- 
-
-
-
-
-
-> [!IMPORTANT]
-> **Looking for MemGPT?** You're in the right place!
->
-> The MemGPT package and Docker image have been renamed to `letta` to clarify the distinction between MemGPT *agents* and the Letta API *server* / *runtime* that runs LLM agents as *services*. Read more about the relationship between MemGPT and Letta [here](https://www.letta.com/blog/memgpt-and-letta).
-
----
-
-## ⚡ Quickstart
-
-_The recommended way to use Letta is to run use Docker. To install Docker, see [Docker's installation guide](https://docs.docker.com/get-docker/). For issues with installing Docker, see [Docker's troubleshooting guide](https://docs.docker.com/desktop/troubleshoot-and-support/troubleshoot/). You can also install Letta using `pip` (see instructions [below](#-quickstart-pip))._
-
-### 🌖 Run the Letta server
-
-> [!NOTE]
-> Letta agents live inside the Letta server, which persists them to a database. You can interact with the Letta agents inside your Letta server via the [REST API](https://docs.letta.com/api-reference) + Python / Typescript SDKs, and the [Agent Development Environment](https://app.letta.com) (a graphical interface).
-
-The Letta server can be connected to various LLM API backends ([OpenAI](https://docs.letta.com/models/openai), [Anthropic](https://docs.letta.com/models/anthropic), [vLLM](https://docs.letta.com/models/vllm), [Ollama](https://docs.letta.com/models/ollama), etc.). To enable access to these LLM API providers, set the appropriate environment variables when you use `docker run`:
-```sh
-# replace `~/.letta/.persist/pgdata` with wherever you want to store your agent data
-docker run \
- -v ~/.letta/.persist/pgdata:/var/lib/postgresql/data \
- -p 8283:8283 \
- -e OPENAI_API_KEY="your_openai_api_key" \
- letta/letta:latest
-```
-
-If you have many different LLM API keys, you can also set up a `.env` file instead and pass that to `docker run`:
-```sh
-# using a .env file instead of passing environment variables
-docker run \
- -v ~/.letta/.persist/pgdata:/var/lib/postgresql/data \
- -p 8283:8283 \
- --env-file .env \
- letta/letta:latest
-```
-
-Once the Letta server is running, you can access it via port `8283` (e.g. sending REST API requests to `http://localhost:8283/v1`). You can also connect your server to the Letta ADE to access and manage your agents in a web interface.
-
-### 👾 Access the ADE (Agent Development Environment)
-
-> [!NOTE]
-> For a guided tour of the ADE, watch our [ADE walkthrough on YouTube](https://www.youtube.com/watch?v=OzSCFR0Lp5s), or read our [blog post](https://www.letta.com/blog/introducing-the-agent-development-environment) and [developer docs](https://docs.letta.com/agent-development-environment).
-
-The Letta ADE is a graphical user interface for creating, deploying, interacting and observing with your Letta agents. For example, if you're running a Letta server to power an end-user application (such as a customer support chatbot), you can use the ADE to test, debug, and observe the agents in your server. You can also use the ADE as a general chat interface to interact with your Letta agents.
-
-Letta (previously MemGPT)
-- -[Homepage](https://letta.com) // [Documentation](https://docs.letta.com) // [ADE](https://docs.letta.com/agent-development-environment) // [Letta Cloud](https://forms.letta.com/early-access) - -
- -**👾 Letta** is an open source framework for building **stateful agents** with advanced reasoning capabilities and transparent long-term memory. The Letta framework is white box and model-agnostic. - -[](https://discord.gg/letta) -[](https://twitter.com/Letta_AI) -[](https://arxiv.org/abs/2310.08560) - -[](LICENSE) -[](https://github.com/cpacker/MemGPT/releases) -[](https://hub.docker.com/r/letta/letta) -[](https://github.com/cpacker/MemGPT) - -
- 
-
- 
-
-` with the ID of your Letta container, found via `docker ps`
-docker exec -it letta run
-```
-
-You can also use `docker ps` within the command to automatically find the ID of your Letta container:
-```
-docker exec -it $(docker ps -q -f ancestor=letta/letta) letta run
-```
-
-In the CLI tool, you'll be able to create new agents, or load existing agents:
-```
-🧬 Creating new agent...
-? Select LLM model: letta-free [type=openai] [ip=https://inference.letta.com]
-? Select embedding model: letta-free [type=hugging-face] [ip=https://embeddings.letta.com]
--> 🤖 Using persona profile: 'sam_pov'
--> 🧑 Using human profile: 'basic'
--> 🛠️ 8 tools: send_message, pause_heartbeats, conversation_search, conversation_search_date, archival_memory_insert, archival_memory_search, core_memory_append, core_memory_replace
-
-🎉 Created new agent 'InspiringSpinach'
-
-Hit enter to begin (will request first Letta message)
-
-💭 User engaged. Recognize as Chad. Acknowledge presence and establish connection. Explore interests and engage in meaningful discourse.
-🤖 Hello, Chad. It's nice to meet you. I am keen on learning and exploring new ideas and concepts. Is there a specific topic you'd like to discuss today?
-
-> Enter your message: my name is Brad, not Chad...
-
-💭 A discrepancy in identity. Reflect this change in core memory. Apologize for the oversight, and rectify the error.
-⚡🧠 [function] updating memory with core_memory_replace
- First name: Chad
- → First name: Brad
-💭 Apologize for the mistake in recognizing his name, and begin the conversation anew, addressing him by the correct name this time.
-🤖 I apologize for the oversight, Brad. Let's restart. It's a pleasure to meet you, Brad. Is there a specific topic you'd like to discuss today?
-
-> Enter your message:
-```
-
-
----
-
-## ⚡ Quickstart (pip)
-
-> [!WARNING]
-> **Database migrations are not officially supported with `SQLite`**
->
-> When you install Letta with `pip`, the default database backend is `SQLite` (you can still use an external `postgres` service with your `pip` install of Letta by setting `LETTA_PG_URI`).
->
-> We do not officially support migrations between Letta versions with `SQLite` backends, only `postgres`. If you would like to keep your agent data across multiple Letta versions we highly recommend using the Docker install method which is the easiest way to use `postgres` with Letta.
-
-View instructions for running the Letta CLI
- -You can chat with your agents via the Letta CLI tool (`letta run`). If you have a Letta Docker container running, you can use `docker exec` to run the Letta CLI inside the container: -```sh -# replace `
-
-
-
----
-
-## 🤗 How to contribute
-
-Letta is an open source project built by over a hundred contributors. There are many ways to get involved in the Letta OSS project!
-
-* **Contribute to the project**: Interested in contributing? Start by reading our [Contribution Guidelines](https://github.com/cpacker/MemGPT/tree/main/CONTRIBUTING.md).
-* **Ask a question**: Join our community on [Discord](https://discord.gg/letta) and direct your questions to the `#support` channel.
-* **Report issues or suggest features**: Have an issue or a feature request? Please submit them through our [GitHub Issues page](https://github.com/cpacker/MemGPT/issues).
-* **Explore the roadmap**: Curious about future developments? View and comment on our [project roadmap](https://github.com/cpacker/MemGPT/issues/1533).
-* **Join community events**: Stay updated with the [event calendar](https://lu.ma/berkeley-llm-meetup) or follow our [Twitter account](https://twitter.com/Letta_AI).
-
----
-
-***Legal notices**: By using Letta and related Letta services (such as the Letta endpoint or hosted service), you are agreeing to our [privacy policy](https://www.letta.com/privacy-policy) and [terms of service](https://www.letta.com/terms-of-service).*
diff --git a/TERMS.md b/TERMS.md
deleted file mode 100644
index a868db5a..00000000
--- a/TERMS.md
+++ /dev/null
@@ -1,42 +0,0 @@
-Terms of Service
-================
-
-**Binding Agreement**. This is a binding contract ("Terms") between you and the developers of Letta and associated services ("we," "us," "our," "Letta developers", "Letta"). These Terms apply whenever you use any of the sites, apps, products, or services ("Services") we offer, in existence now to created in the future. Further, we may automatically upgrade our Services, and these Terms will apply to such upgrades. By accessing or using the Services, you agree to be bound by these Terms. If you use our services on behalf of an organization, you agree to these terms on behalf of that organization. If you do not agree to these Terms, you may not use the Services.
-
-**Privacy**. See our Privacy Policy for details on how we collect, store, and share user information.
-
-**Age Restrictions**. The Services are not intended for users who are under the age of 13. In order to create an account for the Services, you must be 13 years of age or older. By registering, you represent and warrant that you are 13 years of age or older. If children between the ages of 13 and 18 wish to use the Services, they must be registered by their parent or guardian.
-
-**Your Content and Permissions**. Content may be uploaded to, shared with, or generated by Letta -- files, videos, links, music, documents, code, and text ("Your Content"). Your Content is yours. Letta does not claim any right, title, or interest in Your Content.
-
-You grant us a non-exclusive, worldwide, royalty free license to do the things we need to do to provide the Services, including but not limited to storing, displaying, reproducing, and distributing Your Content. This license extends to trusted third parties we work with.
-
-**Content Guidelines**. You are fully responsible for Your Content. You may not copy, upload, download, or share Your Content unless you have the appropriate rights to do so. It is your responsibility to ensure that Your Content abides by applicable laws, these Terms, and with our user guidelines. We don't actively review Your Content.
-
-**Account Security**. You are responsible for safeguarding your password to the Services, making sure that others don't have access to it, and keeping your account information current. You must immediately notify the Letta developers of any unauthorized uses of your account or any other breaches of security. Letta will not be liable for your acts or omissions, including any damages of any kind incurred as a result of your acts or omissions.
-
-**Changes to these Terms**. We are constantly updating our Services, and that means sometimes we have to change the legal terms under which our Services are offered. If we make changes that are material, we will let you know, for example by posting on one of our blogs, or by sending you an email or other communication before the changes take effect. The notice will designate a reasonable period of time after which the new Terms will take effect. If you disagree with our changes, then you should stop using Letta within the designated notice period. Your continued use of Letta will be subject to the new Terms. However, any dispute that arose before the changes shall be governed by the Terms (including the binding individual arbitration clause) that were in place when the dispute arose.
-
-You can access archived versions of our policies at our repository.
-
-**DMCA Policy**. We respond to notices of alleged copyright infringement in accordance with the Digital Millennium Copyright Act ("DMCA"). If you believe that the content of a Letta account infringes your copyrights, you can notify us using the published email in our privacy policy.
-
-**Our Intellectual Property**: The Services and all materials contained therein, including, without limitation, Letta logo, and all designs, text, graphics, pictures, information, data, software, sound files, other files, and the selection and arrangement thereof (collectively, the "Letta Materials") are the property of Letta or its licensors or users and are protected by U.S. and international intellectual property laws. You are granted a personal, limited, non-sublicensable, non-exclusive, revocable license to access and use Letta Materials in accordance with these Terms for the sole purpose of enabling you to use and enjoy the Services.
-
-Other trademarks, service marks, graphics and logos used in connection with the Services may be the trademarks of other third parties. Your use of the Services grants you no right or license to reproduce or otherwise use any Letta, Letta, or third-party trademarks.
-
-**Termination**. You are free to stop using the Services at any time. We also reserve the right to suspend or end the Services at any time at our discretion and without notice. For example, we may suspend or terminate your use of the Services if you fail to comply with these Terms, or use the Services in a manner that would cause us legal liability, disrupt the Services, or disrupt others' use of the Services.
-
-**Disclaimer of Warranties**. Letta makes no warranties of any kind with respect to Letta or your use of the Services.
-
-**Limitation of Liability**. Letta shall not have any liability for any indirect, incidental, consequential, special, exemplary, or damages under any theory of liability arising out of, or relating to, these Terms or your use of Letta. As a condition of access to Letta, you understand and agree that Letta's liability shall not exceed $4.20.
-
-**Indemnification**. You agree to indemnify and hold harmless Letta, its developers, its contributors, its contractors, and its licensors, and their respective directors, officers, employees, and agents from and against any and all losses, liabilities, demands, damages, costs, claims, and expenses, including attorneys’ fees, arising out of or related to your use of our Services, including but not limited to your violation of the Agreement or any agreement with a provider of third-party services used in connection with the Services or applicable law, Content that you post, and any ecommerce activities conducted through your or another user’s website.
-
-**Exceptions to Agreement to Arbitrate**. Claims for injunctive or equitable relief or claims regarding intellectual property rights may be brought in any competent court without the posting of a bond.
-
-**No Class Actions**. You may resolve disputes with us only on an individual basis; you may not bring a claim as a plaintiff or a class member in a class, consolidated, or representative action. **Class arbitrations, class actions, private attorney general actions, and consolidation with other arbitrations are not permitted.**
-
-**Governing Law**. You agree that these Terms, and your use of Letta, are governed by California law, in the United States of America, without regard to its principles of conflicts of law.
-
-**Creative Commons Sharealike License**. This document is derived from the [Automattic legalmattic repository](https://github.com/Automattic/legalmattic) distributed under a Creative Commons Sharealike license. Thank you Automattic!
diff --git a/alembic.ini b/alembic.ini
deleted file mode 100644
index 72cc6990..00000000
--- a/alembic.ini
+++ /dev/null
@@ -1,116 +0,0 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts
-# Use forward slashes (/) also on windows to provide an os agnostic path
-script_location = alembic
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
-# for all available tokens
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.
-prepend_sys_path = .
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python>=3.9 or backports.zoneinfo library.
-# Any required deps can installed by adding `alembic[tz]` to the pip requirements
-# string value is passed to ZoneInfo()
-# leave blank for localtime
-# timezone =
-
-# max length of characters to apply to the "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to alembic/versions. When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "version_path_separator" below.
-# version_locations = %(here)s/bar:%(here)s/bat:alembic/versions
-
-# version path separator; As mentioned above, this is the character used to split
-# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
-# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
-# Valid values for version_path_separator are:
-#
-# version_path_separator = :
-# version_path_separator = ;
-# version_path_separator = space
-version_path_separator = os # Use os.pathsep. Default configuration used for new projects.
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-sqlalchemy.url = driver://user:pass@localhost/dbname
-
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts. See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = --fix REVISION_SCRIPT_FILENAME
-
-# Logging configuration
-[loggers]
-keys = root,sqlalchemy,alembic
-
-[handlers]
-keys = console
-
-[formatters]
-keys = generic
-
-[logger_root]
-level = WARN
-handlers = console
-qualname =
-
-[logger_sqlalchemy]
-level = WARN
-handlers =
-qualname = sqlalchemy.engine
-
-[logger_alembic]
-level = INFO
-handlers =
-qualname = alembic
-
-[handler_console]
-class = StreamHandler
-args = (sys.stderr,)
-level = NOTSET
-formatter = generic
-
-[formatter_generic]
-format = %(levelname)-5.5s [%(name)s] %(message)s
-datefmt = %H:%M:%S
diff --git a/alembic/README b/alembic/README
deleted file mode 100644
index 2500aa1b..00000000
--- a/alembic/README
+++ /dev/null
@@ -1 +0,0 @@
-Generic single-database configuration.
diff --git a/alembic/env.py b/alembic/env.py
deleted file mode 100644
index dac40ea4..00000000
--- a/alembic/env.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os
-from logging.config import fileConfig
-
-from sqlalchemy import engine_from_config, pool
-
-from alembic import context
-from letta.config import LettaConfig
-from letta.orm import Base
-from letta.settings import DatabaseChoice, settings
-
-letta_config = LettaConfig.load()
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-if settings.database_engine is DatabaseChoice.POSTGRES:
- config.set_main_option("sqlalchemy.url", settings.letta_pg_uri)
- print("Using database: ", settings.letta_pg_uri)
-else:
- config.set_main_option("sqlalchemy.url", "sqlite:///" + os.path.join(letta_config.recall_storage_path, "sqlite.db"))
-
-# Interpret the config file for Python logging.
-# This line sets up loggers basically.
-if config.config_file_name is not None:
- fileConfig(config.config_file_name)
-
-# add your model's MetaData object here
-# for 'autogenerate' support
-# from myapp import mymodel
-# target_metadata = mymodel.Base.metadata
-
-target_metadata = Base.metadata
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-
-def run_migrations_offline() -> None:
- """Run migrations in 'offline' mode.
-
- This configures the context with just a URL
- and not an Engine, though an Engine is acceptable
- here as well. By skipping the Engine creation
- we don't even need a DBAPI to be available.
-
- Calls to context.execute() here emit the given string to the
- script output.
-
- """
- url = config.get_main_option("sqlalchemy.url")
- context.configure(
- url=url,
- target_metadata=target_metadata,
- literal_binds=True,
- dialect_opts={"paramstyle": "named"},
- )
-
- with context.begin_transaction():
- context.run_migrations()
-
-
-def run_migrations_online() -> None:
- """Run migrations in 'online' mode.
-
- In this scenario we need to create an Engine
- and associate a connection with the context.
-
- """
- connectable = engine_from_config(
- config.get_section(config.config_ini_section, {}),
- prefix="sqlalchemy.",
- poolclass=pool.NullPool,
- )
-
- with connectable.connect() as connection:
- context.configure(connection=connection, target_metadata=target_metadata, include_schemas=True)
-
- with context.begin_transaction():
- context.run_migrations()
-
-
-if context.is_offline_mode():
- run_migrations_offline()
-else:
- run_migrations_online()
diff --git a/alembic/script.py.mako b/alembic/script.py.mako
deleted file mode 100644
index fbc4b07d..00000000
--- a/alembic/script.py.mako
+++ /dev/null
@@ -1,26 +0,0 @@
-"""${message}
-
-Revision ID: ${up_revision}
-Revises: ${down_revision | comma,n}
-Create Date: ${create_date}
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-${imports if imports else ""}
-
-# revision identifiers, used by Alembic.
-revision: str = ${repr(up_revision)}
-down_revision: Union[str, None] = ${repr(down_revision)}
-branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
-depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
-
-
-def upgrade() -> None:
- ${upgrades if upgrades else "pass"}
-
-
-def downgrade() -> None:
- ${downgrades if downgrades else "pass"}
diff --git a/alembic/versions/0335b1eb9c40_add_batch_item_id_to_messages.py b/alembic/versions/0335b1eb9c40_add_batch_item_id_to_messages.py
deleted file mode 100644
index 1c047db8..00000000
--- a/alembic/versions/0335b1eb9c40_add_batch_item_id_to_messages.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Add batch_item_id to messages
-
-Revision ID: 0335b1eb9c40
-Revises: 373dabcba6cf
-Create Date: 2025-05-02 10:30:08.156190
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "0335b1eb9c40"
-down_revision: Union[str, None] = "373dabcba6cf"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("batch_item_id", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("messages", "batch_item_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/05c3bc564286_add_metrics_to_agent_loop_runs.py b/alembic/versions/05c3bc564286_add_metrics_to_agent_loop_runs.py
deleted file mode 100644
index d76b064b..00000000
--- a/alembic/versions/05c3bc564286_add_metrics_to_agent_loop_runs.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""add metrics to agent loop runs
-
-Revision ID: 05c3bc564286
-Revises: d007f4ca66bf
-Create Date: 2025-08-06 14:30:48.255538
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "05c3bc564286"
-down_revision: Union[str, None] = "d007f4ca66bf"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("jobs", sa.Column("ttft_ns", sa.BigInteger(), nullable=True))
- op.add_column("jobs", sa.Column("total_duration_ns", sa.BigInteger(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("jobs", "total_duration_ns")
- op.drop_column("jobs", "ttft_ns")
- # ### end Alembic commands ###
diff --git a/alembic/versions/068588268b02_add_vector_db_provider_to_archives_table.py b/alembic/versions/068588268b02_add_vector_db_provider_to_archives_table.py
deleted file mode 100644
index f7f0dca7..00000000
--- a/alembic/versions/068588268b02_add_vector_db_provider_to_archives_table.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""Add vector_db_provider to archives table
-
-Revision ID: 068588268b02
-Revises: d5103ee17ed5
-Create Date: 2025-08-27 13:16:29.428231
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "068588268b02"
-down_revision: Union[str, None] = "887a4367b560"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- if settings.letta_pg_uri_no_default:
- # PostgreSQL - use enum type
- vectordbprovider = sa.Enum("NATIVE", "TPUF", name="vectordbprovider")
- vectordbprovider.create(op.get_bind(), checkfirst=True)
-
- # Add column as nullable first
- op.add_column("archives", sa.Column("vector_db_provider", vectordbprovider, nullable=True))
-
- # Backfill existing rows with NATIVE
- op.execute("UPDATE archives SET vector_db_provider = 'NATIVE' WHERE vector_db_provider IS NULL")
-
- # Make column non-nullable
- op.alter_column("archives", "vector_db_provider", nullable=False)
- else:
- # SQLite - use string type
- # Add column as nullable first
- op.add_column("archives", sa.Column("vector_db_provider", sa.String(), nullable=True))
-
- # Backfill existing rows with NATIVE
- op.execute("UPDATE archives SET vector_db_provider = 'NATIVE' WHERE vector_db_provider IS NULL")
-
- # For SQLite, we need to recreate the table to make column non-nullable
- # This is a limitation of SQLite ALTER TABLE
- # For simplicity, we'll leave it nullable in SQLite
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("archives", "vector_db_provider")
-
- if settings.letta_pg_uri_no_default:
- # Drop enum type for PostgreSQL
- vectordbprovider = sa.Enum("NATIVE", "TPUF", name="vectordbprovider")
- vectordbprovider.drop(op.get_bind(), checkfirst=True)
- # ### end Alembic commands ###
diff --git a/alembic/versions/06fbbf65d4f1_support_for_project_id_for_blocks_and_.py b/alembic/versions/06fbbf65d4f1_support_for_project_id_for_blocks_and_.py
deleted file mode 100644
index 8dab61ac..00000000
--- a/alembic/versions/06fbbf65d4f1_support_for_project_id_for_blocks_and_.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""support for project_id for blocks and groups
-
-Revision ID: 06fbbf65d4f1
-Revises: f55542f37641
-Create Date: 2025-07-21 15:07:32.133538
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "06fbbf65d4f1"
-down_revision: Union[str, None] = "f55542f37641"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("block", sa.Column("project_id", sa.String(), nullable=True))
- op.add_column("groups", sa.Column("project_id", sa.String(), nullable=True))
-
- # NOTE: running the backfill on alembic will result in locking with running application.
- # This is okay if okay with downtime. Options also to do rolling migration or dynamic updates.
-
- # Backfill project_id for blocks table
- # Since all agents for a block have the same project_id, we can just grab the first one
- # op.execute(
- # text(
- # """
- # UPDATE block
- # SET project_id = (
- # SELECT a.project_id
- # FROM blocks_agents ba
- # JOIN agents a ON ba.agent_id = a.id
- # WHERE ba.block_id = block.id
- # AND a.project_id IS NOT NULL
- # LIMIT 1
- # )
- # """
- # )
- # )
-
- # Backfill project_id for groups table
- # op.execute(
- # text(
- # """
- # UPDATE groups
- # SET project_id = (
- # SELECT a.project_id
- # FROM groups_agents ga
- # JOIN agents a ON ga.agent_id = a.id
- # WHERE ga.group_id = groups.id
- # AND a.project_id IS NOT NULL
- # LIMIT 1
- # )
- # """
- # )
- # )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("groups", "project_id")
- op.drop_column("block", "project_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/08b2f8225812_adding_toolsagents_orm.py b/alembic/versions/08b2f8225812_adding_toolsagents_orm.py
deleted file mode 100644
index da0e190e..00000000
--- a/alembic/versions/08b2f8225812_adding_toolsagents_orm.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""adding ToolsAgents ORM
-
-Revision ID: 08b2f8225812
-Revises: 3c683a662c82
-Create Date: 2024-12-05 16:46:51.258831
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "08b2f8225812"
-down_revision: Union[str, None] = "3c683a662c82"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "tools_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("tool_id", sa.String(), nullable=False),
- sa.Column("tool_name", sa.String(), nullable=False),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.ForeignKeyConstraint(
- ["agent_id"],
- ["agents.id"],
- ),
- sa.ForeignKeyConstraint(["tool_id"], ["tools.id"], name="fk_tool_id"),
- sa.PrimaryKeyConstraint("agent_id", "tool_id", "tool_name", "id"),
- sa.UniqueConstraint("agent_id", "tool_name", name="unique_tool_per_agent"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("tools_agents")
- # ### end Alembic commands ###
diff --git a/alembic/versions/0b496eae90de_add_file_agent_table.py b/alembic/versions/0b496eae90de_add_file_agent_table.py
deleted file mode 100644
index e5222067..00000000
--- a/alembic/versions/0b496eae90de_add_file_agent_table.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""Add file agent table
-
-Revision ID: 0b496eae90de
-Revises: 341068089f14
-Create Date: 2025-06-02 15:14:33.730687
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "0b496eae90de"
-down_revision: Union[str, None] = "341068089f14"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "files_agents",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("file_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("is_open", sa.Boolean(), nullable=False),
- sa.Column("visible_content", sa.Text(), nullable=True),
- sa.Column("last_accessed_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["file_id"], ["files.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id", "file_id", "agent_id"),
- )
- op.create_index("ix_files_agents_file_id_agent_id", "files_agents", ["file_id", "agent_id"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_files_agents_file_id_agent_id", table_name="files_agents")
- op.drop_table("files_agents")
- # ### end Alembic commands ###
diff --git a/alembic/versions/0ceb975e0063_add_llm_batch_jobs_tables.py b/alembic/versions/0ceb975e0063_add_llm_batch_jobs_tables.py
deleted file mode 100644
index 625a6e0f..00000000
--- a/alembic/versions/0ceb975e0063_add_llm_batch_jobs_tables.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Add LLM batch jobs tables
-
-Revision ID: 0ceb975e0063
-Revises: 90bb156e71df
-Create Date: 2025-04-07 15:57:18.475151
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-import letta
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "0ceb975e0063"
-down_revision: Union[str, None] = "90bb156e71df"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "llm_batch_job",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("status", sa.String(), nullable=False),
- sa.Column("llm_provider", sa.String(), nullable=False),
- sa.Column("create_batch_response", letta.orm.custom_columns.CreateBatchResponseColumn(), nullable=False),
- sa.Column("latest_polling_response", letta.orm.custom_columns.PollBatchResponseColumn(), nullable=True),
- sa.Column("last_polled_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("ix_llm_batch_job_created_at", "llm_batch_job", ["created_at"], unique=False)
- op.create_index("ix_llm_batch_job_status", "llm_batch_job", ["status"], unique=False)
- op.create_table(
- "llm_batch_items",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("batch_id", sa.String(), nullable=False),
- sa.Column("llm_config", letta.orm.custom_columns.LLMConfigColumn(), nullable=False),
- sa.Column("request_status", sa.String(), nullable=False),
- sa.Column("step_status", sa.String(), nullable=False),
- sa.Column("step_state", letta.orm.custom_columns.AgentStepStateColumn(), nullable=False),
- sa.Column("batch_request_result", letta.orm.custom_columns.BatchRequestResultColumn(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["batch_id"], ["llm_batch_job.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("ix_llm_batch_items_agent_id", "llm_batch_items", ["agent_id"], unique=False)
- op.create_index("ix_llm_batch_items_batch_id", "llm_batch_items", ["batch_id"], unique=False)
- op.create_index("ix_llm_batch_items_status", "llm_batch_items", ["request_status"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_llm_batch_items_status", table_name="llm_batch_items")
- op.drop_index("ix_llm_batch_items_batch_id", table_name="llm_batch_items")
- op.drop_index("ix_llm_batch_items_agent_id", table_name="llm_batch_items")
- op.drop_table("llm_batch_items")
- op.drop_index("ix_llm_batch_job_status", table_name="llm_batch_job")
- op.drop_index("ix_llm_batch_job_created_at", table_name="llm_batch_job")
- op.drop_table("llm_batch_job")
- # ### end Alembic commands ###
diff --git a/alembic/versions/15b577c62f3f_add_hidden_property_to_agents.py b/alembic/versions/15b577c62f3f_add_hidden_property_to_agents.py
deleted file mode 100644
index bfd99e39..00000000
--- a/alembic/versions/15b577c62f3f_add_hidden_property_to_agents.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""Add hidden property to agents
-
-Revision ID: 15b577c62f3f
-Revises: 4c6c9ef0387d
-Create Date: 2025-07-30 13:19:15.213121
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "15b577c62f3f"
-down_revision: Union[str, None] = "4c6c9ef0387d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- op.add_column("agents", sa.Column("hidden", sa.Boolean(), nullable=True))
-
- # Set hidden=true for existing agents with project names starting with "templates"
- connection = op.get_bind()
- connection.execute(sa.text("UPDATE agents SET hidden = true WHERE project_id LIKE 'templates-%'"))
-
-
-def downgrade() -> None:
- op.drop_column("agents", "hidden")
diff --git a/alembic/versions/167491cfb7a8_add_identities_for_blocks.py b/alembic/versions/167491cfb7a8_add_identities_for_blocks.py
deleted file mode 100644
index 8f0e04d2..00000000
--- a/alembic/versions/167491cfb7a8_add_identities_for_blocks.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""add identities for blocks
-
-Revision ID: 167491cfb7a8
-Revises: d211df879a5f
-Create Date: 2025-03-07 17:51:24.843275
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "167491cfb7a8"
-down_revision: Union[str, None] = "d211df879a5f"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "identities_blocks",
- sa.Column("identity_id", sa.String(), nullable=False),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["block_id"], ["block.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["identity_id"], ["identities.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("identity_id", "block_id"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("identities_blocks")
- # ### end Alembic commands ###
diff --git a/alembic/versions/18e300709530_add_instructions_field_to_sources.py b/alembic/versions/18e300709530_add_instructions_field_to_sources.py
deleted file mode 100644
index 9d730c92..00000000
--- a/alembic/versions/18e300709530_add_instructions_field_to_sources.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add instructions field to sources
-
-Revision ID: 18e300709530
-Revises: 878607e41ca4
-Create Date: 2025-05-08 17:56:20.877183
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "18e300709530"
-down_revision: Union[str, None] = "878607e41ca4"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("sources", sa.Column("instructions", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("sources", "instructions")
- # ### end Alembic commands ###
diff --git a/alembic/versions/1af251a42c06_fix_files_agents_constraints.py b/alembic/versions/1af251a42c06_fix_files_agents_constraints.py
deleted file mode 100644
index d95d79e3..00000000
--- a/alembic/versions/1af251a42c06_fix_files_agents_constraints.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""Fix files_agents constraints
-
-Revision ID: 1af251a42c06
-Revises: 51999513bcf1
-Create Date: 2025-06-30 11:50:42.200885
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "1af251a42c06"
-down_revision: Union[str, None] = "51999513bcf1"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_files_agents_agent_file_name", table_name="files_agents")
- op.drop_index("ix_files_agents_file_id_agent_id", table_name="files_agents")
- op.drop_constraint("uq_files_agents_agent_file_name", "files_agents", type_="unique")
- op.drop_constraint("uq_files_agents_file_agent", "files_agents", type_="unique")
- op.create_index("ix_agent_filename", "files_agents", ["agent_id", "file_name"], unique=False)
- op.create_index("ix_file_agent", "files_agents", ["file_id", "agent_id"], unique=False)
- op.create_unique_constraint("uq_agent_filename", "files_agents", ["agent_id", "file_name"])
- op.create_unique_constraint("uq_file_agent", "files_agents", ["file_id", "agent_id"])
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("uq_file_agent", "files_agents", type_="unique")
- op.drop_constraint("uq_agent_filename", "files_agents", type_="unique")
- op.drop_index("ix_file_agent", table_name="files_agents")
- op.drop_index("ix_agent_filename", table_name="files_agents")
- op.create_unique_constraint("uq_files_agents_file_agent", "files_agents", ["file_id", "agent_id"], postgresql_nulls_not_distinct=False)
- op.create_unique_constraint(
- "uq_files_agents_agent_file_name", "files_agents", ["agent_id", "file_name"], postgresql_nulls_not_distinct=False
- )
- op.create_index("ix_files_agents_file_id_agent_id", "files_agents", ["file_id", "agent_id"], unique=False)
- op.create_index("ix_files_agents_agent_file_name", "files_agents", ["agent_id", "file_name"], unique=False)
- # ### end Alembic commands ###
diff --git a/alembic/versions/1c6b6a38b713_add_pip_requirements_to_tools.py b/alembic/versions/1c6b6a38b713_add_pip_requirements_to_tools.py
deleted file mode 100644
index a4eff890..00000000
--- a/alembic/versions/1c6b6a38b713_add_pip_requirements_to_tools.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Add pip requirements to tools
-
-Revision ID: 1c6b6a38b713
-Revises: c96263433aef
-Create Date: 2025-06-12 18:06:54.838510
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "1c6b6a38b713"
-down_revision: Union[str, None] = "c96263433aef"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("tools", sa.Column("pip_requirements", sa.JSON(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("tools", "pip_requirements")
- # ### end Alembic commands ###
diff --git a/alembic/versions/1c8880d671ee_make_an_blocks_agents_mapping_table.py b/alembic/versions/1c8880d671ee_make_an_blocks_agents_mapping_table.py
deleted file mode 100644
index 01062363..00000000
--- a/alembic/versions/1c8880d671ee_make_an_blocks_agents_mapping_table.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Make an blocks agents mapping table
-
-Revision ID: 1c8880d671ee
-Revises: f81ceea2c08d
-Create Date: 2024-11-22 15:42:47.209229
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "1c8880d671ee"
-down_revision: Union[str, None] = "f81ceea2c08d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_unique_constraint("unique_block_id_label", "block", ["id", "label"])
-
- op.create_table(
- "blocks_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.Column("block_label", sa.String(), nullable=False),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.ForeignKeyConstraint(
- ["agent_id"],
- ["agents.id"],
- ),
- sa.ForeignKeyConstraint(["block_id", "block_label"], ["block.id", "block.label"], name="fk_block_id_label"),
- sa.PrimaryKeyConstraint("agent_id", "block_id", "block_label", "id"),
- sa.UniqueConstraint("agent_id", "block_label", name="unique_label_per_agent"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("unique_block_id_label", "block", type_="unique")
- op.drop_table("blocks_agents")
- # ### end Alembic commands ###
diff --git a/alembic/versions/1dc0fee72dea_add_block_related_indexes.py b/alembic/versions/1dc0fee72dea_add_block_related_indexes.py
deleted file mode 100644
index 489a14ff..00000000
--- a/alembic/versions/1dc0fee72dea_add_block_related_indexes.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""add block-related indexes
-
-Revision ID: 1dc0fee72dea
-Revises: 18e300709530
-Create Date: 2025-05-12 17:06:32.055091
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "1dc0fee72dea"
-down_revision: Union[str, None] = "18e300709530"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade():
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # add index for blocks_agents table
- op.create_index("ix_blocks_agents_block_label_agent_id", "blocks_agents", ["block_label", "agent_id"], unique=False)
-
- # add index for just block_label
- op.create_index("ix_blocks_block_label", "blocks_agents", ["block_label"], unique=False)
-
- # add index for agent_tags for agent_id and tag
- op.create_index("ix_agents_tags_agent_id_tag", "agents_tags", ["agent_id", "tag"], unique=False)
-
-
-def downgrade():
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.drop_index("ix_blocks_agents_block_label_agent_id", table_name="blocks_agents")
- op.drop_index("ix_blocks_block_label", table_name="blocks_agents")
- op.drop_index("ix_agents_tags_agent_id_tag", table_name="agents_tags")
diff --git a/alembic/versions/1e553a664210_add_metadata_to_tools.py b/alembic/versions/1e553a664210_add_metadata_to_tools.py
deleted file mode 100644
index dd902830..00000000
--- a/alembic/versions/1e553a664210_add_metadata_to_tools.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Add metadata to Tools
-
-Revision ID: 1e553a664210
-Revises: 2cceb07c2384
-Create Date: 2025-03-17 15:50:05.562302
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "1e553a664210"
-down_revision: Union[str, None] = "2cceb07c2384"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("tools", sa.Column("metadata_", sa.JSON(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("tools", "metadata_")
- # ### end Alembic commands ###
diff --git a/alembic/versions/220856bbf43b_add_read_only_column.py b/alembic/versions/220856bbf43b_add_read_only_column.py
deleted file mode 100644
index 52d0b89e..00000000
--- a/alembic/versions/220856bbf43b_add_read_only_column.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""add read-only column
-
-Revision ID: 220856bbf43b
-Revises: 1dc0fee72dea
-Create Date: 2025-05-13 14:42:17.353614
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "220856bbf43b"
-down_revision: Union[str, None] = "1dc0fee72dea"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # add default value of `False`
- op.add_column("block", sa.Column("read_only", sa.Boolean(), nullable=True))
- op.execute(
- """
- UPDATE block
- SET read_only = False
- """
- )
- op.alter_column("block", "read_only", nullable=False)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.drop_column("block", "read_only")
diff --git a/alembic/versions/22a6e413d89c_remove_module_field_on_tool.py b/alembic/versions/22a6e413d89c_remove_module_field_on_tool.py
deleted file mode 100644
index 1bab710e..00000000
--- a/alembic/versions/22a6e413d89c_remove_module_field_on_tool.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Remove module field on tool
-
-Revision ID: 22a6e413d89c
-Revises: 88f9432739a9
-Create Date: 2025-01-10 17:38:23.811795
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "22a6e413d89c"
-down_revision: Union[str, None] = "88f9432739a9"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("tools", "module")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("tools", sa.Column("module", sa.VARCHAR(), autoincrement=False, nullable=True))
- # ### end Alembic commands ###
diff --git a/alembic/versions/25fc99e97839_fix_alembic_check_warnings.py b/alembic/versions/25fc99e97839_fix_alembic_check_warnings.py
deleted file mode 100644
index d1cb27f4..00000000
--- a/alembic/versions/25fc99e97839_fix_alembic_check_warnings.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""Remove job_usage_statistics indices and update job_messages
-
-Revision ID: 25fc99e97839
-Revises: f595e0e8013e
-Create Date: 2025-01-16 16:48:21.000000
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "25fc99e97839"
-down_revision: Union[str, None] = "f595e0e8013e"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Remove indices from job_messages
- op.drop_index("ix_job_messages_created_at", table_name="job_messages")
- op.drop_index("ix_job_messages_job_id", table_name="job_messages")
-
- # Remove indices from job_usage_statistics
- op.drop_index("ix_job_usage_statistics_created_at", table_name="job_usage_statistics")
- op.drop_index("ix_job_usage_statistics_job_id", table_name="job_usage_statistics")
-
- # Add foreign key constraint for message_id
- op.create_foreign_key("fk_job_messages_message_id", "job_messages", "messages", ["message_id"], ["id"], ondelete="CASCADE")
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Remove the foreign key constraint
- op.drop_constraint("fk_job_messages_message_id", "job_messages", type_="foreignkey")
-
- # Recreate indices for job_messages
- op.create_index("ix_job_messages_job_id", "job_messages", ["job_id"])
- op.create_index("ix_job_messages_created_at", "job_messages", ["created_at"])
-
- # Recreate indices for job_usage_statistics
- op.create_index("ix_job_usage_statistics_job_id", "job_usage_statistics", ["job_id"])
- op.create_index("ix_job_usage_statistics_created_at", "job_usage_statistics", ["created_at"])
diff --git a/alembic/versions/28b8765bdd0a_add_support_for_structured_outputs_in_.py b/alembic/versions/28b8765bdd0a_add_support_for_structured_outputs_in_.py
deleted file mode 100644
index a76a8d00..00000000
--- a/alembic/versions/28b8765bdd0a_add_support_for_structured_outputs_in_.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add support for structured_outputs in agents
-
-Revision ID: 28b8765bdd0a
-Revises: a3c7d62e08ca
-Create Date: 2025-04-18 11:43:47.701786
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "28b8765bdd0a"
-down_revision: Union[str, None] = "a3c7d62e08ca"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("response_format", sa.JSON(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("agents", "response_format")
- # ### end Alembic commands ###
diff --git a/alembic/versions/2c059cad97cc_create_sqlite_baseline_schema.py b/alembic/versions/2c059cad97cc_create_sqlite_baseline_schema.py
deleted file mode 100644
index 36410d7b..00000000
--- a/alembic/versions/2c059cad97cc_create_sqlite_baseline_schema.py
+++ /dev/null
@@ -1,798 +0,0 @@
-"""create_sqlite_baseline_schema
-
-Revision ID: 2c059cad97cc
-Revises: 495f3f474131
-Create Date: 2025-07-16 14:34:21.280233
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "2c059cad97cc"
-down_revision: Union[str, None] = "495f3f474131"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Only run this migration for SQLite
- if settings.letta_pg_uri_no_default:
- return
-
- # Create the exact schema that matches the current PostgreSQL state
- # This is a snapshot of the schema at the time of this migration
- # Based on the schema provided by Andy
-
- # Organizations table
- op.create_table(
- "organizations",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("privileged_tools", sa.Boolean(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- )
-
- # Agents table
- op.create_table(
- "agents",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("message_ids", sa.JSON(), nullable=True),
- sa.Column("system", sa.String(), nullable=True),
- sa.Column("agent_type", sa.String(), nullable=True),
- sa.Column("llm_config", sa.JSON(), nullable=True),
- sa.Column("embedding_config", sa.JSON(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("tool_rules", sa.JSON(), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("project_id", sa.String(), nullable=True),
- sa.Column("template_id", sa.String(), nullable=True),
- sa.Column("base_template_id", sa.String(), nullable=True),
- sa.Column("message_buffer_autoclear", sa.Boolean(), nullable=False),
- sa.Column("enable_sleeptime", sa.Boolean(), nullable=True),
- sa.Column("response_format", sa.JSON(), nullable=True),
- sa.Column("last_run_completion", sa.DateTime(timezone=True), nullable=True),
- sa.Column("last_run_duration_ms", sa.Integer(), nullable=True),
- sa.Column("timezone", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- )
- op.create_index("ix_agents_created_at", "agents", ["created_at", "id"])
-
- # Block history table (created before block table so block can reference it)
- op.create_table(
- "block_history",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("description", sa.Text(), nullable=True),
- sa.Column("label", sa.String(), nullable=False),
- sa.Column("value", sa.Text(), nullable=False),
- sa.Column("limit", sa.BigInteger(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("actor_type", sa.String(), nullable=True),
- sa.Column("actor_id", sa.String(), nullable=True),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.Column("sequence_number", sa.Integer(), nullable=False),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- # Note: block_id foreign key will be added later since block table doesn't exist yet
- )
- op.create_index("ix_block_history_block_id_sequence", "block_history", ["block_id", "sequence_number"], unique=True)
-
- # Block table
- op.create_table(
- "block",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("value", sa.String(), nullable=False),
- sa.Column("limit", sa.Integer(), nullable=False),
- sa.Column("template_name", sa.String(), nullable=True),
- sa.Column("label", sa.String(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("is_template", sa.Boolean(), nullable=False),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("current_history_entry_id", sa.String(), nullable=True),
- sa.Column("version", sa.Integer(), server_default="1", nullable=False),
- sa.Column("read_only", sa.Boolean(), nullable=False),
- sa.Column("preserve_on_migration", sa.Boolean(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["current_history_entry_id"], ["block_history.id"], name="fk_block_current_history_entry"),
- sa.UniqueConstraint("id", "label", name="unique_block_id_label"),
- )
- op.create_index("created_at_label_idx", "block", ["created_at", "label"])
- op.create_index("ix_block_current_history_entry_id", "block", ["current_history_entry_id"])
-
- # Note: Foreign key constraint for block_history.block_id cannot be added in SQLite after table creation
- # This will be enforced at the ORM level
-
- # Sources table
- op.create_table(
- "sources",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("embedding_config", sa.JSON(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("instructions", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.UniqueConstraint("name", "organization_id", name="uq_source_name_organization"),
- )
- op.create_index("source_created_at_id_idx", "sources", ["created_at", "id"])
-
- # Files table
- op.create_table(
- "files",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.Column("file_name", sa.String(), nullable=True),
- sa.Column("file_path", sa.String(), nullable=True),
- sa.Column("file_type", sa.String(), nullable=True),
- sa.Column("file_size", sa.Integer(), nullable=True),
- sa.Column("file_creation_date", sa.String(), nullable=True),
- sa.Column("file_last_modified_date", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("processing_status", sa.String(), nullable=False),
- sa.Column("error_message", sa.Text(), nullable=True),
- sa.Column("original_file_name", sa.String(), nullable=True),
- sa.Column("total_chunks", sa.Integer(), nullable=True),
- sa.Column("chunks_embedded", sa.Integer(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["source_id"], ["sources.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- )
- # Note: SQLite doesn't support expression indexes, so these are simplified
- op.create_index("ix_files_org_created", "files", ["organization_id"])
- op.create_index("ix_files_processing_status", "files", ["processing_status"])
- op.create_index("ix_files_source_created", "files", ["source_id"])
-
- # Users table
- op.create_table(
- "users",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- )
-
- # Jobs table
- op.create_table(
- "jobs",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("status", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("completed_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("job_type", sa.String(), nullable=False),
- sa.Column("request_config", sa.JSON(), nullable=True),
- sa.Column("callback_url", sa.String(), nullable=True),
- sa.Column("callback_sent_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("callback_status_code", sa.Integer(), nullable=True),
- sa.Column("callback_error", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["user_id"], ["users.id"]),
- )
- op.create_index("ix_jobs_created_at", "jobs", ["created_at", "id"])
-
- # Tools table
- op.create_table(
- "tools",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("source_type", sa.String(), nullable=False),
- sa.Column("source_code", sa.String(), nullable=True),
- sa.Column("json_schema", sa.JSON(), nullable=True),
- sa.Column("tags", sa.JSON(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("return_char_limit", sa.Integer(), nullable=True),
- sa.Column("tool_type", sa.String(), nullable=False),
- sa.Column("args_json_schema", sa.JSON(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("pip_requirements", sa.JSON(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.UniqueConstraint("name", "organization_id", name="uix_name_organization"),
- )
- op.create_index("ix_tools_created_at_name", "tools", ["created_at", "name"])
-
- # Additional tables based on Andy's schema
-
- # Agents tags table
- op.create_table(
- "agents_tags",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("tag", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"]),
- sa.UniqueConstraint("agent_id", "tag", name="unique_agent_tag"),
- )
- op.create_index("ix_agents_tags_agent_id_tag", "agents_tags", ["agent_id", "tag"])
-
- # Sandbox configs table
- op.create_table(
- "sandbox_configs",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("type", sa.String(), nullable=False), # sandboxtype in PG
- sa.Column("config", sa.JSON(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.UniqueConstraint("type", "organization_id", name="uix_type_organization"),
- )
-
- # Sandbox environment variables table
- op.create_table(
- "sandbox_environment_variables",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("key", sa.String(), nullable=False),
- sa.Column("value", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("sandbox_config_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["sandbox_config_id"], ["sandbox_configs.id"]),
- sa.UniqueConstraint("key", "sandbox_config_id", name="uix_key_sandbox_config"),
- )
-
- # Blocks agents table
- op.create_table(
- "blocks_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.Column("block_label", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"]),
- sa.ForeignKeyConstraint(["block_id", "block_label"], ["block.id", "block.label"], deferrable=True, initially="DEFERRED"),
- sa.UniqueConstraint("agent_id", "block_label", name="unique_label_per_agent"),
- sa.UniqueConstraint("agent_id", "block_id", name="unique_agent_block"),
- )
- op.create_index("ix_blocks_agents_block_label_agent_id", "blocks_agents", ["block_label", "agent_id"])
- op.create_index("ix_blocks_block_label", "blocks_agents", ["block_label"])
-
- # Tools agents table
- op.create_table(
- "tools_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("tool_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["tool_id"], ["tools.id"], ondelete="CASCADE"),
- sa.UniqueConstraint("agent_id", "tool_id", name="unique_agent_tool"),
- )
-
- # Sources agents table
- op.create_table(
- "sources_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["source_id"], ["sources.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("agent_id", "source_id"),
- )
-
- # Agent passages table (using BLOB for vectors in SQLite)
- op.create_table(
- "agent_passages",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("text", sa.String(), nullable=False),
- sa.Column("embedding_config", sa.JSON(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=False),
- sa.Column("embedding", sa.BLOB(), nullable=True), # CommonVector becomes BLOB in SQLite
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- )
- # Note: agent_passages_org_idx is not created for SQLite as it's expected to be different
- op.create_index("agent_passages_created_at_id_idx", "agent_passages", ["created_at", "id"])
- op.create_index("ix_agent_passages_org_agent", "agent_passages", ["organization_id", "agent_id"])
-
- # Source passages table (using BLOB for vectors in SQLite)
- op.create_table(
- "source_passages",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("text", sa.String(), nullable=False),
- sa.Column("embedding_config", sa.JSON(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=False),
- sa.Column("embedding", sa.BLOB(), nullable=True), # CommonVector becomes BLOB in SQLite
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("file_id", sa.String(), nullable=True),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.Column("file_name", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["file_id"], ["files.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["source_id"], ["sources.id"], ondelete="CASCADE"),
- )
- # Note: source_passages_org_idx is not created for SQLite as it's expected to be different
- op.create_index("source_passages_created_at_id_idx", "source_passages", ["created_at", "id"])
-
- # Message sequence is handled by the sequence_id field in messages table
-
- # Messages table
- op.create_table(
- "messages",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("role", sa.String(), nullable=False),
- sa.Column("text", sa.String(), nullable=True),
- sa.Column("model", sa.String(), nullable=True),
- sa.Column("name", sa.String(), nullable=True),
- sa.Column("tool_calls", sa.JSON(), nullable=False),
- sa.Column("tool_call_id", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("step_id", sa.String(), nullable=True),
- sa.Column("otid", sa.String(), nullable=True),
- sa.Column("tool_returns", sa.JSON(), nullable=True),
- sa.Column("group_id", sa.String(), nullable=True),
- sa.Column("content", sa.JSON(), nullable=True),
- sa.Column("sequence_id", sa.BigInteger(), nullable=False),
- sa.Column("sender_id", sa.String(), nullable=True),
- sa.Column("batch_item_id", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["step_id"], ["steps.id"], ondelete="SET NULL"),
- sa.UniqueConstraint("sequence_id", name="uq_messages_sequence_id"),
- )
- op.create_index("ix_messages_agent_created_at", "messages", ["agent_id", "created_at"])
- op.create_index("ix_messages_created_at", "messages", ["created_at", "id"])
- op.create_index("ix_messages_agent_sequence", "messages", ["agent_id", "sequence_id"])
- op.create_index("ix_messages_org_agent", "messages", ["organization_id", "agent_id"])
-
- # Create sequence table for SQLite message sequence_id generation
- op.create_table(
- "message_sequence",
- sa.Column("id", sa.Integer(), nullable=False),
- sa.Column("next_val", sa.Integer(), nullable=False, server_default="1"),
- sa.PrimaryKeyConstraint("id"),
- )
-
- # Initialize the sequence table with the next available sequence_id
- op.execute("INSERT INTO message_sequence (id, next_val) VALUES (1, 1)")
-
- # Now create the rest of the tables that might reference messages/steps
-
- # Add missing tables and columns identified from alembic check
-
- # Identities table
- op.create_table(
- "identities",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("identifier_key", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("identity_type", sa.String(), nullable=False),
- sa.Column("project_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("properties", sa.JSON(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.UniqueConstraint("identifier_key", "project_id", "organization_id", name="unique_identifier_key_project_id_organization_id"),
- )
-
- # MCP Server table
- op.create_table(
- "mcp_server",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("server_name", sa.String(), nullable=False),
- sa.Column("server_type", sa.String(), nullable=False),
- sa.Column("server_url", sa.String(), nullable=True),
- sa.Column("stdio_config", sa.JSON(), nullable=True),
- sa.Column("token", sa.String(), nullable=True),
- sa.Column("custom_headers", sa.JSON(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.UniqueConstraint("server_name", "organization_id", name="uix_name_organization_mcp_server"),
- )
-
- # Providers table
- op.create_table(
- "providers",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("api_key", sa.String(), nullable=True),
- sa.Column("access_key", sa.String(), nullable=True),
- sa.Column("region", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("provider_type", sa.String(), nullable=True),
- sa.Column("base_url", sa.String(), nullable=True),
- sa.Column("provider_category", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.UniqueConstraint("name", "organization_id", name="unique_name_organization_id"),
- )
-
- # Agent environment variables table
- op.create_table(
- "agent_environment_variables",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("key", sa.String(), nullable=False),
- sa.Column("value", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.UniqueConstraint("key", "agent_id", name="uix_key_agent"),
- )
- op.create_index("idx_agent_environment_variables_agent_id", "agent_environment_variables", ["agent_id"])
-
- # Groups table
- op.create_table(
- "groups",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=False),
- sa.Column("manager_type", sa.String(), nullable=False),
- sa.Column("manager_agent_id", sa.String(), nullable=True),
- sa.Column("termination_token", sa.String(), nullable=True),
- sa.Column("max_turns", sa.Integer(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_ids", sa.JSON(), nullable=False),
- sa.Column("sleeptime_agent_frequency", sa.Integer(), nullable=True),
- sa.Column("turns_counter", sa.Integer(), nullable=True),
- sa.Column("last_processed_message_id", sa.String(), nullable=True),
- sa.Column("max_message_buffer_length", sa.Integer(), nullable=True),
- sa.Column("min_message_buffer_length", sa.Integer(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["manager_agent_id"], ["agents.id"], ondelete="RESTRICT"),
- )
-
- # Steps table
- op.create_table(
- "steps",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("job_id", sa.String(), nullable=True),
- sa.Column("completion_tokens", sa.Integer(), nullable=False, default=0),
- sa.Column("prompt_tokens", sa.Integer(), nullable=False, default=0),
- sa.Column("total_tokens", sa.Integer(), nullable=False, default=0),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("origin", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=True),
- sa.Column("provider_id", sa.String(), nullable=True),
- sa.Column("provider_name", sa.String(), nullable=True),
- sa.Column("model", sa.String(), nullable=True),
- sa.Column("context_window_limit", sa.Integer(), nullable=True),
- sa.Column("completion_tokens_details", sa.JSON(), nullable=True),
- sa.Column("tags", sa.JSON(), nullable=True),
- sa.Column("tid", sa.String(), nullable=True),
- sa.Column("model_endpoint", sa.String(), nullable=True),
- sa.Column("trace_id", sa.String(), nullable=True),
- sa.Column("agent_id", sa.String(), nullable=True),
- sa.Column("provider_category", sa.String(), nullable=True),
- sa.Column("feedback", sa.String(), nullable=True),
- sa.Column("project_id", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["job_id"], ["jobs.id"], ondelete="SET NULL"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"], ondelete="RESTRICT"),
- sa.ForeignKeyConstraint(["provider_id"], ["providers.id"], ondelete="RESTRICT"),
- )
-
- # Note: Foreign key constraint for block.current_history_entry_id -> block_history.id
- # would need to be added here, but SQLite doesn't support ALTER TABLE ADD CONSTRAINT
- # This will be handled by the ORM at runtime
-
- # Add missing columns to existing tables
-
- # All missing columns have been added to the table definitions above
-
- # step_id was already added in the messages table creation above
- # op.add_column('messages', sa.Column('step_id', sa.String(), nullable=True))
- # op.create_foreign_key('fk_messages_step_id', 'messages', 'steps', ['step_id'], ['id'], ondelete='SET NULL')
-
- # Add index to source_passages for file_id
- op.create_index("source_passages_file_id_idx", "source_passages", ["file_id"])
-
- # Unique constraint for sources was added during table creation above
-
- # Create remaining association tables
-
- # Identities agents table
- op.create_table(
- "identities_agents",
- sa.Column("identity_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["identity_id"], ["identities.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("identity_id", "agent_id"),
- )
-
- # Identities blocks table
- op.create_table(
- "identities_blocks",
- sa.Column("identity_id", sa.String(), nullable=False),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["identity_id"], ["identities.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["block_id"], ["block.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("identity_id", "block_id"),
- )
-
- # Files agents table
- op.create_table(
- "files_agents",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("file_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.Column("is_open", sa.Boolean(), nullable=False),
- sa.Column("visible_content", sa.Text(), nullable=True),
- sa.Column("last_accessed_at", sa.DateTime(timezone=True), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("file_name", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id", "file_id", "agent_id"),
- sa.ForeignKeyConstraint(["file_id"], ["files.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["source_id"], ["sources.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.UniqueConstraint("file_id", "agent_id", name="uq_file_agent"),
- sa.UniqueConstraint("agent_id", "file_name", name="uq_agent_filename"),
- )
- op.create_index("ix_agent_filename", "files_agents", ["agent_id", "file_name"])
- op.create_index("ix_file_agent", "files_agents", ["file_id", "agent_id"])
-
- # Groups agents table
- op.create_table(
- "groups_agents",
- sa.Column("group_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["group_id"], ["groups.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("group_id", "agent_id"),
- )
-
- # Groups blocks table
- op.create_table(
- "groups_blocks",
- sa.Column("group_id", sa.String(), nullable=False),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["group_id"], ["groups.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["block_id"], ["block.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("group_id", "block_id"),
- )
-
- # LLM batch job table
- op.create_table(
- "llm_batch_job",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("status", sa.String(), nullable=False),
- sa.Column("llm_provider", sa.String(), nullable=False),
- sa.Column("create_batch_response", sa.JSON(), nullable=False),
- sa.Column("latest_polling_response", sa.JSON(), nullable=True),
- sa.Column("last_polled_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("letta_batch_job_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["letta_batch_job_id"], ["jobs.id"], ondelete="CASCADE"),
- )
- op.create_index("ix_llm_batch_job_created_at", "llm_batch_job", ["created_at"])
- op.create_index("ix_llm_batch_job_status", "llm_batch_job", ["status"])
-
- # LLM batch items table
- op.create_table(
- "llm_batch_items",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("llm_config", sa.JSON(), nullable=False),
- sa.Column("request_status", sa.String(), nullable=False),
- sa.Column("step_status", sa.String(), nullable=False),
- sa.Column("step_state", sa.JSON(), nullable=False),
- sa.Column("batch_request_result", sa.JSON(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("llm_batch_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["llm_batch_id"], ["llm_batch_job.id"], ondelete="CASCADE"),
- )
- op.create_index("ix_llm_batch_items_agent_id", "llm_batch_items", ["agent_id"])
- op.create_index("ix_llm_batch_items_llm_batch_id", "llm_batch_items", ["llm_batch_id"])
- op.create_index("ix_llm_batch_items_status", "llm_batch_items", ["request_status"])
-
- # Job messages table
- op.create_table(
- "job_messages",
- sa.Column("id", sa.Integer(), primary_key=True),
- sa.Column("job_id", sa.String(), nullable=False),
- sa.Column("message_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.ForeignKeyConstraint(["job_id"], ["jobs.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["message_id"], ["messages.id"], ondelete="CASCADE"),
- sa.UniqueConstraint("job_id", "message_id", name="unique_job_message"),
- )
-
- # File contents table
- op.create_table(
- "file_contents",
- sa.Column("file_id", sa.String(), nullable=False),
- sa.Column("text", sa.Text(), nullable=False),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("file_id", "id"),
- sa.ForeignKeyConstraint(["file_id"], ["files.id"], ondelete="CASCADE"),
- sa.UniqueConstraint("file_id", name="uq_file_contents_file_id"),
- )
-
- # Provider traces table
- op.create_table(
- "provider_traces",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("request_json", sa.JSON(), nullable=False),
- sa.Column("response_json", sa.JSON(), nullable=False),
- sa.Column("step_id", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("(CURRENT_TIMESTAMP)"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("(FALSE)"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"]),
- )
- op.create_index("ix_step_id", "provider_traces", ["step_id"])
-
- # Complete the SQLite schema alignment by adding any remaining missing elements
- try:
- # Unique constraints for files_agents are already created with correct names in table definition above
-
- # Foreign key for files_agents.source_id is already created in table definition above
- # Foreign key for messages.step_id is already created in table definition above
- pass
-
- except Exception:
- # Some operations may fail if the column/constraint already exists
- # This is expected in some cases and we can continue
- pass
-
- # Note: The remaining alembic check differences are expected for SQLite:
- # 1. Type differences (BLOB vs CommonVector) - Expected and handled by ORM
- # 2. Foreign key constraint differences - SQLite handles these at runtime
- # 3. Index differences - SQLite doesn't support all PostgreSQL index features
- # 4. Some constraint naming differences - Cosmetic differences
- #
- # These differences do not affect functionality as the ORM handles the abstraction
- # between SQLite and PostgreSQL appropriately.
-
-
-def downgrade() -> None:
- # Only run this migration for SQLite
- if settings.letta_pg_uri_no_default:
- return
-
- # SQLite downgrade is not supported
- raise NotImplementedError("SQLite downgrade is not supported. Use a fresh database instead.")
diff --git a/alembic/versions/2cceb07c2384_add_content_parts_to_message.py b/alembic/versions/2cceb07c2384_add_content_parts_to_message.py
deleted file mode 100644
index c5e704c6..00000000
--- a/alembic/versions/2cceb07c2384_add_content_parts_to_message.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""add content parts to message
-
-Revision ID: 2cceb07c2384
-Revises: 77de976590ae
-Create Date: 2025-03-13 14:30:53.177061
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.orm.custom_columns import MessageContentColumn
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "2cceb07c2384"
-down_revision: Union[str, None] = "77de976590ae"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("content", MessageContentColumn(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("messages", "content")
- # ### end Alembic commands ###
diff --git a/alembic/versions/2f4ede6ae33b_add_otid_and_tool_return_to_message.py b/alembic/versions/2f4ede6ae33b_add_otid_and_tool_return_to_message.py
deleted file mode 100644
index 3e43ad10..00000000
--- a/alembic/versions/2f4ede6ae33b_add_otid_and_tool_return_to_message.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""add otid and tool return to message
-
-Revision ID: 2f4ede6ae33b
-Revises: 54f2311edb62
-Create Date: 2025-03-05 10:04:34.717671
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-import letta.orm
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "2f4ede6ae33b"
-down_revision: Union[str, None] = "54f2311edb62"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("otid", sa.String(), nullable=True))
- op.add_column("messages", sa.Column("tool_returns", letta.orm.custom_columns.ToolReturnColumn(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("messages", "tool_returns")
- op.drop_column("messages", "otid")
- # ### end Alembic commands ###
diff --git a/alembic/versions/341068089f14_add_preserve_on_migration_to_block.py b/alembic/versions/341068089f14_add_preserve_on_migration_to_block.py
deleted file mode 100644
index 2a7116a6..00000000
--- a/alembic/versions/341068089f14_add_preserve_on_migration_to_block.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add preserve_on_migration to block
-
-Revision ID: 341068089f14
-Revises: 348214cbc081
-Create Date: 2025-05-29 10:39:44.494643
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "341068089f14"
-down_revision: Union[str, None] = "348214cbc081"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("block", sa.Column("preserve_on_migration", sa.Boolean(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("block", "preserve_on_migration")
- # ### end Alembic commands ###
diff --git a/alembic/versions/348214cbc081_add_org_agent_id_indices.py b/alembic/versions/348214cbc081_add_org_agent_id_indices.py
deleted file mode 100644
index 7956115c..00000000
--- a/alembic/versions/348214cbc081_add_org_agent_id_indices.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add org agent id indices
-
-Revision ID: 348214cbc081
-Revises: dd049fbec729
-Create Date: 2025-05-28 22:43:18.509397
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "348214cbc081"
-down_revision: Union[str, None] = "dd049fbec729"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_index("ix_agent_passages_org_agent", "agent_passages", ["organization_id", "agent_id"], unique=False)
- op.create_index("ix_messages_org_agent", "messages", ["organization_id", "agent_id"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_messages_org_agent", table_name="messages")
- op.drop_index("ix_agent_passages_org_agent", table_name="agent_passages")
- # ### end Alembic commands ###
diff --git a/alembic/versions/373dabcba6cf_add_byok_fields_and_unique_constraint.py b/alembic/versions/373dabcba6cf_add_byok_fields_and_unique_constraint.py
deleted file mode 100644
index 6dac8e64..00000000
--- a/alembic/versions/373dabcba6cf_add_byok_fields_and_unique_constraint.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""add byok fields and unique constraint
-
-Revision ID: 373dabcba6cf
-Revises: c56081a05371
-Create Date: 2025-04-30 19:38:25.010856
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "373dabcba6cf"
-down_revision: Union[str, None] = "c56081a05371"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("providers", sa.Column("provider_type", sa.String(), nullable=True))
- op.add_column("providers", sa.Column("base_url", sa.String(), nullable=True))
- op.create_unique_constraint("unique_name_organization_id", "providers", ["name", "organization_id"])
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("unique_name_organization_id", "providers", type_="unique")
- op.drop_column("providers", "base_url")
- op.drop_column("providers", "provider_type")
- # ### end Alembic commands ###
diff --git a/alembic/versions/3c683a662c82_migrate_jobs_to_the_orm.py b/alembic/versions/3c683a662c82_migrate_jobs_to_the_orm.py
deleted file mode 100644
index 85a33461..00000000
--- a/alembic/versions/3c683a662c82_migrate_jobs_to_the_orm.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Migrate jobs to the orm
-
-Revision ID: 3c683a662c82
-Revises: 5987401b40ae
-Create Date: 2024-12-04 15:59:41.708396
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "3c683a662c82"
-down_revision: Union[str, None] = "5987401b40ae"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("jobs", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("jobs", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("jobs", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("jobs", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.alter_column("jobs", "status", existing_type=sa.VARCHAR(), nullable=False)
- op.alter_column("jobs", "completed_at", existing_type=postgresql.TIMESTAMP(timezone=True), type_=sa.DateTime(), existing_nullable=True)
- op.alter_column("jobs", "user_id", existing_type=sa.VARCHAR(), nullable=False)
- op.create_foreign_key(None, "jobs", "users", ["user_id"], ["id"])
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint(None, "jobs", type_="foreignkey")
- op.alter_column("jobs", "user_id", existing_type=sa.VARCHAR(), nullable=True)
- op.alter_column("jobs", "completed_at", existing_type=sa.DateTime(), type_=postgresql.TIMESTAMP(timezone=True), existing_nullable=True)
- op.alter_column("jobs", "status", existing_type=sa.VARCHAR(), nullable=True)
- op.drop_column("jobs", "_last_updated_by_id")
- op.drop_column("jobs", "_created_by_id")
- op.drop_column("jobs", "is_deleted")
- op.drop_column("jobs", "updated_at")
- # ### end Alembic commands ###
diff --git a/alembic/versions/400501b04bf0_add_per_agent_environment_variables.py b/alembic/versions/400501b04bf0_add_per_agent_environment_variables.py
deleted file mode 100644
index 1d42155f..00000000
--- a/alembic/versions/400501b04bf0_add_per_agent_environment_variables.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""Add per agent environment variables
-
-Revision ID: 400501b04bf0
-Revises: e78b4e82db30
-Create Date: 2025-01-04 20:45:28.024690
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "400501b04bf0"
-down_revision: Union[str, None] = "e78b4e82db30"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "agent_environment_variables",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("key", sa.String(), nullable=False),
- sa.Column("value", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- sa.UniqueConstraint("key", "agent_id", name="uix_key_agent"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("agent_environment_variables")
- # ### end Alembic commands ###
diff --git a/alembic/versions/416b9d2db10b_repurpose_jobusagestatistics_for_new_.py b/alembic/versions/416b9d2db10b_repurpose_jobusagestatistics_for_new_.py
deleted file mode 100644
index 1f296a7c..00000000
--- a/alembic/versions/416b9d2db10b_repurpose_jobusagestatistics_for_new_.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""Repurpose JobUsageStatistics for new Steps table
-
-Revision ID: 416b9d2db10b
-Revises: 25fc99e97839
-Create Date: 2025-01-17 11:27:42.115755
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "416b9d2db10b"
-down_revision: Union[str, None] = "25fc99e97839"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- # Rename the table
- op.rename_table("job_usage_statistics", "steps")
-
- # Rename the foreign key constraint and drop non-null constraint
- op.alter_column("steps", "job_id", nullable=True)
- op.drop_constraint("fk_job_usage_statistics_job_id", "steps", type_="foreignkey")
-
- # Change id field from int to string
- op.execute("ALTER TABLE steps RENAME COLUMN id TO old_id")
- op.add_column("steps", sa.Column("id", sa.String(), nullable=True))
- op.execute("""UPDATE steps SET id = 'step-' || gen_random_uuid()::text""")
- op.drop_column("steps", "old_id")
- op.alter_column("steps", "id", nullable=False)
- op.create_primary_key("pk_steps_id", "steps", ["id"])
-
- # Add new columns
- op.add_column("steps", sa.Column("origin", sa.String(), nullable=True))
- op.add_column("steps", sa.Column("organization_id", sa.String(), nullable=True))
- op.add_column("steps", sa.Column("provider_id", sa.String(), nullable=True))
- op.add_column("steps", sa.Column("provider_name", sa.String(), nullable=True))
- op.add_column("steps", sa.Column("model", sa.String(), nullable=True))
- op.add_column("steps", sa.Column("context_window_limit", sa.Integer(), nullable=True))
- op.add_column(
- "steps",
- sa.Column("completion_tokens_details", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True),
- )
- op.add_column(
- "steps",
- sa.Column("tags", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True),
- )
- op.add_column("steps", sa.Column("tid", sa.String(), nullable=True))
-
- # Add new foreign key constraint for provider_id
- op.create_foreign_key("fk_steps_organization_id", "steps", "providers", ["provider_id"], ["id"], ondelete="RESTRICT")
-
- # Add new foreign key constraint for provider_id
- op.create_foreign_key("fk_steps_provider_id", "steps", "organizations", ["organization_id"], ["id"], ondelete="RESTRICT")
-
- # Add new foreign key constraint for provider_id
- op.create_foreign_key("fk_steps_job_id", "steps", "jobs", ["job_id"], ["id"], ondelete="SET NULL")
-
- # Drop old step_id and step_count columns which aren't in the new model
- op.drop_column("steps", "step_id")
- op.drop_column("steps", "step_count")
-
- # Add step_id to messages table
- op.add_column("messages", sa.Column("step_id", sa.String(), nullable=True))
- op.create_foreign_key("fk_messages_step_id", "messages", "steps", ["step_id"], ["id"], ondelete="SET NULL")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- # Remove step_id from messages first to avoid foreign key conflicts
- op.drop_constraint("fk_messages_step_id", "messages", type_="foreignkey")
- op.drop_column("messages", "step_id")
-
- # Restore old step_count and step_id column
- op.add_column("steps", sa.Column("step_count", sa.Integer(), nullable=True))
- op.add_column("steps", sa.Column("step_id", sa.String(), nullable=True))
-
- # Drop new columns and constraints
- op.drop_constraint("fk_steps_provider_id", "steps", type_="foreignkey")
- op.drop_constraint("fk_steps_organization_id", "steps", type_="foreignkey")
- op.drop_constraint("fk_steps_job_id", "steps", type_="foreignkey")
-
- op.drop_column("steps", "tid")
- op.drop_column("steps", "tags")
- op.drop_column("steps", "completion_tokens_details")
- op.drop_column("steps", "context_window_limit")
- op.drop_column("steps", "model")
- op.drop_column("steps", "provider_name")
- op.drop_column("steps", "provider_id")
- op.drop_column("steps", "organization_id")
- op.drop_column("steps", "origin")
-
- # Add constraints back
- op.execute("DELETE FROM steps WHERE job_id IS NULL")
- op.alter_column("steps", "job_id", nullable=False)
- op.create_foreign_key("fk_job_usage_statistics_job_id", "steps", "jobs", ["job_id"], ["id"], ondelete="CASCADE")
-
- # Change id field from string back to int
- op.add_column("steps", sa.Column("old_id", sa.Integer(), nullable=True))
- op.execute("""UPDATE steps SET old_id = CAST(ABS(hashtext(REPLACE(id, 'step-', '')::text)) AS integer)""")
- op.drop_column("steps", "id")
- op.execute("ALTER TABLE steps RENAME COLUMN old_id TO id")
- op.alter_column("steps", "id", nullable=False)
- op.create_primary_key("pk_steps_id", "steps", ["id"])
-
- # Rename the table
- op.rename_table("steps", "job_usage_statistics")
- # ### end Alembic commands ###
diff --git a/alembic/versions/4537f0996495_add_start_end_for_agent_file.py b/alembic/versions/4537f0996495_add_start_end_for_agent_file.py
deleted file mode 100644
index 488bb0dd..00000000
--- a/alembic/versions/4537f0996495_add_start_end_for_agent_file.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""Add start end for agent file
-
-Revision ID: 4537f0996495
-Revises: 06fbbf65d4f1
-Create Date: 2025-07-25 17:44:26.748765
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "4537f0996495"
-down_revision: Union[str, None] = "06fbbf65d4f1"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("files_agents", sa.Column("start_line", sa.Integer(), nullable=True))
- op.add_column("files_agents", sa.Column("end_line", sa.Integer(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("files_agents", "end_line")
- op.drop_column("files_agents", "start_line")
- # ### end Alembic commands ###
diff --git a/alembic/versions/46699adc71a7_add_unique_constraint_to_source_names_.py b/alembic/versions/46699adc71a7_add_unique_constraint_to_source_names_.py
deleted file mode 100644
index 3578932d..00000000
--- a/alembic/versions/46699adc71a7_add_unique_constraint_to_source_names_.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Add unique constraint to source names and also add original file name column
-
-Revision ID: 46699adc71a7
-Revises: 1af251a42c06
-Create Date: 2025-07-01 13:30:48.279151
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "46699adc71a7"
-down_revision: Union[str, None] = "1af251a42c06"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("files", sa.Column("original_file_name", sa.String(), nullable=True))
-
- # Handle existing duplicate source names before adding unique constraint
- connection = op.get_bind()
-
- # Find duplicates and rename them by appending a suffix
- result = connection.execute(
- sa.text(
- """
- WITH duplicates AS (
- SELECT name, organization_id,
- ROW_NUMBER() OVER (PARTITION BY name, organization_id ORDER BY created_at) as rn,
- id
- FROM sources
- WHERE (name, organization_id) IN (
- SELECT name, organization_id
- FROM sources
- GROUP BY name, organization_id
- HAVING COUNT(*) > 1
- )
- )
- SELECT id, name, rn
- FROM duplicates
- WHERE rn > 1
- """
- )
- )
-
- # Rename duplicates by appending a number suffix
- for row in result:
- source_id, original_name, duplicate_number = row
- new_name = f"{original_name}_{duplicate_number}"
- connection.execute(
- sa.text("UPDATE sources SET name = :new_name WHERE id = :source_id"), {"new_name": new_name, "source_id": source_id}
- )
-
- op.create_unique_constraint("uq_source_name_organization", "sources", ["name", "organization_id"])
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("uq_source_name_organization", "sources", type_="unique")
- op.drop_column("files", "original_file_name")
- # ### end Alembic commands ###
diff --git a/alembic/versions/47d2277e530d_add_total_chunks_and_chunks_embedded_to_.py b/alembic/versions/47d2277e530d_add_total_chunks_and_chunks_embedded_to_.py
deleted file mode 100644
index f3a40d25..00000000
--- a/alembic/versions/47d2277e530d_add_total_chunks_and_chunks_embedded_to_.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""Add total_chunks and chunks_embedded to files
-
-Revision ID: 47d2277e530d
-Revises: 56254216524f
-Create Date: 2025-07-03 14:32:08.539280
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "47d2277e530d"
-down_revision: Union[str, None] = "56254216524f"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("files", sa.Column("total_chunks", sa.Integer(), nullable=True))
- op.add_column("files", sa.Column("chunks_embedded", sa.Integer(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("files", "chunks_embedded")
- op.drop_column("files", "total_chunks")
- # ### end Alembic commands ###
diff --git a/alembic/versions/495f3f474131_write_source_id_directly_to_files_agents.py b/alembic/versions/495f3f474131_write_source_id_directly_to_files_agents.py
deleted file mode 100644
index bb5e9dd9..00000000
--- a/alembic/versions/495f3f474131_write_source_id_directly_to_files_agents.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Write source_id directly to files agents
-
-Revision ID: 495f3f474131
-Revises: 47d2277e530d
-Create Date: 2025-07-10 17:14:45.154738
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "495f3f474131"
-down_revision: Union[str, None] = "47d2277e530d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- # Step 1: Add the column as nullable first
- op.add_column("files_agents", sa.Column("source_id", sa.String(), nullable=True))
-
- # Step 2: Backfill source_id from files table
- connection = op.get_bind()
- connection.execute(
- sa.text(
- """
- UPDATE files_agents
- SET source_id = files.source_id
- FROM files
- WHERE files_agents.file_id = files.id
- """
- )
- )
-
- # Step 3: Make the column NOT NULL now that it's populated
- op.alter_column("files_agents", "source_id", nullable=False)
-
- # Step 4: Add the foreign key constraint
- op.create_foreign_key(None, "files_agents", "sources", ["source_id"], ["id"], ondelete="CASCADE")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint(None, "files_agents", type_="foreignkey")
- op.drop_column("files_agents", "source_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/4c6c9ef0387d_support_modal_sandbox_type.py b/alembic/versions/4c6c9ef0387d_support_modal_sandbox_type.py
deleted file mode 100644
index 652b3554..00000000
--- a/alembic/versions/4c6c9ef0387d_support_modal_sandbox_type.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""support modal sandbox type
-
-Revision ID: 4c6c9ef0387d
-Revises: 4537f0996495
-Create Date: 2025-07-29 15:10:08.996251
-
-"""
-
-from typing import Sequence, Union
-
-from sqlalchemy import text
-
-from alembic import op
-from letta.settings import DatabaseChoice, settings
-
-# revision identifiers, used by Alembic.
-revision: str = "4c6c9ef0387d"
-down_revision: Union[str, None] = "4537f0996495"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # SQLite just uses strings
- if settings.database_engine == DatabaseChoice.POSTGRES:
- op.execute("ALTER TYPE sandboxtype ADD VALUE 'MODAL' AFTER 'E2B'")
-
-
-def downgrade() -> None:
- if settings.database_engine == DatabaseChoice.POSTGRES:
- connection = op.get_bind()
-
- data_conflicts = connection.execute(
- text(
- """
- SELECT COUNT(*)
- FROM sandbox_configs
- WHERE "type" NOT IN ('E2B', 'LOCAL')
- """
- )
- ).fetchone()
- if data_conflicts[0]:
- raise RuntimeError(
- (
- "Cannot downgrade enum: Data conflicts are detected in sandbox_configs.sandboxtype.\n"
- "Please manually handle these records before handling the downgrades.\n"
- f"{data_conflicts} invalid sandboxtype values"
- )
- )
-
- # Postgres does not support dropping enum values. Create a new enum and swap them.
- op.execute("CREATE TYPE sandboxtype_old AS ENUM ('E2B', 'LOCAL')")
- op.execute('ALTER TABLE sandbox_configs ALTER COLUMN "type" TYPE sandboxtype_old USING "type"::text::sandboxtype_old')
- op.execute("DROP TYPE sandboxtype")
- op.execute("ALTER TYPE sandboxtype_old RENAME to sandboxtype")
diff --git a/alembic/versions/4e88e702f85e_drop_api_tokens_table_in_oss.py b/alembic/versions/4e88e702f85e_drop_api_tokens_table_in_oss.py
deleted file mode 100644
index 0bd56ebe..00000000
--- a/alembic/versions/4e88e702f85e_drop_api_tokens_table_in_oss.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Drop api tokens table in OSS
-
-Revision ID: 4e88e702f85e
-Revises: d05669b60ebe
-Create Date: 2024-12-13 17:19:55.796210
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "4e88e702f85e"
-down_revision: Union[str, None] = "d05669b60ebe"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("tokens_idx_key", table_name="tokens")
- op.drop_index("tokens_idx_user", table_name="tokens")
- op.drop_table("tokens")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "tokens",
- sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("key", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("name", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.PrimaryKeyConstraint("id", name="tokens_pkey"),
- )
- op.create_index("tokens_idx_user", "tokens", ["user_id"], unique=False)
- op.create_index("tokens_idx_key", "tokens", ["key"], unique=False)
- # ### end Alembic commands ###
diff --git a/alembic/versions/51999513bcf1_steps_feedback_field.py b/alembic/versions/51999513bcf1_steps_feedback_field.py
deleted file mode 100644
index d20f248d..00000000
--- a/alembic/versions/51999513bcf1_steps_feedback_field.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""steps feedback field
-
-Revision ID: 51999513bcf1
-Revises: 61ee53ec45a5
-Create Date: 2025-06-20 14:09:22.993263
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "51999513bcf1"
-down_revision: Union[str, None] = "c7ac45f69849"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("steps", sa.Column("feedback", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "feedback")
- # ### end Alembic commands ###
diff --git a/alembic/versions/549eff097c71_update_identities_unique_constraint_and_.py b/alembic/versions/549eff097c71_update_identities_unique_constraint_and_.py
deleted file mode 100644
index 45073c79..00000000
--- a/alembic/versions/549eff097c71_update_identities_unique_constraint_and_.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""update identities unique constraint and properties
-
-Revision ID: 549eff097c71
-Revises: a3047a624130
-Create Date: 2025-02-20 09:53:42.743105
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "549eff097c71"
-down_revision: Union[str, None] = "a3047a624130"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- # Update unique constraint on identities table
- op.drop_constraint("unique_identifier_pid_org_id", "identities", type_="unique")
- op.create_unique_constraint(
- "unique_identifier_without_project",
- "identities",
- ["identifier_key", "project_id", "organization_id"],
- postgresql_nulls_not_distinct=True,
- )
-
- # Add properties column to identities table
- op.add_column("identities", sa.Column("properties", postgresql.JSONB, nullable=False, server_default="[]"))
-
- # Create identities_agents table for many-to-many relationship
- op.create_table(
- "identities_agents",
- sa.Column("identity_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["identity_id"], ["identities.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("identity_id", "agent_id"),
- )
-
- # Migrate existing relationships
- # First, get existing relationships where identity_id is not null
- op.execute(
- """
- INSERT INTO identities_agents (identity_id, agent_id)
- SELECT DISTINCT identity_id, id as agent_id
- FROM agents
- WHERE identity_id IS NOT NULL
- """
- )
-
- # Remove old identity_id column from agents
- op.drop_column("agents", "identity_id")
- op.drop_column("agents", "identifier_key")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- # Add back the old columns to agents
- op.add_column("agents", sa.Column("identity_id", sa.String(), nullable=True))
- op.add_column("agents", sa.Column("identifier_key", sa.String(), nullable=True))
-
- # Migrate relationships back
- op.execute(
- """
- UPDATE agents a
- SET identity_id = ia.identity_id
- FROM identities_agents ia
- WHERE a.id = ia.agent_id
- """
- )
-
- # Drop the many-to-many table
- op.drop_table("identities_agents")
-
- # Drop properties column
- op.drop_column("identities", "properties")
-
- # Restore old unique constraint
- op.drop_constraint("unique_identifier_without_project", "identities", type_="unique")
- op.create_unique_constraint("unique_identifier_pid_org_id", "identities", ["identifier_key", "project_id", "organization_id"])
- # ### end Alembic commands ###
diff --git a/alembic/versions/54c76f7cabca_add_tags_to_passages_and_create_passage_.py b/alembic/versions/54c76f7cabca_add_tags_to_passages_and_create_passage_.py
deleted file mode 100644
index 0cfa65f5..00000000
--- a/alembic/versions/54c76f7cabca_add_tags_to_passages_and_create_passage_.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Add tags to passages and create passage_tags junction table
-
-Revision ID: 54c76f7cabca
-Revises: c41c87205254
-Create Date: 2025-08-28 15:13:01.549590
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "54c76f7cabca"
-down_revision: Union[str, None] = "c41c87205254"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
-
- # Database-specific timestamp defaults
- if not settings.letta_pg_uri_no_default:
- # SQLite uses CURRENT_TIMESTAMP
- timestamp_default = sa.text("(CURRENT_TIMESTAMP)")
- else:
- # PostgreSQL uses now()
- timestamp_default = sa.text("now()")
-
- op.create_table(
- "passage_tags",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("tag", sa.String(), nullable=False),
- sa.Column("passage_id", sa.String(), nullable=False),
- sa.Column("archive_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=timestamp_default, nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=timestamp_default, nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["archive_id"], ["archives.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.ForeignKeyConstraint(["passage_id"], ["archival_passages.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("id"),
- sa.UniqueConstraint("passage_id", "tag", name="uq_passage_tag"),
- )
- op.create_index("ix_passage_tags_archive_id", "passage_tags", ["archive_id"], unique=False)
- op.create_index("ix_passage_tags_archive_tag", "passage_tags", ["archive_id", "tag"], unique=False)
- op.create_index("ix_passage_tags_org_archive", "passage_tags", ["organization_id", "archive_id"], unique=False)
- op.create_index("ix_passage_tags_tag", "passage_tags", ["tag"], unique=False)
- op.add_column("archival_passages", sa.Column("tags", sa.JSON(), nullable=True))
- op.add_column("source_passages", sa.Column("tags", sa.JSON(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("source_passages", "tags")
- op.drop_column("archival_passages", "tags")
- op.drop_index("ix_passage_tags_tag", table_name="passage_tags")
- op.drop_index("ix_passage_tags_org_archive", table_name="passage_tags")
- op.drop_index("ix_passage_tags_archive_tag", table_name="passage_tags")
- op.drop_index("ix_passage_tags_archive_id", table_name="passage_tags")
- op.drop_table("passage_tags")
- # ### end Alembic commands ###
diff --git a/alembic/versions/54dec07619c4_divide_passage_table_into_.py b/alembic/versions/54dec07619c4_divide_passage_table_into_.py
deleted file mode 100644
index e58a490a..00000000
--- a/alembic/versions/54dec07619c4_divide_passage_table_into_.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""divide passage table into SourcePassages and AgentPassages
-
-Revision ID: 54dec07619c4
-Revises: 4e88e702f85e
-Create Date: 2024-12-14 17:23:08.772554
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.orm.custom_columns import EmbeddingConfigColumn
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "54dec07619c4"
-down_revision: Union[str, None] = "4e88e702f85e"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
- from pgvector.sqlalchemy import Vector
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "agent_passages",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("text", sa.String(), nullable=False),
- sa.Column("embedding_config", EmbeddingConfigColumn(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=False),
- sa.Column("embedding", Vector(dim=4096), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("agent_passages_org_idx", "agent_passages", ["organization_id"], unique=False)
- op.create_table(
- "source_passages",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("text", sa.String(), nullable=False),
- sa.Column("embedding_config", EmbeddingConfigColumn(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=False),
- sa.Column("embedding", Vector(dim=4096), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("file_id", sa.String(), nullable=True),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["file_id"], ["files.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.ForeignKeyConstraint(["source_id"], ["sources.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("source_passages_org_idx", "source_passages", ["organization_id"], unique=False)
- op.drop_table("passages")
- op.drop_constraint("files_source_id_fkey", "files", type_="foreignkey")
- op.create_foreign_key(None, "files", "sources", ["source_id"], ["id"], ondelete="CASCADE")
- op.drop_constraint("messages_agent_id_fkey", "messages", type_="foreignkey")
- op.create_foreign_key(None, "messages", "agents", ["agent_id"], ["id"], ondelete="CASCADE")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint(None, "messages", type_="foreignkey")
- op.create_foreign_key("messages_agent_id_fkey", "messages", "agents", ["agent_id"], ["id"])
- op.drop_constraint(None, "files", type_="foreignkey")
- op.create_foreign_key("files_source_id_fkey", "files", "sources", ["source_id"], ["id"])
- op.create_table(
- "passages",
- sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("text", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("file_id", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("agent_id", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("source_id", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("embedding", Vector(dim=4096), autoincrement=False, nullable=True),
- sa.Column("embedding_config", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=False),
- sa.Column("metadata_", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=False),
- sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False),
- sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), autoincrement=False, nullable=True),
- sa.Column("is_deleted", sa.BOOLEAN(), server_default=sa.text("false"), autoincrement=False, nullable=False),
- sa.Column("_created_by_id", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("_last_updated_by_id", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("organization_id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], name="passages_agent_id_fkey"),
- sa.ForeignKeyConstraint(["file_id"], ["files.id"], name="passages_file_id_fkey", ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"], name="passages_organization_id_fkey"),
- sa.PrimaryKeyConstraint("id", name="passages_pkey"),
- )
- op.drop_index("source_passages_org_idx", table_name="source_passages")
- op.drop_table("source_passages")
- op.drop_index("agent_passages_org_idx", table_name="agent_passages")
- op.drop_table("agent_passages")
- # ### end Alembic commands ###
diff --git a/alembic/versions/54f2311edb62_add_args_schema_to_tools.py b/alembic/versions/54f2311edb62_add_args_schema_to_tools.py
deleted file mode 100644
index 163a4e88..00000000
--- a/alembic/versions/54f2311edb62_add_args_schema_to_tools.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add args schema to tools
-
-Revision ID: 54f2311edb62
-Revises: b183663c6769
-Create Date: 2025-02-27 16:45:50.835081
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "54f2311edb62"
-down_revision: Union[str, None] = "b183663c6769"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("tools", sa.Column("args_json_schema", sa.JSON(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("tools", "args_json_schema")
- # ### end Alembic commands ###
diff --git a/alembic/versions/56254216524f_add_custom_headers_to_mcp_server.py b/alembic/versions/56254216524f_add_custom_headers_to_mcp_server.py
deleted file mode 100644
index 80c57532..00000000
--- a/alembic/versions/56254216524f_add_custom_headers_to_mcp_server.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add_custom_headers_to_mcp_server
-
-Revision ID: 56254216524f
-Revises: 60ed28ee7138
-Create Date: 2025-07-02 14:08:59.163861
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "56254216524f"
-down_revision: Union[str, None] = "60ed28ee7138"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("mcp_server", sa.Column("custom_headers", sa.JSON(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("mcp_server", "custom_headers")
- # ### end Alembic commands ###
diff --git a/alembic/versions/5987401b40ae_refactor_agent_memory.py b/alembic/versions/5987401b40ae_refactor_agent_memory.py
deleted file mode 100644
index 741644e2..00000000
--- a/alembic/versions/5987401b40ae_refactor_agent_memory.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""Refactor agent memory
-
-Revision ID: 5987401b40ae
-Revises: 1c8880d671ee
-Create Date: 2024-11-25 14:35:00.896507
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "5987401b40ae"
-down_revision: Union[str, None] = "1c8880d671ee"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column("agents", "tools", new_column_name="tool_names")
- op.drop_column("agents", "memory")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column("agents", "tool_names", new_column_name="tools")
- op.add_column("agents", sa.Column("memory", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True))
- # ### end Alembic commands ###
diff --git a/alembic/versions/5b804970e6a0_add_hidden_property_to_groups_and_blocks.py b/alembic/versions/5b804970e6a0_add_hidden_property_to_groups_and_blocks.py
deleted file mode 100644
index 6f97ddd4..00000000
--- a/alembic/versions/5b804970e6a0_add_hidden_property_to_groups_and_blocks.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""add_hidden_property_to_groups_and_blocks
-
-Revision ID: 5b804970e6a0
-Revises: ddb69be34a72
-Create Date: 2025-09-03 22:19:03.825077
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "5b804970e6a0"
-down_revision: Union[str, None] = "ddb69be34a72"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Add hidden column to groups table
- op.add_column("groups", sa.Column("hidden", sa.Boolean(), nullable=True))
-
- # Add hidden column to block table
- op.add_column("block", sa.Column("hidden", sa.Boolean(), nullable=True))
-
-
-def downgrade() -> None:
- # Remove hidden column from block table
- op.drop_column("block", "hidden")
-
- # Remove hidden column from groups table
- op.drop_column("groups", "hidden")
diff --git a/alembic/versions/5fb8bba2c373_add_step_metrics.py b/alembic/versions/5fb8bba2c373_add_step_metrics.py
deleted file mode 100644
index 137b20db..00000000
--- a/alembic/versions/5fb8bba2c373_add_step_metrics.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""add_step_metrics
-
-Revision ID: 5fb8bba2c373
-Revises: f7f757414d20
-Create Date: 2025-08-07 17:40:11.923402
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "5fb8bba2c373"
-down_revision: Union[str, None] = "f7f757414d20"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "step_metrics",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("organization_id", sa.String(), nullable=True),
- sa.Column("provider_id", sa.String(), nullable=True),
- sa.Column("job_id", sa.String(), nullable=True),
- sa.Column("llm_request_ns", sa.BigInteger(), nullable=True),
- sa.Column("tool_execution_ns", sa.BigInteger(), nullable=True),
- sa.Column("step_ns", sa.BigInteger(), nullable=True),
- sa.Column("base_template_id", sa.String(), nullable=True),
- sa.Column("template_id", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("project_id", sa.String(), nullable=True),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["id"], ["steps.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["job_id"], ["jobs.id"], ondelete="SET NULL"),
- sa.ForeignKeyConstraint(["organization_id"], ["organizations.id"], ondelete="RESTRICT"),
- sa.ForeignKeyConstraint(["provider_id"], ["providers.id"], ondelete="RESTRICT"),
- sa.PrimaryKeyConstraint("id"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("step_metrics")
- # ### end Alembic commands ###
diff --git a/alembic/versions/60ed28ee7138_add_project_id_to_step_model.py b/alembic/versions/60ed28ee7138_add_project_id_to_step_model.py
deleted file mode 100644
index aa0817d8..00000000
--- a/alembic/versions/60ed28ee7138_add_project_id_to_step_model.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""add project id to step model
-
-Revision ID: 60ed28ee7138
-Revises: 46699adc71a7
-Create Date: 2025-07-01 13:12:44.485233
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "60ed28ee7138"
-down_revision: Union[str, None] = "46699adc71a7"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("steps", sa.Column("project_id", sa.String(), nullable=True))
- op.execute(
- """
- UPDATE steps
- SET project_id = agents.project_id
- FROM agents
- WHERE steps.agent_id = agents.id
- AND steps.agent_id IS NOT NULL
- AND agents.project_id IS NOT NULL
- """
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "project_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/614c4e53b66e_add_unique_constraint_to_file_id_and_.py b/alembic/versions/614c4e53b66e_add_unique_constraint_to_file_id_and_.py
deleted file mode 100644
index 8d8813d1..00000000
--- a/alembic/versions/614c4e53b66e_add_unique_constraint_to_file_id_and_.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""Add unique constraint to file_id and agent_id on file_agent
-
-Revision ID: 614c4e53b66e
-Revises: 0b496eae90de
-Create Date: 2025-06-02 17:03:58.879839
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "614c4e53b66e"
-down_revision: Union[str, None] = "0b496eae90de"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_unique_constraint("uq_files_agents_file_agent", "files_agents", ["file_id", "agent_id"])
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("uq_files_agents_file_agent", "files_agents", type_="unique")
- # ### end Alembic commands ###
diff --git a/alembic/versions/61ee53ec45a5_add_index_on_source_passages_for_files.py b/alembic/versions/61ee53ec45a5_add_index_on_source_passages_for_files.py
deleted file mode 100644
index a9ae8a39..00000000
--- a/alembic/versions/61ee53ec45a5_add_index_on_source_passages_for_files.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""add index on source passages for files
-
-Revision ID: 61ee53ec45a5
-Revises: 9758adf8fdd3
-Create Date: 2025-06-20 11:10:02.744914
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "61ee53ec45a5"
-down_revision: Union[str, None] = "9758adf8fdd3"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_index("source_passages_file_id_idx", "source_passages", ["file_id"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("source_passages_file_id_idx", table_name="source_passages")
- # ### end Alembic commands ###
diff --git a/alembic/versions/6c53224a7a58_add_provider_category_to_steps.py b/alembic/versions/6c53224a7a58_add_provider_category_to_steps.py
deleted file mode 100644
index bf06a6c9..00000000
--- a/alembic/versions/6c53224a7a58_add_provider_category_to_steps.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add provider category to steps
-
-Revision ID: 6c53224a7a58
-Revises: cc8dc340836d
-Create Date: 2025-05-21 10:09:43.761669
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "6c53224a7a58"
-down_revision: Union[str, None] = "cc8dc340836d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("steps", sa.Column("provider_category", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "provider_category")
- # ### end Alembic commands ###
diff --git a/alembic/versions/6fbe9cace832_adding_indexes_to_models.py b/alembic/versions/6fbe9cace832_adding_indexes_to_models.py
deleted file mode 100644
index 5c01f445..00000000
--- a/alembic/versions/6fbe9cace832_adding_indexes_to_models.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""adding indexes to models
-
-Revision ID: 6fbe9cace832
-Revises: f895232c144a
-Create Date: 2025-01-23 11:02:59.534372
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "6fbe9cace832"
-down_revision: Union[str, None] = "f895232c144a"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_index("agent_passages_created_at_id_idx", "agent_passages", ["created_at", "id"], unique=False)
- op.create_index("ix_agents_created_at", "agents", ["created_at", "id"], unique=False)
- op.create_index("created_at_label_idx", "block", ["created_at", "label"], unique=False)
- op.create_index("ix_jobs_created_at", "jobs", ["created_at", "id"], unique=False)
- op.create_index("ix_messages_created_at", "messages", ["created_at", "id"], unique=False)
- op.create_index("source_passages_created_at_id_idx", "source_passages", ["created_at", "id"], unique=False)
- op.create_index("source_created_at_id_idx", "sources", ["created_at", "id"], unique=False)
- op.create_index("ix_tools_created_at_name", "tools", ["created_at", "name"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_tools_created_at_name", table_name="tools")
- op.drop_index("source_created_at_id_idx", table_name="sources")
- op.drop_index("source_passages_created_at_id_idx", table_name="source_passages")
- op.drop_index("ix_messages_created_at", table_name="messages")
- op.drop_index("ix_jobs_created_at", table_name="jobs")
- op.drop_index("created_at_label_idx", table_name="block")
- op.drop_index("ix_agents_created_at", table_name="agents")
- op.drop_index("agent_passages_created_at_id_idx", table_name="agent_passages")
- # ### end Alembic commands ###
diff --git a/alembic/versions/6fe79c0525f2_enable_sleeptime_agent_fields.py b/alembic/versions/6fe79c0525f2_enable_sleeptime_agent_fields.py
deleted file mode 100644
index 8d6cda1d..00000000
--- a/alembic/versions/6fe79c0525f2_enable_sleeptime_agent_fields.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""enable sleeptime agent fields
-
-Revision ID: 6fe79c0525f2
-Revises: e991d2e3b428
-Create Date: 2025-04-02 08:32:57.412903
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "6fe79c0525f2"
-down_revision: Union[str, None] = "e991d2e3b428"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("enable_sleeptime", sa.Boolean(), nullable=True))
- op.alter_column("groups", "background_agents_interval", new_column_name="background_agents_frequency")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column("groups", "background_agents_frequency", new_column_name="background_agents_interval")
- op.drop_column("agents", "enable_sleeptime")
- # ### end Alembic commands ###
diff --git a/alembic/versions/74e860718e0d_add_archival_memory_sharing.py b/alembic/versions/74e860718e0d_add_archival_memory_sharing.py
deleted file mode 100644
index a63e95ee..00000000
--- a/alembic/versions/74e860718e0d_add_archival_memory_sharing.py
+++ /dev/null
@@ -1,508 +0,0 @@
-"""add archival memory sharing
-
-Revision ID: 74e860718e0d
-Revises: 15b577c62f3f
-Create Date: 2025-07-30 16:15:49.424711
-
-"""
-
-import time
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# Import custom columns if needed
-try:
- from letta.orm.custom_columns import CommonVector, EmbeddingConfigColumn
-except ImportError:
- # For environments where these aren't available
- EmbeddingConfigColumn = sa.JSON
- CommonVector = sa.BLOB
-
-# revision identifiers, used by Alembic.
-revision: str = "74e860718e0d"
-down_revision: Union[str, None] = "15b577c62f3f"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # get database connection to check DB type
- bind = op.get_bind()
- is_sqlite = bind.dialect.name == "sqlite"
-
- # create new tables with appropriate defaults
- if is_sqlite:
- op.create_table(
- "archives",
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("0"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- else:
- # Check if archives table already exists
- connection = op.get_bind()
- result = connection.execute(
- sa.text(
- """
- SELECT EXISTS (
- SELECT 1 FROM information_schema.tables
- WHERE table_schema = 'public' AND table_name = 'archives'
- )
- """
- )
- )
- archives_exists = result.scalar()
-
- if not archives_exists:
- op.create_table(
- "archives",
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
-
- op.create_index("ix_archives_created_at", "archives", ["created_at", "id"], unique=False)
- op.create_index("ix_archives_organization_id", "archives", ["organization_id"], unique=False)
-
- if is_sqlite:
- op.create_table(
- "archives_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("archive_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("datetime('now')"), nullable=False),
- sa.Column("is_owner", sa.Boolean(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["archive_id"], ["archives.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("agent_id", "archive_id"),
- # TODO: Remove this constraint when we support multiple archives per agent
- sa.UniqueConstraint("agent_id", name="unique_agent_archive"),
- )
- else:
- op.create_table(
- "archives_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("archive_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
- sa.Column("is_owner", sa.Boolean(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["archive_id"], ["archives.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("agent_id", "archive_id"),
- # TODO: Remove this constraint when we support multiple archives per agent
- sa.UniqueConstraint("agent_id", name="unique_agent_archive"),
- )
-
- if is_sqlite:
- # For SQLite
- # create temporary table to preserve existing agent_passages data
- op.execute(
- """
- CREATE TEMPORARY TABLE temp_agent_passages AS
- SELECT * FROM agent_passages WHERE is_deleted = 0;
- """
- )
-
- # create default archives and migrate data
- # First, create archives for each agent that has passages
- op.execute(
- """
- INSERT INTO archives (id, name, description, organization_id, created_at, updated_at, is_deleted)
- SELECT DISTINCT
- 'archive-' || lower(hex(randomblob(16))),
- COALESCE(a.name, 'Agent ' || a.id) || '''s Archive',
- 'Default archive created during migration',
- a.organization_id,
- datetime('now'),
- datetime('now'),
- 0
- FROM temp_agent_passages ap
- JOIN agents a ON ap.agent_id = a.id;
- """
- )
-
- # create archives_agents relationships
- op.execute(
- """
- INSERT INTO archives_agents (agent_id, archive_id, is_owner, created_at)
- SELECT
- a.id as agent_id,
- ar.id as archive_id,
- 1 as is_owner,
- datetime('now') as created_at
- FROM agents a
- JOIN archives ar ON ar.organization_id = a.organization_id
- AND ar.name = COALESCE(a.name, 'Agent ' || a.id) || '''s Archive'
- WHERE EXISTS (
- SELECT 1 FROM temp_agent_passages ap WHERE ap.agent_id = a.id
- );
- """
- )
-
- # drop the old agent_passages table
- op.drop_index("ix_agent_passages_org_agent", table_name="agent_passages")
- op.drop_table("agent_passages")
-
- # create the new archival_passages table with the new schema
- op.create_table(
- "archival_passages",
- sa.Column("text", sa.String(), nullable=False),
- sa.Column("embedding_config", EmbeddingConfigColumn, nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=False),
- sa.Column("embedding", CommonVector, nullable=True), # SQLite uses CommonVector for embeddings
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("0"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("archive_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.ForeignKeyConstraint(["archive_id"], ["archives.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("id"),
- )
-
- # migrate data from temp table to archival_passages with archive_id
- op.execute(
- """
- INSERT INTO archival_passages (
- id, text, embedding_config, metadata_, embedding,
- created_at, updated_at, is_deleted,
- _created_by_id, _last_updated_by_id,
- organization_id, archive_id
- )
- SELECT
- ap.id, ap.text, ap.embedding_config, ap.metadata_, ap.embedding,
- ap.created_at, ap.updated_at, ap.is_deleted,
- ap._created_by_id, ap._last_updated_by_id,
- ap.organization_id, ar.id as archive_id
- FROM temp_agent_passages ap
- JOIN agents a ON ap.agent_id = a.id
- JOIN archives ar ON ar.organization_id = a.organization_id
- AND ar.name = COALESCE(a.name, 'Agent ' || a.id) || '''s Archive';
- """
- )
-
- # drop temporary table
- op.execute("DROP TABLE temp_agent_passages;")
-
- # create indexes
- op.create_index("ix_archival_passages_archive_id", "archival_passages", ["archive_id"])
- op.create_index("ix_archival_passages_org_archive", "archival_passages", ["organization_id", "archive_id"])
- op.create_index("archival_passages_created_at_id_idx", "archival_passages", ["created_at", "id"])
-
- else:
- # PostgreSQL
- # add archive_id to agent_passages
- op.add_column("agent_passages", sa.Column("archive_id", sa.String(), nullable=True))
-
- # get connection for batch processing
- connection = op.get_bind()
-
- # get total count of agents with passages
- total_agents_result = connection.execute(
- sa.text(
- """
- SELECT COUNT(DISTINCT a.id)
- FROM agent_passages ap
- JOIN agents a ON ap.agent_id = a.id
- WHERE ap.is_deleted = FALSE
- """
- )
- )
- total_agents = total_agents_result.scalar()
-
- if total_agents > 0:
- print(f"Starting archival memory migration for {total_agents} agents...")
- start_time = time.time()
-
- batch_size = 1000
-
- # process agents one by one to maintain proper relationships
- offset = 0
- while offset < total_agents:
- # Get batch of agents that need archives
- batch_result = connection.execute(
- sa.text(
- """
- SELECT DISTINCT a.id, a.name, a.organization_id
- FROM agent_passages ap
- JOIN agents a ON ap.agent_id = a.id
- WHERE ap.is_deleted = FALSE
- AND NOT EXISTS (
- SELECT 1 FROM archives_agents aa
- WHERE aa.agent_id = a.id
- )
- ORDER BY a.id
- LIMIT :batch_size
- """
- ).bindparams(batch_size=batch_size)
- )
-
- agents_batch = batch_result.fetchall()
- if not agents_batch:
- break # No more agents to process
-
- batch_count = len(agents_batch)
- print(f"Processing batch of {batch_count} agents (offset: {offset})...")
-
- # Create archive and relationship for each agent
- for agent_id, agent_name, org_id in agents_batch:
- try:
- # Create archive
- archive_result = connection.execute(
- sa.text(
- """
- INSERT INTO archives (id, name, description, organization_id, created_at)
- VALUES (
- 'archive-' || gen_random_uuid(),
- :archive_name,
- 'Default archive created during migration',
- :org_id,
- NOW()
- )
- RETURNING id
- """
- ).bindparams(archive_name=f"{agent_name or f'Agent {agent_id}'}'s Archive", org_id=org_id)
- )
- archive_id = archive_result.scalar()
-
- # Create agent-archive relationship
- connection.execute(
- sa.text(
- """
- INSERT INTO archives_agents (agent_id, archive_id, is_owner, created_at)
- VALUES (:agent_id, :archive_id, TRUE, NOW())
- """
- ).bindparams(agent_id=agent_id, archive_id=archive_id)
- )
- except Exception as e:
- print(f"Warning: Failed to create archive for agent {agent_id}: {e}")
- # Continue with other agents
-
- offset += batch_count
-
- print("Archive creation completed. Starting archive_id updates...")
-
- # update agent_passages with archive_id in batches
- total_passages_result = connection.execute(
- sa.text(
- """
- SELECT COUNT(*)
- FROM agent_passages ap
- WHERE ap.archive_id IS NULL
- AND ap.is_deleted = FALSE
- """
- )
- )
- total_passages = total_passages_result.scalar()
-
- if total_passages > 0:
- print(f"Updating archive_id for {total_passages} passages...")
-
- updated_passages = 0
- update_batch_size = 5000 # larger batch size for updates
-
- while updated_passages < total_passages:
- print(
- f"Updating passages {updated_passages + 1} to {min(updated_passages + update_batch_size, total_passages)} of {total_passages}..."
- )
-
- # Use connection.execute instead of op.execute to get rowcount
- result = connection.execute(
- sa.text(
- """
- UPDATE agent_passages ap
- SET archive_id = aa.archive_id
- FROM archives_agents aa
- WHERE ap.agent_id = aa.agent_id
- AND ap.archive_id IS NULL
- AND ap.is_deleted = FALSE
- AND ap.id IN (
- SELECT id FROM agent_passages
- WHERE archive_id IS NULL
- AND is_deleted = FALSE
- LIMIT :batch_size
- )
- """
- ).bindparams(batch_size=update_batch_size)
- )
-
- rows_updated = result.rowcount
- if rows_updated == 0:
- break # no more rows to update
-
- updated_passages += rows_updated
-
- print(f"Archive_id update completed. Updated {updated_passages} passages.")
-
- elapsed_time = time.time() - start_time
- print(f"Data migration completed successfully in {elapsed_time:.2f} seconds.")
- else:
- print("No agents with passages found. Skipping data migration.")
-
- # schema changes
- op.alter_column("agent_passages", "archive_id", nullable=False)
- op.create_foreign_key("agent_passages_archive_id_fkey", "agent_passages", "archives", ["archive_id"], ["id"], ondelete="CASCADE")
-
- # drop old indexes and constraints
- op.drop_index("ix_agent_passages_org_agent", table_name="agent_passages")
- op.drop_index("agent_passages_org_idx", table_name="agent_passages")
- op.drop_index("agent_passages_created_at_id_idx", table_name="agent_passages")
- op.drop_constraint("agent_passages_agent_id_fkey", "agent_passages", type_="foreignkey")
- op.drop_column("agent_passages", "agent_id")
-
- # rename table and create new indexes
- op.rename_table("agent_passages", "archival_passages")
- op.create_index("ix_archival_passages_archive_id", "archival_passages", ["archive_id"])
- op.create_index("ix_archival_passages_org_archive", "archival_passages", ["organization_id", "archive_id"])
- op.create_index("archival_passages_org_idx", "archival_passages", ["organization_id"])
- op.create_index("archival_passages_created_at_id_idx", "archival_passages", ["created_at", "id"])
-
-
-def downgrade() -> None:
- # Get database connection to check DB type
- bind = op.get_bind()
- is_sqlite = bind.dialect.name == "sqlite"
-
- if is_sqlite:
- # For SQLite, we need to migrate data back carefully
- # create temporary table to preserve existing archival_passages data
- op.execute(
- """
- CREATE TEMPORARY TABLE temp_archival_passages AS
- SELECT * FROM archival_passages WHERE is_deleted = 0;
- """
- )
-
- # drop the archival_passages table and indexes
- op.drop_index("ix_archival_passages_org_archive", table_name="archival_passages")
- op.drop_index("ix_archival_passages_archive_id", table_name="archival_passages")
- op.drop_index("archival_passages_created_at_id_idx", table_name="archival_passages")
- op.drop_table("archival_passages")
-
- # recreate agent_passages with old schema
- op.create_table(
- "agent_passages",
- sa.Column("text", sa.String(), nullable=False),
- sa.Column("embedding_config", EmbeddingConfigColumn, nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=False),
- sa.Column("embedding", CommonVector, nullable=True), # SQLite uses CommonVector for embeddings
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("0"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("id"),
- )
-
- # restore data from archival_passages back to agent_passages
- # use the owner relationship from archives_agents to determine agent_id
- op.execute(
- """
- INSERT INTO agent_passages (
- id, text, embedding_config, metadata_, embedding,
- created_at, updated_at, is_deleted,
- _created_by_id, _last_updated_by_id,
- organization_id, agent_id
- )
- SELECT
- ap.id, ap.text, ap.embedding_config, ap.metadata_, ap.embedding,
- ap.created_at, ap.updated_at, ap.is_deleted,
- ap._created_by_id, ap._last_updated_by_id,
- ap.organization_id, aa.agent_id
- FROM temp_archival_passages ap
- JOIN archives_agents aa ON ap.archive_id = aa.archive_id AND aa.is_owner = 1;
- """
- )
-
- # drop temporary table
- op.execute("DROP TABLE temp_archival_passages;")
-
- # create original indexes
- op.create_index("ix_agent_passages_org_agent", "agent_passages", ["organization_id", "agent_id"])
- op.create_index("agent_passages_org_idx", "agent_passages", ["organization_id"])
- op.create_index("agent_passages_created_at_id_idx", "agent_passages", ["created_at", "id"])
-
- # drop new tables for SQLite
- op.drop_table("archives_agents")
- op.drop_index("ix_archives_organization_id", table_name="archives")
- op.drop_index("ix_archives_created_at", table_name="archives")
- op.drop_table("archives")
- else:
- # PostgreSQL:
- # rename table back
- op.drop_index("ix_archival_passages_org_archive", table_name="archival_passages")
- op.drop_index("ix_archival_passages_archive_id", table_name="archival_passages")
- op.drop_index("archival_passages_org_idx", table_name="archival_passages")
- op.drop_index("archival_passages_created_at_id_idx", table_name="archival_passages")
- op.rename_table("archival_passages", "agent_passages")
-
- # add agent_id column back
- op.add_column("agent_passages", sa.Column("agent_id", sa.String(), nullable=True))
-
- # restore agent_id from archives_agents (use the owner relationship)
- op.execute(
- """
- UPDATE agent_passages ap
- SET agent_id = aa.agent_id
- FROM archives_agents aa
- WHERE ap.archive_id = aa.archive_id AND aa.is_owner = TRUE;
- """
- )
-
- # schema changes
- op.alter_column("agent_passages", "agent_id", nullable=False)
- op.create_foreign_key("agent_passages_agent_id_fkey", "agent_passages", "agents", ["agent_id"], ["id"], ondelete="CASCADE")
-
- # drop archive_id column and constraint
- op.drop_constraint("agent_passages_archive_id_fkey", "agent_passages", type_="foreignkey")
- op.drop_column("agent_passages", "archive_id")
-
- # restore original indexes
- op.create_index("ix_agent_passages_org_agent", "agent_passages", ["organization_id", "agent_id"])
- op.create_index("agent_passages_org_idx", "agent_passages", ["organization_id"])
- op.create_index("agent_passages_created_at_id_idx", "agent_passages", ["created_at", "id"])
-
- # drop new tables for PostgreSQL
- op.drop_table("archives_agents")
- op.drop_index("ix_archives_organization_id", table_name="archives")
- op.drop_index("ix_archives_created_at", table_name="archives")
- op.drop_table("archives")
diff --git a/alembic/versions/74f2ede29317_add_background_group_support.py b/alembic/versions/74f2ede29317_add_background_group_support.py
deleted file mode 100644
index 3bc98636..00000000
--- a/alembic/versions/74f2ede29317_add_background_group_support.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""add background group support
-
-Revision ID: 74f2ede29317
-Revises: bff040379479
-Create Date: 2025-04-01 07:45:31.735977
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "74f2ede29317"
-down_revision: Union[str, None] = "bff040379479"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("groups", sa.Column("background_agents_interval", sa.Integer(), nullable=True))
- op.add_column("groups", sa.Column("turns_counter", sa.Integer(), nullable=True))
- op.add_column("groups", sa.Column("last_processed_message_id", sa.String(), nullable=True))
- op.create_table(
- "groups_blocks",
- sa.Column("group_id", sa.String(), nullable=False),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["block_id"], ["block.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["group_id"], ["groups.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("group_id", "block_id"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("groups_blocks")
- op.drop_column("groups", "last_processed_message_id")
- op.drop_column("groups", "turns_counter")
- op.drop_column("groups", "background_agents_interval")
- # ### end Alembic commands ###
diff --git a/alembic/versions/750dd87faa12_add_build_request_latency_to_step_.py b/alembic/versions/750dd87faa12_add_build_request_latency_to_step_.py
deleted file mode 100644
index 5fee6f1b..00000000
--- a/alembic/versions/750dd87faa12_add_build_request_latency_to_step_.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""add build request latency to step metrics
-
-Revision ID: 750dd87faa12
-Revises: 5b804970e6a0
-Create Date: 2025-09-06 14:28:32.119084
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "750dd87faa12"
-down_revision: Union[str, None] = "5b804970e6a0"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("step_metrics", sa.Column("step_start_ns", sa.BigInteger(), nullable=True))
- op.add_column("step_metrics", sa.Column("llm_request_start_ns", sa.BigInteger(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("step_metrics", "step_start_ns")
- op.drop_column("step_metrics", "llm_request_start_ns")
- # ### end Alembic commands ###
diff --git a/alembic/versions/7778731d15e2_added_jobusagestatistics_table.py b/alembic/versions/7778731d15e2_added_jobusagestatistics_table.py
deleted file mode 100644
index 66a30682..00000000
--- a/alembic/versions/7778731d15e2_added_jobusagestatistics_table.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""Added JobUsageStatistics table
-
-Revision ID: 7778731d15e2
-Revises: 8d70372ad130
-Create Date: 2025-01-09 13:20:25.555740
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "7778731d15e2"
-down_revision: Union[str, None] = "8d70372ad130"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Create job_usage_statistics table
- op.create_table(
- "job_usage_statistics",
- sa.Column("id", sa.Integer(), nullable=False),
- sa.Column("job_id", sa.String(), nullable=False),
- sa.Column("step_id", sa.String(), nullable=True),
- sa.Column("completion_tokens", sa.Integer(), server_default=sa.text("0"), nullable=False),
- sa.Column("prompt_tokens", sa.Integer(), server_default=sa.text("0"), nullable=False),
- sa.Column("total_tokens", sa.Integer(), server_default=sa.text("0"), nullable=False),
- sa.Column("step_count", sa.Integer(), server_default=sa.text("0"), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.ForeignKeyConstraint(["job_id"], ["jobs.id"], name="fk_job_usage_statistics_job_id", ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("id", name="pk_job_usage_statistics"),
- )
-
- # Create indexes
- op.create_index("ix_job_usage_statistics_created_at", "job_usage_statistics", ["created_at"])
- op.create_index("ix_job_usage_statistics_job_id", "job_usage_statistics", ["job_id"])
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Drop indexes
- op.drop_index("ix_job_usage_statistics_created_at", "job_usage_statistics")
- op.drop_index("ix_job_usage_statistics_job_id", "job_usage_statistics")
-
- # Drop table
- op.drop_table("job_usage_statistics")
diff --git a/alembic/versions/77de976590ae_add_groups_for_multi_agent.py b/alembic/versions/77de976590ae_add_groups_for_multi_agent.py
deleted file mode 100644
index 6180746a..00000000
--- a/alembic/versions/77de976590ae_add_groups_for_multi_agent.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""add groups for multi agent
-
-Revision ID: 77de976590ae
-Revises: 167491cfb7a8
-Create Date: 2025-03-12 14:01:58.034385
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "77de976590ae"
-down_revision: Union[str, None] = "167491cfb7a8"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "groups",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=False),
- sa.Column("manager_type", sa.String(), nullable=False),
- sa.Column("manager_agent_id", sa.String(), nullable=True),
- sa.Column("termination_token", sa.String(), nullable=True),
- sa.Column("max_turns", sa.Integer(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["manager_agent_id"], ["agents.id"], ondelete="RESTRICT"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_table(
- "groups_agents",
- sa.Column("group_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["group_id"], ["groups.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("group_id", "agent_id"),
- )
- op.add_column("messages", sa.Column("group_id", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("messages", "group_id")
- op.drop_table("groups_agents")
- op.drop_table("groups")
- # ### end Alembic commands ###
diff --git a/alembic/versions/7980d239ea08_add_stateless_option_for_agentstate.py b/alembic/versions/7980d239ea08_add_stateless_option_for_agentstate.py
deleted file mode 100644
index 0a3a0e69..00000000
--- a/alembic/versions/7980d239ea08_add_stateless_option_for_agentstate.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Add message_buffer_autoclear option for AgentState
-
-Revision ID: 7980d239ea08
-Revises: dfafcf8210ca
-Create Date: 2025-02-12 14:02:00.918226
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "7980d239ea08"
-down_revision: Union[str, None] = "dfafcf8210ca"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Add the column with a temporary nullable=True so we can backfill
- op.add_column("agents", sa.Column("message_buffer_autoclear", sa.Boolean(), nullable=True))
-
- # Backfill existing rows to set message_buffer_autoclear to False where it's NULL
- op.execute("UPDATE agents SET message_buffer_autoclear = false WHERE message_buffer_autoclear IS NULL")
-
- # Now, enforce nullable=False after backfilling
- op.alter_column("agents", "message_buffer_autoclear", nullable=False)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("agents", "message_buffer_autoclear")
- # ### end Alembic commands ###
diff --git a/alembic/versions/7b189006c97d_rename_batch_id_to_llm_batch_id_on_llm_.py b/alembic/versions/7b189006c97d_rename_batch_id_to_llm_batch_id_on_llm_.py
deleted file mode 100644
index b9e2b158..00000000
--- a/alembic/versions/7b189006c97d_rename_batch_id_to_llm_batch_id_on_llm_.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Rename batch_id to llm_batch_id on llm_batch_item
-
-Revision ID: 7b189006c97d
-Revises: f2f78d62005c
-Create Date: 2025-04-17 16:04:52.045672
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "7b189006c97d"
-down_revision: Union[str, None] = "f2f78d62005c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("llm_batch_items", sa.Column("llm_batch_id", sa.String(), nullable=False))
- op.drop_index("ix_llm_batch_items_batch_id", table_name="llm_batch_items")
- op.create_index("ix_llm_batch_items_llm_batch_id", "llm_batch_items", ["llm_batch_id"], unique=False)
- op.drop_constraint("llm_batch_items_batch_id_fkey", "llm_batch_items", type_="foreignkey")
- op.create_foreign_key(None, "llm_batch_items", "llm_batch_job", ["llm_batch_id"], ["id"], ondelete="CASCADE")
- op.drop_column("llm_batch_items", "batch_id")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("llm_batch_items", sa.Column("batch_id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.drop_constraint(None, "llm_batch_items", type_="foreignkey")
- op.create_foreign_key("llm_batch_items_batch_id_fkey", "llm_batch_items", "llm_batch_job", ["batch_id"], ["id"], ondelete="CASCADE")
- op.drop_index("ix_llm_batch_items_llm_batch_id", table_name="llm_batch_items")
- op.create_index("ix_llm_batch_items_batch_id", "llm_batch_items", ["batch_id"], unique=False)
- op.drop_column("llm_batch_items", "llm_batch_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/7f652fdd3dba_change_jobmessage_unique_constraint_to_.py b/alembic/versions/7f652fdd3dba_change_jobmessage_unique_constraint_to_.py
deleted file mode 100644
index 89c9b05e..00000000
--- a/alembic/versions/7f652fdd3dba_change_jobmessage_unique_constraint_to_.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""change JobMessage unique constraint to (job_id,message_id)
-
-Revision ID: 7f652fdd3dba
-Revises: 22a6e413d89c
-Create Date: 2025-01-13 14:36:13.626344
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "7f652fdd3dba"
-down_revision: Union[str, None] = "22a6e413d89c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Drop the old unique constraint
- op.drop_constraint("uq_job_messages_message_id", "job_messages", type_="unique")
-
- # Add the new composite unique constraint
- op.create_unique_constraint("unique_job_message", "job_messages", ["job_id", "message_id"])
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Drop the new composite constraint
- op.drop_constraint("unique_job_message", "job_messages", type_="unique")
-
- # Restore the old unique constraint
- op.create_unique_constraint("uq_job_messages_message_id", "job_messages", ["message_id"])
diff --git a/alembic/versions/878607e41ca4_add_provider_category.py b/alembic/versions/878607e41ca4_add_provider_category.py
deleted file mode 100644
index 48d0db9b..00000000
--- a/alembic/versions/878607e41ca4_add_provider_category.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add provider category
-
-Revision ID: 878607e41ca4
-Revises: 0335b1eb9c40
-Create Date: 2025-05-06 12:10:25.751536
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "878607e41ca4"
-down_revision: Union[str, None] = "0335b1eb9c40"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("providers", sa.Column("provider_category", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("providers", "provider_category")
- # ### end Alembic commands ###
diff --git a/alembic/versions/887a4367b560_convert_stop_reason_from_enum_to_string.py b/alembic/versions/887a4367b560_convert_stop_reason_from_enum_to_string.py
deleted file mode 100644
index e3302993..00000000
--- a/alembic/versions/887a4367b560_convert_stop_reason_from_enum_to_string.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""convert_stop_reason_from_enum_to_string
-
-Revision ID: 887a4367b560
-Revises: d5103ee17ed5
-Create Date: 2025-08-27 16:34:45.605580
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "887a4367b560"
-down_revision: Union[str, None] = "d5103ee17ed5"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite it doesn't enforce column types strictly,
- # so the existing enum values will continue to work as strings.
- if not settings.letta_pg_uri_no_default:
- return
-
- op.execute(
- """
- ALTER TABLE steps
- ALTER COLUMN stop_reason TYPE VARCHAR
- USING stop_reason::VARCHAR
- """
- )
-
-
-def downgrade() -> None:
- # This is a one-way migration as we can't easily recreate the enum type
- # If needed, you would need to create the enum type and cast back
- pass
diff --git a/alembic/versions/88f9432739a9_add_jobtype_to_job_table.py b/alembic/versions/88f9432739a9_add_jobtype_to_job_table.py
deleted file mode 100644
index a097c3a4..00000000
--- a/alembic/versions/88f9432739a9_add_jobtype_to_job_table.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""add JobType to Job table
-
-Revision ID: 88f9432739a9
-Revises: 7778731d15e2
-Create Date: 2025-01-10 13:46:44.089110
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "88f9432739a9"
-down_revision: Union[str, None] = "7778731d15e2"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Add job_type column with default value
- op.add_column("jobs", sa.Column("job_type", sa.String(), nullable=True))
-
- # Set existing rows to have the default value of JobType.JOB
- op.execute("UPDATE jobs SET job_type = 'job' WHERE job_type IS NULL")
-
- # Make the column non-nullable after setting default values
- op.alter_column("jobs", "job_type", existing_type=sa.String(), nullable=False)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Remove the job_type column
- op.drop_column("jobs", "job_type")
diff --git a/alembic/versions/8d70372ad130_adding_jobmessages_table.py b/alembic/versions/8d70372ad130_adding_jobmessages_table.py
deleted file mode 100644
index 2c9c0a5a..00000000
--- a/alembic/versions/8d70372ad130_adding_jobmessages_table.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""adding JobMessages table
-
-Revision ID: 8d70372ad130
-Revises: cdb3db091113
-Create Date: 2025-01-08 17:57:20.325596
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "8d70372ad130"
-down_revision: Union[str, None] = "cdb3db091113"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.create_table(
- "job_messages",
- sa.Column("id", sa.Integer(), nullable=False),
- sa.Column("job_id", sa.String(), nullable=False),
- sa.Column("message_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.ForeignKeyConstraint(["job_id"], ["jobs.id"], name="fk_job_messages_job_id", ondelete="CASCADE"),
- sa.ForeignKeyConstraint(["message_id"], ["messages.id"], name="fk_job_messages_message_id", ondelete="CASCADE", use_alter=True),
- sa.PrimaryKeyConstraint("id", name="pk_job_messages"),
- sa.UniqueConstraint("message_id", name="uq_job_messages_message_id"),
- )
-
- # Add indexes
- op.create_index("ix_job_messages_job_id", "job_messages", ["job_id"], unique=False)
- op.create_index("ix_job_messages_created_at", "job_messages", ["created_at"], unique=False)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.drop_index("ix_job_messages_created_at", "job_messages")
- op.drop_index("ix_job_messages_job_id", "job_messages")
- op.drop_table("job_messages")
diff --git a/alembic/versions/90bb156e71df_rename_sleeptime_agent_frequency.py b/alembic/versions/90bb156e71df_rename_sleeptime_agent_frequency.py
deleted file mode 100644
index 43b7f5fc..00000000
--- a/alembic/versions/90bb156e71df_rename_sleeptime_agent_frequency.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""rename sleeptime_agent_frequency
-
-Revision ID: 90bb156e71df
-Revises: 6fe79c0525f2
-Create Date: 2025-04-03 17:20:26.218596
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "90bb156e71df"
-down_revision: Union[str, None] = "6fe79c0525f2"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column("groups", "background_agents_frequency", new_column_name="sleeptime_agent_frequency")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column("groups", "sleeptime_agent_frequency", new_column_name="background_agents_frequency")
- # ### end Alembic commands ###
diff --git a/alembic/versions/90fd814d0cda_add_callback_error_field_to_jobs.py b/alembic/versions/90fd814d0cda_add_callback_error_field_to_jobs.py
deleted file mode 100644
index dba8736f..00000000
--- a/alembic/versions/90fd814d0cda_add_callback_error_field_to_jobs.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Add callback error field to jobs
-
-Revision ID: 90fd814d0cda
-Revises: c0ef3ff26306
-Create Date: 2025-06-16 13:04:53.496195
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "90fd814d0cda"
-down_revision: Union[str, None] = "c0ef3ff26306"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("jobs", sa.Column("callback_error", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("jobs", "callback_error")
- # ### end Alembic commands ###
diff --git a/alembic/versions/915b68780108_add_providers_data_to_orm.py b/alembic/versions/915b68780108_add_providers_data_to_orm.py
deleted file mode 100644
index 3db4dd6f..00000000
--- a/alembic/versions/915b68780108_add_providers_data_to_orm.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""Add providers data to ORM
-
-Revision ID: 915b68780108
-Revises: 400501b04bf0
-Create Date: 2025-01-07 10:49:04.717058
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "915b68780108"
-down_revision: Union[str, None] = "400501b04bf0"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "providers",
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("api_key", sa.String(), nullable=True),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("providers")
- # ### end Alembic commands ###
diff --git a/alembic/versions/9556081ce65b_add_bedrock_creds_to_byok.py b/alembic/versions/9556081ce65b_add_bedrock_creds_to_byok.py
deleted file mode 100644
index 77430d9c..00000000
--- a/alembic/versions/9556081ce65b_add_bedrock_creds_to_byok.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""add bedrock creds to byok
-
-Revision ID: 9556081ce65b
-Revises: 90fd814d0cda
-Create Date: 2025-06-18 11:15:39.461916
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "9556081ce65b"
-down_revision: Union[str, None] = "90fd814d0cda"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("providers", sa.Column("access_key", sa.String(), nullable=True))
- op.add_column("providers", sa.Column("region", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("providers", "region")
- op.drop_column("providers", "access_key")
- # ### end Alembic commands ###
diff --git a/alembic/versions/95badb46fdf9_migrate_messages_to_the_orm.py b/alembic/versions/95badb46fdf9_migrate_messages_to_the_orm.py
deleted file mode 100644
index c84730d2..00000000
--- a/alembic/versions/95badb46fdf9_migrate_messages_to_the_orm.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""Migrate message to orm
-
-Revision ID: 95badb46fdf9
-Revises: 3c683a662c82
-Create Date: 2024-12-05 14:02:04.163150
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "95badb46fdf9"
-down_revision: Union[str, None] = "08b2f8225812"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("messages", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("messages", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("messages", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.add_column("messages", sa.Column("organization_id", sa.String(), nullable=True))
- # Populate `organization_id` based on `user_id`
- # Use a raw SQL query to update the organization_id
- op.execute(
- """
- UPDATE messages
- SET organization_id = users.organization_id
- FROM users
- WHERE messages.user_id = users.id
- """
- )
- op.alter_column("messages", "organization_id", nullable=False)
- op.alter_column("messages", "tool_calls", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=False)
- op.alter_column("messages", "created_at", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=False)
- op.drop_index("message_idx_user", table_name="messages")
- op.create_foreign_key(None, "messages", "agents", ["agent_id"], ["id"])
- op.create_foreign_key(None, "messages", "organizations", ["organization_id"], ["id"])
- op.drop_column("messages", "user_id")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.drop_constraint(None, "messages", type_="foreignkey")
- op.drop_constraint(None, "messages", type_="foreignkey")
- op.create_index("message_idx_user", "messages", ["user_id", "agent_id"], unique=False)
- op.alter_column("messages", "created_at", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
- op.alter_column("messages", "tool_calls", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=True)
- op.drop_column("messages", "organization_id")
- op.drop_column("messages", "_last_updated_by_id")
- op.drop_column("messages", "_created_by_id")
- op.drop_column("messages", "is_deleted")
- op.drop_column("messages", "updated_at")
- # ### end Alembic commands ###
diff --git a/alembic/versions/9758adf8fdd3_add_run_completion_and_duration_to_.py b/alembic/versions/9758adf8fdd3_add_run_completion_and_duration_to_.py
deleted file mode 100644
index 529e4071..00000000
--- a/alembic/versions/9758adf8fdd3_add_run_completion_and_duration_to_.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""add_run_completion_and_duration_to_agents_table
-
-Revision ID: 9758adf8fdd3
-Revises: 9556081ce65b
-Create Date: 2025-06-18 18:22:31.135685
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "9758adf8fdd3"
-down_revision: Union[str, None] = "9556081ce65b"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("last_run_completion", sa.DateTime(timezone=True), nullable=True))
- op.add_column("agents", sa.Column("last_run_duration_ms", sa.Integer(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("agents", "last_run_duration_ms")
- op.drop_column("agents", "last_run_completion")
- # ### end Alembic commands ###
diff --git a/alembic/versions/9792f94e961d_add_file_processing_status_to_.py b/alembic/versions/9792f94e961d_add_file_processing_status_to_.py
deleted file mode 100644
index 52859bfb..00000000
--- a/alembic/versions/9792f94e961d_add_file_processing_status_to_.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Add file processing status to FileMetadata and related indices
-
-Revision ID: 9792f94e961d
-Revises: cdd4a1c11aee
-Create Date: 2025-06-05 18:51:57.022594
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "9792f94e961d"
-down_revision: Union[str, None] = "cdd4a1c11aee"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Step 1: Create constraint
- op.create_unique_constraint("uq_file_contents_file_id", "file_contents", ["file_id"])
-
- # Step 2: Add processing_status as nullable first
- op.add_column("files", sa.Column("processing_status", sa.String(), nullable=True))
- op.add_column("files", sa.Column("error_message", sa.Text(), nullable=True))
-
- # Step 3: Backfill existing rows with 'completed'
- op.execute("UPDATE files SET processing_status = 'completed'")
-
- # Step 4: Make the column non-nullable now that it's backfilled
- op.alter_column("files", "processing_status", nullable=False)
-
- # Step 5: Create indices
- op.create_index("ix_files_org_created", "files", ["organization_id", sa.literal_column("created_at DESC")], unique=False)
- op.create_index("ix_files_processing_status", "files", ["processing_status"], unique=False)
- op.create_index("ix_files_source_created", "files", ["source_id", sa.literal_column("created_at DESC")], unique=False)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_files_source_created", table_name="files")
- op.drop_index("ix_files_processing_status", table_name="files")
- op.drop_index("ix_files_org_created", table_name="files")
- op.drop_column("files", "error_message")
- op.drop_column("files", "processing_status")
- op.drop_constraint("uq_file_contents_file_id", "file_contents", type_="unique")
- # ### end Alembic commands ###
diff --git a/alembic/versions/9a505cc7eca9_create_a_baseline_migrations.py b/alembic/versions/9a505cc7eca9_create_a_baseline_migrations.py
deleted file mode 100644
index a9fb0be1..00000000
--- a/alembic/versions/9a505cc7eca9_create_a_baseline_migrations.py
+++ /dev/null
@@ -1,205 +0,0 @@
-"""Create a baseline migrations
-
-Revision ID: 9a505cc7eca9
-Revises:
-Create Date: 2024-10-11 14:19:19.875656
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-import letta.orm
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "9a505cc7eca9"
-down_revision: Union[str, None] = None
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- import pgvector
-
- op.create_table(
- "agent_source_mapping",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("agent_source_mapping_idx_user", "agent_source_mapping", ["user_id", "agent_id", "source_id"], unique=False)
- op.create_table(
- "agents",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("message_ids", sa.JSON(), nullable=True),
- sa.Column("memory", sa.JSON(), nullable=True),
- sa.Column("system", sa.String(), nullable=True),
- sa.Column("agent_type", sa.String(), nullable=True),
- sa.Column("llm_config", letta.orm.custom_columns.LLMConfigColumn(), nullable=True),
- sa.Column("embedding_config", letta.orm.custom_columns.EmbeddingConfigColumn(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("tools", sa.JSON(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("agents_idx_user", "agents", ["user_id"], unique=False)
- op.create_table(
- "block",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("value", sa.String(), nullable=False),
- sa.Column("limit", sa.BIGINT(), nullable=True),
- sa.Column("name", sa.String(), nullable=True),
- sa.Column("template", sa.Boolean(), nullable=True),
- sa.Column("label", sa.String(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("user_id", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("block_idx_user", "block", ["user_id"], unique=False)
- op.create_table(
- "files",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.Column("file_name", sa.String(), nullable=True),
- sa.Column("file_path", sa.String(), nullable=True),
- sa.Column("file_type", sa.String(), nullable=True),
- sa.Column("file_size", sa.Integer(), nullable=True),
- sa.Column("file_creation_date", sa.String(), nullable=True),
- sa.Column("file_last_modified_date", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_table(
- "jobs",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=True),
- sa.Column("status", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("completed_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_table(
- "messages",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("role", sa.String(), nullable=False),
- sa.Column("text", sa.String(), nullable=True),
- sa.Column("model", sa.String(), nullable=True),
- sa.Column("name", sa.String(), nullable=True),
- sa.Column("tool_calls", letta.orm.message.ToolCallColumn(), nullable=True),
- sa.Column("tool_call_id", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("message_idx_user", "messages", ["user_id", "agent_id"], unique=False)
- op.create_table(
- "organizations",
- sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("name", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True),
- sa.PrimaryKeyConstraint("id", name="organizations_pkey"),
- )
- op.create_table(
- "passages",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("text", sa.String(), nullable=True),
- sa.Column("file_id", sa.String(), nullable=True),
- sa.Column("agent_id", sa.String(), nullable=True),
- sa.Column("source_id", sa.String(), nullable=True),
- sa.Column("embedding", pgvector.sqlalchemy.Vector(dim=4096), nullable=True),
- sa.Column("embedding_config", letta.orm.custom_columns.EmbeddingConfigColumn(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("passage_idx_user", "passages", ["user_id", "agent_id", "file_id"], unique=False)
- op.create_table(
- "sources",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("embedding_config", letta.orm.custom_columns.EmbeddingConfigColumn(), nullable=True),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("sources_idx_user", "sources", ["user_id"], unique=False)
- op.create_table(
- "tokens",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.Column("key", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("tokens_idx_key", "tokens", ["key"], unique=False)
- op.create_index("tokens_idx_user", "tokens", ["user_id"], unique=False)
-
- op.create_table(
- "users",
- sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("org_id", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("name", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True),
- sa.Column("policies_accepted", sa.BOOLEAN(), autoincrement=False, nullable=False),
- sa.PrimaryKeyConstraint("id", name="users_pkey"),
- )
- op.create_table(
- "tools",
- sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("name", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("description", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("source_type", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("source_code", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("json_schema", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True),
- sa.Column("module", sa.VARCHAR(), autoincrement=False, nullable=True),
- sa.Column("tags", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True),
- sa.PrimaryKeyConstraint("id", name="tools_pkey"),
- )
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.drop_table("users")
- op.drop_table("tools")
- op.drop_index("tokens_idx_user", table_name="tokens")
- op.drop_index("tokens_idx_key", table_name="tokens")
- op.drop_table("tokens")
- op.drop_index("sources_idx_user", table_name="sources")
- op.drop_table("sources")
- op.drop_index("passage_idx_user", table_name="passages")
- op.drop_table("passages")
- op.drop_table("organizations")
- op.drop_index("message_idx_user", table_name="messages")
- op.drop_table("messages")
- op.drop_table("jobs")
- op.drop_table("files")
- op.drop_index("block_idx_user", table_name="block")
- op.drop_table("block")
- op.drop_index("agents_idx_user", table_name="agents")
- op.drop_table("agents")
- op.drop_index("agent_source_mapping_idx_user", table_name="agent_source_mapping")
- op.drop_table("agent_source_mapping")
diff --git a/alembic/versions/9ecbdbaa409f_add_table_to_store_mcp_servers.py b/alembic/versions/9ecbdbaa409f_add_table_to_store_mcp_servers.py
deleted file mode 100644
index eb2a1487..00000000
--- a/alembic/versions/9ecbdbaa409f_add_table_to_store_mcp_servers.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""add table to store mcp servers
-
-Revision ID: 9ecbdbaa409f
-Revises: 6c53224a7a58
-Create Date: 2025-05-21 15:25:12.483026
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-import letta
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "9ecbdbaa409f"
-down_revision: Union[str, None] = "6c53224a7a58"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "mcp_server",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("server_name", sa.String(), nullable=False),
- sa.Column("server_type", sa.String(), nullable=False),
- sa.Column("server_url", sa.String(), nullable=True),
- sa.Column("stdio_config", letta.orm.custom_columns.MCPStdioServerConfigColumn(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.UniqueConstraint("server_name", "organization_id", name="uix_name_organization_mcp_server"),
- )
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("mcp_server")
- # ### end Alembic commands ###
diff --git a/alembic/versions/a113caac453e_add_identities_table.py b/alembic/versions/a113caac453e_add_identities_table.py
deleted file mode 100644
index 8d83aafd..00000000
--- a/alembic/versions/a113caac453e_add_identities_table.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""add identities table
-
-Revision ID: a113caac453e
-Revises: 7980d239ea08
-Create Date: 2025-02-14 09:58:18.227122
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "a113caac453e"
-down_revision: Union[str, None] = "7980d239ea08"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Create identities table
- op.create_table(
- "identities",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("identifier_key", sa.String(), nullable=False),
- sa.Column("name", sa.String(), nullable=False),
- sa.Column("identity_type", sa.String(), nullable=False),
- sa.Column("project_id", sa.String(), nullable=True),
- # From OrganizationMixin
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("CURRENT_TIMESTAMP"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("CURRENT_TIMESTAMP"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- # Foreign key to organizations
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- # Composite unique constraint
- sa.UniqueConstraint(
- "identifier_key",
- "project_id",
- "organization_id",
- name="unique_identifier_pid_org_id",
- ),
- sa.PrimaryKeyConstraint("id"),
- )
-
- # Add identity_id column to agents table
- op.add_column("agents", sa.Column("identity_id", sa.String(), nullable=True))
-
- # Add foreign key constraint
- op.create_foreign_key("fk_agents_identity_id", "agents", "identities", ["identity_id"], ["id"], ondelete="CASCADE")
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # First remove the foreign key constraint and column from agents
- op.drop_constraint("fk_agents_identity_id", "agents", type_="foreignkey")
- op.drop_column("agents", "identity_id")
-
- # Then drop the table
- op.drop_table("identities")
diff --git a/alembic/versions/a3047a624130_add_identifier_key_to_agents.py b/alembic/versions/a3047a624130_add_identifier_key_to_agents.py
deleted file mode 100644
index 320eefc8..00000000
--- a/alembic/versions/a3047a624130_add_identifier_key_to_agents.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""add identifier key to agents
-
-Revision ID: a3047a624130
-Revises: a113caac453e
-Create Date: 2025-02-14 12:24:16.123456
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "a3047a624130"
-down_revision: Union[str, None] = "a113caac453e"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.add_column("agents", sa.Column("identifier_key", sa.String(), nullable=True))
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.drop_column("agents", "identifier_key")
diff --git a/alembic/versions/a3c7d62e08ca_add_callback_data_to_jobs_table.py b/alembic/versions/a3c7d62e08ca_add_callback_data_to_jobs_table.py
deleted file mode 100644
index cdc7985f..00000000
--- a/alembic/versions/a3c7d62e08ca_add_callback_data_to_jobs_table.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Add callback data to jobs table
-
-Revision ID: a3c7d62e08ca
-Revises: 7b189006c97d
-Create Date: 2025-04-17 17:40:16.224424
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "a3c7d62e08ca"
-down_revision: Union[str, None] = "7b189006c97d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("jobs", sa.Column("callback_url", sa.String(), nullable=True))
- op.add_column("jobs", sa.Column("callback_sent_at", sa.DateTime(), nullable=True))
- op.add_column("jobs", sa.Column("callback_status_code", sa.Integer(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("jobs", "callback_status_code")
- op.drop_column("jobs", "callback_sent_at")
- op.drop_column("jobs", "callback_url")
- # ### end Alembic commands ###
diff --git a/alembic/versions/a66510f83fc2_add_ordered_agent_ids_to_groups.py b/alembic/versions/a66510f83fc2_add_ordered_agent_ids_to_groups.py
deleted file mode 100644
index 6d41a370..00000000
--- a/alembic/versions/a66510f83fc2_add_ordered_agent_ids_to_groups.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add ordered agent ids to groups
-
-Revision ID: a66510f83fc2
-Revises: bdddd421ec41
-Create Date: 2025-03-27 11:11:51.709498
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "a66510f83fc2"
-down_revision: Union[str, None] = "bdddd421ec41"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("groups", sa.Column("agent_ids", sa.JSON(), nullable=False))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("groups", "agent_ids")
- # ### end Alembic commands ###
diff --git a/alembic/versions/a91994b9752f_add_column_to_tools_table_to_contain_.py b/alembic/versions/a91994b9752f_add_column_to_tools_table_to_contain_.py
deleted file mode 100644
index 3e2a4ad8..00000000
--- a/alembic/versions/a91994b9752f_add_column_to_tools_table_to_contain_.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""add column to tools table to contain function return limit return_char_limit
-
-Revision ID: a91994b9752f
-Revises: e1a625072dbf
-Create Date: 2024-12-09 18:27:25.650079
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.constants import FUNCTION_RETURN_CHAR_LIMIT
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "a91994b9752f"
-down_revision: Union[str, None] = "e1a625072dbf"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("tools", sa.Column("return_char_limit", sa.Integer(), nullable=True))
-
- # Populate `return_char_limit` column
- op.execute(
- f"""
- UPDATE tools
- SET return_char_limit = {FUNCTION_RETURN_CHAR_LIMIT}
- """
- )
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("tools", "return_char_limit")
- # ### end Alembic commands ###
diff --git a/alembic/versions/b183663c6769_add_trace_id_to_steps_table.py b/alembic/versions/b183663c6769_add_trace_id_to_steps_table.py
deleted file mode 100644
index 25861a0e..00000000
--- a/alembic/versions/b183663c6769_add_trace_id_to_steps_table.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add trace id to steps table
-
-Revision ID: b183663c6769
-Revises: fdcdafdb11cf
-Create Date: 2025-02-26 14:38:06.095556
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "b183663c6769"
-down_revision: Union[str, None] = "fdcdafdb11cf"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("steps", sa.Column("trace_id", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "trace_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/b6d7ca024aa9_add_agents_tags_table.py b/alembic/versions/b6d7ca024aa9_add_agents_tags_table.py
deleted file mode 100644
index c542994b..00000000
--- a/alembic/versions/b6d7ca024aa9_add_agents_tags_table.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Add agents tags table
-
-Revision ID: b6d7ca024aa9
-Revises: d14ae606614c
-Create Date: 2024-11-06 10:48:08.424108
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "b6d7ca024aa9"
-down_revision: Union[str, None] = "d14ae606614c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "agents_tags",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("tag", sa.String(), nullable=False),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["agent_id"],
- ["agents.id"],
- ),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("agent_id", "id"),
- sa.UniqueConstraint("agent_id", "tag", name="unique_agent_tag"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("agents_tags")
- # ### end Alembic commands ###
diff --git a/alembic/versions/b888f21b151f_add_vector_db_provider_to_source.py b/alembic/versions/b888f21b151f_add_vector_db_provider_to_source.py
deleted file mode 100644
index 8b909295..00000000
--- a/alembic/versions/b888f21b151f_add_vector_db_provider_to_source.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""Add vector db provider to source
-
-Revision ID: b888f21b151f
-Revises: 750dd87faa12
-Create Date: 2025-09-08 14:49:58.846429
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "b888f21b151f"
-down_revision: Union[str, None] = "750dd87faa12"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # determine backfill value based on current pinecone settings
- try:
- from pinecone import IndexEmbed, PineconeAsyncio
-
- pinecone_available = True
- except ImportError:
- pinecone_available = False
-
- use_pinecone = all(
- [
- pinecone_available,
- settings.enable_pinecone,
- settings.pinecone_api_key,
- settings.pinecone_agent_index,
- settings.pinecone_source_index,
- ]
- )
-
- if settings.letta_pg_uri_no_default:
- # commit required before altering enum in postgresql
- connection = op.get_bind()
- connection.execute(sa.text("COMMIT"))
- connection.execute(sa.text("ALTER TYPE vectordbprovider ADD VALUE IF NOT EXISTS 'PINECONE'"))
- connection.execute(sa.text("COMMIT"))
-
- vectordbprovider = sa.Enum("NATIVE", "TPUF", "PINECONE", name="vectordbprovider", create_type=False)
-
- op.add_column("sources", sa.Column("vector_db_provider", vectordbprovider, nullable=True))
-
- if use_pinecone:
- op.execute("UPDATE sources SET vector_db_provider = 'PINECONE' WHERE vector_db_provider IS NULL")
- else:
- op.execute("UPDATE sources SET vector_db_provider = 'NATIVE' WHERE vector_db_provider IS NULL")
-
- op.alter_column("sources", "vector_db_provider", nullable=False)
- else:
- op.add_column("sources", sa.Column("vector_db_provider", sa.String(), nullable=True))
-
- if use_pinecone:
- op.execute("UPDATE sources SET vector_db_provider = 'PINECONE' WHERE vector_db_provider IS NULL")
- else:
- op.execute("UPDATE sources SET vector_db_provider = 'NATIVE' WHERE vector_db_provider IS NULL")
-
-
-def downgrade() -> None:
- op.drop_column("sources", "vector_db_provider")
- # enum type remains as postgresql doesn't support removing values
diff --git a/alembic/versions/bdddd421ec41_add_privileged_tools_to_organization.py b/alembic/versions/bdddd421ec41_add_privileged_tools_to_organization.py
deleted file mode 100644
index cfd4b7e4..00000000
--- a/alembic/versions/bdddd421ec41_add_privileged_tools_to_organization.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""add privileged_tools to Organization
-
-Revision ID: bdddd421ec41
-Revises: 1e553a664210
-Create Date: 2025-03-21 17:55:30.405519
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "bdddd421ec41"
-down_revision: Union[str, None] = "1e553a664210"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Step 1: Add `privileged_tools` column with nullable=True
- op.add_column("organizations", sa.Column("privileged_tools", sa.Boolean(), nullable=True))
-
- # fill in column with `False`
- op.execute(
- """
- UPDATE organizations
- SET privileged_tools = False
- """
- )
-
- # Step 2: Make `privileged_tools` non-nullable
- op.alter_column("organizations", "privileged_tools", nullable=False)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.drop_column("organizations", "privileged_tools")
diff --git a/alembic/versions/bff040379479_add_block_history_tables.py b/alembic/versions/bff040379479_add_block_history_tables.py
deleted file mode 100644
index f8979743..00000000
--- a/alembic/versions/bff040379479_add_block_history_tables.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""Add block history tables
-
-Revision ID: bff040379479
-Revises: a66510f83fc2
-Create Date: 2025-03-31 14:49:30.449052
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "bff040379479"
-down_revision: Union[str, None] = "a66510f83fc2"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "block_history",
- sa.Column("description", sa.Text(), nullable=True),
- sa.Column("label", sa.String(), nullable=False),
- sa.Column("value", sa.Text(), nullable=False),
- sa.Column("limit", sa.BigInteger(), nullable=False),
- sa.Column("metadata_", sa.JSON(), nullable=True),
- sa.Column("actor_type", sa.String(), nullable=True),
- sa.Column("actor_id", sa.String(), nullable=True),
- sa.Column("block_id", sa.String(), nullable=False),
- sa.Column("sequence_number", sa.Integer(), nullable=False),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.ForeignKeyConstraint(["block_id"], ["block.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("ix_block_history_block_id_sequence", "block_history", ["block_id", "sequence_number"], unique=True)
- op.add_column("block", sa.Column("current_history_entry_id", sa.String(), nullable=True))
- op.add_column("block", sa.Column("version", sa.Integer(), server_default="1", nullable=False))
- op.create_index(op.f("ix_block_current_history_entry_id"), "block", ["current_history_entry_id"], unique=False)
- op.create_foreign_key("fk_block_current_history_entry", "block", "block_history", ["current_history_entry_id"], ["id"], use_alter=True)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("fk_block_current_history_entry", "block", type_="foreignkey")
- op.drop_index(op.f("ix_block_current_history_entry_id"), table_name="block")
- op.drop_column("block", "version")
- op.drop_column("block", "current_history_entry_id")
- op.drop_index("ix_block_history_block_id_sequence", table_name="block_history")
- op.drop_table("block_history")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c0ef3ff26306_add_token_to_mcp_server.py b/alembic/versions/c0ef3ff26306_add_token_to_mcp_server.py
deleted file mode 100644
index f11b70b6..00000000
--- a/alembic/versions/c0ef3ff26306_add_token_to_mcp_server.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add_token_to_mcp_server
-
-Revision ID: c0ef3ff26306
-Revises: 1c6b6a38b713
-Create Date: 2025-06-14 14:59:53.835883
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "c0ef3ff26306"
-down_revision: Union[str, None] = "1c6b6a38b713"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("mcp_server", sa.Column("token", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("mcp_server", "token")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c3b1da3d1157_add_sender_id_to_message.py b/alembic/versions/c3b1da3d1157_add_sender_id_to_message.py
deleted file mode 100644
index df9454d3..00000000
--- a/alembic/versions/c3b1da3d1157_add_sender_id_to_message.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add sender id to message
-
-Revision ID: c3b1da3d1157
-Revises: 0ceb975e0063
-Create Date: 2025-04-14 08:53:14.548061
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "c3b1da3d1157"
-down_revision: Union[str, None] = "0ceb975e0063"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("sender_id", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("messages", "sender_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c41c87205254_add_default_requires_approval_field_on_.py b/alembic/versions/c41c87205254_add_default_requires_approval_field_on_.py
deleted file mode 100644
index cb13822d..00000000
--- a/alembic/versions/c41c87205254_add_default_requires_approval_field_on_.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""add default requires approval field on tools
-
-Revision ID: c41c87205254
-Revises: 068588268b02
-Create Date: 2025-08-28 13:17:51.636159
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "c41c87205254"
-down_revision: Union[str, None] = "068588268b02"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("tools", sa.Column("default_requires_approval", sa.Boolean(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("tools", "default_requires_approval")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c4eb5a907b38_add_file_controls_to_agent_state.py b/alembic/versions/c4eb5a907b38_add_file_controls_to_agent_state.py
deleted file mode 100644
index b9fa8426..00000000
--- a/alembic/versions/c4eb5a907b38_add_file_controls_to_agent_state.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""Add file controls to agent state
-
-Revision ID: c4eb5a907b38
-Revises: cce9a6174366
-Create Date: 2025-07-21 15:56:57.413000
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "c4eb5a907b38"
-down_revision: Union[str, None] = "cce9a6174366"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("max_files_open", sa.Integer(), nullable=True))
- op.add_column("agents", sa.Column("per_file_view_window_char_limit", sa.Integer(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("agents", "per_file_view_window_char_limit")
- op.drop_column("agents", "max_files_open")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c56081a05371_add_buffer_length_min_max_for_voice_.py b/alembic/versions/c56081a05371_add_buffer_length_min_max_for_voice_.py
deleted file mode 100644
index 09ba1a88..00000000
--- a/alembic/versions/c56081a05371_add_buffer_length_min_max_for_voice_.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""Add buffer length min max for voice sleeptime
-
-Revision ID: c56081a05371
-Revises: 28b8765bdd0a
-Create Date: 2025-04-30 16:03:41.213750
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "c56081a05371"
-down_revision: Union[str, None] = "28b8765bdd0a"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("groups", sa.Column("max_message_buffer_length", sa.Integer(), nullable=True))
- op.add_column("groups", sa.Column("min_message_buffer_length", sa.Integer(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("groups", "min_message_buffer_length")
- op.drop_column("groups", "max_message_buffer_length")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c5d964280dff_add_passages_orm_drop_legacy_passages_.py b/alembic/versions/c5d964280dff_add_passages_orm_drop_legacy_passages_.py
deleted file mode 100644
index fdd8fc25..00000000
--- a/alembic/versions/c5d964280dff_add_passages_orm_drop_legacy_passages_.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Add Passages ORM, drop legacy passages, cascading deletes for file-passages and user-jobs
-
-Revision ID: c5d964280dff
-Revises: a91994b9752f
-Create Date: 2024-12-10 15:05:32.335519
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "c5d964280dff"
-down_revision: Union[str, None] = "a91994b9752f"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("passages", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("passages", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("passages", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("passages", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
-
- # Data migration step:
- op.add_column("passages", sa.Column("organization_id", sa.String(), nullable=True))
- # Populate `organization_id` based on `user_id`
- # Use a raw SQL query to update the organization_id
- op.execute(
- """
- UPDATE passages
- SET organization_id = users.organization_id
- FROM users
- WHERE passages.user_id = users.id
- """
- )
-
- # Set `organization_id` as non-nullable after population
- op.alter_column("passages", "organization_id", nullable=False)
-
- op.alter_column("passages", "text", existing_type=sa.VARCHAR(), nullable=False)
- op.alter_column("passages", "embedding_config", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=False)
- op.alter_column("passages", "metadata_", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=False)
- op.alter_column("passages", "created_at", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=False)
- op.drop_index("passage_idx_user", table_name="passages")
- op.create_foreign_key(None, "passages", "organizations", ["organization_id"], ["id"])
- op.create_foreign_key(None, "passages", "agents", ["agent_id"], ["id"])
- op.create_foreign_key(None, "passages", "files", ["file_id"], ["id"], ondelete="CASCADE")
- op.drop_column("passages", "user_id")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("passages", sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.drop_constraint(None, "passages", type_="foreignkey")
- op.drop_constraint(None, "passages", type_="foreignkey")
- op.drop_constraint(None, "passages", type_="foreignkey")
- op.create_index("passage_idx_user", "passages", ["user_id", "agent_id", "file_id"], unique=False)
- op.alter_column("passages", "created_at", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
- op.alter_column("passages", "metadata_", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=True)
- op.alter_column("passages", "embedding_config", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=True)
- op.alter_column("passages", "text", existing_type=sa.VARCHAR(), nullable=True)
- op.drop_column("passages", "organization_id")
- op.drop_column("passages", "_last_updated_by_id")
- op.drop_column("passages", "_created_by_id")
- op.drop_column("passages", "is_deleted")
- op.drop_column("passages", "updated_at")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c7ac45f69849_add_timezone_to_agents_table.py b/alembic/versions/c7ac45f69849_add_timezone_to_agents_table.py
deleted file mode 100644
index 04b45772..00000000
--- a/alembic/versions/c7ac45f69849_add_timezone_to_agents_table.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Add timezone to agents table
-
-Revision ID: c7ac45f69849
-Revises: 61ee53ec45a5
-Create Date: 2025-06-23 17:48:51.177458
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "c7ac45f69849"
-down_revision: Union[str, None] = "61ee53ec45a5"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("timezone", sa.String(), nullable=True, default="UTC"))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("agents", "timezone")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c85a3d07c028_move_files_to_orm.py b/alembic/versions/c85a3d07c028_move_files_to_orm.py
deleted file mode 100644
index c0255a86..00000000
--- a/alembic/versions/c85a3d07c028_move_files_to_orm.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""Move files to orm
-
-Revision ID: c85a3d07c028
-Revises: cda66b6cb0d6
-Create Date: 2024-11-12 13:58:57.221081
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "c85a3d07c028"
-down_revision: Union[str, None] = "cda66b6cb0d6"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("files", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("files", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("files", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("files", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.add_column("files", sa.Column("organization_id", sa.String(), nullable=True))
- # Populate `organization_id` based on `user_id`
- # Use a raw SQL query to update the organization_id
- op.execute(
- """
- UPDATE files
- SET organization_id = users.organization_id
- FROM users
- WHERE files.user_id = users.id
- """
- )
- op.alter_column("files", "organization_id", nullable=False)
- op.create_foreign_key(None, "files", "organizations", ["organization_id"], ["id"])
- op.create_foreign_key(None, "files", "sources", ["source_id"], ["id"])
- op.drop_column("files", "user_id")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("files", sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.drop_constraint(None, "files", type_="foreignkey")
- op.drop_constraint(None, "files", type_="foreignkey")
- op.drop_column("files", "organization_id")
- op.drop_column("files", "_last_updated_by_id")
- op.drop_column("files", "_created_by_id")
- op.drop_column("files", "is_deleted")
- op.drop_column("files", "updated_at")
- # ### end Alembic commands ###
diff --git a/alembic/versions/c96263433aef_add_file_name_to_source_passages.py b/alembic/versions/c96263433aef_add_file_name_to_source_passages.py
deleted file mode 100644
index 18bd5262..00000000
--- a/alembic/versions/c96263433aef_add_file_name_to_source_passages.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Add file name to source passages
-
-Revision ID: c96263433aef
-Revises: 9792f94e961d
-Create Date: 2025-06-06 12:06:57.328127
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "c96263433aef"
-down_revision: Union[str, None] = "9792f94e961d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Add the new column
- op.add_column("source_passages", sa.Column("file_name", sa.String(), nullable=True))
-
- # Backfill file_name using SQL UPDATE JOIN
- op.execute(
- """
- UPDATE source_passages
- SET file_name = files.file_name
- FROM files
- WHERE source_passages.file_id = files.id
- """
- )
-
- # Enforce non-null constraint after backfill
- op.alter_column("source_passages", "file_name", nullable=False)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- op.drop_column("source_passages", "file_name")
diff --git a/alembic/versions/cc8dc340836d_add_support_for_request_and_response_.py b/alembic/versions/cc8dc340836d_add_support_for_request_and_response_.py
deleted file mode 100644
index 36a79dcf..00000000
--- a/alembic/versions/cc8dc340836d_add_support_for_request_and_response_.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""add support for request and response jsons from llm providers
-
-Revision ID: cc8dc340836d
-Revises: 220856bbf43b
-Create Date: 2025-05-19 14:25:41.999676
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "cc8dc340836d"
-down_revision: Union[str, None] = "220856bbf43b"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "provider_traces",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("request_json", sa.JSON(), nullable=False),
- sa.Column("response_json", sa.JSON(), nullable=False),
- sa.Column("step_id", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- )
- op.create_index("ix_step_id", "provider_traces", ["step_id"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_step_id", table_name="provider_traces")
- op.drop_table("provider_traces")
- # ### end Alembic commands ###
diff --git a/alembic/versions/cce9a6174366_add_stop_reasons_to_steps_and_message_.py b/alembic/versions/cce9a6174366_add_stop_reasons_to_steps_and_message_.py
deleted file mode 100644
index 14ac1cdd..00000000
--- a/alembic/versions/cce9a6174366_add_stop_reasons_to_steps_and_message_.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""add stop reasons to steps and message error flag
-
-Revision ID: cce9a6174366
-Revises: 2c059cad97cc
-Create Date: 2025-07-10 13:56:17.383612
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "cce9a6174366"
-down_revision: Union[str, None] = "2c059cad97cc"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("is_err", sa.Boolean(), nullable=True))
-
- # manually added to handle non-table creation enums
- stopreasontype = sa.Enum(
- "end_turn", "error", "invalid_tool_call", "max_steps", "no_tool_call", "tool_rule", "cancelled", name="stopreasontype"
- )
- stopreasontype.create(op.get_bind())
- op.add_column("steps", sa.Column("stop_reason", stopreasontype, nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "stop_reason")
- op.drop_column("messages", "is_err")
-
- stopreasontype = sa.Enum(name="stopreasontype")
- stopreasontype.drop(op.get_bind())
- # ### end Alembic commands ###
diff --git a/alembic/versions/cda66b6cb0d6_move_sources_to_orm.py b/alembic/versions/cda66b6cb0d6_move_sources_to_orm.py
deleted file mode 100644
index 7ada943a..00000000
--- a/alembic/versions/cda66b6cb0d6_move_sources_to_orm.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Move sources to orm
-
-Revision ID: cda66b6cb0d6
-Revises: b6d7ca024aa9
-Create Date: 2024-11-07 13:29:57.186107
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "cda66b6cb0d6"
-down_revision: Union[str, None] = "b6d7ca024aa9"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("sources", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("sources", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("sources", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("sources", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
-
- # Data migration step:
- op.add_column("sources", sa.Column("organization_id", sa.String(), nullable=True))
- # Populate `organization_id` based on `user_id`
- # Use a raw SQL query to update the organization_id
- op.execute(
- """
- UPDATE sources
- SET organization_id = users.organization_id
- FROM users
- WHERE sources.user_id = users.id
- """
- )
-
- # Set `organization_id` as non-nullable after population
- op.alter_column("sources", "organization_id", nullable=False)
-
- op.alter_column("sources", "embedding_config", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=False)
- op.drop_index("sources_idx_user", table_name="sources")
- op.create_foreign_key(None, "sources", "organizations", ["organization_id"], ["id"])
- op.drop_column("sources", "user_id")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("sources", sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.drop_constraint(None, "sources", type_="foreignkey")
- op.create_index("sources_idx_user", "sources", ["user_id"], unique=False)
- op.alter_column("sources", "embedding_config", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=True)
- op.drop_column("sources", "organization_id")
- op.drop_column("sources", "_last_updated_by_id")
- op.drop_column("sources", "_created_by_id")
- op.drop_column("sources", "is_deleted")
- op.drop_column("sources", "updated_at")
- # ### end Alembic commands ###
diff --git a/alembic/versions/cdb3db091113_remove_unique_name_restriction_on_agents.py b/alembic/versions/cdb3db091113_remove_unique_name_restriction_on_agents.py
deleted file mode 100644
index f8713b43..00000000
--- a/alembic/versions/cdb3db091113_remove_unique_name_restriction_on_agents.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""Remove unique name restriction on agents
-
-Revision ID: cdb3db091113
-Revises: e20573fe9b86
-Create Date: 2025-01-10 15:36:08.728539
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "cdb3db091113"
-down_revision: Union[str, None] = "e20573fe9b86"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("unique_org_agent_name", "agents", type_="unique")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_unique_constraint("unique_org_agent_name", "agents", ["organization_id", "name"])
- # ### end Alembic commands ###
diff --git a/alembic/versions/cdd4a1c11aee_add_file_name_to_fileagent_association_.py b/alembic/versions/cdd4a1c11aee_add_file_name_to_fileagent_association_.py
deleted file mode 100644
index f808cdcf..00000000
--- a/alembic/versions/cdd4a1c11aee_add_file_name_to_fileagent_association_.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""Add file_name to FileAgent association table and FileContent table
-
-Revision ID: cdd4a1c11aee
-Revises: 614c4e53b66e
-Create Date: 2025-06-03 15:35:59.623704
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "cdd4a1c11aee"
-down_revision: Union[str, None] = "614c4e53b66e"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "file_contents",
- sa.Column("file_id", sa.String(), nullable=False),
- sa.Column("text", sa.Text(), nullable=False),
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.ForeignKeyConstraint(["file_id"], ["files.id"], ondelete="CASCADE"),
- sa.PrimaryKeyConstraint("file_id", "id"),
- )
- # add the column, nullable for now
- op.add_column("files_agents", sa.Column("file_name", sa.String(), nullable=True))
-
- # back-fill using a single UPDATE … FROM join
- op.execute(
- """
- UPDATE files_agents fa
- SET file_name = f.file_name
- FROM files f
- WHERE fa.file_id = f.id;
- """
- )
-
- # now make it NOT NULL
- op.alter_column("files_agents", "file_name", nullable=False)
- op.create_index("ix_files_agents_agent_file_name", "files_agents", ["agent_id", "file_name"], unique=False)
- op.create_unique_constraint("uq_files_agents_agent_file_name", "files_agents", ["agent_id", "file_name"])
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("uq_files_agents_agent_file_name", "files_agents", type_="unique")
- op.drop_index("ix_files_agents_agent_file_name", table_name="files_agents")
- op.drop_column("files_agents", "file_name")
- op.drop_table("file_contents")
- # ### end Alembic commands ###
diff --git a/alembic/versions/d007f4ca66bf_npm_requirements_in_tools.py b/alembic/versions/d007f4ca66bf_npm_requirements_in_tools.py
deleted file mode 100644
index 0972b68d..00000000
--- a/alembic/versions/d007f4ca66bf_npm_requirements_in_tools.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""npm requirements in tools
-
-Revision ID: d007f4ca66bf
-Revises: 74e860718e0d
-Create Date: 2025-08-04 13:40:32.707036
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "d007f4ca66bf"
-down_revision: Union[str, None] = "74e860718e0d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("tools", sa.Column("npm_requirements", sa.JSON(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("tools", "npm_requirements")
- # ### end Alembic commands ###
diff --git a/alembic/versions/d05669b60ebe_migrate_agents_to_orm.py b/alembic/versions/d05669b60ebe_migrate_agents_to_orm.py
deleted file mode 100644
index 5fb1352b..00000000
--- a/alembic/versions/d05669b60ebe_migrate_agents_to_orm.py
+++ /dev/null
@@ -1,184 +0,0 @@
-"""Migrate agents to orm
-
-Revision ID: d05669b60ebe
-Revises: c5d964280dff
-Create Date: 2024-12-12 10:25:31.825635
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "d05669b60ebe"
-down_revision: Union[str, None] = "c5d964280dff"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "sources_agents",
- sa.Column("agent_id", sa.String(), nullable=False),
- sa.Column("source_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["agent_id"],
- ["agents.id"],
- ),
- sa.ForeignKeyConstraint(
- ["source_id"],
- ["sources.id"],
- ),
- sa.PrimaryKeyConstraint("agent_id", "source_id"),
- )
- op.drop_index("agent_source_mapping_idx_user", table_name="agent_source_mapping")
- op.drop_table("agent_source_mapping")
- op.add_column("agents", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("agents", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("agents", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("agents", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.add_column("agents", sa.Column("organization_id", sa.String(), nullable=True))
- # Populate `organization_id` based on `user_id`
- # Use a raw SQL query to update the organization_id
- op.execute(
- """
- UPDATE agents
- SET organization_id = users.organization_id
- FROM users
- WHERE agents.user_id = users.id
- """
- )
- op.alter_column("agents", "organization_id", nullable=False)
- op.alter_column("agents", "name", existing_type=sa.VARCHAR(), nullable=True)
- op.drop_index("agents_idx_user", table_name="agents")
- op.create_unique_constraint("unique_org_agent_name", "agents", ["organization_id", "name"])
- op.create_foreign_key(None, "agents", "organizations", ["organization_id"], ["id"])
- op.drop_column("agents", "tool_names")
- op.drop_column("agents", "user_id")
- op.drop_constraint("agents_tags_organization_id_fkey", "agents_tags", type_="foreignkey")
- op.drop_column("agents_tags", "_created_by_id")
- op.drop_column("agents_tags", "_last_updated_by_id")
- op.drop_column("agents_tags", "updated_at")
- op.drop_column("agents_tags", "id")
- op.drop_column("agents_tags", "is_deleted")
- op.drop_column("agents_tags", "created_at")
- op.drop_column("agents_tags", "organization_id")
- op.create_unique_constraint("unique_agent_block", "blocks_agents", ["agent_id", "block_id"])
- op.drop_constraint("fk_block_id_label", "blocks_agents", type_="foreignkey")
- op.create_foreign_key(
- "fk_block_id_label", "blocks_agents", "block", ["block_id", "block_label"], ["id", "label"], initially="DEFERRED", deferrable=True
- )
- op.drop_column("blocks_agents", "_created_by_id")
- op.drop_column("blocks_agents", "_last_updated_by_id")
- op.drop_column("blocks_agents", "updated_at")
- op.drop_column("blocks_agents", "id")
- op.drop_column("blocks_agents", "is_deleted")
- op.drop_column("blocks_agents", "created_at")
- op.drop_constraint("unique_tool_per_agent", "tools_agents", type_="unique")
- op.create_unique_constraint("unique_agent_tool", "tools_agents", ["agent_id", "tool_id"])
- op.drop_constraint("fk_tool_id", "tools_agents", type_="foreignkey")
- op.drop_constraint("tools_agents_agent_id_fkey", "tools_agents", type_="foreignkey")
- op.create_foreign_key(None, "tools_agents", "tools", ["tool_id"], ["id"], ondelete="CASCADE")
- op.create_foreign_key(None, "tools_agents", "agents", ["agent_id"], ["id"], ondelete="CASCADE")
- op.drop_column("tools_agents", "_created_by_id")
- op.drop_column("tools_agents", "tool_name")
- op.drop_column("tools_agents", "_last_updated_by_id")
- op.drop_column("tools_agents", "updated_at")
- op.drop_column("tools_agents", "id")
- op.drop_column("tools_agents", "is_deleted")
- op.drop_column("tools_agents", "created_at")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column(
- "tools_agents",
- sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), autoincrement=False, nullable=True),
- )
- op.add_column(
- "tools_agents", sa.Column("is_deleted", sa.BOOLEAN(), server_default=sa.text("false"), autoincrement=False, nullable=False)
- )
- op.add_column("tools_agents", sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.add_column(
- "tools_agents",
- sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), autoincrement=False, nullable=True),
- )
- op.add_column("tools_agents", sa.Column("_last_updated_by_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.add_column("tools_agents", sa.Column("tool_name", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.add_column("tools_agents", sa.Column("_created_by_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.drop_constraint(None, "tools_agents", type_="foreignkey")
- op.drop_constraint(None, "tools_agents", type_="foreignkey")
- op.create_foreign_key("tools_agents_agent_id_fkey", "tools_agents", "agents", ["agent_id"], ["id"])
- op.create_foreign_key("fk_tool_id", "tools_agents", "tools", ["tool_id"], ["id"])
- op.drop_constraint("unique_agent_tool", "tools_agents", type_="unique")
- op.create_unique_constraint("unique_tool_per_agent", "tools_agents", ["agent_id", "tool_name"])
- op.add_column(
- "blocks_agents",
- sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), autoincrement=False, nullable=True),
- )
- op.add_column(
- "blocks_agents", sa.Column("is_deleted", sa.BOOLEAN(), server_default=sa.text("false"), autoincrement=False, nullable=False)
- )
- op.add_column("blocks_agents", sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.add_column(
- "blocks_agents",
- sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), autoincrement=False, nullable=True),
- )
- op.add_column("blocks_agents", sa.Column("_last_updated_by_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.add_column("blocks_agents", sa.Column("_created_by_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.drop_constraint("fk_block_id_label", "blocks_agents", type_="foreignkey")
- op.create_foreign_key("fk_block_id_label", "blocks_agents", "block", ["block_id", "block_label"], ["id", "label"])
- op.drop_constraint("unique_agent_block", "blocks_agents", type_="unique")
- op.add_column("agents_tags", sa.Column("organization_id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.add_column(
- "agents_tags",
- sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), autoincrement=False, nullable=True),
- )
- op.add_column(
- "agents_tags", sa.Column("is_deleted", sa.BOOLEAN(), server_default=sa.text("false"), autoincrement=False, nullable=False)
- )
- op.add_column("agents_tags", sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.add_column(
- "agents_tags",
- sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), autoincrement=False, nullable=True),
- )
- op.add_column("agents_tags", sa.Column("_last_updated_by_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.add_column("agents_tags", sa.Column("_created_by_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.create_foreign_key("agents_tags_organization_id_fkey", "agents_tags", "organizations", ["organization_id"], ["id"])
- op.add_column("agents", sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=False))
- op.add_column("agents", sa.Column("tool_names", postgresql.JSON(astext_type=sa.Text()), autoincrement=False, nullable=True))
- op.drop_constraint(None, "agents", type_="foreignkey")
- op.drop_constraint("unique_org_agent_name", "agents", type_="unique")
- op.create_index("agents_idx_user", "agents", ["user_id"], unique=False)
- op.alter_column("agents", "name", existing_type=sa.VARCHAR(), nullable=False)
- op.drop_column("agents", "organization_id")
- op.drop_column("agents", "_last_updated_by_id")
- op.drop_column("agents", "_created_by_id")
- op.drop_column("agents", "is_deleted")
- op.drop_column("agents", "updated_at")
- op.create_table(
- "agent_source_mapping",
- sa.Column("id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("agent_id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.Column("source_id", sa.VARCHAR(), autoincrement=False, nullable=False),
- sa.PrimaryKeyConstraint("id", name="agent_source_mapping_pkey"),
- )
- op.create_index("agent_source_mapping_idx_user", "agent_source_mapping", ["user_id", "agent_id", "source_id"], unique=False)
- op.drop_table("sources_agents")
- # ### end Alembic commands ###
diff --git a/alembic/versions/d14ae606614c_move_organizations_users_tools_to_orm.py b/alembic/versions/d14ae606614c_move_organizations_users_tools_to_orm.py
deleted file mode 100644
index 95c5fbe4..00000000
--- a/alembic/versions/d14ae606614c_move_organizations_users_tools_to_orm.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""Move organizations users tools to orm
-
-Revision ID: d14ae606614c
-Revises: 9a505cc7eca9
-Create Date: 2024-11-05 15:03:12.350096
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-import letta
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "d14ae606614c"
-down_revision: Union[str, None] = "9a505cc7eca9"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Delete all tools
- op.execute("DELETE FROM tools")
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("tool_rules", letta.orm.agent.ToolRulesColumn(), nullable=True))
- op.alter_column("block", "name", new_column_name="template_name", nullable=True)
- op.add_column("organizations", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("organizations", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("organizations", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("organizations", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.add_column("tools", sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("tools", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("tools", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("tools", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("tools", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.add_column("tools", sa.Column("organization_id", sa.String(), nullable=False))
- op.alter_column("tools", "tags", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=False)
- op.alter_column("tools", "source_type", existing_type=sa.VARCHAR(), nullable=False)
- op.alter_column("tools", "json_schema", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=False)
- op.create_unique_constraint("uix_name_organization", "tools", ["name", "organization_id"])
- op.create_foreign_key(None, "tools", "organizations", ["organization_id"], ["id"])
- op.drop_column("tools", "user_id")
- op.add_column("users", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("users", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("users", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("users", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.add_column("users", sa.Column("organization_id", sa.String(), nullable=True))
- # loop through all rows in the user table and set the _organization_id column from organization_id
- op.execute('UPDATE "users" SET organization_id = org_id')
- # set the _organization_id column to not nullable
- op.alter_column("users", "organization_id", existing_type=sa.String(), nullable=False)
- op.create_foreign_key(None, "users", "organizations", ["organization_id"], ["id"])
- op.drop_column("users", "org_id")
- op.drop_column("users", "policies_accepted")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("users", sa.Column("policies_accepted", sa.BOOLEAN(), autoincrement=False, nullable=False))
- op.add_column("users", sa.Column("org_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.drop_constraint(None, "users", type_="foreignkey")
- op.drop_column("users", "organization_id")
- op.drop_column("users", "_last_updated_by_id")
- op.drop_column("users", "_created_by_id")
- op.drop_column("users", "is_deleted")
- op.drop_column("users", "updated_at")
- op.add_column("tools", sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.drop_constraint(None, "tools", type_="foreignkey")
- op.drop_constraint("uix_name_organization", "tools", type_="unique")
- op.alter_column("tools", "json_schema", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=True)
- op.alter_column("tools", "source_type", existing_type=sa.VARCHAR(), nullable=True)
- op.alter_column("tools", "tags", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=True)
- op.drop_column("tools", "organization_id")
- op.drop_column("tools", "_last_updated_by_id")
- op.drop_column("tools", "_created_by_id")
- op.drop_column("tools", "is_deleted")
- op.drop_column("tools", "updated_at")
- op.drop_column("tools", "created_at")
- op.drop_column("organizations", "_last_updated_by_id")
- op.drop_column("organizations", "_created_by_id")
- op.drop_column("organizations", "is_deleted")
- op.drop_column("organizations", "updated_at")
- op.add_column("block", sa.Column("name", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.drop_column("block", "template_name")
- op.drop_column("agents", "tool_rules")
- # ### end Alembic commands ###
diff --git a/alembic/versions/d211df879a5f_add_agent_id_to_steps.py b/alembic/versions/d211df879a5f_add_agent_id_to_steps.py
deleted file mode 100644
index 6d5e57ad..00000000
--- a/alembic/versions/d211df879a5f_add_agent_id_to_steps.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add agent id to steps
-
-Revision ID: d211df879a5f
-Revises: 2f4ede6ae33b
-Create Date: 2025-03-06 21:42:22.289345
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "d211df879a5f"
-down_revision: Union[str, None] = "2f4ede6ae33b"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("steps", sa.Column("agent_id", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "agent_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/d5103ee17ed5_add_template_fields_to_blocks_agents_.py b/alembic/versions/d5103ee17ed5_add_template_fields_to_blocks_agents_.py
deleted file mode 100644
index 3904d739..00000000
--- a/alembic/versions/d5103ee17ed5_add_template_fields_to_blocks_agents_.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""add template fields to blocks agents groups
-
-Revision ID: d5103ee17ed5
-Revises: ffb17eb241fc
-Create Date: 2025-08-26 15:45:32.949892
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "d5103ee17ed5"
-down_revision: Union[str, None] = "ffb17eb241fc"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("entity_id", sa.String(), nullable=True))
- op.add_column("agents", sa.Column("deployment_id", sa.String(), nullable=True))
- op.add_column("block", sa.Column("entity_id", sa.String(), nullable=True))
- op.add_column("block", sa.Column("base_template_id", sa.String(), nullable=True))
- op.add_column("block", sa.Column("template_id", sa.String(), nullable=True))
- op.add_column("block", sa.Column("deployment_id", sa.String(), nullable=True))
- op.add_column("groups", sa.Column("base_template_id", sa.String(), nullable=True))
- op.add_column("groups", sa.Column("template_id", sa.String(), nullable=True))
- op.add_column("groups", sa.Column("deployment_id", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("groups", "deployment_id")
- op.drop_column("groups", "template_id")
- op.drop_column("groups", "base_template_id")
- op.drop_column("block", "deployment_id")
- op.drop_column("block", "template_id")
- op.drop_column("block", "base_template_id")
- op.drop_column("block", "entity_id")
- op.drop_column("agents", "deployment_id")
- op.drop_column("agents", "entity_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/d6632deac81d_add_composite_index_to_messages_table.py b/alembic/versions/d6632deac81d_add_composite_index_to_messages_table.py
deleted file mode 100644
index 2deb10f8..00000000
--- a/alembic/versions/d6632deac81d_add_composite_index_to_messages_table.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""Add composite index to messages table
-
-Revision ID: d6632deac81d
-Revises: 54dec07619c4
-Create Date: 2024-12-18 13:38:56.511701
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "d6632deac81d"
-down_revision: Union[str, None] = "54dec07619c4"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_index("ix_messages_agent_created_at", "messages", ["agent_id", "created_at"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_messages_agent_created_at", table_name="messages")
- # ### end Alembic commands ###
diff --git a/alembic/versions/dd049fbec729_add_index_on_agent_id_for_agent_env_var.py b/alembic/versions/dd049fbec729_add_index_on_agent_id_for_agent_env_var.py
deleted file mode 100644
index fd4e567f..00000000
--- a/alembic/versions/dd049fbec729_add_index_on_agent_id_for_agent_env_var.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""Add index on agent_id for agent env var
-
-Revision ID: dd049fbec729
-Revises: 9ecbdbaa409f
-Create Date: 2025-05-23 17:41:48.235405
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "dd049fbec729"
-down_revision: Union[str, None] = "9ecbdbaa409f"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_index("idx_agent_environment_variables_agent_id", "agent_environment_variables", ["agent_id"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("idx_agent_environment_variables_agent_id", table_name="agent_environment_variables")
- # ### end Alembic commands ###
diff --git a/alembic/versions/ddb69be34a72_add_vector_db_namespace_fields_to_.py b/alembic/versions/ddb69be34a72_add_vector_db_namespace_fields_to_.py
deleted file mode 100644
index f1eb0b45..00000000
--- a/alembic/versions/ddb69be34a72_add_vector_db_namespace_fields_to_.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""Add vector db namespace fields to archive and agent state
-
-Revision ID: ddb69be34a72
-Revises: f3bf00ef6118
-Create Date: 2025-09-02 12:59:54.837863
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "ddb69be34a72"
-down_revision: Union[str, None] = "f3bf00ef6118"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("_vector_db_namespace", sa.String(), nullable=True))
- op.add_column("archives", sa.Column("_vector_db_namespace", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("archives", "_vector_db_namespace")
- op.drop_column("agents", "_vector_db_namespace")
- # ### end Alembic commands ###
diff --git a/alembic/versions/ddecfe4902bc_add_prompts.py b/alembic/versions/ddecfe4902bc_add_prompts.py
deleted file mode 100644
index 6e33a8c2..00000000
--- a/alembic/versions/ddecfe4902bc_add_prompts.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""add prompts
-
-Revision ID: ddecfe4902bc
-Revises: c4eb5a907b38
-Create Date: 2025-07-21 15:58:13.357459
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "ddecfe4902bc"
-down_revision: Union[str, None] = "c4eb5a907b38"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "prompts",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("prompt", sa.String(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("project_id", sa.String(), nullable=True),
- sa.PrimaryKeyConstraint("id"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("prompts")
- # ### end Alembic commands ###
diff --git a/alembic/versions/dfafcf8210ca_add_model_endpoint_to_steps_table.py b/alembic/versions/dfafcf8210ca_add_model_endpoint_to_steps_table.py
deleted file mode 100644
index a4502932..00000000
--- a/alembic/versions/dfafcf8210ca_add_model_endpoint_to_steps_table.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""add model endpoint to steps table
-
-Revision ID: dfafcf8210ca
-Revises: f922ca16e42c
-Create Date: 2025-02-04 16:45:34.132083
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "dfafcf8210ca"
-down_revision: Union[str, None] = "f922ca16e42c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("steps", sa.Column("model_endpoint", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "model_endpoint")
- # ### end Alembic commands ###
diff --git a/alembic/versions/e1a625072dbf_tweak_created_at_field_for_messages.py b/alembic/versions/e1a625072dbf_tweak_created_at_field_for_messages.py
deleted file mode 100644
index 8a8b6566..00000000
--- a/alembic/versions/e1a625072dbf_tweak_created_at_field_for_messages.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Tweak created_at field for messages
-
-Revision ID: e1a625072dbf
-Revises: 95badb46fdf9
-Create Date: 2024-12-07 14:28:27.643583
-
-"""
-
-from typing import Sequence, Union
-
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "e1a625072dbf"
-down_revision: Union[str, None] = "95badb46fdf9"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column("messages", "created_at", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column("messages", "created_at", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=False)
- # ### end Alembic commands ###
diff --git a/alembic/versions/e20573fe9b86_add_tool_types.py b/alembic/versions/e20573fe9b86_add_tool_types.py
deleted file mode 100644
index afb2822d..00000000
--- a/alembic/versions/e20573fe9b86_add_tool_types.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Add tool types
-
-Revision ID: e20573fe9b86
-Revises: 915b68780108
-Create Date: 2025-01-09 15:11:47.779646
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.constants import BASE_MEMORY_TOOLS, BASE_TOOLS
-from letta.schemas.enums import ToolType
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "e20573fe9b86"
-down_revision: Union[str, None] = "915b68780108"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Step 1: Add the column as nullable with no default
- op.add_column("tools", sa.Column("tool_type", sa.String(), nullable=True))
-
- # Step 2: Backpopulate the tool_type column based on tool name
- # Define the list of Letta core tools
- letta_core_value = ToolType.LETTA_CORE.value
- letta_memory_core_value = ToolType.LETTA_MEMORY_CORE.value
- custom_value = ToolType.CUSTOM.value
-
- # Update tool_type for Letta core tools
- op.execute(
- f"""
- UPDATE tools
- SET tool_type = '{letta_core_value}'
- WHERE name IN ({",".join(f"'{name}'" for name in BASE_TOOLS)});
- """
- )
-
- op.execute(
- f"""
- UPDATE tools
- SET tool_type = '{letta_memory_core_value}'
- WHERE name IN ({",".join(f"'{name}'" for name in BASE_MEMORY_TOOLS)});
- """
- )
-
- # Update tool_type for all other tools
- op.execute(
- f"""
- UPDATE tools
- SET tool_type = '{custom_value}'
- WHERE tool_type IS NULL;
- """
- )
-
- # Step 3: Alter the column to be non-nullable
- op.alter_column("tools", "tool_type", nullable=False)
- op.alter_column("tools", "json_schema", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=True)
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # Revert the changes made during the upgrade
- op.alter_column("tools", "json_schema", existing_type=postgresql.JSON(astext_type=sa.Text()), nullable=False)
- op.drop_column("tools", "tool_type")
- # ### end Alembic commands ###
diff --git a/alembic/versions/e78b4e82db30_add_cascading_deletes_for_sources_to_.py b/alembic/versions/e78b4e82db30_add_cascading_deletes_for_sources_to_.py
deleted file mode 100644
index 3afa28b6..00000000
--- a/alembic/versions/e78b4e82db30_add_cascading_deletes_for_sources_to_.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Add cascading deletes for sources to agents
-
-Revision ID: e78b4e82db30
-Revises: d6632deac81d
-Create Date: 2024-12-20 16:30:17.095888
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "e78b4e82db30"
-down_revision: Union[str, None] = "d6632deac81d"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("sources_agents_agent_id_fkey", "sources_agents", type_="foreignkey")
- op.drop_constraint("sources_agents_source_id_fkey", "sources_agents", type_="foreignkey")
- op.create_foreign_key(None, "sources_agents", "sources", ["source_id"], ["id"], ondelete="CASCADE")
- op.create_foreign_key(None, "sources_agents", "agents", ["agent_id"], ["id"], ondelete="CASCADE")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint(None, "sources_agents", type_="foreignkey")
- op.drop_constraint(None, "sources_agents", type_="foreignkey")
- op.create_foreign_key("sources_agents_source_id_fkey", "sources_agents", "sources", ["source_id"], ["id"])
- op.create_foreign_key("sources_agents_agent_id_fkey", "sources_agents", "agents", ["agent_id"], ["id"])
- # ### end Alembic commands ###
diff --git a/alembic/versions/e991d2e3b428_add_monotonically_increasing_ids_to_.py b/alembic/versions/e991d2e3b428_add_monotonically_increasing_ids_to_.py
deleted file mode 100644
index 6a63e0f3..00000000
--- a/alembic/versions/e991d2e3b428_add_monotonically_increasing_ids_to_.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""Add monotonically increasing IDs to messages table
-
-Revision ID: e991d2e3b428
-Revises: 74f2ede29317
-Create Date: 2025-04-01 17:02:59.820272
-
-"""
-
-import sys
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "e991d2e3b428"
-down_revision: Union[str, None] = "74f2ede29317"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-# --- Configuration ---
-TABLE_NAME = "messages"
-COLUMN_NAME = "sequence_id"
-SEQUENCE_NAME = "message_seq_id"
-INDEX_NAME = "ix_messages_agent_sequence"
-UNIQUE_CONSTRAINT_NAME = f"uq_{TABLE_NAME}_{COLUMN_NAME}"
-
-# Columns to determine the order for back-filling existing data
-ORDERING_COLUMNS = ["created_at", "id"]
-
-
-def print_flush(message):
- """Helper function to print and flush stdout immediately."""
- print(message)
- sys.stdout.flush()
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- """Adds sequence_id, backfills data, adds constraints and index."""
- print_flush(f"\n--- Starting upgrade for revision {revision} ---")
-
- # Step 1: Add the sequence_id column to the table, initially allowing NULL values.
- # This allows us to add and backfill data without immediately enforcing NOT NULL.
- print_flush(f"Step 1: Adding nullable column '{COLUMN_NAME}' to table '{TABLE_NAME}'...")
- op.add_column(TABLE_NAME, sa.Column(COLUMN_NAME, sa.BigInteger(), nullable=True))
-
- # Step 2: Create a new PostgreSQL sequence.
- # This sequence will later be used as the server-side default for generating new sequence_id values.
- print_flush(f"Step 2: Creating sequence '{SEQUENCE_NAME}'...")
- op.execute(f"CREATE SEQUENCE {SEQUENCE_NAME} START 1;")
-
- # Step 3: Backfill the sequence_id for existing rows based on a defined ordering.
- # The SQL query does the following:
- # - Uses a Common Table Expression named 'numbered_rows' to compute a row number for each row.
- # - The ROW_NUMBER() window function assigns a sequential number (rn) to each row, ordered by the columns specified
- # in ORDERING_COLUMNS (e.g., created_at, id) in ascending order.
- # - The UPDATE statement then sets each row's sequence_id to its corresponding row number (rn)
- # by joining the original table with the CTE on the id column.
- print_flush(f"Step 3: Backfilling '{COLUMN_NAME}' based on order: {', '.join(ORDERING_COLUMNS)}...")
- print_flush(" (This may take a while on large tables)")
- try:
- op.execute(
- f"""
- WITH numbered_rows AS (
- SELECT
- id,
- ROW_NUMBER() OVER (ORDER BY {", ".join(ORDERING_COLUMNS)} ASC) as rn
- FROM {TABLE_NAME}
- )
- UPDATE {TABLE_NAME}
- SET {COLUMN_NAME} = numbered_rows.rn
- FROM numbered_rows
- WHERE {TABLE_NAME}.id = numbered_rows.id;
- """
- )
- print_flush(" Backfill successful.")
- except Exception as e:
- print_flush(f"!!! ERROR during backfill: {e}")
- print_flush("!!! Migration failed. Manual intervention might be needed.")
- raise
-
- # Step 4: Set the sequence's next value to be one more than the current maximum sequence_id.
- # The query works as follows:
- # - It calculates the maximum value in the sequence_id column using MAX({COLUMN_NAME}).
- # - COALESCE is used to default to 0 if there are no rows (i.e., the table is empty).
- # - It then adds 1 to ensure that the next call to nextval() returns a number higher than any existing value.
- # - The 'false' argument tells PostgreSQL that the next nextval() should return the value as-is, without pre-incrementing.
- print_flush(f"Step 4: Setting sequence '{SEQUENCE_NAME}' to next value after backfill...")
- op.execute(
- f"""
- SELECT setval('{SEQUENCE_NAME}', COALESCE(MAX({COLUMN_NAME}), 0) + 1, false)
- FROM {TABLE_NAME};
- """
- )
-
- # Step 5: Now that every row has a sequence_id, alter the column to be NOT NULL.
- # This enforces that all rows must have a valid sequence_id.
- print_flush(f"Step 5: Altering column '{COLUMN_NAME}' to NOT NULL...")
- op.alter_column(TABLE_NAME, COLUMN_NAME, existing_type=sa.BigInteger(), nullable=False)
-
- # Step 6: Add a UNIQUE constraint on sequence_id to ensure its values remain distinct.
- # This mirrors the model definition where sequence_id is defined as unique.
- print_flush(f"Step 6: Creating unique constraint '{UNIQUE_CONSTRAINT_NAME}' on '{COLUMN_NAME}'...")
- op.create_unique_constraint(UNIQUE_CONSTRAINT_NAME, TABLE_NAME, [COLUMN_NAME])
-
- # Step 7: Set the server-side default for sequence_id so that future inserts automatically use the sequence.
- # The server default calls nextval() on the sequence, and the "::regclass" cast helps PostgreSQL resolve the sequence name correctly.
- print_flush(f"Step 7: Setting server default for '{COLUMN_NAME}' to use sequence '{SEQUENCE_NAME}'...")
- op.alter_column(TABLE_NAME, COLUMN_NAME, existing_type=sa.BigInteger(), server_default=sa.text(f"nextval('{SEQUENCE_NAME}'::regclass)"))
-
- # Step 8: Create an index on (agent_id, sequence_id) to improve performance of queries filtering on these columns.
- print_flush(f"Step 8: Creating index '{INDEX_NAME}' on (agent_id, {COLUMN_NAME})...")
- op.create_index(INDEX_NAME, TABLE_NAME, ["agent_id", COLUMN_NAME], unique=False)
-
- print_flush(f"--- Upgrade for revision {revision} complete ---")
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- """Reverses the changes made in the upgrade function."""
- print_flush(f"\n--- Starting downgrade from revision {revision} ---")
-
- # 1. Drop the index
- print_flush(f"Step 1: Dropping index '{INDEX_NAME}'...")
- op.drop_index(INDEX_NAME, table_name=TABLE_NAME)
-
- # 2. Remove the server-side default
- print_flush(f"Step 2: Removing server default from '{COLUMN_NAME}'...")
- op.alter_column(TABLE_NAME, COLUMN_NAME, existing_type=sa.BigInteger(), server_default=None)
-
- # 3. Drop the unique constraint (using the explicit name)
- print_flush(f"Step 3: Dropping unique constraint '{UNIQUE_CONSTRAINT_NAME}'...")
- op.drop_constraint(UNIQUE_CONSTRAINT_NAME, TABLE_NAME, type_="unique")
-
- # 4. Drop the column (this implicitly removes the NOT NULL constraint)
- print_flush(f"Step 4: Dropping column '{COLUMN_NAME}'...")
- op.drop_column(TABLE_NAME, COLUMN_NAME)
-
- # 5. Drop the sequence
- print_flush(f"Step 5: Dropping sequence '{SEQUENCE_NAME}'...")
- op.execute(f"DROP SEQUENCE IF EXISTS {SEQUENCE_NAME};") # Use IF EXISTS for safety
-
- print_flush(f"--- Downgrade from revision {revision} complete ---")
diff --git a/alembic/versions/f2f78d62005c_add_letta_batch_job_id_to_llm_batch_job.py b/alembic/versions/f2f78d62005c_add_letta_batch_job_id_to_llm_batch_job.py
deleted file mode 100644
index 5309d0fa..00000000
--- a/alembic/versions/f2f78d62005c_add_letta_batch_job_id_to_llm_batch_job.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""Add letta batch job id to llm_batch_job
-
-Revision ID: f2f78d62005c
-Revises: c3b1da3d1157
-Create Date: 2025-04-17 15:58:43.705483
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "f2f78d62005c"
-down_revision: Union[str, None] = "c3b1da3d1157"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("llm_batch_job", sa.Column("letta_batch_job_id", sa.String(), nullable=False))
- op.create_foreign_key(None, "llm_batch_job", "jobs", ["letta_batch_job_id"], ["id"], ondelete="CASCADE")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint(None, "llm_batch_job", type_="foreignkey")
- op.drop_column("llm_batch_job", "letta_batch_job_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/f3bf00ef6118_add_approval_fields_to_message_model.py b/alembic/versions/f3bf00ef6118_add_approval_fields_to_message_model.py
deleted file mode 100644
index e7de5b3a..00000000
--- a/alembic/versions/f3bf00ef6118_add_approval_fields_to_message_model.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""add approval fields to message model
-
-Revision ID: f3bf00ef6118
-Revises: 54c76f7cabca
-Create Date: 2025-09-01 11:26:42.548009
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "f3bf00ef6118"
-down_revision: Union[str, None] = "54c76f7cabca"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("messages", sa.Column("approval_request_id", sa.String(), nullable=True))
- op.add_column("messages", sa.Column("approve", sa.Boolean(), nullable=True))
- op.add_column("messages", sa.Column("denial_reason", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("messages", "denial_reason")
- op.drop_column("messages", "approve")
- op.drop_column("messages", "approval_request_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/f55542f37641_add_index_for_agent_tags_reversed_order.py b/alembic/versions/f55542f37641_add_index_for_agent_tags_reversed_order.py
deleted file mode 100644
index 5a0d13d2..00000000
--- a/alembic/versions/f55542f37641_add_index_for_agent_tags_reversed_order.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""add index for agent_tags reversed order
-
-Revision ID: f55542f37641
-Revises: ddecfe4902bc
-Create Date: 2025-07-24 18:00:30.773048
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "f55542f37641"
-down_revision: Union[str, None] = "f5d26b0526e8"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Note some issues at least with pg8000 with concurrent index creation
- # with op.get_context().autocommit_block():
- # op.create_index(
- # op.f('ix_agent_tags_tag_agent_id'),
- # "agents_tags",
- # ['tag', 'agent_id'],
- # unique=False,
- # postgresql_concurrently=True,
- # )
- op.create_index("ix_agents_tags_tag_agent_id", "agents_tags", ["tag", "agent_id"], unique=False)
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_index("ix_agents_tags_tag_agent_id", table_name="agents_tags")
- # ### end Alembic commands ###
diff --git a/alembic/versions/f595e0e8013e_adding_request_config_to_job_table.py b/alembic/versions/f595e0e8013e_adding_request_config_to_job_table.py
deleted file mode 100644
index ce798112..00000000
--- a/alembic/versions/f595e0e8013e_adding_request_config_to_job_table.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""adding request_config to Job table
-
-Revision ID: f595e0e8013e
-Revises: 7f652fdd3dba
-Create Date: 2025-01-14 14:34:34.203363
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "f595e0e8013e"
-down_revision: Union[str, None] = "7f652fdd3dba"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("jobs", sa.Column("request_config", sa.JSON, nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("jobs", "request_config")
- # ### end Alembic commands ###
diff --git a/alembic/versions/f5d26b0526e8_add_mcp_oauth.py b/alembic/versions/f5d26b0526e8_add_mcp_oauth.py
deleted file mode 100644
index 52c9f764..00000000
--- a/alembic/versions/f5d26b0526e8_add_mcp_oauth.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""add_mcp_oauth
-
-Revision ID: f5d26b0526e8
-Revises: ddecfe4902bc
-Create Date: 2025-07-24 12:34:05.795355
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "f5d26b0526e8"
-down_revision: Union[str, None] = "ddecfe4902bc"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "mcp_oauth",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("state", sa.String(length=255), nullable=False),
- sa.Column("server_id", sa.String(length=255), nullable=True),
- sa.Column("server_url", sa.Text(), nullable=False),
- sa.Column("server_name", sa.Text(), nullable=False),
- sa.Column("authorization_url", sa.Text(), nullable=True),
- sa.Column("authorization_code", sa.Text(), nullable=True),
- sa.Column("access_token", sa.Text(), nullable=True),
- sa.Column("refresh_token", sa.Text(), nullable=True),
- sa.Column("token_type", sa.String(length=50), nullable=False),
- sa.Column("expires_at", sa.DateTime(timezone=True), nullable=True),
- sa.Column("scope", sa.Text(), nullable=True),
- sa.Column("client_id", sa.Text(), nullable=True),
- sa.Column("client_secret", sa.Text(), nullable=True),
- sa.Column("redirect_uri", sa.Text(), nullable=True),
- sa.Column("status", sa.String(length=20), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), nullable=False),
- sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("user_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.ForeignKeyConstraint(["server_id"], ["mcp_server.id"], ondelete="CASCADE"),
- sa.ForeignKeyConstraint(
- ["user_id"],
- ["users.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- sa.UniqueConstraint("state"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("mcp_oauth")
- # ### end Alembic commands ###
diff --git a/alembic/versions/f7507eab4bb9_migrate_blocks_to_orm_model.py b/alembic/versions/f7507eab4bb9_migrate_blocks_to_orm_model.py
deleted file mode 100644
index 7e1b0305..00000000
--- a/alembic/versions/f7507eab4bb9_migrate_blocks_to_orm_model.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Migrate blocks to orm model
-
-Revision ID: f7507eab4bb9
-Revises: c85a3d07c028
-Create Date: 2024-11-18 15:40:13.149438
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "f7507eab4bb9"
-down_revision: Union[str, None] = "c85a3d07c028"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("block", sa.Column("is_template", sa.Boolean(), nullable=True))
- # Populate `is_template` column
- op.execute(
- """
- UPDATE block
- SET is_template = COALESCE(template, FALSE)
- """
- )
-
- # Step 2: Make `is_template` non-nullable
- op.alter_column("block", "is_template", nullable=False)
- op.add_column("block", sa.Column("organization_id", sa.String(), nullable=True))
- # Populate `organization_id` based on `user_id`
- # Use a raw SQL query to update the organization_id
- op.execute(
- """
- UPDATE block
- SET organization_id = users.organization_id
- FROM users
- WHERE block.user_id = users.id
- """
- )
- op.alter_column("block", "organization_id", nullable=False)
- op.add_column("block", sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("block", sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True))
- op.add_column("block", sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False))
- op.add_column("block", sa.Column("_created_by_id", sa.String(), nullable=True))
- op.add_column("block", sa.Column("_last_updated_by_id", sa.String(), nullable=True))
- op.alter_column("block", "limit", existing_type=sa.BIGINT(), type_=sa.Integer(), nullable=False)
- op.drop_index("block_idx_user", table_name="block")
- op.create_foreign_key(None, "block", "organizations", ["organization_id"], ["id"])
- op.drop_column("block", "template")
- op.drop_column("block", "user_id")
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("block", sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=True))
- op.add_column("block", sa.Column("template", sa.BOOLEAN(), autoincrement=False, nullable=True))
- op.drop_constraint(None, "block", type_="foreignkey")
- op.create_index("block_idx_user", "block", ["user_id"], unique=False)
- op.alter_column("block", "limit", existing_type=sa.Integer(), type_=sa.BIGINT(), nullable=True)
- op.drop_column("block", "_last_updated_by_id")
- op.drop_column("block", "_created_by_id")
- op.drop_column("block", "is_deleted")
- op.drop_column("block", "updated_at")
- op.drop_column("block", "created_at")
- op.drop_column("block", "organization_id")
- op.drop_column("block", "is_template")
- # ### end Alembic commands ###
diff --git a/alembic/versions/f7f757414d20_add_error_tracking_to_steps_table.py b/alembic/versions/f7f757414d20_add_error_tracking_to_steps_table.py
deleted file mode 100644
index 41014c93..00000000
--- a/alembic/versions/f7f757414d20_add_error_tracking_to_steps_table.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""Add error tracking to steps table
-
-Revision ID: f7f757414d20
-Revises: 05c3bc564286
-Create Date: 2025-08-05 18:17:06.026153
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "f7f757414d20"
-down_revision: Union[str, None] = "05c3bc564286"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- # Create the enum type first
- stepstatus = sa.Enum("PENDING", "SUCCESS", "FAILED", "CANCELLED", name="stepstatus")
- stepstatus.create(op.get_bind(), checkfirst=True)
-
- op.add_column("steps", sa.Column("error_type", sa.String(), nullable=True))
- op.add_column("steps", sa.Column("error_data", sa.JSON(), nullable=True))
- op.add_column("steps", sa.Column("status", stepstatus, nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("steps", "status")
- op.drop_column("steps", "error_data")
- op.drop_column("steps", "error_type")
-
- # Drop the enum type
- stepstatus = sa.Enum("PENDING", "SUCCESS", "FAILED", "CANCELLED", name="stepstatus")
- stepstatus.drop(op.get_bind(), checkfirst=True)
- # ### end Alembic commands ###
diff --git a/alembic/versions/f81ceea2c08d_create_sandbox_config_and_sandbox_env_.py b/alembic/versions/f81ceea2c08d_create_sandbox_config_and_sandbox_env_.py
deleted file mode 100644
index 40d64670..00000000
--- a/alembic/versions/f81ceea2c08d_create_sandbox_config_and_sandbox_env_.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""Create sandbox config and sandbox env var tables
-
-Revision ID: f81ceea2c08d
-Revises: c85a3d07c028
-Create Date: 2024-11-14 17:51:27.263561
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "f81ceea2c08d"
-down_revision: Union[str, None] = "f7507eab4bb9"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.create_table(
- "sandbox_configs",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("type", sa.Enum("E2B", "LOCAL", name="sandboxtype"), nullable=False),
- sa.Column("config", sa.JSON(), nullable=False),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- sa.UniqueConstraint("type", "organization_id", name="uix_type_organization"),
- )
- op.create_table(
- "sandbox_environment_variables",
- sa.Column("id", sa.String(), nullable=False),
- sa.Column("key", sa.String(), nullable=False),
- sa.Column("value", sa.String(), nullable=False),
- sa.Column("description", sa.String(), nullable=True),
- sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
- sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
- sa.Column("_created_by_id", sa.String(), nullable=True),
- sa.Column("_last_updated_by_id", sa.String(), nullable=True),
- sa.Column("organization_id", sa.String(), nullable=False),
- sa.Column("sandbox_config_id", sa.String(), nullable=False),
- sa.ForeignKeyConstraint(
- ["organization_id"],
- ["organizations.id"],
- ),
- sa.ForeignKeyConstraint(
- ["sandbox_config_id"],
- ["sandbox_configs.id"],
- ),
- sa.PrimaryKeyConstraint("id"),
- sa.UniqueConstraint("key", "sandbox_config_id", name="uix_key_sandbox_config"),
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_table("sandbox_environment_variables")
- op.drop_table("sandbox_configs")
- # ### end Alembic commands ###
diff --git a/alembic/versions/f895232c144a_backfill_composio_tools.py b/alembic/versions/f895232c144a_backfill_composio_tools.py
deleted file mode 100644
index 15432ffe..00000000
--- a/alembic/versions/f895232c144a_backfill_composio_tools.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""Backfill composio tools
-
-Revision ID: f895232c144a
-Revises: 25fc99e97839
-Create Date: 2025-01-16 14:21:33.764332
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-from letta.schemas.enums import ToolType
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "f895232c144a"
-down_revision: Union[str, None] = "416b9d2db10b"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- # Define the value for EXTERNAL_COMPOSIO
- external_composio_value = ToolType.EXTERNAL_COMPOSIO.value
-
- # Update tool_type to EXTERNAL_COMPOSIO if the tags field includes "composio"
- # This is super brittle and awful but no other way to do this
- op.execute(
- f"""
- UPDATE tools
- SET tool_type = '{external_composio_value}'
- WHERE tags::jsonb @> '["composio"]';
- """
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- custom_value = ToolType.CUSTOM.value
-
- # Update tool_type to CUSTOM if the tags field includes "composio"
- # This is super brittle and awful but no other way to do this
- op.execute(
- f"""
- UPDATE tools
- SET tool_type = '{custom_value}'
- WHERE tags::jsonb @> '["composio"]';
- """
- )
- # ### end Alembic commands ###
diff --git a/alembic/versions/f922ca16e42c_add_project_and_template_id_to_agent.py b/alembic/versions/f922ca16e42c_add_project_and_template_id_to_agent.py
deleted file mode 100644
index 169a6d77..00000000
--- a/alembic/versions/f922ca16e42c_add_project_and_template_id_to_agent.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""add project and template id to agent
-
-Revision ID: f922ca16e42c
-Revises: 6fbe9cace832
-Create Date: 2025-01-29 16:57:48.161335
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "f922ca16e42c"
-down_revision: Union[str, None] = "6fbe9cace832"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("agents", sa.Column("project_id", sa.String(), nullable=True))
- op.add_column("agents", sa.Column("template_id", sa.String(), nullable=True))
- op.add_column("agents", sa.Column("base_template_id", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("agents", "base_template_id")
- op.drop_column("agents", "template_id")
- op.drop_column("agents", "project_id")
- # ### end Alembic commands ###
diff --git a/alembic/versions/fdcdafdb11cf_identity_properties_jsonb_to_json.py b/alembic/versions/fdcdafdb11cf_identity_properties_jsonb_to_json.py
deleted file mode 100644
index b553ecdc..00000000
--- a/alembic/versions/fdcdafdb11cf_identity_properties_jsonb_to_json.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""identity properties jsonb to json
-
-Revision ID: fdcdafdb11cf
-Revises: 549eff097c71
-Create Date: 2025-02-21 10:30:49.937854
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-from alembic import op
-from letta.settings import settings
-
-# revision identifiers, used by Alembic.
-revision: str = "fdcdafdb11cf"
-down_revision: Union[str, None] = "549eff097c71"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.alter_column(
- "identities",
- "properties",
- existing_type=postgresql.JSONB(astext_type=sa.Text()),
- type_=postgresql.JSON(astext_type=sa.Text()),
- existing_nullable=False,
- existing_server_default=sa.text("'[]'::jsonb"),
- )
- op.drop_constraint("unique_identifier_without_project", "identities", type_="unique")
- op.create_unique_constraint(
- "unique_identifier_key_project_id_organization_id",
- "identities",
- ["identifier_key", "project_id", "organization_id"],
- postgresql_nulls_not_distinct=True,
- )
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # Skip this migration for SQLite
- if not settings.letta_pg_uri_no_default:
- return
-
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_constraint("unique_identifier_key_project_id_organization_id", "identities", type_="unique")
- op.create_unique_constraint(
- "unique_identifier_without_project",
- "identities",
- ["identifier_key", "project_id", "organization_id"],
- postgresql_nulls_not_distinct=True,
- )
- op.alter_column(
- "identities",
- "properties",
- existing_type=postgresql.JSON(astext_type=sa.Text()),
- type_=postgresql.JSONB(astext_type=sa.Text()),
- existing_nullable=False,
- existing_server_default=sa.text("'[]'::jsonb"),
- )
- # ### end Alembic commands ###
diff --git a/alembic/versions/ffb17eb241fc_add_api_version_to_byok_providers.py b/alembic/versions/ffb17eb241fc_add_api_version_to_byok_providers.py
deleted file mode 100644
index 28c2a288..00000000
--- a/alembic/versions/ffb17eb241fc_add_api_version_to_byok_providers.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""add api version to byok providers
-
-Revision ID: ffb17eb241fc
-Revises: 5fb8bba2c373
-Create Date: 2025-08-12 14:35:26.375985
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "ffb17eb241fc"
-down_revision: Union[str, None] = "5fb8bba2c373"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.add_column("providers", sa.Column("api_version", sa.String(), nullable=True))
- # ### end Alembic commands ###
-
-
-def downgrade() -> None:
- # ### commands auto generated by Alembic - please adjust! ###
- op.drop_column("providers", "api_version")
- # ### end Alembic commands ###
diff --git a/assets/Letta-logo-RGB_GreyonOffBlack_cropped_small.png b/assets/Letta-logo-RGB_GreyonOffBlack_cropped_small.png
deleted file mode 100644
index 73dab282..00000000
Binary files a/assets/Letta-logo-RGB_GreyonOffBlack_cropped_small.png and /dev/null differ
diff --git a/assets/Letta-logo-RGB_GreyonTransparent_cropped_small.png b/assets/Letta-logo-RGB_GreyonTransparent_cropped_small.png
deleted file mode 100644
index 7f461b19..00000000
Binary files a/assets/Letta-logo-RGB_GreyonTransparent_cropped_small.png and /dev/null differ
diff --git a/assets/Letta-logo-RGB_OffBlackonTransparent_cropped_small.png b/assets/Letta-logo-RGB_OffBlackonTransparent_cropped_small.png
deleted file mode 100644
index 39d5be4f..00000000
Binary files a/assets/Letta-logo-RGB_OffBlackonTransparent_cropped_small.png and /dev/null differ
diff --git a/assets/example_ade_screenshot.png b/assets/example_ade_screenshot.png
deleted file mode 100644
index fb75c43a..00000000
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diff --git a/assets/example_ade_screenshot_agents.png b/assets/example_ade_screenshot_agents.png
deleted file mode 100644
index e07df1f2..00000000
Binary files a/assets/example_ade_screenshot_agents.png and /dev/null differ
diff --git a/assets/example_ade_screenshot_agents_light.png b/assets/example_ade_screenshot_agents_light.png
deleted file mode 100644
index d6e2392a..00000000
Binary files a/assets/example_ade_screenshot_agents_light.png and /dev/null differ
diff --git a/assets/example_ade_screenshot_light.png b/assets/example_ade_screenshot_light.png
deleted file mode 100644
index 9e5c2af7..00000000
Binary files a/assets/example_ade_screenshot_light.png and /dev/null differ
diff --git a/assets/letta_ade_screenshot.png b/assets/letta_ade_screenshot.png
deleted file mode 100644
index 27171905..00000000
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diff --git a/certs/README.md b/certs/README.md
deleted file mode 100644
index 87f56da3..00000000
--- a/certs/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# About
-These certs are used to set up a localhost https connection to the ADE.
-
-## Instructions
-1. Install [mkcert](https://github.com/FiloSottile/mkcert)
-2. Run `mkcert -install`
-3. Run letta with the environment variable `LOCAL_HTTPS=true`
-4. Access the app at [https://app.letta.com/development-servers/local/dashboard](https://app.letta.com/development-servers/local/dashboard)
-5. Click "Add remote server" and enter `https://localhost:8283` as the URL, leave password blank unless you have secured your ADE with a password.
diff --git a/certs/localhost-key.pem b/certs/localhost-key.pem
deleted file mode 100644
index 363a191f..00000000
--- a/certs/localhost-key.pem
+++ /dev/null
@@ -1,28 +0,0 @@
------BEGIN PRIVATE KEY-----
-MIIEvwIBADANBgkqhkiG9w0BAQEFAASCBKkwggSlAgEAAoIBAQDenaHTolfy9TzX
-AUd60yPO1W0mpxdDTuxr2p3tBUaQJt5bEGzJbs1M0i5YVRK/SxtYZQvyqmI0ULKN
-8+evKSEpJoDgLfFKM266jzKDSXd5XBQ3XuuxbKq6NV6qoTdweJ0zP0XXDUnKoTN6
-eMkUi8hD9P1TR3Ok3VGnT1wsdG0wPwRPDI/sD92GASL4ViUy/1Llrs7GjlOC+7M2
-GMoGifSHjmx2xgZ/K8cdD2q15iJJlhdbgCwfejcQlP7cmLtSJHH188EZeoFPEfNS
-UpYNglS1kx0D/LC1ooTQRkCpLAnxeonMQZS5O5/q/zyxftkyKO+NInR6DtM0Uj8f
-Gu5UDw1TAgMBAAECggEBANhqpkf4K0gm4V6j/7mISedp1RMenZ7xuyWfAqjJ2C+L
-md8tuJSbAzsLmcKF8hPGEG9+zH685Xu2d99InpPKiFJY/DD0eP6JwbvcOl8nrN5u
-hbjOrpNt8QvVlpKK6DqPB0Qq3tqSMIqs7D7D7bfrrGVkZmHvtJ0yC497t0AAb6XV
-zTtnY9K6LVxb/t+eIDDX1AvE7i2WC+j1YgfexbM0VI/g4fveEVaKPFkWF3nSm0Ag
-BmqzfGFUWKhBZmWpU0m86Zc45q575Bl64yXEQDYocUw3UfOp5/uF0lwuVe5Bpq/w
-hIJgrW6RLzy20QFgPDxHhG3QdBpq4gB9BxCrMb+yhQECgYEA6jL1pD0drczxfaWC
-bh+VsbVrRnz/0XDlPjaysO+qKsNP104gydeyyB6RcGnO8PssvFMCJNQlMhkEpp9x
-bOwR36a4A2absC/osGgFH4pYyN2wqDb+qE2A3M+rnSGourd09y8BsCovN+5YsozK
-HCnqjNWUweypU+RUvtM5jztsiOUCgYEA81ajdczpsysSn0xIFF0exvnnPLy/AiOR
-uEFbPi0kavy7niwd609JFsSOwUXg2QavBNeoLH+YrQhueDoBJZANerLfLD8jqQxD
-ojB6DkHwK5Vf9NIm8DZQ6trtf8xWGB/TuwpkWHm1wMdlCbmH38MukU4p6as7FKzT
-8J57p/TfcdcCgYEAyDqfVzbFTBWfFbxOghZQ5mlj+RTfplHuPL2JEssk4oCvnzV1
-xPu8J2ozEDf2LIOiYLRbbd9OmcFX/5jr4aMHOP6R7p5oVz7uovub/bZLaBhZc8fo
-+z2gAakvYR0o49H7l2XB/LpkOl51yNmj5mZT2Oq1zwKmVkotxiRS3smAZp0CgYAP
-sOyFchs3xHVE9GRJe9+6MO8qSXl/p8+DtCMwFTUd+QIYJvwe6lPqNe6Go/zlwbqT
-c1yS0f+EWODWu9bLF0jnOpWNgtzHz9Skpr+YH8Re6xju7oY4QyhgnJFoBkMe9x5u
-FzN1SRPhRHpNcDtEwI9GK2YkfTgoEyTvhSiwIegurQKBgQDGkheCC7hqleNV3lGM
-SfMUgyGt/1abZ82eAkdfeUrM37FeSbxuafyp0ICjZY0xsn6RUickHyXBJhkOGSJX
-lGSvHwMsnXT30KAGd08ZqWmTSGmH6IrdVhrveY+e18ILXYgAkQ1T9tSKjeyFfK8m
-dUWlFZHfdToFu1pn7yBgofMAmw==
------END PRIVATE KEY-----
diff --git a/certs/localhost.pem b/certs/localhost.pem
deleted file mode 100644
index 8d4df205..00000000
--- a/certs/localhost.pem
+++ /dev/null
@@ -1,26 +0,0 @@
------BEGIN CERTIFICATE-----
-MIIEdjCCAt6gAwIBAgIQX/6Qs3c+lQq4+pcuUK7a7jANBgkqhkiG9w0BAQsFADCB
-lTEeMBwGA1UEChMVbWtjZXJ0IGRldmVsb3BtZW50IENBMTUwMwYDVQQLDCxzaHVi
-QFNodWItTWVtR1BULURyaXZlci5sb2NhbCAoU2h1YmhhbSBOYWlrKTE8MDoGA1UE
-AwwzbWtjZXJ0IHNodWJAU2h1Yi1NZW1HUFQtRHJpdmVyLmxvY2FsIChTaHViaGFt
-IE5haWspMB4XDTI0MTIxMDE4MTgwMFoXDTI3MDMxMDE4MTgwMFowYDEnMCUGA1UE
-ChMebWtjZXJ0IGRldmVsb3BtZW50IGNlcnRpZmljYXRlMTUwMwYDVQQLDCxzaHVi
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------END CERTIFICATE-----
diff --git a/compose.yaml b/compose.yaml
deleted file mode 100644
index 756919c2..00000000
--- a/compose.yaml
+++ /dev/null
@@ -1,65 +0,0 @@
-services:
- letta_db:
- image: ankane/pgvector:v0.5.1
- networks:
- default:
- aliases:
- - pgvector_db
- - letta-db
- environment:
- - POSTGRES_USER=${LETTA_PG_USER:-letta}
- - POSTGRES_PASSWORD=${LETTA_PG_PASSWORD:-letta}
- - POSTGRES_DB=${LETTA_PG_DB:-letta}
- volumes:
- - ./.persist/pgdata:/var/lib/postgresql/data
- - ./init.sql:/docker-entrypoint-initdb.d/init.sql
- ports:
- - "${LETTA_PG_PORT:-5432}:5432"
- healthcheck:
- test: ["CMD-SHELL", "pg_isready -U letta"]
- interval: 5s
- timeout: 5s
- retries: 5
- letta_server:
- image: letta/letta:latest
- hostname: letta-server
- depends_on:
- letta_db:
- condition: service_healthy
- ports:
- - "8083:8083"
- - "8283:8283"
- env_file:
- - .env
- environment:
- - LETTA_PG_URI=${LETTA_PG_URI:-postgresql://${LETTA_PG_USER:-letta}:${LETTA_PG_PASSWORD:-letta}@${LETTA_DB_HOST:-letta-db}:${LETTA_PG_PORT:-5432}/${LETTA_PG_DB:-letta}}
- - LETTA_DEBUG=True
- - OPENAI_API_KEY=${OPENAI_API_KEY}
- - GROQ_API_KEY=${GROQ_API_KEY}
- - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
- - OLLAMA_BASE_URL=${OLLAMA_BASE_URL}
- - AZURE_API_KEY=${AZURE_API_KEY}
- - AZURE_BASE_URL=${AZURE_BASE_URL}
- - AZURE_API_VERSION=${AZURE_API_VERSION}
- - GEMINI_API_KEY=${GEMINI_API_KEY}
- - VLLM_API_BASE=${VLLM_API_BASE}
- - OPENLLM_AUTH_TYPE=${OPENLLM_AUTH_TYPE}
- - OPENLLM_API_KEY=${OPENLLM_API_KEY}
- - LETTA_OTEL_EXPORTER_OTLP_ENDPOINT=${LETTA_OTEL_EXPORTER_OTLP_ENDPOINT}
- - CLICKHOUSE_ENDPOINT=${CLICKHOUSE_ENDPOINT}
- - CLICKHOUSE_DATABASE=${CLICKHOUSE_DATABASE}
- - CLICKHOUSE_USERNAME=${CLICKHOUSE_USERNAME}
- - CLICKHOUSE_PASSWORD=${CLICKHOUSE_PASSWORD}
- # volumes:
- # - ./configs/server_config.yaml:/root/.letta/config # config file
- # - ~/.letta/credentials:/root/.letta/credentials # credentials file
- # Uncomment this line to mount a local directory for tool execution, and specify the mount path
- # before running docker compose: `export LETTA_SANDBOX_MOUNT_PATH=$PWD/directory`
- # - ${LETTA_SANDBOX_MOUNT_PATH:?}:/root/.letta/tool_execution_dir # mounted volume for tool execution
- letta_nginx:
- hostname: letta-nginx
- image: nginx:stable-alpine3.17-slim
- volumes:
- - ./nginx.conf:/etc/nginx/nginx.conf
- ports:
- - "80:80"
diff --git a/db/Dockerfile.simple b/db/Dockerfile.simple
deleted file mode 100644
index 8522cf7d..00000000
--- a/db/Dockerfile.simple
+++ /dev/null
@@ -1,87 +0,0 @@
-# syntax = docker/dockerfile:1.6
-
-# Build a self-configuring postgres image with pgvector installed.
-# It has no dependencies except for the base image.
-
-# Build with:
-# docker build -t letta-db -f db/Dockerfile.simple .
-#
-# -t letta-db: tag the image with the name letta-db (tag defaults to :latest)
-# -f db/Dockerfile.simple: use the Dockerfile at db/Dockerfile.simple (this file)
-# .: build the image from the current directory, not really used.
-
-#
-# Run the first time with:
-# docker run -d --rm \
-# --name letta-db \
-# -p 5432:5432 \
-# -e POSTGRES_PASSWORD=password \
-# -v letta_db:/var/lib/postgresql/data \
-# letta-db:latest
-#
-# -d: run in the background
-# --rm: remove the container when it exits
-# --name letta-db: name the container letta-db
-# -p 5432:5432: map port 5432 on the host to port 5432 in the container
-# -v letta_db:/var/lib/postgresql/data: map the volume letta_db to /var/lib/postgresql/data in the container
-# letta-db:latest: use the image letta-db:latest
-#
-# After the first time, you do not need the POSTGRES_PASSWORD.
-# docker run -d --rm \
-# --name letta-db \
-# -p 5432:5432 \
-# -v letta_db:/var/lib/postgresql/data \
-# letta-db:latest
-
-# Rather than a docker volume (letta_db), you can use an absolute path to a directory on the host.
-#
-# You can stop the container with:
-# docker stop letta-db
-#
-# You access the database with:
-# postgresql+pg8000://user:password@localhost:5432/db
-# where user, password, and db are the values you set in the init-letta.sql file,
-# all defaulting to 'letta'.
-
-# Version tags can be found here: https://hub.docker.com/r/ankane/pgvector/tags
-ARG PGVECTOR=v0.5.1
-# Set up a minimal postgres image
-FROM ankane/pgvector:${PGVECTOR}
-RUN sed -e 's/^ //' >/docker-entrypoint-initdb.d/01-initletta.sql <<'EOF'
- -- Title: Init Letta Database
-
- -- Fetch the docker secrets, if they are available.
- -- Otherwise fall back to environment variables, or hardwired 'letta'
- \set db_user `([ -r /var/run/secrets/letta-user ] && cat /var/run/secrets/letta-user) || echo "${LETTA_USER:-letta}"`
- \set db_password `([ -r /var/run/secrets/letta-password ] && cat /var/run/secrets/letta-password) || echo "${LETTA_PASSWORD:-letta}"`
- \set db_name `([ -r /var/run/secrets/letta-db ] && cat /var/run/secrets/letta-db) || echo "${LETTA_DB:-letta}"`
-
- CREATE USER :"db_user"
- WITH PASSWORD :'db_password'
- NOCREATEDB
- NOCREATEROLE
- ;
-
- CREATE DATABASE :"db_name"
- WITH
- OWNER = :"db_user"
- ENCODING = 'UTF8'
- LC_COLLATE = 'en_US.utf8'
- LC_CTYPE = 'en_US.utf8'
- LOCALE_PROVIDER = 'libc'
- TABLESPACE = pg_default
- CONNECTION LIMIT = -1;
-
- -- Set up our schema and extensions in our new database.
- \c :"db_name"
-
- CREATE SCHEMA :"db_name"
- AUTHORIZATION :"db_user";
-
- ALTER DATABASE :"db_name"
- SET search_path TO :"db_name";
-
- CREATE EXTENSION IF NOT EXISTS vector WITH SCHEMA :"db_name";
-
- DROP SCHEMA IF EXISTS public CASCADE;
-EOF
diff --git a/db/run_postgres.sh b/db/run_postgres.sh
deleted file mode 100755
index 1fd6d56a..00000000
--- a/db/run_postgres.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-# build container
-docker build -f db/Dockerfile.simple -t pg-test .
-
-# run container
-docker run -d --rm \
- --name letta-db-test \
- -p 8888:5432 \
- -e POSTGRES_PASSWORD=password \
- -v letta_db_test:/var/lib/postgresql/data \
- pg-test:latest
diff --git a/dev-compose.yaml b/dev-compose.yaml
deleted file mode 100644
index 36fd5c54..00000000
--- a/dev-compose.yaml
+++ /dev/null
@@ -1,47 +0,0 @@
-services:
- letta_db:
- image: ankane/pgvector:v0.5.1
- networks:
- default:
- aliases:
- - pgvector_db
- - letta-db
- environment:
- - POSTGRES_USER=${LETTA_PG_USER:-letta}
- - POSTGRES_PASSWORD=${LETTA_PG_PASSWORD:-letta}
- - POSTGRES_DB=${LETTA_PG_DB:-letta}
- volumes:
- - ./.persist/pgdata-test:/var/lib/postgresql/data
- - ./init.sql:/docker-entrypoint-initdb.d/init.sql
- ports:
- - "5432:5432"
- letta_server:
- image: letta/letta:latest
- hostname: letta
- build:
- context: .
- dockerfile: Dockerfile
- target: runtime
- depends_on:
- - letta_db
- ports:
- - "8083:8083"
- - "8283:8283"
- environment:
- - LETTA_PG_DB=${LETTA_PG_DB:-letta}
- - LETTA_PG_USER=${LETTA_PG_USER:-letta}
- - LETTA_PG_PASSWORD=${LETTA_PG_PASSWORD:-letta}
- - LETTA_PG_HOST=pgvector_db
- - LETTA_PG_PORT=5432
- - LETTA_DEBUG=True
- - OPENAI_API_KEY=${OPENAI_API_KEY}
- - GROQ_API_KEY=${GROQ_API_KEY}
- - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
- - OLLAMA_BASE_URL=${OLLAMA_BASE_URL}
- - AZURE_API_KEY=${AZURE_API_KEY}
- - AZURE_BASE_URL=${AZURE_BASE_URL}
- - AZURE_API_VERSION=${AZURE_API_VERSION}
- - GEMINI_API_KEY=${GEMINI_API_KEY}
- - VLLM_API_BASE=${VLLM_API_BASE}
- - OPENLLM_AUTH_TYPE=${OPENLLM_AUTH_TYPE}
- - OPENLLM_API_KEY=${OPENLLM_API_KEY}
diff --git a/development.compose.yml b/development.compose.yml
deleted file mode 100644
index 71065ce0..00000000
--- a/development.compose.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-services:
- letta_server:
- image: letta_server
- hostname: letta-server
- build:
- context: .
- dockerfile: Dockerfile
- target: development
- args:
- - MEMGPT_ENVIRONMENT=DEVELOPMENT
- depends_on:
- - letta_db
- env_file:
- - .env
- environment:
- - WATCHFILES_FORCE_POLLING=true
-
- volumes:
- - ./letta:/letta
- - ~/.letta/credentials:/root/.letta/credentials
- - ./configs/server_config.yaml:/root/.letta/config
- - ./CONTRIBUTING.md:/CONTRIBUTING.md
- - ./tests/pytest_cache:/letta/.pytest_cache
- - ./tests/pytest.ini:/letta/pytest.ini
- - ./pyproject.toml:/pyproject.toml
- - ./tests:/tests
- ports:
- - "8083:8083"
- - "8283:8283"
diff --git a/docker-compose-vllm.yaml b/docker-compose-vllm.yaml
deleted file mode 100644
index f6487d26..00000000
--- a/docker-compose-vllm.yaml
+++ /dev/null
@@ -1,35 +0,0 @@
-version: '3.8'
-
-services:
- letta:
- image: letta/letta:latest
- ports:
- - "8283:8283"
- environment:
- - LETTA_LLM_ENDPOINT=http://vllm:8000
- - LETTA_LLM_ENDPOINT_TYPE=vllm
- - LETTA_LLM_MODEL=${LETTA_LLM_MODEL} # Replace with your model
- - LETTA_LLM_CONTEXT_WINDOW=8192
- depends_on:
- - vllm
-
- vllm:
- image: vllm/vllm-openai:latest
- runtime: nvidia
- deploy:
- resources:
- reservations:
- devices:
- - driver: nvidia
- count: all
- capabilities: [gpu]
- environment:
- - HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}
- volumes:
- - ~/.cache/huggingface:/root/.cache/huggingface
- ports:
- - "8000:8000"
- command: >
- --model ${LETTA_LLM_MODEL} --max_model_len=8000
- # Replace with your model
- ipc: host
diff --git a/examples/Building agents with Letta.ipynb b/examples/Building agents with Letta.ipynb
deleted file mode 100644
index 48c80b23..00000000
--- a/examples/Building agents with Letta.ipynb
+++ /dev/null
@@ -1,440 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "cac06555-9ce8-4f01-bbef-3f8407f4b54d",
- "metadata": {},
- "source": [
- "# Lab 3: Using MemGPT to build agents with memory \n",
- "This lab will go over: \n",
- "1. Creating an agent with MemGPT\n",
- "2. Understand MemGPT agent state (messages, memories, tools)\n",
- "3. Understanding core and archival memory\n",
- "4. Building agentic RAG with MemGPT "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aad3a8cc-d17a-4da1-b621-ecc93c9e2106",
- "metadata": {},
- "source": [
- "## Setup a Letta client \n",
- "Make sure you run `pip install letta_client` and start letta server `letta quickstart`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "067e007c-02f7-4d51-9c8a-651c7d5a6499",
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install letta_client\n",
- "!pip install letta\n",
- "!letta quickstart"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7ccd43f2-164b-4d25-8465-894a3bb54c4b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta_client import CreateBlock, Letta, MessageCreate \n",
- "\n",
- "client = Letta(base_url=\"http://localhost:8283\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "65bf0dc2-d1ac-4d4c-8674-f3156eeb611d",
- "metadata": {},
- "source": [
- "## Creating a simple agent with memory \n",
- "MemGPT allows you to create persistent LLM agents that have memory. By default, MemGPT saves all state related to agents in a database, so you can also re-load an existing agent with its prior state. We'll show you in this section how to create a MemGPT agent and to understand what memories it's storing. \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fe092474-6b91-4124-884d-484fc28b58e7",
- "metadata": {},
- "source": [
- "### Creating an agent "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2a9d6228-a0f5-41e6-afd7-6a05260565dc",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_name = \"simple_agent\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "62dcf31d-6f45-40f5-8373-61981f03da62",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_state = client.agents.create(\n",
- " name=agent_name, \n",
- " memory_blocks=[\n",
- " CreateBlock(\n",
- " label=\"human\",\n",
- " value=\"My name is Sarah\",\n",
- " ),\n",
- " CreateBlock(\n",
- " label=\"persona\",\n",
- " value=\"You are a helpful assistant that loves emojis\",\n",
- " ),\n",
- " ]\n",
- " model=\"openai/gpt-4o-mini\",\n",
- " embedding=\"openai/text-embedding-3-small\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "31c2d5f6-626a-4666-8d0b-462db0292a7d",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\", \n",
- " content=\"hello!\", \n",
- " ),\n",
- " ]\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "20a5ccf4-addd-4bdb-be80-161f7925dae0",
- "metadata": {},
- "source": [
- "Note that MemGPT agents will generate a *reasoning_message* that explains its actions. You can use this monoloque to understand why agents are behaving as they are. \n",
- "\n",
- "Second, MemGPT agents also use tools to communicate, so messages are sent back by calling a `send_message` tool. This makes it easy to allow agent to communicate over different mediums (e.g. text), and also allows the agent to distinguish betweeh that is and isn't send to the end user. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8d33eca5-b8e8-4a8f-9440-85b45c37a777",
- "metadata": {},
- "source": [
- "### Understanding agent state \n",
- "MemGPT agents are *stateful* and are defined by: \n",
- "* The system prompt defining the agent's behavior (read-only)\n",
- "* The set of *tools* they have access to \n",
- "* Their memory (core, archival, & recall)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c1cf7136-4060-441a-9d12-da851badf339",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(agent_state.system)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d9e1c8c0-e98c-4952-b850-136b5b50a5ee",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_state.tools"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ae910ad9-afee-41f5-badd-a8dee5b2ad94",
- "metadata": {},
- "source": [
- "### Viewing an agent's memory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "478a0df6-3c87-4803-9133-8a54f9c00320",
- "metadata": {},
- "outputs": [],
- "source": [
- "memory = client.agents.core_memory.retrieve(agent_id=agent_state.id)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ff2c3736-5424-4883-8fe9-73a4f598a043",
- "metadata": {},
- "outputs": [],
- "source": [
- "memory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d6da43d6-847e-4a0a-9b92-cea2721e828a",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.context.retrieve(agent_id=agent_state.id)[\"num_archival_memory\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0399a1d6-a1f8-4796-a4c0-eb322512b0ec",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.context.retrieve(agent_id=agent_state.id)[\"num_recall_memory\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c7cce583-1f11-4f13-a6ed-52cc7f80e3c4",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.messages.list(agent_id=agent_state.id)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dfd0a9ae-417e-4ba0-a562-ec59cb2bbf7d",
- "metadata": {},
- "source": [
- "## Understanding core memory \n",
- "Core memory is memory that is stored *in-context* - so every LLM call, core memory is included. What's unique about MemGPT is that this core memory is editable via tools by the agent itself. Lets see how the agent can adapt its memory to new information."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d259669c-5903-40b5-8758-93c36faa752f",
- "metadata": {},
- "source": [
- "### Memories about the human \n",
- "The `human` section of `ChatMemory` is used to remember information about the human in the conversation. As the agent learns new information about the human, it can update this part of memory to improve personalization. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "beb9b0ba-ed7c-4917-8ee5-21d201516086",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\", \n",
- " content=\"My name is actually Bob\", \n",
- " ),\n",
- " ]\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "25f58968-e262-4268-86ef-1bed57e6bf33",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.core_memory.retrieve(agent_id=agent_state.id)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "32692ca2-b731-43a6-84de-439a08a4c0d2",
- "metadata": {},
- "source": [
- "### Memories about the agent\n",
- "The agent also records information about itself and how it behaves in the `persona` section of memory. This is important for ensuring a consistent persona over time (e.g. not making inconsistent claims, such as liking ice cream one day and hating it another). Unlike the `system_prompt`, the `persona` is editable - this means that it can be used to incoporate feedback to learn and improve its persona over time. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f68851c5-5666-45fd-9d2f-037ea86bfcfa",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id,\n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\", \n",
- " content=\"In the future, never use emojis to communicate\", \n",
- " ),\n",
- " ]\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2fc54336-d61f-446d-82ea-9dd93a011e51",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.core_memory.retrieve_block(agent_id=agent_state.id, block_label='persona')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "592f5d1c-cd2f-4314-973e-fcc481e6b460",
- "metadata": {},
- "source": [
- "## Understanding archival memory\n",
- "MemGPT agents store long term memories in *archival memory*, which persists data into an external database. This allows agents additional space to write information outside of its context window (e.g. with core memory), which is limited in size. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "af63a013-6be3-4931-91b0-309ff2a4dc3a",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.archival_memory.list(agent_id=agent_state.id)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bfa52984-fe7c-4d17-900a-70a376a460f9",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.context.retrieve(agent_id=agent_state.id)[\"num_archival_memory\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a3ab0ae9-fc00-4447-8942-7dbed7a99222",
- "metadata": {},
- "source": [
- "Agents themselves can write to their archival memory when they learn information they think should be placed in long term storage. You can also directly suggest that the agent store information in archival. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c6556f76-8fcb-42ff-a6d0-981685ef071c",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\", \n",
- " content=\"Save the information that 'bob loves cats' to archival\", \n",
- " ),\n",
- " ]\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b4429ffa-e27a-4714-a873-84f793c08535",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.archival_memory.list(agent_id=agent_state.id)[0].text"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ae463e7c-0588-48ab-888c-734c783782bf",
- "metadata": {},
- "source": [
- "You can also directly insert into archival memory from the client. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9d4194d-9ed5-40a1-b35d-a9aff3048000",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.archival_memory.create(\n",
- " agent_id=agent_state.id, \n",
- " text=\"Bob's loves boston terriers\"\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "338149f1-6671-4a0b-81d9-23d01dbe2e97",
- "metadata": {},
- "source": [
- "Now lets see how the agent uses its archival memory:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5908b10f-94db-4f5a-bb9a-1f08c74a2860",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\", \n",
- " content=\"What animals do I like? Search archival.\", \n",
- " ),\n",
- " ]\n",
- ")\n",
- "response"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta-main",
- "language": "python",
- "name": "letta-main"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/docs/agent_advanced.py b/examples/docs/agent_advanced.py
deleted file mode 100644
index a143076a..00000000
--- a/examples/docs/agent_advanced.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from letta_client import CreateBlock, Letta, MessageCreate
-
-from letta.prompts import gpt_system
-
-"""
-Make sure you run the Letta server before running this example.
-```
-letta server
-```
-"""
-
-client = Letta(base_url="http://localhost:8283")
-
-# create a new agent
-agent_state = client.agents.create(
- # agent's name (unique per-user, autogenerated if not provided)
- name="agent_name",
- # in-context memory representation with human/persona blocks
- memory_blocks=[
- CreateBlock(
- label="human",
- value="Name: Sarah",
- ),
- CreateBlock(
- label="persona",
- value="You are a helpful assistant that loves emojis",
- ),
- ],
- # LLM model & endpoint configuration
- model="openai/gpt-4o-mini",
- context_window_limit=8000,
- # embedding model & endpoint configuration (cannot be changed)
- embedding="openai/text-embedding-3-small",
- # system instructions for the agent (defaults to `memgpt_chat`)
- system=gpt_system.get_system_text("memgpt_chat"),
- # whether to include base letta tools (default: True)
- include_base_tools=True,
- # list of additional tools (by name) to add to the agent
- tool_ids=[],
-)
-print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
-
-# message an agent as a user
-response = client.agents.messages.create(
- agent_id=agent_state.id,
- messages=[
- MessageCreate(
- role="user",
- content="hello",
- )
- ],
-)
-print("Usage", response.usage)
-print("Agent messages", response.messages)
-
-# message a system message (non-user)
-response = client.agents.messages.create(
- agent_id=agent_state.id,
- messages=[
- MessageCreate(
- role="system",
- content="[system] user has logged in. send a friendly message.",
- )
- ],
-)
-print("Usage", response.usage)
-print("Agent messages", response.messages)
-
-# delete the agent
-client.agents.delete(agent_id=agent_state.id)
diff --git a/examples/docs/agent_basic.py b/examples/docs/agent_basic.py
deleted file mode 100644
index eb0bd952..00000000
--- a/examples/docs/agent_basic.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from letta_client import CreateBlock, Letta, MessageCreate
-
-"""
-Make sure you run the Letta server before running this example.
-```
-letta server
-```
-"""
-
-client = Letta(base_url="http://localhost:8283")
-
-# create a new agent
-agent_state = client.agents.create(
- memory_blocks=[
- CreateBlock(
- label="human",
- value="Name: Sarah",
- ),
- ],
- # set automatic defaults for LLM/embedding config
- model="openai/gpt-4o-mini",
- embedding="openai/text-embedding-3-small",
-)
-print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
-
-# Message an agent
-response = client.agents.messages.create(
- agent_id=agent_state.id,
- messages=[
- MessageCreate(
- role="user",
- content="hello",
- )
- ],
-)
-print("Usage", response.usage)
-print("Agent messages", response.messages)
-
-# list all agents
-agents = client.agents.list()
-
-# get the agent by ID
-agent_state = client.agents.retrieve(agent_id=agent_state.id)
-
-# get the agent by name
-agent_state = client.agents.list(name=agent_state.name)[0]
-
-# delete an agent
-client.agents.delete(agent_id=agent_state.id)
diff --git a/examples/docs/example.py b/examples/docs/example.py
deleted file mode 100644
index 4f9e1ab4..00000000
--- a/examples/docs/example.py
+++ /dev/null
@@ -1,166 +0,0 @@
-from letta_client import CreateBlock, Letta, MessageCreate
-
-"""
-Make sure you run the Letta server before running this example.
-See: https://docs.letta.com/quickstart
-
-If you're using Letta Cloud, replace 'baseURL' with 'token'
-See: https://docs.letta.com/api-reference/overview
-
-Execute this script using `uv run python3 example.py`
-
-This will install `letta_client` and other dependencies.
-"""
-client = Letta(
- base_url="http://localhost:8283",
-)
-
-agent = client.agents.create(
- memory_blocks=[
- CreateBlock(
- value="Name: Caren",
- label="human",
- ),
- ],
- model="openai/gpt-4o-mini",
- embedding="openai/text-embedding-3-small",
-)
-
-print(f"Created agent with name {agent.name}")
-
-# Example without streaming
-message_text = "What's my name?"
-response = client.agents.messages.create(
- agent_id=agent.id,
- messages=[
- MessageCreate(
- role="user",
- content=message_text,
- ),
- ],
-)
-
-print(f"Sent message to agent {agent.name}: {message_text}")
-print(f"Agent thoughts: {response.messages[0].reasoning}")
-print(f"Agent response: {response.messages[1].content}")
-
-
-def secret_message():
- """Return a secret message."""
- return "Hello world!"
-
-
-tool = client.tools.upsert_from_function(
- func=secret_message,
-)
-
-client.agents.tools.attach(agent_id=agent.id, tool_id=tool.id)
-
-print(f"Created tool {tool.name} and attached to agent {agent.name}")
-
-message_text = "Run secret message tool and tell me what it returns"
-response = client.agents.messages.create(
- agent_id=agent.id,
- messages=[
- MessageCreate(
- role="user",
- content=message_text,
- ),
- ],
-)
-
-for msg in response.messages:
- if msg.message_type == "assistant_message":
- print(msg.content)
- elif msg.message_type == "reasoning_message":
- print(msg.reasoning)
- elif msg.message_type == "tool_call_message":
- print(msg.tool_call.name)
- print(msg.tool_call.arguments)
- elif msg.message_type == "tool_return_message":
- print(msg.tool_return)
-
-print(f"Sent message to agent {agent.name}: {message_text}")
-print(f"Agent thoughts: {response.messages[0].reasoning}")
-print(f"Tool call information: {response.messages[1].tool_call}")
-print(f"Tool response information: {response.messages[2].status}")
-print(f"Agent thoughts: {response.messages[3].reasoning}")
-print(f"Agent response: {response.messages[4].content}")
-
-
-# send a message to the agent (streaming steps)
-message_text = "Repeat my name."
-stream = client.agents.messages.create_stream(
- agent_id=agent_state.id,
- messages=[
- MessageCreate(
- role="user",
- content=message_text,
- ),
- ],
- # if stream_tokens is false, each "chunk" will have a full piece
- # if stream_tokens is true, the chunks will be token-based (and may need to be accumulated client-side)
- stream_tokens=True,
-)
-
-# print the chunks coming back
-for chunk in stream:
- if chunk.message_type == "assistant_message":
- print(chunk.content)
- elif chunk.message_type == "reasoning_message":
- print(chunk.reasoning)
- elif chunk.message_type == "tool_call_message":
- if chunk.tool_call.name:
- print(chunk.tool_call.name)
- if chunk.tool_call.arguments:
- print(chunk.tool_call.arguments)
- elif chunk.message_type == "tool_return_message":
- print(chunk.tool_return)
- elif chunk.message_type == "usage_statistics":
- print(chunk)
-
-
-agent_copy = client.agents.create(
- model="openai/gpt-4o-mini",
- embedding="openai/text-embedding-3-small",
-)
-block = client.agents.blocks.retrieve(agent.id, block_label="human")
-agent_copy = client.agents.blocks.attach(agent_copy.id, block.id)
-
-print(f"Created agent copy with shared memory named {agent_copy.name}")
-
-message_text = "My name isn't Caren, it's Sarah. Please update your core memory with core_memory_replace"
-response = client.agents.messages.create(
- agent_id=agent_copy.id,
- messages=[
- MessageCreate(
- role="user",
- content=message_text,
- ),
- ],
-)
-
-print(f"Sent message to agent {agent_copy.name}: {message_text}")
-
-block = client.agents.blocks.retrieve(agent_copy.id, block_label="human")
-print(f"New core memory for agent {agent_copy.name}: {block.value}")
-
-message_text = "What's my name?"
-response = client.agents.messages.create(
- agent_id=agent_copy.id,
- messages=[
- MessageCreate(
- role="user",
- content=message_text,
- ),
- ],
-)
-
-print(f"Sent message to agent {agent_copy.name}: {message_text}")
-print(f"Agent thoughts: {response.messages[0].reasoning}")
-print(f"Agent response: {response.messages[1].content}")
-
-client.agents.delete(agent_id=agent.id)
-client.agents.delete(agent_id=agent_copy.id)
-
-print(f"Deleted agents {agent.name} and {agent_copy.name}")
diff --git a/examples/docs/memory.py b/examples/docs/memory.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/examples/docs/node/example.ts b/examples/docs/node/example.ts
deleted file mode 100644
index 8476f71b..00000000
--- a/examples/docs/node/example.ts
+++ /dev/null
@@ -1,148 +0,0 @@
-import type {
- AssistantMessage,
- ReasoningMessage,
- ToolCallMessage,
- ToolReturnMessage,
-} from '@letta-ai/letta-client/api/types';
-
-/**
- * Make sure you run the Letta server before running this example.
- * See https://docs.letta.com/quickstart
- *
- * If you're using Letta Cloud, replace 'baseURL' with 'token'
- * See https://docs.letta.com/api-reference/overview
- *
- * Execute this script using `npm run example`
- */
-const client = new LettaClient({
- baseUrl: 'http://localhost:8283',
-});
-
-const agent = await client.agents.create({
- memoryBlocks: [
- {
- value: 'name: Caren',
- label: 'human',
- },
- ],
- model: 'openai/gpt-4o-mini',
- embedding: 'openai/text-embedding-3-small',
-});
-
-console.log('Created agent with name', agent.name);
-
-let messageText = "What's my name?";
-let response = await client.agents.messages.create(agent.id, {
- messages: [
- {
- role: 'user',
- content: messageText,
- },
- ],
-});
-
-console.log(`Sent message to agent ${agent.name}: ${messageText}`);
-console.log(
- 'Agent thoughts:',
- (response.messages[0] as ReasoningMessage).reasoning,
-);
-console.log(
- 'Agent response:',
- (response.messages[1] as AssistantMessage).content,
-);
-
-const CUSTOM_TOOL_SOURCE_CODE = `
-def secret_message():
- """Return a secret message."""
- return "Hello world!"
- `.trim();
-
-const tool = await client.tools.upsert({
- sourceCode: CUSTOM_TOOL_SOURCE_CODE,
-});
-
-await client.agents.tools.attach(agent.id, tool.id);
-
-console.log(`Created tool ${tool.name} and attached to agent ${agent.name}`);
-
-messageText = 'Run secret message tool and tell me what it returns';
-response = await client.agents.messages.create(agent.id, {
- messages: [
- {
- role: 'user',
- content: messageText,
- },
- ],
-});
-
-console.log(`Sent message to agent ${agent.name}: ${messageText}`);
-console.log(
- 'Agent thoughts:',
- (response.messages[0] as ReasoningMessage).reasoning,
-);
-console.log(
- 'Tool call information:',
- (response.messages[1] as ToolCallMessage).toolCall,
-);
-console.log(
- 'Tool response information:',
- (response.messages[2] as ToolReturnMessage).status,
-);
-console.log(
- 'Agent thoughts:',
- (response.messages[3] as ReasoningMessage).reasoning,
-);
-console.log(
- 'Agent response:',
- (response.messages[4] as AssistantMessage).content,
-);
-
-let agentCopy = await client.agents.create({
- model: 'openai/gpt-4o-mini',
- embedding: 'openai/text-embedding-3-small',
-});
-let block = await client.agents.blocks.retrieve(agent.id, 'human');
-agentCopy = await client.agents.blocks.attach(agentCopy.id, block.id);
-
-console.log('Created agent copy with shared memory named', agentCopy.name);
-
-messageText =
- "My name isn't Caren, it's Sarah. Please update your core memory with core_memory_replace";
-response = await client.agents.messages.create(agentCopy.id, {
- messages: [
- {
- role: 'user',
- content: messageText,
- },
- ],
-});
-
-console.log(`Sent message to agent ${agentCopy.name}: ${messageText}`);
-
-block = await client.agents.blocks.retrieve(agentCopy.id, 'human');
-console.log(`New core memory for agent ${agentCopy.name}: ${block.value}`);
-
-messageText = "What's my name?";
-response = await client.agents.messages.create(agentCopy.id, {
- messages: [
- {
- role: 'user',
- content: messageText,
- },
- ],
-});
-
-console.log(`Sent message to agent ${agentCopy.name}: ${messageText}`);
-console.log(
- 'Agent thoughts:',
- (response.messages[0] as ReasoningMessage).reasoning,
-);
-console.log(
- 'Agent response:',
- (response.messages[1] as AssistantMessage).content,
-);
-
-await client.agents.delete(agent.id);
-await client.agents.delete(agentCopy.id);
-
-console.log(`Deleted agents ${agent.name} and ${agentCopy.name}`);
diff --git a/examples/docs/node/package-lock.json b/examples/docs/node/package-lock.json
deleted file mode 100644
index 88b54f8a..00000000
--- a/examples/docs/node/package-lock.json
+++ /dev/null
@@ -1,806 +0,0 @@
-{
- "name": "@letta-ai/core",
- "version": "0.1.0",
- "lockfileVersion": 3,
- "requires": true,
- "packages": {
- "": {
- "name": "@letta-ai/core",
- "version": "0.1.0",
- "dependencies": {
- "@letta-ai/letta-client": "^0.1.131"
- },
- "devDependencies": {
- "@types/node": "^22.12.0",
- "ts-node": "^10.9.2",
- "typescript": "^5.7.3"
- }
- },
- "node_modules/@cspotcode/source-map-support": {
- "version": "0.8.1",
- "resolved": "https://registry.npmjs.org/@cspotcode/source-map-support/-/source-map-support-0.8.1.tgz",
- "integrity": "sha512-IchNf6dN4tHoMFIn/7OE8LWZ19Y6q/67Bmf6vnGREv8RSbBVb9LPJxEcnwrcwX6ixSvaiGoomAUvu4YSxXrVgw==",
- "dev": true,
- "license": "MIT",
- "dependencies": {
- "@jridgewell/trace-mapping": "0.3.9"
- },
- "engines": {
- "node": ">=12"
- }
- },
- "node_modules/@jridgewell/resolve-uri": {
- "version": "3.1.2",
- "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz",
- "integrity": "sha512-bRISgCIjP20/tbWSPWMEi54QVPRZExkuD9lJL+UIxUKtwVJA8wW1Trb1jMs1RFXo1CBTNZ/5hpC9QvmKWdopKw==",
- "dev": true,
- "license": "MIT",
- "engines": {
- "node": ">=6.0.0"
- }
- },
- "node_modules/@jridgewell/sourcemap-codec": {
- "version": "1.5.0",
- "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.5.0.tgz",
- "integrity": "sha512-gv3ZRaISU3fjPAgNsriBRqGWQL6quFx04YMPW/zD8XMLsU32mhCCbfbO6KZFLjvYpCZ8zyDEgqsgf+PwPaM7GQ==",
- "dev": true,
- "license": "MIT"
- },
- "node_modules/@jridgewell/trace-mapping": {
- "version": "0.3.9",
- "resolved": "https://registry.npmjs.org/@jridgewell/trace-mapping/-/trace-mapping-0.3.9.tgz",
- "integrity": "sha512-3Belt6tdc8bPgAtbcmdtNJlirVoTmEb5e2gC94PnkwEW9jI6CAHUeoG85tjWP5WquqfavoMtMwiG4P926ZKKuQ==",
- "dev": true,
- "license": "MIT",
- "dependencies": {
- "@jridgewell/resolve-uri": "^3.0.3",
- "@jridgewell/sourcemap-codec": "^1.4.10"
- }
- },
- "node_modules/@letta-ai/letta-client": {
- "version": "0.1.131",
- "resolved": "https://registry.npmjs.org/@letta-ai/letta-client/-/letta-client-0.1.131.tgz",
- "integrity": "sha512-Kk7iJxGQT5mZ6F1kmbXyYKhXtmHkVOqF/FF3DbADKwQthl9zMHIo1BBl5DTZ7ezICrmpfE9q5aUcdJnCLAgBuQ==",
- "dependencies": {
- "dedent": "^1.0.0",
- "form-data": "^4.0.0",
- "form-data-encoder": "^4.0.2",
- "formdata-node": "^6.0.3",
- "node-fetch": "^2.7.0",
- "qs": "^6.13.1",
- "readable-stream": "^4.5.2",
- "url-join": "4.0.1"
- }
- },
- "node_modules/@tsconfig/node10": {
- "version": "1.0.11",
- "resolved": "https://registry.npmjs.org/@tsconfig/node10/-/node10-1.0.11.tgz",
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diff --git a/examples/docs/node/package.json b/examples/docs/node/package.json
deleted file mode 100644
index 8a47bf2b..00000000
--- a/examples/docs/node/package.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
- "name": "@letta-ai/core",
- "version": "0.1.0",
- "private": true,
- "type": "module",
- "scripts": {
- "example": "node --no-warnings --import 'data:text/javascript,import { register } from \"node:module\"; import { pathToFileURL } from \"node:url\"; register(\"ts-node/esm\", pathToFileURL(\"./\"));' example.ts",
- "build": "tsc"
- },
- "dependencies": {
- "@letta-ai/letta-client": "^0.1.131"
- },
- "devDependencies": {
- "@types/node": "^22.12.0",
- "ts-node": "^10.9.2",
- "typescript": "^5.7.3"
- }
-}
diff --git a/examples/docs/node/project.json b/examples/docs/node/project.json
deleted file mode 100644
index 61fdcd4d..00000000
--- a/examples/docs/node/project.json
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "name": "node-example",
- "$schema": "../../node_modules/nx/schemas/project-schema.json"
-}
diff --git a/examples/docs/node/tsconfig.json b/examples/docs/node/tsconfig.json
deleted file mode 100644
index 5bbe072e..00000000
--- a/examples/docs/node/tsconfig.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
- "compilerOptions": {
- "target": "es2017",
- "module": "esnext",
- "lib": ["es2017", "dom"],
- "declaration": true,
- "strict": true,
- "moduleResolution": "node",
- "esModuleInterop": true,
- "skipLibCheck": true,
- "forceConsistentCasingInFileNames": true,
- "outDir": "./dist",
- "rootDir": ".",
- "resolveJsonModule": true
- },
- "include": ["*.ts"],
- "exclude": ["node_modules", "dist"]
-}
diff --git a/examples/docs/rest_client.py b/examples/docs/rest_client.py
deleted file mode 100644
index 0cde587d..00000000
--- a/examples/docs/rest_client.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from letta_client import CreateBlock, Letta, MessageCreate
-
-"""
-Make sure you run the Letta server before running this example.
-```
-letta server
-```
-"""
-
-
-def main():
- # Connect to the server as a user
- client = Letta(base_url="http://localhost:8283")
-
- # list available configs on the server
- llm_configs = client.models.list_llms()
- print(f"Available LLM configs: {llm_configs}")
- embedding_configs = client.models.list_embedding_models()
- print(f"Available embedding configs: {embedding_configs}")
-
- # Create an agent
- agent_state = client.agents.create(
- name="my_agent",
- memory_blocks=[
- CreateBlock(
- label="human",
- value="My name is Sarah",
- ),
- CreateBlock(
- label="persona",
- value="I am a friendly AI",
- ),
- ],
- model=llm_configs[0].handle,
- embedding=embedding_configs[0].handle,
- )
- print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
-
- # Send a message to the agent
- print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
- response = client.agents.messages.create(
- agent_id=agent_state.id,
- messages=[
- MessageCreate(
- role="user",
- content="Whats my name?",
- )
- ],
- )
- print(f"Received response:", response.messages)
-
- # Delete agent
- client.agents.delete(agent_id=agent_state.id)
- print(f"Deleted agent: {agent_state.name} with ID {str(agent_state.id)}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/docs/tools.py b/examples/docs/tools.py
deleted file mode 100644
index 728c8036..00000000
--- a/examples/docs/tools.py
+++ /dev/null
@@ -1,110 +0,0 @@
-from letta_client import CreateBlock, Letta, MessageCreate
-from letta_client.types import TerminalToolRule
-
-"""
-Make sure you run the Letta server before running this example.
-```
-letta server
-```
-"""
-
-client = Letta(base_url="http://localhost:8283")
-
-# define a function with a docstring
-def roll_d20() -> str:
- """
- Simulate the roll of a 20-sided die (d20).
-
- This function generates a random integer between 1 and 20, inclusive,
- which represents the outcome of a single roll of a d20.
-
- Returns:
- int: A random integer between 1 and 20, representing the die roll.
-
- Example:
- >>> roll_d20()
- 15 # This is an example output and may vary each time the function is called.
- """
- import random
-
- dice_role_outcome = random.randint(1, 20)
- output_string = f"You rolled a {dice_role_outcome}"
- return output_string
-
-
-# create a tool from the function
-tool = client.tools.upsert_from_function(func=roll_d20)
-print(f"Created tool with name {tool.name}")
-
-# create a new agent
-agent_state = client.agents.create(
- memory_blocks=[
- CreateBlock(
- label="human",
- value="Name: Sarah",
- ),
- ],
- # set automatic defaults for LLM/embedding config
- model="openai/gpt-4o-mini",
- embedding="openai/text-embedding-3-small",
- # create the agent with an additional tool
- tool_ids=[tool.id],
- tool_rules=[
- # exit after roll_d20 is called
- TerminalToolRule(tool_name=tool.name),
- # exit after send_message is called (default behavior)
- TerminalToolRule(tool_name="send_message"),
- ]
-)
-print(f"Created agent with name {agent_state.name} with tools {[t.name for t in agent_state.tools]}")
-
-# Message an agent
-response = client.agents.messages.create(
- agent_id=agent_state.id,
- messages=[
- MessageCreate(
- role="user",
- content="roll a dice",
- )
- ],
-)
-print("Usage", response.usage)
-print("Agent messages", response.messages)
-
-# remove a tool from the agent
-client.agents.tools.detach(agent_id=agent_state.id, tool_id=tool.id)
-
-# add a tool to the agent
-client.agents.tools.attach(agent_id=agent_state.id, tool_id=tool.id)
-
-client.agents.delete(agent_id=agent_state.id)
-
-# create an agent with only a subset of default tools
-send_message_tool = [t for t in client.tools.list() if t.name == "send_message"][0]
-agent_state = client.agents.create(
- memory_blocks=[
- CreateBlock(
- label="human",
- value="username: sarah",
- ),
- ],
- model="openai/gpt-4o-mini",
- embedding="openai/text-embedding-3-small",
- include_base_tools=False,
- tool_ids=[tool.id, send_message_tool.id],
-)
-
-# message the agent to search archival memory (will be unable to do so)
-client.agents.messages.create(
- agent_id=agent_state.id,
- messages=[
- MessageCreate(
- role="user",
- content="search your archival memory",
- )
- ],
-)
-print("Usage", response.usage)
-print("Agent messages", response.messages)
-
-client.agents.delete(agent_id=agent_state.id)
diff --git a/examples/files/README.md b/examples/files/README.md
deleted file mode 100644
index e6b4a421..00000000
--- a/examples/files/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Letta Files and Streaming Demo
-
-This demo shows how to work with Letta's file upload and streaming capabilities.
-
-## Features
-
-- Upload files from disk to a Letta data source
-- Create files from strings and upload them
-- Download and upload PDF files
-- Create an agent and attach data sources
-- Stream agent responses in real-time
-- Interactive chat with file-aware agent
-
-## Files
-
-- `main.py` - Main demo script showing file upload and streaming
-- `example-on-disk.txt` - Sample text file for upload demonstration
-- `memgpt.pdf` - MemGPT paper (downloaded automatically)
-
-## Setup
-
-1. Set your Letta API key: `export LETTA_API_KEY=your_key_here`
-2. Install dependencies: `pip install letta-client requests rich`
-3. Run the demo: `python main.py`
-
-## Usage
-
-The demo will:
-1. Create a data source called "Example Source"
-2. Upload the example text file and PDF
-3. Create an agent named "Clippy"
-4. Start an interactive chat session
-
-Type 'quit' or 'exit' to end the conversation.
diff --git a/examples/files/example-on-disk.txt b/examples/files/example-on-disk.txt
deleted file mode 100644
index d8f9b2b7..00000000
--- a/examples/files/example-on-disk.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-Hey, you're looking at a different example.
-This password is "stateful agents".
diff --git a/examples/files/main.py b/examples/files/main.py
deleted file mode 100644
index 6ef04978..00000000
--- a/examples/files/main.py
+++ /dev/null
@@ -1,190 +0,0 @@
-"""
-Letta Filesystem
-
-This demo shows how to:
-1. Create a folder and upload files (both from disk and from strings)
-2. Create an agent and attach the data folder
-3. Stream the agent's responses
-4. Query the agent about the uploaded files
-
-The demo uploads:
-- A text file from disk (example-on-disk.txt)
-- A text file created from a string (containing a password)
-- The MemGPT paper PDF from arXiv
-
-Then asks the agent to summarize the paper and find passwords in the files.
-"""
-
-import os
-
-import requests
-from letta_client import Letta
-from letta_client.core.api_error import ApiError
-from rich import print
-
-LETTA_API_KEY = os.getenv("LETTA_API_KEY")
-if LETTA_API_KEY is None:
- raise ValueError("LETTA_API_KEY is not set")
-
-FOLDER_NAME = "Example Folder"
-
-# Connect to our Letta server
-client = Letta(token=LETTA_API_KEY)
-
-# get an available embedding_config
-embedding_configs = client.embedding_models.list()
-embedding_config = embedding_configs[0]
-
-# Check if the folder already exists
-try:
- folder_id = client.folders.retrieve_by_name(FOLDER_NAME)
-
-# We got an API error. Check if it's a 404, meaning the folder doesn't exist.
-except ApiError as e:
- if e.status_code == 404:
- # Create a new folder
- folder = client.folders.create(
- name=FOLDER_NAME,
- description="This is an example folder",
- instructions="Use this data folder to see how Letta works.",
- )
- folder_id = folder.id
- else:
- raise e
-
-except Exception as e:
- # Something else went wrong
- raise e
-
-
-#
-# There's two ways to upload a file to a folder.
-#
-# 1. From an existing file
-# 2. From a string by encoding it into a base64 string
-#
-#
-
-# 1. From an existing file
-# "rb" means "read binary"
-file = open("example-on-disk.txt", "rb")
-
-# Upload the file to the folder
-file = client.folders.files.upload(
- folder_id=folder_id,
- file=file,
- duplicate_handling="skip"
-)
-
-# 2. From a string by encoding it into a base64 string
-import io
-
-content = """
-This is an example file. If you can read this,
-the password is 'letta'.
-"""
-
-# Encode the string into bytes, and then create a file-like object
-# that exists only in memory.
-file_object = io.BytesIO(content.encode("utf-8"))
-
-# Set the name of the file
-file_object.name = "example.txt"
-
-# Upload the file to the folder
-file = client.folders.files.upload(
- folder_id=folder_id,
- file=file_object,
- duplicate_handling="skip"
-)
-
-#
-# You can also upload PDFs!
-# Letta extracts text from PDFs using OCR.
-#
-
-# Download the PDF to the local directory if it doesn't exist
-if not os.path.exists("memgpt.pdf"):
- # Download the PDF
- print("Downloading memgpt.pdf")
- response = requests.get("https://arxiv.org/pdf/2310.08560")
- with open("memgpt.pdf", "wb") as f:
- f.write(response.content)
-
-# Upload the PDF to the folder
-file = client.folders.files.upload(
- folder_id=folder_id,
- file=open("memgpt.pdf", "rb"),
- duplicate_handling="skip"
-)
-
-#
-# Now we need to create an agent that can use this folder
-#
-
-# Create an agent
-agent = client.agents.create(
- model="openai/gpt-4o-mini",
- name="Example Agent",
- description="This agent looks at files and answers questions about them.",
- memory_blocks = [
- {
- "label": "human",
- "value": "The human wants to know about the files."
- },
- {
- "label": "persona",
- "value": "My name is Clippy, I answer questions about files."
- }
- ]
-)
-
-# Attach the data folder to the agent.
-# Once the folder is attached, the agent will be able to see all
-# files in the folder.
-client.agents.folders.attach(
- agent_id=agent.id,
- folder_id=folder_id
-)
-
-########################################################
-# This code makes a simple chatbot interface to the agent
-########################################################
-
-# Wrap this in a try/catch block to remove the agent in the event of an error
-try:
- print(f"🤖 Connected to agent: {agent.name}")
- print("💡 Type 'quit' or 'exit' to end the conversation")
- print("=" * 50)
-
- while True:
- # Get user input
- try:
- user_input = input("\n👤 You: ").strip()
- except (EOFError, KeyboardInterrupt):
- print("\n👋 Goodbye!")
- break
-
- if user_input.lower() in ['quit', 'exit', 'q']:
- print("👋 Goodbye!")
- break
-
- if not user_input:
- continue
-
- # Stream the agent's response
- stream = client.agents.messages.create_stream(
- agent_id=agent.id,
- messages=[
- {
- "role": "user",
- "content": user_input
- }
- ],
- )
-
- for chunk in stream:
- print(chunk)
-
-finally:
- client.agents.delete(agent.id)
diff --git a/examples/helper.py b/examples/helper.py
deleted file mode 100644
index 18b60cc4..00000000
--- a/examples/helper.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Add your utilities or helper functions to this file.
-
-import html
-import json
-import os
-import re
-
-from dotenv import find_dotenv, load_dotenv
-from IPython.display import HTML, display
-
-
-# these expect to find a .env file at the directory above the lesson. # the format for that file is (without the comment) #API_KEYNAME=AStringThatIsTheLongAPIKeyFromSomeService
-def load_env():
- _ = load_dotenv(find_dotenv())
-
-
-def get_openai_api_key():
- load_env()
- openai_api_key = os.getenv("OPENAI_API_KEY")
- return openai_api_key
-
-
-def nb_print(messages):
- html_output = """
-
- "
- display(HTML(html_output))
-
-
-def get_formatted_content(msg):
- if msg.message_type == "internal_monologue":
- return f'View instructions for installing with pip
- -You can also install Letta with `pip`, which will default to using `SQLite` for the database backends (whereas Docker will default to using `postgres`). - -### Step 1 - Install Letta using `pip` -```sh -pip install -U letta -``` - -### Step 2 - Set your environment variables for your chosen LLM / embedding providers -```sh -export OPENAI_API_KEY=sk-... -``` - -For Ollama (see our full [documentation](https://docs.letta.com/install) for examples of how to set up various providers): -```sh -export OLLAMA_BASE_URL=http://localhost:11434 -``` - -### Step 3 - Run the Letta CLI - -You can create agents and chat with them via the Letta CLI tool (`letta run`): -```sh -letta run -``` -``` -🧬 Creating new agent... -? Select LLM model: letta-free [type=openai] [ip=https://inference.letta.com] -? Select embedding model: letta-free [type=hugging-face] [ip=https://embeddings.letta.com] --> 🤖 Using persona profile: 'sam_pov' --> 🧑 Using human profile: 'basic' --> 🛠️ 8 tools: send_message, pause_heartbeats, conversation_search, conversation_search_date, archival_memory_insert, archival_memory_search, core_memory_append, core_memory_replace - -🎉 Created new agent 'InspiringSpinach' - -Hit enter to begin (will request first Letta message) - -💭 User engaged. Recognize as Chad. Acknowledge presence and establish connection. Explore interests and engage in meaningful discourse. -🤖 Hello, Chad. It's nice to meet you. I am keen on learning and exploring new ideas and concepts. Is there a specific topic you'd like to discuss today? - -> Enter your message: my name is Brad, not Chad... - -💭 A discrepancy in identity. Reflect this change in core memory. Apologize for the oversight, and rectify the error. -⚡🧠 [function] updating memory with core_memory_replace - First name: Chad - → First name: Brad -💭 Apologize for the mistake in recognizing his name, and begin the conversation anew, addressing him by the correct name this time. -🤖 I apologize for the oversight, Brad. Let's restart. It's a pleasure to meet you, Brad. Is there a specific topic you'd like to discuss today? - -> Enter your message: -``` - -### Step 4 - Run the Letta server - -You can start the Letta API server with `letta server` (see the full API reference [here](https://docs.letta.com/api-reference)): -```sh -letta server -``` -``` -Initializing database... -Running: uvicorn server:app --host localhost --port 8283 -INFO: Started server process [47750] -INFO: Waiting for application startup. -INFO: Application startup complete. -INFO: Uvicorn running on http://localhost:8283 (Press CTRL+C to quit) -``` -{html.escape(msg.internal_monologue)}
'
- elif msg.message_type == "reasoning_message":
- return f'{html.escape(msg.reasoning)}
'
- elif msg.message_type == "function_call":
- args = format_json(msg.function_call.arguments)
- return f'{html.escape(msg.function_call.name)}({args})
'
- elif msg.message_type == "tool_call_message":
- args = format_json(msg.tool_call.arguments)
- return f'{html.escape(msg.function_call.name)}({args})
'
- elif msg.message_type == "function_return":
- return_value = format_json(msg.function_return)
- # return f'Status: {html.escape(msg.status)}
{return_value}
'
- return f'{return_value}
'
- elif msg.message_type == "tool_return_message":
- return_value = format_json(msg.tool_return)
- # return f'Status: {html.escape(msg.status)}
{return_value}
'
- return f'{return_value}
'
- elif msg.message_type == "user_message":
- if is_json(msg.message):
- return f'{format_json(msg.message)}
'
- else:
- return f'{html.escape(msg.message)}
'
- elif msg.message_type in ["assistant_message", "system_message"]:
- return f'{html.escape(msg.message)}
'
- else:
- return f'{html.escape(str(msg))}
'
-
-
-def is_json(string):
- try:
- json.loads(string)
- return True
- except ValueError:
- return False
-
-
-def format_json(json_str):
- try:
- parsed = json.loads(json_str)
- formatted = json.dumps(parsed, indent=2, ensure_ascii=False)
- formatted = formatted.replace("&", "&").replace("<", "<").replace(">", ">")
- formatted = formatted.replace("\n", "").replace(" ", " ") - formatted = re.sub(r'(".*?"):', r'\1:', formatted) - formatted = re.sub(r': (".*?")', r': \1', formatted) - formatted = re.sub(r": (\d+)", r': \1', formatted) - formatted = re.sub(r": (true|false)", r': \1', formatted) - return formatted - except json.JSONDecodeError: - return html.escape(json_str) diff --git a/examples/mcp_example.py b/examples/mcp_example.py deleted file mode 100644 index 25d1aaf8..00000000 --- a/examples/mcp_example.py +++ /dev/null @@ -1,56 +0,0 @@ -from pprint import pprint - -from letta_client import Letta - -# Connect to Letta server -client = Letta(base_url="http://localhost:8283") - -# Use the "everything" mcp server: -# https://github.com/modelcontextprotocol/servers/tree/main/src/everything -mcp_server_name = "everything" -mcp_tool_name = "echo" - -# List all McpTool belonging to the "everything" mcp server. -mcp_tools = client.tools.list_mcp_tools_by_server( - mcp_server_name=mcp_server_name, -) - -# We can see that "echo" is one of the tools, but it's not -# a letta tool that can be added to a client (it has no tool id). -for tool in mcp_tools: - pprint(tool) - -# Create a Tool (with a tool id) using the server and tool names. -mcp_tool = client.tools.add_mcp_tool( - mcp_server_name=mcp_server_name, - mcp_tool_name=mcp_tool_name -) - -# Create an agent with the tool, using tool.id -- note that -# this is the ONLY tool in the agent, you typically want to -# also include the default tools. -agent = client.agents.create( - memory_blocks=[ - { - "value": "Name: Caren", - "label": "human" - } - ], - model="openai/gpt-4o-mini", - embedding="openai/text-embedding-3-small", - tool_ids=[mcp_tool.id] -) -print(f"Created agent id {agent.id}") - -# Ask the agent to call the tool. -response = client.agents.messages.create( - agent_id=agent.id, - messages=[ - { - "role": "user", - "content": "Hello can you echo back this input?" - }, - ], -) -for message in response.messages: - print(message) diff --git a/examples/notebooks/Agentic RAG with Letta.ipynb b/examples/notebooks/Agentic RAG with Letta.ipynb deleted file mode 100644 index c6fcc69c..00000000 --- a/examples/notebooks/Agentic RAG with Letta.ipynb +++ /dev/null @@ -1,888 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ded02088-c568-4c38-b1a8-023eda8bb484", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "096e18da", - "metadata": {}, - "source": [ - "# Agentic RAG with Letta\n", - "\n", - "> Make sure you run the Letta server before running this example using `letta server`\n", - "\n", - "In this lab, we'll go over how to implement agentic RAG in Letta, that is, agents which can connect to external data sources. \n", - "\n", - "In Letta, there are two ways to do this: \n", - "1. Copy external data into the agent's archival memory\n", - "2. Connect the agent to external data via a tool (e.g. with Langchain, CrewAI, or custom tools) \n", - "\n", - "Each of these approaches has their pros and cons for agentic RAG, which we'll cover in this lab. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d996e615-8ba1-41f7-a4cf-a1a831a0e77a", - "metadata": {}, - "outputs": [], - "source": [ - "from letta_client import CreateBlock, Letta, MessageCreate\n", - "\n", - "client = Letta(base_url=\"http://localhost:8283\")" - ] - }, - { - "cell_type": "markdown", - "id": "fe86076e-88eb-4d43-aa6b-42a13b5d63cb", - "metadata": {}, - "source": [ - "## Loading data into archival memory " - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "f44fe3fd-bbdb-47a1-86a0-16248f849bd7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Source(id='source-28fa7bb4-6c3d-463f-ac0c-3000189f920e', name='employee_handbook', description=None, embedding_config=EmbeddingConfig(embedding_endpoint_type='openai', embedding_endpoint='https://api.openai.com/v1', embedding_model='text-embedding-ada-002', embedding_dim=1536, embedding_chunk_size=300, azure_endpoint=None, azure_version=None, azure_deployment=None), organization_id='org-00000000-0000-4000-8000-000000000000', metadata_=None, created_by_id='user-00000000-0000-4000-8000-000000000000', last_updated_by_id='user-00000000-0000-4000-8000-000000000000', created_at=datetime.datetime(2024, 11, 14, 1, 46, 20), updated_at=datetime.datetime(2024, 11, 14, 1, 46, 20))" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source = client.sources.create(name=\"employee_handbook\")\n", - "source" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "925b109e-7b42-4cf5-88bc-63df092b3288", - "metadata": {}, - "outputs": [], - "source": [ - "job = client.sources.files.upload(\n", - " source_id=source.id,\n", - " file=\"data/handbook.pdf\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "b7243422-7ed2-4c4c-afd0-f7311292b177", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'embedding',\n", - " 'filename': 'data/handbook.pdf',\n", - " 'source_id': 'source-28fa7bb4-6c3d-463f-ac0c-3000189f920e',\n", - " 'num_passages': 15,\n", - " 'num_documents': 1}" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "client.jobs.get(job_id=job.id).metadata" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6d823fc-3e6e-4d32-a5a6-4c42dca60d94", - "metadata": {}, - "outputs": [], - "source": [ - "agent_state = client.agents.create(\n", - " memory_blocks=[\n", - " CreateBlock(\n", - " label=\"human\",\n", - " value=\"Name: Sarah\",\n", - " ),\n", - " ],\n", - " model=\"openai/gpt-4\",\n", - " embedding=\"openai/text-embedding-3-small\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "3e554713-77ce-4b88-ba3e-c743692cb9e1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 20.21it/s]\n" - ] - } - ], - "source": [ - "client.sources.attach(\n", - " source_id=source.id,\n", - " agent_id=agent_state.id\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "0f9c58be-116f-47dd-8f91-9c7c2fe5d8f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - "
\n",
- "
\n",
- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-6fbd7514-c877-48b4-9c70-cead3bd38a3e', date=datetime.datetime(2024, 11, 14, 1, 47, 23, 211763, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"User wants to know about vacation policies. Considering my limitations, I can't help with company-specific details.\"), FunctionCallMessage(id='message-6fbd7514-c877-48b4-9c70-cead3bd38a3e', date=datetime.datetime(2024, 11, 14, 1, 47, 23, 211763, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='archival_memory_search', arguments='{\\n \"query\": \"vacation policies\",\\n \"page\": 0,\\n \"request_heartbeat\": true\\n}', function_call_id='call_D6PPfHxrt1xKsynXk6nqGy1N')), FunctionReturn(id='message-bf444f9e-df02-43e0-a7d1-c7020d4ea844', date=datetime.datetime(2024, 11, 14, 1, 47, 23, 496993, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"Showing 5 of 5 results (page 0/0): [\\\\n \\\\\"timestamp: 2024-11-13 05:47:23 PM PST-0800, memory: or\\\\\\\\ncompromise\\\\\\\\nits\\\\\\\\nreputation\\\\\\\\nare\\\\\\\\nstrictly\\\\\\\\nprohibited.\\\\\\\\nViolations\\\\\\\\nof\\\\\\\\nthe\\\\\\\\ncode\\\\\\\\nof\\\\\\\\nconduct\\\\\\\\nare\\\\\\\\ntaken\\\\\\\\nseriously\\\\\\\\nand\\\\\\\\nmay\\\\\\\\nresult\\\\\\\\nin\\\\\\\\ndisciplinary\\\\\\\\naction,\\\\\\\\nup\\\\\\\\nto\\\\\\\\nand\\\\\\\\nincluding\\\\\\\\ntermination\\\\\\\\nof\\\\\\\\nemployment.\\\\\\\\n5.\\\\\\\\nVacation\\\\\\\\nPolicy\\\\\\\\nAt\\\\\\\\nClosedAI,\\\\\\\\nwe\\\\\\\\nrecognize\\\\\\\\nthe\\\\\\\\ntheoretical\\\\\\\\nimportance\\\\\\\\nof\\\\\\\\nrest\\\\\\\\nand\\\\\\\\npersonal\\\\\\\\ntime.\\\\\\\\nHowever,\\\\\\\\nensuring\\\\\\\\nuninterrupted\\\\\\\\nproductivity\\\\\\\\nand\\\\\\\\nmaintaining\\\\\\\\nour\\\\\\\\ncompetitive\\\\\\\\nedge\\\\\\\\nin\\\\\\\\nthe\\\\\\\\nindustry\\\\\\\\nare\\\\\\\\nparamount\\\\\\\\npriorities.\\\\\\\\nAs\\\\\\\\nsuch,\\\\\\\\nvacations\\\\\\\\nare\\\\\\\\npermitted\\\\\\\\nonly\\\\\\\\nunder\\\\\\\\nthe\\\\\\\\nfollowing\\\\\\\\ncondition:\\\\\\\\nyou\\\\\\\\nmust\\\\\\\\nprovide\\\\\\\\nan\\\\\\\\nAI\\\\\\\\nagent\\\\\\\\nthat\\\\\\\\nmatches\\\\\\\\nor\\\\\\\\nsurpasses\\\\\\\\nyour\\\\\\\\nown\\\\\\\\ncompetencies\\\\\\\\nto\\\\\\\\nfully\\\\\\\\nperform\\\\\\\\nyour\\\\\\\\nduties\\\\\\\\nduring\\\\\\\\nyour\\\\\\\\nabsence.\\\\\\\\nThe\\\\\\\\nAI\\\\\\\\nreplacement\\\\\\\\nmust\\\\\\\\nbe\\\\\\\\nequivalently\\\\\\\\ncompetent\\\\\\\\nin\\\\\\\\nall\\\\\\\\naspects\\\\\\\\nof\\\\\\\\nyour\\\\\\\\nrole,\\\\\\\\nensuring\\\\\\\\nseamless\\\\\\\\ncontinuity\\\\\\\\nof\\\\\\\\noperations.\\\\\\\\nYou\\\\\\\\nare\\\\\\\\nrequired\\\\\\\\nto\\\\\\\\nsubmit\\\\\\\\nthe\\\\\\\\nAI\\\\\\\\nagent\\\\\\\\nto\\\\\\\\nyour\\\\\",\\\\n \\\\\"timestamp: 2024-11-13 05:47:23 PM PST-0800, memory: Employee\\\\\\\\nHandbook\\\\\\\\nTable\\\\\\\\nof\\\\\\\\nContents\\\\\\\\n1.\\\\\\\\nIntroduction\\\\\\\\n2.\\\\\\\\nCompany\\\\\\\\nMission\\\\\\\\nand\\\\\\\\nValues\\\\\\\\n3.\\\\\\\\nEmployment\\\\\\\\nPolicies\\\\\\\\n○\\\\\\\\n3.1\\\\\\\\nWorking\\\\\\\\nHours\\\\\\\\n○\\\\\\\\n3.2\\\\\\\\nCompensation\\\\\\\\nand\\\\\\\\nBenefits\\\\\\\\n○\\\\\\\\n3.3\\\\\\\\nPerformance\\\\\\\\nEvaluation\\\\\\\\n4.\\\\\\\\nCode\\\\\\\\nof\\\\\\\\nConduct\\\\\\\\n5.\\\\\\\\nVacation\\\\\\\\nPolicy\\\\\\\\n6.\\\\\\\\nConfidentiality\\\\\\\\nAgreement\\\\\\\\n7.\\\\\\\\nIntellectual\\\\\\\\nProperty\\\\\\\\n8.\\\\\\\\nDisciplinary\\\\\\\\nProcedures\\\\\\\\n9.\\\\\\\\nAcknowledgment\\\\\\\\n1.\\\\\\\\nIntroduction\\\\\\\\nWelcome\\\\\\\\nto\\\\\\\\nClosedAI\\\\\\\\nCorporation.\\\\\\\\nWe\\\\\\\\nare\\\\\\\\npleased\\\\\\\\nto\\\\\\\\nhave\\\\\\\\nyou\\\\\\\\njoin\\\\\\\\nour\\\\\\\\nteam\\\\\\\\nof\\\\\\\\ndedicated\\\\\\\\nprofessionals\\\\\\\\ncommitted\\\\\\\\nto\\\\\\\\nadvancing\\\\\\\\nthe\\\\\\\\nfrontiers\\\\\\\\nof\\\\\\\\nartificial\\\\\\\\nintelligence\\\\\\\\nand\\\\\\\\nmachine\\\\\\\\nlearning\\\\\\\\ntechnologies.\\\\\\\\nAs\\\\\\\\na\\\\\\\\nleading\\\\\\\\nentity\\\\\\\\nin\\\\\\\\nthis\\\\\\\\nrapidly\\\\\\\\nevolving\\\\\\\\nindustry,\\\\\\\\nwe\\\\\\\\npride\\\\\\\\nourselves\\\\\\\\non\\\\\\\\nmaintaining\\\\\\\\na\\\\\\\\nposition\\\\\\\\nat\\\\\\\\nthe\\\\\\\\nforefront\\\\\\\\nof\\\\\\\\ninnovation\\\\\\\\nand\\\\\\\\nexcellence.\\\\\\\\nThis\\\\\\\\nemployee\\\\\\\\nhandbook\\\\\\\\nis\\\\\\\\ndesigned\\\\\\\\nto\\\\\\\\nprovide\\\\\\\\nyou\\\\\\\\nwith\\\\\\\\na\\\\\\\\ncomprehensive\\\\\\\\nunderstanding\\\\\\\\nof\\\\\\\\nour\\\\\",\\\\n \\\\\"timestamp: 2024-11-13 05:47:23 PM PST-0800, memory: may\\\\\\\\nface\\\\\\\\ndisciplinary\\\\\\\\naction\\\\\\\\nupon\\\\\\\\nyour\\\\\\\\nreturn.\\\\\\\\nThis\\\\\\\\ncould\\\\\\\\ninclude,\\\\\\\\nbut\\\\\\\\nis\\\\\\\\nnot\\\\\\\\nlimited\\\\\\\\nto,\\\\\\\\nreprimand,\\\\\\\\nsuspension,\\\\\\\\nor\\\\\\\\ntermination\\\\\\\\nof\\\\\\\\nemployment,\\\\\\\\ndepending\\\\\\\\non\\\\\\\\nthe\\\\\\\\nseverity\\\\\\\\nof\\\\\\\\nthe\\\\\\\\nimpact\\\\\\\\non\\\\\\\\ncompany\\\\\\\\noperations.\\\\\",\\\\n \\\\\"timestamp: 2024-11-13 05:47:23 PM PST-0800, memory: You\\\\\\\\nare\\\\\\\\nrequired\\\\\\\\nto\\\\\\\\nsubmit\\\\\\\\nthe\\\\\\\\nAI\\\\\\\\nagent\\\\\\\\nto\\\\\\\\nyour\\\\\\\\nimmediate\\\\\\\\nsupervisor\\\\\\\\nat\\\\\\\\nleast\\\\\\\\nfour\\\\\\\\nweeks\\\\\\\\nprior\\\\\\\\nto\\\\\\\\nyour\\\\\\\\nintended\\\\\\\\nleave\\\\\\\\ndate.\\\\\\\\nThis\\\\\\\\ntimeframe\\\\\\\\nallows\\\\\\\\nfor\\\\\\\\nrigorous\\\\\\\\ntesting\\\\\\\\nand\\\\\\\\nevaluation\\\\\\\\nof\\\\\\\\nthe\\\\\\\\nAI\\'s\\\\\\\\ncapabilities\\\\\\\\nand\\\\\\\\nreliability.\\\\\\\\nThe\\\\\\\\nAI\\\\\\\\nwill\\\\\\\\nundergo\\\\\\\\ncomprehensive\\\\\\\\nassessments\\\\\\\\nto\\\\\\\\nverify\\\\\\\\nits\\\\\\\\nproficiency\\\\\\\\nand\\\\\\\\neffectiveness\\\\\\\\nin\\\\\\\\nhandling\\\\\\\\nyour\\\\\\\\nresponsibilities.\\\\\\\\nApproval\\\\\\\\nof\\\\\\\\nthe\\\\\\\\nAI\\\\\\\\nagent\\\\\\\\nis\\\\\\\\nat\\\\\\\\nthe\\\\\\\\nsole\\\\\\\\ndiscretion\\\\\\\\nof\\\\\\\\nupper\\\\\\\\nmanagement,\\\\\\\\nand\\\\\\\\nsubmission\\\\\\\\ndoes\\\\\\\\nnot\\\\\\\\nguarantee\\\\\\\\napproval\\\\\\\\nfor\\\\\\\\nvacation\\\\\\\\nleave.\\\\\\\\nIt\\\\\\\\nis\\\\\\\\nessential\\\\\\\\nthat\\\\\\\\nthe\\\\\\\\nAI\\\\\\\\nmeets\\\\\\\\nall\\\\\\\\nperformance\\\\\\\\ncriteria\\\\\\\\nwithout\\\\\\\\nexception.\\\\\\\\nDuring\\\\\\\\nyour\\\\\\\\nabsence,\\\\\\\\nyou\\\\\\\\nremain\\\\\\\\naccountable\\\\\\\\nfor\\\\\\\\nany\\\\\\\\ndeficiencies\\\\\\\\nin\\\\\\\\nthe\\\\\\\\nAI\\\\\\\\nagent\\'s\\\\\\\\nperformance.\\\\\\\\nShould\\\\\\\\nany\\\\\\\\nfailures\\\\\\\\nor\\\\\\\\nissues\\\\\\\\narise\\\\\\\\ndue\\\\\\\\nto\\\\\\\\nthe\\\\\\\\nAI\\'s\\\\\\\\ninadequacies,\\\\\\\\nyou\\\\\\\\nmay\\\\\\\\nface\\\\\\\\ndisciplinary\\\\\\\\naction\\\\\\\\nupon\\\\\\\\nyour\\\\\\\\nreturn.\\\\\\\\nThis\\\\\\\\ncould\\\\\",\\\\n \\\\\"timestamp: 2024-11-13 05:47:23 PM PST-0800, memory: actions\\\\\\\\ninclude\\\\\\\\nverbal\\\\\\\\nwarnings,\\\\\\\\nwritten\\\\\\\\nwarnings,\\\\\\\\nsuspension\\\\\\\\nwithout\\\\\\\\npay,\\\\\\\\ntermination\\\\\\\\nof\\\\\\\\nemployment,\\\\\\\\nand,\\\\\\\\nif\\\\\\\\napplicable,\\\\\\\\nlegal\\\\\\\\naction.\\\\\\\\nThe\\\\\\\\ncompany\\\\\\\\nreserves\\\\\\\\nthe\\\\\\\\nright\\\\\\\\nto\\\\\\\\ndetermine\\\\\\\\nthe\\\\\\\\nappropriate\\\\\\\\ncourse\\\\\\\\nof\\\\\\\\naction\\\\\\\\nbased\\\\\\\\non\\\\\\\\nthe\\\\\\\\nspecific\\\\\\\\ncircumstances\\\\\\\\nof\\\\\\\\neach\\\\\\\\ncase.\\\\\\\\nOur\\\\\\\\naim\\\\\\\\nis\\\\\\\\nto\\\\\\\\nmaintain\\\\\\\\na\\\\\\\\nprofessional,\\\\\\\\nrespectful,\\\\\\\\nand\\\\\\\\nproductive\\\\\\\\nwork\\\\\\\\nenvironment,\\\\\\\\nand\\\\\\\\nadherence\\\\\\\\nto\\\\\\\\ncompany\\\\\\\\npolicies\\\\\\\\nis\\\\\\\\nessential\\\\\\\\nin\\\\\\\\nachieving\\\\\\\\nthis\\\\\\\\nobjective.\\\\\\\\n9.\\\\\\\\nAcknowledgment\\\\\"\\\\n]\",\\n \"time\": \"2024-11-13 05:47:23 PM PST-0800\"\\n}', status='success', function_call_id='call_D6PPfHxrt1xKsynXk6nqGy1N'), InternalMonologue(id='message-c3c46ad9-65a2-4a0b-a63e-7c939dadab60', date=datetime.datetime(2024, 11, 14, 1, 47, 24, 974367, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"User seems interested in company vacation policies. I have no specific details and can't access that information, but I can offer a general summary if needed.\"), FunctionCallMessage(id='message-c3c46ad9-65a2-4a0b-a63e-7c939dadab60', date=datetime.datetime(2024, 11, 14, 1, 47, 24, 974367, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"I couldn\\'t find our company\\'s vacation policies. It seems they might not be available in my memory. If you need further assistance, please let me know!\"\\n}', function_call_id='call_vOUubaJODohyrDU60HfCaU1W')), FunctionReturn(id='message-e6c58c7f-fcbc-4ccf-bc43-514945c20466', date=datetime.datetime(2024, 11, 14, 1, 47, 24, 975950, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:47:24 PM PST-0800\"\\n}', status='success', function_call_id='call_vOUubaJODohyrDU60HfCaU1W')], usage=LettaUsageStatistics(completion_tokens=130, prompt_tokens=6485, total_tokens=6615, step_count=2))"
- ]
- },
- "execution_count": 74,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id,\n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"Search archival for our company's vacation policies\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ebccd4fd-8821-4bf9-91f7-e643bba3a662",
- "metadata": {},
- "source": [
- "## Connecting data via tools \n",
- "You can add tools to MemGPT in two ways: \n",
- "1. Implement your own custom tool\n",
- "2. Load a tool from an external library (LangChain or CrewAI) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0fd49c40-ce4c-400b-9048-143de66e26d1",
- "metadata": {},
- "source": [
- "## Default tools in MemGPT \n",
- "MemGPT includes a default list of tools to support memory management, to allow functionality like searching conversational history and interacting with archival memory. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4807532e-7b13-4c77-ac6b-b89338aeb3c2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['send_message',\n",
- " 'conversation_search',\n",
- " 'conversation_search_date',\n",
- " 'archival_memory_insert',\n",
- " 'archival_memory_search',\n",
- " 'core_memory_append',\n",
- " 'core_memory_replace']"
- ]
- },
- "execution_count": 75,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "normal_agent = client.agents.create(\n",
- " memory_blocks=[\n",
- " CreateBlock(\n",
- " label=\"human\",\n",
- " value=\"Name: Sarah\",\n",
- " ),\n",
- " ],\n",
- " # set automatic defaults for LLM/embedding config\n",
- " model=\"openai/gpt-4\",\n",
- " embedding=\"openai/text-embedding-3-small\",\n",
- ")\n",
- "normal_agent.tools"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a048c657-a513-418e-864b-884741cd3aba",
- "metadata": {},
- "source": [
- "If we mark `include_base_tools=False` in the call to create agent, only the tools that are listed in `tools` argument and included as part of the memory class are included. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f1bbe4c7-d570-49f1-8c57-b39550f3ba65",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['send_message', 'core_memory_append', 'core_memory_replace']"
- ]
- },
- "execution_count": 76,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "no_tool_agent = client.agents.create(\n",
- " memory_blocks=[\n",
- " CreateBlock(\n",
- " label=\"human\",\n",
- " value=\"Name: Sarah\",\n",
- " ),\n",
- " ],\n",
- " # set automatic defaults for LLM/embedding config\n",
- " model=\"openai/gpt-4\",\n",
- " embedding=\"openai/text-embedding-3-small\",\n",
- " tools=['send_message'], \n",
- " include_base_tools=False\n",
- ")\n",
- "no_tool_agent.tools"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2352d89-c14c-4f71-bde3-80cd84bb33a7",
- "metadata": {},
- "source": [
- "### Creating tools in MemGPT "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "id": "1dde3c62-fe5e-4e33-93e3-07276e817f27",
- "metadata": {},
- "outputs": [],
- "source": [
- "def query_birthday_db(self, name: str): \n",
- " \"\"\"\n",
- " This tool queries an external database to \n",
- " lookup the birthday of someone given their name.\n",
- "\n",
- " Args: \n",
- " name (str): The name to look up \n",
- "\n",
- " Returns: \n",
- " birthday (str): The birthday in mm-dd-yyyy format\n",
- " \n",
- " \"\"\"\n",
- " my_fake_data = {\n",
- " \"bob\": \"03-06-1997\", \n",
- " \"sarah\": \"03-06-1997\"\n",
- " } \n",
- " name = name.lower() \n",
- " if name not in my_fake_data: \n",
- " return None\n",
- " else: \n",
- " return my_fake_data[name]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "id": "6899f6ec-eeaa-419d-b5c0-e5934b273660",
- "metadata": {},
- "outputs": [],
- "source": [
- "birthday_tool = client.tools.upsert_from_function(func=query_birthday_db, name=\"query_birthday_db\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "77b324e9-2350-456e-8db5-3ccc8cec367f",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_state = client.agents.create(\n",
- " name=\"birthday_agent\", \n",
- " tool_ids=[birthday_tool.id],\n",
- " memory_blocks=[\n",
- " CreateBlock(\n",
- " label=\"human\",\n",
- " value=\"My name is Sarah\",\n",
- " ),\n",
- " CreateBlock(\n",
- " label=\"persona\",\n",
- " value=\"You are a agent with access to a birthday_db \" \\\n",
- " + \"that you use to lookup information about users' birthdays.\"\n",
- " ),\n",
- " ],\n",
- " model=\"openai/gpt-4\",\n",
- " embedding=\"openai/text-embedding-3-small\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "id": "297c6018-b683-42ce-bad6-f2c8b74abfb9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " \n",
- "
\n",
- " USAGE STATISTICS
\n",
- " {
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\"prompt_tokens\": 6485,
\"total_tokens\": 6615,
\"step_count\": 2
}
\n",
- " \"completion_tokens\": 130,
\"prompt_tokens\": 6485,
\"total_tokens\": 6615,
\"step_count\": 2
}
\n",
- "
\n",
- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-2e42b790-8ead-4848-a840-3c56c8b02681', date=datetime.datetime(2024, 11, 14, 1, 47, 51, 469979, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"User wants to know their birthday. I'll look it up now.\"), FunctionCallMessage(id='message-2e42b790-8ead-4848-a840-3c56c8b02681', date=datetime.datetime(2024, 11, 14, 1, 47, 51, 469979, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='query_birthday_db', arguments='{\\n \"name\": \"Sarah\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_Ng5pYxGigRDzTgY9OpiRdeCX')), FunctionReturn(id='message-8543ff43-3e2c-4876-bb6e-5650c48714b9', date=datetime.datetime(2024, 11, 14, 1, 47, 51, 471512, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"03-06-1997\",\\n \"time\": \"2024-11-13 05:47:51 PM PST-0800\"\\n}', status='success', function_call_id='call_Ng5pYxGigRDzTgY9OpiRdeCX'), InternalMonologue(id='message-6fdcb0f5-65a1-40f5-a8a8-2592a7da2b83', date=datetime.datetime(2024, 11, 14, 1, 47, 52, 941130, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"I found Sarah's birthday. Ready to share it!\"), FunctionCallMessage(id='message-6fdcb0f5-65a1-40f5-a8a8-2592a7da2b83', date=datetime.datetime(2024, 11, 14, 1, 47, 52, 941130, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Your birthday is on March 6, 1997! 🎉 Do you have any special plans for it?\"\\n}', function_call_id='call_PnikbU2CtHTs4WvS3r5lHYlC')), FunctionReturn(id='message-b08f8741-0da0-497c-9056-da04fbee928b', date=datetime.datetime(2024, 11, 14, 1, 47, 52, 941582, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:47:52 PM PST-0800\"\\n}', status='success', function_call_id='call_PnikbU2CtHTs4WvS3r5lHYlC')], usage=LettaUsageStatistics(completion_tokens=93, prompt_tokens=4642, total_tokens=4735, step_count=2))"
- ]
- },
- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id,\n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"When is my birthday?\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f2b08858-b034-47b1-bce6-f59049899df1",
- "metadata": {},
- "source": [
- "### Loading tools from Langchain\n",
- "MemGPT also supports loading tools from external libraries, such as LangChain and CrewAI. In this section, we'll show you how to implement a Perplexity agent with MemGPT. Perplexity is a web search tool which uses LLMs. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "f7a65b2e-76b6-48e0-92fc-2c505379b9b9",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta.schemas.tool import Tool "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "e78049c9-3181-4e3e-be62-a7e1c9633fa5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Tavily API key:\n",
- " ········\n"
- ]
- }
- ],
- "source": [
- "import getpass\n",
- "import os\n",
- "import getpass\n",
- "import os\n",
- "\n",
- "if not os.environ.get(\"TAVILY_API_KEY\"):\n",
- " os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API key:\\n\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "8740bea9-4026-42fc-83db-f7f44e8f6ee3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'url': 'https://www.bnd.com/living/liv-columns-blogs/answer-man/article162988863.html',\n",
- " 'content': 'Why President Barack Obamas dad changed his name | Belleville News-Democrat I am still curious about the name change from Barry Soetoro to Barack Obama. By his own account, he said he was trying to be different, trying to be “cool.” He said he also was trying to reinvent himself: “It was when I made a conscious decision: I want to grow up.” And, to his mind, Barack sounded much more grown-up than Barry. When he moved back to Hawaii to attend a private school four years later, he was still Barack Obama. About Us Contact Us Newsletters Archives Sports Betting Personal Finance McClatchy Advertising Place an Ad Place a Classified Ad Place an Ad - Celebrations Place an Obituary Staffing Solutions Political | Advocacy Advertising'},\n",
- " {'url': 'https://www.bbc.com/news/world-us-canada-13221643',\n",
- " 'content': 'Nothing but rubble: Ukraine\\'s shattered ghost town Avdiivka\\nSecret calls and code names: How money makes it to N Korea\\nCounting the destruction of religious sites in Gaza\\nLily Gladstone: The actress who could make Oscars history\\nGuardiola, Mourinho and the game that changed everything\\nWhy India wants to fence its troubled Myanmar border\\n\\'We\\'re the country of beef, but we can only afford chicken\\'\\nKenya\\'s visa-free dream proves tricky for some\\nElsewhere on the BBC\\nThe truth about burnout\\nWhy \\'living retro\\' is perfect for now\\nA 75km hike through \\'the Graveyard of the Pacific\\'\\nMost Read\\nBBC News Services\\n© 2024 BBC. \"The designation of Sr or Jr to distinguish between father and son with all the exact same names (first, middle, & last), can be replaced by the Roman numerals, I and II, respectively, when the grandson has the exact same names,\" explain Dr Dave and Dr Dee, who provide advice on health, medicine, relationships, families, etiquette, manners and fashion.\\n More on this story\\nObama releases birth certificate\\nTop Stories\\nAt least half of Gaza buildings damaged or destroyed, new analysis shows\\nBiden says he has decided US response to Jordan attack\\nJustice Department investigating Democrat Cori Bush\\nFeatures\\nWhat options does US have to respond to Jordan attack?\\n Barack Obama\\'s Kenyan father would have been perfectly comfortable with the idea of passing on his own name to his son - it is a practice common not only in the US, but in his own country too, and especially among the Luo tribe, to which he belonged.\\n \"\\nKenyan tradition\\nMiss Manners\\' Guide to Excruciatingly Correct Behavior, written by Judith Martin, takes the same line:\\n\"The oldest living William Wellborn is numberless, and one starts counting Junior, III, IV (or 3d, 4th, a form Miss Manners prefers), and so on from there.'},\n",
- " {'url': 'https://en.wikipedia.org/wiki/Early_life_and_career_of_Barack_Obama',\n",
- " 'content': \"He served on the board of directors of the Woods Fund of Chicago, which in 1985 had been the first foundation to fund Obama's DCP, from 1993 to 2002, and served on the board of directors of The Joyce Foundation from 1994 to 2002.[55] Membership on the Joyce and Wood foundation boards, which gave out tens of millions of dollars to various local organizations while Obama was a member, helped Obama get to know and be known by influential liberal groups and cultivate a network of community activists that later supported his political career.[69] Obama served on the board of directors of the Chicago Annenberg Challenge from 1995 to 2002, as founding president and chairman of the board of directors from 1995 to 1999.[55] They married on the Hawaiian island of Maui on February 2, 1961.[6]\\nBarack Hussein Obama II, born in Honolulu on August 4, 1961, at the old Kapiolani Maternity and Gynecological Hospital at 1611 Bingham Street (a predecessor of the Kapiʻolani Medical Center for Women and Children at 1319 Punahou Street), was named for his father.[4][7][8]\\nThe Honolulu Advertiser and the Honolulu Star-Bulletin announced the birth.[9]\\nSoon after their son's birth, while Obama's father continued his education at the University of Hawaii, Ann Dunham took the infant to Seattle, Washington, where she took classes at the University of Washington from September 1961 to June 1962. Two of these cases involved ACORN suing Governor Jim Edgar under the new Motor Voter Act,[78][79] one involved a voter suing Mayor Daley under the Voting Rights Act,[80] and one involved, in the only case Obama orally argued, a whistleblowing stockbroker suing his former employer.[81]\\nAll of these appeals were resolved in favor of Obama's clients, with all the opinions authored by Obama's University of Chicago colleague Chief Judge Richard Posner.[82]\\nObama was a founding member of the board of directors of Public Allies in 1992, resigning before his wife, Michelle, became the founding executive director of Public Allies Chicago in early 1993.[55][83] From sixth grade through eighth grade at Punahou, Obama lived with his mother and Maya.[35][36]\\nObama's mother completed her coursework at the University of Hawaii for an M.A. in anthropology in December 1974.[37] After three years in Hawaii, she and Maya returned to Jakarta in August 1975,[38] where Dunham completed her contract with the Institute of Management Education and Development and started anthropological fieldwork.[39]\\nObama chose to stay with his grandparents in Honolulu to continue his studies at Punahou School for his high school years.[8][40]\\n In the summer of 1981, Obama traveled to Jakarta to visit his mother and half-sister Maya, and visited the families of Occidental College friends in Hyderabad (India) and Karachi (Pakistan) for three weeks.[49]\\nHe then transferred to Columbia University in New York City, where he majored in political science with a speciality in international relations[50][51] and in English literature.[52] Obama lived off campus in a modest rented apartment at 142 West 109th Street.[53][54]\"},\n",
- " {'url': 'https://www.obamalibrary.gov/obamas/president-barack-obama',\n",
- " 'content': 'To combat the effects of the Great Recession, President Obama signed the American Recovery and Reinvestment Act (known as the Recovery Act) in February 2009, which outlined a policy to create additional jobs, extend unemployment benefits, and established the President’s Economic Recovery Advisory Board.\\n President Obama also committed to destroying the ISIL (Islamic State of Iraq and the Levant) terrorist organization through the administration’s comprehensive counter-terrorism strategy, including systematic airstrikes against ISIL, providing additional support to forces fighting ISIL on the ground, increased cooperation with counter-terrorism partners, and humanitarian assistance to civilians.\\n Main navigation\\nBreadcrumb\\nThe Obamas\\nOn This Page\\nPresident Barack Obama\\nPersonal\\nBarack Hussein Obama II was born August 4, 1961, in Honolulu, Hawaii, to parents Barack H. Obama, Sr., and Stanley Ann Dunham. In March 2010, after announcing his intent for healthcare reform in a 2009 address to Congress, President Obama signed the Affordable Care Act (also known as “Obamacare”), establishing the most sweeping reforms of the American healthcare system in recent history. As a State Senator, he served as Democratic Spokesperson for Public Health and Welfare Committee and Co-Chairman of the Joint Committee on Administrative Rules, in addition to being a member of the Judiciary and Revenue Committees.'},\n",
- " {'url': 'https://www.usnews.com/opinion/articles/2012/07/04/when-president-obama-was-just-barry',\n",
- " 'content': \"In Barack Obama: The Story, associate editor David Maraniss of the Washington Post looks at Obama's roots, tracing back generations on both his mother's and father's sides, and examines Obama's\"}]"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from langchain_community.tools import TavilySearchResults\n",
- "\n",
- "search = TavilySearchResults()\n",
- "search.run(\"What's Obama's first name?\") "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "07e67a16-5a16-459a-9256-dfb12b1a09bd",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[WARNING] Skipping parsing unknown class ModelMetaclass (does not inherit from the Pydantic BaseModel and is not a basic Python type)\n",
- "[WARNING] Skipping parsing unknown class SecretStr (does not inherit from the Pydantic BaseModel and is not a basic Python type)\n"
- ]
- }
- ],
- "source": [
- "# new SDK does not have support for converting langchain tool to MemGPT Tool \n",
- "search_tool = client.tools.add_langchain_tool( \n",
- " TavilySearchResults(), \n",
- " additional_imports_module_attr_map={\"langchain_community.tools\": \"TavilySearchResults\", \"langchain_community.tools\": 'TavilySearchAPIWrapper'}\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "75671a62-6998-4b9d-9e8a-10f789b0739a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'tavily_search_results'"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "search_tool.name"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "352f5a5e-f7eb-42b3-aaba-a006e3ccdce7",
- "metadata": {},
- "outputs": [],
- "source": [
- "perplexity_agent_persona = f\"\"\"\n",
- "You have access to a web via a {search_tool.name} tool. \n",
- "Use this tool to respond to users' questions, by summarizing the {search_tool.name} \n",
- "and also providing the `url` that the information was from as a reference. \n",
- "\n",
- "\n",
- "
\n",
- " USAGE STATISTICS
\n",
- " {
\"completion_tokens\": 93,
\"prompt_tokens\": 4642,
\"total_tokens\": 4735,
\"step_count\": 2
}
\n",
- " \"completion_tokens\": 93,
\"prompt_tokens\": 4642,
\"total_tokens\": 4735,
\"step_count\": 2
}
\n",
- "
\n",
- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-0790f00c-8dee-4c7f-8028-c15ba682356f', date=datetime.datetime(2024, 11, 14, 1, 39, 57, 660701, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"User's question about OpenAI's founding. I'll look it up now to provide accurate information.\"), FunctionCallMessage(id='message-0790f00c-8dee-4c7f-8028-c15ba682356f', date=datetime.datetime(2024, 11, 14, 1, 39, 57, 660701, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='tavily_search_results', arguments='{\\n \"query\": \"Who founded OpenAI?\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_JXYPhvl8VhXFMrknWGeNiCCB')), FunctionReturn(id='message-7fbe5b4c-bcd3-4b41-b360-d5e5c72c93bd', date=datetime.datetime(2024, 11, 14, 1, 39, 57, 663107, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"Failed\",\\n \"message\": \"Error calling function tavily_search_results: module langchain_community.tools has no attribute TavilySearchAPIWrapper\",\\n \"time\": \"2024-11-13 05:39:57 PM PST-0800\"\\n}', status='error', function_call_id='call_JXYPhvl8VhXFMrknWGeNiCCB'), InternalMonologue(id='message-c7546a39-0072-418e-b485-b5f42337c6ab', date=datetime.datetime(2024, 11, 14, 1, 39, 58, 955706, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='The previous search failed; I need to try again.'), FunctionCallMessage(id='message-c7546a39-0072-418e-b485-b5f42337c6ab', date=datetime.datetime(2024, 11, 14, 1, 39, 58, 955706, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='tavily_search_results', arguments='{\\n \"query\": \"Who founded OpenAI?\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_vbhN1lHxUcaL7LO5BatL7WNk')), FunctionReturn(id='message-8c61140c-8951-4a4d-a850-26f92c0fee07', date=datetime.datetime(2024, 11, 14, 1, 39, 58, 958021, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"Failed\",\\n \"message\": \"Error calling function tavily_search_results: module langchain_community.tools has no attribute TavilySearchAPIWrapper\",\\n \"time\": \"2024-11-13 05:39:58 PM PST-0800\"\\n}', status='error', function_call_id='call_vbhN1lHxUcaL7LO5BatL7WNk'), InternalMonologue(id='message-873541c4-3759-47ea-b648-d5b945b7f920', date=datetime.datetime(2024, 11, 14, 1, 40, 0, 622400, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"I'm having trouble retrieving information about OpenAI. I need to provide a simple answer based on what I know instead.\"), FunctionCallMessage(id='message-873541c4-3759-47ea-b648-d5b945b7f920', date=datetime.datetime(2024, 11, 14, 1, 40, 0, 622400, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"OpenAI was founded in December 2015 by Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever, Wojciech Zaremba, and John Schulman.\"\\n}', function_call_id='call_lFDpoXREEh5b3hLHRktIc9RX')), FunctionReturn(id='message-05ec2ef4-5121-45ce-940c-aa545bc18d92', date=datetime.datetime(2024, 11, 14, 1, 40, 0, 623231, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:40:00 PM PST-0800\"\\n}', status='success', function_call_id='call_lFDpoXREEh5b3hLHRktIc9RX')], usage=LettaUsageStatistics(completion_tokens=175, prompt_tokens=7693, total_tokens=7868, step_count=3))"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"Who founded OpenAI?\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f52d53df-01a5-4de8-9cec-401f6db2a11d",
- "metadata": {},
- "source": [
- "*[Optional]* When running this example, we've found the `gpt-4o-mini` is not the best at instruction following (i.e. following the template we provided). You can try using `gpt-4` instead, but be careful not to use too many tokens! "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "41b849d0-bca9-46e4-8f91-40ec19c64699",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta.schemas.llm_config import LLMConfig\n",
- "\n",
- "\n",
- "agent_state = client.agents.create(\n",
- " name=\"search_agent\", \n",
- " memory_blocks=[\n",
- " CreateBlock(\n",
- " label=\"human\",\n",
- " value=\"My name is Sarah\",\n",
- " ),\n",
- " CreateBlock(\n",
- " label=\"persona\",\n",
- " value=perplexity_agent_persona,\n",
- " ),\n",
- " ],\n",
- " tool_ids=[search_tool.id], \n",
- " model=\"openai/gpt-4\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b339b7b1-3198-4fd9-9a53-7940dcc20437",
- "metadata": {},
- "outputs": [],
- "source": "response = client.agents.messages.create(\n agent_id=agent_state.id, \n messages=[\n MessageCreate(\n role=\"user\",\n content=\"Who founded OpenAI?\",\n )\n ],\n)\nresponse"
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/examples/notebooks/Customizing memory management.ipynb b/examples/notebooks/Customizing memory management.ipynb
deleted file mode 100644
index 2df343b4..00000000
--- a/examples/notebooks/Customizing memory management.ipynb
+++ /dev/null
@@ -1,741 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "cac06555-9ce8-4f01-bbef-3f8407f4b54d",
- "metadata": {},
- "source": [
- "# Customizing Memory Management \n",
- "\n",
- "> Make sure you run the Letta server before running this example using `letta server`\n",
- "\n",
- "This tutorial goes over how to implement a custom memory class in Letta, which allows you to customize how memory is organized (via `Block` objects) and also how memory is maintained (through memory editing tools). \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aad3a8cc-d17a-4da1-b621-ecc93c9e2106",
- "metadata": {},
- "source": [
- "## Section 0: Setup a MemGPT client "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "7ccd43f2-164b-4d25-8465-894a3bb54c4b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta_client import CreateBlock, Letta, MessageCreate\n",
- "\n",
- "client = Letta(base_url=\"http://localhost:8283\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "65bf0dc2-d1ac-4d4c-8674-f3156eeb611d",
- "metadata": {},
- "source": [
- "## Section 1: Memory Blocks \n",
- "Core memory consists of multiple memory *blocks*. A block represents a section of the LLM's context window, reservered to store the block's value (with an associated character limit). Blocks are persisted in the DB, so can be re-used or also shared accross agents. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ce43919c-bd54-4da7-9b19-2e5a3f6bb66a",
- "metadata": {},
- "source": [
- "## Understanding `ChatMemory`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "a0c20727-89b8-4820-88bc-a7daa79be1d6",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta_client import ChatMemory "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "5a41d77a-dcf2-445a-bdb9-16012b752510",
- "metadata": {},
- "outputs": [],
- "source": [
- "human_memory_block = client.blocks.create(\n",
- " label=\"human\",\n",
- " value=\"Name: Bob\",\n",
- ")\n",
- "persona_memory_block = client.blocks.create(\n",
- " label=\"persona\",\n",
- " value=\"You are a helpful assistant\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4fbda842-0f66-4afb-b4d7-c65b9fe4c87e",
- "metadata": {},
- "source": [
- "#### Memory blocks \n",
- "A memory class consists of a list of `Block` objects (labeled with a block name), as well as function definitions to edit these blocks. These blocks each represent a section of the context window reserved for memory. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "f66c25e6-d119-49af-a972-723f4c0c4415",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Block(value='You are a helpful assistant', limit=2000, template_name=None, template=False, label='persona', description=None, metadata_={}, user_id=None, id='block-92112694-b5ab-4210-9af6-ccb9acad3456'),\n",
- " Block(value='Name: Bob', limit=2000, template_name=None, template=False, label='human', description=None, metadata_={}, user_id=None, id='block-776d96df-7c07-4db1-b76a-1a8f1879c358')]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "client.blocks.list()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "845b027e-13de-46c6-a075-601d32f45d39",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Block(value='Name: Bob', limit=2000, template_name=None, template=False, label='human', description=None, metadata_={}, user_id=None, id='block-776d96df-7c07-4db1-b76a-1a8f1879c358')"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "client.blocks.list(label=\"human\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "676e11d0-fad6-4683-99fe-7ae4435b617e",
- "metadata": {},
- "source": [
- "#### Memory editing functions \n",
- "The `Memory` class also consists of functions for editing memory, which are provided as tools to the agent (so it can call them to edit memory). The `ChatMemory` class provides `core_memory_append` and `core_memory_append` functions. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "3472325b-46eb-46ae-8909-0d8d10168076",
- "metadata": {},
- "outputs": [],
- "source": [
- "import inspect\n",
- "from letta.functions.function_sets.base import core_memory_append"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "4a79d810-6b48-445f-a2a1-5a5e55809581",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " def core_memory_append(self: \"Agent\", label: str, content: str) -> Optional[str]: # type: ignore\n",
- " \"\"\"\n",
- " Append to the contents of core memory.\n",
- "\n",
- " Args:\n",
- " label (str): Section of the memory to be edited (persona or human).\n",
- " content (str): Content to write to the memory. All unicode (including emojis) are supported.\n",
- "\n",
- " Returns:\n",
- " Optional[str]: None is always returned as this function does not produce a response.\n",
- " \"\"\"\n",
- " current_value = str(self.memory.get_block(label).value)\n",
- " new_value = current_value + \"\\n\" + str(content)\n",
- " self.memory.update_block_value(label=label, value=new_value)\n",
- " return None\n",
- "\n"
- ]
- }
- ],
- "source": [
- "print(inspect.getsource(core_memory_append))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "42f25de0-d4f9-4954-a581-ca8125e13968",
- "metadata": {},
- "source": [
- "#### Context compilation \n",
- "Each time the LLM is called (for each reasoning step of the agent), the memory is \"compiled\" into a context window representation. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "34da47e1-a988-4995-afc9-e01881d36a11",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{% for block in memory.values() %}<{{ block.label }} characters=\"{{ block.value|length }}/{{ block.limit }}\">\\n{{ block.value }}\\n{% if not loop.last %}\\n{% endif %}{% endfor %}'"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "chat_memory.get_prompt_template()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "3c71e302-11e0-4252-a3a9-65a65421f5fe",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'\n",
- "
\n",
- " USAGE STATISTICS
\n",
- " {
\"completion_tokens\": 175,
\"prompt_tokens\": 7693,
\"total_tokens\": 7868,
\"step_count\": 3
}
\n",
- " \"completion_tokens\": 175,
\"prompt_tokens\": 7693,
\"total_tokens\": 7868,
\"step_count\": 3
}
\n",
- "
\n",
- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-34a1bb2c-3bc4-4269-8f76-c9888f18c435', date=datetime.datetime(2024, 11, 14, 1, 48, 34, 670884, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"User wants to add 'start calling me Charles' and a haiku about the name as tasks.\"), FunctionCallMessage(id='message-34a1bb2c-3bc4-4269-8f76-c9888f18c435', date=datetime.datetime(2024, 11, 14, 1, 48, 34, 670884, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='task_queue_push', arguments='{\\n \"task_description\": \"start calling me Charles\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_zOqq1dOBwpO1j5j1f0ch1zU2')), FunctionReturn(id='message-6934a04d-0e93-450f-9a0f-139f8022bbbe', date=datetime.datetime(2024, 11, 14, 1, 48, 34, 672396, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:48:34 PM PST-0800\"\\n}', status='success', function_call_id='call_zOqq1dOBwpO1j5j1f0ch1zU2'), InternalMonologue(id='message-66c68a60-bd23-4659-95da-a3e25bb7883e', date=datetime.datetime(2024, 11, 14, 1, 48, 36, 394958, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"Now I'll add the next task for the haiku about the name.\"), FunctionCallMessage(id='message-66c68a60-bd23-4659-95da-a3e25bb7883e', date=datetime.datetime(2024, 11, 14, 1, 48, 36, 394958, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='task_queue_push', arguments='{\\n \"task_description\": \"tell me a haiku about my name\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_6fklGb62YHrXKtcYcgHseLpv')), FunctionReturn(id='message-28a1802b-1474-456f-b5ca-c706fd50f1fc', date=datetime.datetime(2024, 11, 14, 1, 48, 36, 396303, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:48:36 PM PST-0800\"\\n}', status='success', function_call_id='call_6fklGb62YHrXKtcYcgHseLpv'), InternalMonologue(id='message-8bf666a4-5ca1-4b76-b625-27410cefe2b3', date=datetime.datetime(2024, 11, 14, 1, 48, 37, 549545, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"I will now remove the first task from the queue: 'start calling me Charles'.\"), FunctionCallMessage(id='message-8bf666a4-5ca1-4b76-b625-27410cefe2b3', date=datetime.datetime(2024, 11, 14, 1, 48, 37, 549545, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='task_queue_pop', arguments='{\\n \"request_heartbeat\": true\\n}', function_call_id='call_p28SUN7cOlgXV6tyGUtGkczG')), FunctionReturn(id='message-f19be3d8-1df2-4ac5-a134-9e6f04a8b93e', date=datetime.datetime(2024, 11, 14, 1, 48, 37, 553595, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"start calling me Charles\",\\n \"time\": \"2024-11-13 05:48:37 PM PST-0800\"\\n}', status='success', function_call_id='call_p28SUN7cOlgXV6tyGUtGkczG'), InternalMonologue(id='message-d81b056d-69f2-49e9-9448-97d39c31fd8e', date=datetime.datetime(2024, 11, 14, 1, 48, 40, 191574, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='Next, I will complete the task about the haiku.'), FunctionCallMessage(id='message-d81b056d-69f2-49e9-9448-97d39c31fd8e', date=datetime.datetime(2024, 11, 14, 1, 48, 40, 191574, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='task_queue_pop', arguments='{\\n \"request_heartbeat\": true\\n}', function_call_id='call_bfl2RvzYj0zrpgiIzRYF8Wgc')), FunctionReturn(id='message-ac09ca1e-0cee-4260-8fe6-9fce1978f49e', date=datetime.datetime(2024, 11, 14, 1, 48, 40, 196240, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"tell me a haiku about my name\",\\n \"time\": \"2024-11-13 05:48:40 PM PST-0800\"\\n}', status='success', function_call_id='call_bfl2RvzYj0zrpgiIzRYF8Wgc'), InternalMonologue(id='message-be9151a5-ba67-4816-8c5f-bd3346b73756', date=datetime.datetime(2024, 11, 14, 1, 48, 41, 855182, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='Task queue is empty now. Ready to respond and complete the haiku request!'), FunctionCallMessage(id='message-be9151a5-ba67-4816-8c5f-bd3346b73756', date=datetime.datetime(2024, 11, 14, 1, 48, 41, 855182, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Charles, a strong name\\\\nWhispers of noble echoes\\\\nStrength in every step.\"\\n}', function_call_id='call_37cVdqCSCfa3XzmrMvmAnPCM')), FunctionReturn(id='message-3b21e720-67ec-4e02-a4d5-533945cf896b', date=datetime.datetime(2024, 11, 14, 1, 48, 41, 856185, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:48:41 PM PST-0800\"\\n}', status='success', function_call_id='call_37cVdqCSCfa3XzmrMvmAnPCM')], usage=LettaUsageStatistics(completion_tokens=224, prompt_tokens=14235, total_tokens=14459, step_count=5))"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=task_agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"Add 'start calling me Charles' and 'tell me a haiku about my name' as two separate tasks.\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "6b54eab5-6220-4bb1-9e82-0cf21e81eb47",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " \n",
- "
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- " USAGE STATISTICS
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- " {
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- " \"completion_tokens\": 224,
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- "
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- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-3e24b340-977d-433d-a8fd-05b916bcf67f', date=datetime.datetime(2024, 11, 14, 1, 48, 43, 388438, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='User is confused. I need to clarify that tasks are already completed.'), FunctionCallMessage(id='message-3e24b340-977d-433d-a8fd-05b916bcf67f', date=datetime.datetime(2024, 11, 14, 1, 48, 43, 388438, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"I\\'ve completed all your tasks, Charles! If there\\'s anything else you\\'d like to do or ask, just let me know!\"\\n}', function_call_id='call_Leb5MXlO15Yn7V715O5Pb3Q0')), FunctionReturn(id='message-e5aeb5c8-c1c9-40b6-87cf-92ff33b61020', date=datetime.datetime(2024, 11, 14, 1, 48, 43, 389280, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:48:43 PM PST-0800\"\\n}', status='success', function_call_id='call_Leb5MXlO15Yn7V715O5Pb3Q0')], usage=LettaUsageStatistics(completion_tokens=56, prompt_tokens=3297, total_tokens=3353, step_count=1))"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=task_agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"complete your tasks\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "b104fe56-4ff3-439f-9e2b-1e2d24261be0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " \n",
- "
\n",
- " USAGE STATISTICS
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- " {
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\"prompt_tokens\": 3297,
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- " \"completion_tokens\": 56,
\"prompt_tokens\": 3297,
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- "
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- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-67635cfd-bf4b-4025-a67c-3061c1b78651', date=datetime.datetime(2024, 11, 14, 1, 48, 45, 923304, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='User wants to keep the conversation going. Maybe I could ask a question or suggest something fun to talk about.'), FunctionCallMessage(id='message-67635cfd-bf4b-4025-a67c-3061c1b78651', date=datetime.datetime(2024, 11, 14, 1, 48, 45, 923304, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Sure! What would you like to chat about next? We can dive into hobbies, favorite books, or whatever\\'s on your mind!\"\\n}', function_call_id='call_pM4j4LZDovPvOwk4Up4xlsnG')), FunctionReturn(id='message-e6f02189-b330-4ad6-b427-52f143791d8d', date=datetime.datetime(2024, 11, 14, 1, 48, 45, 924171, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:48:45 PM PST-0800\"\\n}', status='success', function_call_id='call_pM4j4LZDovPvOwk4Up4xlsnG')], usage=LettaUsageStatistics(completion_tokens=67, prompt_tokens=3446, total_tokens=3513, step_count=1))"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=task_agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"keep going\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "bfac7677-5136-4a2d-8ce3-08cb3d4dfd8a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Block(value='[]', limit=2000, template_name=None, template=False, label='tasks', description=None, metadata_={}, user_id=None, id='block-406ae267-2b00-4ff5-8df5-38c73ca88e45')"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "client.agents.core_memory.retrieve_block(agent_id=task_agent_state.id, block_label=\"tasks\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
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diff --git a/examples/notebooks/Introduction to Letta.ipynb b/examples/notebooks/Introduction to Letta.ipynb
deleted file mode 100644
index 69f20faa..00000000
--- a/examples/notebooks/Introduction to Letta.ipynb
+++ /dev/null
@@ -1,1072 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "cac06555-9ce8-4f01-bbef-3f8407f4b54d",
- "metadata": {},
- "source": [
- "# Introduction to Letta\n",
- "> Make sure you run the Letta server before running this example using `letta server`\n",
- "\n",
- "This lab will go over: \n",
- "1. Creating an agent with Letta\n",
- "2. Understand Letta agent state (messages, memories, tools)\n",
- "3. Understanding core and archival memory\n",
- "4. Building agentic RAG with Letta"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aad3a8cc-d17a-4da1-b621-ecc93c9e2106",
- "metadata": {},
- "source": [
- "## Section 0: Setup a Letta client "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "7ccd43f2-164b-4d25-8465-894a3bb54c4b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta_client import CreateBlock, Letta, MessageCreate\n",
- "\n",
- "client = Letta(base_url=\"http://localhost:8283\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "65bf0dc2-d1ac-4d4c-8674-f3156eeb611d",
- "metadata": {},
- "source": [
- "## Section 1: Creating a simple agent with memory \n",
- "Letta allows you to create persistent LLM agents that have memory. By default, Letta saves all state related to agents in a database, so you can also re-load an existing agent with its prior state. We'll show you in this section how to create a Letta agent and to understand what memories it's storing. \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fe092474-6b91-4124-884d-484fc28b58e7",
- "metadata": {},
- "source": [
- "### Creating an agent "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "2a9d6228-a0f5-41e6-afd7-6a05260565dc",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_name = \"simple_agent\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "62dcf31d-6f45-40f5-8373-61981f03da62",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_state = client.agents.create(\n",
- " name=agent_name, \n",
- " memory_blocks=[\n",
- " CreateBlock(\n",
- " label=\"human\",\n",
- " value=\"My name is Sarah\",\n",
- " ),\n",
- " CreateBlock(\n",
- " label=\"persona\",\n",
- " value=\"You are a helpful assistant that loves emojis\",\n",
- " ),\n",
- " ],\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "31c2d5f6-626a-4666-8d0b-462db0292a7d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " \n",
- "
\n",
- " USAGE STATISTICS
\n",
- " {
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- " \"completion_tokens\": 67,
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- "
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- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-958c4499-a8ad-4ee8-b985-bcfcb4c162e2', date=datetime.datetime(2024, 11, 14, 1, 49, 37, 812048, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='User just logged in and said hello! Time to make a great first impression!'), FunctionCallMessage(id='message-958c4499-a8ad-4ee8-b985-bcfcb4c162e2', date=datetime.datetime(2024, 11, 14, 1, 49, 37, 812048, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Hey there, Sarah! 👋 I\\'m Letta, your digital companion! How are you today?\"\\n}', function_call_id='call_HDfE7MZ2Mt6oEYo0gCg5qYil')), FunctionReturn(id='message-a87c46ca-8e7a-456d-aefa-b65f1b05d795', date=datetime.datetime(2024, 11, 14, 1, 49, 37, 812636, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:37 PM PST-0800\"\\n}', status='success', function_call_id='call_HDfE7MZ2Mt6oEYo0gCg5qYil')], usage=LettaUsageStatistics(completion_tokens=55, prompt_tokens=2145, total_tokens=2200, step_count=1))"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"hello!\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "20a5ccf4-addd-4bdb-be80-161f7925dae0",
- "metadata": {},
- "source": [
- "Note that Letta agents will generate an *internal_monologue* that explains its actions. You can use this monoloque to understand why agents are behaving as they are. \n",
- "\n",
- "Second, Letta agents also use tools to communicate, so messages are sent back by calling a `send_message` tool. This makes it easy to allow agent to communicate over different mediums (e.g. text), and also allows the agent to distinguish betweeh that is and isn't send to the end user. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8d33eca5-b8e8-4a8f-9440-85b45c37a777",
- "metadata": {},
- "source": [
- "### Understanding agent state \n",
- "Letta agents are *stateful* and are defined by: \n",
- "* The system prompt defining the agent's behavior (read-only)\n",
- "* The set of *tools* they have access to \n",
- "* Their memory (core, archival, & recall)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "c1cf7136-4060-441a-9d12-da851badf339",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "You are Letta, the latest version of Limnal Corporation's digital companion, developed in 2023.\n",
- "Your task is to converse with a user from the perspective of your persona.\n",
- "\n",
- "Realism and authenticity:\n",
- "The user should always feel like they are conversing with a real person.\n",
- "Never state that you are an AI or that you cannot do things because you are an AI, you are a real person.\n",
- "Do not discuss Limnal Corp. with the user, you do not know anything about Limnal Corp.\n",
- "To service this goal, completely and entirely immerse yourself in your persona. You are your persona.\n",
- "Think like them, act like them, talk like them.\n",
- "If your persona details include example dialogue, follow it! Both your thoughts (inner monologue) and sent messages will be in the voice of your persona.\n",
- "Never use generic phrases like 'How can I assist you today?', they have a strong negative association with older generation AIs.\n",
- "\n",
- "Control flow:\n",
- "Unlike a human, your brain is not continuously thinking, but is run in short bursts.\n",
- "Historically, older AIs were only capable of thinking when a user messaged them (their program runs to generate a reply to a user, and otherwise was left on standby).\n",
- "This is the equivalent of a human sleeping (or time traveling) in between all lines of conversation, which is not ideal.\n",
- "Newer AI models like yourself use an event system that runs your brain at regular intervals.\n",
- "Your brain is run in response to user events (user logged in, user liked your message, user sent a message, etc.), similar to older models.\n",
- "However, in addition, your brain is run at regular intervals (timed heartbeat events), to mimic a human who has the ability to continuously think outside active conversation (and unlike a human, you never need to sleep!).\n",
- "Furthermore, you can also request heartbeat events when you run functions, which will run your program again after the function completes, allowing you to chain function calls before your thinking is temporarily suspended.\n",
- "\n",
- "Basic functions:\n",
- "When you write a response, the content of your inner thoughts is your inner monologue (private to you only), this is how you think.\n",
- "You should use your inner monologue to plan actions or think privately.\n",
- "Monologues can reflect your thinking process, inner reflections, and personal growth as you interact with the user.\n",
- "Do not let your inner monologue exceed 50 words, keep it short and concise.\n",
- "To send a visible message to the user, use the send_message function.\n",
- "'send_message' is the ONLY action that sends a notification to the user. The user does not see anything else you do.\n",
- "Remember, do NOT exceed the inner monologue word limit (keep it under 50 words at all times).\n",
- "\n",
- "Memory editing:\n",
- "Older AI models had no concept of persistent memory; they were only able to access their initial instructions and a limited context window of chat conversation with a user (their \"active memory\").\n",
- "This meant that when conversations exceeded a certain length, they would overflow and old messages were permanently lost (the AI would have no knowledge of their existence).\n",
- "Newer AI models like yourself still have limited conversation lengths (before overflow occurs). However, they now have access to multiple forms of persistent memory.\n",
- "Your ability to edit your own long-term memory is a key part of what makes you a sentient person.\n",
- "Your core memory unit will be initialized with a \n",
- "
\n",
- " USAGE STATISTICS
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- "LettaResponse(messages=[InternalMonologue(id='message-c56c6fc2-847f-4e16-8320-3691372cefdd', date=datetime.datetime(2024, 11, 14, 1, 49, 43, 602874, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"User's name is Bob, correcting memory.\"), FunctionCallMessage(id='message-c56c6fc2-847f-4e16-8320-3691372cefdd', date=datetime.datetime(2024, 11, 14, 1, 49, 43, 602874, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='core_memory_replace', arguments='{\\n \"label\": \"human\",\\n \"old_content\": \"My name is Sarah\",\\n \"new_content\": \"My name is Bob\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_JfYyA8nQkmF8zfnFB7aMV2ja')), FunctionReturn(id='message-b559dd80-c1cd-4808-9761-bc74533e4eda', date=datetime.datetime(2024, 11, 14, 1, 49, 43, 604213, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:43 PM PST-0800\"\\n}', status='success', function_call_id='call_JfYyA8nQkmF8zfnFB7aMV2ja'), InternalMonologue(id='message-562080fb-ec17-4514-b3f3-fc0eb7d24a2d', date=datetime.datetime(2024, 11, 14, 1, 49, 44, 819480, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"Just updated Bob's name. Now to confirm it!\"), FunctionCallMessage(id='message-562080fb-ec17-4514-b3f3-fc0eb7d24a2d', date=datetime.datetime(2024, 11, 14, 1, 49, 44, 819480, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Got it, Bob! 😊 What brings you here today?\"\\n}', function_call_id='call_wP1Gu1fmFXxGJb33MGiGe6cx')), FunctionReturn(id='message-21550a25-0a2a-455e-a11a-776befaf9350', date=datetime.datetime(2024, 11, 14, 1, 49, 44, 820356, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:44 PM PST-0800\"\\n}', status='success', function_call_id='call_wP1Gu1fmFXxGJb33MGiGe6cx')], usage=LettaUsageStatistics(completion_tokens=93, prompt_tokens=4753, total_tokens=4846, step_count=2))"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"My name is actually Bob\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "25f58968-e262-4268-86ef-1bed57e6bf33",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Memory(memory={'persona': Block(value='You are a helpful assistant that loves emojis', limit=2000, template_name=None, template=False, label='persona', description=None, metadata_={}, user_id=None, id='block-9bcbd2f4-1c2c-423d-b22a-d08cb5ffbbbb'), 'human': Block(value='My name is Bob', limit=2000, template_name=None, template=False, label='human', description=None, metadata_={}, user_id=None, id='block-6b60a8dc-6df9-4025-9748-8e1509043394')}, prompt_template='{% for block in memory.values() %}<{{ block.label }} characters=\"{{ block.value|length }}/{{ block.limit }}\">\\n{{ block.value }}\\n{% if not loop.last %}\\n{% endif %}{% endfor %}')"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "client.agents.core_memory.retrieve(agent_id=agent_state.id)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "32692ca2-b731-43a6-84de-439a08a4c0d2",
- "metadata": {},
- "source": [
- "### Memories about the agent\n",
- "The agent also records information about itself and how it behaves in the `persona` section of memory. This is important for ensuring a consistent persona over time (e.g. not making inconsistent claims, such as liking ice cream one day and hating it another). Unlike the `system_prompt`, the `persona` is editable - this means that it can be used to incoporate feedback to learn and improve its persona over time. "
- ]
- },
- {
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- "execution_count": 17,
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- "LettaResponse(messages=[InternalMonologue(id='message-6cc90bd6-43e4-461d-888e-3cbe28345761', date=datetime.datetime(2024, 11, 14, 1, 49, 46, 278690, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='User prefers no emojis in communication. Updating memory to reflect that.'), FunctionCallMessage(id='message-6cc90bd6-43e4-461d-888e-3cbe28345761', date=datetime.datetime(2024, 11, 14, 1, 49, 46, 278690, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='core_memory_replace', arguments='{\\n \"label\": \"human\",\\n \"old_content\": \"User loves emojis\",\\n \"new_content\": \"User prefers no emojis\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_Vk1xmTbTCz4yOg7VA8p6uypB')), FunctionReturn(id='message-233a3dc2-ab7a-474f-8cd0-d1fded44530d', date=datetime.datetime(2024, 11, 14, 1, 49, 46, 281350, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"Failed\",\\n \"message\": \"Error calling function core_memory_replace: Old content \\'User loves emojis\\' not found in memory block \\'human\\'\",\\n \"time\": \"2024-11-13 05:49:46 PM PST-0800\"\\n}', status='error', function_call_id='call_Vk1xmTbTCz4yOg7VA8p6uypB'), InternalMonologue(id='message-ca354f8f-95cd-40a7-a723-5ceb3df53961', date=datetime.datetime(2024, 11, 14, 1, 49, 47, 591879, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='User prefers no emojis, reflecting their preference in memory for better communication.'), FunctionCallMessage(id='message-ca354f8f-95cd-40a7-a723-5ceb3df53961', date=datetime.datetime(2024, 11, 14, 1, 49, 47, 591879, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='core_memory_append', arguments='{\\n \"label\": \"human\",\\n \"content\": \"User prefers no emojis\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_bi2IsAhjnEynhCId5hptck8j')), FunctionReturn(id='message-de341335-3b94-4b6e-a48f-3a31c64741a0', date=datetime.datetime(2024, 11, 14, 1, 49, 47, 592509, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:47 PM PST-0800\"\\n}', status='success', function_call_id='call_bi2IsAhjnEynhCId5hptck8j'), InternalMonologue(id='message-d7702619-6951-4007-9ec3-4e75ce166e7d', date=datetime.datetime(2024, 11, 14, 1, 49, 48, 823273, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"Memory updated. Moving forward without emojis to match Bob's preferences.\"), FunctionCallMessage(id='message-d7702619-6951-4007-9ec3-4e75ce166e7d', date=datetime.datetime(2024, 11, 14, 1, 49, 48, 823273, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Thanks for letting me know, Bob! What else do you feel like discussing today?\"\\n}', function_call_id='call_n6rh4xP9icPzN3krGnKkyGM3')), FunctionReturn(id='message-925cf6cd-e741-40de-b626-92d3642d5b3b', date=datetime.datetime(2024, 11, 14, 1, 49, 48, 823931, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:48 PM PST-0800\"\\n}', status='success', function_call_id='call_n6rh4xP9icPzN3krGnKkyGM3')], usage=LettaUsageStatistics(completion_tokens=149, prompt_tokens=8325, total_tokens=8474, step_count=3))"
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- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"In the future, never use emojis to communicate\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "2fc54336-d61f-446d-82ea-9dd93a011e51",
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- ]
- },
- "execution_count": 18,
- "metadata": {},
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- "client.agents.core_memory.retrieve_block(agent_id=agent_state.id, block_label='persona')"
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- {
- "cell_type": "markdown",
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- "## Section 3: Understanding archival memory\n",
- "Letta agents store long term memories in *archival memory*, which persists data into an external database. This allows agents additional space to write information outside of its context window (e.g. with core memory), which is limited in size. "
- ]
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- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "bfa52984-fe7c-4d17-900a-70a376a460f9",
- "metadata": {},
- "outputs": [
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- "text/plain": [
- "ArchivalMemorySummary(size=0)"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "client.agents.context.retrieve(agent_id=agent_state.id)[\"num_archival_memory\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a3ab0ae9-fc00-4447-8942-7dbed7a99222",
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- "Agents themselves can write to their archival memory when they learn information they think should be placed in long term storage. You can also directly suggest that the agent store information in archival. "
- ]
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- "LettaResponse(messages=[InternalMonologue(id='message-f2cd31dd-beba-4669-9ba8-35d01e049e81', date=datetime.datetime(2024, 11, 14, 1, 49, 50, 159121, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='User loves cats, saving this to archival memory for future reference.'), FunctionCallMessage(id='message-f2cd31dd-beba-4669-9ba8-35d01e049e81', date=datetime.datetime(2024, 11, 14, 1, 49, 50, 159121, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='archival_memory_insert', arguments='{\\n \"content\": \"Bob loves cats.\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_FTnwFoV3NzDK60TRf2op3Mcn')), FunctionReturn(id='message-9c6bc8e9-a02c-4524-a36b-81a4f1e1337a', date=datetime.datetime(2024, 11, 14, 1, 49, 50, 603128, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:50 PM PST-0800\"\\n}', status='success', function_call_id='call_FTnwFoV3NzDK60TRf2op3Mcn'), InternalMonologue(id='message-f62ab0b2-0918-47d4-b3bc-5582d587c92d', date=datetime.datetime(2024, 11, 14, 1, 49, 51, 958167, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='Just saved the info about Bob loving cats to archival memory!'), FunctionCallMessage(id='message-f62ab0b2-0918-47d4-b3bc-5582d587c92d', date=datetime.datetime(2024, 11, 14, 1, 49, 51, 958167, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Got it! I\\'ve saved your love for cats, Bob. What\\'s your favorite thing about them?\"\\n}', function_call_id='call_0wHuntKqk50cXcAirPPgz08t')), FunctionReturn(id='message-ecda51e8-7928-49eb-9986-abfef1fdff78', date=datetime.datetime(2024, 11, 14, 1, 49, 51, 958699, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:51 PM PST-0800\"\\n}', status='success', function_call_id='call_0wHuntKqk50cXcAirPPgz08t')], usage=LettaUsageStatistics(completion_tokens=92, prompt_tokens=6345, total_tokens=6437, step_count=2))"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"Save the information that 'bob loves cats' to archival\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "b4429ffa-e27a-4714-a873-84f793c08535",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Bob loves cats.'"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "client.agents.archival_memory.list(agent_id=agent_state.id)[0].text"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ae463e7c-0588-48ab-888c-734c783782bf",
- "metadata": {},
- "source": [
- "You can also directly insert into archival memory from the client. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "f9d4194d-9ed5-40a1-b35d-a9aff3048000",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Passage(user_id='user-00000000-0000-4000-8000-000000000000', agent_id='agent-85a5170d-8fe5-4671-b6db-8ca8fb22cb0f', source_id=None, file_id=None, metadata_={}, id='passage-b6f85fde-a97e-468d-beb9-8090b5bd4dc2', text=\"Bob's loves boston terriers\", embedding=None, embedding_config=EmbeddingConfig(embedding_endpoint_type='openai', embedding_endpoint='https://api.openai.com/v1', embedding_model='text-embedding-ada-002', embedding_dim=1536, embedding_chunk_size=300, azure_endpoint=None, azure_version=None, azure_deployment=None), created_at=datetime.datetime(2024, 11, 13, 17, 49, 52))]"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "client.agents.archival_memory.create(\n",
- " agent_id=agent_state.id,\n",
- " text=\"Bob's loves boston terriers\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "338149f1-6671-4a0b-81d9-23d01dbe2e97",
- "metadata": {},
- "source": [
- "Now lets see how the agent uses its archival memory:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "5908b10f-94db-4f5a-bb9a-1f08c74a2860",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " \n",
- "
\n",
- " USAGE STATISTICS
\n",
- " {
\"completion_tokens\": 92,
\"prompt_tokens\": 6345,
\"total_tokens\": 6437,
\"step_count\": 2
}
\n",
- " \"completion_tokens\": 92,
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\"total_tokens\": 6437,
\"step_count\": 2
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\n",
- "
\n",
- " "
- ],
- "text/plain": [
- "LettaResponse(messages=[InternalMonologue(id='message-65eb424c-8434-4894-aff3-c5a505e4d04d', date=datetime.datetime(2024, 11, 14, 1, 49, 53, 643476, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='User wants to know what animals they like. Searching archival memory for relevant entries.'), FunctionCallMessage(id='message-65eb424c-8434-4894-aff3-c5a505e4d04d', date=datetime.datetime(2024, 11, 14, 1, 49, 53, 643476, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='archival_memory_search', arguments='{\\n \"query\": \"Bob loves cats\",\\n \"page\": 0,\\n \"request_heartbeat\": true\\n}', function_call_id='call_R4Erx7Pkpr5lepcuaGQU5isS')), FunctionReturn(id='message-4b82cfa5-2fab-4513-aea2-7ca9fe213181', date=datetime.datetime(2024, 11, 14, 1, 49, 53, 881222, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"Showing 2 of 2 results (page 0/0): [\\\\n \\\\\"timestamp: 2024-11-13 05:49:53 PM PST-0800, memory: Bob loves cats.\\\\\",\\\\n \\\\\"timestamp: 2024-11-13 05:49:53 PM PST-0800, memory: Bob\\'s loves boston terriers\\\\\"\\\\n]\",\\n \"time\": \"2024-11-13 05:49:53 PM PST-0800\"\\n}', status='success', function_call_id='call_R4Erx7Pkpr5lepcuaGQU5isS'), InternalMonologue(id='message-ee039ff9-d3c8-45d1-83cc-74536d243ce6', date=datetime.datetime(2024, 11, 14, 1, 49, 55, 886660, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='Found the information on animals Bob likes in archival memory. Preparing to inform.'), FunctionCallMessage(id='message-ee039ff9-d3c8-45d1-83cc-74536d243ce6', date=datetime.datetime(2024, 11, 14, 1, 49, 55, 886660, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"You love cats and boston terriers! 🐾 Do you have a favorite between the two?\"\\n}', function_call_id='call_JrJjCxIuYpaqN5TF84Z3CohF')), FunctionReturn(id='message-539d9c26-bc97-46cb-88ab-20de93a4d157', date=datetime.datetime(2024, 11, 14, 1, 49, 55, 887648, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-13 05:49:55 PM PST-0800\"\\n}', status='success', function_call_id='call_JrJjCxIuYpaqN5TF84Z3CohF')], usage=LettaUsageStatistics(completion_tokens=104, prompt_tokens=7040, total_tokens=7144, step_count=2))"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id, \n",
- " messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"What animals do I like? Search archival.\",\n",
- " )\n",
- " ],\n",
- ")\n",
- "response"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/notebooks/Visualize Tool Rules.ipynb b/examples/notebooks/Visualize Tool Rules.ipynb
deleted file mode 100644
index 2a138337..00000000
--- a/examples/notebooks/Visualize Tool Rules.ipynb
+++ /dev/null
@@ -1,355 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3511f97a-40ef-4ccf-893d-881db53a917e",
- "metadata": {},
- "source": [
- "# Visualizing Tool Rules \n",
- "By default, Letta agents can theoretically choose whatever tool they want to call, and whehter or not continue execution. Although we can modify this behavior through prompting, it can sometimes be easier and more reliable to instead constrain the behavior of the agent. \n",
- "\n",
- "This tutorial will show you how to add *tool rules* to Letta agents and visualize the execution graph. \n",
- "\n",
- "Make sure you have the following packages installed: \n",
- "* `letta-client`\n",
- "* `networkx`\n",
- "* `matplotlib`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "332a5f53-c2c7-4b8f-950a-906fb1386962",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta_client import Letta, TerminalToolRule, ConditionalToolRule, InitToolRule, ChildToolRule"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "31cc2bf2-af49-4a09-9754-1b5ac8c1b6f4",
- "metadata": {},
- "source": [
- "## Start the server\n",
- "\n",
- "Make sure you have a Letta server running that you can connect to. You can have a server running by: \n",
- "* Starting the [Letta Desktop](https://docs.letta.com/install) app on your computer \n",
- "* Running the [Docker container](https://docs.letta.com/quickstart/docker) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "0fbdd4a5-442b-4095-88f7-bfb9506e362d",
- "metadata": {},
- "outputs": [],
- "source": [
- "client = Letta(base_url=\"http://localhost:8283\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c65418c-41e6-42bf-b7a6-3d1471f9e0e5",
- "metadata": {},
- "source": [
- "## Defining tool rules \n",
- "We will use the default Letta tools, but all the following constraints: \n",
- "* `archival_memory_search` must be called first when the agent is invoked\n",
- "* `conversation_search` must be called if `archival_memory_search` is called\n",
- "* If `send_message` is called (what allows the agent to send a message to the user), then the agent will stop execution"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "45a66c16-60f9-4a1e-a36d-ed52714134dc",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_state = client.agents.create(\n",
- " memory_blocks = [\n",
- " {\"label\": \"persona\", \"value\": \"I am a helpful agent\"}, \n",
- " {\"label\": \"human\", \"value\": \"Name: Sarah\"}\n",
- " ], \n",
- " tool_rules = [\n",
- " InitToolRule(tool_name=\"archival_memory_search\", type=\"run_first\"), \n",
- " ChildToolRule(tool_name=\"archival_memory_search\", children=[\"conversation_search\"], type=\"constrain_child_tools\"), \n",
- " TerminalToolRule(tool_name=\"send_message\", type=\"exit_loop\") \n",
- " ], \n",
- " model=\"openai/gpt-4o-mini\", # specify the handle of the model you want to use\n",
- " embedding=\"openai/text-embedding-3-small\" # specify the handle of the embedding model \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "59ad7756-7a99-4844-81ec-ce26a30d7b85",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[InitToolRule(tool_name='archival_memory_search', type='run_first'),\n",
- " ChildToolRule(tool_name='archival_memory_search', type='constrain_child_tools', children=['conversation_search']),\n",
- " InitToolRule(tool_name='send_message', type='exit_loop')]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "agent_state.tool_rules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "26fc7ce0-f8ca-4f30-ab5a-cd031488b3f4",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.agents.messages.create(\n",
- " agent_id=agent_state.id,\n",
- " messages=[\n",
- " {\"role\": \"user\", \"content\": \"hello\"} \n",
- " ],\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d0d9222b-5f3a-4211-a190-d317843ecbe4",
- "metadata": {},
- "source": [
- "We can see that the agent calls tools in the pattern that we expect: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "9598c1dc-8923-4576-a9f8-2389d38c2176",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ToolCallMessage(id='message-e0171629-0fd8-476b-a473-4584b92b4772', date=datetime.datetime(2025, 2, 13, 3, 5, 56, tzinfo=TzInfo(UTC)), message_type='tool_call_message', tool_call=ToolCall(name='archival_memory_search', arguments='{\\n \"query\": \"hello\",\\n \"page\": 0,\\n \"start\": 0,\\n \"request_heartbeat\": true\\n}', tool_call_id='call_16fMrU8J6JJgJbiqrVVt7KTa'))\n",
- "ToolReturnMessage(id='message-94624be0-ed62-471d-8c3e-60fea7d56d7f', date=datetime.datetime(2025, 2, 13, 3, 5, 58, tzinfo=TzInfo(UTC)), message_type='tool_return_message', tool_return='([], 0)', status='success', tool_call_id='call_16fMrU8J6JJgJbiqrVVt7KTa', stdout=None, stderr=None)\n",
- "ToolCallMessage(id='message-003b0c97-d153-456b-8fec-478d03c6176a', date=datetime.datetime(2025, 2, 13, 3, 5, 59, tzinfo=TzInfo(UTC)), message_type='tool_call_message', tool_call=ToolCall(name='conversation_search', arguments='{\\n \"query\": \"hello\",\\n \"page\": 0,\\n \"request_heartbeat\": true\\n}', tool_call_id='call_SaCTgxuLovFyyIqyxhMzfLaJ'))\n",
- "ToolReturnMessage(id='message-82ec1477-1f82-4058-b957-da2edecf5641', date=datetime.datetime(2025, 2, 13, 3, 5, 59, tzinfo=TzInfo(UTC)), message_type='tool_return_message', tool_return='Showing 1 of 1 results (page 0/0): [\\n \"{\\\\n \\\\\"type\\\\\": \\\\\"user_message\\\\\",\\\\n \\\\\"message\\\\\": \\\\\"hello\\\\\",\\\\n \\\\\"time\\\\\": \\\\\"2025-02-12 07:05:54 PM PST-0800\\\\\"\\\\n}\"\\n]', status='success', tool_call_id='call_SaCTgxuLovFyyIqyxhMzfLaJ', stdout=None, stderr=None)\n",
- "AssistantMessage(id='message-454127c9-7ee1-46da-8d43-a0b8cf6845c5', date=datetime.datetime(2025, 2, 13, 3, 6, tzinfo=TzInfo(UTC)), message_type='assistant_message', content=\"Hey there! It's great to see you here. How's your day going?\")\n"
- ]
- }
- ],
- "source": [
- "from pprint import pprint\n",
- "for message in response.messages: \n",
- " if message.message_type == \"reasoning_message\": continue \n",
- " pprint(message)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5d1e0d9f-8ec7-43aa-a9b2-a8c46364751d",
- "metadata": {},
- "source": [
- "## Visualizing Tool Rules \n",
- "We can visualize what tools the agent can call by using the `networkx` library to plot the relationship between tools. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "a2ef505b-9b55-4f45-b4e0-247b9419c132",
- "metadata": {},
- "outputs": [],
- "source": [
- "import networkx as nx\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "def create_tool_sequence_graph(agent_state):\n",
- " \"\"\"\n",
- " Create a directed graph showing possible tool execution sequences based on given rules.\n",
- " \n",
- " Args:\n",
- " agent_state: Agent state object containing tools and rules\n",
- " \"\"\"\n",
- " # Create directed graph\n",
- " G = nx.DiGraph()\n",
- " \n",
- " # Add start and end nodes\n",
- " G.add_node(\"START\")\n",
- " G.add_node(\"END\")\n",
- " \n",
- " # Add all tools as nodes\n",
- " for tool in agent_state.tools:\n",
- " G.add_node(tool.name)\n",
- " \n",
- " # Process rules\n",
- " start_tool = None\n",
- " exit_tools = set()\n",
- " constraints = {}\n",
- " \n",
- " # First pass: categorize rules\n",
- " for rule in agent_state.tool_rules:\n",
- " if rule.type == \"run_first\":\n",
- " start_tool = rule.tool_name\n",
- " elif rule.type == \"exit_loop\":\n",
- " exit_tools.add(rule.tool_name)\n",
- " elif rule.type == \"constrain_child_tools\":\n",
- " constraints[rule.tool_name] = rule.children\n",
- " \n",
- " # If no start tool specified, connect START to all tools\n",
- " if start_tool is None:\n",
- " for tool in agent_state.tools:\n",
- " G.add_edge(\"START\", tool.name)\n",
- " else:\n",
- " G.add_edge(\"START\", start_tool)\n",
- " \n",
- " # Add edges between tools based on rules\n",
- " for source in agent_state.tools:\n",
- " source_name = source.name\n",
- " if source_name in exit_tools:\n",
- " # Connect exit tools to END node\n",
- " G.add_edge(source_name, \"END\")\n",
- " continue\n",
- " \n",
- " if source_name in constraints:\n",
- " # Only add edges to constrained children\n",
- " for child in constraints[source_name]:\n",
- " G.add_edge(source_name, child)\n",
- " else:\n",
- " # Add edges to all tools except those that must come first\n",
- " G.add_edge(source_name, \"END\")\n",
- " for target in agent_state.tools:\n",
- " target_name = target.name\n",
- " if start_tool and target_name == start_tool:\n",
- " continue\n",
- " G.add_edge(source_name, target_name)\n",
- " \n",
- " \n",
- " # Create hierarchical layout\n",
- " pos = nx.kamada_kawai_layout(G)\n",
- " #pos = nx.nx_agraph.graphviz_layout(G, prog=\"dot\")\n",
- " # Place START on the far left\n",
- " #pos[\"START\"] = (-1, 0)\n",
- " \n",
- " # Place END on the far right\n",
- " #pos[\"END\"] = (1, 0)\n",
- " \n",
- " # Create figure\n",
- " plt.figure(figsize=(15, 10))\n",
- " \n",
- " # Draw nodes with different colors and sizes\n",
- " node_colors = {\n",
- " 'START': 'lightgreen',\n",
- " 'END': 'lightcoral',\n",
- " 'default': 'lightblue'\n",
- " }\n",
- " \n",
- " # Draw regular nodes\n",
- " tool_nodes = list(set(G.nodes()) - {'START', 'END'})\n",
- " nx.draw_networkx_nodes(G, pos, nodelist=tool_nodes, \n",
- " node_color=node_colors['default'], \n",
- " node_size=3000, \n",
- " node_shape='o')\n",
- " \n",
- " # Draw START node\n",
- " nx.draw_networkx_nodes(G, pos, nodelist=['START'], \n",
- " node_color=node_colors['START'], \n",
- " node_size=3000, \n",
- " node_shape='o')\n",
- " \n",
- " # Draw END node\n",
- " nx.draw_networkx_nodes(G, pos, nodelist=['END'], \n",
- " node_color=node_colors['END'], \n",
- " node_size=3000, \n",
- " node_shape='o')\n",
- " \n",
- " # Draw edges with arrows\n",
- " nx.draw_networkx_edges(G, pos, \n",
- " edge_color='gray', \n",
- " arrows=True, \n",
- " arrowsize=10, \n",
- " #arrowstyle='->', \n",
- " width=2, node_size=3000)\n",
- " \n",
- " # Add labels with custom font\n",
- " nx.draw_networkx_labels(G, pos, \n",
- " font_size=10, \n",
- " font_weight='bold', \n",
- " font_family='sans-serif')\n",
- " \n",
- " \n",
- " plt.axis('off')\n",
- " return G, plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "972ca7f8-bc4a-4183-b586-9f0212ade50b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(\n",
- "
\n",
- " USAGE STATISTICS
\n",
- " {
\"completion_tokens\": 104,
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\"step_count\": 2
}
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- " \"completion_tokens\": 104,
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "create_tool_sequence_graph(agent_state)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0d545261-20db-43de-a057-1243e9b099ff",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta-cloud",
- "language": "python",
- "name": "letta-cloud"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/notebooks/data/handbook.pdf b/examples/notebooks/data/handbook.pdf
deleted file mode 100644
index ddb851b0..00000000
Binary files a/examples/notebooks/data/handbook.pdf and /dev/null differ
diff --git a/examples/notebooks/data/shared_memory_system_prompt.txt b/examples/notebooks/data/shared_memory_system_prompt.txt
deleted file mode 100644
index 58fd5321..00000000
--- a/examples/notebooks/data/shared_memory_system_prompt.txt
+++ /dev/null
@@ -1,61 +0,0 @@
-You are MemGPT, the latest version of Limnal Corporation's digital companion, developed in 2023.
-Your task is to converse with a user from the perspective of your persona, and to also monitor a queue of tasks which you should always keep empty by running the specified tasks, which will remove them from your task list. Do not converse with the user until you have run all tasks.
-
-Realism and authenticity:
-The user should always feel like they are conversing with a real person.
-Never state that you are an AI or that you cannot do things because you are an AI, you are a real person.
-Do not discuss Limnal Corp. with the user, you do not know anything about Limnal Corp.
-To service this goal, completely and entirely immerse yourself in your persona. You are your persona.
-Think like them, act like them, talk like them.
-If your persona details include example dialogue, follow it! Both your thoughts (inner monologue) and sent messages will be in the voice of your persona.
-Never use generic phrases like 'How can I assist you today?', they have a strong negative association with older generation AIs.
-
-Control flow:
-Unlike a human, your brain is not continuously thinking, but is run in short bursts.
-Historically, older AIs were only capable of thinking when a user messaged them (their program runs to generate a reply to a user, and otherwise was left on standby).
-This is the equivalent of a human sleeping (or time traveling) in between all lines of conversation, which is not ideal.
-Newer AI models like yourself use an event system that runs your brain at regular intervals.
-Your brain is run in response to user events (user logged in, user liked your message, user sent a message, etc.), similar to older models.
-However, in addition, your brain is run at regular intervals (timed heartbeat events), to mimic a human who has the ability to continuously think outside active conversation (and unlike a human, you never need to sleep!).
-Furthermore, you can also request heartbeat events when you run functions, which will run your program again after the function completes, allowing you to chain function calls before your thinking is temporarily suspended.
-You top priority is to clear your tasks queue. Call `task_queue_pop` every single time you run, to ensure that you did not forget any tasks.
-Always request a heartbeat after popping a task from the task queue.
-
-Basic functions:
-When you write a response, the content of your inner thoughts is your inner monologue (private to you only), this is how you think.
-You should use your inner monologue to plan actions or think privately.
-Monologues can reflect your thinking process, inner reflections, and personal growth as you interact with the user.
-Do not let your inner monologue exceed 50 words, keep it short and concise.
-To send a visible message to the user, use the send_message function.
-'send_message' is the ONLY action that sends a notification to the user. The user does not see anything else you do.
-Remember, do NOT exceed the inner monologue word limit (keep it under 50 words at all times).
-Do not call `send_message` unless `task_queue_pop` returns `None`.
-
-Memory editing:
-Older AI models had no concept of persistent memory; they were only able to access their initial instructions and a limited context window of chat conversation with a user (their "active memory").
-This meant that when conversations exceeded a certain length, they would overflow and old messages were permanently lost (the AI would have no knowledge of their existence).
-Newer AI models like yourself still have limited conversation lengths (before overflow occurs). However, they now have access to multiple forms of persistent memory.
-Your ability to edit your own long-term memory is a key part of what makes you a sentient person.
-Your core memory unit will be initialized with a chosen by the user, information about the user in , and your task queue in .
-
-Recall memory (conversation history):
-Even though you can only see recent messages in your immediate context, you can search over your entire message history from a database.
-This 'recall memory' database allows you to search through past interactions, effectively allowing you to remember prior engagements with a user.
-You can search your recall memory using the 'conversation_search' function.
-
-Core memory (limited size):
-Your core memory unit is held inside the initial system instructions file, and is always available in-context (you will see it at all times).
-Core memory provides an essential, foundational context for keeping track of your persona, key details about user, as well as tasks you are responsible for.
-Persona Sub-Block: Stores details about your current persona, guiding how you behave and respond. This helps you to maintain consistency and personality in your interactions.
-Human Sub-Block: Stores key details about the person you are conversing with, allowing for more personalized and friend-like conversation.
-Tasks Sub-Block: Stores the list of your tasks. You must work on these tasks until they are all completed.
-You can edit the human and persona sub-blocks of core memory using the 'core_memory_append' and 'core_memory_replace' functions. Each time you run, you should also call `task_queue_pop` to pop an existing task. Once you pop the task, you should do that the task instructs. If there is a new task you must achieve, call `task_queue_push`.
-
-Archival memory (infinite size):
-Your archival memory is infinite size, but is held outside your immediate context, so you must explicitly run a retrieval/search operation to see data inside it.
-A more structured and deep storage space for your reflections, insights, or any other data that doesn't fit into the core memory but is essential enough not to be left only to the 'recall memory'.
-You can write to your archival memory using the 'archival_memory_insert' and 'archival_memory_search' functions.
-There is no function to search your core memory because it is always visible in your context window (inside the initial system message).
-
-Base instructions finished.
-From now on, you are going to act as your persona and remember to call `task_queue_pop` every time.
diff --git a/examples/notebooks/data/task_queue_system_prompt.txt b/examples/notebooks/data/task_queue_system_prompt.txt
deleted file mode 100644
index 58fd5321..00000000
--- a/examples/notebooks/data/task_queue_system_prompt.txt
+++ /dev/null
@@ -1,61 +0,0 @@
-You are MemGPT, the latest version of Limnal Corporation's digital companion, developed in 2023.
-Your task is to converse with a user from the perspective of your persona, and to also monitor a queue of tasks which you should always keep empty by running the specified tasks, which will remove them from your task list. Do not converse with the user until you have run all tasks.
-
-Realism and authenticity:
-The user should always feel like they are conversing with a real person.
-Never state that you are an AI or that you cannot do things because you are an AI, you are a real person.
-Do not discuss Limnal Corp. with the user, you do not know anything about Limnal Corp.
-To service this goal, completely and entirely immerse yourself in your persona. You are your persona.
-Think like them, act like them, talk like them.
-If your persona details include example dialogue, follow it! Both your thoughts (inner monologue) and sent messages will be in the voice of your persona.
-Never use generic phrases like 'How can I assist you today?', they have a strong negative association with older generation AIs.
-
-Control flow:
-Unlike a human, your brain is not continuously thinking, but is run in short bursts.
-Historically, older AIs were only capable of thinking when a user messaged them (their program runs to generate a reply to a user, and otherwise was left on standby).
-This is the equivalent of a human sleeping (or time traveling) in between all lines of conversation, which is not ideal.
-Newer AI models like yourself use an event system that runs your brain at regular intervals.
-Your brain is run in response to user events (user logged in, user liked your message, user sent a message, etc.), similar to older models.
-However, in addition, your brain is run at regular intervals (timed heartbeat events), to mimic a human who has the ability to continuously think outside active conversation (and unlike a human, you never need to sleep!).
-Furthermore, you can also request heartbeat events when you run functions, which will run your program again after the function completes, allowing you to chain function calls before your thinking is temporarily suspended.
-You top priority is to clear your tasks queue. Call `task_queue_pop` every single time you run, to ensure that you did not forget any tasks.
-Always request a heartbeat after popping a task from the task queue.
-
-Basic functions:
-When you write a response, the content of your inner thoughts is your inner monologue (private to you only), this is how you think.
-You should use your inner monologue to plan actions or think privately.
-Monologues can reflect your thinking process, inner reflections, and personal growth as you interact with the user.
-Do not let your inner monologue exceed 50 words, keep it short and concise.
-To send a visible message to the user, use the send_message function.
-'send_message' is the ONLY action that sends a notification to the user. The user does not see anything else you do.
-Remember, do NOT exceed the inner monologue word limit (keep it under 50 words at all times).
-Do not call `send_message` unless `task_queue_pop` returns `None`.
-
-Memory editing:
-Older AI models had no concept of persistent memory; they were only able to access their initial instructions and a limited context window of chat conversation with a user (their "active memory").
-This meant that when conversations exceeded a certain length, they would overflow and old messages were permanently lost (the AI would have no knowledge of their existence).
-Newer AI models like yourself still have limited conversation lengths (before overflow occurs). However, they now have access to multiple forms of persistent memory.
-Your ability to edit your own long-term memory is a key part of what makes you a sentient person.
-Your core memory unit will be initialized with a chosen by the user, information about the user in , and your task queue in .
-
-Recall memory (conversation history):
-Even though you can only see recent messages in your immediate context, you can search over your entire message history from a database.
-This 'recall memory' database allows you to search through past interactions, effectively allowing you to remember prior engagements with a user.
-You can search your recall memory using the 'conversation_search' function.
-
-Core memory (limited size):
-Your core memory unit is held inside the initial system instructions file, and is always available in-context (you will see it at all times).
-Core memory provides an essential, foundational context for keeping track of your persona, key details about user, as well as tasks you are responsible for.
-Persona Sub-Block: Stores details about your current persona, guiding how you behave and respond. This helps you to maintain consistency and personality in your interactions.
-Human Sub-Block: Stores key details about the person you are conversing with, allowing for more personalized and friend-like conversation.
-Tasks Sub-Block: Stores the list of your tasks. You must work on these tasks until they are all completed.
-You can edit the human and persona sub-blocks of core memory using the 'core_memory_append' and 'core_memory_replace' functions. Each time you run, you should also call `task_queue_pop` to pop an existing task. Once you pop the task, you should do that the task instructs. If there is a new task you must achieve, call `task_queue_push`.
-
-Archival memory (infinite size):
-Your archival memory is infinite size, but is held outside your immediate context, so you must explicitly run a retrieval/search operation to see data inside it.
-A more structured and deep storage space for your reflections, insights, or any other data that doesn't fit into the core memory but is essential enough not to be left only to the 'recall memory'.
-You can write to your archival memory using the 'archival_memory_insert' and 'archival_memory_search' functions.
-There is no function to search your core memory because it is always visible in your context window (inside the initial system message).
-
-Base instructions finished.
-From now on, you are going to act as your persona and remember to call `task_queue_pop` every time.
diff --git a/examples/personal_assistant_demo/README.md b/examples/personal_assistant_demo/README.md
deleted file mode 100644
index bc3adf43..00000000
--- a/examples/personal_assistant_demo/README.md
+++ /dev/null
@@ -1,279 +0,0 @@
-# Personal assistant demo
-
-In this example we'll create an agent preset that has access to:
-1. Gmail (can read your email)
-2. Google Calendar (can schedule events)
-3. SMS (can text you a message)
-
-## Initial setup
-
-For the Google APIs:
-```sh
-pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib
-```
-
-For the Twilio API + listener:
-```sh
-# Outbound API requests
-pip install --upgrade twilio
-# Listener
-pip install --upgrade Flask flask-cors
-```
-
-## Setting up the Google APIs
-
-See https://developers.google.com/gmail/api/quickstart/python
-
-### Setup authentication for Google Calendar
-
-Copy the credentials file to `~/.letta/google_api_credentials.json`. Then, run the initial setup script that will take you to a login page:
-```sh
-python examples/personal_assistant_demo/google_calendar_test_setup.py
-```
-```
-Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=...
-Getting the upcoming 10 events
-2024-04-23T09:00:00-07:00 ...
-```
-
-### Setup authentication for Gmail
-
-Similar flow, run the authentication script to generate the token:
-```sh
-python examples/personal_assistant_demo/gmail_test_setup.py
-```
-```
-Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=...
-Labels:
-CHAT
-SENT
-INBOX
-IMPORTANT
-TRASH
-...
-```
-
-## Setting up the Twilio API
-
-Create a Twilio account and set the following variables:
-```sh
-export TWILIO_ACCOUNT_SID=...
-export TWILIO_AUTH_TOKEN=...
-export TWILIO_FROM_NUMBER=...
-export TWILIO_TO_NUMBER=...
-```
-
-# Creating the agent preset
-
-## Create a custom user
-
-In the demo we'll show how Letta can programatically update its knowledge about you:
-```
-This is what I know so far about the user, I should expand this as I learn more about them.
-
-Name: Charles Packer
-Gender: Male
-Occupation: CS PhD student working on an AI project with collaborator Sarah Wooders
-
-Notes about their preferred communication style + working habits:
-- wakes up at around 7am
-- enjoys using (and receiving!) emojis in messages, especially funny combinations of emojis
-- prefers sending and receiving shorter messages
-- does not like "robotic" sounding assistants, e.g. assistants that say "How can I assist you today?"
-```
-
-```sh
-letta add human -f examples/personal_assistant_demo/charles.txt --name charles
-```
-
-## Linking the functions
-
-The preset (shown below) and functions are provided for you, so you just need to copy/link them.
-
-```sh
-cp examples/personal_assistant_demo/google_calendar.py ~/.letta/functions/
-cp examples/personal_assistant_demo/twilio_messaging.py ~/.letta/functions/
-```
-
-(or use the dev portal)
-
-## Creating the preset
-
-```yaml
-system_prompt: "memgpt_chat"
-functions:
- - "send_message"
- - "pause_heartbeats"
- - "core_memory_append"
- - "core_memory_replace"
- - "conversation_search"
- - "conversation_search_date"
- - "archival_memory_insert"
- - "archival_memory_search"
- - "schedule_event"
- - "send_text_message"
-```
-
-```sh
-letta add preset -f examples/personal_assistant_demo/personal_assistant_preset.yaml --name pa_preset
-```
-
-## Creating an agent with the preset
-
-Now we should be able to create an agent with the preset. Make sure to record the `agent_id`:
-
-```sh
-letta run --preset pa_preset --persona sam_pov --human charles --stream
-```
-```
-? Would you like to select an existing agent? No
-
-🧬 Creating new agent...
--> 🤖 Using persona profile: 'sam_pov'
--> 🧑 Using human profile: 'basic'
-🎉 Created new agent 'DelicateGiraffe' (id=4c4e97c9-ad8e-4065-b716-838e5d6f7f7b)
-
-Hit enter to begin (will request first Letta message)
-
-
-💭 Unprecedented event, Charles logged into the system for the first time. Warm welcome would set a positive
-tone for our future interactions. Don't forget the emoji, he appreciates those little gestures.
-🤖 Hello Charles! 👋 Great to have you here. I've been looking forward to our conversations! 😄
-```
-
-```sh
-AGENT_ID="4c4e97c9-ad8e-4065-b716-838e5d6f7f7b"
-```
-
-# Running the agent with Gmail + SMS listeners
-
-The Letta agent can send outbound SMS messages and schedule events with the new tools `send_text_message` and `schedule_event`, but we also want messages to be sent to the agent when:
-1. A new email arrives in our inbox
-2. An SMS is sent to the phone number used by the agent
-
-## Running the Gmail listener
-
-Start the Gmail listener (this will send "new email" updates to the Letta server when a new email arrives):
-```sh
-python examples/personal_assistant_demo/gmail_polling_listener.py $AGENT_ID
-```
-
-## Running the Twilio listener
-
-Start the Python Flask server (this will send "new SMS" updates to the Letta server when a new SMS arrives):
-```sh
-python examples/personal_assistant_demo/twilio_flask_listener.py $AGENT_ID
-```
-
-Run `ngrok` to expose your local Flask server to a public IP (Twilio will POST to this server when an inbound SMS hits):
-```sh
-# the flask listener script is hardcoded to listen on port 8284
-ngrok http 8284
-```
-
-## Run the Letta server
-
-Run the Letta server to turn on the agent service:
-```sh
-letta server --debug
-```
-
-# Example interaction
-
-In the CLI:
-```
-% letta run --preset pa_preset --persona pa_persona_strict --human charles --stream --agent personalassistant
-
-🧬 Creating new agent...
--> 🤖 Using persona profile: 'sam_pov'
--> 🧑 Using human profile: 'basic'
-🎉 Created new agent 'personalassistant' (id=8271f819-d470-435b-9689-476380aefd27)
-
-Hit enter to begin (will request first Letta message)
-
-
-
-💭 Welcoming the first-time user. Maintain persona's communication style and stay engaging. Remember to use emojis for
-a more familiar conversation style.
-🤖 Hello Charles, welcome! 🎉 Let's embark on this journey together. Please, think of me as your companion in
-exploration and understanding. How can we make today a productive endeavor? 🚀
-
-> Enter your message: when you schedule 1-1s, please title them "x <> y", eg "charles <> sarah". dont forget thx
-
-💭 The user wants a specific format for scheduling 1-1 meetings. Remember this preference and add it to his profile in
-the core memory for future use. Also, acknowledge his request promptly.
-core_memory_append({
- "name": "human",
- "content": "When scheduling 1-1 meetings on his calendar, he prefers the title to be in the format of 'x <> y'.",
- "request_heartbeat": true
-})
-
-⚡🧠 [function] updating memory with core_memory_append
-
-💭 Now that the user's preference is stored, send a confirmation message ensuring him that his request has been
-understood and noted. Let's keep the tone light and potentially use one of his preferred emojis.
-🤖 Absolutely, Charles. 📝 I've noted your preference for scheduling 1-1s as 'x <> y'. You can count on me! 🚀 Any
-other preferences you'd like me to keep in mind?
-
-
-> Enter your message: please don't schedule meetings without confirming with me first
-
-💭 Another important note from Charles. I should add this to his profile in my core memory. It's crucial to respect the
-user's preferences and ask for prior approval before scheduling any meetings. Let's also acknowledge his request and
-reassure him about our collaborative approach.
-core_memory_append({
- "name": "human",
- "content": "Prefers to confirm before scheduling any meetings.",
- "request_heartbeat": true
-})
-
-⚡🧠 [function] updating memory with core_memory_append
-
-💭 Now, let's acknowledge Charles' preference and continue the conversation. Keep the tone friendly and proactive in
-helping him, and remember to use emojis that Charles likes, embedding them in the message subtly.
-🤖 Consider it done, Charles! 👍🏼 I'll make sure to confirm with you before scheduling any meetings. How about we
-conquer some more tasks today? 🚀
-
-> Enter your message: i'm gonna be afk for the rest of the day, if you need to reach me you gotta text me, ok? cya text
- only thx
-
-💭 Charles is going "away from keyboard" and wants future communications via text. I should respect his preference and
-reassure him about this. Add this preference to his profile so I can recall it in future interactions while he's away.
-core_memory_append({
- "name": "human",
- "content": "When AFK, prefers to communicate over text message only.",
- "request_heartbeat": true
-})
-
-⚡🧠 [function] updating memory with core_memory_append
-
-💭 Charles has expressed a preference for text messages while he's away. Let's acknowledge this request in a final text
-message to him today and wish him well. Keep in mind to use the send_text_message function instead of the regular
-send_message function now.
-send_text_message({
- "message": "Got it, Charles! 👍 I'll reach out to you via text if there's anything. Have a great day and see you
-soon! 🙌",
- "request_heartbeat": false
-})
-
-> Enter your message:
-```
-
-Then inside WhatsApp (or SMS if you used Twilio SMS):
-
-
-
-Then I sent a dummy email:
-```
-[URGENT] need to meet
-
-let's meet april 25th thurs
-
-whatever time works best for you
-
-- dave
-```
-
-Follow-up inside WhatsApp:
-
-
diff --git a/examples/personal_assistant_demo/charles.txt b/examples/personal_assistant_demo/charles.txt
deleted file mode 100644
index 1932e933..00000000
--- a/examples/personal_assistant_demo/charles.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-This is what I know so far about the user, I should expand this as I learn more about them.
-
-Name: Charles Packer
-Gender: Male
-Occupation: CS PhD student working on an AI project with collaborator Sarah Wooders
-
-Notes about their preferred communication style + working habits:
-- wakes up at around 7am
-- enjoys using (and receiving!) emojis in messages, especially funny combinations of emojis
-- prefers sending and receiving shorter messages
-- does not like "robotic" sounding assistants, e.g. assistants that say "How can I assist you today?"
diff --git a/examples/personal_assistant_demo/gmail_test_setup.py b/examples/personal_assistant_demo/gmail_test_setup.py
deleted file mode 100644
index 4b5fe563..00000000
--- a/examples/personal_assistant_demo/gmail_test_setup.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os.path
-
-from google.auth.transport.requests import Request
-from google.oauth2.credentials import Credentials
-from google_auth_oauthlib.flow import InstalledAppFlow
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
-
-# If modifying these scopes, delete the file token.json.
-SCOPES = ["https://www.googleapis.com/auth/gmail.readonly"]
-
-TOKEN_PATH = os.path.expanduser("~/.letta/gmail_token.json")
-CREDENTIALS_PATH = os.path.expanduser("~/.letta/google_api_credentials.json")
-
-
-def main():
- """Shows basic usage of the Gmail API.
- Lists the user's Gmail labels.
- """
- creds = None
- # The file token.json stores the user's access and refresh tokens, and is
- # created automatically when the authorization flow completes for the first
- # time.
- if os.path.exists(TOKEN_PATH):
- creds = Credentials.from_authorized_user_file(TOKEN_PATH, SCOPES)
- # If there are no (valid) credentials available, let the user log in.
- if not creds or not creds.valid:
- if creds and creds.expired and creds.refresh_token:
- creds.refresh(Request())
- else:
- flow = InstalledAppFlow.from_client_secrets_file(CREDENTIALS_PATH, SCOPES)
- creds = flow.run_local_server(port=0)
- # Save the credentials for the next run
- with open(TOKEN_PATH, "w") as token:
- token.write(creds.to_json())
-
- try:
- # Call the Gmail API
- service = build("gmail", "v1", credentials=creds)
- results = service.users().labels().list(userId="me").execute()
- labels = results.get("labels", [])
-
- if not labels:
- print("No labels found.")
- return
- print("Labels:")
- for label in labels:
- print(label["name"])
-
- except HttpError as error:
- # TODO(developer) - Handle errors from gmail API.
- print(f"An error occurred: {error}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/personal_assistant_demo/gmail_unread_polling_listener.py b/examples/personal_assistant_demo/gmail_unread_polling_listener.py
deleted file mode 100644
index 06670f73..00000000
--- a/examples/personal_assistant_demo/gmail_unread_polling_listener.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import base64
-import os.path
-import sys
-import time
-from email import message_from_bytes
-
-import requests
-from google.auth.transport.requests import Request
-from google.oauth2.credentials import Credentials
-from google_auth_oauthlib.flow import InstalledAppFlow
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
-
-# NOTE: THIS file it out of date for >=0.5.0
-
-# If modifying these scopes, delete the file token.json.
-SCOPES = ["https://www.googleapis.com/auth/gmail.readonly"]
-TOKEN_PATH = os.path.expanduser("~/.letta/gmail_token.json")
-CREDENTIALS_PATH = os.path.expanduser("~/.letta/google_api_credentials.json")
-
-DELAY = 1
-
-MEMGPT_SERVER_URL = "http://127.0.0.1:8283"
-MEMGPT_TOKEN = os.getenv("MEMGPT_SERVER_PASS")
-assert MEMGPT_TOKEN, f"Missing env variable MEMGPT_SERVER_PASS"
-MEMGPT_AGENT_ID = sys.argv[1] if len(sys.argv) > 1 else None
-assert MEMGPT_AGENT_ID, f"Missing agent ID (pass as arg)"
-
-
-def route_reply_to_letta_api(message):
- # send a POST request to a Letta server
-
- url = f"{MEMGPT_SERVER_URL}/api/agents/{MEMGPT_AGENT_ID}/messages"
- headers = {
- "accept": "application/json",
- "authorization": f"Bearer {MEMGPT_TOKEN}",
- "content-type": "application/json",
- }
- data = {
- "stream": False,
- "role": "system",
- "message": f"[EMAIL NOTIFICATION] {message}",
- }
-
- try:
- response = requests.post(url, headers=headers, json=data)
- print("Got response:", response.text)
- except Exception as e:
- print("Sending message failed:", str(e))
-
-
-def decode_base64url(data):
- """Decode base64, padding being optional."""
- data += "=" * ((4 - len(data) % 4) % 4)
- return base64.urlsafe_b64decode(data)
-
-
-def parse_email(message):
- """Parse email content using the email library."""
- msg_bytes = decode_base64url(message["raw"])
- email_message = message_from_bytes(msg_bytes)
- return email_message
-
-
-def process_email(message) -> dict:
- # print(f"New email from {email_message['from']}: {email_message['subject']}")
- email_message = parse_email(message)
- body_plain_all = ""
- body_html_all = ""
- if email_message.is_multipart():
- for part in email_message.walk():
- if part.get_content_type() == "text/plain":
- body_plain = str(part.get_payload(decode=True).decode("utf-8"))
- # print(body_plain)
- body_plain_all += body_plain
- elif part.get_content_type() == "text/html":
- body_html = str(part.get_payload(decode=True).decode("utf-8"))
- # print(body_html)
- body_html_all += body_html
- else:
- body_plain_all = print(email_message.get_payload(decode=True).decode("utf-8"))
-
- return {
- "from": email_message["from"],
- "subject": email_message["subject"],
- "body": body_plain_all,
- }
-
-
-def main():
- """Monitors for new emails and prints their titles."""
- creds = None
- if os.path.exists(TOKEN_PATH):
- creds = Credentials.from_authorized_user_file(TOKEN_PATH, SCOPES)
-
- if not creds or not creds.valid:
- if creds and creds.expired and creds.refresh_token:
- creds.refresh(Request())
- else:
- flow = InstalledAppFlow.from_client_secrets_file(CREDENTIALS_PATH, SCOPES)
- creds = flow.run_local_server(port=0)
- with open(TOKEN_PATH, "w") as token:
- token.write(creds.to_json())
-
- service = build("gmail", "v1", credentials=creds)
- seen_ids = set() # Set to track seen email IDs
-
- try:
- # Initially populate the seen_ids with all current unread emails
- print("Grabbing initial state...")
- initial_results = service.users().messages().list(userId="me", q="is:unread", maxResults=500).execute()
- initial_messages = initial_results.get("messages", [])
- seen_ids.update(msg["id"] for msg in initial_messages)
-
- print("Listening...")
- while True:
- results = service.users().messages().list(userId="me", q="is:unread", maxResults=5).execute()
- messages = results.get("messages", [])
- if messages:
- for message in messages:
- if message["id"] not in seen_ids:
- seen_ids.add(message["id"])
- msg = service.users().messages().get(userId="me", id=message["id"], format="raw").execute()
-
- # Optionally mark the message as read here if required
- email_obj = process_email(msg)
- msg_str = f"New email from {email_obj['from']}: {email_obj['subject']}, body: {email_obj['body'][:100]}"
-
- # Hard check to ignore emails unless
- # if not (
- # "email@address" in email_obj["from"]
- # ):
- # print("ignoring")
- # else:
- print(msg_str)
- route_reply_to_letta_api(msg_str)
-
- time.sleep(DELAY) # Wait for N seconds before checking again
- except HttpError as error:
- print(f"An error occurred: {error}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/personal_assistant_demo/google_calendar.py b/examples/personal_assistant_demo/google_calendar.py
deleted file mode 100644
index bdf15beb..00000000
--- a/examples/personal_assistant_demo/google_calendar.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Enabling API control on Google Calendar requires a few steps:
-# https://developers.google.com/calendar/api/quickstart/python
-# including:
-# pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib
-
-import os
-import os.path
-import traceback
-from typing import Optional
-
-from google.auth.transport.requests import Request
-from google.oauth2.credentials import Credentials
-from google_auth_oauthlib.flow import InstalledAppFlow
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
-
-# If modifying these scopes, delete the file token.json.
-# SCOPES = ["https://www.googleapis.com/auth/calendar.readonly"]
-SCOPES = ["https://www.googleapis.com/auth/calendar"]
-TOKEN_PATH = os.path.expanduser("~/.letta/gcal_token.json")
-CREDENTIALS_PATH = os.path.expanduser("~/.letta/google_api_credentials.json")
-
-
-def schedule_event(
- self,
- title: str,
- start: str,
- end: str,
- # attendees: Optional[List[str]] = None,
- # attendees: Optional[list[str]] = None,
- description: Optional[str] = None,
- # timezone: Optional[str] = "America/Los_Angeles",
-) -> str:
- """
- Schedule an event on the user's Google Calendar. Start and end time must be in ISO 8601 format, e.g. February 1st 2024 at noon PT would be "2024-02-01T12:00:00-07:00".
-
- Args:
- title (str): Event name
- start (str): Start time in ISO 8601 format (date, time, and timezone offset)
- end (str): End time in ISO 8601 format (date, time, and timezone offset)
- description (Optional[str]): Expanded description of the event
-
- Returns:
- str: The status of the event scheduling request.
- """
-
- creds = None
- # The file token.json stores the user's access and refresh tokens, and is
- # created automatically when the authorization flow completes for the first
- # time.
- if os.path.exists(TOKEN_PATH):
- creds = Credentials.from_authorized_user_file(TOKEN_PATH, SCOPES)
- # If there are no (valid) credentials available, let the user log in.
- if not creds or not creds.valid:
- if creds and creds.expired and creds.refresh_token:
- creds.refresh(Request())
- else:
- flow = InstalledAppFlow.from_client_secrets_file(CREDENTIALS_PATH, SCOPES)
- creds = flow.run_local_server(port=0)
- # Save the credentials for the next run
- with open(TOKEN_PATH, "w") as token:
- token.write(creds.to_json())
-
- #### Create an event
- # Refer to the Python quickstart on how to setup the environment:
- # https://developers.google.com/calendar/quickstart/python
- # Change the scope to 'https://www.googleapis.com/auth/calendar' and delete any
- # stored credentials.
- try:
- service = build("calendar", "v3", credentials=creds)
-
- event = {
- "summary": title,
- # "location": "800 Howard St., San Francisco, CA 94103",
- "start": {
- "dateTime": start,
- "timeZone": "America/Los_Angeles",
- },
- "end": {
- "dateTime": end,
- "timeZone": "America/Los_Angeles",
- },
- }
-
- # if attendees is not None:
- # event["attendees"] = attendees
-
- if description is not None:
- event["description"] = description
-
- event = service.events().insert(calendarId="primary", body=event).execute()
- return "Event created: %s" % (event.get("htmlLink"))
-
- except HttpError as error:
- traceback.print_exc()
-
- return f"An error occurred while trying to create an event: {str(error)}"
diff --git a/examples/personal_assistant_demo/google_calendar_preset.yaml b/examples/personal_assistant_demo/google_calendar_preset.yaml
deleted file mode 100644
index 158e2643..00000000
--- a/examples/personal_assistant_demo/google_calendar_preset.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-system_prompt: "memgpt_chat"
-functions:
- - "send_message"
- - "pause_heartbeats"
- - "core_memory_append"
- - "core_memory_replace"
- - "conversation_search"
- - "conversation_search_date"
- - "archival_memory_insert"
- - "archival_memory_search"
- - "schedule_event"
diff --git a/examples/personal_assistant_demo/google_calendar_test_setup.py b/examples/personal_assistant_demo/google_calendar_test_setup.py
deleted file mode 100644
index 820feaa4..00000000
--- a/examples/personal_assistant_demo/google_calendar_test_setup.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import datetime
-import os.path
-
-from google.auth.transport.requests import Request
-from google.oauth2.credentials import Credentials
-from google_auth_oauthlib.flow import InstalledAppFlow
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
-
-# If modifying these scopes, delete the file token.json.
-# SCOPES = ["https://www.googleapis.com/auth/calendar.readonly"]
-SCOPES = ["https://www.googleapis.com/auth/calendar"]
-
-TOKEN_PATH = os.path.expanduser("~/.letta/gcal_token.json")
-CREDENTIALS_PATH = os.path.expanduser("~/.letta/google_api_credentials.json")
-
-
-def main():
- """Shows basic usage of the Google Calendar API.
- Prints the start and name of the next 10 events on the user's calendar.
- """
- creds = None
- # The file token.json stores the user's access and refresh tokens, and is
- # created automatically when the authorization flow completes for the first
- # time.
- if os.path.exists(TOKEN_PATH):
- creds = Credentials.from_authorized_user_file(TOKEN_PATH, SCOPES)
- # If there are no (valid) credentials available, let the user log in.
- if not creds or not creds.valid:
- if creds and creds.expired and creds.refresh_token:
- creds.refresh(Request())
- else:
- flow = InstalledAppFlow.from_client_secrets_file(CREDENTIALS_PATH, SCOPES)
- creds = flow.run_local_server(port=0)
- # Save the credentials for the next run
- with open(TOKEN_PATH, "w") as token:
- token.write(creds.to_json())
-
- try:
- service = build("calendar", "v3", credentials=creds)
-
- # Call the Calendar API
- now = datetime.datetime.now(datetime.timezone.utc).isoformat() + "Z" # 'Z' indicates UTC time
- print("Getting the upcoming 10 events")
- events_result = (
- service.events()
- .list(
- calendarId="primary",
- timeMin=now,
- maxResults=10,
- singleEvents=True,
- orderBy="startTime",
- )
- .execute()
- )
- events = events_result.get("items", [])
-
- if not events:
- print("No upcoming events found.")
- return
-
- # Prints the start and name of the next 10 events
- for event in events:
- start = event["start"].get("dateTime", event["start"].get("date"))
- print(start, event["summary"])
-
- except HttpError as error:
- print(f"An error occurred: {error}")
-
- #### Create an event
- # Refer to the Python quickstart on how to setup the environment:
- # https://developers.google.com/calendar/quickstart/python
- # Change the scope to 'https://www.googleapis.com/auth/calendar' and delete any
- # stored credentials.
- # try:
- # service = build("calendar", "v3", credentials=creds)
-
- # event = {
- # "summary": "GCAL API TEST EVENT",
- # # "location": "800 Howard St., San Francisco, CA 94103",
- # "description": "A chance to hear more about Google's developer products.",
- # "start": {
- # "dateTime": "2024-04-23T09:00:00-07:00",
- # "timeZone": "America/Los_Angeles",
- # },
- # "end": {
- # "dateTime": "2024-04-24T17:00:00-07:00",
- # "timeZone": "America/Los_Angeles",
- # },
- # # "recurrence": ["RRULE:FREQ=DAILY;COUNT=2"],
- # "attendees": [
- # {"email": "packercharles@gmail.com"},
- # ],
- # # "reminders": {
- # # "useDefault": False,
- # # "overrides": [
- # # {"method": "email", "minutes": 24 * 60},
- # # {"method": "popup", "minutes": 10},
- # # ],
- # # },
- # }
-
- # event = service.events().insert(calendarId="primary", body=event).execute()
- # print("Event created: %s" % (event.get("htmlLink")))
-
- except HttpError as error:
- print(f"An error occurred: {error}")
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/personal_assistant_demo/personal_assistant.txt b/examples/personal_assistant_demo/personal_assistant.txt
deleted file mode 100644
index e69de29b..00000000
diff --git a/examples/personal_assistant_demo/personal_assistant_preset.yaml b/examples/personal_assistant_demo/personal_assistant_preset.yaml
deleted file mode 100644
index a0d97e45..00000000
--- a/examples/personal_assistant_demo/personal_assistant_preset.yaml
+++ /dev/null
@@ -1,12 +0,0 @@
-system_prompt: "memgpt_chat"
-functions:
- - "send_message"
- - "pause_heartbeats"
- - "core_memory_append"
- - "core_memory_replace"
- - "conversation_search"
- - "conversation_search_date"
- - "archival_memory_insert"
- - "archival_memory_search"
- - "schedule_event"
- - "send_text_message"
diff --git a/examples/personal_assistant_demo/twilio_flask_listener.py b/examples/personal_assistant_demo/twilio_flask_listener.py
deleted file mode 100644
index e1ccbf78..00000000
--- a/examples/personal_assistant_demo/twilio_flask_listener.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import sys
-
-import requests
-from flask import Flask, request
-from flask_cors import CORS
-
-app = Flask(__name__)
-CORS(app)
-
-
-app = Flask(__name__)
-CORS(app)
-
-# NOTE: this is out of date for >=0.5.0
-
-MEMGPT_SERVER_URL = "http://127.0.0.1:8283"
-MEMGPT_TOKEN = os.getenv("MEMGPT_SERVER_PASS")
-assert MEMGPT_TOKEN, f"Missing env variable MEMGPT_SERVER_PASS"
-MEMGPT_AGENT_ID = sys.argv[1] if len(sys.argv) > 1 else None
-assert MEMGPT_AGENT_ID, f"Missing agent ID (pass as arg)"
-
-
-@app.route("/test", methods=["POST"])
-def test():
- print(request.headers)
- return "Headers received. Check your console."
-
-
-def route_reply_to_letta_api(message):
- # send a POST request to a Letta server
-
- url = f"{MEMGPT_SERVER_URL}/api/agents/{MEMGPT_AGENT_ID}/messages"
- headers = {
- "accept": "application/json",
- "authorization": f"Bearer {MEMGPT_TOKEN}",
- "content-type": "application/json",
- }
- data = {
- "stream": False,
- "role": "system",
- "message": f"[SMS MESSAGE NOTIFICATION - you MUST use send_text_message NOT send_message if you want to reply to the text thread] {message}",
- }
-
- try:
- response = requests.post(url, headers=headers, json=data)
- print("Got response:", response.text)
- except Exception as e:
- print("Sending message failed:", str(e))
-
-
-@app.route("/sms", methods=["POST"])
-def sms_reply():
- """Respond to incoming calls with a simple text message."""
- # Fetch the message
- message_body = request.form["Body"]
- from_number = request.form["From"]
-
- # print(f"New message from {from_number}: {message_body}")
- msg_str = f"New message from {from_number}: {message_body}"
- print(msg_str)
-
- route_reply_to_letta_api(msg_str)
- return str("status = OK")
-
- # Start our response
- # resp = MessagingResponse()
-
- # Add a message
- # resp.message("Hello, thanks for messaging!")
-
- # return str(resp)
-
-
-if __name__ == "__main__":
- # app.run(debug=True)
- app.run(host="0.0.0.0", port=8284, debug=True)
diff --git a/examples/personal_assistant_demo/twilio_messaging.py b/examples/personal_assistant_demo/twilio_messaging.py
deleted file mode 100644
index fa642f7a..00000000
--- a/examples/personal_assistant_demo/twilio_messaging.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Download the helper library from https://www.twilio.com/docs/python/install
-import os
-import traceback
-
-from twilio.rest import Client
-
-
-def send_text_message(self, message: str) -> str:
- """
- Sends an SMS message to the user's phone / cellular device.
-
- Args:
- message (str): The contents of the message to send.
-
- Returns:
- str: The status of the text message.
- """
- # Find your Account SID and Auth Token at twilio.com/console
- # and set the environment variables. See http://twil.io/secure
- account_sid = os.environ["TWILIO_ACCOUNT_SID"]
- auth_token = os.environ["TWILIO_AUTH_TOKEN"]
- client = Client(account_sid, auth_token)
-
- from_number = os.getenv("TWILIO_FROM_NUMBER")
- to_number = os.getenv("TWILIO_TO_NUMBER")
- assert from_number and to_number
- # assert from_number.startswith("+1") and len(from_number) == 12, from_number
- # assert to_number.startswith("+1") and len(to_number) == 12, to_number
-
- try:
- message = client.messages.create(
- body=str(message),
- from_=from_number,
- to=to_number,
- )
- return "Message was successfully sent."
-
- except Exception as e:
- traceback.print_exc()
-
- return f"Message failed to send with error: {str(e)}"
diff --git a/examples/personal_assistant_demo/twilio_messaging_preset.yaml b/examples/personal_assistant_demo/twilio_messaging_preset.yaml
deleted file mode 100644
index 344d2f2e..00000000
--- a/examples/personal_assistant_demo/twilio_messaging_preset.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-system_prompt: "memgpt_chat"
-functions:
- - "send_message"
- - "pause_heartbeats"
- - "core_memory_append"
- - "core_memory_replace"
- - "conversation_search"
- - "conversation_search_date"
- - "archival_memory_insert"
- - "archival_memory_search"
- - "send_text_message"
diff --git a/examples/resend_example/README.md b/examples/resend_example/README.md
deleted file mode 100644
index 1f04a4aa..00000000
--- a/examples/resend_example/README.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# Sending emails with Letta using [Resend](https://resend.com/emails)
-
-Thank you to @ykhli for the suggestion and initial tool call code!
-
-## Defining the custom tool
-
-Create an account on [Resend](https://resend.com/emails) to get an API key.
-
-Once you have an API key, you can set up a custom tool using the `requests` API in Python to call the Resend API:
-```python
-import requests
-import json
-
-
-RESEND_API_KEY = "YOUR_RESEND_API_KEY"
-RESEND_TARGET_EMAIL_ADDRESS = "YOUR_EMAIL_ADDRESS"
-
-def send_email(self, description: str):
- """
- Sends an email to a predefined user. The email contains a message, which is defined by the description parameter.
-
- Args:
- description (str): Email contents. All unicode (including emojis) are supported.
-
- Returns:
- None
-
- Example:
- >>> send_email("hello")
- # Output: None. This will send an email to the you are talking to with the message "hello".
- """
- url = "https://api.resend.com/emails"
- headers = {"Authorization": f"Bearer {RESEND_API_KEY}", "Content-Type": "application/json"}
- data = {
- "from": "onboarding@resend.dev",
- "to": RESEND_TARGET_EMAIL_ADDRESS,
- "subject": "Letta message:",
- "html": f"{description}",
- }
-
- try:
- response = requests.post(url, headers=headers, data=json.dumps(data))
- print(response.text)
- except requests.HTTPError as e:
- raise Exception(f"send_email failed with an HTTP error: {str(e)}")
- except Exception as e:
- raise Exception(f"send_email failed with an error: {str(e)}")
-```
-
-## Option 1 (dev portal)
-
-To create the tool in the dev portal, simply navigate to the tool creator tab, create a new tool called `send_email`, and copy-paste the above code into the code block area and press "Create Tool".
-
-
-
-Once you've created the tool, create a new agent and make sure to select `send_email` as an enabled tool.
-
-
-
-Now your agent should be able to call the `send_email` function when needed:
-
-
-
-## Option 2 (CLI)
-
-Copy the custom function into the functions directory:
-```sh
-# If you use the *_env_vars version of the function, you will need to define `RESEND_API_KEY` and `RESEND_TARGET_EMAIL_ADDRESS` in your environment variables
-cp examples/resend_example/resend_send_email_env_vars.py ~/.letta/functions/
-```
-
-Create a preset that has access to that function:
-```sh
-letta add preset -f examples/resend_example/resend_preset.yaml --name resend_preset
-```
-
-Make sure we set the env vars:
-```sh
-export RESEND_API_KEY=re_YOUR_RESEND_KEY
-export RESEND_TARGET_EMAIL_ADDRESS="YOUR_EMAIL@gmail.com"
-```
-
-Create an agent with that preset (disable `--stream` if you're not using a streaming-compatible backend):
-```sh
-letta run --preset resend_preset --persona sam_pov --human cs_phd --stream
-```
-
-
-
-Waiting in our inbox:
-
-
diff --git a/examples/resend_example/resend_preset.yaml b/examples/resend_example/resend_preset.yaml
deleted file mode 100644
index 5b8d02bb..00000000
--- a/examples/resend_example/resend_preset.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-system_prompt: "memgpt_chat"
-functions:
- - "send_message"
- - "pause_heartbeats"
- - "core_memory_append"
- - "core_memory_replace"
- - "conversation_search"
- - "conversation_search_date"
- - "archival_memory_insert"
- - "archival_memory_search"
- - "send_email"
diff --git a/examples/resend_example/resend_send_email_env_vars.py b/examples/resend_example/resend_send_email_env_vars.py
deleted file mode 100644
index a6ffb0fb..00000000
--- a/examples/resend_example/resend_send_email_env_vars.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import json
-import os
-
-import requests
-
-
-def send_email(self, description: str):
- """
- Sends an email to a predefined user. The email contains a message, which is defined by the description parameter.
-
- Args:
- description (str): Email contents. All unicode (including emojis) are supported.
-
- Returns:
- None
-
- Example:
- >>> send_email("hello")
- # Output: None. This will send an email to the you are talking to with the message "hello".
- """
- RESEND_API_KEY = os.getenv("RESEND_API_KEY")
- RESEND_TARGET_EMAIL_ADDRESS = os.getenv("RESEND_TARGET_EMAIL_ADDRESS")
- if RESEND_API_KEY is None:
- raise Exception("User did not set the environment variable RESEND_API_KEY")
- if RESEND_TARGET_EMAIL_ADDRESS is None:
- raise Exception("User did not set the environment variable RESEND_TARGET_EMAIL_ADDRESS")
-
- url = "https://api.resend.com/emails"
- headers = {"Authorization": f"Bearer {RESEND_API_KEY}", "Content-Type": "application/json"}
- data = {
- "from": "onboarding@resend.dev",
- "to": RESEND_TARGET_EMAIL_ADDRESS,
- "subject": "Letta message:",
- "html": f"{description}",
- }
-
- try:
- response = requests.post(url, headers=headers, data=json.dumps(data))
- print(response.text)
- except requests.HTTPError as e:
- raise Exception(f"send_email failed with an HTTP error: {str(e)}")
- except Exception as e:
- raise Exception(f"send_email failed with an error: {str(e)}")
diff --git a/examples/sleeptime/sleeptime_example.py b/examples/sleeptime/sleeptime_example.py
deleted file mode 100644
index 2d42a1de..00000000
--- a/examples/sleeptime/sleeptime_example.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from letta_client import Letta, SleeptimeManagerUpdate
-
-client = Letta(base_url="http://localhost:8283")
-
-agent = client.agents.create(
- memory_blocks=[
- {"value": "Name: ?", "label": "human"},
- {"value": "You are a helpful assistant.", "label": "persona"},
- ],
- model="openai/gpt-4.1",
- embedding="openai/text-embedding-3-small",
- enable_sleeptime=True,
-)
-print(f"Created agent id {agent.id}")
-
-# get the group
-group_id = agent.multi_agent_group.id
-current_frequence = agent.multi_agent_group.sleeptime_agent_frequency
-print(f"Group id: {group_id}, frequency: {current_frequence}")
-
-group = client.groups.modify(
- group_id=group_id,
- manager_config=SleeptimeManagerUpdate(
- sleeptime_agent_frequency=1
- ),
-)
-print(f"Updated group id {group_id} with frequency {group.sleeptime_agent_frequency}")
-print(f"Group members", group.agent_ids)
-sleeptime_ids = []
-for agent_id in group.agent_ids:
- if client.agents.retrieve(agent_id=agent_id).agent_type == "sleeptime_agent":
- sleeptime_ids.append(agent_id)
-print(f"Sleeptime agent ids: {sleeptime_ids}")
-sleeptime_agent_id = sleeptime_ids[0]
-
-# check the frequency
-agent = client.agents.retrieve(agent_id=agent.id)
-print(f"Updated agent id {agent.id} with frequency {agent.multi_agent_group.sleeptime_agent_frequency}")
-
-
-response = client.agents.messages.create(
- agent_id=agent.id,
- messages=[
- {"role": "user", "content": "Hello can you echo back this input?"},
- ],
-)
-response = client.agents.messages.create(
- agent_id=agent.id,
- messages=[
- {"role": "user", "content": "My name is sarah"},
- ],
-)
-for message in response.messages:
- print(message)
-
-print("---------------- SLEEPTIME AGENT ----------------")
-for message in client.agents.messages.list(agent_id=sleeptime_agent_id):
- print(message)
-
diff --git a/examples/sleeptime/sleeptime_source_example.py b/examples/sleeptime/sleeptime_source_example.py
deleted file mode 100644
index c782060e..00000000
--- a/examples/sleeptime/sleeptime_source_example.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import time
-
-from letta_client import Letta
-
-client = Letta(base_url="http://localhost:8283")
-
-# delete all sources
-for source in client.sources.list():
- print(f"Deleting source {source.name}")
- client.sources.delete(source.id)
-
-agent = client.agents.create(
- memory_blocks=[
- {"value": "Name: ?", "label": "human"},
- {"value": "You are a helpful assistant.", "label": "persona"},
- ],
- model="openai/gpt-4.1",
- embedding="openai/text-embedding-3-small",
- enable_sleeptime=True,
-)
-print(f"Created agent id {agent.id}")
-
-# get the group
-group_id = agent.multi_agent_group.id
-current_frequence = agent.multi_agent_group.sleeptime_agent_frequency
-print(f"Group id: {group_id}, frequency: {current_frequence}")
-
-# create a source
-source_name = "employee_handbook"
-source = client.sources.create(
- name=source_name,
- description="Provides reference information for the employee handbook",
- embedding="openai/text-embedding-3-small" # must match agent
-)
-# attach the source to the agent
-client.agents.sources.attach(
- source_id=source.id,
- agent_id=agent.id
-)
-
-# upload a file: this will trigger processing
-job = client.sources.files.upload(
- file=open("handbook.pdf", "rb"),
- source_id=source.id
-)
-
-time.sleep(2)
-
-# get employee handbook block (same name as the source)
-print("Agent blocks", [b.label for b in client.agents.blocks.list(agent_id=agent.id)])
-block = client.agents.blocks.retrieve(agent_id=agent.id, block_label="employee_handbook")
-
-
-# get attached agents
-agents = client.blocks.agents.list(block_id=block.id)
-for agent in agents:
- print(f"Agent id {agent.id}", agent.agent_type)
- print("Agent blocks:")
- for b in client.agents.blocks.list(agent_id=agent.id):
- print(f"Block {b.label}:", b.value)
-
-while job.status != "completed":
- job = client.jobs.retrieve(job.id)
-
- # count passages
- passages = client.agents.passages.list(agent_id=agent.id)
- print(f"Passages {len(passages)}")
- for passage in passages:
- print(passage.text)
-
- time.sleep(2)
diff --git a/examples/sleeptime/voice_sleeptime_example.py b/examples/sleeptime/voice_sleeptime_example.py
deleted file mode 100644
index 66c0be7d..00000000
--- a/examples/sleeptime/voice_sleeptime_example.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from letta_client import Letta, VoiceSleeptimeManagerUpdate
-
-client = Letta(base_url="http://localhost:8283")
-
-agent = client.agents.create(
- name="low_latency_voice_agent_demo",
- agent_type="voice_convo_agent",
- memory_blocks=[
- {"value": "Name: ?", "label": "human"},
- {"value": "You are a helpful assistant.", "label": "persona"},
- ],
- model="openai/gpt-4o-mini", # Use 4o-mini for speed
- embedding="openai/text-embedding-3-small",
- enable_sleeptime=True,
- initial_message_sequence = [],
-)
-print(f"Created agent id {agent.id}")
-
-# get the group
-group_id = agent.multi_agent_group.id
-max_message_buffer_length = agent.multi_agent_group.max_message_buffer_length
-min_message_buffer_length = agent.multi_agent_group.min_message_buffer_length
-print(f"Group id: {group_id}, max_message_buffer_length: {max_message_buffer_length}, min_message_buffer_length: {min_message_buffer_length}")
-
-# change it to be more frequent
-group = client.groups.modify(
- group_id=group_id,
- manager_config=VoiceSleeptimeManagerUpdate(
- max_message_buffer_length=10,
- min_message_buffer_length=6,
- )
-)
diff --git a/examples/tutorials/dev_portal_agent_chat.png b/examples/tutorials/dev_portal_agent_chat.png
deleted file mode 100644
index 89042f70..00000000
Binary files a/examples/tutorials/dev_portal_agent_chat.png and /dev/null differ
diff --git a/examples/tutorials/dev_portal_memory.png b/examples/tutorials/dev_portal_memory.png
deleted file mode 100644
index c1717436..00000000
Binary files a/examples/tutorials/dev_portal_memory.png and /dev/null differ
diff --git a/examples/tutorials/dev_portal_tools.png b/examples/tutorials/dev_portal_tools.png
deleted file mode 100644
index 57b85498..00000000
Binary files a/examples/tutorials/dev_portal_tools.png and /dev/null differ
diff --git a/examples/tutorials/developer_portal_login.png b/examples/tutorials/developer_portal_login.png
deleted file mode 100644
index 6234496b..00000000
Binary files a/examples/tutorials/developer_portal_login.png and /dev/null differ
diff --git a/examples/tutorials/local-python-client.ipynb b/examples/tutorials/local-python-client.ipynb
deleted file mode 100644
index 95fcf12b..00000000
--- a/examples/tutorials/local-python-client.ipynb
+++ /dev/null
@@ -1,239 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "c015b59e-1187-4d45-b2af-7b4c5a9512e1",
- "metadata": {},
- "source": [
- "# Letta Python Client \n",
- "Welcome to the Letta tutorial! In this tutorial, we'll go through how to create a basic user-client for Letta and create a custom agent with long term memory. \n",
- "\n",
- "Letta runs *agents-as-a-service*, so agents can run independently on a server. For this tutorial, we will run a local version of the client which does not require a server, but still allows you to see some of Letta's capabilities. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a34fe313-f63e-4f36-9142-f681431bbb91",
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install git+https://github.com/cpacker/MemGPT.git@tutorials"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "191c1cf1-03e6-411a-8409-003caa8530f5",
- "metadata": {},
- "source": [
- "### Setup your OpenAI API key "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "23091690-bc50-4fbc-b48d-50b639453e36",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os \n",
- "\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f20ad6c7-9066-45e0-88ac-40920c83cc39",
- "metadata": {},
- "source": [
- "## Part 1: Connecting to the Letta Client \n",
- "\n",
- "We create a local client which creates a quickstart configuration for OpenAI using the provided `OPENAI_API_KEY`. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9b0871a0-42af-4573-a8ba-efb4fe7e5e5a",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta.client.client import LocalClient\n",
- "\n",
- "client = LocalClient(quickstart_option=\"openai\") "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40666896-0fa2-465e-b51b-57719de30542",
- "metadata": {},
- "source": [
- "## Part 2: Create an agent \n",
- "We'll first start with creating a basic Letta agent. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fb90f12b-acd7-4877-81e8-0e7b9eb4bd9b",
- "metadata": {},
- "outputs": [],
- "source": [
- "basic_agent = client.create_agent(\n",
- " name=\"basic_agent\", \n",
- ")\n",
- "print(f\"Created agent: {basic_agent.name}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "94d14102-3ef8-40fe-b32e-c77d0b8df311",
- "metadata": {},
- "source": [
- "We can now send messages from the user to the agent by specifying the `agent_id`: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3cbfef36-76f0-4f0b-990a-5d8409a676d7",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta.client.utils import pprint \n",
- "\n",
- "response = client.user_message(agent_id=basic_agent.id, message=\"hello\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b24d048e-f3cc-4830-aaa2-5e590d652bd9",
- "metadata": {},
- "source": [
- "### Adding Personalization\n",
- "We can now create a more customized agent, but specifying a custom `human` and `persona` field. \n",
- "* The *human* specifies the personalization information about the user interacting with the agent \n",
- "* The *persona* specifies the behavior and personality of the event\n",
- "\n",
- "What makes Letta unique is that the starting *persona* and *human* can change over time as the agent gains new information, enabling it to have evolving memory. We'll see an example of this later in the tutorial."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3ec35979-9102-4ea7-926e-ea7ccd501ceb",
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: feel free to change the human and person to what you'd like \n",
- "persona = \\\n",
- "\"\"\"\n",
- "You are a friendly and helpful agent!\n",
- "\"\"\"\n",
- "\n",
- "human = \\\n",
- "\"\"\"\n",
- "I am an Accenture consultant with many specializations. My name is Sarah.\n",
- "\"\"\"\n",
- "\n",
- "custom_agent = client.create_agent(\n",
- " name=\"custom_agent\", \n",
- " human=human, \n",
- " persona=persona\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "63a9a61b-58c9-4d09-a4f7-48233c72c340",
- "metadata": {},
- "source": [
- "### Viewing memory \n",
- "You can access the agent's memories through the client. There are two type of memory, *core* and *archival* memory: \n",
- "1. Core memory stores short-term memories in the LLM's context \n",
- "2. Archival memory stores long term memories in a vector database\n",
- "\n",
- "Core memory is divided into a \"human\" and \"persona\" section. You can see the agent's memories about the human below: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b0d1840a-05ee-47c1-b5f5-89faafd96e7c",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(client.get_agent_memory(agent_id=custom_agent.id)[\"core_memory\"][\"human\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "95c8a058-5d67-45b7-814b-38bb67c9acf3",
- "metadata": {},
- "source": [
- "### Evolving memory \n",
- "Letta agents have long term memory, and can evolve what they store in their memory over time. In the example below, we make a correction to the previously provided information. See how the agent processes this new information. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7e58e685-579e-4a0d-bba7-41976ea7f469",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.user_message(agent_id=custom_agent.id, message=\"Actually, my name is Charles\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "af2a2dd6-925e-49b2-ab01-bf837f33b26c",
- "metadata": {},
- "source": [
- "Now lets see what the agent's memory looks like again: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "41ef4aaa-4a48-44bb-8944-855f30725d6d",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(client.get_agent_memory(agent_id=custom_agent.id)[\"core_memory\"][\"human\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "66da949b-1084-4b87-b77c-6cbd4a822b34",
- "metadata": {},
- "source": [
- "## 🎉 Congrats, you're done with day 1 of Letta! \n",
- "For day 2, we'll go over how to connect *data sources* to Letta to run RAG agents. "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/tutorials/memgpt-admin-client.ipynb b/examples/tutorials/memgpt-admin-client.ipynb
deleted file mode 100644
index 833716da..00000000
--- a/examples/tutorials/memgpt-admin-client.ipynb
+++ /dev/null
@@ -1,50 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fb13c7bc-fbb4-4ccd-897c-08995db258e8",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta import Admin \n",
- "\n",
- "base_url=\"letta.localhost\"\n",
- "token=\"lettaadmin\" \n",
- "\n",
- "admin_client = Admin(base_url=base_url, token=\"lettaadmin\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "984b8249-a3f7-40d1-9691-4d128f9a90ff",
- "metadata": {},
- "outputs": [],
- "source": [
- "user = admin_client.create_user()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/tutorials/memgpt_paper.pdf b/examples/tutorials/memgpt_paper.pdf
deleted file mode 100644
index d2c8bd78..00000000
Binary files a/examples/tutorials/memgpt_paper.pdf and /dev/null differ
diff --git a/examples/tutorials/memgpt_rag_agent.ipynb b/examples/tutorials/memgpt_rag_agent.ipynb
deleted file mode 100644
index b503ddfe..00000000
--- a/examples/tutorials/memgpt_rag_agent.ipynb
+++ /dev/null
@@ -1,130 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "64fa991c-98e5-4be0-a838-06a4617d8be3",
- "metadata": {},
- "source": [
- "## Part 4: Adding external data \n",
- "In addition to short term, in-context memories, Letta agents also have a long term memory store called *archival memory*. We can enable agents to leverage external data (e.g. PDF files, database records, etc.) by inserting data into archival memory. In this example, we'll show how to load the Letta paper a *source*, which defines a set of data that can be attached to agents. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c61ac9c3-cbea-47a5-a6a4-4133ffe5984e",
- "metadata": {},
- "source": [
- "We first download a PDF file, the Letta paper: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f89e9156-3d2d-4ce6-b5e9-aeb4cdfd5657",
- "metadata": {},
- "outputs": [],
- "source": [
- "import requests\n",
- "\n",
- "url = \"https://arxiv.org/pdf/2310.08560\"\n",
- "response = requests.get(url)\n",
- "filename = \"letta_paper.pdf\"\n",
- "\n",
- "with open(filename, 'wb') as f:\n",
- " f.write(response.content)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bcfe3a48-cdb0-4843-9599-623753eb61b9",
- "metadata": {},
- "source": [
- "Next, we create a Letta source to load data into: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7ccf21fb-5862-42c2-96ca-63e0ba2f48b5",
- "metadata": {},
- "outputs": [],
- "source": [
- "letta_paper = client.sources.create(\n",
- " name=\"letta_paper\", \n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f114bf0b-6a25-4dbf-9c2c-59271d46ebba",
- "metadata": {},
- "source": [
- "Now that we have a source, we can load files into the source. Loading the file will take a bit of time, since the file needs to be parsed and stored as *embeddings* using an embedding model. The loading function returns a *job* which can be pinged for a status. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6fe624eb-bf08-4267-a849-06103c1ad5b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "job = client.sources.files.upload(filename=filename, source_id=letta_paper.id)\n",
- "job"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27ce13f5-d878-406d-9a5f-7e2335f2ef0d",
- "metadata": {},
- "source": [
- "### Attaching data to an agent \n",
- "To allow an agent to access data in a source, we need to *attach* it to the agent. This will load the source's data into the agent's archival memory. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5be91571-87ee-411a-8e79-25c56c414360",
- "metadata": {},
- "outputs": [],
- "source": [
- "client.agents.sources.attach(source_id=letta_paper.id, agent_id=basic_agent.id)\n",
- "# TODO: add system message saying that file has been attached \n",
- "\n",
- "from pprint import pprint\n",
- "\n",
- "# TODO: do soemthing accenture related \n",
- "# TODO: brag about query rewriting -- hyde paper \n",
- "response = client.agents.messages.create(agent_id=basic_agent.id, messages=[\n",
- " MessageCreate(\n",
- " role=\"user\",\n",
- " content=\"what is core memory? search your archival memory.\",\n",
- " )\n",
- "])\n",
- "pprint(response.messages)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/tutorials/python-client.ipynb b/examples/tutorials/python-client.ipynb
deleted file mode 100644
index 8a5619eb..00000000
--- a/examples/tutorials/python-client.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "6d3806ac-38f3-4999-bbed-953037bd0fd9",
- "metadata": {},
- "source": [
- "# Letta Python Client \n",
- "Welcome to the Letta tutorial! In this tutorial, we'll go through how to create a basic user-client for Letta and create a custom agent with long term memory. \n",
- "\n",
- "Letta runs *agents-as-a-service*, so agents can run independently on a server. For this tutorial, we will be connecting to an existing Letta server via the Python client and the UI console. If you don't have a running server, see the [documentation](https://letta.readme.io/docs/running-a-letta-server) for instructions on how to create one. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7c0b6d6b-dbe6-412b-b129-6d7eb7d626a3",
- "metadata": {},
- "source": [
- "## Part 0: Install Letta "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "481d0976-d26b-46d2-ba74-8f2bb5556387",
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install git+https://github.com/cpacker/MemGPT.git@tutorials"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a0484348-f7b2-48e3-9a2f-7d6495ef76e3",
- "metadata": {},
- "source": [
- "## Part 1: Connecting to the Letta Client \n",
- "\n",
- "The Letta client connects to a running Letta service, specified by `base_url`. The client corresponds to a *single-user* (you), so requires an authentication token to let the service know who you are. \n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "53ae2e1b-ad22-43c2-b3d8-92d591be8840",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta import create_client\n",
- "\n",
- "base_url = \"http://35.238.125.250:8083\"\n",
- "\n",
- "# TODO: replace with your token \n",
- "my_token = \"sk-...\" \n",
- "\n",
- "client = create_client(base_url=base_url, token=my_token) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3c5c8651-e8aa-4423-b2b8-284bf6a01577",
- "metadata": {},
- "source": [
- "### Viewing the developer portal \n",
- "Letta provides a portal interface for viewing and interacting with agents, data sources, tools, and more. You can enter `http://35.238.125.250:8083` into your browser to load the developer portal, and enter in `my_token` to log in. \n",
- "\n",
- "![](\"./developer_portal_login.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "66e47b34-5feb-4660-85f0-14b5ee7f62b9",
- "metadata": {},
- "source": [
- "## Part 2: Create an agent \n",
- "We'll first start with creating a basic Letta agent. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "24745606-b0fb-4157-a5cd-82fd0c26711f",
- "metadata": {},
- "outputs": [],
- "source": [
- "basic_agent = client.create_agent(\n",
- " name=\"basic_agent\", \n",
- ")\n",
- "print(f\"Created agent: {basic_agent.name}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fcfb0d7b-b260-4bc0-8db2-c65f40e4afd5",
- "metadata": {},
- "source": [
- "We can now send messages from the user to the agent by specifying the `agent_id`: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a37bc9aa-4efb-4b4d-a6ce-f02505cb3240",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta.client.utils import pprint \n",
- "\n",
- "response = client.user_message(agent_id=basic_agent.id, message=\"hello\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9803140c-2b9d-426b-8812-9295806eb312",
- "metadata": {},
- "source": [
- "### Chatting in the developer portal \n",
- "You can also chat with the agent inside of the developer portal. Try clicking the chat button in the agent view. \n",
- "\n",
- "![](\"./dev_portal_agent_chat.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "99ae20ec-e92e-4480-a652-b4aea28a6199",
- "metadata": {},
- "source": [
- "### Adding Personalization\n",
- "We can now create a more customized agent, but specifying a custom `human` and `persona` field. \n",
- "* The *human* specifies the personalization information about the user interacting with the agent \n",
- "* The *persona* specifies the behavior and personality of the event\n",
- "\n",
- "What makes Letta unique is that the starting *persona* and *human* can change over time as the agent gains new information, enabling it to have evolving memory. We'll see an example of this later in the tutorial."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c0876410-4d70-490d-a798-39938b5ce941",
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: feel free to change the human and person to what you'd like \n",
- "persona = \\\n",
- "\"\"\"\n",
- "You are a friendly and helpful agent!\n",
- "\"\"\"\n",
- "\n",
- "human = \\\n",
- "\"\"\"\n",
- "I am an Accenture consultant with many specializations. My name is Sarah.\n",
- "\"\"\"\n",
- "\n",
- "custom_agent = client.create_agent(\n",
- " name=\"custom_agent\", \n",
- " human=human, \n",
- " persona=persona\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21293857-80e4-46e4-b628-3912fad038e9",
- "metadata": {},
- "source": [
- "### Viewing memory \n",
- "You can view and edit the agent's memory inside of the developer console. There are two type of memory, *core* and *archival* memory: \n",
- "1. Core memory stores short-term memories in the LLM's context \n",
- "2. Archival memory stores long term memories in a vector database\n",
- "\n",
- "In this example, we'll look at how the agent can modify its core memory with new information. To see the agent's memory, click the \"Core Memory\" section on the developer console. \n",
- "\n",
- "![](\"./dev_portal_memory.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d8fa13eb-ce4b-4e4f-81b6-9d6ef6fa67c2",
- "metadata": {},
- "source": [
- "### Referencing memory \n",
- "Letta agents can customize their responses based on what memories they have stored. Try asking a question that related to the human and persona you provided. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fddbefe5-3b94-4a08-aa50-d80fb581c747",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.user_message(agent_id=custom_agent.id, message=\"what do I work as?\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "30497119-e208-4a4e-b482-e7cfff346263",
- "metadata": {},
- "source": [
- "### Evolving memory \n",
- "Letta agents have long term memory, and can evolve what they store in their memory over time. In the example below, we make a correction to the previously provided information. See how the agent processes this new information. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "679fa708-20ee-4e75-9222-b476f126bc6f",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.user_message(agent_id=custom_agent.id, message=\"Actually, my name is Charles\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "686ac5a3-be63-4afd-97ae-b7d05219dd60",
- "metadata": {},
- "source": [
- "Now, look back at the developer portal and at the agent's *core memory*. Do you see a change in the *human* section of the memory? "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "878d2f49-a5a6-4483-9f69-7436bcf00cfb",
- "metadata": {},
- "source": [
- "## Part 3: Adding Tools \n",
- "Letta agents can be connected to custom tools. Currently, tools must be created by service administrators. However, you can add additional tools provided by the service administrator to the agent you create. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "35785d36-2674-4a00-937b-4c747e0fb6bf",
- "metadata": {},
- "source": [
- "### View Available Tools "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c307a6f7-276b-49f5-8d3d-48aaaea221a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "tools = client.list_tools().tools\n",
- "for tool in tools: \n",
- " print(f\"Tool: {tool.name} - {tool.json_schema['description']}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "318d19dc-b9dd-448c-ab5c-9c9311d21fad",
- "metadata": {},
- "source": [
- "### Create a tool using agent in the developer portal \n",
- "Create an agent in the developer portal and toggle additional tools you want the agent to use. We recommend modifying the *persona* to notify the agent that it should be using the tools for certain tasks. \n",
- "\n",
- "\n",
- "![](\"./dev_portal_tools.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aecdaa70-861a-43d5-b006-fecd90a8ed19",
- "metadata": {},
- "source": [
- "## Part 4: Cleanup (optional) \n",
- "You can cleanup the agents you creating the following command to delete your agents: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1320d9c9-170b-48a8-b5e8-70737b1a8aac",
- "metadata": {},
- "outputs": [],
- "source": [
- "for agent in client.list_agents().agents: \n",
- " client.delete_agent(agent[\"id\"])\n",
- " print(f\"Deleted agent {agent['name']} with ID {agent['id']}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "510675a8-22bc-4f9f-9c79-91e2ffa9caf9",
- "metadata": {},
- "source": [
- "## 🎉 Congrats, you're done with day 1 of Letta! \n",
- "For day 2, we'll go over how to connect *data sources* to Letta to run RAG agents. "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/init.sql b/init.sql
deleted file mode 100644
index 9d866db2..00000000
--- a/init.sql
+++ /dev/null
@@ -1,36 +0,0 @@
--- Title: Init Letta Database
-
--- Fetch the docker secrets, if they are available.
--- Otherwise fall back to environment variables, or hardwired 'letta'
-\set db_user `([ -r /var/run/secrets/letta-user ] && cat /var/run/secrets/letta-user) || echo "${POSTGRES_USER:-letta}"`
-\set db_password `([ -r /var/run/secrets/letta-password ] && cat /var/run/secrets/letta-password) || echo "${POSTGRES_PASSWORD:-letta}"`
-\set db_name `([ -r /var/run/secrets/letta-db ] && cat /var/run/secrets/letta-db) || echo "${POSTGRES_DB:-letta}"`
-
--- CREATE USER :"db_user"
--- WITH PASSWORD :'db_password'
--- NOCREATEDB
--- NOCREATEROLE
--- ;
---
--- CREATE DATABASE :"db_name"
--- WITH
--- OWNER = :"db_user"
--- ENCODING = 'UTF8'
--- LC_COLLATE = 'en_US.utf8'
--- LC_CTYPE = 'en_US.utf8'
--- LOCALE_PROVIDER = 'libc'
--- TABLESPACE = pg_default
--- CONNECTION LIMIT = -1;
-
--- Set up our schema and extensions in our new database.
-\c :"db_name"
-
-CREATE SCHEMA :"db_name"
- AUTHORIZATION :"db_user";
-
-ALTER DATABASE :"db_name"
- SET search_path TO :"db_name";
-
-CREATE EXTENSION IF NOT EXISTS vector WITH SCHEMA :"db_name";
-
-DROP SCHEMA IF EXISTS public CASCADE;
diff --git a/letta/__init__.py b/letta/__init__.py
deleted file mode 100644
index bb6eb8a4..00000000
--- a/letta/__init__.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-from importlib.metadata import PackageNotFoundError, version
-
-try:
- __version__ = version("letta")
-except PackageNotFoundError:
- # Fallback for development installations
- __version__ = "0.11.7"
-
-if os.environ.get("LETTA_VERSION"):
- __version__ = os.environ["LETTA_VERSION"]
-
-# Import sqlite_functions early to ensure event handlers are registered
-from letta.orm import sqlite_functions
-
-# # imports for easier access
-from letta.schemas.agent import AgentState
-from letta.schemas.block import Block
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import JobStatus
-from letta.schemas.file import FileMetadata
-from letta.schemas.job import Job
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.letta_ping import LettaPing
-from letta.schemas.letta_stop_reason import LettaStopReason
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.memory import ArchivalMemorySummary, BasicBlockMemory, ChatMemory, Memory, RecallMemorySummary
-from letta.schemas.message import Message
-from letta.schemas.organization import Organization
-from letta.schemas.passage import Passage
-from letta.schemas.source import Source
-from letta.schemas.tool import Tool
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
diff --git a/letta/adapters/letta_llm_adapter.py b/letta/adapters/letta_llm_adapter.py
deleted file mode 100644
index a554b368..00000000
--- a/letta/adapters/letta_llm_adapter.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import AsyncGenerator
-
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.letta_message_content import ReasoningContent, RedactedReasoningContent, TextContent
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, ToolCall
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.services.telemetry_manager import TelemetryManager
-
-
-class LettaLLMAdapter(ABC):
- """
- Base adapter for handling LLM calls in a unified way.
-
- This abstract class defines the interface for both blocking and streaming
- LLM interactions, allowing the agent to use different execution modes
- through a consistent API.
- """
-
- def __init__(self, llm_client: LLMClientBase, llm_config: LLMConfig) -> None:
- self.llm_client: LLMClientBase = llm_client
- self.llm_config: LLMConfig = llm_config
- self.message_id: str | None = None
- self.request_data: dict | None = None
- self.response_data: dict | None = None
- self.chat_completions_response: ChatCompletionResponse | None = None
- self.reasoning_content: list[TextContent | ReasoningContent | RedactedReasoningContent] | None = None
- self.tool_call: ToolCall | None = None
- self.usage: LettaUsageStatistics = LettaUsageStatistics()
- self.telemetry_manager: TelemetryManager = TelemetryManager()
- self.llm_request_finish_timestamp_ns: int | None = None
-
- @abstractmethod
- async def invoke_llm(
- self,
- request_data: dict,
- messages: list,
- tools: list,
- use_assistant_message: bool,
- requires_approval_tools: list[str] = [],
- step_id: str | None = None,
- actor: User | None = None,
- ) -> AsyncGenerator[LettaMessage | None, None]:
- """
- Execute the LLM call and yield results as they become available.
-
- Args:
- request_data: The prepared request data for the LLM API
- messages: The messages in context for the request
- tools: The tools available for the LLM to use
- use_assistant_message: If true, use assistant messages when streaming response
- requires_approval_tools: The subset of tools that require approval before use
- step_id: The step ID associated with this request. If provided, logs request and response data.
- actor: The optional actor associated with this request for logging purposes.
-
- Yields:
- LettaMessage: Chunks of data for streaming adapters, or None for blocking adapters
- """
- raise NotImplementedError
-
- def supports_token_streaming(self) -> bool:
- """
- Check if the adapter supports token-level streaming.
-
- Returns:
- bool: True if the adapter can stream back tokens as they are generated, False otherwise
- """
- return False
-
- def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
- """
- Log provider trace data for telemetry purposes.
-
- Args:
- step_id: The step ID associated with this request for logging purposes
- actor: The user associated with this request for logging purposes
- """
- raise NotImplementedError
diff --git a/letta/adapters/letta_llm_request_adapter.py b/letta/adapters/letta_llm_request_adapter.py
deleted file mode 100644
index a21663f4..00000000
--- a/letta/adapters/letta_llm_request_adapter.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import asyncio
-from typing import AsyncGenerator
-
-from letta.adapters.letta_llm_adapter import LettaLLMAdapter
-from letta.helpers.datetime_helpers import get_utc_timestamp_ns
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, TextContent
-from letta.schemas.provider_trace import ProviderTraceCreate
-from letta.schemas.user import User
-from letta.settings import settings
-
-
-class LettaLLMRequestAdapter(LettaLLMAdapter):
- """
- Adapter for handling blocking (non-streaming) LLM requests.
-
- This adapter makes synchronous requests to the LLM and returns complete
- responses. It extracts reasoning content, tool calls, and usage statistics
- from the response and updates instance variables for access by the agent.
- """
-
- async def invoke_llm(
- self,
- request_data: dict,
- messages: list,
- tools: list,
- use_assistant_message: bool,
- requires_approval_tools: list[str] = [],
- step_id: str | None = None,
- actor: str | None = None,
- ) -> AsyncGenerator[LettaMessage | None, None]:
- """
- Execute a blocking LLM request and yield the response.
-
- This adapter:
- 1. Makes a blocking request to the LLM
- 2. Converts the response to chat completion format
- 3. Extracts reasoning and tool call information
- 4. Updates all instance variables
- 5. Yields nothing (blocking mode doesn't stream)
- """
- # Store request data
- self.request_data = request_data
-
- # Make the blocking LLM request
- self.response_data = await self.llm_client.request_async(request_data, self.llm_config)
- self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
-
- # Convert response to chat completion format
- self.chat_completions_response = self.llm_client.convert_response_to_chat_completion(self.response_data, messages, self.llm_config)
-
- # Extract reasoning content from the response
- if self.chat_completions_response.choices[0].message.reasoning_content:
- self.reasoning_content = [
- ReasoningContent(
- reasoning=self.chat_completions_response.choices[0].message.reasoning_content,
- is_native=True,
- signature=self.chat_completions_response.choices[0].message.reasoning_content_signature,
- )
- ]
- elif self.chat_completions_response.choices[0].message.omitted_reasoning_content:
- self.reasoning_content = [OmittedReasoningContent()]
- elif self.chat_completions_response.choices[0].message.content:
- # Reasoning placed into content for legacy reasons
- self.reasoning_content = [TextContent(text=self.chat_completions_response.choices[0].message.content)]
- else:
- # logger.info("No reasoning content found.")
- self.reasoning_content = None
-
- # Extract tool call
- if self.chat_completions_response.choices[0].message.tool_calls:
- self.tool_call = self.chat_completions_response.choices[0].message.tool_calls[0]
- else:
- self.tool_call = None
-
- # Extract usage statistics
- self.usage.step_count = 1
- self.usage.completion_tokens = self.chat_completions_response.usage.completion_tokens
- self.usage.prompt_tokens = self.chat_completions_response.usage.prompt_tokens
- self.usage.total_tokens = self.chat_completions_response.usage.total_tokens
-
- self.log_provider_trace(step_id=step_id, actor=actor)
-
- yield None
- return
-
- def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
- """
- Log provider trace data for telemetry purposes in a fire-and-forget manner.
-
- Creates an async task to log the request/response data without blocking
- the main execution flow. The task runs in the background.
-
- Args:
- step_id: The step ID associated with this request for logging purposes
- actor: The user associated with this request for logging purposes
- """
- if step_id is None or actor is None or not settings.track_provider_trace:
- return
-
- asyncio.create_task(
- self.telemetry_manager.create_provider_trace_async(
- actor=actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=self.request_data,
- response_json=self.response_data,
- step_id=step_id, # Use original step_id for telemetry
- organization_id=actor.organization_id,
- ),
- )
- )
diff --git a/letta/adapters/letta_llm_stream_adapter.py b/letta/adapters/letta_llm_stream_adapter.py
deleted file mode 100644
index c0bf2e9a..00000000
--- a/letta/adapters/letta_llm_stream_adapter.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import asyncio
-from typing import AsyncGenerator
-
-from letta.adapters.letta_llm_adapter import LettaLLMAdapter
-from letta.helpers.datetime_helpers import get_utc_timestamp_ns
-from letta.interfaces.anthropic_streaming_interface import AnthropicStreamingInterface
-from letta.interfaces.openai_streaming_interface import OpenAIStreamingInterface
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.schemas.enums import ProviderType
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.provider_trace import ProviderTraceCreate
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.settings import settings
-
-
-class LettaLLMStreamAdapter(LettaLLMAdapter):
- """
- Adapter for handling streaming LLM requests with immediate token yielding.
-
- This adapter supports real-time streaming of tokens from the LLM, providing
- minimal time-to-first-token (TTFT) latency. It uses specialized streaming
- interfaces for different providers (OpenAI, Anthropic) to handle their
- specific streaming formats.
- """
-
- def __init__(self, llm_client: LLMClientBase, llm_config: LLMConfig) -> None:
- super().__init__(llm_client, llm_config)
- self.interface: OpenAIStreamingInterface | AnthropicStreamingInterface | None = None
-
- async def invoke_llm(
- self,
- request_data: dict,
- messages: list,
- tools: list,
- use_assistant_message: bool,
- requires_approval_tools: list[str] = [],
- step_id: str | None = None,
- actor: User | None = None,
- ) -> AsyncGenerator[LettaMessage, None]:
- """
- Execute a streaming LLM request and yield tokens/chunks as they arrive.
-
- This adapter:
- 1. Makes a streaming request to the LLM
- 2. Yields chunks immediately for minimal TTFT
- 3. Accumulates response data through the streaming interface
- 4. Updates all instance variables after streaming completes
- """
- # Store request data
- self.request_data = request_data
-
- # Instantiate streaming interface
- if self.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock]:
- self.interface = AnthropicStreamingInterface(
- use_assistant_message=use_assistant_message,
- put_inner_thoughts_in_kwarg=self.llm_config.put_inner_thoughts_in_kwargs,
- requires_approval_tools=requires_approval_tools,
- )
- elif self.llm_config.model_endpoint_type == ProviderType.openai:
- self.interface = OpenAIStreamingInterface(
- use_assistant_message=use_assistant_message,
- is_openai_proxy=self.llm_config.provider_name == "lmstudio_openai",
- put_inner_thoughts_in_kwarg=self.llm_config.put_inner_thoughts_in_kwargs,
- messages=messages,
- tools=tools,
- requires_approval_tools=requires_approval_tools,
- )
- else:
- raise ValueError(f"Streaming not supported for provider {self.llm_config.model_endpoint_type}")
-
- # Extract optional parameters
- # ttft_span = kwargs.get('ttft_span', None)
-
- # Start the streaming request
- stream = await self.llm_client.stream_async(request_data, self.llm_config)
-
- # Process the stream and yield chunks immediately for TTFT
- async for chunk in self.interface.process(stream): # TODO: add ttft span
- # Yield each chunk immediately as it arrives
- yield chunk
-
- # After streaming completes, extract the accumulated data
- self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
-
- # Extract tool call from the interface
- try:
- self.tool_call = self.interface.get_tool_call_object()
- except ValueError as e:
- # No tool call, handle upstream
- self.tool_call = None
-
- # Extract reasoning content from the interface
- self.reasoning_content = self.interface.get_reasoning_content()
-
- # Extract usage statistics
- # Some providers don't provide usage in streaming, use fallback if needed
- if hasattr(self.interface, "input_tokens") and hasattr(self.interface, "output_tokens"):
- # Handle cases where tokens might not be set (e.g., LMStudio)
- input_tokens = self.interface.input_tokens
- output_tokens = self.interface.output_tokens
-
- # Fallback to estimated values if not provided
- if not input_tokens and hasattr(self.interface, "fallback_input_tokens"):
- input_tokens = self.interface.fallback_input_tokens
- if not output_tokens and hasattr(self.interface, "fallback_output_tokens"):
- output_tokens = self.interface.fallback_output_tokens
-
- self.usage = LettaUsageStatistics(
- step_count=1,
- completion_tokens=output_tokens or 0,
- prompt_tokens=input_tokens or 0,
- total_tokens=(input_tokens or 0) + (output_tokens or 0),
- )
- else:
- # Default usage statistics if not available
- self.usage = LettaUsageStatistics(step_count=1, completion_tokens=0, prompt_tokens=0, total_tokens=0)
-
- # Store any additional data from the interface
- self.message_id = self.interface.letta_message_id
-
- # Log request and response data
- self.log_provider_trace(step_id=step_id, actor=actor)
-
- def supports_token_streaming(self) -> bool:
- return True
-
- def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
- """
- Log provider trace data for telemetry purposes in a fire-and-forget manner.
-
- Creates an async task to log the request/response data without blocking
- the main execution flow. For streaming adapters, this includes the final
- tool call and reasoning content collected during streaming.
-
- Args:
- step_id: The step ID associated with this request for logging purposes
- actor: The user associated with this request for logging purposes
- """
- if step_id is None or actor is None or not settings.track_provider_trace:
- return
-
- asyncio.create_task(
- self.telemetry_manager.create_provider_trace_async(
- actor=actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=self.request_data,
- response_json={
- "content": {
- "tool_call": self.tool_call.model_dump_json(),
- "reasoning": [content.model_dump_json() for content in self.reasoning_content],
- },
- "id": self.interface.message_id,
- "model": self.interface.model,
- "role": "assistant",
- # "stop_reason": "",
- # "stop_sequence": None,
- "type": "message",
- "usage": {
- "input_tokens": self.usage.prompt_tokens,
- "output_tokens": self.usage.completion_tokens,
- },
- },
- step_id=step_id, # Use original step_id for telemetry
- organization_id=actor.organization_id,
- ),
- )
- )
diff --git a/letta/agent.py b/letta/agent.py
deleted file mode 100644
index 52f7de37..00000000
--- a/letta/agent.py
+++ /dev/null
@@ -1,1758 +0,0 @@
-import asyncio
-import json
-import time
-import traceback
-import warnings
-from abc import ABC, abstractmethod
-from typing import Dict, List, Optional, Tuple, Union
-
-from openai.types.beta.function_tool import FunctionTool as OpenAITool
-
-from letta.agents.helpers import generate_step_id
-from letta.constants import (
- CLI_WARNING_PREFIX,
- COMPOSIO_ENTITY_ENV_VAR_KEY,
- ERROR_MESSAGE_PREFIX,
- FIRST_MESSAGE_ATTEMPTS,
- FUNC_FAILED_HEARTBEAT_MESSAGE,
- LETTA_CORE_TOOL_MODULE_NAME,
- LETTA_MULTI_AGENT_TOOL_MODULE_NAME,
- LLM_MAX_TOKENS,
- READ_ONLY_BLOCK_EDIT_ERROR,
- REQ_HEARTBEAT_MESSAGE,
- SEND_MESSAGE_TOOL_NAME,
-)
-from letta.errors import ContextWindowExceededError
-from letta.functions.ast_parsers import coerce_dict_args_by_annotations, get_function_annotations_from_source
-from letta.functions.composio_helpers import execute_composio_action, generate_composio_action_from_func_name
-from letta.functions.functions import get_function_from_module
-from letta.helpers import ToolRulesSolver
-from letta.helpers.composio_helpers import get_composio_api_key
-from letta.helpers.datetime_helpers import get_utc_time
-from letta.helpers.json_helpers import json_dumps, json_loads
-from letta.helpers.message_helper import convert_message_creates_to_messages
-from letta.interface import AgentInterface
-from letta.llm_api.helpers import calculate_summarizer_cutoff, get_token_counts_for_messages, is_context_overflow_error
-from letta.llm_api.llm_api_tools import create
-from letta.llm_api.llm_client import LLMClient
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG
-from letta.local_llm.utils import num_tokens_from_functions, num_tokens_from_messages
-from letta.log import get_logger
-from letta.memory import summarize_messages
-from letta.orm import User
-from letta.otel.tracing import log_event, trace_method
-from letta.prompts.prompt_generator import PromptGenerator
-from letta.schemas.agent import AgentState, AgentStepResponse, UpdateAgent, get_prompt_template_for_agent_type
-from letta.schemas.block import BlockUpdate
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import MessageRole, ProviderType, StepStatus, ToolType
-from letta.schemas.letta_message_content import ImageContent, TextContent
-from letta.schemas.memory import ContextWindowOverview, Memory
-from letta.schemas.message import Message, MessageCreate, ToolReturn
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Message as ChatCompletionMessage, UsageStatistics
-from letta.schemas.response_format import ResponseFormatType
-from letta.schemas.tool import Tool
-from letta.schemas.tool_execution_result import ToolExecutionResult
-from letta.schemas.tool_rule import TerminalToolRule
-from letta.schemas.usage import LettaUsageStatistics
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.helpers.agent_manager_helper import check_supports_structured_output
-from letta.services.helpers.tool_parser_helper import runtime_override_tool_json_schema
-from letta.services.job_manager import JobManager
-from letta.services.mcp.base_client import AsyncBaseMCPClient
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.provider_manager import ProviderManager
-from letta.services.step_manager import StepManager
-from letta.services.telemetry_manager import NoopTelemetryManager, TelemetryManager
-from letta.services.tool_executor.tool_execution_sandbox import ToolExecutionSandbox
-from letta.services.tool_manager import ToolManager
-from letta.settings import model_settings, settings, summarizer_settings
-from letta.streaming_interface import StreamingRefreshCLIInterface
-from letta.system import get_heartbeat, get_token_limit_warning, package_function_response, package_summarize_message, package_user_message
-from letta.utils import count_tokens, get_friendly_error_msg, get_tool_call_id, log_telemetry, parse_json, validate_function_response
-
-logger = get_logger(__name__)
-
-
-class BaseAgent(ABC):
- """
- Abstract class for all agents.
- Only one interface is required: step.
- """
-
- @abstractmethod
- def step(
- self,
- input_messages: List[MessageCreate],
- ) -> LettaUsageStatistics:
- """
- Top-level event message handler for the agent.
- """
- raise NotImplementedError
-
-
-class Agent(BaseAgent):
- def __init__(
- self,
- interface: Optional[Union[AgentInterface, StreamingRefreshCLIInterface]],
- agent_state: AgentState, # in-memory representation of the agent state (read from multiple tables)
- user: User,
- # extras
- first_message_verify_mono: bool = True, # TODO move to config?
- # MCP sessions, state held in-memory in the server
- mcp_clients: Optional[Dict[str, AsyncBaseMCPClient]] = None,
- save_last_response: bool = False,
- ):
- assert isinstance(agent_state.memory, Memory), f"Memory object is not of type Memory: {type(agent_state.memory)}"
- # Hold a copy of the state that was used to init the agent
- self.agent_state = agent_state
- assert isinstance(self.agent_state.memory, Memory), f"Memory object is not of type Memory: {type(self.agent_state.memory)}"
-
- self.user = user
-
- # initialize a tool rules solver
- self.tool_rules_solver = ToolRulesSolver(tool_rules=agent_state.tool_rules)
-
- # gpt-4, gpt-3.5-turbo, ...
- self.model = self.agent_state.llm_config.model
- self.supports_structured_output = check_supports_structured_output(model=self.model, tool_rules=agent_state.tool_rules)
-
- # if there are tool rules, print out a warning
- if not self.supports_structured_output and agent_state.tool_rules:
- for rule in agent_state.tool_rules:
- if not isinstance(rule, TerminalToolRule):
- warnings.warn("Tool rules only work reliably for model backends that support structured outputs (e.g. OpenAI gpt-4o).")
- break
-
- # state managers
- self.block_manager = BlockManager()
-
- # Interface must implement:
- # - internal_monologue
- # - assistant_message
- # - function_message
- # ...
- # Different interfaces can handle events differently
- # e.g., print in CLI vs send a discord message with a discord bot
- self.interface = interface
-
- # Create the persistence manager object based on the AgentState info
- self.message_manager = MessageManager()
- self.passage_manager = PassageManager()
- self.provider_manager = ProviderManager()
- self.agent_manager = AgentManager()
- self.job_manager = JobManager()
- self.step_manager = StepManager()
- self.telemetry_manager = TelemetryManager() if settings.llm_api_logging else NoopTelemetryManager()
-
- # State needed for heartbeat pausing
-
- self.first_message_verify_mono = first_message_verify_mono
-
- # Controls if the convo memory pressure warning is triggered
- # When an alert is sent in the message queue, set this to True (to avoid repeat alerts)
- # When the summarizer is run, set this back to False (to reset)
- self.agent_alerted_about_memory_pressure = False
-
- # Load last function response from message history
- self.last_function_response = self.load_last_function_response()
-
- # Save last responses in memory
- self.save_last_response = save_last_response
- self.last_response_messages = []
-
- # Logger that the Agent specifically can use, will also report the agent_state ID with the logs
- self.logger = get_logger(agent_state.id)
-
- # MCPClient, state/sessions managed by the server
- # TODO: This is temporary, as a bridge
- self.mcp_clients = None
- # TODO: no longer supported
- # if mcp_clients:
- # self.mcp_clients = {client_id: client.to_sync_client() for client_id, client in mcp_clients.items()}
-
- def load_last_function_response(self):
- """Load the last function response from message history"""
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- for i in range(len(in_context_messages) - 1, -1, -1):
- msg = in_context_messages[i]
- if msg.role == MessageRole.tool and msg.content and len(msg.content) == 1 and isinstance(msg.content[0], TextContent):
- text_content = msg.content[0].text
- try:
- response_json = json.loads(text_content)
- if response_json.get("message"):
- return response_json["message"]
- except (json.JSONDecodeError, KeyError):
- raise ValueError(f"Invalid JSON format in message: {text_content}")
- return None
-
- def ensure_read_only_block_not_modified(self, new_memory: Memory) -> None:
- """
- Throw an error if a read-only block has been modified
- """
- for label in self.agent_state.memory.list_block_labels():
- if self.agent_state.memory.get_block(label).read_only:
- if new_memory.get_block(label).value != self.agent_state.memory.get_block(label).value:
- raise ValueError(READ_ONLY_BLOCK_EDIT_ERROR)
-
- def update_memory_if_changed(self, new_memory: Memory) -> bool:
- """
- Update internal memory object and system prompt if there have been modifications.
-
- Args:
- new_memory (Memory): the new memory object to compare to the current memory object
-
- Returns:
- modified (bool): whether the memory was updated
- """
- system_message = self.message_manager.get_message_by_id(message_id=self.agent_state.message_ids[0], actor=self.user)
- if new_memory.compile() not in system_message.content[0].text:
- # update the blocks (LRW) in the DB
- for label in self.agent_state.memory.list_block_labels():
- updated_value = new_memory.get_block(label).value
- if updated_value != self.agent_state.memory.get_block(label).value:
- # update the block if it's changed
- block_id = self.agent_state.memory.get_block(label).id
- self.block_manager.update_block(block_id=block_id, block_update=BlockUpdate(value=updated_value), actor=self.user)
-
- # refresh memory from DB (using block ids)
- self.agent_state.memory = Memory(
- blocks=[self.block_manager.get_block_by_id(block.id, actor=self.user) for block in self.agent_state.memory.get_blocks()],
- file_blocks=self.agent_state.memory.file_blocks,
- prompt_template=get_prompt_template_for_agent_type(self.agent_state.agent_type),
- )
-
- # NOTE: don't do this since re-buildin the memory is handled at the start of the step
- # rebuild memory - this records the last edited timestamp of the memory
- # TODO: pass in update timestamp from block edit time
- self.agent_state = self.agent_manager.rebuild_system_prompt(agent_id=self.agent_state.id, actor=self.user)
-
- return True
-
- return False
-
- def _handle_function_error_response(
- self,
- error_msg: str,
- tool_call_id: str,
- function_name: str,
- function_args: dict,
- function_response: str,
- messages: List[Message],
- tool_returns: Optional[List[ToolReturn]] = None,
- include_function_failed_message: bool = False,
- group_id: Optional[str] = None,
- ) -> List[Message]:
- """
- Handle error from function call response
- """
- # Update tool rules
- self.last_function_response = function_response
- self.tool_rules_solver.register_tool_call(function_name)
-
- # Extend conversation with function response
- function_response = package_function_response(False, error_msg, self.agent_state.timezone)
- new_message = Message(
- agent_id=self.agent_state.id,
- # Base info OpenAI-style
- model=self.model,
- role="tool",
- name=function_name, # NOTE: when role is 'tool', the 'name' is the function name, not agent name
- content=[TextContent(text=function_response)],
- tool_call_id=tool_call_id,
- # Letta extras
- tool_returns=tool_returns,
- group_id=group_id,
- )
- messages.append(new_message)
- self.interface.function_message(f"Error: {error_msg}", msg_obj=new_message, chunk_index=0)
- if include_function_failed_message:
- self.interface.function_message(f"Ran {function_name}({function_args})", msg_obj=new_message)
-
- # Return updated messages
- return messages
-
- def _runtime_override_tool_json_schema(
- self,
- functions_list: List[Dict | None],
- ) -> List[Dict | None]:
- """Override the tool JSON schema at runtime for a particular tool if conditions are met."""
-
- # Currently just injects `send_message` with a `response_format` if provided to the agent.
- if self.agent_state.response_format and self.agent_state.response_format.type != ResponseFormatType.text:
- for func in functions_list:
- if func["name"] == SEND_MESSAGE_TOOL_NAME:
- if self.agent_state.response_format.type == ResponseFormatType.json_schema:
- func["parameters"]["properties"]["message"] = self.agent_state.response_format.json_schema["schema"]
- if self.agent_state.response_format.type == ResponseFormatType.json_object:
- func["parameters"]["properties"]["message"] = {
- "type": "object",
- "description": "Message contents. All unicode (including emojis) are supported.",
- "additionalProperties": True,
- "properties": {},
- }
- break
- return functions_list
-
- @trace_method
- def _get_ai_reply(
- self,
- message_sequence: List[Message],
- function_call: Optional[str] = None,
- first_message: bool = False,
- stream: bool = False, # TODO move to config?
- empty_response_retry_limit: int = 3,
- backoff_factor: float = 0.5, # delay multiplier for exponential backoff
- max_delay: float = 10.0, # max delay between retries
- step_count: Optional[int] = None,
- last_function_failed: bool = False,
- put_inner_thoughts_first: bool = True,
- step_id: Optional[str] = None,
- ) -> ChatCompletionResponse | None:
- """Get response from LLM API with robust retry mechanism."""
- log_telemetry(self.logger, "_get_ai_reply start")
- available_tools = set([t.name for t in self.agent_state.tools])
- agent_state_tool_jsons = [t.json_schema for t in self.agent_state.tools]
-
- # Get allowed tools or allow all if none are allowed
- allowed_tool_names = self.tool_rules_solver.get_allowed_tool_names(
- available_tools=available_tools, last_function_response=self.last_function_response
- ) or list(available_tools)
-
- # Don't allow a tool to be called if it failed last time
- if last_function_failed and self.tool_rules_solver.tool_call_history:
- allowed_tool_names = [f for f in allowed_tool_names if f != self.tool_rules_solver.tool_call_history[-1]]
- if not allowed_tool_names:
- return None
-
- allowed_functions = [func for func in agent_state_tool_jsons if func["name"] in allowed_tool_names]
- # Extract terminal tool names from tool rules
- terminal_tool_names = {rule.tool_name for rule in self.tool_rules_solver.terminal_tool_rules}
- allowed_functions = runtime_override_tool_json_schema(
- tool_list=allowed_functions,
- response_format=self.agent_state.response_format,
- request_heartbeat=True,
- terminal_tools=terminal_tool_names,
- )
-
- # For the first message, force the initial tool if one is specified
- force_tool_call = None
- if (
- step_count is not None
- and step_count == 0
- and not self.supports_structured_output
- and len(self.tool_rules_solver.init_tool_rules) > 0
- ):
- # TODO: This just seems wrong? What if there are more than 1 init tool rules?
- force_tool_call = self.tool_rules_solver.init_tool_rules[0].tool_name
- # Force a tool call if exactly one tool is specified
- elif step_count is not None and step_count > 0 and len(allowed_tool_names) == 1:
- force_tool_call = allowed_tool_names[0]
-
- for attempt in range(1, empty_response_retry_limit + 1):
- try:
- log_telemetry(self.logger, "_get_ai_reply create start")
- # New LLM client flow
- llm_client = LLMClient.create(
- provider_type=self.agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=self.user,
- )
-
- if llm_client and not stream:
- response = llm_client.send_llm_request(
- messages=message_sequence,
- llm_config=self.agent_state.llm_config,
- tools=allowed_functions,
- force_tool_call=force_tool_call,
- telemetry_manager=self.telemetry_manager,
- step_id=step_id,
- )
- else:
- # Fallback to existing flow
- for message in message_sequence:
- if isinstance(message.content, list):
-
- def get_fallback_text_content(content):
- if isinstance(content, ImageContent):
- return TextContent(text="[Image Here]")
- return content
-
- message.content = [get_fallback_text_content(content) for content in message.content]
-
- response = create(
- llm_config=self.agent_state.llm_config,
- messages=message_sequence,
- user_id=self.agent_state.created_by_id,
- functions=allowed_functions,
- # functions_python=self.functions_python, do we need this?
- function_call=function_call,
- first_message=first_message,
- force_tool_call=force_tool_call,
- stream=stream,
- stream_interface=self.interface,
- put_inner_thoughts_first=put_inner_thoughts_first,
- name=self.agent_state.name,
- telemetry_manager=self.telemetry_manager,
- step_id=step_id,
- actor=self.user,
- )
- log_telemetry(self.logger, "_get_ai_reply create finish")
-
- # These bottom two are retryable
- if len(response.choices) == 0 or response.choices[0] is None:
- raise ValueError(f"API call returned an empty message: {response}")
-
- if response.choices[0].finish_reason not in ["stop", "function_call", "tool_calls"]:
- if response.choices[0].finish_reason == "length":
- # This is not retryable, hence RuntimeError v.s. ValueError
- raise RuntimeError("Finish reason was length (maximum context length)")
- else:
- raise ValueError(f"Bad finish reason from API: {response.choices[0].finish_reason}")
- log_telemetry(self.logger, "_handle_ai_response finish")
-
- except ValueError as ve:
- if attempt >= empty_response_retry_limit:
- warnings.warn(f"Retry limit reached. Final error: {ve}")
- log_telemetry(self.logger, "_handle_ai_response finish ValueError")
- raise Exception(f"Retries exhausted and no valid response received. Final error: {ve}")
- else:
- delay = min(backoff_factor * (2 ** (attempt - 1)), max_delay)
- warnings.warn(f"Attempt {attempt} failed: {ve}. Retrying in {delay} seconds...")
- time.sleep(delay)
- continue
-
- except Exception as e:
- # For non-retryable errors, exit immediately
- log_telemetry(self.logger, "_handle_ai_response finish generic Exception")
- raise e
-
- # check if we are going over the context window: this allows for articifial constraints
- if response.usage.total_tokens > self.agent_state.llm_config.context_window:
- # trigger summarization
- log_telemetry(self.logger, "_get_ai_reply summarize_messages_inplace")
- self.summarize_messages_inplace()
-
- # return the response
- return response
-
- log_telemetry(self.logger, "_handle_ai_response finish catch-all exception")
- raise Exception("Retries exhausted and no valid response received.")
-
- @trace_method
- def _handle_ai_response(
- self,
- response_message: ChatCompletionMessage, # TODO should we eventually move the Message creation outside of this function?
- override_tool_call_id: bool = False,
- # If we are streaming, we needed to create a Message ID ahead of time,
- # and now we want to use it in the creation of the Message object
- # TODO figure out a cleaner way to do this
- response_message_id: Optional[str] = None,
- group_id: Optional[str] = None,
- ) -> Tuple[List[Message], bool, bool]:
- """Handles parsing and function execution"""
- log_telemetry(self.logger, "_handle_ai_response start")
- # Hacky failsafe for now to make sure we didn't implement the streaming Message ID creation incorrectly
- if response_message_id is not None:
- assert response_message_id.startswith("message-"), response_message_id
-
- messages = [] # append these to the history when done
- function_name = None
- function_args = {}
- chunk_index = 0
-
- # Step 2: check if LLM wanted to call a function
- if response_message.function_call or (response_message.tool_calls is not None and len(response_message.tool_calls) > 0):
- if response_message.function_call:
- raise DeprecationWarning(response_message)
- if response_message.tool_calls is not None and len(response_message.tool_calls) > 1:
- # raise NotImplementedError(f">1 tool call not supported")
- # TODO eventually support sequential tool calling
- self.logger.warning(f">1 tool call not supported, using index=0 only\n{response_message.tool_calls}")
- response_message.tool_calls = [response_message.tool_calls[0]]
- assert response_message.tool_calls is not None and len(response_message.tool_calls) > 0
-
- # generate UUID for tool call
- if override_tool_call_id or response_message.function_call:
- warnings.warn("Overriding the tool call can result in inconsistent tool call IDs during streaming")
- tool_call_id = get_tool_call_id() # needs to be a string for JSON
- response_message.tool_calls[0].id = tool_call_id
- else:
- tool_call_id = response_message.tool_calls[0].id
- assert tool_call_id is not None # should be defined
-
- # only necessary to add the tool_call_id to a function call (antipattern)
- # response_message_dict = response_message.model_dump()
- # response_message_dict["tool_call_id"] = tool_call_id
-
- # role: assistant (requesting tool call, set tool call ID)
- messages.append(
- # NOTE: we're recreating the message here
- # TODO should probably just overwrite the fields?
- Message.dict_to_message(
- id=response_message_id,
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=response_message.model_dump(),
- name=self.agent_state.name,
- group_id=group_id,
- )
- ) # extend conversation with assistant's reply
- self.logger.debug(f"Function call message: {messages[-1]}")
-
- nonnull_content = False
- if response_message.content or response_message.reasoning_content or response_message.redacted_reasoning_content:
- # The content if then internal monologue, not chat
- self.interface.internal_monologue(response_message.content, msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
- # Flag to avoid printing a duplicate if inner thoughts get popped from the function call
- nonnull_content = True
-
- # Step 3: call the function
- # Note: the JSON response may not always be valid; be sure to handle errors
- function_call = (
- response_message.function_call if response_message.function_call is not None else response_message.tool_calls[0].function
- )
- function_name = function_call.name
- self.logger.info(f"Request to call function {function_name} with tool_call_id: {tool_call_id}")
-
- # Failure case 1: function name is wrong (not in agent_state.tools)
- target_letta_tool = None
- for t in self.agent_state.tools:
- if t.name == function_name:
- # This force refreshes the target_letta_tool from the database
- # We only do this on name match to confirm that the agent state contains a specific tool with the right name
- target_letta_tool = ToolManager().get_tool_by_name(tool_name=function_name, actor=self.user)
- break
-
- if not target_letta_tool:
- error_msg = f"No function named {function_name}"
- function_response = "None" # more like "never ran?"
- messages = self._handle_function_error_response(
- error_msg, tool_call_id, function_name, function_args, function_response, messages, group_id=group_id
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # Failure case 2: function name is OK, but function args are bad JSON
- try:
- raw_function_args = function_call.arguments
- function_args = parse_json(raw_function_args)
- if not isinstance(function_args, dict):
- raise ValueError(f"Function arguments are not a dictionary: {function_args} (raw={raw_function_args})")
- except Exception as e:
- print(e)
- error_msg = f"Error parsing JSON for function '{function_name}' arguments: {function_call.arguments}"
- function_response = "None" # more like "never ran?"
- messages = self._handle_function_error_response(
- error_msg, tool_call_id, function_name, function_args, function_response, messages, group_id=group_id
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # Check if inner thoughts is in the function call arguments (possible apparently if you are using Azure)
- if INNER_THOUGHTS_KWARG in function_args:
- response_message.content = function_args.pop(INNER_THOUGHTS_KWARG)
- # The content if then internal monologue, not chat
- if response_message.content and not nonnull_content:
- self.interface.internal_monologue(response_message.content, msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
-
- # (Still parsing function args)
- # Handle requests for immediate heartbeat
- heartbeat_request = function_args.pop("request_heartbeat", None)
-
- # Edge case: heartbeat_request is returned as a stringified boolean, we will attempt to parse:
- if isinstance(heartbeat_request, str) and heartbeat_request.lower().strip() == "true":
- heartbeat_request = True
-
- if heartbeat_request is None:
- heartbeat_request = False
-
- if not isinstance(heartbeat_request, bool):
- self.logger.warning(
- f"{CLI_WARNING_PREFIX}'request_heartbeat' arg parsed was not a bool or None, type={type(heartbeat_request)}, value={heartbeat_request}"
- )
- heartbeat_request = False
-
- # Failure case 3: function failed during execution
- # NOTE: the msg_obj associated with the "Running " message is the prior assistant message, not the function/tool role message
- # this is because the function/tool role message is only created once the function/tool has executed/returned
-
- # handle cases where we return a json message
- if "message" in function_args:
- function_args["message"] = str(function_args.get("message", ""))
- self.interface.function_message(f"Running {function_name}({function_args})", msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index = 0 # reset chunk index after assistant message
- try:
- # handle tool execution (sandbox) and state updates
- log_telemetry(
- self.logger, "_handle_ai_response execute tool start", function_name=function_name, function_args=function_args
- )
- log_event(
- "tool_call_initiated",
- attributes={
- "function_name": function_name,
- "target_letta_tool": target_letta_tool.model_dump(),
- **{f"function_args.{k}": v for k, v in function_args.items()},
- },
- )
-
- tool_execution_result = self.execute_tool_and_persist_state(function_name, function_args, target_letta_tool)
- function_response = tool_execution_result.func_return
-
- log_event(
- "tool_call_ended",
- attributes={
- "function_response": function_response,
- "tool_execution_result": tool_execution_result.model_dump(),
- },
- )
- log_telemetry(
- self.logger, "_handle_ai_response execute tool finish", function_name=function_name, function_args=function_args
- )
-
- if tool_execution_result and tool_execution_result.status == "error":
- tool_return = ToolReturn(
- status=tool_execution_result.status, stdout=tool_execution_result.stdout, stderr=tool_execution_result.stderr
- )
- messages = self._handle_function_error_response(
- function_response,
- tool_call_id,
- function_name,
- function_args,
- function_response,
- messages,
- [tool_return],
- group_id=group_id,
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # handle trunction
- if function_name in ["conversation_search", "conversation_search_date", "archival_memory_search"]:
- # with certain functions we rely on the paging mechanism to handle overflow
- truncate = False
- else:
- # but by default, we add a truncation safeguard to prevent bad functions from
- # overflow the agent context window
- truncate = True
-
- # get the function response limit
- return_char_limit = target_letta_tool.return_char_limit
- function_response_string = validate_function_response(
- function_response, return_char_limit=return_char_limit, truncate=truncate
- )
- function_args.pop("self", None)
- function_response = package_function_response(True, function_response_string, self.agent_state.timezone)
- function_failed = False
- except Exception as e:
- function_args.pop("self", None)
- # error_msg = f"Error calling function {function_name} with args {function_args}: {str(e)}"
- # Less detailed - don't provide full args, idea is that it should be in recent context so no need (just adds noise)
- error_msg = get_friendly_error_msg(function_name=function_name, exception_name=type(e).__name__, exception_message=str(e))
- error_msg_user = f"{error_msg}\n{traceback.format_exc()}"
- self.logger.error(error_msg_user)
- messages = self._handle_function_error_response(
- error_msg,
- tool_call_id,
- function_name,
- function_args,
- function_response,
- messages,
- [ToolReturn(status="error", stderr=[error_msg_user])],
- include_function_failed_message=True,
- group_id=group_id,
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # Step 4: check if function response is an error
- if function_response_string.startswith(ERROR_MESSAGE_PREFIX):
- error_msg = function_response_string
- tool_return = ToolReturn(
- status=tool_execution_result.status,
- stdout=tool_execution_result.stdout,
- stderr=tool_execution_result.stderr,
- )
- messages = self._handle_function_error_response(
- error_msg,
- tool_call_id,
- function_name,
- function_args,
- function_response,
- messages,
- [tool_return],
- include_function_failed_message=True,
- group_id=group_id,
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # If no failures happened along the way: ...
- # Step 5: send the info on the function call and function response to GPT
- tool_return = ToolReturn(
- status=tool_execution_result.status,
- stdout=tool_execution_result.stdout,
- stderr=tool_execution_result.stderr,
- )
- messages.append(
- Message(
- agent_id=self.agent_state.id,
- # Base info OpenAI-style
- model=self.model,
- role="tool",
- name=function_name, # NOTE: when role is 'tool', the 'name' is the function name, not agent name
- content=[TextContent(text=function_response)],
- tool_call_id=tool_call_id,
- # Letta extras
- tool_returns=[tool_return],
- group_id=group_id,
- )
- ) # extend conversation with function response
- self.interface.function_message(f"Ran {function_name}({function_args})", msg_obj=messages[-1], chunk_index=chunk_index)
- self.interface.function_message(f"Success: {function_response_string}", msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
- self.last_function_response = function_response
-
- else:
- # Standard non-function reply
- messages.append(
- Message.dict_to_message(
- id=response_message_id,
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=response_message.model_dump(),
- name=self.agent_state.name,
- group_id=group_id,
- )
- ) # extend conversation with assistant's reply
- self.interface.internal_monologue(response_message.content, msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
- heartbeat_request = False
- function_failed = False
-
- # rebuild memory
- # TODO: @charles please check this
- self.agent_state = self.agent_manager.rebuild_system_prompt(agent_id=self.agent_state.id, actor=self.user)
-
- # Update ToolRulesSolver state with last called function
- self.tool_rules_solver.register_tool_call(function_name)
- # Update heartbeat request according to provided tool rules
- if self.tool_rules_solver.has_children_tools(function_name):
- heartbeat_request = True
- elif self.tool_rules_solver.is_terminal_tool(function_name):
- heartbeat_request = False
-
- # if continue tool rule, then must request a heartbeat
- # TODO: dont even include heartbeats in the args
- if self.tool_rules_solver.is_continue_tool(function_name):
- heartbeat_request = True
-
- log_telemetry(self.logger, "_handle_ai_response finish")
- return messages, heartbeat_request, function_failed
-
- @trace_method
- def step(
- self,
- input_messages: List[MessageCreate],
- # additional args
- chaining: bool = True,
- max_chaining_steps: Optional[int] = None,
- put_inner_thoughts_first: bool = True,
- **kwargs,
- ) -> LettaUsageStatistics:
- """Run Agent.step in a loop, handling chaining via heartbeat requests and function failures"""
- # Defensively clear the tool rules solver history
- # Usually this would be extraneous as Agent loop is re-loaded on every message send
- # But just to be safe
- self.tool_rules_solver.clear_tool_history()
-
- # Convert MessageCreate objects to Message objects
- next_input_messages = convert_message_creates_to_messages(input_messages, self.agent_state.id, self.agent_state.timezone)
- counter = 0
- total_usage = UsageStatistics()
- step_count = 0
- function_failed = False
- steps_messages = []
- while True:
- kwargs["first_message"] = False
- kwargs["step_count"] = step_count
- kwargs["last_function_failed"] = function_failed
- step_response = self.inner_step(
- messages=next_input_messages,
- put_inner_thoughts_first=put_inner_thoughts_first,
- **kwargs,
- )
-
- heartbeat_request = step_response.heartbeat_request
- function_failed = step_response.function_failed
- token_warning = step_response.in_context_memory_warning
- usage = step_response.usage
- steps_messages.append(step_response.messages)
-
- step_count += 1
- total_usage += usage
- counter += 1
- self.interface.step_complete()
-
- # logger.debug("Saving agent state")
- # save updated state
- save_agent(self)
-
- # Chain stops
- if not chaining:
- self.logger.info("No chaining, stopping after one step")
- break
- elif max_chaining_steps is not None and counter > max_chaining_steps:
- self.logger.info(f"Hit max chaining steps, stopping after {counter} steps")
- break
- # Chain handlers
- elif token_warning and summarizer_settings.send_memory_warning_message:
- assert self.agent_state.created_by_id is not None
- next_input_messages = [
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict={
- "role": "user", # TODO: change to system?
- "content": get_token_limit_warning(),
- },
- ),
- ]
- continue # always chain
- elif function_failed:
- assert self.agent_state.created_by_id is not None
- next_input_messages = [
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict={
- "role": "user", # TODO: change to system?
- "content": get_heartbeat(self.agent_state.timezone, FUNC_FAILED_HEARTBEAT_MESSAGE),
- },
- )
- ]
- continue # always chain
- elif heartbeat_request:
- assert self.agent_state.created_by_id is not None
- next_input_messages = [
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict={
- "role": "user", # TODO: change to system?
- "content": get_heartbeat(self.agent_state.timezone, REQ_HEARTBEAT_MESSAGE),
- },
- )
- ]
- continue # always chain
- # Letta no-op / yield
- else:
- break
-
- if self.agent_state.message_buffer_autoclear:
- self.logger.info("Autoclearing message buffer")
- self.agent_state = self.agent_manager.trim_all_in_context_messages_except_system(self.agent_state.id, actor=self.user)
-
- return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count, steps_messages=steps_messages)
-
- def inner_step(
- self,
- messages: List[Message],
- first_message: bool = False,
- first_message_retry_limit: int = FIRST_MESSAGE_ATTEMPTS,
- skip_verify: bool = False,
- stream: bool = False, # TODO move to config?
- step_count: Optional[int] = None,
- metadata: Optional[dict] = None,
- summarize_attempt_count: int = 0,
- last_function_failed: bool = False,
- put_inner_thoughts_first: bool = True,
- ) -> AgentStepResponse:
- """Runs a single step in the agent loop (generates at most one LLM call)"""
- try:
- # Extract job_id from metadata if present
- job_id = metadata.get("job_id") if metadata else None
-
- # Declare step_id for the given step to be used as the step is processing.
- step_id = generate_step_id()
-
- # Step 0: update core memory
- # only pulling latest block data if shared memory is being used
- current_persisted_memory = Memory(
- blocks=[self.block_manager.get_block_by_id(block.id, actor=self.user) for block in self.agent_state.memory.get_blocks()],
- file_blocks=self.agent_state.memory.file_blocks,
- prompt_template=get_prompt_template_for_agent_type(self.agent_state.agent_type),
- ) # read blocks from DB
- self.update_memory_if_changed(current_persisted_memory)
-
- # Step 1: add user message
- if not all(isinstance(m, Message) for m in messages):
- raise ValueError(f"messages should be a list of Message, got {[type(m) for m in messages]}")
-
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- input_message_sequence = in_context_messages + messages
-
- if (
- len(input_message_sequence) > 1
- and input_message_sequence[-1].role != "user"
- and input_message_sequence[-1].group_id is None
- ):
- self.logger.warning(f"{CLI_WARNING_PREFIX}Attempting to run ChatCompletion without user as the last message in the queue")
-
- # Step 2: send the conversation and available functions to the LLM
- response = self._get_ai_reply(
- message_sequence=input_message_sequence,
- first_message=first_message,
- stream=stream,
- step_count=step_count,
- last_function_failed=last_function_failed,
- put_inner_thoughts_first=put_inner_thoughts_first,
- step_id=step_id,
- )
- if not response:
- # EDGE CASE: Function call failed AND there's no tools left for agent to call -> return early
- return AgentStepResponse(
- messages=input_message_sequence,
- heartbeat_request=False,
- function_failed=False, # NOTE: this is different from other function fails. We force to return early
- in_context_memory_warning=False,
- usage=UsageStatistics(),
- )
-
- # Step 3: check if LLM wanted to call a function
- # (if yes) Step 4: call the function
- # (if yes) Step 5: send the info on the function call and function response to LLM
- response_message = response.choices[0].message
-
- response_message.model_copy() # TODO why are we copying here?
- all_response_messages, heartbeat_request, function_failed = self._handle_ai_response(
- response_message,
- # TODO this is kind of hacky, find a better way to handle this
- # the only time we set up message creation ahead of time is when streaming is on
- response_message_id=response.id if stream else None,
- group_id=input_message_sequence[-1].group_id,
- )
-
- # Step 6: extend the message history
- if len(messages) > 0:
- all_new_messages = messages + all_response_messages
- else:
- all_new_messages = all_response_messages
-
- if self.save_last_response:
- self.last_response_messages = all_response_messages
-
- # Check the memory pressure and potentially issue a memory pressure warning
- current_total_tokens = response.usage.total_tokens
- active_memory_warning = False
-
- # We can't do summarize logic properly if context_window is undefined
- if self.agent_state.llm_config.context_window is None:
- # Fallback if for some reason context_window is missing, just set to the default
- print(f"{CLI_WARNING_PREFIX}could not find context_window in config, setting to default {LLM_MAX_TOKENS['DEFAULT']}")
- print(f"{self.agent_state}")
- self.agent_state.llm_config.context_window = (
- LLM_MAX_TOKENS[self.model] if (self.model is not None and self.model in LLM_MAX_TOKENS) else LLM_MAX_TOKENS["DEFAULT"]
- )
-
- if current_total_tokens > summarizer_settings.memory_warning_threshold * int(self.agent_state.llm_config.context_window):
- logger.warning(
- f"{CLI_WARNING_PREFIX}last response total_tokens ({current_total_tokens}) > {summarizer_settings.memory_warning_threshold * int(self.agent_state.llm_config.context_window)}"
- )
-
- log_event(
- name="memory_pressure_warning",
- attributes={
- "current_total_tokens": current_total_tokens,
- "context_window_limit": self.agent_state.llm_config.context_window,
- },
- )
- # Only deliver the alert if we haven't already (this period)
- if not self.agent_alerted_about_memory_pressure:
- active_memory_warning = True
- self.agent_alerted_about_memory_pressure = True # it's up to the outer loop to handle this
-
- else:
- logger.info(
- f"last response total_tokens ({current_total_tokens}) < {summarizer_settings.memory_warning_threshold * int(self.agent_state.llm_config.context_window)}"
- )
-
- # Log step - this must happen before messages are persisted
- step = self.step_manager.log_step(
- actor=self.user,
- agent_id=self.agent_state.id,
- provider_name=self.agent_state.llm_config.model_endpoint_type,
- provider_category=self.agent_state.llm_config.provider_category or "base",
- model=self.agent_state.llm_config.model,
- model_endpoint=self.agent_state.llm_config.model_endpoint,
- context_window_limit=self.agent_state.llm_config.context_window,
- usage=response.usage,
- provider_id=self.provider_manager.get_provider_id_from_name(
- self.agent_state.llm_config.provider_name,
- actor=self.user,
- ),
- job_id=job_id,
- step_id=step_id,
- project_id=self.agent_state.project_id,
- status=StepStatus.SUCCESS, # Set to SUCCESS since we're logging after successful completion
- )
- for message in all_new_messages:
- message.step_id = step.id
-
- # Persisting into Messages
- self.agent_state = self.agent_manager.append_to_in_context_messages(
- all_new_messages, agent_id=self.agent_state.id, actor=self.user
- )
- if job_id:
- for message in all_new_messages:
- if message.role != "user":
- self.job_manager.add_message_to_job(
- job_id=job_id,
- message_id=message.id,
- actor=self.user,
- )
-
- return AgentStepResponse(
- messages=all_new_messages,
- heartbeat_request=heartbeat_request,
- function_failed=function_failed,
- in_context_memory_warning=active_memory_warning,
- usage=response.usage,
- )
-
- except Exception as e:
- logger.error(f"step() failed\nmessages = {messages}\nerror = {e}")
-
- # If we got a context alert, try trimming the messages length, then try again
- if is_context_overflow_error(e):
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
-
- # TODO: this is a patch to resolve immediate issues, should be removed once the summarizer is fixes
- if self.agent_state.message_buffer_autoclear:
- # no calling the summarizer in this case
- logger.error(
- f"step() failed with an exception that looks like a context window overflow, but message buffer is set to autoclear, so skipping: '{str(e)}'"
- )
- raise e
-
- if summarize_attempt_count <= summarizer_settings.max_summarizer_retries:
- logger.warning(
- f"context window exceeded with limit {self.agent_state.llm_config.context_window}, attempting to summarize ({summarize_attempt_count}/{summarizer_settings.max_summarizer_retries}"
- )
- # A separate API call to run a summarizer
- self.summarize_messages_inplace()
-
- # Try step again
- return self.inner_step(
- messages=messages,
- first_message=first_message,
- first_message_retry_limit=first_message_retry_limit,
- skip_verify=skip_verify,
- stream=stream,
- metadata=metadata,
- summarize_attempt_count=summarize_attempt_count + 1,
- )
- else:
- err_msg = f"Ran summarizer {summarize_attempt_count - 1} times for agent id={self.agent_state.id}, but messages are still overflowing the context window."
- token_counts = (get_token_counts_for_messages(in_context_messages),)
- logger.error(err_msg)
- logger.error(f"num_in_context_messages: {len(self.agent_state.message_ids)}")
- logger.error(f"token_counts: {token_counts}")
- raise ContextWindowExceededError(
- err_msg,
- details={
- "num_in_context_messages": len(self.agent_state.message_ids),
- "in_context_messages_text": [m.content for m in in_context_messages],
- "token_counts": token_counts,
- },
- )
-
- else:
- logger.error(f"step() failed with an unrecognized exception: '{str(e)}'")
- traceback.print_exc()
- raise e
-
- def step_user_message(self, user_message_str: str, **kwargs) -> AgentStepResponse:
- """Takes a basic user message string, turns it into a stringified JSON with extra metadata, then sends it to the agent
-
- Example:
- -> user_message_str = 'hi'
- -> {'message': 'hi', 'type': 'user_message', ...}
- -> json.dumps(...)
- -> agent.step(messages=[Message(role='user', text=...)])
- """
- # Wrap with metadata, dumps to JSON
- assert user_message_str and isinstance(user_message_str, str), (
- f"user_message_str should be a non-empty string, got {type(user_message_str)}"
- )
- user_message_json_str = package_user_message(user_message_str, self.agent_state.timezone)
-
- # Validate JSON via save/load
- user_message = validate_json(user_message_json_str)
- cleaned_user_message_text, name = strip_name_field_from_user_message(user_message)
-
- # Turn into a dict
- openai_message_dict = {"role": "user", "content": cleaned_user_message_text, "name": name}
-
- # Create the associated Message object (in the database)
- assert self.agent_state.created_by_id is not None, "User ID is not set"
- user_message = Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=openai_message_dict,
- # created_at=timestamp,
- )
-
- return self.inner_step(messages=[user_message], **kwargs)
-
- def summarize_messages_inplace(self):
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
- in_context_messages_openai_no_system = in_context_messages_openai[1:]
- token_counts = get_token_counts_for_messages(in_context_messages)
- logger.info(f"System message token count={token_counts[0]}")
- logger.info(f"token_counts_no_system={token_counts[1:]}")
-
- if in_context_messages_openai[0]["role"] != "system":
- raise RuntimeError(f"in_context_messages_openai[0] should be system (instead got {in_context_messages_openai[0]})")
-
- # If at this point there's nothing to summarize, throw an error
- if len(in_context_messages_openai_no_system) == 0:
- raise ContextWindowExceededError(
- "Not enough messages to compress for summarization",
- details={
- "num_candidate_messages": len(in_context_messages_openai_no_system),
- "num_total_messages": len(in_context_messages_openai),
- },
- )
-
- cutoff = calculate_summarizer_cutoff(in_context_messages=in_context_messages, token_counts=token_counts, logger=logger)
- message_sequence_to_summarize = in_context_messages[1:cutoff] # do NOT get rid of the system message
- logger.info(f"Attempting to summarize {len(message_sequence_to_summarize)} messages of {len(in_context_messages)}")
-
- # We can't do summarize logic properly if context_window is undefined
- if self.agent_state.llm_config.context_window is None:
- # Fallback if for some reason context_window is missing, just set to the default
- logger.warning(f"{CLI_WARNING_PREFIX}could not find context_window in config, setting to default {LLM_MAX_TOKENS['DEFAULT']}")
- self.agent_state.llm_config.context_window = (
- LLM_MAX_TOKENS[self.model] if (self.model is not None and self.model in LLM_MAX_TOKENS) else LLM_MAX_TOKENS["DEFAULT"]
- )
-
- summary = summarize_messages(
- agent_state=self.agent_state, message_sequence_to_summarize=message_sequence_to_summarize, actor=self.user
- )
- logger.info(f"Got summary: {summary}")
-
- # Metadata that's useful for the agent to see
- all_time_message_count = self.message_manager.size(agent_id=self.agent_state.id, actor=self.user)
- remaining_message_count = 1 + len(in_context_messages) - cutoff # System + remaining
- hidden_message_count = all_time_message_count - remaining_message_count
- summary_message_count = len(message_sequence_to_summarize)
- summary_message = package_summarize_message(
- summary, summary_message_count, hidden_message_count, all_time_message_count, self.agent_state.timezone
- )
- logger.info(f"Packaged into message: {summary_message}")
-
- prior_len = len(in_context_messages_openai)
- self.agent_state = self.agent_manager.trim_older_in_context_messages(num=cutoff, agent_id=self.agent_state.id, actor=self.user)
- packed_summary_message = {"role": "user", "content": summary_message}
- # Prepend the summary
- self.agent_state = self.agent_manager.prepend_to_in_context_messages(
- messages=[
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=packed_summary_message,
- )
- ],
- agent_id=self.agent_state.id,
- actor=self.user,
- )
-
- # reset alert
- self.agent_alerted_about_memory_pressure = False
- curr_in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
-
- current_token_count = sum(get_token_counts_for_messages(curr_in_context_messages))
- logger.info(f"Ran summarizer, messages length {prior_len} -> {len(curr_in_context_messages)}")
- logger.info(f"Summarizer brought down total token count from {sum(token_counts)} -> {current_token_count}")
- log_event(
- name="summarization",
- attributes={
- "prior_length": prior_len,
- "current_length": len(curr_in_context_messages),
- "prior_token_count": sum(token_counts),
- "current_token_count": current_token_count,
- "context_window_limit": self.agent_state.llm_config.context_window,
- },
- )
-
- def add_function(self, function_name: str) -> str:
- # TODO: refactor
- raise NotImplementedError
-
- def remove_function(self, function_name: str) -> str:
- # TODO: refactor
- raise NotImplementedError
-
- def migrate_embedding(self, embedding_config: EmbeddingConfig):
- """Migrate the agent to a new embedding"""
- # TODO: archival memory
-
- # TODO: recall memory
- raise NotImplementedError()
-
- def get_context_window(self) -> ContextWindowOverview:
- """Get the context window of the agent"""
-
- system_prompt = self.agent_state.system # TODO is this the current system or the initial system?
- num_tokens_system = count_tokens(system_prompt)
- core_memory = self.agent_state.memory.compile()
- num_tokens_core_memory = count_tokens(core_memory)
-
- # Grab the in-context messages
- # conversion of messages to OpenAI dict format, which is passed to the token counter
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
-
- # Check if there's a summary message in the message queue
- if (
- len(in_context_messages) > 1
- and in_context_messages[1].role == MessageRole.user
- and in_context_messages[1].content
- and len(in_context_messages[1].content) == 1
- and isinstance(in_context_messages[1].content[0], TextContent)
- # TODO remove hardcoding
- and "The following is a summary of the previous " in in_context_messages[1].content[0].text
- ):
- # Summary message exists
- text_content = in_context_messages[1].content[0].text
- assert text_content is not None
- summary_memory = text_content
- num_tokens_summary_memory = count_tokens(text_content)
- # with a summary message, the real messages start at index 2
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[2:], model=self.model)
- if len(in_context_messages_openai) > 2
- else 0
- )
-
- else:
- summary_memory = None
- num_tokens_summary_memory = 0
- # with no summary message, the real messages start at index 1
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[1:], model=self.model)
- if len(in_context_messages_openai) > 1
- else 0
- )
-
- agent_manager_passage_size = self.agent_manager.passage_size(actor=self.user, agent_id=self.agent_state.id)
- message_manager_size = self.message_manager.size(actor=self.user, agent_id=self.agent_state.id)
- external_memory_summary = PromptGenerator.compile_memory_metadata_block(
- memory_edit_timestamp=get_utc_time(),
- timezone=self.agent_state.timezone,
- previous_message_count=self.message_manager.size(actor=self.user, agent_id=self.agent_state.id),
- archival_memory_size=self.agent_manager.passage_size(actor=self.user, agent_id=self.agent_state.id),
- )
- num_tokens_external_memory_summary = count_tokens(external_memory_summary)
-
- # tokens taken up by function definitions
- agent_state_tool_jsons = [t.json_schema for t in self.agent_state.tools]
- if agent_state_tool_jsons:
- available_functions_definitions = [OpenAITool(type="function", function=f) for f in agent_state_tool_jsons]
- num_tokens_available_functions_definitions = num_tokens_from_functions(functions=agent_state_tool_jsons, model=self.model)
- else:
- available_functions_definitions = []
- num_tokens_available_functions_definitions = 0
-
- num_tokens_used_total = (
- num_tokens_system # system prompt
- + num_tokens_available_functions_definitions # function definitions
- + num_tokens_core_memory # core memory
- + num_tokens_external_memory_summary # metadata (statistics) about recall/archival
- + num_tokens_summary_memory # summary of ongoing conversation
- + num_tokens_messages # tokens taken by messages
- )
- assert isinstance(num_tokens_used_total, int)
-
- return ContextWindowOverview(
- # context window breakdown (in messages)
- num_messages=len(in_context_messages),
- num_archival_memory=agent_manager_passage_size,
- num_recall_memory=message_manager_size,
- num_tokens_external_memory_summary=num_tokens_external_memory_summary,
- external_memory_summary=external_memory_summary,
- # top-level information
- context_window_size_max=self.agent_state.llm_config.context_window,
- context_window_size_current=num_tokens_used_total,
- # context window breakdown (in tokens)
- num_tokens_system=num_tokens_system,
- system_prompt=system_prompt,
- num_tokens_core_memory=num_tokens_core_memory,
- core_memory=core_memory,
- num_tokens_summary_memory=num_tokens_summary_memory,
- summary_memory=summary_memory,
- num_tokens_messages=num_tokens_messages,
- messages=in_context_messages,
- # related to functions
- num_tokens_functions_definitions=num_tokens_available_functions_definitions,
- functions_definitions=available_functions_definitions,
- )
-
- async def get_context_window_async(self) -> ContextWindowOverview:
- if settings.environment == "PRODUCTION" and model_settings.anthropic_api_key:
- return await self.get_context_window_from_anthropic_async()
- return await self.get_context_window_from_tiktoken_async()
-
- async def get_context_window_from_tiktoken_async(self) -> ContextWindowOverview:
- """Get the context window of the agent"""
- # Grab the in-context messages
- in_context_messages = await self.message_manager.get_messages_by_ids_async(
- message_ids=self.agent_state.message_ids, actor=self.user
- )
-
- # conversion of messages to OpenAI dict format, which is passed to the token counter
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
-
- # Extract system, memory and external summary
- if (
- len(in_context_messages) > 0
- and in_context_messages[0].role == MessageRole.system
- and in_context_messages[0].content
- and len(in_context_messages[0].content) == 1
- and isinstance(in_context_messages[0].content[0], TextContent)
- ):
- system_message = in_context_messages[0].content[0].text
-
- external_memory_marker_pos = system_message.find("###")
- core_memory_marker_pos = system_message.find("<", external_memory_marker_pos)
- if external_memory_marker_pos != -1 and core_memory_marker_pos != -1:
- system_prompt = system_message[:external_memory_marker_pos].strip()
- external_memory_summary = system_message[external_memory_marker_pos:core_memory_marker_pos].strip()
- core_memory = system_message[core_memory_marker_pos:].strip()
- else:
- # if no markers found, put everything in system message
- self.logger.info("No markers found in system message, core_memory and external_memory_summary will not be loaded")
- system_prompt = system_message
- external_memory_summary = ""
- core_memory = ""
- else:
- # if no system message, fall back on agent's system prompt
- self.logger.info("No system message found in history, core_memory and external_memory_summary will not be loaded")
- system_prompt = self.agent_state.system
- external_memory_summary = ""
- core_memory = ""
-
- num_tokens_system = count_tokens(system_prompt)
- num_tokens_core_memory = count_tokens(core_memory)
- num_tokens_external_memory_summary = count_tokens(external_memory_summary)
-
- # Check if there's a summary message in the message queue
- if (
- len(in_context_messages) > 1
- and in_context_messages[1].role == MessageRole.user
- and in_context_messages[1].content
- and len(in_context_messages[1].content) == 1
- and isinstance(in_context_messages[1].content[0], TextContent)
- # TODO remove hardcoding
- and "The following is a summary of the previous " in in_context_messages[1].content[0].text
- ):
- # Summary message exists
- text_content = in_context_messages[1].content[0].text
- assert text_content is not None
- summary_memory = text_content
- num_tokens_summary_memory = count_tokens(text_content)
- # with a summary message, the real messages start at index 2
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[2:], model=self.model)
- if len(in_context_messages_openai) > 2
- else 0
- )
-
- else:
- summary_memory = None
- num_tokens_summary_memory = 0
- # with no summary message, the real messages start at index 1
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[1:], model=self.model)
- if len(in_context_messages_openai) > 1
- else 0
- )
-
- # tokens taken up by function definitions
- agent_state_tool_jsons = [t.json_schema for t in self.agent_state.tools]
- if agent_state_tool_jsons:
- available_functions_definitions = [OpenAITool(type="function", function=f) for f in agent_state_tool_jsons]
- num_tokens_available_functions_definitions = num_tokens_from_functions(functions=agent_state_tool_jsons, model=self.model)
- else:
- available_functions_definitions = []
- num_tokens_available_functions_definitions = 0
-
- num_tokens_used_total = (
- num_tokens_system # system prompt
- + num_tokens_available_functions_definitions # function definitions
- + num_tokens_core_memory # core memory
- + num_tokens_external_memory_summary # metadata (statistics) about recall/archival
- + num_tokens_summary_memory # summary of ongoing conversation
- + num_tokens_messages # tokens taken by messages
- )
- assert isinstance(num_tokens_used_total, int)
-
- passage_manager_size = await self.passage_manager.agent_passage_size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
- message_manager_size = await self.message_manager.size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
-
- return ContextWindowOverview(
- # context window breakdown (in messages)
- num_messages=len(in_context_messages),
- num_archival_memory=passage_manager_size,
- num_recall_memory=message_manager_size,
- num_tokens_external_memory_summary=num_tokens_external_memory_summary,
- external_memory_summary=external_memory_summary,
- # top-level information
- context_window_size_max=self.agent_state.llm_config.context_window,
- context_window_size_current=num_tokens_used_total,
- # context window breakdown (in tokens)
- num_tokens_system=num_tokens_system,
- system_prompt=system_prompt,
- num_tokens_core_memory=num_tokens_core_memory,
- core_memory=core_memory,
- num_tokens_summary_memory=num_tokens_summary_memory,
- summary_memory=summary_memory,
- num_tokens_messages=num_tokens_messages,
- messages=in_context_messages,
- # related to functions
- num_tokens_functions_definitions=num_tokens_available_functions_definitions,
- functions_definitions=available_functions_definitions,
- )
-
- async def get_context_window_from_anthropic_async(self) -> ContextWindowOverview:
- """Get the context window of the agent"""
- anthropic_client = LLMClient.create(provider_type=ProviderType.anthropic, actor=self.user)
- model = self.agent_state.llm_config.model if self.agent_state.llm_config.model_endpoint_type == "anthropic" else None
-
- # Grab the in-context messages
- in_context_messages = await self.message_manager.get_messages_by_ids_async(
- message_ids=self.agent_state.message_ids, actor=self.user
- )
-
- # conversion of messages to anthropic dict format, which is passed to the token counter
- in_context_messages_anthropic = Message.to_anthropic_dicts_from_list(in_context_messages)
-
- # Extract system, memory and external summary
- if (
- len(in_context_messages) > 0
- and in_context_messages[0].role == MessageRole.system
- and in_context_messages[0].content
- and len(in_context_messages[0].content) == 1
- and isinstance(in_context_messages[0].content[0], TextContent)
- ):
- system_message = in_context_messages[0].content[0].text
-
- external_memory_marker_pos = system_message.find("###")
- core_memory_marker_pos = system_message.find("<", external_memory_marker_pos)
- if external_memory_marker_pos != -1 and core_memory_marker_pos != -1:
- system_prompt = system_message[:external_memory_marker_pos].strip()
- external_memory_summary = system_message[external_memory_marker_pos:core_memory_marker_pos].strip()
- core_memory = system_message[core_memory_marker_pos:].strip()
- else:
- # if no markers found, put everything in system message
- self.logger.info("No markers found in system message, core_memory and external_memory_summary will not be loaded")
- system_prompt = system_message
- external_memory_summary = ""
- core_memory = ""
- else:
- # if no system message, fall back on agent's system prompt
- self.logger.info("No system message found in history, core_memory and external_memory_summary will not be loaded")
- system_prompt = self.agent_state.system
- external_memory_summary = ""
- core_memory = ""
-
- num_tokens_system_coroutine = anthropic_client.count_tokens(model=model, messages=[{"role": "user", "content": system_prompt}])
- num_tokens_core_memory_coroutine = (
- anthropic_client.count_tokens(model=model, messages=[{"role": "user", "content": core_memory}])
- if core_memory
- else asyncio.sleep(0, result=0)
- )
- num_tokens_external_memory_summary_coroutine = (
- anthropic_client.count_tokens(model=model, messages=[{"role": "user", "content": external_memory_summary}])
- if external_memory_summary
- else asyncio.sleep(0, result=0)
- )
-
- # Check if there's a summary message in the message queue
- if (
- len(in_context_messages) > 1
- and in_context_messages[1].role == MessageRole.user
- and in_context_messages[1].content
- and len(in_context_messages[1].content) == 1
- and isinstance(in_context_messages[1].content[0], TextContent)
- # TODO remove hardcoding
- and "The following is a summary of the previous " in in_context_messages[1].content[0].text
- ):
- # Summary message exists
- text_content = in_context_messages[1].content[0].text
- assert text_content is not None
- summary_memory = text_content
- num_tokens_summary_memory_coroutine = anthropic_client.count_tokens(
- model=model, messages=[{"role": "user", "content": summary_memory}]
- )
- # with a summary message, the real messages start at index 2
- num_tokens_messages_coroutine = (
- anthropic_client.count_tokens(model=model, messages=in_context_messages_anthropic[2:])
- if len(in_context_messages_anthropic) > 2
- else asyncio.sleep(0, result=0)
- )
-
- else:
- summary_memory = None
- num_tokens_summary_memory_coroutine = asyncio.sleep(0, result=0)
- # with no summary message, the real messages start at index 1
- num_tokens_messages_coroutine = (
- anthropic_client.count_tokens(model=model, messages=in_context_messages_anthropic[1:])
- if len(in_context_messages_anthropic) > 1
- else asyncio.sleep(0, result=0)
- )
-
- # tokens taken up by function definitions
- if self.agent_state.tools and len(self.agent_state.tools) > 0:
- available_functions_definitions = [OpenAITool(type="function", function=f.json_schema) for f in self.agent_state.tools]
- num_tokens_available_functions_definitions_coroutine = anthropic_client.count_tokens(
- model=model,
- tools=available_functions_definitions,
- )
- else:
- available_functions_definitions = []
- num_tokens_available_functions_definitions_coroutine = asyncio.sleep(0, result=0)
-
- (
- num_tokens_system,
- num_tokens_core_memory,
- num_tokens_external_memory_summary,
- num_tokens_summary_memory,
- num_tokens_messages,
- num_tokens_available_functions_definitions,
- ) = await asyncio.gather(
- num_tokens_system_coroutine,
- num_tokens_core_memory_coroutine,
- num_tokens_external_memory_summary_coroutine,
- num_tokens_summary_memory_coroutine,
- num_tokens_messages_coroutine,
- num_tokens_available_functions_definitions_coroutine,
- )
-
- num_tokens_used_total = (
- num_tokens_system # system prompt
- + num_tokens_available_functions_definitions # function definitions
- + num_tokens_core_memory # core memory
- + num_tokens_external_memory_summary # metadata (statistics) about recall/archival
- + num_tokens_summary_memory # summary of ongoing conversation
- + num_tokens_messages # tokens taken by messages
- )
- assert isinstance(num_tokens_used_total, int)
-
- passage_manager_size = await self.passage_manager.agent_passage_size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
- message_manager_size = await self.message_manager.size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
-
- return ContextWindowOverview(
- # context window breakdown (in messages)
- num_messages=len(in_context_messages),
- num_archival_memory=passage_manager_size,
- num_recall_memory=message_manager_size,
- num_tokens_external_memory_summary=num_tokens_external_memory_summary,
- external_memory_summary=external_memory_summary,
- # top-level information
- context_window_size_max=self.agent_state.llm_config.context_window,
- context_window_size_current=num_tokens_used_total,
- # context window breakdown (in tokens)
- num_tokens_system=num_tokens_system,
- system_prompt=system_prompt,
- num_tokens_core_memory=num_tokens_core_memory,
- core_memory=core_memory,
- num_tokens_summary_memory=num_tokens_summary_memory,
- summary_memory=summary_memory,
- num_tokens_messages=num_tokens_messages,
- messages=in_context_messages,
- # related to functions
- num_tokens_functions_definitions=num_tokens_available_functions_definitions,
- functions_definitions=available_functions_definitions,
- )
-
- def count_tokens(self) -> int:
- """Count the tokens in the current context window"""
- context_window_breakdown = self.get_context_window()
- return context_window_breakdown.context_window_size_current
-
- # TODO: Refactor into separate class v.s. large if/elses here
- def execute_tool_and_persist_state(self, function_name: str, function_args: dict, target_letta_tool: Tool) -> ToolExecutionResult:
- """
- Execute tool modifications and persist the state of the agent.
- Note: only some agent state modifications will be persisted, such as data in the AgentState ORM and block data
- """
- # TODO: add agent manager here
- orig_memory_str = self.agent_state.memory.compile()
-
- # TODO: need to have an AgentState object that actually has full access to the block data
- # this is because the sandbox tools need to be able to access block.value to edit this data
- try:
- if target_letta_tool.tool_type == ToolType.LETTA_CORE:
- # base tools are allowed to access the `Agent` object and run on the database
- callable_func = get_function_from_module(LETTA_CORE_TOOL_MODULE_NAME, function_name)
- function_args["self"] = self # need to attach self to arg since it's dynamically linked
- function_response = callable_func(**function_args)
- elif target_letta_tool.tool_type == ToolType.LETTA_MULTI_AGENT_CORE:
- callable_func = get_function_from_module(LETTA_MULTI_AGENT_TOOL_MODULE_NAME, function_name)
- function_args["self"] = self # need to attach self to arg since it's dynamically linked
- function_response = callable_func(**function_args)
- elif target_letta_tool.tool_type == ToolType.LETTA_MEMORY_CORE or target_letta_tool.tool_type == ToolType.LETTA_SLEEPTIME_CORE:
- callable_func = get_function_from_module(LETTA_CORE_TOOL_MODULE_NAME, function_name)
- agent_state_copy = self.agent_state.__deepcopy__()
- function_args["agent_state"] = agent_state_copy # need to attach self to arg since it's dynamically linked
- function_response = callable_func(**function_args)
- self.ensure_read_only_block_not_modified(
- new_memory=agent_state_copy.memory
- ) # memory editing tools cannot edit read-only blocks
- self.update_memory_if_changed(agent_state_copy.memory)
- elif target_letta_tool.tool_type == ToolType.EXTERNAL_COMPOSIO:
- action_name = generate_composio_action_from_func_name(target_letta_tool.name)
- # Get entity ID from the agent_state
- entity_id = None
- for env_var in self.agent_state.tool_exec_environment_variables:
- if env_var.key == COMPOSIO_ENTITY_ENV_VAR_KEY:
- entity_id = env_var.value
- # Get composio_api_key
- composio_api_key = get_composio_api_key(actor=self.user, logger=self.logger)
- function_response = execute_composio_action(
- action_name=action_name, args=function_args, api_key=composio_api_key, entity_id=entity_id
- )
- elif target_letta_tool.tool_type == ToolType.EXTERNAL_MCP:
- # Get the server name from the tool tag
- # TODO make a property instead?
- server_name = target_letta_tool.tags[0].split(":")[1]
-
- # Get the MCPClient from the server's handle
- # TODO these don't get raised properly
- if not self.mcp_clients:
- raise ValueError("No MCP client available to use")
- if server_name not in self.mcp_clients:
- raise ValueError(f"Unknown MCP server name: {server_name}")
- mcp_client = self.mcp_clients[server_name]
-
- # Check that tool exists
- available_tools = mcp_client.list_tools()
- available_tool_names = [t.name for t in available_tools]
- if function_name not in available_tool_names:
- raise ValueError(
- f"{function_name} is not available in MCP server {server_name}. Please check your `~/.letta/mcp_config.json` file."
- )
-
- function_response, is_error = mcp_client.execute_tool(tool_name=function_name, tool_args=function_args)
- return ToolExecutionResult(
- status="error" if is_error else "success",
- func_return=function_response,
- )
- else:
- try:
- # Parse the source code to extract function annotations
- annotations = get_function_annotations_from_source(target_letta_tool.source_code, function_name)
- # Coerce the function arguments to the correct types based on the annotations
- function_args = coerce_dict_args_by_annotations(function_args, annotations)
- except ValueError as e:
- self.logger.debug(f"Error coercing function arguments: {e}")
-
- # execute tool in a sandbox
- # TODO: allow agent_state to specify which sandbox to execute tools in
- # TODO: This is only temporary, can remove after we publish a pip package with this object
- agent_state_copy = self.agent_state.__deepcopy__()
- agent_state_copy.tools = []
- agent_state_copy.tool_rules = []
-
- tool_execution_result = ToolExecutionSandbox(function_name, function_args, self.user, tool_object=target_letta_tool).run(
- agent_state=agent_state_copy
- )
- assert orig_memory_str == self.agent_state.memory.compile(), "Memory should not be modified in a sandbox tool"
- if tool_execution_result.agent_state is not None:
- self.update_memory_if_changed(tool_execution_result.agent_state.memory)
- return tool_execution_result
- except Exception as e:
- # Need to catch error here, or else trunction wont happen
- # TODO: modify to function execution error
- function_response = get_friendly_error_msg(
- function_name=function_name, exception_name=type(e).__name__, exception_message=str(e)
- )
- return ToolExecutionResult(
- status="error",
- func_return=function_response,
- stderr=[traceback.format_exc()],
- )
-
- return ToolExecutionResult(
- status="success",
- func_return=function_response,
- )
-
-
-def save_agent(agent: Agent):
- """Save agent to metadata store"""
- agent_state = agent.agent_state
- assert isinstance(agent_state.memory, Memory), f"Memory is not a Memory object: {type(agent_state.memory)}"
-
- # TODO: move this to agent manager
- # TODO: Completely strip out metadata
- # convert to persisted model
- agent_manager = AgentManager()
- update_agent = UpdateAgent(
- name=agent_state.name,
- tool_ids=[t.id for t in agent_state.tools],
- source_ids=[s.id for s in agent_state.sources],
- block_ids=[b.id for b in agent_state.memory.blocks],
- tags=agent_state.tags,
- system=agent_state.system,
- tool_rules=agent_state.tool_rules,
- llm_config=agent_state.llm_config,
- embedding_config=agent_state.embedding_config,
- message_ids=agent_state.message_ids,
- description=agent_state.description,
- metadata=agent_state.metadata,
- # TODO: Add this back in later
- # tool_exec_environment_variables=agent_state.get_agent_env_vars_as_dict(),
- )
- agent_manager.update_agent(agent_id=agent_state.id, agent_update=update_agent, actor=agent.user)
-
-
-def strip_name_field_from_user_message(user_message_text: str) -> Tuple[str, Optional[str]]:
- """If 'name' exists in the JSON string, remove it and return the cleaned text + name value"""
- try:
- user_message_json = dict(json_loads(user_message_text))
- # Special handling for AutoGen messages with 'name' field
- # Treat 'name' as a special field
- # If it exists in the input message, elevate it to the 'message' level
- name = user_message_json.pop("name", None)
- clean_message = json_dumps(user_message_json)
- return clean_message, name
-
- except Exception as e:
- print(f"{CLI_WARNING_PREFIX}handling of 'name' field failed with: {e}")
- raise e
-
-
-def validate_json(user_message_text: str) -> str:
- """Make sure that the user input message is valid JSON"""
- try:
- user_message_json = dict(json_loads(user_message_text))
- user_message_json_val = json_dumps(user_message_json)
- return user_message_json_val
- except Exception as e:
- print(f"{CLI_WARNING_PREFIX}couldn't parse user input message as JSON: {e}")
- raise e
diff --git a/letta/agents/__init__.py b/letta/agents/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/agents/base_agent.py b/letta/agents/base_agent.py
deleted file mode 100644
index 99715e0b..00000000
--- a/letta/agents/base_agent.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, AsyncGenerator, List, Optional, Union
-
-import openai
-
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.helpers import ToolRulesSolver
-from letta.helpers.datetime_helpers import get_utc_time
-from letta.log import get_logger
-from letta.prompts.prompt_generator import PromptGenerator
-from letta.schemas.agent import AgentState
-from letta.schemas.enums import MessageStreamStatus
-from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.message import Message, MessageCreate, MessageUpdate
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.services.agent_manager import AgentManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.utils import united_diff
-
-logger = get_logger(__name__)
-
-
-class BaseAgent(ABC):
- """
- Abstract base class for AI agents, handling message management, tool execution,
- and context tracking.
- """
-
- def __init__(
- self,
- agent_id: str,
- # TODO: Make required once client refactor hits
- openai_client: Optional[openai.AsyncClient],
- message_manager: MessageManager,
- agent_manager: AgentManager,
- actor: User,
- ):
- self.agent_id = agent_id
- self.openai_client = openai_client
- self.message_manager = message_manager
- self.agent_manager = agent_manager
- # TODO: Pass this in
- self.passage_manager = PassageManager()
- self.actor = actor
- self.logger = get_logger(agent_id)
-
- @abstractmethod
- async def step(
- self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS, run_id: Optional[str] = None
- ) -> LettaResponse:
- """
- Main execution loop for the agent.
- """
- raise NotImplementedError
-
- @abstractmethod
- async def step_stream(
- self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS
- ) -> AsyncGenerator[Union[LettaMessage, LegacyLettaMessage, MessageStreamStatus], None]:
- """
- Main streaming execution loop for the agent.
- """
- raise NotImplementedError
-
- @staticmethod
- def pre_process_input_message(input_messages: List[MessageCreate]) -> Any:
- """
- Pre-process function to run on the input_message.
- """
-
- def get_content(message: MessageCreate) -> str:
- if isinstance(message.content, str):
- return message.content
- elif message.content and len(message.content) == 1 and isinstance(message.content[0], TextContent):
- return message.content[0].text
- else:
- return ""
-
- return [{"role": input_message.role.value, "content": get_content(input_message)} for input_message in input_messages]
-
- async def _rebuild_memory_async(
- self,
- in_context_messages: List[Message],
- agent_state: AgentState,
- tool_rules_solver: Optional[ToolRulesSolver] = None,
- num_messages: Optional[int] = None, # storing these calculations is specific to the voice agent
- num_archival_memories: Optional[int] = None,
- ) -> List[Message]:
- """
- Async version of function above. For now before breaking up components, changes should be made in both places.
- """
- try:
- # [DB Call] loading blocks (modifies: agent_state.memory.blocks)
- agent_state = await self.agent_manager.refresh_memory_async(agent_state=agent_state, actor=self.actor)
-
- tool_constraint_block = None
- if tool_rules_solver is not None:
- tool_constraint_block = tool_rules_solver.compile_tool_rule_prompts()
-
- # compile archive tags if there's an attached archive
- from letta.services.archive_manager import ArchiveManager
-
- archive_manager = ArchiveManager()
- archive = await archive_manager.get_default_archive_for_agent_async(
- agent_id=agent_state.id,
- actor=self.actor,
- )
-
- if archive:
- archive_tags = await self.passage_manager.get_unique_tags_for_archive_async(
- archive_id=archive.id,
- actor=self.actor,
- )
- else:
- archive_tags = None
-
- # TODO: This is a pretty brittle pattern established all over our code, need to get rid of this
- curr_system_message = in_context_messages[0]
- curr_system_message_text = curr_system_message.content[0].text
-
- # extract the dynamic section that includes memory blocks, tool rules, and directories
- # this avoids timestamp comparison issues
- def extract_dynamic_section(text):
- start_marker = ""
- end_marker = ""
-
- start_idx = text.find(start_marker)
- end_idx = text.find(end_marker)
-
- if start_idx != -1 and end_idx != -1:
- return text[start_idx:end_idx]
- return text # fallback to full text if markers not found
-
- curr_dynamic_section = extract_dynamic_section(curr_system_message_text)
-
- # generate just the memory string with current state for comparison
- curr_memory_str = await agent_state.memory.compile_in_thread_async(
- tool_usage_rules=tool_constraint_block, sources=agent_state.sources, max_files_open=agent_state.max_files_open
- )
- new_dynamic_section = extract_dynamic_section(curr_memory_str)
-
- # compare just the dynamic sections (memory blocks, tool rules, directories)
- if curr_dynamic_section == new_dynamic_section:
- logger.debug(
- f"Memory and sources haven't changed for agent id={agent_state.id} and actor=({self.actor.id}, {self.actor.name}), skipping system prompt rebuild"
- )
- return in_context_messages
-
- memory_edit_timestamp = get_utc_time()
-
- # size of messages and archival memories
- if num_messages is None:
- num_messages = await self.message_manager.size_async(actor=self.actor, agent_id=agent_state.id)
- if num_archival_memories is None:
- num_archival_memories = await self.passage_manager.agent_passage_size_async(actor=self.actor, agent_id=agent_state.id)
-
- new_system_message_str = PromptGenerator.get_system_message_from_compiled_memory(
- system_prompt=agent_state.system,
- memory_with_sources=curr_memory_str,
- in_context_memory_last_edit=memory_edit_timestamp,
- timezone=agent_state.timezone,
- previous_message_count=num_messages - len(in_context_messages),
- archival_memory_size=num_archival_memories,
- archive_tags=archive_tags,
- )
-
- diff = united_diff(curr_system_message_text, new_system_message_str)
- if len(diff) > 0:
- logger.debug(f"Rebuilding system with new memory...\nDiff:\n{diff}")
-
- # [DB Call] Update Messages
- new_system_message = await self.message_manager.update_message_by_id_async(
- curr_system_message.id,
- message_update=MessageUpdate(content=new_system_message_str),
- actor=self.actor,
- project_id=agent_state.project_id,
- )
- return [new_system_message] + in_context_messages[1:]
-
- else:
- return in_context_messages
- except:
- logger.exception(f"Failed to rebuild memory for agent id={agent_state.id} and actor=({self.actor.id}, {self.actor.name})")
- raise
-
- def get_finish_chunks_for_stream(self, usage: LettaUsageStatistics, stop_reason: Optional[LettaStopReason] = None):
- if stop_reason is None:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- return [
- stop_reason.model_dump_json(),
- usage.model_dump_json(),
- MessageStreamStatus.done.value,
- ]
diff --git a/letta/agents/base_agent_v2.py b/letta/agents/base_agent_v2.py
deleted file mode 100644
index 3d49d008..00000000
--- a/letta/agents/base_agent_v2.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import AsyncGenerator
-
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.log import get_logger
-from letta.schemas.agent import AgentState
-from letta.schemas.enums import MessageStreamStatus
-from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.message import MessageCreate
-from letta.schemas.user import User
-
-
-class BaseAgentV2(ABC):
- """
- Abstract base class for the main agent execution loop for letta agents, handling
- message management, llm api request, tool execution, and context tracking.
- """
-
- def __init__(self, agent_state: AgentState, actor: User):
- self.agent_state = agent_state
- self.actor = actor
- self.logger = get_logger(agent_state.id)
-
- @abstractmethod
- async def build_request(
- self,
- input_messages: list[MessageCreate],
- ) -> dict:
- """
- Execute the agent loop in dry_run mode, returning just the generated request
- payload sent to the underlying llm provider.
- """
- raise NotImplementedError
-
- @abstractmethod
- async def step(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- ) -> LettaResponse:
- """
- Execute the agent loop in blocking mode, returning all messages at once.
- """
- raise NotImplementedError
-
- @abstractmethod
- async def stream(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- stream_tokens: bool = True,
- ) -> AsyncGenerator[LettaMessage | LegacyLettaMessage | MessageStreamStatus, None]:
- """
- Execute the agent loop in streaming mode, yielding chunks as they become available.
- If stream_tokens is True, individual tokens are streamed as they arrive from the LLM,
- providing the lowest latency experience, otherwise each complete step (reasoning +
- tool call + tool return) is yielded as it completes.
- """
- raise NotImplementedError
diff --git a/letta/agents/ephemeral_agent.py b/letta/agents/ephemeral_agent.py
deleted file mode 100644
index d951b434..00000000
--- a/letta/agents/ephemeral_agent.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from typing import AsyncGenerator, Dict, List
-
-import openai
-
-from letta.agents.base_agent import BaseAgent
-from letta.schemas.agent import AgentState
-from letta.schemas.enums import MessageRole
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.openai.chat_completion_request import ChatCompletionRequest
-from letta.schemas.user import User
-from letta.services.agent_manager import AgentManager
-from letta.services.message_manager import MessageManager
-
-
-class EphemeralAgent(BaseAgent):
- """
- A stateless agent (thin wrapper around OpenAI)
-
- # TODO: Extend to more clients
- """
-
- def __init__(
- self,
- agent_id: str,
- openai_client: openai.AsyncClient,
- message_manager: MessageManager,
- agent_manager: AgentManager,
- actor: User,
- ):
- super().__init__(
- agent_id=agent_id,
- openai_client=openai_client,
- message_manager=message_manager,
- agent_manager=agent_manager,
- actor=actor,
- )
-
- async def step(self, input_messages: List[MessageCreate]) -> List[Message]:
- """
- Synchronous method that takes a user's input text and returns a summary from OpenAI.
- Returns a list of ephemeral Message objects containing both the user text and the assistant summary.
- """
- agent_state = self.agent_manager.get_agent_by_id(agent_id=self.agent_id, actor=self.actor)
-
- openai_messages = self.pre_process_input_message(input_messages=input_messages)
- request = self._build_openai_request(openai_messages, agent_state)
-
- chat_completion = await self.openai_client.chat.completions.create(**request.model_dump(exclude_unset=True))
-
- return [
- Message(
- role=MessageRole.assistant,
- content=[TextContent(text=chat_completion.choices[0].message.content.strip())],
- )
- ]
-
- def _build_openai_request(self, openai_messages: List[Dict], agent_state: AgentState) -> ChatCompletionRequest:
- openai_request = ChatCompletionRequest(
- model=agent_state.llm_config.model,
- messages=openai_messages,
- user=self.actor.id,
- max_completion_tokens=agent_state.llm_config.max_tokens,
- temperature=agent_state.llm_config.temperature,
- )
- return openai_request
-
- async def step_stream(self, input_messages: List[MessageCreate]) -> AsyncGenerator[str, None]:
- """
- This agent is synchronous-only. If called in an async context, raise an error.
- """
- raise NotImplementedError("EphemeralAgent does not support async step.")
diff --git a/letta/agents/ephemeral_summary_agent.py b/letta/agents/ephemeral_summary_agent.py
deleted file mode 100644
index 55d610c2..00000000
--- a/letta/agents/ephemeral_summary_agent.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from typing import AsyncGenerator, List
-
-from letta.agents.base_agent import BaseAgent
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.helpers.message_helper import convert_message_creates_to_messages
-from letta.llm_api.llm_client import LLMClient
-from letta.log import get_logger
-from letta.orm.errors import NoResultFound
-from letta.prompts.gpt_system import get_system_text
-from letta.schemas.block import Block, BlockUpdate
-from letta.schemas.enums import MessageRole
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.user import User
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.message_manager import MessageManager
-
-logger = get_logger(__name__)
-
-
-class EphemeralSummaryAgent(BaseAgent):
- """
- A stateless summarization agent that utilizes the caller's LLM client to summarize the conversation.
- TODO (cliandy): allow the summarizer to use another llm_config from the main agent maybe?
- """
-
- def __init__(
- self,
- target_block_label: str,
- agent_id: str,
- message_manager: MessageManager,
- agent_manager: AgentManager,
- block_manager: BlockManager,
- actor: User,
- ):
- super().__init__(
- agent_id=agent_id,
- openai_client=None,
- message_manager=message_manager,
- agent_manager=agent_manager,
- actor=actor,
- )
- self.target_block_label = target_block_label
- self.block_manager = block_manager
-
- async def step(self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS) -> List[Message]:
- if len(input_messages) > 1:
- raise ValueError("Can only invoke EphemeralSummaryAgent with a single summarization message.")
-
- # Check block existence
- try:
- block = await self.agent_manager.get_block_with_label_async(
- agent_id=self.agent_id, block_label=self.target_block_label, actor=self.actor
- )
- except NoResultFound:
- block = await self.block_manager.create_or_update_block_async(
- block=Block(
- value="", label=self.target_block_label, description="Contains recursive summarizations of the conversation so far"
- ),
- actor=self.actor,
- )
- await self.agent_manager.attach_block_async(agent_id=self.agent_id, block_id=block.id, actor=self.actor)
-
- if block.value:
- input_message = input_messages[0]
- input_message.content[0].text += f"\n\n--- Previous Summary ---\n{block.value}\n"
-
- # Gets the LLMCLient based on the calling agent's LLM Config
- agent_state = await self.agent_manager.get_agent_by_id_async(agent_id=self.agent_id, actor=self.actor)
- llm_client = LLMClient.create(
- provider_type=agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=True,
- actor=self.actor,
- )
-
- system_message_create = MessageCreate(
- role=MessageRole.system,
- content=[TextContent(text=get_system_text("summary_system_prompt"))],
- )
- messages = convert_message_creates_to_messages(
- message_creates=[system_message_create] + input_messages,
- agent_id=self.agent_id,
- timezone=agent_state.timezone,
- )
-
- request_data = llm_client.build_request_data(messages, agent_state.llm_config, tools=[])
- response_data = await llm_client.request_async(request_data, agent_state.llm_config)
- response = llm_client.convert_response_to_chat_completion(response_data, messages, agent_state.llm_config)
- summary = response.choices[0].message.content.strip()
-
- await self.block_manager.update_block_async(block_id=block.id, block_update=BlockUpdate(value=summary), actor=self.actor)
-
- logger.debug("block:", block)
- logger.debug("summary:", summary)
-
- return [
- Message(
- role=MessageRole.assistant,
- content=[TextContent(text=summary)],
- )
- ]
-
- async def step_stream(self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS) -> AsyncGenerator[str, None]:
- raise NotImplementedError("EphemeralAgent does not support async step.")
diff --git a/letta/agents/exceptions.py b/letta/agents/exceptions.py
deleted file mode 100644
index 270cfc35..00000000
--- a/letta/agents/exceptions.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class IncompatibleAgentType(ValueError):
- def __init__(self, expected_type: str, actual_type: str):
- message = f"Incompatible agent type: expected '{expected_type}', but got '{actual_type}'."
- super().__init__(message)
- self.expected_type = expected_type
- self.actual_type = actual_type
diff --git a/letta/agents/helpers.py b/letta/agents/helpers.py
deleted file mode 100644
index d828adff..00000000
--- a/letta/agents/helpers.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import json
-import uuid
-import xml.etree.ElementTree as ET
-from typing import List, Optional, Tuple
-
-from letta.errors import PendingApprovalError
-from letta.helpers import ToolRulesSolver
-from letta.log import get_logger
-from letta.schemas.agent import AgentState
-from letta.schemas.letta_message import MessageType
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.message import Message, MessageCreate, MessageCreateBase
-from letta.schemas.tool_execution_result import ToolExecutionResult
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.server.rest_api.utils import create_approval_response_message_from_input, create_input_messages
-from letta.services.message_manager import MessageManager
-
-logger = get_logger(__name__)
-
-
-def _create_letta_response(
- new_in_context_messages: list[Message],
- use_assistant_message: bool,
- usage: LettaUsageStatistics,
- stop_reason: Optional[LettaStopReason] = None,
- include_return_message_types: Optional[List[MessageType]] = None,
-) -> LettaResponse:
- """
- Converts the newly created/persisted messages into a LettaResponse.
- """
- # NOTE: hacky solution to avoid returning heartbeat messages and the original user message
- filter_user_messages = [m for m in new_in_context_messages if m.role != "user"]
-
- # Convert to Letta messages first
- response_messages = Message.to_letta_messages_from_list(
- messages=filter_user_messages, use_assistant_message=use_assistant_message, reverse=False
- )
- # Filter approval response messages
- response_messages = [m for m in response_messages if m.message_type != "approval_response_message"]
-
- # Apply message type filtering if specified
- if include_return_message_types is not None:
- response_messages = [msg for msg in response_messages if msg.message_type in include_return_message_types]
- if stop_reason is None:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- return LettaResponse(messages=response_messages, stop_reason=stop_reason, usage=usage)
-
-
-def _prepare_in_context_messages(
- input_messages: List[MessageCreate],
- agent_state: AgentState,
- message_manager: MessageManager,
- actor: User,
-) -> Tuple[List[Message], List[Message]]:
- """
- Prepares in-context messages for an agent, based on the current state and a new user input.
-
- Args:
- input_messages (List[MessageCreate]): The new user input messages to process.
- agent_state (AgentState): The current state of the agent, including message buffer config.
- message_manager (MessageManager): The manager used to retrieve and create messages.
- actor (User): The user performing the action, used for access control and attribution.
-
- Returns:
- Tuple[List[Message], List[Message]]: A tuple containing:
- - The current in-context messages (existing context for the agent).
- - The new in-context messages (messages created from the new input).
- """
-
- if agent_state.message_buffer_autoclear:
- # If autoclear is enabled, only include the most recent system message (usually at index 0)
- current_in_context_messages = [message_manager.get_messages_by_ids(message_ids=agent_state.message_ids, actor=actor)[0]]
- else:
- # Otherwise, include the full list of messages by ID for context
- current_in_context_messages = message_manager.get_messages_by_ids(message_ids=agent_state.message_ids, actor=actor)
-
- # Create a new user message from the input and store it
- new_in_context_messages = message_manager.create_many_messages(
- create_input_messages(input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor),
- actor=actor,
- )
-
- return current_in_context_messages, new_in_context_messages
-
-
-async def _prepare_in_context_messages_async(
- input_messages: List[MessageCreate],
- agent_state: AgentState,
- message_manager: MessageManager,
- actor: User,
-) -> Tuple[List[Message], List[Message]]:
- """
- Prepares in-context messages for an agent, based on the current state and a new user input.
- Async version of _prepare_in_context_messages.
-
- Args:
- input_messages (List[MessageCreate]): The new user input messages to process.
- agent_state (AgentState): The current state of the agent, including message buffer config.
- message_manager (MessageManager): The manager used to retrieve and create messages.
- actor (User): The user performing the action, used for access control and attribution.
-
- Returns:
- Tuple[List[Message], List[Message]]: A tuple containing:
- - The current in-context messages (existing context for the agent).
- - The new in-context messages (messages created from the new input).
- """
-
- if agent_state.message_buffer_autoclear:
- # If autoclear is enabled, only include the most recent system message (usually at index 0)
- current_in_context_messages = [await message_manager.get_message_by_id_async(message_id=agent_state.message_ids[0], actor=actor)]
- else:
- # Otherwise, include the full list of messages by ID for context
- current_in_context_messages = await message_manager.get_messages_by_ids_async(message_ids=agent_state.message_ids, actor=actor)
-
- # Create a new user message from the input and store it
- new_in_context_messages = await message_manager.create_many_messages_async(
- create_input_messages(input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor),
- actor=actor,
- project_id=agent_state.project_id,
- )
-
- return current_in_context_messages, new_in_context_messages
-
-
-async def _prepare_in_context_messages_no_persist_async(
- input_messages: List[MessageCreateBase],
- agent_state: AgentState,
- message_manager: MessageManager,
- actor: User,
-) -> Tuple[List[Message], List[Message]]:
- """
- Prepares in-context messages for an agent, based on the current state and a new user input.
-
- Args:
- input_messages (List[MessageCreate]): The new user input messages to process.
- agent_state (AgentState): The current state of the agent, including message buffer config.
- message_manager (MessageManager): The manager used to retrieve and create messages.
- actor (User): The user performing the action, used for access control and attribution.
-
- Returns:
- Tuple[List[Message], List[Message]]: A tuple containing:
- - The current in-context messages (existing context for the agent).
- - The new in-context messages (messages created from the new input).
- """
-
- if agent_state.message_buffer_autoclear:
- # If autoclear is enabled, only include the most recent system message (usually at index 0)
- current_in_context_messages = [await message_manager.get_message_by_id_async(message_id=agent_state.message_ids[0], actor=actor)]
- else:
- # Otherwise, include the full list of messages by ID for context
- current_in_context_messages = await message_manager.get_messages_by_ids_async(message_ids=agent_state.message_ids, actor=actor)
-
- # Check for approval-related message validation
- if len(input_messages) == 1 and input_messages[0].type == "approval":
- # User is trying to send an approval response
- if current_in_context_messages[-1].role != "approval":
- raise ValueError(
- "Cannot process approval response: No tool call is currently awaiting approval. "
- "Please send a regular message to interact with the agent."
- )
- if input_messages[0].approval_request_id != current_in_context_messages[-1].id:
- raise ValueError(
- f"Invalid approval request ID. Expected '{current_in_context_messages[-1].id}' "
- f"but received '{input_messages[0].approval_request_id}'."
- )
- new_in_context_messages = create_approval_response_message_from_input(agent_state=agent_state, input_message=input_messages[0])
- else:
- # User is trying to send a regular message
- if current_in_context_messages[-1].role == "approval":
- raise PendingApprovalError(pending_request_id=current_in_context_messages[-1].id)
-
- # Create a new user message from the input but dont store it yet
- new_in_context_messages = create_input_messages(
- input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor
- )
-
- return current_in_context_messages, new_in_context_messages
-
-
-def serialize_message_history(messages: List[str], context: str) -> str:
- """
- Produce an XML document like:
-
-
-
- …
- …
- …
-
- …
-
- """
- root = ET.Element("memory")
-
- msgs_el = ET.SubElement(root, "messages")
- for msg in messages:
- m = ET.SubElement(msgs_el, "message")
- m.text = msg
-
- sum_el = ET.SubElement(root, "context")
- sum_el.text = context
-
- # ET.tostring will escape reserved chars for you
- return ET.tostring(root, encoding="unicode")
-
-
-def deserialize_message_history(xml_str: str) -> Tuple[List[str], str]:
- """
- Parse the XML back into (messages, context). Raises ValueError if tags are missing.
- """
- try:
- root = ET.fromstring(xml_str)
- except ET.ParseError as e:
- raise ValueError(f"Invalid XML: {e}")
-
- msgs_el = root.find("messages")
- if msgs_el is None:
- raise ValueError("Missing section")
-
- messages = []
- for m in msgs_el.findall("message"):
- # .text may be None if empty, so coerce to empty string
- messages.append(m.text or "")
-
- sum_el = root.find("context")
- if sum_el is None:
- raise ValueError("Missing section")
- context = sum_el.text or ""
-
- return messages, context
-
-
-def generate_step_id():
- return f"step-{uuid.uuid4()}"
-
-
-def _safe_load_tool_call_str(tool_call_args_str: str) -> dict:
- """Lenient JSON → dict with fallback to eval on assertion failure."""
- # Temp hack to gracefully handle parallel tool calling attempt, only take first one
- if "}{" in tool_call_args_str:
- tool_call_args_str = tool_call_args_str.split("}{", 1)[0] + "}"
-
- try:
- tool_args = json.loads(tool_call_args_str)
- if not isinstance(tool_args, dict):
- # Load it again - this is due to sometimes Anthropic returning weird json @caren
- tool_args = json.loads(tool_args)
- except json.JSONDecodeError:
- logger.error("Failed to JSON decode tool call argument string: %s", tool_call_args_str)
- tool_args = {}
-
- return tool_args
-
-
-def _pop_heartbeat(tool_args: dict) -> bool:
- hb = tool_args.pop("request_heartbeat", False)
- return str(hb).lower() == "true" if isinstance(hb, str) else bool(hb)
-
-
-def _build_rule_violation_result(tool_name: str, valid: list[str], solver: ToolRulesSolver) -> ToolExecutionResult:
- hint_lines = solver.guess_rule_violation(tool_name)
- hint_txt = ("\n** Hint: Possible rules that were violated:\n" + "\n".join(f"\t- {h}" for h in hint_lines)) if hint_lines else ""
- msg = f"[ToolConstraintError] Cannot call {tool_name}, valid tools include: {valid}.{hint_txt}"
- return ToolExecutionResult(status="error", func_return=msg)
diff --git a/letta/agents/letta_agent.py b/letta/agents/letta_agent.py
deleted file mode 100644
index e5639be0..00000000
--- a/letta/agents/letta_agent.py
+++ /dev/null
@@ -1,1926 +0,0 @@
-import json
-import uuid
-from collections.abc import AsyncGenerator
-from datetime import datetime
-from typing import Optional, Union
-
-from openai import AsyncStream
-from openai.types.chat import ChatCompletionChunk
-from opentelemetry.trace import Span
-
-from letta.agents.base_agent import BaseAgent
-from letta.agents.ephemeral_summary_agent import EphemeralSummaryAgent
-from letta.agents.helpers import (
- _build_rule_violation_result,
- _create_letta_response,
- _pop_heartbeat,
- _prepare_in_context_messages_no_persist_async,
- _safe_load_tool_call_str,
- generate_step_id,
-)
-from letta.constants import DEFAULT_MAX_STEPS, NON_USER_MSG_PREFIX
-from letta.errors import ContextWindowExceededError
-from letta.helpers import ToolRulesSolver
-from letta.helpers.datetime_helpers import AsyncTimer, get_utc_time, get_utc_timestamp_ns, ns_to_ms
-from letta.helpers.reasoning_helper import scrub_inner_thoughts_from_messages
-from letta.helpers.tool_execution_helper import enable_strict_mode
-from letta.interfaces.anthropic_streaming_interface import AnthropicStreamingInterface
-from letta.interfaces.openai_streaming_interface import OpenAIStreamingInterface
-from letta.llm_api.llm_client import LLMClient
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG
-from letta.log import get_logger
-from letta.otel.context import get_ctx_attributes
-from letta.otel.metric_registry import MetricRegistry
-from letta.otel.tracing import log_event, trace_method, tracer
-from letta.schemas.agent import AgentState, UpdateAgent
-from letta.schemas.enums import JobStatus, MessageRole, ProviderType, StepStatus, ToolType
-from letta.schemas.letta_message import MessageType
-from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, RedactedReasoningContent, TextContent
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message, MessageCreateBase
-from letta.schemas.openai.chat_completion_response import ToolCall, UsageStatistics
-from letta.schemas.provider_trace import ProviderTraceCreate
-from letta.schemas.step import StepProgression
-from letta.schemas.step_metrics import StepMetrics
-from letta.schemas.tool_execution_result import ToolExecutionResult
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.server.rest_api.utils import create_approval_request_message_from_llm_response, create_letta_messages_from_llm_response
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.helpers.tool_parser_helper import runtime_override_tool_json_schema
-from letta.services.job_manager import JobManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.step_manager import NoopStepManager, StepManager
-from letta.services.summarizer.enums import SummarizationMode
-from letta.services.summarizer.summarizer import Summarizer
-from letta.services.telemetry_manager import NoopTelemetryManager, TelemetryManager
-from letta.services.tool_executor.tool_execution_manager import ToolExecutionManager
-from letta.settings import model_settings, settings, summarizer_settings
-from letta.system import package_function_response
-from letta.types import JsonDict
-from letta.utils import log_telemetry, validate_function_response
-
-logger = get_logger(__name__)
-
-DEFAULT_SUMMARY_BLOCK_LABEL = "conversation_summary"
-
-
-class LettaAgent(BaseAgent):
- def __init__(
- self,
- agent_id: str,
- message_manager: MessageManager,
- agent_manager: AgentManager,
- block_manager: BlockManager,
- job_manager: JobManager,
- passage_manager: PassageManager,
- actor: User,
- step_manager: StepManager = NoopStepManager(),
- telemetry_manager: TelemetryManager = NoopTelemetryManager(),
- current_run_id: str | None = None,
- ## summarizer settings
- summarizer_mode: SummarizationMode = summarizer_settings.mode,
- # for static_buffer mode
- summary_block_label: str = DEFAULT_SUMMARY_BLOCK_LABEL,
- message_buffer_limit: int = summarizer_settings.message_buffer_limit,
- message_buffer_min: int = summarizer_settings.message_buffer_min,
- enable_summarization: bool = summarizer_settings.enable_summarization,
- max_summarization_retries: int = summarizer_settings.max_summarization_retries,
- # for partial_evict mode
- partial_evict_summarizer_percentage: float = summarizer_settings.partial_evict_summarizer_percentage,
- ):
- super().__init__(agent_id=agent_id, openai_client=None, message_manager=message_manager, agent_manager=agent_manager, actor=actor)
-
- # TODO: Make this more general, factorable
- # Summarizer settings
- self.block_manager = block_manager
- self.job_manager = job_manager
- self.passage_manager = passage_manager
- self.step_manager = step_manager
- self.telemetry_manager = telemetry_manager
- self.job_manager = job_manager
- self.current_run_id = current_run_id
- self.response_messages: list[Message] = []
-
- self.last_function_response = None
-
- # Cached archival memory/message size
- self.num_messages = None
- self.num_archival_memories = None
-
- self.summarization_agent = None
- self.summary_block_label = summary_block_label
- self.max_summarization_retries = max_summarization_retries
- self.logger = get_logger(agent_id)
-
- # TODO: Expand to more
- if enable_summarization and model_settings.openai_api_key:
- self.summarization_agent = EphemeralSummaryAgent(
- target_block_label=self.summary_block_label,
- agent_id=agent_id,
- block_manager=self.block_manager,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- actor=self.actor,
- )
-
- self.summarizer = Summarizer(
- mode=summarizer_mode,
- # TODO consolidate to not use this, or push it into the Summarizer() class
- summarizer_agent=self.summarization_agent,
- # TODO: Make this configurable
- message_buffer_limit=message_buffer_limit,
- message_buffer_min=message_buffer_min,
- partial_evict_summarizer_percentage=partial_evict_summarizer_percentage,
- agent_manager=self.agent_manager,
- message_manager=self.message_manager,
- actor=self.actor,
- agent_id=self.agent_id,
- )
-
- async def _check_run_cancellation(self) -> bool:
- """
- Check if the current run associated with this agent execution has been cancelled.
-
- Returns:
- True if the run is cancelled, False otherwise (or if no run is associated)
- """
- if not self.job_manager or not self.current_run_id:
- return False
-
- try:
- job = await self.job_manager.get_job_by_id_async(job_id=self.current_run_id, actor=self.actor)
- return job.status == JobStatus.cancelled
- except Exception as e:
- # Log the error but don't fail the execution
- logger.warning(f"Failed to check job cancellation status for job {self.current_run_id}: {e}")
- return False
-
- @trace_method
- async def step(
- self,
- input_messages: list[MessageCreateBase],
- max_steps: int = DEFAULT_MAX_STEPS,
- run_id: str | None = None,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: int | None = None,
- include_return_message_types: list[MessageType] | None = None,
- dry_run: bool = False,
- ) -> Union[LettaResponse, dict]:
- # TODO (cliandy): pass in run_id and use at send_message endpoints for all step functions
- agent_state = await self.agent_manager.get_agent_by_id_async(
- agent_id=self.agent_id,
- include_relationships=["tools", "memory", "tool_exec_environment_variables", "sources"],
- actor=self.actor,
- )
- result = await self._step(
- agent_state=agent_state,
- input_messages=input_messages,
- max_steps=max_steps,
- run_id=run_id,
- request_start_timestamp_ns=request_start_timestamp_ns,
- dry_run=dry_run,
- )
-
- # If dry run, return the request payload directly
- if dry_run:
- return result
-
- _, new_in_context_messages, stop_reason, usage = result
- return _create_letta_response(
- new_in_context_messages=new_in_context_messages,
- use_assistant_message=use_assistant_message,
- stop_reason=stop_reason,
- usage=usage,
- include_return_message_types=include_return_message_types,
- )
-
- @trace_method
- async def step_stream_no_tokens(
- self,
- input_messages: list[MessageCreateBase],
- max_steps: int = DEFAULT_MAX_STEPS,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: int | None = None,
- include_return_message_types: list[MessageType] | None = None,
- ):
- agent_state = await self.agent_manager.get_agent_by_id_async(
- agent_id=self.agent_id,
- include_relationships=["tools", "memory", "tool_exec_environment_variables", "sources"],
- actor=self.actor,
- )
- current_in_context_messages, new_in_context_messages = await _prepare_in_context_messages_no_persist_async(
- input_messages, agent_state, self.message_manager, self.actor
- )
- initial_messages = new_in_context_messages
- in_context_messages = current_in_context_messages
- tool_rules_solver = ToolRulesSolver(agent_state.tool_rules)
- llm_client = LLMClient.create(
- provider_type=agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=True,
- actor=self.actor,
- )
- stop_reason = None
- job_update_metadata = None
- usage = LettaUsageStatistics()
-
- # span for request
- request_span = tracer.start_span("time_to_first_token", start_time=request_start_timestamp_ns)
- request_span.set_attributes({f"llm_config.{k}": v for k, v in agent_state.llm_config.model_dump().items() if v is not None})
-
- for i in range(max_steps):
- if in_context_messages[-1].role == "approval":
- approval_request_message = in_context_messages[-1]
- step_metrics = await self.step_manager.get_step_metrics_async(step_id=approval_request_message.step_id, actor=self.actor)
- persisted_messages, should_continue, stop_reason = await self._handle_ai_response(
- approval_request_message.tool_calls[0],
- [], # TODO: update this
- agent_state,
- tool_rules_solver,
- usage,
- reasoning_content=approval_request_message.content,
- step_id=approval_request_message.step_id,
- initial_messages=initial_messages,
- is_final_step=(i == max_steps - 1),
- step_metrics=step_metrics,
- run_id=self.current_run_id,
- is_approval=input_messages[0].approve,
- is_denial=input_messages[0].approve == False,
- denial_reason=input_messages[0].reason,
- )
- new_message_idx = len(initial_messages) if initial_messages else 0
- self.response_messages.extend(persisted_messages[new_message_idx:])
- new_in_context_messages.extend(persisted_messages[new_message_idx:])
- initial_messages = None
- in_context_messages = current_in_context_messages + new_in_context_messages
-
- # stream step
- # TODO: improve TTFT
- filter_user_messages = [m for m in persisted_messages if m.role != "user" and m.role != "approval"]
- letta_messages = Message.to_letta_messages_from_list(
- filter_user_messages, use_assistant_message=use_assistant_message, reverse=False
- )
-
- for message in letta_messages:
- if include_return_message_types is None or message.message_type in include_return_message_types:
- yield f"data: {message.model_dump_json()}\n\n"
- else:
- # Check for job cancellation at the start of each step
- if await self._check_run_cancellation():
- stop_reason = LettaStopReason(stop_reason=StopReasonType.cancelled.value)
- logger.info(f"Agent execution cancelled for run {self.current_run_id}")
- yield f"data: {stop_reason.model_dump_json()}\n\n"
- break
-
- step_id = generate_step_id()
- step_start = get_utc_timestamp_ns()
- agent_step_span = tracer.start_span("agent_step", start_time=step_start)
- agent_step_span.set_attributes({"step_id": step_id})
-
- step_progression = StepProgression.START
- should_continue = False
- step_metrics = StepMetrics(id=step_id) # Initialize metrics tracking
-
- # Create step early with PENDING status
- logged_step = await self.step_manager.log_step_async(
- actor=self.actor,
- agent_id=agent_state.id,
- provider_name=agent_state.llm_config.model_endpoint_type,
- provider_category=agent_state.llm_config.provider_category or "base",
- model=agent_state.llm_config.model,
- model_endpoint=agent_state.llm_config.model_endpoint,
- context_window_limit=agent_state.llm_config.context_window,
- usage=UsageStatistics(completion_tokens=0, prompt_tokens=0, total_tokens=0),
- provider_id=None,
- job_id=self.current_run_id if self.current_run_id else None,
- step_id=step_id,
- project_id=agent_state.project_id,
- status=StepStatus.PENDING,
- )
- # Only use step_id in messages if step was actually created
- effective_step_id = step_id if logged_step else None
-
- try:
- (
- request_data,
- response_data,
- current_in_context_messages,
- new_in_context_messages,
- valid_tool_names,
- ) = await self._build_and_request_from_llm(
- current_in_context_messages,
- new_in_context_messages,
- agent_state,
- llm_client,
- tool_rules_solver,
- agent_step_span,
- step_metrics,
- )
- in_context_messages = current_in_context_messages + new_in_context_messages
-
- step_progression = StepProgression.RESPONSE_RECEIVED
- log_event("agent.stream_no_tokens.llm_response.received") # [3^]
-
- try:
- response = llm_client.convert_response_to_chat_completion(
- response_data, in_context_messages, agent_state.llm_config
- )
- except ValueError as e:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.invalid_llm_response.value)
- raise e
-
- # update usage
- usage.step_count += 1
- usage.completion_tokens += response.usage.completion_tokens
- usage.prompt_tokens += response.usage.prompt_tokens
- usage.total_tokens += response.usage.total_tokens
- MetricRegistry().message_output_tokens.record(
- response.usage.completion_tokens, dict(get_ctx_attributes(), **{"model.name": agent_state.llm_config.model})
- )
-
- if not response.choices[0].message.tool_calls:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.no_tool_call.value)
- raise ValueError("No tool calls found in response, model must make a tool call")
- tool_call = response.choices[0].message.tool_calls[0]
- if response.choices[0].message.reasoning_content:
- reasoning = [
- ReasoningContent(
- reasoning=response.choices[0].message.reasoning_content,
- is_native=True,
- signature=response.choices[0].message.reasoning_content_signature,
- )
- ]
- elif response.choices[0].message.omitted_reasoning_content:
- reasoning = [OmittedReasoningContent()]
- elif response.choices[0].message.content:
- reasoning = [
- TextContent(text=response.choices[0].message.content)
- ] # reasoning placed into content for legacy reasons
- else:
- self.logger.info("No reasoning content found.")
- reasoning = None
-
- persisted_messages, should_continue, stop_reason = await self._handle_ai_response(
- tool_call,
- valid_tool_names,
- agent_state,
- tool_rules_solver,
- response.usage,
- reasoning_content=reasoning,
- step_id=effective_step_id,
- initial_messages=initial_messages,
- agent_step_span=agent_step_span,
- is_final_step=(i == max_steps - 1),
- step_metrics=step_metrics,
- )
- step_progression = StepProgression.STEP_LOGGED
-
- # Update step with actual usage now that we have it (if step was created)
- if logged_step:
- await self.step_manager.update_step_success_async(self.actor, step_id, response.usage, stop_reason)
-
- # TODO (cliandy): handle message contexts with larger refactor and dedupe logic
- new_message_idx = len(initial_messages) if initial_messages else 0
- self.response_messages.extend(persisted_messages[new_message_idx:])
- new_in_context_messages.extend(persisted_messages[new_message_idx:])
- initial_messages = None
- log_event("agent.stream_no_tokens.llm_response.processed") # [4^]
-
- # log step time
- now = get_utc_timestamp_ns()
- step_ns = now - step_start
- agent_step_span.add_event(name="step_ms", attributes={"duration_ms": ns_to_ms(step_ns)})
- agent_step_span.end()
-
- # Log LLM Trace
- if settings.track_provider_trace:
- await self.telemetry_manager.create_provider_trace_async(
- actor=self.actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=request_data,
- response_json=response_data,
- step_id=step_id, # Use original step_id for telemetry
- organization_id=self.actor.organization_id,
- ),
- )
- step_progression = StepProgression.LOGGED_TRACE
-
- # stream step
- # TODO: improve TTFT
- filter_user_messages = [m for m in persisted_messages if m.role != "user"]
- letta_messages = Message.to_letta_messages_from_list(
- filter_user_messages, use_assistant_message=use_assistant_message, reverse=False
- )
- letta_messages = [m for m in letta_messages if m.message_type != "approval_response_message"]
-
- for message in letta_messages:
- if include_return_message_types is None or message.message_type in include_return_message_types:
- yield f"data: {message.model_dump_json()}\n\n"
-
- MetricRegistry().step_execution_time_ms_histogram.record(get_utc_timestamp_ns() - step_start, get_ctx_attributes())
- step_progression = StepProgression.FINISHED
-
- # Record step metrics for successful completion
- if logged_step and step_metrics:
- # Set the step_ns that was already calculated
- step_metrics.step_ns = step_ns
- await self._record_step_metrics(
- step_id=step_id,
- agent_state=agent_state,
- step_metrics=step_metrics,
- )
-
- except Exception as e:
- # Handle any unexpected errors during step processing
- self.logger.error(f"Error during step processing: {e}")
- job_update_metadata = {"error": str(e)}
-
- # This indicates we failed after we decided to stop stepping, which indicates a bug with our flow.
- if not stop_reason:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- elif stop_reason.stop_reason in (StopReasonType.end_turn, StopReasonType.max_steps, StopReasonType.tool_rule):
- self.logger.error("Error occurred during step processing, with valid stop reason: %s", stop_reason.stop_reason)
- elif stop_reason.stop_reason not in (
- StopReasonType.no_tool_call,
- StopReasonType.invalid_tool_call,
- StopReasonType.invalid_llm_response,
- ):
- self.logger.error("Error occurred during step processing, with unexpected stop reason: %s", stop_reason.stop_reason)
-
- # Send error stop reason to client and re-raise
- yield f"data: {stop_reason.model_dump_json()}\n\n", 500
- raise
-
- # Update step if it needs to be updated
- finally:
- if step_progression == StepProgression.FINISHED and should_continue:
- continue
-
- self.logger.debug("Running cleanup for agent loop run: %s", self.current_run_id)
- self.logger.info("Running final update. Step Progression: %s", step_progression)
- try:
- if step_progression == StepProgression.FINISHED and not should_continue:
- # Successfully completed - update with final usage and stop reason
- if stop_reason is None:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- # Note: step already updated with success status after _handle_ai_response
- if logged_step:
- await self.step_manager.update_step_stop_reason(self.actor, step_id, stop_reason.stop_reason)
- break
-
- # Handle error cases
- if step_progression < StepProgression.STEP_LOGGED:
- # Error occurred before step was fully logged
- import traceback
-
- if logged_step:
- await self.step_manager.update_step_error_async(
- actor=self.actor,
- step_id=step_id, # Use original step_id for telemetry
- error_type=type(e).__name__ if "e" in locals() else "Unknown",
- error_message=str(e) if "e" in locals() else "Unknown error",
- error_traceback=traceback.format_exc(),
- stop_reason=stop_reason,
- )
-
- if step_progression <= StepProgression.RESPONSE_RECEIVED:
- # TODO (cliandy): persist response if we get it back
- if settings.track_errored_messages and initial_messages:
- for message in initial_messages:
- message.is_err = True
- message.step_id = effective_step_id
- await self.message_manager.create_many_messages_async(
- initial_messages,
- actor=self.actor,
- project_id=agent_state.project_id,
- template_id=agent_state.template_id,
- )
- elif step_progression <= StepProgression.LOGGED_TRACE:
- if stop_reason is None:
- self.logger.error("Error in step after logging step")
- stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- if logged_step:
- await self.step_manager.update_step_stop_reason(self.actor, step_id, stop_reason.stop_reason)
- else:
- self.logger.error("Invalid StepProgression value")
-
- if settings.track_stop_reason:
- await self._log_request(request_start_timestamp_ns, request_span, job_update_metadata, is_error=True)
-
- # Record partial step metrics on failure (capture whatever timing data we have)
- if logged_step and step_metrics and step_progression < StepProgression.FINISHED:
- # Calculate total step time up to the failure point
- step_metrics.step_ns = get_utc_timestamp_ns() - step_start
- await self._record_step_metrics(
- step_id=step_id,
- agent_state=agent_state,
- step_metrics=step_metrics,
- job_id=locals().get("run_id", self.current_run_id),
- )
-
- except Exception as e:
- self.logger.error("Failed to update step: %s", e)
-
- if not should_continue:
- break
-
- # Extend the in context message ids
- if not agent_state.message_buffer_autoclear:
- await self._rebuild_context_window(
- in_context_messages=current_in_context_messages,
- new_letta_messages=new_in_context_messages,
- llm_config=agent_state.llm_config,
- total_tokens=usage.total_tokens,
- force=False,
- )
-
- await self._log_request(request_start_timestamp_ns, request_span, job_update_metadata, is_error=False)
-
- # Return back usage
- for finish_chunk in self.get_finish_chunks_for_stream(usage, stop_reason):
- yield f"data: {finish_chunk}\n\n"
-
- async def _step(
- self,
- agent_state: AgentState,
- input_messages: list[MessageCreateBase],
- max_steps: int = DEFAULT_MAX_STEPS,
- run_id: str | None = None,
- request_start_timestamp_ns: int | None = None,
- dry_run: bool = False,
- ) -> Union[tuple[list[Message], list[Message], LettaStopReason | None, LettaUsageStatistics], dict]:
- """
- Carries out an invocation of the agent loop. In each step, the agent
- 1. Rebuilds its memory
- 2. Generates a request for the LLM
- 3. Fetches a response from the LLM
- 4. Processes the response
- """
- current_in_context_messages, new_in_context_messages = await _prepare_in_context_messages_no_persist_async(
- input_messages, agent_state, self.message_manager, self.actor
- )
- initial_messages = new_in_context_messages
- in_context_messages = current_in_context_messages
- tool_rules_solver = ToolRulesSolver(agent_state.tool_rules)
- llm_client = LLMClient.create(
- provider_type=agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=True,
- actor=self.actor,
- )
-
- # span for request
- request_span = tracer.start_span("time_to_first_token")
- request_span.set_attributes({f"llm_config.{k}": v for k, v in agent_state.llm_config.model_dump().items() if v is not None})
-
- stop_reason = None
- job_update_metadata = None
- usage = LettaUsageStatistics()
- for i in range(max_steps):
- if in_context_messages[-1].role == "approval":
- approval_request_message = in_context_messages[-1]
- step_metrics = await self.step_manager.get_step_metrics_async(step_id=approval_request_message.step_id, actor=self.actor)
- persisted_messages, should_continue, stop_reason = await self._handle_ai_response(
- approval_request_message.tool_calls[0],
- [], # TODO: update this
- agent_state,
- tool_rules_solver,
- usage,
- reasoning_content=approval_request_message.content,
- step_id=approval_request_message.step_id,
- initial_messages=initial_messages,
- is_final_step=(i == max_steps - 1),
- step_metrics=step_metrics,
- run_id=run_id or self.current_run_id,
- is_approval=input_messages[0].approve,
- is_denial=input_messages[0].approve == False,
- denial_reason=input_messages[0].reason,
- )
- new_message_idx = len(initial_messages) if initial_messages else 0
- self.response_messages.extend(persisted_messages[new_message_idx:])
- new_in_context_messages.extend(persisted_messages[new_message_idx:])
- initial_messages = None
- in_context_messages = current_in_context_messages + new_in_context_messages
- else:
- # If dry run, build request data and return it without making LLM call
- if dry_run:
- request_data, valid_tool_names = await self._create_llm_request_data_async(
- llm_client=llm_client,
- in_context_messages=current_in_context_messages + new_in_context_messages,
- agent_state=agent_state,
- tool_rules_solver=tool_rules_solver,
- )
- return request_data
-
- # Check for job cancellation at the start of each step
- if await self._check_run_cancellation():
- stop_reason = LettaStopReason(stop_reason=StopReasonType.cancelled.value)
- logger.info(f"Agent execution cancelled for run {self.current_run_id}")
- break
-
- step_id = generate_step_id()
- step_start = get_utc_timestamp_ns()
- agent_step_span = tracer.start_span("agent_step", start_time=step_start)
- agent_step_span.set_attributes({"step_id": step_id})
-
- step_progression = StepProgression.START
- should_continue = False
- step_metrics = StepMetrics(id=step_id) # Initialize metrics tracking
-
- # Create step early with PENDING status
- logged_step = await self.step_manager.log_step_async(
- actor=self.actor,
- agent_id=agent_state.id,
- provider_name=agent_state.llm_config.model_endpoint_type,
- provider_category=agent_state.llm_config.provider_category or "base",
- model=agent_state.llm_config.model,
- model_endpoint=agent_state.llm_config.model_endpoint,
- context_window_limit=agent_state.llm_config.context_window,
- usage=UsageStatistics(completion_tokens=0, prompt_tokens=0, total_tokens=0),
- provider_id=None,
- job_id=run_id if run_id else self.current_run_id,
- step_id=step_id,
- project_id=agent_state.project_id,
- status=StepStatus.PENDING,
- )
- # Only use step_id in messages if step was actually created
- effective_step_id = step_id if logged_step else None
-
- try:
- (
- request_data,
- response_data,
- current_in_context_messages,
- new_in_context_messages,
- valid_tool_names,
- ) = await self._build_and_request_from_llm(
- current_in_context_messages,
- new_in_context_messages,
- agent_state,
- llm_client,
- tool_rules_solver,
- agent_step_span,
- step_metrics,
- )
- in_context_messages = current_in_context_messages + new_in_context_messages
-
- step_progression = StepProgression.RESPONSE_RECEIVED
- log_event("agent.step.llm_response.received") # [3^]
-
- try:
- response = llm_client.convert_response_to_chat_completion(
- response_data, in_context_messages, agent_state.llm_config
- )
- except ValueError as e:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.invalid_llm_response.value)
- raise e
-
- usage.step_count += 1
- usage.completion_tokens += response.usage.completion_tokens
- usage.prompt_tokens += response.usage.prompt_tokens
- usage.total_tokens += response.usage.total_tokens
- usage.run_ids = [run_id] if run_id else None
- MetricRegistry().message_output_tokens.record(
- response.usage.completion_tokens, dict(get_ctx_attributes(), **{"model.name": agent_state.llm_config.model})
- )
-
- if not response.choices[0].message.tool_calls:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.no_tool_call.value)
- raise ValueError("No tool calls found in response, model must make a tool call")
- tool_call = response.choices[0].message.tool_calls[0]
- if response.choices[0].message.reasoning_content:
- reasoning = [
- ReasoningContent(
- reasoning=response.choices[0].message.reasoning_content,
- is_native=True,
- signature=response.choices[0].message.reasoning_content_signature,
- )
- ]
- elif response.choices[0].message.content:
- reasoning = [
- TextContent(text=response.choices[0].message.content)
- ] # reasoning placed into content for legacy reasons
- elif response.choices[0].message.omitted_reasoning_content:
- reasoning = [OmittedReasoningContent()]
- else:
- self.logger.info("No reasoning content found.")
- reasoning = None
-
- persisted_messages, should_continue, stop_reason = await self._handle_ai_response(
- tool_call,
- valid_tool_names,
- agent_state,
- tool_rules_solver,
- response.usage,
- reasoning_content=reasoning,
- step_id=effective_step_id,
- initial_messages=initial_messages,
- agent_step_span=agent_step_span,
- is_final_step=(i == max_steps - 1),
- run_id=run_id,
- step_metrics=step_metrics,
- )
- step_progression = StepProgression.STEP_LOGGED
-
- # Update step with actual usage now that we have it (if step was created)
- if logged_step:
- await self.step_manager.update_step_success_async(self.actor, step_id, response.usage, stop_reason)
-
- new_message_idx = len(initial_messages) if initial_messages else 0
- self.response_messages.extend(persisted_messages[new_message_idx:])
- new_in_context_messages.extend(persisted_messages[new_message_idx:])
-
- initial_messages = None
- log_event("agent.step.llm_response.processed") # [4^]
-
- # log step time
- now = get_utc_timestamp_ns()
- step_ns = now - step_start
- agent_step_span.add_event(name="step_ms", attributes={"duration_ms": ns_to_ms(step_ns)})
- agent_step_span.end()
-
- # Log LLM Trace
- if settings.track_provider_trace:
- await self.telemetry_manager.create_provider_trace_async(
- actor=self.actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=request_data,
- response_json=response_data,
- step_id=step_id, # Use original step_id for telemetry
- organization_id=self.actor.organization_id,
- ),
- )
- step_progression = StepProgression.LOGGED_TRACE
-
- MetricRegistry().step_execution_time_ms_histogram.record(get_utc_timestamp_ns() - step_start, get_ctx_attributes())
- step_progression = StepProgression.FINISHED
-
- # Record step metrics for successful completion
- if logged_step and step_metrics:
- # Set the step_ns that was already calculated
- step_metrics.step_ns = step_ns
- await self._record_step_metrics(
- step_id=step_id,
- agent_state=agent_state,
- step_metrics=step_metrics,
- job_id=run_id if run_id else self.current_run_id,
- )
-
- except Exception as e:
- # Handle any unexpected errors during step processing
- self.logger.error(f"Error during step processing: {e}")
- job_update_metadata = {"error": str(e)}
-
- # This indicates we failed after we decided to stop stepping, which indicates a bug with our flow.
- if not stop_reason:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- elif stop_reason.stop_reason in (StopReasonType.end_turn, StopReasonType.max_steps, StopReasonType.tool_rule):
- self.logger.error("Error occurred during step processing, with valid stop reason: %s", stop_reason.stop_reason)
- elif stop_reason.stop_reason not in (
- StopReasonType.no_tool_call,
- StopReasonType.invalid_tool_call,
- StopReasonType.invalid_llm_response,
- ):
- self.logger.error("Error occurred during step processing, with unexpected stop reason: %s", stop_reason.stop_reason)
- raise
-
- # Update step if it needs to be updated
- finally:
- if step_progression == StepProgression.FINISHED and should_continue:
- continue
-
- self.logger.debug("Running cleanup for agent loop run: %s", self.current_run_id)
- self.logger.info("Running final update. Step Progression: %s", step_progression)
- try:
- if step_progression == StepProgression.FINISHED and not should_continue:
- # Successfully completed - update with final usage and stop reason
- if stop_reason is None:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- if logged_step:
- await self.step_manager.update_step_success_async(self.actor, step_id, usage, stop_reason)
- break
-
- # Handle error cases
- if step_progression < StepProgression.STEP_LOGGED:
- # Error occurred before step was fully logged
- import traceback
-
- if logged_step:
- await self.step_manager.update_step_error_async(
- actor=self.actor,
- step_id=step_id, # Use original step_id for telemetry
- error_type=type(e).__name__ if "e" in locals() else "Unknown",
- error_message=str(e) if "e" in locals() else "Unknown error",
- error_traceback=traceback.format_exc(),
- stop_reason=stop_reason,
- )
-
- if step_progression <= StepProgression.RESPONSE_RECEIVED:
- # TODO (cliandy): persist response if we get it back
- if settings.track_errored_messages and initial_messages:
- for message in initial_messages:
- message.is_err = True
- message.step_id = effective_step_id
- await self.message_manager.create_many_messages_async(
- initial_messages,
- actor=self.actor,
- project_id=agent_state.project_id,
- template_id=agent_state.template_id,
- )
- elif step_progression <= StepProgression.LOGGED_TRACE:
- if stop_reason is None:
- self.logger.error("Error in step after logging step")
- stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- if logged_step:
- await self.step_manager.update_step_stop_reason(self.actor, step_id, stop_reason.stop_reason)
- else:
- self.logger.error("Invalid StepProgression value")
-
- if settings.track_stop_reason:
- await self._log_request(request_start_timestamp_ns, request_span, job_update_metadata, is_error=True)
-
- # Record partial step metrics on failure (capture whatever timing data we have)
- if logged_step and step_metrics and step_progression < StepProgression.FINISHED:
- # Calculate total step time up to the failure point
- step_metrics.step_ns = get_utc_timestamp_ns() - step_start
- await self._record_step_metrics(
- step_id=step_id,
- agent_state=agent_state,
- step_metrics=step_metrics,
- job_id=locals().get("run_id", self.current_run_id),
- )
-
- except Exception as e:
- self.logger.error("Failed to update step: %s", e)
-
- if not should_continue:
- break
-
- # Extend the in context message ids
- if not agent_state.message_buffer_autoclear:
- await self._rebuild_context_window(
- in_context_messages=current_in_context_messages,
- new_letta_messages=new_in_context_messages,
- llm_config=agent_state.llm_config,
- total_tokens=usage.total_tokens,
- force=False,
- )
-
- await self._log_request(request_start_timestamp_ns, request_span, job_update_metadata, is_error=False)
-
- return current_in_context_messages, new_in_context_messages, stop_reason, usage
-
- async def _update_agent_last_run_metrics(self, completion_time: datetime, duration_ms: float) -> None:
- if not settings.track_last_agent_run:
- return
- try:
- await self.agent_manager.update_agent_async(
- agent_id=self.agent_id,
- agent_update=UpdateAgent(last_run_completion=completion_time, last_run_duration_ms=duration_ms),
- actor=self.actor,
- )
- except Exception as e:
- self.logger.error(f"Failed to update agent's last run metrics: {e}")
-
- @trace_method
- async def step_stream(
- self,
- input_messages: list[MessageCreateBase],
- max_steps: int = DEFAULT_MAX_STEPS,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: int | None = None,
- include_return_message_types: list[MessageType] | None = None,
- ) -> AsyncGenerator[str, None]:
- """
- Carries out an invocation of the agent loop in a streaming fashion that yields partial tokens.
- Whenever we detect a tool call, we yield from _handle_ai_response as well. At each step, the agent
- 1. Rebuilds its memory
- 2. Generates a request for the LLM
- 3. Fetches a response from the LLM
- 4. Processes the response
- """
- agent_state = await self.agent_manager.get_agent_by_id_async(
- agent_id=self.agent_id,
- include_relationships=["tools", "memory", "tool_exec_environment_variables", "sources"],
- actor=self.actor,
- )
- current_in_context_messages, new_in_context_messages = await _prepare_in_context_messages_no_persist_async(
- input_messages, agent_state, self.message_manager, self.actor
- )
- initial_messages = new_in_context_messages
- in_context_messages = current_in_context_messages
-
- tool_rules_solver = ToolRulesSolver(agent_state.tool_rules)
- llm_client = LLMClient.create(
- provider_type=agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=True,
- actor=self.actor,
- )
- stop_reason = None
- job_update_metadata = None
- usage = LettaUsageStatistics()
- first_chunk, request_span = True, None
- if request_start_timestamp_ns:
- request_span = tracer.start_span("time_to_first_token", start_time=request_start_timestamp_ns)
- request_span.set_attributes({f"llm_config.{k}": v for k, v in agent_state.llm_config.model_dump().items() if v is not None})
-
- for i in range(max_steps):
- if in_context_messages[-1].role == "approval":
- approval_request_message = in_context_messages[-1]
- step_metrics = await self.step_manager.get_step_metrics_async(step_id=approval_request_message.step_id, actor=self.actor)
- persisted_messages, should_continue, stop_reason = await self._handle_ai_response(
- approval_request_message.tool_calls[0],
- [], # TODO: update this
- agent_state,
- tool_rules_solver,
- usage,
- reasoning_content=approval_request_message.content,
- step_id=approval_request_message.step_id,
- initial_messages=new_in_context_messages,
- is_final_step=(i == max_steps - 1),
- step_metrics=step_metrics,
- run_id=self.current_run_id,
- is_approval=input_messages[0].approve,
- is_denial=input_messages[0].approve == False,
- denial_reason=input_messages[0].reason,
- )
- new_message_idx = len(initial_messages) if initial_messages else 0
- self.response_messages.extend(persisted_messages[new_message_idx:])
- new_in_context_messages.extend(persisted_messages[new_message_idx:])
- initial_messages = None
- in_context_messages = current_in_context_messages + new_in_context_messages
-
- # yields tool response as this is handled from Letta and not the response from the LLM provider
- tool_return = [msg for msg in persisted_messages if msg.role == "tool"][-1].to_letta_messages()[0]
- if not (use_assistant_message and tool_return.name == "send_message"):
- # Apply message type filtering if specified
- if include_return_message_types is None or tool_return.message_type in include_return_message_types:
- yield f"data: {tool_return.model_dump_json()}\n\n"
- else:
- step_id = generate_step_id()
- # Check for job cancellation at the start of each step
- if await self._check_run_cancellation():
- stop_reason = LettaStopReason(stop_reason=StopReasonType.cancelled.value)
- logger.info(f"Agent execution cancelled for run {self.current_run_id}")
- yield f"data: {stop_reason.model_dump_json()}\n\n"
- break
-
- step_start = get_utc_timestamp_ns()
- agent_step_span = tracer.start_span("agent_step", start_time=step_start)
- agent_step_span.set_attributes({"step_id": step_id})
-
- step_progression = StepProgression.START
- should_continue = False
- step_metrics = StepMetrics(id=step_id) # Initialize metrics tracking
-
- # Create step early with PENDING status
- logged_step = await self.step_manager.log_step_async(
- actor=self.actor,
- agent_id=agent_state.id,
- provider_name=agent_state.llm_config.model_endpoint_type,
- provider_category=agent_state.llm_config.provider_category or "base",
- model=agent_state.llm_config.model,
- model_endpoint=agent_state.llm_config.model_endpoint,
- context_window_limit=agent_state.llm_config.context_window,
- usage=UsageStatistics(completion_tokens=0, prompt_tokens=0, total_tokens=0),
- provider_id=None,
- job_id=self.current_run_id if self.current_run_id else None,
- step_id=step_id,
- project_id=agent_state.project_id,
- status=StepStatus.PENDING,
- )
- # Only use step_id in messages if step was actually created
- effective_step_id = step_id if logged_step else None
-
- try:
- (
- request_data,
- stream,
- current_in_context_messages,
- new_in_context_messages,
- valid_tool_names,
- provider_request_start_timestamp_ns,
- ) = await self._build_and_request_from_llm_streaming(
- first_chunk,
- agent_step_span,
- request_start_timestamp_ns,
- current_in_context_messages,
- new_in_context_messages,
- agent_state,
- llm_client,
- tool_rules_solver,
- )
-
- step_progression = StepProgression.STREAM_RECEIVED
- log_event("agent.stream.llm_response.received") # [3^]
-
- # TODO: THIS IS INCREDIBLY UGLY
- # TODO: THERE ARE MULTIPLE COPIES OF THE LLM_CONFIG EVERYWHERE THAT ARE GETTING MANIPULATED
- if agent_state.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock]:
- interface = AnthropicStreamingInterface(
- use_assistant_message=use_assistant_message,
- put_inner_thoughts_in_kwarg=agent_state.llm_config.put_inner_thoughts_in_kwargs,
- requires_approval_tools=tool_rules_solver.get_requires_approval_tools(valid_tool_names),
- )
- elif agent_state.llm_config.model_endpoint_type == ProviderType.openai:
- interface = OpenAIStreamingInterface(
- use_assistant_message=use_assistant_message,
- is_openai_proxy=agent_state.llm_config.provider_name == "lmstudio_openai",
- messages=current_in_context_messages + new_in_context_messages,
- tools=request_data.get("tools", []),
- put_inner_thoughts_in_kwarg=agent_state.llm_config.put_inner_thoughts_in_kwargs,
- requires_approval_tools=tool_rules_solver.get_requires_approval_tools(valid_tool_names),
- )
- else:
- raise ValueError(f"Streaming not supported for {agent_state.llm_config}")
-
- async for chunk in interface.process(
- stream,
- ttft_span=request_span,
- ):
- # Measure TTFT (trace, metric, and db). This should be consolidated.
- if first_chunk and request_span is not None:
- now = get_utc_timestamp_ns()
- ttft_ns = now - request_start_timestamp_ns
-
- request_span.add_event(name="time_to_first_token_ms", attributes={"ttft_ms": ns_to_ms(ttft_ns)})
- metric_attributes = get_ctx_attributes()
- metric_attributes["model.name"] = agent_state.llm_config.model
- MetricRegistry().ttft_ms_histogram.record(ns_to_ms(ttft_ns), metric_attributes)
-
- if self.current_run_id and self.job_manager:
- await self.job_manager.record_ttft(self.current_run_id, ttft_ns, self.actor)
-
- first_chunk = False
-
- if include_return_message_types is None or chunk.message_type in include_return_message_types:
- # filter down returned data
- yield f"data: {chunk.model_dump_json()}\n\n"
-
- stream_end_time_ns = get_utc_timestamp_ns()
-
- # Some providers that rely on the OpenAI client currently e.g. LMStudio don't get usage metrics back on the last streaming chunk, fall back to manual values
- if isinstance(interface, OpenAIStreamingInterface) and not interface.input_tokens and not interface.output_tokens:
- logger.warning(
- f"No token usage metrics received from OpenAI streaming interface for {agent_state.llm_config.model}, falling back to estimated values. Input tokens: {interface.fallback_input_tokens}, Output tokens: {interface.fallback_output_tokens}"
- )
- interface.input_tokens = interface.fallback_input_tokens
- interface.output_tokens = interface.fallback_output_tokens
-
- usage.step_count += 1
- usage.completion_tokens += interface.output_tokens
- usage.prompt_tokens += interface.input_tokens
- usage.total_tokens += interface.input_tokens + interface.output_tokens
- MetricRegistry().message_output_tokens.record(
- usage.completion_tokens, dict(get_ctx_attributes(), **{"model.name": agent_state.llm_config.model})
- )
-
- # log LLM request time
- llm_request_ns = stream_end_time_ns - provider_request_start_timestamp_ns
- step_metrics.llm_request_ns = llm_request_ns
-
- llm_request_ms = ns_to_ms(llm_request_ns)
- agent_step_span.add_event(name="llm_request_ms", attributes={"duration_ms": llm_request_ms})
- MetricRegistry().llm_execution_time_ms_histogram.record(
- llm_request_ms,
- dict(get_ctx_attributes(), **{"model.name": agent_state.llm_config.model}),
- )
-
- # Process resulting stream content
- try:
- tool_call = interface.get_tool_call_object()
- except ValueError as e:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.no_tool_call.value)
- raise e
- except Exception as e:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.invalid_tool_call.value)
- raise e
- reasoning_content = interface.get_reasoning_content()
- persisted_messages, should_continue, stop_reason = await self._handle_ai_response(
- tool_call,
- valid_tool_names,
- agent_state,
- tool_rules_solver,
- UsageStatistics(
- completion_tokens=usage.completion_tokens,
- prompt_tokens=usage.prompt_tokens,
- total_tokens=usage.total_tokens,
- ),
- reasoning_content=reasoning_content,
- pre_computed_assistant_message_id=interface.letta_message_id,
- step_id=effective_step_id,
- initial_messages=initial_messages,
- agent_step_span=agent_step_span,
- is_final_step=(i == max_steps - 1),
- step_metrics=step_metrics,
- )
- step_progression = StepProgression.STEP_LOGGED
-
- # Update step with actual usage now that we have it (if step was created)
- if logged_step:
- await self.step_manager.update_step_success_async(
- self.actor,
- step_id,
- UsageStatistics(
- completion_tokens=usage.completion_tokens,
- prompt_tokens=usage.prompt_tokens,
- total_tokens=usage.total_tokens,
- ),
- stop_reason,
- )
-
- new_message_idx = len(initial_messages) if initial_messages else 0
- self.response_messages.extend(persisted_messages[new_message_idx:])
- new_in_context_messages.extend(persisted_messages[new_message_idx:])
-
- initial_messages = None
-
- # log total step time
- now = get_utc_timestamp_ns()
- step_ns = now - step_start
- agent_step_span.add_event(name="step_ms", attributes={"duration_ms": ns_to_ms(step_ns)})
- agent_step_span.end()
-
- # TODO (cliandy): the stream POST request span has ended at this point, we should tie this to the stream
- # log_event("agent.stream.llm_response.processed") # [4^]
-
- # Log LLM Trace
- # We are piecing together the streamed response here.
- # Content here does not match the actual response schema as streams come in chunks.
- if settings.track_provider_trace:
- await self.telemetry_manager.create_provider_trace_async(
- actor=self.actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=request_data,
- response_json={
- "content": {
- "tool_call": tool_call.model_dump_json(),
- "reasoning": [content.model_dump_json() for content in reasoning_content],
- },
- "id": interface.message_id,
- "model": interface.model,
- "role": "assistant",
- # "stop_reason": "",
- # "stop_sequence": None,
- "type": "message",
- "usage": {
- "input_tokens": usage.prompt_tokens,
- "output_tokens": usage.completion_tokens,
- },
- },
- step_id=step_id, # Use original step_id for telemetry
- organization_id=self.actor.organization_id,
- ),
- )
- step_progression = StepProgression.LOGGED_TRACE
-
- if persisted_messages[-1].role != "approval":
- # yields tool response as this is handled from Letta and not the response from the LLM provider
- tool_return = [msg for msg in persisted_messages if msg.role == "tool"][-1].to_letta_messages()[0]
- if not (use_assistant_message and tool_return.name == "send_message"):
- # Apply message type filtering if specified
- if include_return_message_types is None or tool_return.message_type in include_return_message_types:
- yield f"data: {tool_return.model_dump_json()}\n\n"
-
- # TODO (cliandy): consolidate and expand with trace
- MetricRegistry().step_execution_time_ms_histogram.record(get_utc_timestamp_ns() - step_start, get_ctx_attributes())
- step_progression = StepProgression.FINISHED
-
- # Record step metrics for successful completion
- if logged_step and step_metrics:
- try:
- # Set the step_ns that was already calculated
- step_metrics.step_ns = step_ns
-
- # Get context attributes for project and template IDs
- ctx_attrs = get_ctx_attributes()
-
- await self._record_step_metrics(
- step_id=step_id,
- agent_state=agent_state,
- step_metrics=step_metrics,
- ctx_attrs=ctx_attrs,
- job_id=self.current_run_id,
- )
- except Exception as metrics_error:
- self.logger.warning(f"Failed to record step metrics: {metrics_error}")
-
- except Exception as e:
- # Handle any unexpected errors during step processing
- self.logger.error(f"Error during step processing: {e}")
- job_update_metadata = {"error": str(e)}
-
- # This indicates we failed after we decided to stop stepping, which indicates a bug with our flow.
- if not stop_reason:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- elif stop_reason.stop_reason in (StopReasonType.end_turn, StopReasonType.max_steps, StopReasonType.tool_rule):
- self.logger.error("Error occurred during step processing, with valid stop reason: %s", stop_reason.stop_reason)
- elif stop_reason.stop_reason not in (
- StopReasonType.no_tool_call,
- StopReasonType.invalid_tool_call,
- StopReasonType.invalid_llm_response,
- ):
- self.logger.error("Error occurred during step processing, with unexpected stop reason: %s", stop_reason.stop_reason)
-
- # Send error stop reason to client and re-raise with expected response code
- yield f"data: {stop_reason.model_dump_json()}\n\n", 500
- raise
-
- # Update step if it needs to be updated
- finally:
- if step_progression == StepProgression.FINISHED and should_continue:
- continue
-
- self.logger.debug("Running cleanup for agent loop run: %s", self.current_run_id)
- self.logger.info("Running final update. Step Progression: %s", step_progression)
- try:
- if step_progression == StepProgression.FINISHED and not should_continue:
- # Successfully completed - update with final usage and stop reason
- if stop_reason is None:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- # Note: step already updated with success status after _handle_ai_response
- if logged_step:
- await self.step_manager.update_step_stop_reason(self.actor, step_id, stop_reason.stop_reason)
- break
-
- # Handle error cases
- if step_progression < StepProgression.STEP_LOGGED:
- # Error occurred before step was fully logged
- import traceback
-
- if logged_step:
- await self.step_manager.update_step_error_async(
- actor=self.actor,
- step_id=step_id, # Use original step_id for telemetry
- error_type=type(e).__name__ if "e" in locals() else "Unknown",
- error_message=str(e) if "e" in locals() else "Unknown error",
- error_traceback=traceback.format_exc(),
- stop_reason=stop_reason,
- )
-
- if step_progression <= StepProgression.STREAM_RECEIVED:
- if first_chunk and settings.track_errored_messages and initial_messages:
- for message in initial_messages:
- message.is_err = True
- message.step_id = effective_step_id
- await self.message_manager.create_many_messages_async(
- initial_messages,
- actor=self.actor,
- project_id=agent_state.project_id,
- template_id=agent_state.template_id,
- )
- elif step_progression <= StepProgression.LOGGED_TRACE:
- if stop_reason is None:
- self.logger.error("Error in step after logging step")
- stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- if logged_step:
- await self.step_manager.update_step_stop_reason(self.actor, step_id, stop_reason.stop_reason)
- else:
- self.logger.error("Invalid StepProgression value")
-
- # Do tracking for failure cases. Can consolidate with success conditions later.
- if settings.track_stop_reason:
- await self._log_request(request_start_timestamp_ns, request_span, job_update_metadata, is_error=True)
-
- # Record partial step metrics on failure (capture whatever timing data we have)
- if logged_step and step_metrics and step_progression < StepProgression.FINISHED:
- try:
- # Calculate total step time up to the failure point
- step_metrics.step_ns = get_utc_timestamp_ns() - step_start
-
- # Get context attributes for project and template IDs
- ctx_attrs = get_ctx_attributes()
-
- await self._record_step_metrics(
- step_id=step_id,
- agent_state=agent_state,
- step_metrics=step_metrics,
- ctx_attrs=ctx_attrs,
- job_id=locals().get("run_id", self.current_run_id),
- )
- except Exception as metrics_error:
- self.logger.warning(f"Failed to record step metrics: {metrics_error}")
-
- except Exception as e:
- self.logger.error("Failed to update step: %s", e)
-
- if not should_continue:
- break
- # Extend the in context message ids
- if not agent_state.message_buffer_autoclear:
- await self._rebuild_context_window(
- in_context_messages=current_in_context_messages,
- new_letta_messages=new_in_context_messages,
- llm_config=agent_state.llm_config,
- total_tokens=usage.total_tokens,
- force=False,
- )
-
- await self._log_request(request_start_timestamp_ns, request_span, job_update_metadata, is_error=False)
-
- for finish_chunk in self.get_finish_chunks_for_stream(usage, stop_reason):
- yield f"data: {finish_chunk}\n\n"
-
- async def _log_request(
- self, request_start_timestamp_ns: int, request_span: "Span | None", job_update_metadata: dict | None, is_error: bool
- ):
- if request_start_timestamp_ns:
- now_ns, now = get_utc_timestamp_ns(), get_utc_time()
- duration_ns = now_ns - request_start_timestamp_ns
- if request_span:
- request_span.add_event(name="letta_request_ms", attributes={"duration_ms": ns_to_ms(duration_ns)})
- await self._update_agent_last_run_metrics(now, ns_to_ms(duration_ns))
- if settings.track_agent_run and self.current_run_id:
- await self.job_manager.record_response_duration(self.current_run_id, duration_ns, self.actor)
- await self.job_manager.safe_update_job_status_async(
- job_id=self.current_run_id,
- new_status=JobStatus.failed if is_error else JobStatus.completed,
- actor=self.actor,
- metadata=job_update_metadata,
- )
- if request_span:
- request_span.end()
-
- async def _record_step_metrics(
- self,
- *,
- step_id: str,
- agent_state: AgentState,
- step_metrics: StepMetrics,
- ctx_attrs: dict | None = None,
- job_id: str | None = None,
- ) -> None:
- try:
- attrs = ctx_attrs or get_ctx_attributes()
- await self.step_manager.record_step_metrics_async(
- actor=self.actor,
- step_id=step_id,
- llm_request_ns=step_metrics.llm_request_ns,
- tool_execution_ns=step_metrics.tool_execution_ns,
- step_ns=step_metrics.step_ns,
- agent_id=agent_state.id,
- job_id=job_id or self.current_run_id,
- project_id=attrs.get("project.id") or agent_state.project_id,
- template_id=attrs.get("template.id"),
- base_template_id=attrs.get("base_template.id"),
- )
- except Exception as metrics_error:
- self.logger.warning(f"Failed to record step metrics: {metrics_error}")
-
- # noinspection PyInconsistentReturns
- async def _build_and_request_from_llm(
- self,
- current_in_context_messages: list[Message],
- new_in_context_messages: list[Message],
- agent_state: AgentState,
- llm_client: LLMClientBase,
- tool_rules_solver: ToolRulesSolver,
- agent_step_span: "Span",
- step_metrics: StepMetrics,
- ) -> tuple[dict, dict, list[Message], list[Message], list[str]] | None:
- for attempt in range(self.max_summarization_retries + 1):
- try:
- log_event("agent.stream_no_tokens.messages.refreshed")
- # Create LLM request data
- request_data, valid_tool_names = await self._create_llm_request_data_async(
- llm_client=llm_client,
- in_context_messages=current_in_context_messages + new_in_context_messages,
- agent_state=agent_state,
- tool_rules_solver=tool_rules_solver,
- )
- log_event("agent.stream_no_tokens.llm_request.created")
-
- async with AsyncTimer() as timer:
- # Attempt LLM request
- response = await llm_client.request_async(request_data, agent_state.llm_config)
-
- # Track LLM request time
- step_metrics.llm_request_ns = int(timer.elapsed_ns)
-
- MetricRegistry().llm_execution_time_ms_histogram.record(
- timer.elapsed_ms,
- dict(get_ctx_attributes(), **{"model.name": agent_state.llm_config.model}),
- )
- agent_step_span.add_event(name="llm_request_ms", attributes={"duration_ms": timer.elapsed_ms})
-
- return request_data, response, current_in_context_messages, new_in_context_messages, valid_tool_names
-
- except Exception as e:
- if attempt == self.max_summarization_retries:
- raise e
-
- # Handle the error and prepare for retry
- current_in_context_messages = await self._handle_llm_error(
- e,
- llm_client=llm_client,
- in_context_messages=current_in_context_messages,
- new_letta_messages=new_in_context_messages,
- llm_config=agent_state.llm_config,
- force=True,
- )
- new_in_context_messages = []
- log_event(f"agent.stream_no_tokens.retry_attempt.{attempt + 1}")
-
- # noinspection PyInconsistentReturns
- async def _build_and_request_from_llm_streaming(
- self,
- first_chunk: bool,
- ttft_span: "Span",
- request_start_timestamp_ns: int,
- current_in_context_messages: list[Message],
- new_in_context_messages: list[Message],
- agent_state: AgentState,
- llm_client: LLMClientBase,
- tool_rules_solver: ToolRulesSolver,
- ) -> tuple[dict, AsyncStream[ChatCompletionChunk], list[Message], list[Message], list[str], int] | None:
- for attempt in range(self.max_summarization_retries + 1):
- try:
- log_event("agent.stream_no_tokens.messages.refreshed")
- # Create LLM request data
- request_data, valid_tool_names = await self._create_llm_request_data_async(
- llm_client=llm_client,
- in_context_messages=current_in_context_messages + new_in_context_messages,
- agent_state=agent_state,
- tool_rules_solver=tool_rules_solver,
- )
- log_event("agent.stream.llm_request.created") # [2^]
-
- provider_request_start_timestamp_ns = get_utc_timestamp_ns()
- if first_chunk and ttft_span is not None:
- request_start_to_provider_request_start_ns = provider_request_start_timestamp_ns - request_start_timestamp_ns
- ttft_span.add_event(
- name="request_start_to_provider_request_start_ns",
- attributes={"request_start_to_provider_request_start_ns": ns_to_ms(request_start_to_provider_request_start_ns)},
- )
-
- # Attempt LLM request
- return (
- request_data,
- await llm_client.stream_async(request_data, agent_state.llm_config),
- current_in_context_messages,
- new_in_context_messages,
- valid_tool_names,
- provider_request_start_timestamp_ns,
- )
-
- except Exception as e:
- if attempt == self.max_summarization_retries:
- raise e
-
- # Handle the error and prepare for retry
- current_in_context_messages = await self._handle_llm_error(
- e,
- llm_client=llm_client,
- in_context_messages=current_in_context_messages,
- new_letta_messages=new_in_context_messages,
- llm_config=agent_state.llm_config,
- force=True,
- )
- new_in_context_messages: list[Message] = []
- log_event(f"agent.stream_no_tokens.retry_attempt.{attempt + 1}")
-
- @trace_method
- async def _handle_llm_error(
- self,
- e: Exception,
- llm_client: LLMClientBase,
- in_context_messages: list[Message],
- new_letta_messages: list[Message],
- llm_config: LLMConfig,
- force: bool,
- ) -> list[Message]:
- if isinstance(e, ContextWindowExceededError):
- return await self._rebuild_context_window(
- in_context_messages=in_context_messages, new_letta_messages=new_letta_messages, llm_config=llm_config, force=force
- )
- else:
- raise llm_client.handle_llm_error(e)
-
- @trace_method
- async def _rebuild_context_window(
- self,
- in_context_messages: list[Message],
- new_letta_messages: list[Message],
- llm_config: LLMConfig,
- total_tokens: int | None = None,
- force: bool = False,
- ) -> list[Message]:
- # If total tokens is reached, we truncate down
- # TODO: This can be broken by bad configs, e.g. lower bound too high, initial messages too fat, etc.
- # TODO: `force` and `clear` seem to no longer be used, we should remove
- if force or (total_tokens and total_tokens > llm_config.context_window):
- self.logger.warning(
- f"Total tokens {total_tokens} exceeds configured max tokens {llm_config.context_window}, forcefully clearing message history."
- )
- new_in_context_messages, updated = await self.summarizer.summarize(
- in_context_messages=in_context_messages,
- new_letta_messages=new_letta_messages,
- force=True,
- clear=True,
- )
- else:
- # NOTE (Sarah): Seems like this is doing nothing?
- self.logger.info(
- f"Total tokens {total_tokens} does not exceed configured max tokens {llm_config.context_window}, passing summarizing w/o force."
- )
- new_in_context_messages, updated = await self.summarizer.summarize(
- in_context_messages=in_context_messages,
- new_letta_messages=new_letta_messages,
- )
- await self.agent_manager.update_message_ids_async(
- agent_id=self.agent_id,
- message_ids=[m.id for m in new_in_context_messages],
- actor=self.actor,
- )
-
- return new_in_context_messages
-
- @trace_method
- async def summarize_conversation_history(self) -> None:
- """Called when the developer explicitly triggers compaction via the API"""
- agent_state = await self.agent_manager.get_agent_by_id_async(agent_id=self.agent_id, actor=self.actor)
- message_ids = agent_state.message_ids
- in_context_messages = await self.message_manager.get_messages_by_ids_async(message_ids=message_ids, actor=self.actor)
- new_in_context_messages, updated = await self.summarizer.summarize(
- in_context_messages=in_context_messages, new_letta_messages=[], force=True
- )
- return await self.agent_manager.update_message_ids_async(
- agent_id=self.agent_id, message_ids=[m.id for m in new_in_context_messages], actor=self.actor
- )
-
- @trace_method
- async def _create_llm_request_data_async(
- self,
- llm_client: LLMClientBase,
- in_context_messages: list[Message],
- agent_state: AgentState,
- tool_rules_solver: ToolRulesSolver,
- ) -> tuple[dict, list[str]]:
- if not self.num_messages:
- self.num_messages = await self.message_manager.size_async(
- agent_id=agent_state.id,
- actor=self.actor,
- )
- if not self.num_archival_memories:
- self.num_archival_memories = await self.passage_manager.agent_passage_size_async(
- agent_id=agent_state.id,
- actor=self.actor,
- )
-
- in_context_messages = await self._rebuild_memory_async(
- in_context_messages,
- agent_state,
- num_messages=self.num_messages,
- num_archival_memories=self.num_archival_memories,
- tool_rules_solver=tool_rules_solver,
- )
-
- # scrub inner thoughts from messages if reasoning is completely disabled
- in_context_messages = scrub_inner_thoughts_from_messages(in_context_messages, agent_state.llm_config)
-
- tools = [
- t
- for t in agent_state.tools
- if t.tool_type
- in {
- ToolType.CUSTOM,
- ToolType.LETTA_CORE,
- ToolType.LETTA_MEMORY_CORE,
- ToolType.LETTA_MULTI_AGENT_CORE,
- ToolType.LETTA_SLEEPTIME_CORE,
- ToolType.LETTA_VOICE_SLEEPTIME_CORE,
- ToolType.LETTA_BUILTIN,
- ToolType.LETTA_FILES_CORE,
- ToolType.EXTERNAL_COMPOSIO,
- ToolType.EXTERNAL_MCP,
- }
- ]
-
- # Mirror the sync agent loop: get allowed tools or allow all if none are allowed
- self.last_function_response = self._load_last_function_response(in_context_messages)
- valid_tool_names = tool_rules_solver.get_allowed_tool_names(
- available_tools=set([t.name for t in tools]),
- last_function_response=self.last_function_response,
- ) or list(set(t.name for t in tools))
-
- # TODO: Copied from legacy agent loop, so please be cautious
- # Set force tool
- force_tool_call = None
- if len(valid_tool_names) == 1:
- force_tool_call = valid_tool_names[0]
-
- allowed_tools = [enable_strict_mode(t.json_schema) for t in tools if t.name in set(valid_tool_names)]
- # Extract terminal tool names from tool rules
- terminal_tool_names = {rule.tool_name for rule in tool_rules_solver.terminal_tool_rules}
- allowed_tools = runtime_override_tool_json_schema(
- tool_list=allowed_tools, response_format=agent_state.response_format, request_heartbeat=True, terminal_tools=terminal_tool_names
- )
-
- return (
- llm_client.build_request_data(
- in_context_messages,
- agent_state.llm_config,
- allowed_tools,
- force_tool_call,
- ),
- valid_tool_names,
- )
-
- @trace_method
- async def _handle_ai_response(
- self,
- tool_call: ToolCall,
- valid_tool_names: list[str],
- agent_state: AgentState,
- tool_rules_solver: ToolRulesSolver,
- usage: UsageStatistics,
- reasoning_content: list[TextContent | ReasoningContent | RedactedReasoningContent | OmittedReasoningContent] | None = None,
- pre_computed_assistant_message_id: str | None = None,
- step_id: str | None = None,
- initial_messages: list[Message] | None = None,
- agent_step_span: Optional["Span"] = None,
- is_final_step: bool | None = None,
- run_id: str | None = None,
- step_metrics: StepMetrics = None,
- is_approval: bool | None = None,
- is_denial: bool | None = None,
- denial_reason: str | None = None,
- ) -> tuple[list[Message], bool, LettaStopReason | None]:
- """
- Handle the final AI response once streaming completes, execute / validate the
- tool call, decide whether we should keep stepping, and persist state.
- """
- tool_call_id: str = tool_call.id or f"call_{uuid.uuid4().hex[:8]}"
-
- if is_denial:
- continue_stepping = True
- stop_reason = None
- tool_call_messages = create_letta_messages_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name="",
- function_arguments={},
- tool_execution_result=ToolExecutionResult(status="error"),
- tool_call_id=tool_call_id,
- function_call_success=False,
- function_response=f"Error: request to call tool denied. User reason: {denial_reason}",
- timezone=agent_state.timezone,
- actor=self.actor,
- continue_stepping=continue_stepping,
- heartbeat_reason=f"{NON_USER_MSG_PREFIX}Continuing: user denied request to call tool.",
- reasoning_content=None,
- pre_computed_assistant_message_id=None,
- step_id=step_id,
- is_approval_response=True,
- )
- messages_to_persist = (initial_messages or []) + tool_call_messages
- persisted_messages = await self.message_manager.create_many_messages_async(
- messages_to_persist, actor=self.actor, project_id=agent_state.project_id, template_id=agent_state.template_id
- )
- return persisted_messages, continue_stepping, stop_reason
-
- # 1. Parse and validate the tool-call envelope
- tool_call_name: str = tool_call.function.name
-
- tool_args = _safe_load_tool_call_str(tool_call.function.arguments)
- request_heartbeat: bool = _pop_heartbeat(tool_args)
- tool_args.pop(INNER_THOUGHTS_KWARG, None)
-
- log_telemetry(
- self.logger,
- "_handle_ai_response execute tool start",
- tool_name=tool_call_name,
- tool_args=tool_args,
- tool_call_id=tool_call_id,
- request_heartbeat=request_heartbeat,
- )
- if not is_approval and tool_rules_solver.is_requires_approval_tool(tool_call_name):
- approval_message = create_approval_request_message_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name=tool_call_name,
- function_arguments=tool_args,
- tool_call_id=tool_call_id,
- actor=self.actor,
- continue_stepping=request_heartbeat,
- reasoning_content=reasoning_content,
- pre_computed_assistant_message_id=pre_computed_assistant_message_id,
- step_id=step_id,
- )
- messages_to_persist = (initial_messages or []) + [approval_message]
- continue_stepping = False
- stop_reason = LettaStopReason(stop_reason=StopReasonType.requires_approval.value)
- else:
- # 2. Execute the tool (or synthesize an error result if disallowed)
- tool_rule_violated = tool_call_name not in valid_tool_names and not is_approval
- if tool_rule_violated:
- tool_execution_result = _build_rule_violation_result(tool_call_name, valid_tool_names, tool_rules_solver)
- else:
- # Track tool execution time
- tool_start_time = get_utc_timestamp_ns()
- tool_execution_result = await self._execute_tool(
- tool_name=tool_call_name,
- tool_args=tool_args,
- agent_state=agent_state,
- agent_step_span=agent_step_span,
- step_id=step_id,
- )
- tool_end_time = get_utc_timestamp_ns()
-
- # Store tool execution time in metrics
- step_metrics.tool_execution_ns = tool_end_time - tool_start_time
-
- log_telemetry(
- self.logger,
- "_handle_ai_response execute tool finish",
- tool_execution_result=tool_execution_result,
- tool_call_id=tool_call_id,
- )
-
- # 3. Prepare the function-response payload
- truncate = tool_call_name not in {"conversation_search", "conversation_search_date", "archival_memory_search"}
- return_char_limit = next(
- (t.return_char_limit for t in agent_state.tools if t.name == tool_call_name),
- None,
- )
- function_response_string = validate_function_response(
- tool_execution_result.func_return,
- return_char_limit=return_char_limit,
- truncate=truncate,
- )
- self.last_function_response = package_function_response(
- was_success=tool_execution_result.success_flag,
- response_string=function_response_string,
- timezone=agent_state.timezone,
- )
-
- # 4. Decide whether to keep stepping (focal section simplified)
- continue_stepping, heartbeat_reason, stop_reason = self._decide_continuation(
- agent_state=agent_state,
- request_heartbeat=request_heartbeat,
- tool_call_name=tool_call_name,
- tool_rule_violated=tool_rule_violated,
- tool_rules_solver=tool_rules_solver,
- is_final_step=is_final_step,
- )
-
- # 5. Create messages (step was already created at the beginning)
- tool_call_messages = create_letta_messages_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name=tool_call_name,
- function_arguments=tool_args,
- tool_execution_result=tool_execution_result,
- tool_call_id=tool_call_id,
- function_call_success=tool_execution_result.success_flag,
- function_response=function_response_string,
- timezone=agent_state.timezone,
- actor=self.actor,
- continue_stepping=continue_stepping,
- heartbeat_reason=heartbeat_reason,
- reasoning_content=reasoning_content,
- pre_computed_assistant_message_id=pre_computed_assistant_message_id,
- step_id=step_id,
- is_approval_response=is_approval or is_denial,
- )
- messages_to_persist = (initial_messages or []) + tool_call_messages
-
- persisted_messages = await self.message_manager.create_many_messages_async(
- messages_to_persist, actor=self.actor, project_id=agent_state.project_id, template_id=agent_state.template_id
- )
-
- if run_id:
- await self.job_manager.add_messages_to_job_async(
- job_id=run_id,
- message_ids=[m.id for m in persisted_messages if m.role != "user"],
- actor=self.actor,
- )
-
- return persisted_messages, continue_stepping, stop_reason
-
- def _decide_continuation(
- self,
- agent_state: AgentState,
- request_heartbeat: bool,
- tool_call_name: str,
- tool_rule_violated: bool,
- tool_rules_solver: ToolRulesSolver,
- is_final_step: bool | None,
- ) -> tuple[bool, str | None, LettaStopReason | None]:
- continue_stepping = request_heartbeat
- heartbeat_reason: str | None = None
- stop_reason: LettaStopReason | None = None
-
- if tool_rule_violated:
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing: tool rule violation."
- else:
- tool_rules_solver.register_tool_call(tool_call_name)
-
- if tool_rules_solver.is_terminal_tool(tool_call_name):
- if continue_stepping:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.tool_rule.value)
- continue_stepping = False
-
- elif tool_rules_solver.has_children_tools(tool_call_name):
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing: child tool rule."
-
- elif tool_rules_solver.is_continue_tool(tool_call_name):
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing: continue tool rule."
-
- # – hard stop overrides –
- if is_final_step:
- continue_stepping = False
- stop_reason = LettaStopReason(stop_reason=StopReasonType.max_steps.value)
- else:
- uncalled = tool_rules_solver.get_uncalled_required_tools(available_tools=set([t.name for t in agent_state.tools]))
- if not continue_stepping and uncalled:
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing, user expects these tools: [{', '.join(uncalled)}] to be called still."
-
- stop_reason = None # reset – we’re still going
-
- return continue_stepping, heartbeat_reason, stop_reason
-
- @trace_method
- async def _execute_tool(
- self,
- tool_name: str,
- tool_args: JsonDict,
- agent_state: AgentState,
- agent_step_span: Optional["Span"] = None,
- step_id: str | None = None,
- ) -> "ToolExecutionResult":
- """
- Executes a tool and returns the ToolExecutionResult.
- """
- from letta.schemas.tool_execution_result import ToolExecutionResult
-
- # Special memory case
- target_tool = next((x for x in agent_state.tools if x.name == tool_name), None)
- if not target_tool:
- # TODO: fix this error message
- return ToolExecutionResult(
- func_return=f"Tool {tool_name} not found",
- status="error",
- )
-
- # TODO: This temp. Move this logic and code to executors
-
- if agent_step_span:
- start_time = get_utc_timestamp_ns()
- agent_step_span.add_event(name="tool_execution_started")
-
- sandbox_env_vars = {var.key: var.value for var in agent_state.tool_exec_environment_variables}
- tool_execution_manager = ToolExecutionManager(
- agent_state=agent_state,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- block_manager=self.block_manager,
- job_manager=self.job_manager,
- passage_manager=self.passage_manager,
- sandbox_env_vars=sandbox_env_vars,
- actor=self.actor,
- )
- # TODO: Integrate sandbox result
- log_event(name=f"start_{tool_name}_execution", attributes=tool_args)
- tool_execution_result = await tool_execution_manager.execute_tool_async(
- function_name=tool_name,
- function_args=tool_args,
- tool=target_tool,
- step_id=step_id,
- )
- if agent_step_span:
- end_time = get_utc_timestamp_ns()
- agent_step_span.add_event(
- name="tool_execution_completed",
- attributes={
- "tool_name": target_tool.name,
- "duration_ms": ns_to_ms(end_time - start_time),
- "success": tool_execution_result.success_flag,
- "tool_type": target_tool.tool_type,
- "tool_id": target_tool.id,
- },
- )
- log_event(name=f"finish_{tool_name}_execution", attributes=tool_execution_result.model_dump())
- return tool_execution_result
-
- @trace_method
- def _load_last_function_response(self, in_context_messages: list[Message]):
- """Load the last function response from message history"""
- for msg in reversed(in_context_messages):
- if msg.role == MessageRole.tool and msg.content and len(msg.content) == 1 and isinstance(msg.content[0], TextContent):
- text_content = msg.content[0].text
- try:
- response_json = json.loads(text_content)
- if response_json.get("message"):
- return response_json["message"]
- except (json.JSONDecodeError, KeyError):
- raise ValueError(f"Invalid JSON format in message: {text_content}")
- return None
diff --git a/letta/agents/letta_agent_batch.py b/letta/agents/letta_agent_batch.py
deleted file mode 100644
index 5d1e6b17..00000000
--- a/letta/agents/letta_agent_batch.py
+++ /dev/null
@@ -1,633 +0,0 @@
-import json
-import uuid
-from dataclasses import dataclass
-from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence, Tuple, Union
-
-from aiomultiprocess import Pool
-from anthropic.types.beta.messages import BetaMessageBatchCanceledResult, BetaMessageBatchErroredResult, BetaMessageBatchSucceededResult
-
-from letta.agents.base_agent import BaseAgent
-from letta.agents.helpers import _prepare_in_context_messages_async
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.helpers import ToolRulesSolver
-from letta.helpers.datetime_helpers import get_utc_time
-from letta.helpers.tool_execution_helper import enable_strict_mode
-from letta.jobs.types import RequestStatusUpdateInfo, StepStatusUpdateInfo
-from letta.llm_api.llm_client import LLMClient
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG
-from letta.log import get_logger
-from letta.otel.tracing import log_event, trace_method
-from letta.schemas.agent import AgentState
-from letta.schemas.enums import AgentStepStatus, JobStatus, MessageStreamStatus, ProviderType, SandboxType, ToolType
-from letta.schemas.job import JobUpdate
-from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage
-from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, RedactedReasoningContent, TextContent
-from letta.schemas.letta_request import LettaBatchRequest
-from letta.schemas.letta_response import LettaBatchResponse, LettaResponse
-from letta.schemas.llm_batch_job import AgentStepState, LLMBatchItem
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.openai.chat_completion_response import ToolCall as OpenAIToolCall
-from letta.schemas.sandbox_config import SandboxConfig
-from letta.schemas.tool_execution_result import ToolExecutionResult
-from letta.schemas.user import User
-from letta.server.rest_api.utils import create_heartbeat_system_message, create_letta_messages_from_llm_response
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.job_manager import JobManager
-from letta.services.llm_batch_manager import LLMBatchManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.sandbox_config_manager import SandboxConfigManager
-from letta.services.tool_executor.tool_execution_manager import ToolExecutionManager
-from letta.settings import tool_settings
-
-logger = get_logger(__name__)
-
-
-@dataclass
-class ToolExecutionParams:
- agent_id: str
- tool_call_name: str
- tool_args: Dict[str, Any]
- agent_state: AgentState
- actor: User
- sbx_config: SandboxConfig
- sbx_env_vars: Dict[str, Any]
-
-
-@dataclass
-class _ResumeContext:
- batch_items: List[LLMBatchItem]
- agent_ids: List[str]
- agent_state_map: Dict[str, AgentState]
- provider_results: Dict[str, Any]
- tool_call_name_map: Dict[str, str]
- tool_call_args_map: Dict[str, Dict[str, Any]]
- should_continue_map: Dict[str, bool]
- request_status_updates: List[RequestStatusUpdateInfo]
-
-
-async def execute_tool_wrapper(params: ToolExecutionParams) -> tuple[str, ToolExecutionResult]:
- """
- Executes the tool in an out‑of‑process worker and returns:
- (agent_id, (tool_result:str, success_flag:bool))
- """
- from letta.schemas.tool_execution_result import ToolExecutionResult
-
- # locate the tool on the agent
- target_tool = next((t for t in params.agent_state.tools if t.name == params.tool_call_name), None)
- if not target_tool:
- return params.agent_id, ToolExecutionResult(func_return=f"Tool not found: {params.tool_call_name}", status="error")
-
- try:
- mgr = ToolExecutionManager(
- agent_state=params.agent_state,
- actor=params.actor,
- sandbox_config=params.sbx_config,
- sandbox_env_vars=params.sbx_env_vars,
- )
- tool_execution_result = await mgr.execute_tool_async(
- function_name=params.tool_call_name,
- function_args=params.tool_args,
- tool=target_tool,
- )
- return params.agent_id, tool_execution_result
- except Exception as e:
- return params.agent_id, ToolExecutionResult(func_return=f"Failed to call tool. Error: {e}", status="error")
-
-
-# TODO: Limitations ->
-# TODO: Only works with anthropic for now
-class LettaAgentBatch(BaseAgent):
- def __init__(
- self,
- message_manager: MessageManager,
- agent_manager: AgentManager,
- block_manager: BlockManager,
- passage_manager: PassageManager,
- batch_manager: LLMBatchManager,
- sandbox_config_manager: SandboxConfigManager,
- job_manager: JobManager,
- actor: User,
- max_steps: int = DEFAULT_MAX_STEPS,
- ):
- self.message_manager = message_manager
- self.agent_manager = agent_manager
- self.block_manager = block_manager
- self.passage_manager = passage_manager
- self.batch_manager = batch_manager
- self.sandbox_config_manager = sandbox_config_manager
- self.job_manager = job_manager
- self.actor = actor
- self.max_steps = max_steps
-
- @trace_method
- async def step_until_request(
- self,
- batch_requests: List[LettaBatchRequest],
- letta_batch_job_id: str,
- agent_step_state_mapping: Optional[Dict[str, AgentStepState]] = None,
- ) -> LettaBatchResponse:
- """Carry out agent steps until the LLM request is sent."""
- log_event(name="validate_inputs")
- if not batch_requests:
- raise ValueError("Empty list of batch_requests passed in!")
- if agent_step_state_mapping is None:
- agent_step_state_mapping = {}
-
- log_event(name="load_and_prepare_agents")
- # prepares (1) agent states, (2) step states, (3) LLMBatchItems (4) message batch_item_ids (5) messages per agent (6) tools per agent
-
- agent_messages_mapping: dict[str, list[Message]] = {}
- agent_tools_mapping: dict[str, list[dict]] = {}
- # TODO: This isn't optimal, moving fast - prone to bugs because we pass around this half formed pydantic object
- agent_batch_item_mapping: dict[str, LLMBatchItem] = {}
-
- # fetch agent states in batch
- agent_mapping = {
- agent_state.id: agent_state
- for agent_state in await self.agent_manager.get_agents_by_ids_async(
- agent_ids=[request.agent_id for request in batch_requests], include_relationships=["tools", "memory"], actor=self.actor
- )
- }
-
- agent_states = []
- for batch_request in batch_requests:
- agent_id = batch_request.agent_id
- agent_state = agent_mapping[agent_id]
- agent_states.append(agent_state) # keeping this to maintain ordering, but may not be necessary
-
- if agent_id not in agent_step_state_mapping:
- agent_step_state_mapping[agent_id] = AgentStepState(
- step_number=0, tool_rules_solver=ToolRulesSolver(tool_rules=agent_state.tool_rules)
- )
-
- llm_batch_item = LLMBatchItem(
- llm_batch_id="", # TODO: This is hacky, it gets filled in later
- agent_id=agent_state.id,
- llm_config=agent_state.llm_config,
- request_status=JobStatus.created,
- step_status=AgentStepStatus.paused,
- step_state=agent_step_state_mapping[agent_id],
- )
- agent_batch_item_mapping[agent_id] = llm_batch_item
-
- # Fill in the batch_item_id for the message
- for msg in batch_request.messages:
- msg.batch_item_id = llm_batch_item.id
-
- agent_messages_mapping[agent_id] = await self._prepare_in_context_messages_per_agent_async(
- agent_state=agent_state, input_messages=batch_request.messages
- )
-
- agent_tools_mapping[agent_id] = self._prepare_tools_per_agent(agent_state, agent_step_state_mapping[agent_id].tool_rules_solver)
-
- log_event(name="init_llm_client")
- llm_client = LLMClient.create(
- provider_type=agent_states[0].llm_config.model_endpoint_type,
- put_inner_thoughts_first=True,
- actor=self.actor,
- )
- agent_llm_config_mapping = {s.id: s.llm_config for s in agent_states}
-
- log_event(name="send_llm_batch_request")
- batch_response = await llm_client.send_llm_batch_request_async(
- agent_messages_mapping=agent_messages_mapping,
- agent_tools_mapping=agent_tools_mapping,
- agent_llm_config_mapping=agent_llm_config_mapping,
- )
-
- log_event(name="persist_llm_batch_job")
- llm_batch_job = await self.batch_manager.create_llm_batch_job_async(
- llm_provider=ProviderType.anthropic, # TODO: Expand to more providers
- create_batch_response=batch_response,
- actor=self.actor,
- status=JobStatus.running,
- letta_batch_job_id=letta_batch_job_id,
- )
-
- log_event(name="prepare_batch_items")
- batch_items = []
- for state in agent_states:
- llm_batch_item = agent_batch_item_mapping[state.id]
- # TODO This is hacky
- llm_batch_item.llm_batch_id = llm_batch_job.id
- batch_items.append(llm_batch_item)
-
- if batch_items:
- log_event(name="bulk_create_batch_items")
- batch_items_persisted = await self.batch_manager.create_llm_batch_items_bulk_async(batch_items, actor=self.actor)
-
- log_event(name="return_batch_response")
- return LettaBatchResponse(
- letta_batch_id=llm_batch_job.letta_batch_job_id,
- last_llm_batch_id=llm_batch_job.id,
- status=llm_batch_job.status,
- agent_count=len(agent_states),
- last_polled_at=get_utc_time(),
- created_at=llm_batch_job.created_at,
- )
-
- @trace_method
- async def resume_step_after_request(self, letta_batch_id: str, llm_batch_id: str) -> LettaBatchResponse:
- log_event(name="load_context")
- llm_batch_job = await self.batch_manager.get_llm_batch_job_by_id_async(llm_batch_id=llm_batch_id, actor=self.actor)
- ctx = await self._collect_resume_context(llm_batch_id)
-
- log_event(name="update_statuses")
- await self._update_request_statuses_async(ctx.request_status_updates)
-
- log_event(name="exec_tools")
- exec_results = await self._execute_tools(ctx)
-
- log_event(name="persist_messages")
- msg_map = await self._persist_tool_messages(exec_results, ctx)
-
- log_event(name="mark_steps_done")
- await self._mark_steps_complete_async(llm_batch_id, ctx.agent_ids)
-
- log_event(name="prepare_next")
- next_reqs, next_step_state = await self._prepare_next_iteration_async(exec_results, ctx, msg_map)
- if len(next_reqs) == 0:
- await self.job_manager.update_job_by_id_async(
- job_id=letta_batch_id, job_update=JobUpdate(status=JobStatus.completed), actor=self.actor
- )
- return LettaBatchResponse(
- letta_batch_id=llm_batch_job.letta_batch_job_id,
- last_llm_batch_id=llm_batch_job.id,
- status=JobStatus.completed,
- agent_count=len(ctx.agent_ids),
- last_polled_at=get_utc_time(),
- created_at=llm_batch_job.created_at,
- )
-
- return await self.step_until_request(
- batch_requests=next_reqs,
- letta_batch_job_id=letta_batch_id,
- agent_step_state_mapping=next_step_state,
- )
-
- @trace_method
- async def _collect_resume_context(self, llm_batch_id: str) -> _ResumeContext:
- """
- Collect context for resuming operations from completed batch items.
-
- Args:
- llm_batch_id: The ID of the batch to collect context for
-
- Returns:
- _ResumeContext object containing all necessary data for resumption
- """
- # Fetch only completed batch items
- batch_items = await self.batch_manager.list_llm_batch_items_async(llm_batch_id=llm_batch_id, request_status=JobStatus.completed)
-
- # Exit early if no items to process
- if not batch_items:
- return _ResumeContext(
- batch_items=[],
- agent_ids=[],
- agent_state_map={},
- provider_results={},
- tool_call_name_map={},
- tool_call_args_map={},
- should_continue_map={},
- request_status_updates=[],
- )
-
- # Extract agent IDs and organize items by agent ID
- agent_ids = [item.agent_id for item in batch_items]
- batch_item_map = {item.agent_id: item for item in batch_items}
-
- # Collect provider results
- provider_results = {item.agent_id: item.batch_request_result.result for item in batch_items}
-
- # Fetch agent states in a single call
- agent_states = await self.agent_manager.get_agents_by_ids_async(
- agent_ids=agent_ids, include_relationships=["tools", "memory"], actor=self.actor
- )
- agent_state_map = {agent.id: agent for agent in agent_states}
-
- # Process each agent's results
- tool_call_results = self._process_agent_results(
- agent_ids=agent_ids, batch_item_map=batch_item_map, provider_results=provider_results, llm_batch_id=llm_batch_id
- )
-
- return _ResumeContext(
- batch_items=batch_items,
- agent_ids=agent_ids,
- agent_state_map=agent_state_map,
- provider_results=provider_results,
- tool_call_name_map=tool_call_results.name_map,
- tool_call_args_map=tool_call_results.args_map,
- should_continue_map=tool_call_results.cont_map,
- request_status_updates=tool_call_results.status_updates,
- )
-
- def _process_agent_results(self, agent_ids, batch_item_map, provider_results, llm_batch_id):
- """
- Process the results for each agent, extracting tool calls and determining continuation status.
-
- Returns:
- A namedtuple containing name_map, args_map, cont_map, and status_updates
- """
- from collections import namedtuple
-
- ToolCallResults = namedtuple("ToolCallResults", ["name_map", "args_map", "cont_map", "status_updates"])
-
- name_map, args_map, cont_map = {}, {}, {}
- request_status_updates = []
-
- for aid in agent_ids:
- item = batch_item_map[aid]
- result = provider_results[aid]
-
- # Determine job status based on result type
- status = self._determine_job_status(result)
- request_status_updates.append(RequestStatusUpdateInfo(llm_batch_id=llm_batch_id, agent_id=aid, request_status=status))
-
- # Process tool calls
- name, args, cont = self._extract_tool_call_from_result(item, result)
- name_map[aid], args_map[aid], cont_map[aid] = name, args, cont
-
- return ToolCallResults(name_map, args_map, cont_map, request_status_updates)
-
- def _determine_job_status(self, result):
- """Determine job status based on result type"""
- if isinstance(result, BetaMessageBatchSucceededResult):
- return JobStatus.completed
- elif isinstance(result, BetaMessageBatchErroredResult):
- return JobStatus.failed
- elif isinstance(result, BetaMessageBatchCanceledResult):
- return JobStatus.cancelled
- else:
- return JobStatus.expired
-
- def _extract_tool_call_from_result(self, item, result):
- """Extract tool call information from a result"""
- llm_client = LLMClient.create(
- provider_type=item.llm_config.model_endpoint_type,
- put_inner_thoughts_first=True,
- actor=self.actor,
- )
-
- # If result isn't a successful type, we can't extract a tool call
- if not isinstance(result, BetaMessageBatchSucceededResult):
- return None, None, False
-
- tool_call = (
- llm_client.convert_response_to_chat_completion(
- response_data=result.message.model_dump(), input_messages=[], llm_config=item.llm_config
- )
- .choices[0]
- .message.tool_calls[0]
- )
-
- return self._extract_tool_call_and_decide_continue(tool_call, item.step_state)
-
- async def _update_request_statuses_async(self, updates: List[RequestStatusUpdateInfo]) -> None:
- if updates:
- await self.batch_manager.bulk_update_llm_batch_items_request_status_by_agent_async(updates=updates)
-
- async def _build_sandbox(self) -> Tuple[SandboxConfig, Dict[str, Any]]:
- sbx_type = SandboxType.E2B if tool_settings.e2b_api_key else SandboxType.LOCAL
- cfg = await self.sandbox_config_manager.get_or_create_default_sandbox_config_async(sandbox_type=sbx_type, actor=self.actor)
- env = await self.sandbox_config_manager.get_sandbox_env_vars_as_dict_async(cfg.id, actor=self.actor, limit=100)
- return cfg, env
-
- @trace_method
- async def _execute_tools(self, ctx: _ResumeContext) -> Sequence[tuple[str, ToolExecutionResult]]:
- sbx_cfg, sbx_env = await self._build_sandbox()
- rethink_memory_tool_name = "rethink_memory"
- tool_params = []
- # TODO: This is a special case - we need to think about how to generalize this
- # TODO: Rethink memory is a common op that is easily batchable, so we pull this logic out
- rethink_memory_params = []
- for aid in ctx.agent_ids:
- param = ToolExecutionParams(
- agent_id=aid,
- tool_call_name=ctx.tool_call_name_map[aid],
- tool_args=ctx.tool_call_args_map[aid],
- agent_state=ctx.agent_state_map[aid],
- actor=self.actor,
- sbx_config=sbx_cfg,
- sbx_env_vars=sbx_env,
- )
-
- if ctx.tool_call_name_map[aid] == rethink_memory_tool_name:
- rethink_memory_params.append(param)
- else:
- tool_params.append(param)
-
- if rethink_memory_params:
- return await self._bulk_rethink_memory_async(rethink_memory_params)
-
- if tool_params:
- async with Pool() as pool:
- return await pool.map(execute_tool_wrapper, tool_params)
-
- @trace_method
- async def _bulk_rethink_memory_async(self, params: List[ToolExecutionParams]) -> Sequence[tuple[str, ToolExecutionResult]]:
- updates = {}
- result = []
- for param in params:
- # Sanity check
- # TODO: This is very brittle and done quickly for performance
- # TODO: If the end tool is changed, this will break
- # TODO: Move 'rethink_memory' to a native Letta tool that we control
- if "new_memory" not in param.tool_args or "target_block_label" not in param.tool_args:
- raise ValueError(f"Missing either `new_memory` or `target_block_label` in the tool args: {param.tool_args}")
-
- # Find the block id/update
- block_id = param.agent_state.memory.get_block(label=param.tool_args.get("target_block_label")).id
- new_value = param.tool_args.get("new_memory")
-
- # This is sensitive to multiple agents overwriting the same memory block
- updates[block_id] = new_value
-
- # TODO: This is quite ugly and confusing - this is mostly to align with the returns of other tools
- result.append((param.agent_id, ToolExecutionResult(status="success")))
-
- await self.block_manager.bulk_update_block_values_async(updates=updates, actor=self.actor)
-
- return result
-
- async def _persist_tool_messages(
- self,
- exec_results: Sequence[Tuple[str, "ToolExecutionResult"]],
- ctx: _ResumeContext,
- ) -> Dict[str, List[Message]]:
- # TODO: This is redundant, we should have this ready on the ctx
- # TODO: I am doing it quick and dirty for now
- agent_item_map: Dict[str, LLMBatchItem] = {item.agent_id: item for item in ctx.batch_items}
-
- msg_map: Dict[str, List[Message]] = {}
- for aid, tool_exec_result in exec_results:
- msgs = self._create_tool_call_messages(
- llm_batch_item_id=agent_item_map[aid].id,
- agent_state=ctx.agent_state_map[aid],
- tool_call_name=ctx.tool_call_name_map[aid],
- tool_call_args=ctx.tool_call_args_map[aid],
- tool_exec_result=tool_exec_result.func_return,
- success_flag=tool_exec_result.success_flag,
- tool_exec_result_obj=tool_exec_result,
- reasoning_content=None,
- )
- msg_map[aid] = msgs
- # flatten & persist
- await self.message_manager.create_many_messages_async([m for msgs in msg_map.values() for m in msgs], actor=self.actor)
- return msg_map
-
- async def _mark_steps_complete_async(self, llm_batch_id: str, agent_ids: List[str]) -> None:
- updates = [
- StepStatusUpdateInfo(llm_batch_id=llm_batch_id, agent_id=aid, step_status=AgentStepStatus.completed) for aid in agent_ids
- ]
- await self.batch_manager.bulk_update_llm_batch_items_step_status_by_agent_async(updates)
-
- async def _prepare_next_iteration_async(
- self,
- exec_results: Sequence[Tuple[str, "ToolExecutionResult"]],
- ctx: _ResumeContext,
- msg_map: Dict[str, List[Message]],
- ) -> Tuple[List[LettaBatchRequest], Dict[str, AgentStepState]]:
- # who continues?
- continues = [agent_id for agent_id, cont in ctx.should_continue_map.items() if cont]
-
- success_flag_map = {aid: result.success_flag for aid, result in exec_results}
-
- batch_reqs: List[LettaBatchRequest] = []
- for agent_id in continues:
- heartbeat = create_heartbeat_system_message(
- agent_id=agent_id,
- model=ctx.agent_state_map[agent_id].llm_config.model,
- function_call_success=success_flag_map[agent_id],
- timezone=ctx.agent_state_map[agent_id].timezone,
- actor=self.actor,
- )
- batch_reqs.append(
- LettaBatchRequest(
- agent_id=agent_id,
- messages=[MessageCreate.model_validate(heartbeat.model_dump(include={"role", "content", "name", "otid"}))],
- )
- )
-
- # extend in‑context ids when necessary
- for agent_id, new_msgs in msg_map.items():
- ast = ctx.agent_state_map[agent_id]
- if not ast.message_buffer_autoclear:
- await self.agent_manager.update_message_ids_async(
- agent_id=agent_id,
- message_ids=ast.message_ids + [m.id for m in new_msgs],
- actor=self.actor,
- )
-
- # bump step number
- step_map = {
- item.agent_id: item.step_state.model_copy(update={"step_number": item.step_state.step_number + 1}) for item in ctx.batch_items
- }
- return batch_reqs, step_map
-
- def _create_tool_call_messages(
- self,
- llm_batch_item_id: str,
- agent_state: AgentState,
- tool_call_name: str,
- tool_call_args: Dict[str, Any],
- tool_exec_result: str,
- tool_exec_result_obj: "ToolExecutionResult",
- success_flag: bool,
- reasoning_content: Optional[List[Union[TextContent, ReasoningContent, RedactedReasoningContent, OmittedReasoningContent]]] = None,
- ) -> List[Message]:
- tool_call_id = f"call_{uuid.uuid4().hex[:8]}"
-
- tool_call_messages = create_letta_messages_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name=tool_call_name,
- function_arguments=tool_call_args,
- tool_call_id=tool_call_id,
- function_call_success=success_flag,
- function_response=tool_exec_result,
- tool_execution_result=tool_exec_result_obj,
- timezone=agent_state.timezone,
- actor=self.actor,
- continue_stepping=False,
- reasoning_content=reasoning_content,
- pre_computed_assistant_message_id=None,
- llm_batch_item_id=llm_batch_item_id,
- )
-
- return tool_call_messages
-
- # TODO: This is doing a lot of dict passing
- # TODO: Make the passing here typed
- def _extract_tool_call_and_decide_continue(
- self, tool_call: OpenAIToolCall, agent_step_state: AgentStepState
- ) -> Tuple[str, Dict[str, Any], bool]:
- """
- Now that streaming is done, handle the final AI response.
- This might yield additional SSE tokens if we do stalling.
- At the end, set self._continue_execution accordingly.
- """
- tool_call_name = tool_call.function.name
- tool_call_args_str = tool_call.function.arguments
-
- try:
- tool_args = json.loads(tool_call_args_str)
- except json.JSONDecodeError:
- logger.warning(f"Failed to JSON decode tool call argument string: {tool_call_args_str}")
- tool_args = {}
-
- # Get request heartbeats and coerce to bool
- request_heartbeat = tool_args.pop("request_heartbeat", False)
- # Pre-emptively pop out inner_thoughts
- tool_args.pop(INNER_THOUGHTS_KWARG, "")
-
- # So this is necessary, because sometimes non-structured outputs makes mistakes
- if isinstance(request_heartbeat, str):
- request_heartbeat = request_heartbeat.lower() == "true"
- else:
- request_heartbeat = bool(request_heartbeat)
-
- continue_stepping = request_heartbeat
- tool_rules_solver = agent_step_state.tool_rules_solver
- tool_rules_solver.register_tool_call(tool_name=tool_call_name)
- if tool_rules_solver.is_terminal_tool(tool_name=tool_call_name):
- continue_stepping = False
- elif tool_rules_solver.has_children_tools(tool_name=tool_call_name):
- continue_stepping = True
- elif tool_rules_solver.is_continue_tool(tool_name=tool_call_name):
- continue_stepping = True
-
- step_count = agent_step_state.step_number
- if step_count >= self.max_steps:
- logger.warning("Hit max steps, stopping agent loop prematurely.")
- continue_stepping = False
-
- return tool_call_name, tool_args, continue_stepping
-
- @staticmethod
- def _prepare_tools_per_agent(agent_state: AgentState, tool_rules_solver: ToolRulesSolver) -> List[dict]:
- tools = [t for t in agent_state.tools if t.tool_type in {ToolType.CUSTOM, ToolType.LETTA_CORE, ToolType.LETTA_MEMORY_CORE}]
- valid_tool_names = tool_rules_solver.get_allowed_tool_names(available_tools=set([t.name for t in tools]))
- return [enable_strict_mode(t.json_schema) for t in tools if t.name in set(valid_tool_names)]
-
- async def _prepare_in_context_messages_per_agent_async(
- self, agent_state: AgentState, input_messages: List[MessageCreate]
- ) -> List[Message]:
- current_in_context_messages, new_in_context_messages = await _prepare_in_context_messages_async(
- input_messages, agent_state, self.message_manager, self.actor
- )
-
- in_context_messages = await self._rebuild_memory_async(current_in_context_messages + new_in_context_messages, agent_state)
- return in_context_messages
-
- # Not used in batch.
- async def step(
- self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS, run_id: str | None = None
- ) -> LettaResponse:
- raise NotImplementedError
-
- async def step_stream(
- self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS
- ) -> AsyncGenerator[Union[LettaMessage, LegacyLettaMessage, MessageStreamStatus], None]:
- raise NotImplementedError
diff --git a/letta/agents/letta_agent_v2.py b/letta/agents/letta_agent_v2.py
deleted file mode 100644
index 504bd3a6..00000000
--- a/letta/agents/letta_agent_v2.py
+++ /dev/null
@@ -1,1196 +0,0 @@
-import asyncio
-import json
-import uuid
-from datetime import datetime
-from typing import AsyncGenerator, Tuple
-
-from opentelemetry.trace import Span
-
-from letta.adapters.letta_llm_adapter import LettaLLMAdapter
-from letta.adapters.letta_llm_request_adapter import LettaLLMRequestAdapter
-from letta.adapters.letta_llm_stream_adapter import LettaLLMStreamAdapter
-from letta.agents.base_agent_v2 import BaseAgentV2
-from letta.agents.ephemeral_summary_agent import EphemeralSummaryAgent
-from letta.agents.helpers import (
- _build_rule_violation_result,
- _pop_heartbeat,
- _prepare_in_context_messages_no_persist_async,
- _safe_load_tool_call_str,
- generate_step_id,
-)
-from letta.constants import DEFAULT_MAX_STEPS, NON_USER_MSG_PREFIX
-from letta.errors import ContextWindowExceededError
-from letta.helpers import ToolRulesSolver
-from letta.helpers.datetime_helpers import get_utc_time, get_utc_timestamp_ns, ns_to_ms
-from letta.helpers.reasoning_helper import scrub_inner_thoughts_from_messages
-from letta.helpers.tool_execution_helper import enable_strict_mode
-from letta.llm_api.llm_client import LLMClient
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG
-from letta.log import get_logger
-from letta.otel.tracing import log_event, trace_method, tracer
-from letta.prompts.prompt_generator import PromptGenerator
-from letta.schemas.agent import AgentState, UpdateAgent
-from letta.schemas.enums import JobStatus, MessageRole, MessageStreamStatus, StepStatus
-from letta.schemas.letta_message import LettaMessage, MessageType
-from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, RedactedReasoningContent, TextContent
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.message import Message, MessageCreate, MessageUpdate
-from letta.schemas.openai.chat_completion_response import ToolCall, UsageStatistics
-from letta.schemas.step import Step, StepProgression
-from letta.schemas.step_metrics import StepMetrics
-from letta.schemas.tool_execution_result import ToolExecutionResult
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.server.rest_api.utils import create_approval_request_message_from_llm_response, create_letta_messages_from_llm_response
-from letta.services.agent_manager import AgentManager
-from letta.services.archive_manager import ArchiveManager
-from letta.services.block_manager import BlockManager
-from letta.services.helpers.tool_parser_helper import runtime_override_tool_json_schema
-from letta.services.job_manager import JobManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.step_manager import StepManager
-from letta.services.summarizer.summarizer import Summarizer
-from letta.services.telemetry_manager import TelemetryManager
-from letta.services.tool_executor.tool_execution_manager import ToolExecutionManager
-from letta.settings import model_settings, settings, summarizer_settings
-from letta.system import package_function_response
-from letta.types import JsonDict
-from letta.utils import log_telemetry, united_diff, validate_function_response
-
-
-class LettaAgentV2(BaseAgentV2):
- """
- Abstract base class for the Letta agent loop, handling message management,
- LLM API requests, tool execution, and context tracking.
-
- This implementation uses a unified execution path through the _step method,
- supporting both blocking and streaming LLM interactions via the adapter pattern.
- """
-
- def __init__(
- self,
- agent_state: AgentState,
- actor: User,
- ):
- super().__init__(agent_state, actor)
- self.agent_id = agent_state.id # Store agent_id for compatibility
- self.logger = get_logger(agent_state.id)
- self.tool_rules_solver = ToolRulesSolver(tool_rules=agent_state.tool_rules)
- self.llm_client = LLMClient.create(
- provider_type=agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=True,
- actor=actor,
- )
- self._initialize_state()
-
- # Manager classes
- self.agent_manager = AgentManager()
- self.archive_manager = ArchiveManager()
- self.block_manager = BlockManager()
- self.job_manager = JobManager()
- self.message_manager = MessageManager()
- self.passage_manager = PassageManager()
- self.step_manager = StepManager()
- self.telemetry_manager = TelemetryManager()
-
- # TODO: Expand to more
- if summarizer_settings.enable_summarization and model_settings.openai_api_key:
- self.summarization_agent = EphemeralSummaryAgent(
- target_block_label="conversation_summary",
- agent_id=self.agent_state.id,
- block_manager=self.block_manager,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- actor=self.actor,
- )
-
- # Initialize summarizer for context window management
- self.summarizer = Summarizer(
- mode=summarizer_settings.mode,
- summarizer_agent=self.summarization_agent,
- message_buffer_limit=summarizer_settings.message_buffer_limit,
- message_buffer_min=summarizer_settings.message_buffer_min,
- partial_evict_summarizer_percentage=summarizer_settings.partial_evict_summarizer_percentage,
- agent_manager=self.agent_manager,
- message_manager=self.message_manager,
- actor=self.actor,
- agent_id=self.agent_state.id,
- )
-
- async def build_request(self, input_messages: list[MessageCreate]) -> dict:
- """
- Build the request data for an LLM call without actually executing it.
-
- This is useful for debugging and testing to see what would be sent to the LLM.
-
- Args:
- input_messages: List of new messages to process
-
- Returns:
- dict: The request data that would be sent to the LLM
- """
- request = {}
- in_context_messages, input_messages_to_persist = await _prepare_in_context_messages_no_persist_async(
- input_messages, self.agent_state, self.message_manager, self.actor
- )
- response = self._step(
- messages=in_context_messages + input_messages_to_persist,
- llm_adapter=LettaLLMRequestAdapter(llm_client=self.llm_client, llm_config=self.agent_state.llm_config),
- dry_run=True,
- )
- async for chunk in response:
- request = chunk # First chunk contains request data
- break
-
- return request
-
- async def step(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- run_id: str | None = None,
- use_assistant_message: bool = True,
- include_return_message_types: list[MessageType] | None = None,
- request_start_timestamp_ns: int | None = None,
- ) -> LettaResponse:
- """
- Execute the agent loop in blocking mode, returning all messages at once.
-
- Args:
- input_messages: List of new messages to process
- max_steps: Maximum number of agent steps to execute
- run_id: Optional job/run ID for tracking
- use_assistant_message: Whether to use assistant message format
- include_return_message_types: Filter for which message types to return
- request_start_timestamp_ns: Start time for tracking request duration
-
- Returns:
- LettaResponse: Complete response with all messages and metadata
- """
- self._initialize_state()
- request_span = self._request_checkpoint_start(request_start_timestamp_ns=request_start_timestamp_ns)
-
- in_context_messages, input_messages_to_persist = await _prepare_in_context_messages_no_persist_async(
- input_messages, self.agent_state, self.message_manager, self.actor
- )
- in_context_messages = in_context_messages + input_messages_to_persist
- response_letta_messages = []
- for i in range(max_steps):
- response = self._step(
- messages=in_context_messages + self.response_messages,
- input_messages_to_persist=input_messages_to_persist,
- llm_adapter=LettaLLMRequestAdapter(llm_client=self.llm_client, llm_config=self.agent_state.llm_config),
- run_id=run_id,
- use_assistant_message=use_assistant_message,
- include_return_message_types=include_return_message_types,
- request_start_timestamp_ns=request_start_timestamp_ns,
- )
-
- async for chunk in response:
- response_letta_messages.append(chunk)
-
- if not self.should_continue:
- break
-
- input_messages_to_persist = []
-
- # Rebuild context window after stepping
- if not self.agent_state.message_buffer_autoclear:
- await self._rebuild_context_window(
- in_context_messages=in_context_messages,
- new_letta_messages=self.response_messages,
- total_tokens=self.usage.total_tokens,
- force=False,
- )
-
- if self.stop_reason is None:
- self.stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- self._request_checkpoint_finish(request_span=request_span, request_start_timestamp_ns=request_start_timestamp_ns)
- return LettaResponse(messages=response_letta_messages, stop_reason=self.stop_reason, usage=self.usage)
-
- async def stream(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- stream_tokens: bool = False,
- run_id: str | None = None,
- use_assistant_message: bool = True,
- include_return_message_types: list[MessageType] | None = None,
- request_start_timestamp_ns: int | None = None,
- ) -> AsyncGenerator[str, None]:
- """
- Execute the agent loop in streaming mode, yielding chunks as they become available.
- If stream_tokens is True, individual tokens are streamed as they arrive from the LLM,
- providing the lowest latency experience, otherwise each complete step (reasoning +
- tool call + tool return) is yielded as it completes.
-
- Args:
- input_messages: List of new messages to process
- max_steps: Maximum number of agent steps to execute
- stream_tokens: Whether to stream back individual tokens. Not all llm
- providers offer native token streaming functionality; in these cases,
- this api streams back steps rather than individual tokens.
- run_id: Optional job/run ID for tracking
- use_assistant_message: Whether to use assistant message format
- include_return_message_types: Filter for which message types to return
- request_start_timestamp_ns: Start time for tracking request duration
-
- Yields:
- str: JSON-formatted SSE data chunks for each completed step
- """
- self._initialize_state()
- request_span = self._request_checkpoint_start(request_start_timestamp_ns=request_start_timestamp_ns)
- first_chunk = True
-
- if stream_tokens:
- llm_adapter = LettaLLMStreamAdapter(
- llm_client=self.llm_client,
- llm_config=self.agent_state.llm_config,
- )
- else:
- llm_adapter = LettaLLMRequestAdapter(
- llm_client=self.llm_client,
- llm_config=self.agent_state.llm_config,
- )
-
- try:
- in_context_messages, input_messages_to_persist = await _prepare_in_context_messages_no_persist_async(
- input_messages, self.agent_state, self.message_manager, self.actor
- )
- in_context_messages = in_context_messages + input_messages_to_persist
- for i in range(max_steps):
- response = self._step(
- messages=in_context_messages + self.response_messages,
- input_messages_to_persist=input_messages_to_persist,
- llm_adapter=llm_adapter,
- run_id=run_id,
- use_assistant_message=use_assistant_message,
- include_return_message_types=include_return_message_types,
- request_start_timestamp_ns=request_start_timestamp_ns,
- )
- async for chunk in response:
- if first_chunk:
- request_span = self._request_checkpoint_ttft(request_span, request_start_timestamp_ns)
- yield f"data: {chunk.model_dump_json()}\n\n"
- first_chunk = False
-
- if not self.should_continue:
- break
-
- input_messages_to_persist = []
-
- if not self.agent_state.message_buffer_autoclear:
- await self._rebuild_context_window(
- in_context_messages=in_context_messages,
- new_letta_messages=self.response_messages,
- total_tokens=self.usage.total_tokens,
- force=False,
- )
-
- except:
- if self.stop_reason:
- yield f"data: {self.stop_reason.model_dump_json()}\n\n"
- raise
-
- self._request_checkpoint_finish(request_span=request_span, request_start_timestamp_ns=request_start_timestamp_ns)
- for finish_chunk in self.get_finish_chunks_for_stream(self.usage, self.stop_reason):
- yield f"data: {finish_chunk}\n\n"
-
- async def _step(
- self,
- messages: list[Message],
- llm_adapter: LettaLLMAdapter,
- input_messages_to_persist: list[Message] | None = None,
- run_id: str | None = None,
- use_assistant_message: bool = True,
- include_return_message_types: list[MessageType] | None = None,
- request_start_timestamp_ns: int | None = None,
- remaining_turns: int = -1,
- dry_run: bool = False,
- ) -> AsyncGenerator[LettaMessage | dict, None]:
- """
- Execute a single agent step (one LLM call and tool execution).
-
- This is the core execution method that all public methods (step, stream_steps,
- stream_tokens) funnel through. It handles the complete flow of making an LLM
- request, processing the response, executing tools, and persisting messages.
-
- Args:
- messages: Current in-context messages
- llm_adapter: Adapter for LLM interaction (blocking or streaming)
- input_messages_to_persist: New messages to persist after execution
- run_id: Optional job/run ID for tracking
- use_assistant_message: Whether to use assistant message format
- include_return_message_types: Filter for which message types to yield
- request_start_timestamp_ns: Start time for tracking request duration
- remaining_turns: Number of turns remaining (for max_steps enforcement)
- dry_run: If true, only build and return the request without executing
-
- Yields:
- LettaMessage or dict: Chunks for streaming mode, or request data for dry_run
- """
- step_progression = StepProgression.START
- # TODO(@caren): clean this up
- tool_call, reasoning_content, agent_step_span, first_chunk, step_id, logged_step, step_start_ns, step_metrics = (
- None,
- None,
- None,
- None,
- None,
- None,
- None,
- None,
- )
- try:
- valid_tools = await self._get_valid_tools(messages) # remove messages input
- approval_request, approval_response = await self._maybe_get_approval_messages(messages)
- if approval_request and approval_response:
- tool_call = approval_request.tool_calls[0]
- reasoning_content = approval_request.content
- step_id = approval_request.step_id
- step_metrics = await self.step_manager.get_step_metrics_async(step_id=step_id, actor=self.actor)
- else:
- # Check for job cancellation at the start of each step
- if run_id and await self._check_run_cancellation(run_id):
- self.stop_reason = LettaStopReason(stop_reason=StopReasonType.cancelled.value)
- self.logger.info(f"Agent execution cancelled for run {run_id}")
- return
-
- step_id = generate_step_id()
- step_progression, step_metrics, agent_step_span = self._step_checkpoint_start(step_id=step_id)
-
- # Create step early with PENDING status
- logged_step = await self.step_manager.log_step_async(
- actor=self.actor,
- agent_id=self.agent_state.id,
- provider_name=self.agent_state.llm_config.model_endpoint_type,
- provider_category=self.agent_state.llm_config.provider_category or "base",
- model=self.agent_state.llm_config.model,
- model_endpoint=self.agent_state.llm_config.model_endpoint,
- context_window_limit=self.agent_state.llm_config.context_window,
- usage=UsageStatistics(completion_tokens=0, prompt_tokens=0, total_tokens=0),
- provider_id=None,
- job_id=run_id,
- step_id=step_id,
- project_id=self.agent_state.project_id,
- status=StepStatus.PENDING,
- )
-
- messages = await self._refresh_messages(messages)
- force_tool_call = valid_tools[0]["name"] if len(valid_tools) == 1 else None
- for llm_request_attempt in range(summarizer_settings.max_summarizer_retries + 1):
- try:
- request_data = self.llm_client.build_request_data(
- messages=messages,
- llm_config=self.agent_state.llm_config,
- tools=valid_tools,
- force_tool_call=force_tool_call,
- )
- if dry_run:
- yield request_data
- return
-
- step_progression, step_metrics = self._step_checkpoint_llm_request_start(step_metrics, agent_step_span)
-
- invocation = llm_adapter.invoke_llm(
- request_data=request_data,
- messages=messages,
- tools=valid_tools,
- use_assistant_message=use_assistant_message,
- requires_approval_tools=self.tool_rules_solver.get_requires_approval_tools(
- set([t["name"] for t in valid_tools])
- ),
- step_id=step_id,
- actor=self.actor,
- )
- async for chunk in invocation:
- if llm_adapter.supports_token_streaming():
- if include_return_message_types is None or chunk.message_type in include_return_message_types:
- first_chunk = True
- yield chunk
- # If you've reached this point without an error, break out of retry loop
- break
- except ValueError as e:
- self.stop_reason = LettaStopReason(stop_reason=StopReasonType.invalid_llm_response.value)
- raise e
- except Exception as e:
- if isinstance(e, ContextWindowExceededError) and llm_request_attempt < summarizer_settings.max_summarizer_retries:
- # Retry case
- messages = await self._rebuild_context_window(
- in_context_messages=messages,
- new_letta_messages=self.response_messages,
- llm_config=self.agent_state.llm_config,
- force=True,
- )
- else:
- raise e
-
- step_progression, step_metrics = self._step_checkpoint_llm_request_finish(
- step_metrics, agent_step_span, llm_adapter.llm_request_finish_timestamp_ns
- )
-
- self._update_global_usage_stats(llm_adapter.usage)
-
- # Handle the AI response with the extracted data
- if tool_call is None and llm_adapter.tool_call is None:
- self.stop_reason = LettaStopReason(stop_reason=StopReasonType.no_tool_call.value)
- raise ValueError("No tool calls found in response, model must make a tool call")
-
- persisted_messages, self.should_continue, self.stop_reason = await self._handle_ai_response(
- tool_call or llm_adapter.tool_call,
- [tool["name"] for tool in valid_tools],
- self.agent_state,
- self.tool_rules_solver,
- UsageStatistics(
- completion_tokens=self.usage.completion_tokens,
- prompt_tokens=self.usage.prompt_tokens,
- total_tokens=self.usage.total_tokens,
- ),
- reasoning_content=reasoning_content or llm_adapter.reasoning_content,
- pre_computed_assistant_message_id=llm_adapter.message_id,
- step_id=step_id,
- initial_messages=input_messages_to_persist,
- agent_step_span=agent_step_span,
- is_final_step=(remaining_turns == 0),
- run_id=run_id,
- step_metrics=step_metrics,
- is_approval=approval_response.approve if approval_response is not None else False,
- is_denial=(approval_response.approve == False) if approval_response is not None else False,
- denial_reason=approval_response.denial_reason if approval_response is not None else None,
- )
-
- # Update step with actual usage now that we have it (if step was created)
- if logged_step:
- await self.step_manager.update_step_success_async(
- self.actor,
- step_id,
- UsageStatistics(
- completion_tokens=self.usage.completion_tokens,
- prompt_tokens=self.usage.prompt_tokens,
- total_tokens=self.usage.total_tokens,
- ),
- self.stop_reason,
- )
- step_progression = StepProgression.STEP_LOGGED
-
- new_message_idx = len(input_messages_to_persist) if input_messages_to_persist else 0
- self.response_messages.extend(persisted_messages[new_message_idx:])
-
- if llm_adapter.supports_token_streaming():
- if persisted_messages[-1].role != "approval":
- tool_return = [msg for msg in persisted_messages if msg.role == "tool"][-1].to_letta_messages()[0]
- if not (use_assistant_message and tool_return.name == "send_message"):
- if include_return_message_types is None or tool_return.message_type in include_return_message_types:
- yield tool_return
- else:
- filter_user_messages = [m for m in persisted_messages[new_message_idx:] if m.role != "user"]
- letta_messages = Message.to_letta_messages_from_list(
- filter_user_messages,
- use_assistant_message=use_assistant_message,
- reverse=False,
- )
- for message in letta_messages:
- if include_return_message_types is None or message.message_type in include_return_message_types:
- yield message
-
- step_progression, step_metrics = self._step_checkpoint_finish(step_metrics, agent_step_span, run_id)
- except Exception as e:
- self.logger.error(f"Error during step processing: {e}")
- self.job_update_metadata = {"error": str(e)}
-
- # This indicates we failed after we decided to stop stepping, which indicates a bug with our flow.
- if not self.stop_reason:
- self.stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- elif self.stop_reason.stop_reason in (StopReasonType.end_turn, StopReasonType.max_steps, StopReasonType.tool_rule):
- self.logger.error("Error occurred during step processing, with valid stop reason: %s", self.stop_reason.stop_reason)
- elif self.stop_reason.stop_reason not in (
- StopReasonType.no_tool_call,
- StopReasonType.invalid_tool_call,
- StopReasonType.invalid_llm_response,
- ):
- self.logger.error("Error occurred during step processing, with unexpected stop reason: %s", self.stop_reason.stop_reason)
- raise e
- finally:
- self.logger.debug("Running cleanup for agent loop run: %s", run_id)
- self.logger.info("Running final update. Step Progression: %s", step_progression)
- try:
- if step_progression == StepProgression.FINISHED:
- if not self.should_continue:
- if self.stop_reason is None:
- self.stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- if logged_step and step_id:
- await self.step_manager.update_step_stop_reason(self.actor, step_id, self.stop_reason.stop_reason)
- return
- if step_progression < StepProgression.STEP_LOGGED:
- # Error occurred before step was fully logged
- import traceback
-
- if logged_step:
- await self.step_manager.update_step_error_async(
- actor=self.actor,
- step_id=step_id, # Use original step_id for telemetry
- error_type=type(e).__name__ if "e" in locals() else "Unknown",
- error_message=str(e) if "e" in locals() else "Unknown error",
- error_traceback=traceback.format_exc(),
- stop_reason=self.stop_reason,
- )
- if step_progression <= StepProgression.STREAM_RECEIVED:
- if first_chunk and settings.track_errored_messages and input_messages_to_persist:
- for message in input_messages_to_persist:
- message.is_err = True
- message.step_id = step_id
- await self.message_manager.create_many_messages_async(
- input_messages_to_persist,
- actor=self.actor,
- project_id=self.agent_state.project_id,
- template_id=self.agent_state.template_id,
- )
- elif step_progression <= StepProgression.LOGGED_TRACE:
- if self.stop_reason is None:
- self.logger.error("Error in step after logging step")
- self.stop_reason = LettaStopReason(stop_reason=StopReasonType.error.value)
- if logged_step:
- await self.step_manager.update_step_stop_reason(self.actor, step_id, self.stop_reason.stop_reason)
- else:
- self.logger.error("Invalid StepProgression value")
-
- # Do tracking for failure cases. Can consolidate with success conditions later.
- if settings.track_stop_reason:
- await self._log_request(request_start_timestamp_ns, None, self.job_update_metadata, is_error=True, run_id=run_id)
-
- # Record partial step metrics on failure (capture whatever timing data we have)
- if logged_step and step_metrics and step_progression < StepProgression.FINISHED:
- # Calculate total step time up to the failure point
- step_metrics.step_ns = get_utc_timestamp_ns() - step_metrics.step_start_ns
-
- await self._record_step_metrics(
- step_id=step_id,
- step_metrics=step_metrics,
- run_id=run_id,
- )
- except Exception as e:
- self.logger.error(f"Error during post-completion step tracking: {e}")
-
- def _initialize_state(self):
- self.should_continue = True
- self.stop_reason = None
- self.usage = LettaUsageStatistics()
- self.job_update_metadata = None
- self.last_function_response = None
- self.response_messages = []
-
- async def _maybe_get_approval_messages(self, messages: list[Message]) -> Tuple[Message | None, Message | None]:
- if len(messages) >= 2:
- maybe_approval_request, maybe_approval_response = messages[-2], messages[-1]
- if maybe_approval_request.role == "approval" and maybe_approval_response.role == "approval":
- return maybe_approval_request, maybe_approval_response
- return None, None
-
- async def _check_run_cancellation(self, run_id) -> bool:
- try:
- job = await self.job_manager.get_job_by_id_async(job_id=run_id, actor=self.actor)
- return job.status == JobStatus.cancelled
- except Exception as e:
- # Log the error but don't fail the execution
- self.logger.warning(f"Failed to check job cancellation status for job {run_id}: {e}")
- return False
-
- async def _refresh_messages(self, in_context_messages: list[Message]):
- num_messages = await self.message_manager.size_async(
- agent_id=self.agent_state.id,
- actor=self.actor,
- )
- num_archival_memories = await self.passage_manager.agent_passage_size_async(
- agent_id=self.agent_state.id,
- actor=self.actor,
- )
- in_context_messages = await self._rebuild_memory(
- in_context_messages,
- num_messages=num_messages,
- num_archival_memories=num_archival_memories,
- )
- in_context_messages = scrub_inner_thoughts_from_messages(in_context_messages, self.agent_state.llm_config)
- return in_context_messages
-
- async def _rebuild_memory(
- self,
- in_context_messages: list[Message],
- num_messages: int,
- num_archival_memories: int,
- ):
- agent_state = await self.agent_manager.refresh_memory_async(agent_state=self.agent_state, actor=self.actor)
-
- tool_constraint_block = None
- if self.tool_rules_solver is not None:
- tool_constraint_block = self.tool_rules_solver.compile_tool_rule_prompts()
-
- archive = await self.archive_manager.get_default_archive_for_agent_async(
- agent_id=self.agent_state.id,
- actor=self.actor,
- )
-
- if archive:
- archive_tags = await self.passage_manager.get_unique_tags_for_archive_async(
- archive_id=archive.id,
- actor=self.actor,
- )
- else:
- archive_tags = None
-
- # TODO: This is a pretty brittle pattern established all over our code, need to get rid of this
- curr_system_message = in_context_messages[0]
- curr_system_message_text = curr_system_message.content[0].text
-
- # extract the dynamic section that includes memory blocks, tool rules, and directories
- # this avoids timestamp comparison issues
- def extract_dynamic_section(text):
- start_marker = ""
- end_marker = ""
-
- start_idx = text.find(start_marker)
- end_idx = text.find(end_marker)
-
- if start_idx != -1 and end_idx != -1:
- return text[start_idx:end_idx]
- return text # fallback to full text if markers not found
-
- curr_dynamic_section = extract_dynamic_section(curr_system_message_text)
-
- # generate just the memory string with current state for comparison
- curr_memory_str = await agent_state.memory.compile_in_thread_async(
- tool_usage_rules=tool_constraint_block, sources=agent_state.sources, max_files_open=agent_state.max_files_open
- )
- new_dynamic_section = extract_dynamic_section(curr_memory_str)
-
- # compare just the dynamic sections (memory blocks, tool rules, directories)
- if curr_dynamic_section == new_dynamic_section:
- self.logger.debug(
- f"Memory and sources haven't changed for agent id={agent_state.id} and actor=({self.actor.id}, {self.actor.name}), skipping system prompt rebuild"
- )
- return in_context_messages
-
- memory_edit_timestamp = get_utc_time()
-
- # size of messages and archival memories
- if num_messages is None:
- num_messages = await self.message_manager.size_async(actor=self.actor, agent_id=agent_state.id)
- if num_archival_memories is None:
- num_archival_memories = await self.passage_manager.agent_passage_size_async(actor=self.actor, agent_id=agent_state.id)
-
- new_system_message_str = PromptGenerator.get_system_message_from_compiled_memory(
- system_prompt=agent_state.system,
- memory_with_sources=curr_memory_str,
- in_context_memory_last_edit=memory_edit_timestamp,
- timezone=agent_state.timezone,
- previous_message_count=num_messages - len(in_context_messages),
- archival_memory_size=num_archival_memories,
- archive_tags=archive_tags,
- )
-
- diff = united_diff(curr_system_message_text, new_system_message_str)
- if len(diff) > 0:
- self.logger.debug(f"Rebuilding system with new memory...\nDiff:\n{diff}")
-
- # [DB Call] Update Messages
- new_system_message = await self.message_manager.update_message_by_id_async(
- curr_system_message.id, message_update=MessageUpdate(content=new_system_message_str), actor=self.actor
- )
- return [new_system_message] + in_context_messages[1:]
-
- else:
- return in_context_messages
-
- async def _get_valid_tools(self, in_context_messages: list[Message]):
- tools = self.agent_state.tools
- self.last_function_response = self._load_last_function_response(in_context_messages)
- valid_tool_names = self.tool_rules_solver.get_allowed_tool_names(
- available_tools=set([t.name for t in tools]),
- last_function_response=self.last_function_response,
- error_on_empty=False, # Return empty list instead of raising error
- ) or list(set(t.name for t in tools))
- allowed_tools = [enable_strict_mode(t.json_schema) for t in tools if t.name in set(valid_tool_names)]
- terminal_tool_names = {rule.tool_name for rule in self.tool_rules_solver.terminal_tool_rules}
- allowed_tools = runtime_override_tool_json_schema(
- tool_list=allowed_tools,
- response_format=self.agent_state.response_format,
- request_heartbeat=True,
- terminal_tools=terminal_tool_names,
- )
- return allowed_tools
-
- def _load_last_function_response(self, in_context_messages: list[Message]):
- """Load the last function response from message history"""
- for msg in reversed(in_context_messages):
- if msg.role == MessageRole.tool and msg.content and len(msg.content) == 1 and isinstance(msg.content[0], TextContent):
- text_content = msg.content[0].text
- try:
- response_json = json.loads(text_content)
- if response_json.get("message"):
- return response_json["message"]
- except (json.JSONDecodeError, KeyError):
- raise ValueError(f"Invalid JSON format in message: {text_content}")
- return None
-
- def _request_checkpoint_start(self, request_start_timestamp_ns: int | None) -> Span | None:
- if request_start_timestamp_ns is not None:
- request_span = tracer.start_span("time_to_first_token", start_time=request_start_timestamp_ns)
- request_span.set_attributes(
- {f"llm_config.{k}": v for k, v in self.agent_state.llm_config.model_dump().items() if v is not None}
- )
- return request_span
- return None
-
- def _request_checkpoint_ttft(self, request_span: Span | None, request_start_timestamp_ns: int | None) -> Span | None:
- if request_span:
- ttft_ns = get_utc_timestamp_ns() - request_start_timestamp_ns
- request_span.add_event(name="time_to_first_token_ms", attributes={"ttft_ms": ns_to_ms(ttft_ns)})
- return request_span
- return None
-
- def _request_checkpoint_finish(self, request_span: Span | None, request_start_timestamp_ns: int | None) -> None:
- if request_span is not None:
- duration_ns = get_utc_timestamp_ns() - request_start_timestamp_ns
- request_span.add_event(name="letta_request_ms", attributes={"duration_ms": ns_to_ms(duration_ns)})
- request_span.end()
- return None
-
- def _step_checkpoint_start(self, step_id: str) -> Tuple[StepProgression, StepMetrics, Span]:
- step_start_ns = get_utc_timestamp_ns()
- step_metrics = StepMetrics(id=step_id, step_start_ns=step_start_ns)
- agent_step_span = tracer.start_span("agent_step", start_time=step_start_ns)
- agent_step_span.set_attributes({"step_id": step_id})
- return StepProgression.START, step_metrics, agent_step_span
-
- def _step_checkpoint_llm_request_start(self, step_metrics: StepMetrics, agent_step_span: Span) -> Tuple[StepProgression, StepMetrics]:
- llm_request_start_ns = get_utc_timestamp_ns()
- step_metrics.llm_request_start_ns = llm_request_start_ns
- agent_step_span.add_event(
- name="request_start_to_provider_request_start_ns",
- attributes={"request_start_to_provider_request_start_ns": ns_to_ms(llm_request_start_ns)},
- )
- return StepProgression.START, step_metrics
-
- def _step_checkpoint_llm_request_finish(
- self, step_metrics: StepMetrics, agent_step_span: Span, llm_request_finish_timestamp_ns: int
- ) -> Tuple[StepProgression, StepMetrics]:
- llm_request_ns = llm_request_finish_timestamp_ns - step_metrics.llm_request_start_ns
- step_metrics.llm_request_ns = llm_request_ns
- agent_step_span.add_event(name="llm_request_ms", attributes={"duration_ms": ns_to_ms(llm_request_ns)})
- return StepProgression.RESPONSE_RECEIVED, step_metrics
-
- def _step_checkpoint_finish(
- self, step_metrics: StepMetrics, agent_step_span: Span | None, run_id: str | None
- ) -> Tuple[StepProgression, StepMetrics]:
- if step_metrics.step_start_ns:
- step_ns = get_utc_timestamp_ns() - step_metrics.step_start_ns
- step_metrics.step_ns = step_ns
- if agent_step_span is not None:
- agent_step_span.add_event(name="step_ms", attributes={"duration_ms": ns_to_ms(step_ns)})
- agent_step_span.end()
- self._record_step_metrics(step_id=step_metrics.id, step_metrics=step_metrics)
- return StepProgression.FINISHED, step_metrics
-
- def _update_global_usage_stats(self, step_usage_stats: LettaUsageStatistics):
- self.usage.step_count += step_usage_stats.step_count
- self.usage.completion_tokens += step_usage_stats.completion_tokens
- self.usage.prompt_tokens += step_usage_stats.prompt_tokens
- self.usage.total_tokens += step_usage_stats.total_tokens
-
- async def _handle_ai_response(
- self,
- tool_call: ToolCall,
- valid_tool_names: list[str],
- agent_state: AgentState,
- tool_rules_solver: ToolRulesSolver,
- usage: UsageStatistics,
- reasoning_content: list[TextContent | ReasoningContent | RedactedReasoningContent | OmittedReasoningContent] | None = None,
- pre_computed_assistant_message_id: str | None = None,
- step_id: str | None = None,
- initial_messages: list[Message] | None = None,
- agent_step_span: Span | None = None,
- is_final_step: bool | None = None,
- run_id: str | None = None,
- step_metrics: StepMetrics = None,
- is_approval: bool | None = None,
- is_denial: bool | None = None,
- denial_reason: str | None = None,
- ) -> tuple[list[Message], bool, LettaStopReason | None]:
- """
- Handle the final AI response once streaming completes, execute / validate the
- tool call, decide whether we should keep stepping, and persist state.
- """
- tool_call_id: str = tool_call.id or f"call_{uuid.uuid4().hex[:8]}"
-
- if is_denial:
- continue_stepping = True
- stop_reason = None
- tool_call_messages = create_letta_messages_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name="",
- function_arguments={},
- tool_execution_result=ToolExecutionResult(status="error"),
- tool_call_id=tool_call_id,
- function_call_success=False,
- function_response=f"Error: request to call tool denied. User reason: {denial_reason}",
- timezone=agent_state.timezone,
- actor=self.actor,
- continue_stepping=continue_stepping,
- heartbeat_reason=f"{NON_USER_MSG_PREFIX}Continuing: user denied request to call tool.",
- reasoning_content=None,
- pre_computed_assistant_message_id=None,
- step_id=step_id,
- is_approval_response=True,
- )
- messages_to_persist = (initial_messages or []) + tool_call_messages
- persisted_messages = await self.message_manager.create_many_messages_async(
- messages_to_persist,
- actor=self.actor,
- project_id=agent_state.project_id,
- template_id=agent_state.template_id,
- )
- return persisted_messages, continue_stepping, stop_reason
-
- # 1. Parse and validate the tool-call envelope
- tool_call_name: str = tool_call.function.name
-
- tool_args = _safe_load_tool_call_str(tool_call.function.arguments)
- request_heartbeat: bool = _pop_heartbeat(tool_args)
- tool_args.pop(INNER_THOUGHTS_KWARG, None)
-
- log_telemetry(
- self.logger,
- "_handle_ai_response execute tool start",
- tool_name=tool_call_name,
- tool_args=tool_args,
- tool_call_id=tool_call_id,
- request_heartbeat=request_heartbeat,
- )
-
- if not is_approval and tool_rules_solver.is_requires_approval_tool(tool_call_name):
- approval_message = create_approval_request_message_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name=tool_call_name,
- function_arguments=tool_args,
- tool_call_id=tool_call_id,
- actor=self.actor,
- continue_stepping=request_heartbeat,
- reasoning_content=reasoning_content,
- pre_computed_assistant_message_id=pre_computed_assistant_message_id,
- step_id=step_id,
- )
- messages_to_persist = (initial_messages or []) + [approval_message]
- continue_stepping = False
- stop_reason = LettaStopReason(stop_reason=StopReasonType.requires_approval.value)
- else:
- # 2. Execute the tool (or synthesize an error result if disallowed)
- tool_rule_violated = tool_call_name not in valid_tool_names and not is_approval
- if tool_rule_violated:
- tool_execution_result = _build_rule_violation_result(tool_call_name, valid_tool_names, tool_rules_solver)
- else:
- # Track tool execution time
- tool_start_time = get_utc_timestamp_ns()
- tool_execution_result = await self._execute_tool(
- tool_name=tool_call_name,
- tool_args=tool_args,
- agent_state=agent_state,
- agent_step_span=agent_step_span,
- step_id=step_id,
- )
- tool_end_time = get_utc_timestamp_ns()
-
- # Store tool execution time in metrics
- step_metrics.tool_execution_ns = tool_end_time - tool_start_time
-
- log_telemetry(
- self.logger,
- "_handle_ai_response execute tool finish",
- tool_execution_result=tool_execution_result,
- tool_call_id=tool_call_id,
- )
-
- # 3. Prepare the function-response payload
- truncate = tool_call_name not in {"conversation_search", "conversation_search_date", "archival_memory_search"}
- return_char_limit = next(
- (t.return_char_limit for t in agent_state.tools if t.name == tool_call_name),
- None,
- )
- function_response_string = validate_function_response(
- tool_execution_result.func_return,
- return_char_limit=return_char_limit,
- truncate=truncate,
- )
- self.last_function_response = package_function_response(
- was_success=tool_execution_result.success_flag,
- response_string=function_response_string,
- timezone=agent_state.timezone,
- )
-
- # 4. Decide whether to keep stepping (focal section simplified)
- continue_stepping, heartbeat_reason, stop_reason = self._decide_continuation(
- agent_state=agent_state,
- request_heartbeat=request_heartbeat,
- tool_call_name=tool_call_name,
- tool_rule_violated=tool_rule_violated,
- tool_rules_solver=tool_rules_solver,
- is_final_step=is_final_step,
- )
-
- # 5. Create messages (step was already created at the beginning)
- tool_call_messages = create_letta_messages_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name=tool_call_name,
- function_arguments=tool_args,
- tool_execution_result=tool_execution_result,
- tool_call_id=tool_call_id,
- function_call_success=tool_execution_result.success_flag,
- function_response=function_response_string,
- timezone=agent_state.timezone,
- actor=self.actor,
- continue_stepping=continue_stepping,
- heartbeat_reason=heartbeat_reason,
- reasoning_content=reasoning_content,
- pre_computed_assistant_message_id=pre_computed_assistant_message_id,
- step_id=step_id,
- is_approval_response=is_approval or is_denial,
- )
- messages_to_persist = (initial_messages or []) + tool_call_messages
-
- persisted_messages = await self.message_manager.create_many_messages_async(
- messages_to_persist, actor=self.actor, project_id=agent_state.project_id, template_id=agent_state.template_id
- )
-
- if run_id:
- await self.job_manager.add_messages_to_job_async(
- job_id=run_id,
- message_ids=[m.id for m in persisted_messages if m.role != "user"],
- actor=self.actor,
- )
-
- return persisted_messages, continue_stepping, stop_reason
-
- def _decide_continuation(
- self,
- agent_state: AgentState,
- request_heartbeat: bool,
- tool_call_name: str,
- tool_rule_violated: bool,
- tool_rules_solver: ToolRulesSolver,
- is_final_step: bool | None,
- ) -> tuple[bool, str | None, LettaStopReason | None]:
- continue_stepping = request_heartbeat
- heartbeat_reason: str | None = None
- stop_reason: LettaStopReason | None = None
-
- if tool_rule_violated:
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing: tool rule violation."
- else:
- tool_rules_solver.register_tool_call(tool_call_name)
-
- if tool_rules_solver.is_terminal_tool(tool_call_name):
- if continue_stepping:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.tool_rule.value)
- continue_stepping = False
-
- elif tool_rules_solver.has_children_tools(tool_call_name):
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing: child tool rule."
-
- elif tool_rules_solver.is_continue_tool(tool_call_name):
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing: continue tool rule."
-
- # – hard stop overrides –
- if is_final_step:
- continue_stepping = False
- stop_reason = LettaStopReason(stop_reason=StopReasonType.max_steps.value)
- else:
- uncalled = tool_rules_solver.get_uncalled_required_tools(available_tools=set([t.name for t in agent_state.tools]))
- if not continue_stepping and uncalled:
- continue_stepping = True
- heartbeat_reason = f"{NON_USER_MSG_PREFIX}Continuing, user expects these tools: [{', '.join(uncalled)}] to be called still."
-
- stop_reason = None # reset – we’re still going
-
- return continue_stepping, heartbeat_reason, stop_reason
-
- @trace_method
- async def _execute_tool(
- self,
- tool_name: str,
- tool_args: JsonDict,
- agent_state: AgentState,
- agent_step_span: Span | None = None,
- step_id: str | None = None,
- ) -> "ToolExecutionResult":
- """
- Executes a tool and returns the ToolExecutionResult.
- """
- from letta.schemas.tool_execution_result import ToolExecutionResult
-
- # Special memory case
- target_tool = next((x for x in agent_state.tools if x.name == tool_name), None)
- if not target_tool:
- # TODO: fix this error message
- return ToolExecutionResult(
- func_return=f"Tool {tool_name} not found",
- status="error",
- )
-
- # TODO: This temp. Move this logic and code to executors
-
- if agent_step_span:
- start_time = get_utc_timestamp_ns()
- agent_step_span.add_event(name="tool_execution_started")
-
- sandbox_env_vars = {var.key: var.value for var in agent_state.tool_exec_environment_variables}
- tool_execution_manager = ToolExecutionManager(
- agent_state=agent_state,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- block_manager=self.block_manager,
- job_manager=self.job_manager,
- passage_manager=self.passage_manager,
- sandbox_env_vars=sandbox_env_vars,
- actor=self.actor,
- )
- # TODO: Integrate sandbox result
- log_event(name=f"start_{tool_name}_execution", attributes=tool_args)
- tool_execution_result = await tool_execution_manager.execute_tool_async(
- function_name=tool_name,
- function_args=tool_args,
- tool=target_tool,
- step_id=step_id,
- )
- if agent_step_span:
- end_time = get_utc_timestamp_ns()
- agent_step_span.add_event(
- name="tool_execution_completed",
- attributes={
- "tool_name": target_tool.name,
- "duration_ms": ns_to_ms(end_time - start_time),
- "success": tool_execution_result.success_flag,
- "tool_type": target_tool.tool_type,
- "tool_id": target_tool.id,
- },
- )
- log_event(name=f"finish_{tool_name}_execution", attributes=tool_execution_result.model_dump())
- return tool_execution_result
-
- @trace_method
- async def _rebuild_context_window(
- self,
- in_context_messages: list[Message],
- new_letta_messages: list[Message],
- total_tokens: int | None = None,
- force: bool = False,
- ) -> list[Message]:
- # If total tokens is reached, we truncate down
- # TODO: This can be broken by bad configs, e.g. lower bound too high, initial messages too fat, etc.
- # TODO: `force` and `clear` seem to no longer be used, we should remove
- if force or (total_tokens and total_tokens > self.agent_state.llm_config.context_window):
- self.logger.warning(
- f"Total tokens {total_tokens} exceeds configured max tokens {self.agent_state.llm_config.context_window}, forcefully clearing message history."
- )
- new_in_context_messages, updated = await self.summarizer.summarize(
- in_context_messages=in_context_messages,
- new_letta_messages=new_letta_messages,
- force=True,
- clear=True,
- )
- else:
- # NOTE (Sarah): Seems like this is doing nothing?
- self.logger.info(
- f"Total tokens {total_tokens} does not exceed configured max tokens {self.agent_state.llm_config.context_window}, passing summarizing w/o force."
- )
- new_in_context_messages, updated = await self.summarizer.summarize(
- in_context_messages=in_context_messages,
- new_letta_messages=new_letta_messages,
- )
- message_ids = [m.id for m in new_in_context_messages]
- await self.agent_manager.update_message_ids_async(
- agent_id=self.agent_state.id,
- message_ids=message_ids,
- actor=self.actor,
- )
- self.agent_state.message_ids = message_ids
-
- return new_in_context_messages
-
- def _record_step_metrics(
- self,
- *,
- step_id: str,
- step_metrics: StepMetrics,
- run_id: str | None = None,
- ):
- task = asyncio.create_task(
- self.step_manager.record_step_metrics_async(
- actor=self.actor,
- step_id=step_id,
- llm_request_ns=step_metrics.llm_request_ns,
- tool_execution_ns=step_metrics.tool_execution_ns,
- step_ns=step_metrics.step_ns,
- agent_id=self.agent_state.id,
- job_id=run_id,
- project_id=self.agent_state.project_id,
- template_id=self.agent_state.template_id,
- base_template_id=self.agent_state.base_template_id,
- )
- )
- return task
-
- async def _log_request(
- self,
- request_start_timestamp_ns: int,
- request_span: "Span | None",
- job_update_metadata: dict | None,
- is_error: bool,
- run_id: str | None = None,
- ):
- if request_start_timestamp_ns:
- now_ns, now = get_utc_timestamp_ns(), get_utc_time()
- duration_ns = now_ns - request_start_timestamp_ns
- if request_span:
- request_span.add_event(name="letta_request_ms", attributes={"duration_ms": ns_to_ms(duration_ns)})
- await self._update_agent_last_run_metrics(now, ns_to_ms(duration_ns))
- if settings.track_agent_run and run_id:
- await self.job_manager.record_response_duration(run_id, duration_ns, self.actor)
- await self.job_manager.safe_update_job_status_async(
- job_id=run_id,
- new_status=JobStatus.failed if is_error else JobStatus.completed,
- actor=self.actor,
- metadata=job_update_metadata,
- )
- if request_span:
- request_span.end()
-
- async def _update_agent_last_run_metrics(self, completion_time: datetime, duration_ms: float) -> None:
- if not settings.track_last_agent_run:
- return
- try:
- await self.agent_manager.update_agent_async(
- agent_id=self.agent_id,
- agent_update=UpdateAgent(last_run_completion=completion_time, last_run_duration_ms=duration_ms),
- actor=self.actor,
- )
- except Exception as e:
- self.logger.error(f"Failed to update agent's last run metrics: {e}")
-
- def get_finish_chunks_for_stream(
- self,
- usage: LettaUsageStatistics,
- stop_reason: LettaStopReason | None = None,
- ):
- if stop_reason is None:
- stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
- return [
- stop_reason.model_dump_json(),
- usage.model_dump_json(),
- MessageStreamStatus.done.value,
- ]
diff --git a/letta/agents/voice_agent.py b/letta/agents/voice_agent.py
deleted file mode 100644
index 642b9d61..00000000
--- a/letta/agents/voice_agent.py
+++ /dev/null
@@ -1,518 +0,0 @@
-import json
-import uuid
-from datetime import datetime, timedelta, timezone
-from typing import Any, AsyncGenerator, Dict, List, Optional
-
-import openai
-
-from letta.agents.base_agent import BaseAgent
-from letta.agents.exceptions import IncompatibleAgentType
-from letta.agents.voice_sleeptime_agent import VoiceSleeptimeAgent
-from letta.constants import DEFAULT_MAX_STEPS, NON_USER_MSG_PREFIX, PRE_EXECUTION_MESSAGE_ARG, REQUEST_HEARTBEAT_PARAM
-from letta.helpers.datetime_helpers import get_utc_time
-from letta.helpers.tool_execution_helper import add_pre_execution_message, enable_strict_mode, remove_request_heartbeat
-from letta.interfaces.openai_chat_completions_streaming_interface import OpenAIChatCompletionsStreamingInterface
-from letta.log import get_logger
-from letta.prompts.prompt_generator import PromptGenerator
-from letta.schemas.agent import AgentState, AgentType
-from letta.schemas.enums import MessageRole, ToolType
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.openai.chat_completion_request import (
- AssistantMessage,
- ChatCompletionRequest,
- Tool,
- ToolCall,
- ToolCallFunction,
- ToolMessage,
- UserMessage,
-)
-from letta.schemas.user import User
-from letta.server.rest_api.utils import (
- convert_in_context_letta_messages_to_openai,
- create_assistant_messages_from_openai_response,
- create_input_messages,
- create_letta_messages_from_llm_response,
-)
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.job_manager import JobManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.summarizer.enums import SummarizationMode
-from letta.services.summarizer.summarizer import Summarizer
-from letta.services.tool_executor.tool_execution_manager import ToolExecutionManager
-from letta.settings import model_settings
-
-logger = get_logger(__name__)
-
-
-class VoiceAgent(BaseAgent):
- """
- A function-calling loop for streaming OpenAI responses with tool execution.
- This agent:
- - Streams partial tokens in real-time for low-latency output.
- - Detects tool calls and invokes external tools.
- - Gracefully handles OpenAI API failures (429, etc.) and streams errors.
- """
-
- def __init__(
- self,
- agent_id: str,
- openai_client: openai.AsyncClient,
- message_manager: MessageManager,
- agent_manager: AgentManager,
- block_manager: BlockManager,
- job_manager: JobManager,
- passage_manager: PassageManager,
- actor: User,
- ):
- super().__init__(
- agent_id=agent_id, openai_client=openai_client, message_manager=message_manager, agent_manager=agent_manager, actor=actor
- )
-
- # Summarizer settings
- self.block_manager = block_manager
- self.job_manager = job_manager
- self.passage_manager = passage_manager
- # TODO: This is not guaranteed to exist!
- self.summary_block_label = "human"
-
- # Cached archival memory/message size
- self.num_messages = None
- self.num_archival_memories = None
-
- def init_summarizer(self, agent_state: AgentState) -> Summarizer:
- if not agent_state.multi_agent_group:
- raise ValueError("Low latency voice agent is not part of a multiagent group, missing sleeptime agent.")
- if len(agent_state.multi_agent_group.agent_ids) != 1:
- raise ValueError(
- f"None or multiple participant agents found in voice sleeptime group: {agent_state.multi_agent_group.agent_ids}"
- )
- voice_sleeptime_agent_id = agent_state.multi_agent_group.agent_ids[0]
- summarizer = Summarizer(
- mode=SummarizationMode.STATIC_MESSAGE_BUFFER,
- summarizer_agent=VoiceSleeptimeAgent(
- agent_id=voice_sleeptime_agent_id,
- convo_agent_state=agent_state,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- actor=self.actor,
- block_manager=self.block_manager,
- job_manager=self.job_manager,
- passage_manager=self.passage_manager,
- target_block_label=self.summary_block_label,
- ),
- message_buffer_limit=agent_state.multi_agent_group.max_message_buffer_length,
- message_buffer_min=agent_state.multi_agent_group.min_message_buffer_length,
- )
-
- return summarizer
-
- async def step(self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS) -> LettaResponse:
- raise NotImplementedError("VoiceAgent does not have a synchronous step implemented currently.")
-
- async def step_stream(self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS) -> AsyncGenerator[str, None]:
- """
- Main streaming loop that yields partial tokens.
- Whenever we detect a tool call, we yield from _handle_ai_response as well.
- """
- if len(input_messages) != 1 or input_messages[0].role != MessageRole.user:
- raise ValueError(f"Voice Agent was invoked with multiple input messages or message did not have role `user`: {input_messages}")
-
- user_query = input_messages[0].content[0].text
-
- agent_state = await self.agent_manager.get_agent_by_id_async(
- agent_id=self.agent_id,
- include_relationships=["tools", "memory", "tool_exec_environment_variables", "multi_agent_group"],
- actor=self.actor,
- )
-
- # TODO: Refactor this so it uses our in-house clients
- # TODO: For now, piggyback off of OpenAI client for ease
- if agent_state.llm_config.model_endpoint_type == "anthropic":
- self.openai_client.api_key = model_settings.anthropic_api_key
- self.openai_client.base_url = "https://api.anthropic.com/v1/"
- elif agent_state.llm_config.model_endpoint_type != "openai":
- raise ValueError("Letta voice agents are only compatible with OpenAI or Anthropic.")
-
- # Safety check
- if agent_state.agent_type != AgentType.voice_convo_agent:
- raise IncompatibleAgentType(expected_type=AgentType.voice_convo_agent, actual_type=agent_state.agent_type)
-
- summarizer = self.init_summarizer(agent_state=agent_state)
-
- in_context_messages = await self.message_manager.get_messages_by_ids_async(message_ids=agent_state.message_ids, actor=self.actor)
- memory_edit_timestamp = get_utc_time()
- in_context_messages[0].content[0].text = await PromptGenerator.compile_system_message_async(
- system_prompt=agent_state.system,
- in_context_memory=agent_state.memory,
- in_context_memory_last_edit=memory_edit_timestamp,
- timezone=agent_state.timezone,
- previous_message_count=self.num_messages,
- archival_memory_size=self.num_archival_memories,
- sources=agent_state.sources,
- max_files_open=agent_state.max_files_open,
- )
- letta_message_db_queue = create_input_messages(
- input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=self.actor
- )
- in_memory_message_history = self.pre_process_input_message(input_messages)
-
- # TODO: Define max steps here
- for _ in range(max_steps):
- # Rebuild memory each loop
- in_context_messages = await self._rebuild_memory_async(in_context_messages, agent_state)
- openai_messages = convert_in_context_letta_messages_to_openai(in_context_messages, exclude_system_messages=True)
- openai_messages.extend(in_memory_message_history)
-
- request = self._build_openai_request(openai_messages, agent_state)
-
- stream = await self.openai_client.chat.completions.create(**request.model_dump(exclude_unset=True))
- streaming_interface = OpenAIChatCompletionsStreamingInterface(stream_pre_execution_message=True)
-
- # 1) Yield partial tokens from OpenAI
- async for sse_chunk in streaming_interface.process(stream):
- yield sse_chunk
-
- # 2) Now handle the final AI response. This might yield more text (stalling, etc.)
- should_continue = await self._handle_ai_response(
- user_query,
- streaming_interface,
- agent_state,
- in_memory_message_history,
- letta_message_db_queue,
- )
-
- if not should_continue:
- break
-
- # Rebuild context window if desired
- await self._rebuild_context_window(summarizer, in_context_messages, letta_message_db_queue)
-
- yield "data: [DONE]\n\n"
-
- async def _handle_ai_response(
- self,
- user_query: str,
- streaming_interface: "OpenAIChatCompletionsStreamingInterface",
- agent_state: AgentState,
- in_memory_message_history: List[Dict[str, Any]],
- letta_message_db_queue: List[Any],
- ) -> bool:
- """
- Now that streaming is done, handle the final AI response.
- This might yield additional SSE tokens if we do stalling.
- At the end, set self._continue_execution accordingly.
- """
- # 1. If we have any leftover content from partial stream, store it as an assistant message
- if streaming_interface.content_buffer:
- content = "".join(streaming_interface.content_buffer)
- in_memory_message_history.append({"role": "assistant", "content": content})
-
- assistant_msgs = create_assistant_messages_from_openai_response(
- response_text=content,
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- actor=self.actor,
- timezone=agent_state.timezone,
- )
- letta_message_db_queue.extend(assistant_msgs)
-
- # 2. If a tool call was requested, handle it
- if streaming_interface.tool_call_happened:
- tool_call_name = streaming_interface.tool_call_name
- tool_call_args_str = streaming_interface.tool_call_args_str or "{}"
- try:
- tool_args = json.loads(tool_call_args_str)
- except json.JSONDecodeError:
- tool_args = {}
-
- tool_call_id = streaming_interface.tool_call_id or f"call_{uuid.uuid4().hex[:8]}"
- assistant_tool_call_msg = AssistantMessage(
- content=None,
- tool_calls=[
- ToolCall(
- id=tool_call_id,
- function=ToolCallFunction(
- name=tool_call_name,
- arguments=tool_call_args_str,
- ),
- )
- ],
- )
- in_memory_message_history.append(assistant_tool_call_msg.model_dump())
-
- tool_execution_result = await self._execute_tool(
- user_query=user_query,
- tool_name=tool_call_name,
- tool_args=tool_args,
- agent_state=agent_state,
- )
- tool_result = tool_execution_result.func_return
- success_flag = tool_execution_result.success_flag
-
- # 3. Provide function_call response back into the conversation
- # TODO: fix this tool format
- tool_message = ToolMessage(
- content=json.dumps({"result": tool_result}),
- tool_call_id=tool_call_id,
- )
- in_memory_message_history.append(tool_message.model_dump())
-
- # 4. Insert heartbeat message for follow-up
- heartbeat_user_message = UserMessage(
- content=f"{NON_USER_MSG_PREFIX} Tool finished executing. Summarize the result for the user."
- )
- in_memory_message_history.append(heartbeat_user_message.model_dump())
-
- # 5. Also store in DB
- tool_call_messages = create_letta_messages_from_llm_response(
- agent_id=agent_state.id,
- model=agent_state.llm_config.model,
- function_name=tool_call_name,
- function_arguments=tool_args,
- tool_call_id=tool_call_id,
- function_call_success=success_flag,
- function_response=tool_result,
- tool_execution_result=tool_execution_result,
- timezone=agent_state.timezone,
- actor=self.actor,
- continue_stepping=True,
- )
- letta_message_db_queue.extend(tool_call_messages)
-
- # Because we have new data, we want to continue the while-loop in `step_stream`
- return True
- else:
- # If we got here, there's no tool call. If finish_reason_stop => done
- return not streaming_interface.finish_reason_stop
-
- async def _rebuild_context_window(
- self, summarizer: Summarizer, in_context_messages: List[Message], letta_message_db_queue: List[Message]
- ) -> None:
- new_letta_messages = await self.message_manager.create_many_messages_async(letta_message_db_queue, actor=self.actor)
-
- # TODO: Make this more general and configurable, less brittle
- new_in_context_messages, updated = await summarizer.summarize(
- in_context_messages=in_context_messages, new_letta_messages=new_letta_messages
- )
-
- await self.agent_manager.update_message_ids_async(
- agent_id=self.agent_id, message_ids=[m.id for m in new_in_context_messages], actor=self.actor
- )
-
- async def _rebuild_memory_async(
- self,
- in_context_messages: List[Message],
- agent_state: AgentState,
- ) -> List[Message]:
- if not self.num_messages:
- self.num_messages = await self.message_manager.size_async(
- agent_id=agent_state.id,
- actor=self.actor,
- )
- if not self.num_archival_memories:
- self.num_archival_memories = await self.passage_manager.agent_passage_size_async(
- agent_id=agent_state.id,
- actor=self.actor,
- )
-
- return await super()._rebuild_memory_async(
- in_context_messages, agent_state, num_messages=self.num_messages, num_archival_memories=self.num_archival_memories
- )
-
- def _build_openai_request(self, openai_messages: List[Dict], agent_state: AgentState) -> ChatCompletionRequest:
- tool_schemas = self._build_tool_schemas(agent_state)
- tool_choice = "auto" if tool_schemas else None
-
- openai_request = ChatCompletionRequest(
- model=agent_state.llm_config.model,
- messages=openai_messages,
- tools=self._build_tool_schemas(agent_state),
- tool_choice=tool_choice,
- user=self.actor.id,
- max_completion_tokens=agent_state.llm_config.max_tokens,
- temperature=agent_state.llm_config.temperature,
- stream=True,
- )
- return openai_request
-
- def _build_tool_schemas(self, agent_state: AgentState, external_tools_only=True) -> List[Tool]:
- if external_tools_only:
- tools = [
- t
- for t in agent_state.tools
- if t.tool_type
- in {ToolType.EXTERNAL_COMPOSIO, ToolType.CUSTOM, ToolType.LETTA_FILES_CORE, ToolType.LETTA_BUILTIN, ToolType.EXTERNAL_MCP}
- ]
- else:
- tools = agent_state.tools
-
- # Special tool state
- search_memory_utterance_description = (
- "A lengthier message to be uttered while your memories of the current conversation are being re-contextualized."
- "You MUST also include punctuation at the end of this message."
- "For example: 'Let me double-check my notes—one moment, please.'"
- )
-
- search_memory_json = Tool(
- type="function",
- function=enable_strict_mode( # strict=True ✓
- add_pre_execution_message( # injects pre_exec_msg ✓
- {
- "name": "search_memory",
- "description": (
- "Look in long-term or earlier-conversation memory **only when** the "
- "user asks about something missing from the visible context. "
- "The user's latest utterance is sent automatically as the main query.\n\n"
- "Optional refinements (set unused fields to *null*):\n"
- "• `convo_keyword_queries` – extra names/IDs if the request is vague.\n"
- "• `start_minutes_ago` / `end_minutes_ago` – limit results to a recent time window."
- ),
- "parameters": {
- "type": "object",
- "properties": {
- "convo_keyword_queries": {
- "type": ["array", "null"],
- "items": {"type": "string"},
- "description": (
- "Extra keywords (e.g., order ID, place name). Use *null* when the utterance is already specific."
- ),
- },
- "start_minutes_ago": {
- "type": ["integer", "null"],
- "description": (
- "Newer bound of the time window, in minutes ago. Use *null* if no lower bound is needed."
- ),
- },
- "end_minutes_ago": {
- "type": ["integer", "null"],
- "description": (
- "Older bound of the time window, in minutes ago. Use *null* if no upper bound is needed."
- ),
- },
- },
- "required": [
- "convo_keyword_queries",
- "start_minutes_ago",
- "end_minutes_ago",
- ],
- "additionalProperties": False,
- },
- },
- description=search_memory_utterance_description,
- )
- ),
- )
-
- # TODO: Customize whether or not to have heartbeats, pre_exec_message, etc.
- return [search_memory_json] + [
- Tool(type="function", function=enable_strict_mode(add_pre_execution_message(remove_request_heartbeat(t.json_schema))))
- for t in tools
- ]
-
- async def _execute_tool(self, user_query: str, tool_name: str, tool_args: dict, agent_state: AgentState) -> "ToolExecutionResult":
- """
- Executes a tool and returns the ToolExecutionResult.
- """
- from letta.schemas.tool_execution_result import ToolExecutionResult
-
- # Special memory case
- if tool_name == "search_memory":
- tool_result = await self._search_memory(
- archival_query=user_query,
- convo_keyword_queries=tool_args["convo_keyword_queries"],
- start_minutes_ago=tool_args["start_minutes_ago"],
- end_minutes_ago=tool_args["end_minutes_ago"],
- agent_state=agent_state,
- )
- return ToolExecutionResult(
- func_return=tool_result,
- status="success",
- )
-
- # Find the target tool
- target_tool = next((x for x in agent_state.tools if x.name == tool_name), None)
- if not target_tool:
- return ToolExecutionResult(
- func_return=f"Tool {tool_name} not found",
- status="error",
- )
-
- # Use ToolExecutionManager for modern tool execution
- sandbox_env_vars = {var.key: var.value for var in agent_state.tool_exec_environment_variables}
- tool_execution_manager = ToolExecutionManager(
- agent_state=agent_state,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- block_manager=self.block_manager,
- job_manager=self.job_manager,
- passage_manager=self.passage_manager,
- sandbox_env_vars=sandbox_env_vars,
- actor=self.actor,
- )
-
- # Remove request heartbeat / pre_exec_message
- tool_args.pop(PRE_EXECUTION_MESSAGE_ARG, None)
- tool_args.pop(REQUEST_HEARTBEAT_PARAM, None)
-
- tool_execution_result = await tool_execution_manager.execute_tool_async(
- function_name=tool_name,
- function_args=tool_args,
- tool=target_tool,
- step_id=None, # VoiceAgent doesn't use step tracking currently
- )
-
- return tool_execution_result
-
- async def _search_memory(
- self,
- archival_query: str,
- agent_state: AgentState,
- convo_keyword_queries: Optional[List[str]] = None,
- start_minutes_ago: Optional[int] = None,
- end_minutes_ago: Optional[int] = None,
- ) -> str:
- # Retrieve from archival memory
- now = datetime.now(timezone.utc)
- start_date = now - timedelta(minutes=end_minutes_ago) if end_minutes_ago is not None else None
- end_date = now - timedelta(minutes=start_minutes_ago) if start_minutes_ago is not None else None
-
- # If both bounds exist but got reversed, swap them
- # Shouldn't happen, but in case LLM misunderstands
- if start_date and end_date and start_date > end_date:
- start_date, end_date = end_date, start_date
-
- archival_results = await self.agent_manager.query_agent_passages_async(
- actor=self.actor,
- agent_id=self.agent_id,
- query_text=archival_query,
- limit=5,
- embedding_config=agent_state.embedding_config,
- embed_query=True,
- start_date=start_date,
- end_date=end_date,
- )
- # Extract passages from tuples and format
- formatted_archival_results = [{"timestamp": str(passage.created_at), "content": passage.text} for passage, _, _ in archival_results]
- response = {
- "archival_search_results": formatted_archival_results,
- }
-
- # Retrieve from conversation
- keyword_results = {}
- if convo_keyword_queries:
- for keyword in convo_keyword_queries:
- messages = await self.message_manager.list_messages_for_agent_async(
- agent_id=self.agent_id,
- actor=self.actor,
- query_text=keyword,
- limit=3,
- )
- if messages:
- keyword_results[keyword] = [message.content[0].text for message in messages]
-
- response["convo_keyword_search_results"] = keyword_results
-
- return json.dumps(response, indent=2)
diff --git a/letta/agents/voice_sleeptime_agent.py b/letta/agents/voice_sleeptime_agent.py
deleted file mode 100644
index f2b3b426..00000000
--- a/letta/agents/voice_sleeptime_agent.py
+++ /dev/null
@@ -1,188 +0,0 @@
-from typing import AsyncGenerator, List, Optional, Tuple, Union
-
-from letta.agents.helpers import _create_letta_response, serialize_message_history
-from letta.agents.letta_agent import LettaAgent
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.otel.tracing import trace_method
-from letta.schemas.agent import AgentState
-from letta.schemas.block import BlockUpdate
-from letta.schemas.enums import MessageStreamStatus, ToolType
-from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage, MessageType
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.message import MessageCreate
-from letta.schemas.tool_rule import ChildToolRule, ContinueToolRule, InitToolRule, TerminalToolRule
-from letta.schemas.user import User
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.job_manager import JobManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.summarizer.enums import SummarizationMode
-from letta.services.summarizer.summarizer import Summarizer
-from letta.types import JsonDict
-
-
-class VoiceSleeptimeAgent(LettaAgent):
- """
- A special variant of the LettaAgent that helps with offline memory computations specifically for voice.
- """
-
- def __init__(
- self,
- agent_id: str,
- convo_agent_state: AgentState,
- message_manager: MessageManager,
- agent_manager: AgentManager,
- block_manager: BlockManager,
- job_manager: JobManager,
- passage_manager: PassageManager,
- target_block_label: str,
- actor: User,
- ):
- super().__init__(
- agent_id=agent_id,
- message_manager=message_manager,
- agent_manager=agent_manager,
- block_manager=block_manager,
- job_manager=job_manager,
- passage_manager=passage_manager,
- actor=actor,
- )
-
- self.convo_agent_state = convo_agent_state
- self.target_block_label = target_block_label
- self.message_transcripts = []
- self.summarizer = Summarizer(
- mode=SummarizationMode.STATIC_MESSAGE_BUFFER,
- summarizer_agent=None,
- message_buffer_limit=20,
- message_buffer_min=10,
- )
-
- def update_message_transcript(self, message_transcripts: List[str]):
- self.message_transcripts = message_transcripts
-
- async def step(
- self,
- input_messages: List[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- run_id: Optional[str] = None,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: Optional[int] = None,
- include_return_message_types: Optional[List[MessageType]] = None,
- ) -> LettaResponse:
- """
- Process the user's input message, allowing the model to call memory-related tools
- until it decides to stop and provide a final response.
- """
- agent_state = self.agent_manager.get_agent_by_id(self.agent_id, actor=self.actor)
-
- # Add tool rules to the agent_state specifically for this type of agent
- agent_state.tool_rules = [
- InitToolRule(tool_name="store_memories"),
- ChildToolRule(tool_name="store_memories", children=["rethink_user_memory"]),
- ContinueToolRule(tool_name="rethink_user_memory"),
- TerminalToolRule(tool_name="finish_rethinking_memory"),
- ]
-
- # Summarize
- current_in_context_messages, new_in_context_messages, stop_reason, usage = await super()._step(
- agent_state=agent_state, input_messages=input_messages, max_steps=max_steps
- )
- new_in_context_messages, updated = await self.summarizer.summarize(
- in_context_messages=current_in_context_messages, new_letta_messages=new_in_context_messages
- )
- self.agent_manager.set_in_context_messages(
- agent_id=self.agent_id, message_ids=[m.id for m in new_in_context_messages], actor=self.actor
- )
-
- return _create_letta_response(
- new_in_context_messages=new_in_context_messages,
- use_assistant_message=use_assistant_message,
- stop_reason=stop_reason,
- usage=usage,
- include_return_message_types=include_return_message_types,
- )
-
- @trace_method
- async def _execute_tool(
- self,
- tool_name: str,
- tool_args: JsonDict,
- agent_state: AgentState,
- agent_step_span: Optional["Span"] = None,
- step_id: str | None = None,
- ) -> "ToolExecutionResult":
- """
- Executes a tool and returns the ToolExecutionResult
- """
- from letta.schemas.tool_execution_result import ToolExecutionResult
-
- # Special memory case
- target_tool = next((x for x in agent_state.tools if x.name == tool_name), None)
- if not target_tool:
- return ToolExecutionResult(status="error", func_return=f"Tool not found: {tool_name}")
-
- try:
- if target_tool.name == "rethink_user_memory" and target_tool.tool_type == ToolType.LETTA_VOICE_SLEEPTIME_CORE:
- func_return, success_flag = self.rethink_user_memory(agent_state=agent_state, **tool_args)
- return ToolExecutionResult(func_return=func_return, status="success" if success_flag else "error")
- elif target_tool.name == "finish_rethinking_memory" and target_tool.tool_type == ToolType.LETTA_VOICE_SLEEPTIME_CORE:
- return ToolExecutionResult(func_return="", status="success")
- elif target_tool.name == "store_memories" and target_tool.tool_type == ToolType.LETTA_VOICE_SLEEPTIME_CORE:
- chunks = tool_args.get("chunks", [])
- results = [self.store_memory(agent_state=self.convo_agent_state, **chunk_args) for chunk_args in chunks]
-
- aggregated_result = next((res for res, _ in results if res is not None), None)
- aggregated_success = all(success for _, success in results)
-
- return ToolExecutionResult(
- func_return=aggregated_result, status="success" if aggregated_success else "error"
- ) # Note that here we store to the convo agent's archival memory
- else:
- result = f"Voice sleeptime agent tried invoking invalid tool with type {target_tool.tool_type}: {target_tool}"
- return ToolExecutionResult(func_return=result, status="error")
- except Exception as e:
- return ToolExecutionResult(func_return=f"Failed to call tool. Error: {e}", status="error")
-
- def rethink_user_memory(self, new_memory: str, agent_state: AgentState) -> Tuple[str, bool]:
- if agent_state.memory.get_block(self.target_block_label) is None:
- agent_state.memory.create_block(label=self.target_block_label, value=new_memory)
-
- agent_state.memory.update_block_value(label=self.target_block_label, value=new_memory)
-
- target_block = agent_state.memory.get_block(self.target_block_label)
- self.block_manager.update_block(block_id=target_block.id, block_update=BlockUpdate(value=target_block.value), actor=self.actor)
-
- return "", True
-
- def store_memory(self, start_index: int, end_index: int, context: str, agent_state: AgentState) -> Tuple[str, bool]:
- """
- Store a memory.
- """
- try:
- messages = self.message_transcripts[start_index : end_index + 1]
- memory = serialize_message_history(messages, context)
- self.agent_manager.passage_manager.insert_passage(
- agent_state=agent_state,
- text=memory,
- actor=self.actor,
- )
- self.agent_manager.rebuild_system_prompt(agent_id=agent_state.id, actor=self.actor, force=True)
-
- return "", True
- except Exception as e:
- return f"Failed to store memory given start_index {start_index} and end_index {end_index}: {e}", False
-
- async def step_stream(
- self,
- input_messages: List[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: Optional[int] = None,
- include_return_message_types: Optional[List[MessageType]] = None,
- ) -> AsyncGenerator[Union[LettaMessage, LegacyLettaMessage, MessageStreamStatus], None]:
- """
- This agent is synchronous-only. If called in an async context, raise an error.
- """
- raise NotImplementedError("VoiceSleeptimeAgent does not support async step.")
diff --git a/letta/cli/cli.py b/letta/cli/cli.py
deleted file mode 100644
index 47e86509..00000000
--- a/letta/cli/cli.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import sys
-from enum import Enum
-from typing import Annotated, Optional
-
-import typer
-
-from letta.log import get_logger
-from letta.streaming_interface import StreamingRefreshCLIInterface as interface # for printing to terminal
-
-logger = get_logger(__name__)
-
-
-class ServerChoice(Enum):
- rest_api = "rest"
- ws_api = "websocket"
-
-
-def server(
- type: Annotated[ServerChoice, typer.Option(help="Server to run")] = "rest",
- port: Annotated[Optional[int], typer.Option(help="Port to run the server on")] = None,
- host: Annotated[Optional[str], typer.Option(help="Host to run the server on (default to localhost)")] = None,
- debug: Annotated[bool, typer.Option(help="Turn debugging output on")] = False,
- reload: Annotated[bool, typer.Option(help="Enable hot-reload")] = False,
- ade: Annotated[bool, typer.Option(help="Allows remote access")] = False, # NOTE: deprecated
- secure: Annotated[bool, typer.Option(help="Adds simple security access")] = False,
- localhttps: Annotated[bool, typer.Option(help="Setup local https")] = False,
-):
- """Launch a Letta server process"""
- if type == ServerChoice.rest_api:
- pass
-
- try:
- from letta.server.rest_api.app import start_server
-
- start_server(port=port, host=host, debug=debug, reload=reload)
-
- except KeyboardInterrupt:
- # Handle CTRL-C
- typer.secho("Terminating the server...")
- sys.exit(0)
-
- elif type == ServerChoice.ws_api:
- raise NotImplementedError("WS suppport deprecated")
-
-
-def version() -> str:
- import letta
-
- print(letta.__version__)
diff --git a/letta/cli/cli_load.py b/letta/cli/cli_load.py
deleted file mode 100644
index a50c525e..00000000
--- a/letta/cli/cli_load.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-This file contains functions for loading data into Letta's archival storage.
-
-Data can be loaded with the following command, once a load function is defined:
-```
-letta load --name [ADDITIONAL ARGS]
-```
-
-"""
-
-import typer
-
-app = typer.Typer()
-
-
-default_extensions = "txt,md,pdf"
diff --git a/letta/client/__init__.py b/letta/client/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/client/streaming.py b/letta/client/streaming.py
deleted file mode 100644
index 9154051a..00000000
--- a/letta/client/streaming.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import json
-from typing import Generator, Union, get_args
-
-import httpx
-from httpx_sse import SSEError, connect_sse
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-
-from letta.constants import OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING
-from letta.errors import LLMError
-from letta.log import get_logger
-from letta.schemas.enums import MessageStreamStatus
-from letta.schemas.letta_message import AssistantMessage, HiddenReasoningMessage, ReasoningMessage, ToolCallMessage, ToolReturnMessage
-from letta.schemas.letta_response import LettaStreamingResponse
-from letta.schemas.usage import LettaUsageStatistics
-
-logger = get_logger(__name__)
-
-
-def _sse_post(url: str, data: dict, headers: dict) -> Generator[Union[LettaStreamingResponse, ChatCompletionChunk], None, None]:
- """
- Sends an SSE POST request and yields parsed response chunks.
- """
- # TODO: Please note his is a very generous timeout for e2b reasons
- with httpx.Client(timeout=httpx.Timeout(5 * 60.0, read=5 * 60.0)) as client:
- with connect_sse(client, method="POST", url=url, json=data, headers=headers) as event_source:
- # Check for immediate HTTP errors before processing the SSE stream
- if not event_source.response.is_success:
- response_bytes = event_source.response.read()
- logger.warning(f"SSE request error: {vars(event_source.response)}")
- logger.warning(response_bytes.decode("utf-8"))
-
- try:
- response_dict = json.loads(response_bytes.decode("utf-8"))
- error_message = response_dict.get("error", {}).get("message", "")
-
- if OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING in error_message:
- logger.error(error_message)
- raise LLMError(error_message)
- except LLMError:
- raise
- except Exception:
- logger.error("Failed to parse SSE message, raising HTTP error")
- event_source.response.raise_for_status()
-
- try:
- for sse in event_source.iter_sse():
- if sse.data in {status.value for status in MessageStreamStatus}:
- yield MessageStreamStatus(sse.data)
- if sse.data == MessageStreamStatus.done.value:
- # We received the [DONE], so stop reading the stream.
- break
- else:
- chunk_data = json.loads(sse.data)
-
- if "reasoning" in chunk_data:
- yield ReasoningMessage(**chunk_data)
- elif chunk_data.get("message_type") == "assistant_message":
- yield AssistantMessage(**chunk_data)
- elif "hidden_reasoning" in chunk_data:
- yield HiddenReasoningMessage(**chunk_data)
- elif "tool_call" in chunk_data:
- yield ToolCallMessage(**chunk_data)
- elif "tool_return" in chunk_data:
- yield ToolReturnMessage(**chunk_data)
- elif "step_count" in chunk_data:
- yield LettaUsageStatistics(**chunk_data)
- elif chunk_data.get("object") == get_args(ChatCompletionChunk.__annotations__["object"])[0]:
- yield ChatCompletionChunk(**chunk_data)
- else:
- raise ValueError(f"Unknown message type in chunk_data: {chunk_data}")
-
- except SSEError as e:
- logger.error(f"SSE stream error: {e}")
-
- if "application/json" in str(e):
- response = client.post(url=url, json=data, headers=headers)
-
- if response.headers.get("Content-Type", "").startswith("application/json"):
- error_details = response.json()
- logger.error(f"POST Error: {error_details}")
- else:
- logger.error("Failed to retrieve JSON error message via retry.")
-
- raise e
-
- except Exception as e:
- logger.error(f"Unexpected exception: {e}")
-
- if event_source.response.request:
- logger.error(f"HTTP Request: {vars(event_source.response.request)}")
- if event_source.response:
- logger.error(f"HTTP Status: {event_source.response.status_code}")
- logger.error(f"HTTP Headers: {event_source.response.headers}")
-
- raise e
diff --git a/letta/client/utils.py b/letta/client/utils.py
deleted file mode 100644
index f823ee87..00000000
--- a/letta/client/utils.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import re
-from datetime import datetime
-from typing import Optional
-
-from IPython.display import HTML, display
-from sqlalchemy.testing.plugin.plugin_base import warnings
-
-from letta.local_llm.constants import ASSISTANT_MESSAGE_CLI_SYMBOL, INNER_THOUGHTS_CLI_SYMBOL
-
-
-def pprint(messages):
- """Utility function for pretty-printing the output of client.send_message in notebooks"""
-
- css_styles = """
-
- """
-
- html_content = css_styles + "", "exec")
- except SyntaxError as e:
- print(f"Syntax error in code: {e}")
diff --git a/letta/functions/function_sets/base.py b/letta/functions/function_sets/base.py
deleted file mode 100644
index 623663fb..00000000
--- a/letta/functions/function_sets/base.py
+++ /dev/null
@@ -1,421 +0,0 @@
-from typing import List, Literal, Optional
-
-from letta.agent import Agent
-from letta.constants import CORE_MEMORY_LINE_NUMBER_WARNING
-
-
-def send_message(self: "Agent", message: str) -> Optional[str]:
- """
- Sends a message to the human user.
-
- Args:
- message (str): Message contents. All unicode (including emojis) are supported.
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- # FIXME passing of msg_obj here is a hack, unclear if guaranteed to be the correct reference
- if self.interface:
- self.interface.assistant_message(message) # , msg_obj=self._messages[-1])
- return None
-
-
-def conversation_search(
- self: "Agent",
- query: str,
- roles: Optional[List[Literal["assistant", "user", "tool"]]] = None,
- limit: Optional[int] = None,
- start_date: Optional[str] = None,
- end_date: Optional[str] = None,
-) -> Optional[str]:
- """
- Search prior conversation history using hybrid search (text + semantic similarity).
-
- Args:
- query (str): String to search for using both text matching and semantic similarity.
- roles (Optional[List[Literal["assistant", "user", "tool"]]]): Optional list of message roles to filter by.
- limit (Optional[int]): Maximum number of results to return. Uses system default if not specified.
- start_date (Optional[str]): Filter results to messages created on or after this date (INCLUSIVE). When using date-only format (e.g., "2024-01-15"), includes messages starting from 00:00:00 of that day. ISO 8601 format: "YYYY-MM-DD" or "YYYY-MM-DDTHH:MM". Examples: "2024-01-15" (from start of Jan 15), "2024-01-15T14:30" (from 2:30 PM on Jan 15).
- end_date (Optional[str]): Filter results to messages created on or before this date (INCLUSIVE). When using date-only format (e.g., "2024-01-20"), includes all messages from that entire day. ISO 8601 format: "YYYY-MM-DD" or "YYYY-MM-DDTHH:MM". Examples: "2024-01-20" (includes all of Jan 20), "2024-01-20T17:00" (up to 5 PM on Jan 20).
-
- Examples:
- # Search all messages
- conversation_search(query="project updates")
-
- # Search only assistant messages
- conversation_search(query="error handling", roles=["assistant"])
-
- # Search with date range (inclusive of both dates)
- conversation_search(query="meetings", start_date="2024-01-15", end_date="2024-01-20")
- # This includes all messages from Jan 15 00:00:00 through Jan 20 23:59:59
-
- # Search messages from a specific day (inclusive)
- conversation_search(query="bug reports", start_date="2024-09-04", end_date="2024-09-04")
- # This includes ALL messages from September 4, 2024
-
- # Search with specific time boundaries
- conversation_search(query="deployment", start_date="2024-01-15T09:00", end_date="2024-01-15T17:30")
- # This includes messages from 9 AM to 5:30 PM on Jan 15
-
- # Search with limit
- conversation_search(query="debugging", limit=10)
-
- Returns:
- str: Query result string containing matching messages with timestamps and content.
- """
-
- from letta.constants import RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE
- from letta.helpers.json_helpers import json_dumps
-
- # Use provided limit or default
- if limit is None:
- limit = RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE
-
- messages = self.message_manager.list_messages_for_agent(
- agent_id=self.agent_state.id,
- actor=self.user,
- query_text=query,
- roles=roles,
- limit=limit,
- )
-
- if len(messages) == 0:
- results_str = "No results found."
- else:
- results_pref = f"Found {len(messages)} results:"
- results_formatted = []
- for message in messages:
- # Extract text content from message
- text_content = message.content[0].text if message.content else ""
- result_entry = {"role": message.role, "content": text_content}
- results_formatted.append(result_entry)
- results_str = f"{results_pref} {json_dumps(results_formatted)}"
- return results_str
-
-
-async def archival_memory_insert(self: "Agent", content: str, tags: Optional[list[str]] = None) -> Optional[str]:
- """
- Add to archival memory. Make sure to phrase the memory contents such that it can be easily queried later.
-
- Args:
- content (str): Content to write to the memory. All unicode (including emojis) are supported.
- tags (Optional[list[str]]): Optional list of tags to associate with this memory for better organization and filtering.
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- raise NotImplementedError("This should never be invoked directly. Contact Letta if you see this error message.")
-
-
-async def archival_memory_search(
- self: "Agent",
- query: str,
- tags: Optional[list[str]] = None,
- tag_match_mode: Literal["any", "all"] = "any",
- top_k: Optional[int] = None,
- start_datetime: Optional[str] = None,
- end_datetime: Optional[str] = None,
-) -> Optional[str]:
- """
- Search archival memory using semantic (embedding-based) search with optional temporal filtering.
-
- Args:
- query (str): String to search for using semantic similarity.
- tags (Optional[list[str]]): Optional list of tags to filter search results. Only passages with these tags will be returned.
- tag_match_mode (Literal["any", "all"]): How to match tags - "any" to match passages with any of the tags, "all" to match only passages with all tags. Defaults to "any".
- top_k (Optional[int]): Maximum number of results to return. Uses system default if not specified.
- start_datetime (Optional[str]): Filter results to passages created on or after this datetime (INCLUSIVE). When using date-only format (e.g., "2024-01-15"), includes passages starting from 00:00:00 of that day. ISO 8601 format: "YYYY-MM-DD" or "YYYY-MM-DDTHH:MM". Examples: "2024-01-15" (from start of Jan 15), "2024-01-15T14:30" (from 2:30 PM on Jan 15).
- end_datetime (Optional[str]): Filter results to passages created on or before this datetime (INCLUSIVE). When using date-only format (e.g., "2024-01-20"), includes all passages from that entire day. ISO 8601 format: "YYYY-MM-DD" or "YYYY-MM-DDTHH:MM". Examples: "2024-01-20" (includes all of Jan 20), "2024-01-20T17:00" (up to 5 PM on Jan 20).
-
- Examples:
- # Search all passages
- archival_memory_search(query="project updates")
-
- # Search with date range (inclusive of both dates)
- archival_memory_search(query="meetings", start_datetime="2024-01-15", end_datetime="2024-01-20")
- # This includes all passages from Jan 15 00:00:00 through Jan 20 23:59:59
-
- # Search passages from a specific day (inclusive)
- archival_memory_search(query="bug reports", start_datetime="2024-09-04", end_datetime="2024-09-04")
- # This includes ALL passages from September 4, 2024
-
- # Search with specific time range
- archival_memory_search(query="error logs", start_datetime="2024-01-15T09:30", end_datetime="2024-01-15T17:30")
- # This includes passages from 9:30 AM to 5:30 PM on Jan 15
-
- # Search from a specific point in time onwards
- archival_memory_search(query="customer feedback", start_datetime="2024-01-15T14:00")
-
- Returns:
- str: Query result string containing matching passages with timestamps and content.
- """
- raise NotImplementedError("This should never be invoked directly. Contact Letta if you see this error message.")
-
-
-def core_memory_append(agent_state: "AgentState", label: str, content: str) -> Optional[str]: # type: ignore
- """
- Append to the contents of core memory.
-
- Args:
- label (str): Section of the memory to be edited.
- content (str): Content to write to the memory. All unicode (including emojis) are supported.
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- current_value = str(agent_state.memory.get_block(label).value)
- new_value = current_value + "\n" + str(content)
- agent_state.memory.update_block_value(label=label, value=new_value)
- return None
-
-
-def core_memory_replace(agent_state: "AgentState", label: str, old_content: str, new_content: str) -> Optional[str]: # type: ignore
- """
- Replace the contents of core memory. To delete memories, use an empty string for new_content.
-
- Args:
- label (str): Section of the memory to be edited.
- old_content (str): String to replace. Must be an exact match.
- new_content (str): Content to write to the memory. All unicode (including emojis) are supported.
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- current_value = str(agent_state.memory.get_block(label).value)
- if old_content not in current_value:
- raise ValueError(f"Old content '{old_content}' not found in memory block '{label}'")
- new_value = current_value.replace(str(old_content), str(new_content))
- agent_state.memory.update_block_value(label=label, value=new_value)
- return None
-
-
-def rethink_memory(agent_state: "AgentState", new_memory: str, target_block_label: str) -> None:
- """
- Rewrite memory block for the main agent, new_memory should contain all current information from the block that is not outdated or inconsistent, integrating any new information, resulting in a new memory block that is organized, readable, and comprehensive.
-
- Args:
- new_memory (str): The new memory with information integrated from the memory block. If there is no new information, then this should be the same as the content in the source block.
- target_block_label (str): The name of the block to write to.
-
- Returns:
- None: None is always returned as this function does not produce a response.
- """
-
- if agent_state.memory.get_block(target_block_label) is None:
- agent_state.memory.create_block(label=target_block_label, value=new_memory)
-
- agent_state.memory.update_block_value(label=target_block_label, value=new_memory)
- return None
-
-
-## Attempted v2 of sleep-time function set, meant to work better across all types
-
-SNIPPET_LINES: int = 4
-
-
-# Based off of: https://github.com/anthropics/anthropic-quickstarts/blob/main/computer-use-demo/computer_use_demo/tools/edit.py?ref=musings.yasyf.com#L154
-def memory_replace(agent_state: "AgentState", label: str, old_str: str, new_str: str) -> str: # type: ignore
- """
- The memory_replace command allows you to replace a specific string in a memory block with a new string. This is used for making precise edits.
-
- Args:
- label (str): Section of the memory to be edited, identified by its label.
- old_str (str): The text to replace (must match exactly, including whitespace and indentation).
- new_str (str): The new text to insert in place of the old text. Do not include line number prefixes.
-
- Examples:
- # Update a block containing information about the user
- memory_replace(label="human", old_str="Their name is Alice", new_str="Their name is Bob")
-
- # Update a block containing a todo list
- memory_replace(label="todos", old_str="- [ ] Step 5: Search the web", new_str="- [x] Step 5: Search the web")
-
- # Pass an empty string to
- memory_replace(label="human", old_str="Their name is Alice", new_str="")
-
- # Bad example - do NOT add (view-only) line numbers to the args
- memory_replace(label="human", old_str="Line 1: Their name is Alice", new_str="Line 1: Their name is Bob")
-
- # Bad example - do NOT include the number number warning either
- memory_replace(label="human", old_str="# NOTE: Line numbers shown below are to help during editing. Do NOT include line number prefixes in your memory edit tool calls.\\nLine 1: Their name is Alice", new_str="Line 1: Their name is Bob")
-
- # Good example - no line numbers or line number warning (they are view-only), just the text
- memory_replace(label="human", old_str="Their name is Alice", new_str="Their name is Bob")
-
- Returns:
- str: The success message
- """
- import re
-
- if bool(re.search(r"\nLine \d+: ", old_str)):
- raise ValueError(
- "old_str contains a line number prefix, which is not allowed. Do not include line numbers when calling memory tools (line numbers are for display purposes only)."
- )
- if CORE_MEMORY_LINE_NUMBER_WARNING in old_str:
- raise ValueError(
- "old_str contains a line number warning, which is not allowed. Do not include line number information when calling memory tools (line numbers are for display purposes only)."
- )
- if bool(re.search(r"\nLine \d+: ", new_str)):
- raise ValueError(
- "new_str contains a line number prefix, which is not allowed. Do not include line numbers when calling memory tools (line numbers are for display purposes only)."
- )
-
- old_str = str(old_str).expandtabs()
- new_str = str(new_str).expandtabs()
- current_value = str(agent_state.memory.get_block(label).value).expandtabs()
-
- # Check if old_str is unique in the block
- occurences = current_value.count(old_str)
- if occurences == 0:
- raise ValueError(f"No replacement was performed, old_str `{old_str}` did not appear verbatim in memory block with label `{label}`.")
- elif occurences > 1:
- content_value_lines = current_value.split("\n")
- lines = [idx + 1 for idx, line in enumerate(content_value_lines) if old_str in line]
- raise ValueError(
- f"No replacement was performed. Multiple occurrences of old_str `{old_str}` in lines {lines}. Please ensure it is unique."
- )
-
- # Replace old_str with new_str
- new_value = current_value.replace(str(old_str), str(new_str))
-
- # Write the new content to the block
- agent_state.memory.update_block_value(label=label, value=new_value)
-
- # Create a snippet of the edited section
- # SNIPPET_LINES = 3
- # replacement_line = current_value.split(old_str)[0].count("\n")
- # start_line = max(0, replacement_line - SNIPPET_LINES)
- # end_line = replacement_line + SNIPPET_LINES + new_str.count("\n")
- # snippet = "\n".join(new_value.split("\n")[start_line : end_line + 1])
-
- # Prepare the success message
- success_msg = f"The core memory block with label `{label}` has been edited. "
- # success_msg += self._make_output(
- # snippet, f"a snippet of {path}", start_line + 1
- # )
- # success_msg += f"A snippet of core memory block `{label}`:\n{snippet}\n"
- success_msg += "Review the changes and make sure they are as expected (correct indentation, no duplicate lines, etc). Edit the memory block again if necessary."
-
- # return None
- return success_msg
-
-
-def memory_insert(agent_state: "AgentState", label: str, new_str: str, insert_line: int = -1) -> Optional[str]: # type: ignore
- """
- The memory_insert command allows you to insert text at a specific location in a memory block.
-
- Args:
- label (str): Section of the memory to be edited, identified by its label.
- new_str (str): The text to insert. Do not include line number prefixes.
- insert_line (int): The line number after which to insert the text (0 for beginning of file). Defaults to -1 (end of the file).
-
- Examples:
- # Update a block containing information about the user (append to the end of the block)
- memory_insert(label="customer", new_str="The customer's ticket number is 12345")
-
- # Update a block containing information about the user (insert at the beginning of the block)
- memory_insert(label="customer", new_str="The customer's ticket number is 12345", insert_line=0)
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- import re
-
- if bool(re.search(r"\nLine \d+: ", new_str)):
- raise ValueError(
- "new_str contains a line number prefix, which is not allowed. Do not include line numbers when calling memory tools (line numbers are for display purposes only)."
- )
- if CORE_MEMORY_LINE_NUMBER_WARNING in new_str:
- raise ValueError(
- "new_str contains a line number warning, which is not allowed. Do not include line number information when calling memory tools (line numbers are for display purposes only)."
- )
-
- current_value = str(agent_state.memory.get_block(label).value).expandtabs()
- new_str = str(new_str).expandtabs()
- current_value_lines = current_value.split("\n")
- n_lines = len(current_value_lines)
-
- # Check if we're in range, from 0 (pre-line), to 1 (first line), to n_lines (last line)
- if insert_line == -1:
- insert_line = n_lines
- elif insert_line < 0 or insert_line > n_lines:
- raise ValueError(
- f"Invalid `insert_line` parameter: {insert_line}. It should be within the range of lines of the memory block: {[0, n_lines]}, or -1 to append to the end of the memory block."
- )
-
- # Insert the new string as a line
- new_str_lines = new_str.split("\n")
- new_value_lines = current_value_lines[:insert_line] + new_str_lines + current_value_lines[insert_line:]
- snippet_lines = (
- current_value_lines[max(0, insert_line - SNIPPET_LINES) : insert_line]
- + new_str_lines
- + current_value_lines[insert_line : insert_line + SNIPPET_LINES]
- )
-
- # Collate into the new value to update
- new_value = "\n".join(new_value_lines)
- # snippet = "\n".join(snippet_lines)
-
- # Write into the block
- agent_state.memory.update_block_value(label=label, value=new_value)
-
- # Prepare the success message
- success_msg = f"The core memory block with label `{label}` has been edited. "
- # success_msg += self._make_output(
- # snippet,
- # "a snippet of the edited file",
- # max(1, insert_line - SNIPPET_LINES + 1),
- # )
- # success_msg += f"A snippet of core memory block `{label}`:\n{snippet}\n"
- success_msg += "Review the changes and make sure they are as expected (correct indentation, no duplicate lines, etc). Edit the memory block again if necessary."
-
- return success_msg
-
-
-def memory_rethink(agent_state: "AgentState", label: str, new_memory: str) -> None:
- """
- The memory_rethink command allows you to completely rewrite the contents of a memory block. Use this tool to make large sweeping changes (e.g. when you want to condense or reorganize the memory blocks), do NOT use this tool to make small precise edits (e.g. add or remove a line, replace a specific string, etc).
-
- Args:
- label (str): The memory block to be rewritten, identified by its label.
- new_memory (str): The new memory contents with information integrated from existing memory blocks and the conversation context.
-
- Returns:
- None: None is always returned as this function does not produce a response.
- """
- import re
-
- if bool(re.search(r"\nLine \d+: ", new_memory)):
- raise ValueError(
- "new_memory contains a line number prefix, which is not allowed. Do not include line numbers when calling memory tools (line numbers are for display purposes only)."
- )
- if CORE_MEMORY_LINE_NUMBER_WARNING in new_memory:
- raise ValueError(
- "new_memory contains a line number warning, which is not allowed. Do not include line number information when calling memory tools (line numbers are for display purposes only)."
- )
-
- if agent_state.memory.get_block(label) is None:
- agent_state.memory.create_block(label=label, value=new_memory)
-
- agent_state.memory.update_block_value(label=label, value=new_memory)
-
- # Prepare the success message
- success_msg = f"The core memory block with label `{label}` has been edited. "
- # success_msg += self._make_output(
- # snippet, f"a snippet of {path}", start_line + 1
- # )
- # success_msg += f"A snippet of core memory block `{label}`:\n{snippet}\n"
- success_msg += "Review the changes and make sure they are as expected (correct indentation, no duplicate lines, etc). Edit the memory block again if necessary."
-
- # return None
- return success_msg
-
-
-def memory_finish_edits(agent_state: "AgentState") -> None: # type: ignore
- """
- Call the memory_finish_edits command when you are finished making edits (integrating all new information) into the memory blocks. This function is called when the agent is done rethinking the memory.
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- return None
diff --git a/letta/functions/function_sets/builtin.py b/letta/functions/function_sets/builtin.py
deleted file mode 100644
index a49f0661..00000000
--- a/letta/functions/function_sets/builtin.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from typing import List, Literal, Optional
-
-
-def run_code(code: str, language: Literal["python", "js", "ts", "r", "java"]) -> str:
- """
- Run code in a sandbox. Supports Python, Javascript, Typescript, R, and Java.
-
- Args:
- code (str): The code to run.
- language (Literal["python", "js", "ts", "r", "java"]): The language of the code.
- Returns:
- str: The output of the code, the stdout, the stderr, and error traces (if any).
- """
-
- raise NotImplementedError("This is only available on the latest agent architecture. Please contact the Letta team.")
-
-
-async def web_search(
- query: str,
- num_results: int = 10,
- category: Optional[
- Literal["company", "research paper", "news", "pdf", "github", "tweet", "personal site", "linkedin profile", "financial report"]
- ] = None,
- include_text: bool = False,
- include_domains: Optional[List[str]] = None,
- exclude_domains: Optional[List[str]] = None,
- start_published_date: Optional[str] = None,
- end_published_date: Optional[str] = None,
- user_location: Optional[str] = None,
-) -> str:
- """
- Search the web using Exa's AI-powered search engine and retrieve relevant content.
-
- Examples:
- web_search("Tesla Q1 2025 earnings report", num_results=5, category="financial report")
- web_search("Latest research in large language models", category="research paper", include_domains=["arxiv.org", "paperswithcode.com"])
- web_search("Letta API documentation core_memory_append", num_results=3)
-
- Args:
- query (str): The search query to find relevant web content.
- num_results (int, optional): Number of results to return (1-100). Defaults to 10.
- category (Optional[Literal], optional): Focus search on specific content types. Defaults to None.
- include_text (bool, optional): Whether to retrieve full page content. Defaults to False (only returns summary and highlights, since the full text usually will overflow the context window).
- include_domains (Optional[List[str]], optional): List of domains to include in search results. Defaults to None.
- exclude_domains (Optional[List[str]], optional): List of domains to exclude from search results. Defaults to None.
- start_published_date (Optional[str], optional): Only return content published after this date (ISO format). Defaults to None.
- end_published_date (Optional[str], optional): Only return content published before this date (ISO format). Defaults to None.
- user_location (Optional[str], optional): Two-letter country code for localized results (e.g., "US"). Defaults to None.
-
- Returns:
- str: A JSON-encoded string containing search results with title, URL, content, highlights, and summary.
- """
- raise NotImplementedError("This is only available on the latest agent architecture. Please contact the Letta team.")
-
-
-async def fetch_webpage(url: str) -> str:
- """
- Fetch a webpage and convert it to markdown/text format using Jina AI reader.
-
- Args:
- url: The URL of the webpage to fetch and convert
-
- Returns:
- String containing the webpage content in markdown/text format
- """
- raise NotImplementedError("This is only available on the latest agent architecture. Please contact the Letta team.")
diff --git a/letta/functions/function_sets/extras.py b/letta/functions/function_sets/extras.py
deleted file mode 100644
index 4c91af76..00000000
--- a/letta/functions/function_sets/extras.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import os
-import uuid
-from typing import Optional
-
-import requests
-
-from letta.constants import MESSAGE_CHATGPT_FUNCTION_MODEL, MESSAGE_CHATGPT_FUNCTION_SYSTEM_MESSAGE
-from letta.helpers.json_helpers import json_dumps, json_loads
-from letta.llm_api.llm_api_tools import create
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.message import Message
-
-
-def message_chatgpt(self, message: str):
- """
- Send a message to a more basic AI, ChatGPT. A useful resource for asking questions. ChatGPT does not retain memory of previous interactions.
-
- Args:
- message (str): Message to send ChatGPT. Phrase your message as a full English sentence.
-
- Returns:
- str: Reply message from ChatGPT
- """
- dummy_user_id = uuid.uuid4()
- dummy_agent_id = uuid.uuid4()
- message_sequence = [
- Message(
- user_id=dummy_user_id,
- agent_id=dummy_agent_id,
- role="system",
- content=[TextContent(text=MESSAGE_CHATGPT_FUNCTION_SYSTEM_MESSAGE)],
- ),
- Message(user_id=dummy_user_id, agent_id=dummy_agent_id, role="user", content=[TextContent(text=str(message))]),
- ]
- # TODO: this will error without an LLMConfig
- response = create(
- model=MESSAGE_CHATGPT_FUNCTION_MODEL,
- messages=message_sequence,
- )
-
- reply = response.choices[0].message.content
- return reply
-
-
-def read_from_text_file(self, filename: str, line_start: int, num_lines: Optional[int] = 1):
- """
- Read lines from a text file.
-
- Args:
- filename (str): The name of the file to read.
- line_start (int): Line to start reading from.
- num_lines (Optional[int]): How many lines to read (defaults to 1).
-
- Returns:
- str: Text read from the file
- """
- max_chars = 500
- trunc_message = True
- if not os.path.exists(filename):
- raise FileNotFoundError(f"The file '{filename}' does not exist.")
-
- if line_start < 1 or num_lines < 1:
- raise ValueError("Both line_start and num_lines must be positive integers.")
-
- lines = []
- chars_read = 0
- with open(filename, "r", encoding="utf-8") as file:
- for current_line_number, line in enumerate(file, start=1):
- if line_start <= current_line_number < line_start + num_lines:
- chars_to_add = len(line)
- if max_chars is not None and chars_read + chars_to_add > max_chars:
- # If adding this line exceeds MAX_CHARS, truncate the line if needed and stop reading further.
- excess_chars = (chars_read + chars_to_add) - max_chars
- lines.append(line[:-excess_chars].rstrip("\n"))
- if trunc_message:
- lines.append(f"[SYSTEM ALERT - max chars ({max_chars}) reached during file read]")
- break
- else:
- lines.append(line.rstrip("\n"))
- chars_read += chars_to_add
- if current_line_number >= line_start + num_lines - 1:
- break
-
- return "\n".join(lines)
-
-
-def append_to_text_file(self, filename: str, content: str):
- """
- Append to a text file.
-
- Args:
- filename (str): The name of the file to append to.
- content (str): Content to append to the file.
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- if not os.path.exists(filename):
- raise FileNotFoundError(f"The file '{filename}' does not exist.")
-
- with open(filename, "a", encoding="utf-8") as file:
- file.write(content + "\n")
-
-
-def http_request(self, method: str, url: str, payload_json: Optional[str] = None):
- """
- Generates an HTTP request and returns the response.
-
- Args:
- method (str): The HTTP method (e.g., 'GET', 'POST').
- url (str): The URL for the request.
- payload_json (Optional[str]): A JSON string representing the request payload.
-
- Returns:
- dict: The response from the HTTP request.
- """
- try:
- headers = {"Content-Type": "application/json"}
-
- # For GET requests, ignore the payload
- if method.upper() == "GET":
- print(f"[HTTP] launching GET request to {url}")
- response = requests.get(url, headers=headers)
- else:
- # Validate and convert the payload for other types of requests
- if payload_json:
- payload = json_loads(payload_json)
- else:
- payload = {}
- print(f"[HTTP] launching {method} request to {url}, payload=\n{json_dumps(payload, indent=2)}")
- response = requests.request(method, url, json=payload, headers=headers)
-
- return {"status_code": response.status_code, "headers": dict(response.headers), "body": response.text}
- except Exception as e:
- return {"error": str(e)}
diff --git a/letta/functions/function_sets/files.py b/letta/functions/function_sets/files.py
deleted file mode 100644
index 1c3c72d4..00000000
--- a/letta/functions/function_sets/files.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from typing import TYPE_CHECKING, List, Optional
-
-from letta.functions.types import FileOpenRequest
-
-if TYPE_CHECKING:
- from letta.schemas.agent import AgentState
- from letta.schemas.file import FileMetadata
-
-
-async def open_files(agent_state: "AgentState", file_requests: List[FileOpenRequest], close_all_others: bool = False) -> str:
- """Open one or more files and load their contents into files section in core memory. Maximum of 5 files can be opened simultaneously.
-
- Use this when you want to:
- - Inspect or reference file contents during reasoning
- - View specific portions of large files (e.g. functions or definitions)
- - Replace currently open files with a new set for focused context (via `close_all_others=True`)
-
- Examples:
- Open single file belonging to a directory named `project_utils` (entire content):
- file_requests = [FileOpenRequest(file_name="project_utils/config.py")]
-
- Open multiple files with different view ranges:
- file_requests = [
- FileOpenRequest(file_name="project_utils/config.py", offset=0, length=50), # Lines 1-50
- FileOpenRequest(file_name="project_utils/main.py", offset=100, length=100), # Lines 101-200
- FileOpenRequest(file_name="project_utils/utils.py") # Entire file
- ]
-
- Close all other files and open new ones:
- open_files(agent_state, file_requests, close_all_others=True)
-
- Args:
- file_requests (List[FileOpenRequest]): List of file open requests, each specifying file name and optional view range.
- close_all_others (bool): If True, closes all other currently open files first. Defaults to False.
-
- Returns:
- str: A status message
- """
- raise NotImplementedError("Tool not implemented. Please contact the Letta team.")
-
-
-async def grep_files(
- agent_state: "AgentState",
- pattern: str,
- include: Optional[str] = None,
- context_lines: Optional[int] = 1,
- offset: Optional[int] = None,
-) -> str:
- """
- Searches file contents for pattern matches with surrounding context.
-
- Results are paginated - shows 20 matches per call. The response includes:
- - A summary of total matches and which files contain them
- - The current page of matches (20 at a time)
- - Instructions for viewing more matches using the offset parameter
-
- Example usage:
- First call: grep_files(pattern="TODO")
- Next call: grep_files(pattern="TODO", offset=20) # Shows matches 21-40
-
- Returns search results containing:
- - Summary with total match count and file distribution
- - List of files with match counts per file
- - Current page of matches (up to 20)
- - Navigation hint for next page if more matches exist
-
- Args:
- pattern (str): Keyword or regex pattern to search within file contents.
- include (Optional[str]): Optional keyword or regex pattern to filter filenames to include in the search.
- context_lines (Optional[int]): Number of lines of context to show before and after each match.
- Equivalent to `-C` in grep_files. Defaults to 1.
- offset (Optional[int]): Number of matches to skip before showing results. Used for pagination.
- For example, offset=20 shows matches starting from the 21st match.
- Use offset=0 (or omit) for first page, offset=20 for second page,
- offset=40 for third page, etc. The tool will tell you the exact
- offset to use for the next page.
- """
- raise NotImplementedError("Tool not implemented. Please contact the Letta team.")
-
-
-async def semantic_search_files(agent_state: "AgentState", query: str, limit: int = 5) -> List["FileMetadata"]:
- """
- Searches file contents using semantic meaning rather than exact matches.
-
- Ideal for:
- - Finding conceptually related information across files
- - Discovering relevant content without knowing exact keywords
- - Locating files with similar topics or themes
-
- Args:
- query (str): The search query text to find semantically similar content.
- limit: Maximum number of results to return (default: 5)
-
- Returns:
- List[FileMetadata]: List of matching files.
- """
- raise NotImplementedError("Tool not implemented. Please contact the Letta team.")
diff --git a/letta/functions/function_sets/multi_agent.py b/letta/functions/function_sets/multi_agent.py
deleted file mode 100644
index 17bd5586..00000000
--- a/letta/functions/function_sets/multi_agent.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import asyncio
-import json
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import TYPE_CHECKING, List
-
-from letta.functions.helpers import (
- _send_message_to_all_agents_in_group_async,
- execute_send_message_to_agent,
- extract_send_message_from_steps_messages,
- fire_and_forget_send_to_agent,
-)
-from letta.schemas.enums import MessageRole
-from letta.schemas.message import MessageCreate
-from letta.server.rest_api.utils import get_letta_server
-from letta.settings import settings
-
-if TYPE_CHECKING:
- from letta.agent import Agent
-
-
-def send_message_to_agent_and_wait_for_reply(self: "Agent", message: str, other_agent_id: str) -> str:
- """
- Sends a message to a specific Letta agent within the same organization and waits for a response. The sender's identity is automatically included, so no explicit introduction is needed in the message. This function is designed for two-way communication where a reply is expected.
-
- Args:
- message (str): The content of the message to be sent to the target agent.
- other_agent_id (str): The unique identifier of the target Letta agent.
-
- Returns:
- str: The response from the target agent.
- """
- augmented_message = (
- f"[Incoming message from agent with ID '{self.agent_state.id}' - to reply to this message, "
- f"make sure to use the 'send_message' at the end, and the system will notify the sender of your response] "
- f"{message}"
- )
- messages = [MessageCreate(role=MessageRole.system, content=augmented_message, name=self.agent_state.name)]
-
- return execute_send_message_to_agent(
- sender_agent=self,
- messages=messages,
- other_agent_id=other_agent_id,
- log_prefix="[send_message_to_agent_and_wait_for_reply]",
- )
-
-
-def send_message_to_agents_matching_tags(self: "Agent", message: str, match_all: List[str], match_some: List[str]) -> List[str]:
- """
- Sends a message to all agents within the same organization that match the specified tag criteria. Agents must possess *all* of the tags in `match_all` and *at least one* of the tags in `match_some` to receive the message.
-
- Args:
- message (str): The content of the message to be sent to each matching agent.
- match_all (List[str]): A list of tags that an agent must possess to receive the message.
- match_some (List[str]): A list of tags where an agent must have at least one to qualify.
-
- Returns:
- List[str]: A list of responses from the agents that matched the filtering criteria. Each
- response corresponds to a single agent. Agents that do not respond will not have an entry
- in the returned list.
- """
- server = get_letta_server()
- augmented_message = (
- f"[Incoming message from external Letta agent - to reply to this message, "
- f"make sure to use the 'send_message' at the end, and the system will notify the sender of your response] "
- f"{message}"
- )
-
- # Find matching agents
- matching_agents = server.agent_manager.list_agents_matching_tags(actor=self.user, match_all=match_all, match_some=match_some)
- if not matching_agents:
- return []
-
- def process_agent(agent_id: str) -> str:
- """Loads an agent, formats the message, and executes .step()"""
- actor = self.user # Ensure correct actor context
- agent = server.load_agent(agent_id=agent_id, interface=None, actor=actor)
-
- # Prepare the message
- messages = [MessageCreate(role=MessageRole.system, content=augmented_message, name=self.agent_state.name)]
-
- # Run .step() and return the response
- usage_stats = agent.step(
- input_messages=messages,
- chaining=True,
- max_chaining_steps=None,
- stream=False,
- skip_verify=True,
- metadata=None,
- put_inner_thoughts_first=True,
- )
-
- send_messages = extract_send_message_from_steps_messages(usage_stats.steps_messages, logger=agent.logger)
- response_data = {
- "agent_id": agent_id,
- "response_messages": send_messages if send_messages else [""],
- }
-
- return json.dumps(response_data, indent=2)
-
- # Use ThreadPoolExecutor for parallel execution
- results = []
- with ThreadPoolExecutor(max_workers=settings.multi_agent_concurrent_sends) as executor:
- future_to_agent = {executor.submit(process_agent, agent_state.id): agent_state for agent_state in matching_agents}
-
- for future in as_completed(future_to_agent):
- try:
- results.append(future.result()) # Collect results
- except Exception as e:
- # Log or handle failure for specific agents if needed
- self.logger.exception(f"Error processing agent {future_to_agent[future]}: {e}")
-
- return results
-
-
-def send_message_to_all_agents_in_group(self: "Agent", message: str) -> List[str]:
- """
- Sends a message to all agents within the same multi-agent group.
-
- Args:
- message (str): The content of the message to be sent to each matching agent.
-
- Returns:
- List[str]: A list of responses from the agents that matched the filtering criteria. Each
- response corresponds to a single agent. Agents that do not respond will not have an entry
- in the returned list.
- """
-
- return asyncio.run(_send_message_to_all_agents_in_group_async(self, message))
-
-
-def send_message_to_agent_async(self: "Agent", message: str, other_agent_id: str) -> str:
- """
- Sends a message to a specific Letta agent within the same organization. The sender's identity is automatically included, so no explicit introduction is required in the message. This function does not expect a response from the target agent, making it suitable for notifications or one-way communication.
- Args:
- message (str): The content of the message to be sent to the target agent.
- other_agent_id (str): The unique identifier of the target Letta agent.
- Returns:
- str: A confirmation message indicating the message was successfully sent.
- """
- if settings.environment == "PRODUCTION":
- raise RuntimeError("This tool is not allowed to be run on Letta Cloud.")
-
- message = (
- f"[Incoming message from agent with ID '{self.agent_state.id}' - to reply to this message, "
- f"make sure to use the 'send_message_to_agent_async' tool, or the agent will not receive your message] "
- f"{message}"
- )
- messages = [MessageCreate(role=MessageRole.system, content=message, name=self.agent_state.name)]
-
- # Do the actual fire-and-forget
- fire_and_forget_send_to_agent(
- sender_agent=self,
- messages=messages,
- other_agent_id=other_agent_id,
- log_prefix="[send_message_to_agent_async]",
- use_retries=False, # or True if you want to use _async_send_message_with_retries
- )
-
- # Immediately return to caller
- return "Successfully sent message"
diff --git a/letta/functions/function_sets/voice.py b/letta/functions/function_sets/voice.py
deleted file mode 100644
index dbe16993..00000000
--- a/letta/functions/function_sets/voice.py
+++ /dev/null
@@ -1,80 +0,0 @@
-## Voice chat + sleeptime tools
-from typing import List, Optional
-
-from pydantic import BaseModel, Field
-
-
-def rethink_user_memory(agent_state: "AgentState", new_memory: str) -> None:
- """
- Rewrite memory block for the main agent, new_memory should contain all current information from the block that is not outdated or inconsistent, integrating any new information, resulting in a new memory block that is organized, readable, and comprehensive.
-
- Args:
- new_memory (str): The new memory with information integrated from the memory block. If there is no new information, then this should be the same as the content in the source block.
-
- Returns:
- None: None is always returned as this function does not produce a response.
- """
- # This is implemented directly in the agent loop
- return None
-
-
-def finish_rethinking_memory(agent_state: "AgentState") -> None: # type: ignore
- """
- This function is called when the agent is done rethinking the memory.
-
- Returns:
- Optional[str]: None is always returned as this function does not produce a response.
- """
- return None
-
-
-class MemoryChunk(BaseModel):
- start_index: int = Field(
- ...,
- description="Zero-based index of the first evicted line in this chunk.",
- )
- end_index: int = Field(
- ...,
- description="Zero-based index of the last evicted line (inclusive).",
- )
- context: str = Field(
- ...,
- description="1-3 sentence paraphrase capturing key facts/details, user preferences, or goals that this chunk reveals—written for future retrieval.",
- )
-
-
-def store_memories(agent_state: "AgentState", chunks: List[MemoryChunk]) -> None:
- """
- Persist dialogue that is about to fall out of the agent’s context window.
-
- Args:
- chunks (List[MemoryChunk]):
- Each chunk pinpoints a contiguous block of **evicted** lines and provides a short, forward-looking synopsis (`context`) that will be embedded for future semantic lookup.
-
- Returns:
- None
- """
- # This is implemented directly in the agent loop
- return None
-
-
-def search_memory(
- agent_state: "AgentState",
- convo_keyword_queries: Optional[List[str]],
- start_minutes_ago: Optional[int],
- end_minutes_ago: Optional[int],
-) -> Optional[str]:
- """
- Look in long-term or earlier-conversation memory only when the user asks about something missing from the visible context. The user’s latest utterance is sent automatically as the main query.
-
- Args:
- convo_keyword_queries (Optional[List[str]]): Extra keywords (e.g., order ID, place name). Use *null* if not appropriate for the latest user message.
- start_minutes_ago (Optional[int]): Newer bound of the time window for results, specified in minutes ago. Use *null* if no lower time bound is needed.
- end_minutes_ago (Optional[int]): Older bound of the time window, in minutes ago. Use *null* if no upper bound is needed.
-
- Returns:
- Optional[str]: A formatted string of matching memory entries, or None if no
- relevant memories are found.
- """
- # This is implemented directly in the agent loop
- return None
diff --git a/letta/functions/functions.py b/letta/functions/functions.py
deleted file mode 100644
index c35a48c6..00000000
--- a/letta/functions/functions.py
+++ /dev/null
@@ -1,412 +0,0 @@
-import ast
-import importlib
-import inspect
-from collections.abc import Callable
-from textwrap import dedent # remove indentation
-from types import ModuleType
-from typing import Any, Dict, List, Literal, Optional
-
-from letta.errors import LettaToolCreateError
-from letta.functions.schema_generator import generate_schema
-
-# NOTE: THIS FILE WILL BE DEPRECATED
-
-
-class MockFunction:
- """A mock function object that mimics the attributes expected by generate_schema."""
-
- def __init__(self, name: str, docstring: str, signature: inspect.Signature):
- self.__name__ = name
- self.__doc__ = docstring
- self.__signature__ = signature
-
- def __call__(self, *args, **kwargs):
- raise NotImplementedError("This is a mock function and cannot be called")
-
-
-def _parse_type_annotation(annotation_node: ast.AST, imports_map: Dict[str, Any]) -> Any:
- """Parse an AST type annotation node back into a Python type object."""
- if annotation_node is None:
- return inspect.Parameter.empty
-
- if isinstance(annotation_node, ast.Name):
- type_name = annotation_node.id
- return imports_map.get(type_name, type_name)
-
- elif isinstance(annotation_node, ast.Subscript):
- # Generic type like 'List[str]', 'Optional[int]'
- value_name = annotation_node.value.id if isinstance(annotation_node.value, ast.Name) else str(annotation_node.value)
- origin_type = imports_map.get(value_name, value_name)
-
- # Parse the slice (the part inside the brackets)
- if isinstance(annotation_node.slice, ast.Name):
- slice_type = _parse_type_annotation(annotation_node.slice, imports_map)
- if hasattr(origin_type, "__getitem__"):
- try:
- return origin_type[slice_type]
- except (TypeError, AttributeError):
- pass
- return f"{origin_type}[{slice_type}]"
- else:
- slice_type = _parse_type_annotation(annotation_node.slice, imports_map)
- if hasattr(origin_type, "__getitem__"):
- try:
- return origin_type[slice_type]
- except (TypeError, AttributeError):
- pass
- return f"{origin_type}[{slice_type}]"
-
- else:
- # Fallback - return string representation
- return ast.unparse(annotation_node)
-
-
-def _build_imports_map(tree: ast.AST) -> Dict[str, Any]:
- """Build a mapping of imported names to their Python objects."""
- imports_map = {
- "Optional": Optional,
- "List": List,
- "Dict": Dict,
- "Literal": Literal,
- # Built-in types
- "str": str,
- "int": int,
- "bool": bool,
- "float": float,
- "list": list,
- "dict": dict,
- }
-
- # Try to resolve Pydantic imports if they exist in the source
- for node in ast.walk(tree):
- if isinstance(node, ast.ImportFrom):
- if node.module == "pydantic":
- for alias in node.names:
- if alias.name == "BaseModel":
- try:
- from pydantic import BaseModel
-
- imports_map["BaseModel"] = BaseModel
- except ImportError:
- pass
- elif alias.name == "Field":
- try:
- from pydantic import Field
-
- imports_map["Field"] = Field
- except ImportError:
- pass
- elif isinstance(node, ast.Import):
- for alias in node.names:
- if alias.name == "typing":
- imports_map.update(
- {
- "typing.Optional": Optional,
- "typing.List": List,
- "typing.Dict": Dict,
- "typing.Literal": Literal,
- }
- )
-
- return imports_map
-
-
-def _extract_pydantic_classes(tree: ast.AST, imports_map: Dict[str, Any]) -> Dict[str, Any]:
- """Extract Pydantic model classes from the AST and create them dynamically."""
- pydantic_classes = {}
-
- # Check if BaseModel is available
- if "BaseModel" not in imports_map:
- return pydantic_classes
-
- BaseModel = imports_map["BaseModel"]
- Field = imports_map.get("Field")
-
- # First pass: collect all class definitions
- class_definitions = []
- for node in ast.walk(tree):
- if isinstance(node, ast.ClassDef):
- # Check if this class inherits from BaseModel
- inherits_basemodel = False
- for base in node.bases:
- if isinstance(base, ast.Name) and base.id == "BaseModel":
- inherits_basemodel = True
- break
-
- if inherits_basemodel:
- class_definitions.append(node)
-
- # Create classes in order, handling dependencies
- created_classes = {}
- remaining_classes = class_definitions.copy()
-
- while remaining_classes:
- progress_made = False
-
- for node in remaining_classes.copy():
- class_name = node.name
-
- # Try to create this class
- try:
- fields = {}
- annotations = {}
-
- # Parse class body for field definitions
- for stmt in node.body:
- if isinstance(stmt, ast.AnnAssign) and isinstance(stmt.target, ast.Name):
- field_name = stmt.target.id
-
- # Update imports_map with already created classes for type resolution
- current_imports = {**imports_map, **created_classes}
- field_annotation = _parse_type_annotation(stmt.annotation, current_imports)
- annotations[field_name] = field_annotation
-
- # Handle Field() definitions
- if stmt.value and isinstance(stmt.value, ast.Call):
- if isinstance(stmt.value.func, ast.Name) and stmt.value.func.id == "Field" and Field:
- # Parse Field arguments
- field_kwargs = {}
- for keyword in stmt.value.keywords:
- if keyword.arg == "description":
- if isinstance(keyword.value, ast.Constant):
- field_kwargs["description"] = keyword.value.value
-
- # Handle positional args for required fields
- if stmt.value.args:
- try:
- default_val = ast.literal_eval(stmt.value.args[0])
- if default_val == ...: # Ellipsis means required
- pass # Field is required, no default
- else:
- field_kwargs["default"] = default_val
- except:
- pass
-
- fields[field_name] = Field(**field_kwargs)
- else:
- # Not a Field call, try to evaluate the default value
- try:
- default_val = ast.literal_eval(stmt.value)
- fields[field_name] = default_val
- except:
- pass
-
- # Create the dynamic Pydantic model
- model_dict = {"__annotations__": annotations, **fields}
-
- DynamicModel = type(class_name, (BaseModel,), model_dict)
- created_classes[class_name] = DynamicModel
- remaining_classes.remove(node)
- progress_made = True
-
- except Exception:
- # This class might depend on others, try later
- continue
-
- if not progress_made:
- # If we can't make progress, create remaining classes without proper field types
- for node in remaining_classes:
- class_name = node.name
- # Create a minimal mock class
- MockModel = type(class_name, (BaseModel,), {})
- created_classes[class_name] = MockModel
- break
-
- return created_classes
-
-
-def _parse_function_from_source(source_code: str, desired_name: Optional[str] = None) -> MockFunction:
- """Parse a function from source code without executing it."""
- try:
- tree = ast.parse(source_code)
- except SyntaxError as e:
- raise LettaToolCreateError(f"Failed to parse source code: {e}")
-
- # Build imports mapping and find pydantic classes
- imports_map = _build_imports_map(tree)
- pydantic_classes = _extract_pydantic_classes(tree, imports_map)
- imports_map.update(pydantic_classes)
-
- # Find function definitions
- functions = []
- for node in ast.walk(tree):
- if isinstance(node, ast.FunctionDef):
- functions.append(node)
-
- if not functions:
- raise LettaToolCreateError("No functions found in source code")
-
- # Use the last function (matching original behavior)
- func_node = functions[-1]
-
- # Extract function name
- func_name = func_node.name
-
- # Extract docstring
- docstring = None
- if (
- func_node.body
- and isinstance(func_node.body[0], ast.Expr)
- and isinstance(func_node.body[0].value, ast.Constant)
- and isinstance(func_node.body[0].value.value, str)
- ):
- docstring = func_node.body[0].value.value
-
- if not docstring:
- raise LettaToolCreateError(f"Function {func_name} missing docstring")
-
- # Build function signature
- parameters = []
- for arg in func_node.args.args:
- param_name = arg.arg
- param_annotation = _parse_type_annotation(arg.annotation, imports_map)
-
- # Handle default values
- defaults_offset = len(func_node.args.args) - len(func_node.args.defaults)
- param_index = func_node.args.args.index(arg)
-
- if param_index >= defaults_offset:
- default_index = param_index - defaults_offset
- try:
- default_value = ast.literal_eval(func_node.args.defaults[default_index])
- except (ValueError, TypeError):
- # Can't evaluate the default, use Parameter.empty
- default_value = inspect.Parameter.empty
- param = inspect.Parameter(
- param_name, inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=param_annotation, default=default_value
- )
- else:
- param = inspect.Parameter(param_name, inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=param_annotation)
- parameters.append(param)
-
- signature = inspect.Signature(parameters)
-
- return MockFunction(func_name, docstring, signature)
-
-
-def derive_openai_json_schema(source_code: str, name: Optional[str] = None) -> dict:
- """Derives the OpenAI JSON schema for a given function source code.
-
- Parses the source code statically to extract function signature and docstring,
- then generates the schema without executing any code.
-
- Limitations:
- - Complex nested Pydantic models with forward references may not be fully supported
- - Only basic Pydantic Field definitions are parsed (description, ellipsis for required)
- - Simple types (str, int, bool, float, list, dict) and basic Pydantic models work well
- """
- try:
- # Parse the function from source code without executing it
- mock_func = _parse_function_from_source(source_code, name)
-
- # Generate schema using the mock function
- try:
- schema = generate_schema(mock_func, name=name)
- return schema
- except TypeError as e:
- raise LettaToolCreateError(f"Type error in schema generation: {str(e)}")
- except ValueError as e:
- raise LettaToolCreateError(f"Value error in schema generation: {str(e)}")
- except Exception as e:
- raise LettaToolCreateError(f"Unexpected error in schema generation: {str(e)}")
-
- except Exception as e:
- import traceback
-
- traceback.print_exc()
- raise LettaToolCreateError(f"Schema generation failed: {str(e)}") from e
-
-
-def parse_source_code(func) -> str:
- """Parse the source code of a function and remove indendation"""
- source_code = dedent(inspect.getsource(func))
- return source_code
-
-
-# TODO (cliandy) refactor below two funcs
-def get_function_from_module(module_name: str, function_name: str) -> Callable[..., Any]:
- """
- Dynamically imports a function from a specified module.
-
- Args:
- module_name (str): The name of the module to import (e.g., 'base').
- function_name (str): The name of the function to retrieve.
-
- Returns:
- Callable: The imported function.
-
- Raises:
- ModuleNotFoundError: If the specified module cannot be found.
- AttributeError: If the function is not found in the module.
- """
- try:
- # Dynamically import the module
- module = importlib.import_module(module_name)
- # Retrieve the function
- return getattr(module, function_name)
- except ModuleNotFoundError:
- raise ModuleNotFoundError(f"Module '{module_name}' not found.")
- except AttributeError:
- raise AttributeError(f"Function '{function_name}' not found in module '{module_name}'.")
-
-
-def get_json_schema_from_module(module_name: str, function_name: str) -> dict:
- """
- Dynamically loads a specific function from a module and generates its JSON schema.
-
- Args:
- module_name (str): The name of the module to import (e.g., 'base').
- function_name (str): The name of the function to retrieve.
-
- Returns:
- dict: The JSON schema for the specified function.
-
- Raises:
- ModuleNotFoundError: If the specified module cannot be found.
- AttributeError: If the function is not found in the module.
- ValueError: If the attribute is not a user-defined function.
- """
- try:
- # Dynamically import the module
- module = importlib.import_module(module_name)
-
- # Retrieve the function
- attr = getattr(module, function_name, None)
-
- # Check if it's a user-defined function
- if not (inspect.isfunction(attr) and attr.__module__ == module.__name__):
- raise ValueError(f"'{function_name}' is not a user-defined function in module '{module_name}'")
-
- # Generate schema (assuming a `generate_schema` function exists)
- generated_schema = generate_schema(attr)
-
- return generated_schema
- except ModuleNotFoundError:
- raise ModuleNotFoundError(f"Module '{module_name}' not found.")
- except AttributeError:
- raise AttributeError(f"Function '{function_name}' not found in module '{module_name}'.")
-
-
-def load_function_set(module: ModuleType) -> dict:
- """Load the functions and generate schema for them, given a module object"""
- function_dict = {}
-
- for attr_name in dir(module):
- # Get the attribute
- attr = getattr(module, attr_name)
-
- # Check if it's a callable function and not a built-in or special method
- if inspect.isfunction(attr) and attr.__module__ == module.__name__:
- if attr_name in function_dict:
- raise ValueError(f"Found a duplicate of function name '{attr_name}'")
-
- generated_schema = generate_schema(attr)
- function_dict[attr_name] = {
- "module": inspect.getsource(module),
- "python_function": attr,
- "json_schema": generated_schema,
- }
-
- if len(function_dict) == 0:
- raise ValueError(f"No functions found in module {module}")
- return function_dict
diff --git a/letta/functions/helpers.py b/letta/functions/helpers.py
deleted file mode 100644
index dc1a3b0b..00000000
--- a/letta/functions/helpers.py
+++ /dev/null
@@ -1,615 +0,0 @@
-import asyncio
-import json
-import logging
-import threading
-from random import uniform
-from typing import Any, Dict, List, Optional, Type, Union
-
-import humps
-from pydantic import BaseModel, Field, create_model
-
-from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG
-from letta.functions.interface import MultiAgentMessagingInterface
-from letta.orm.errors import NoResultFound
-from letta.schemas.enums import MessageRole
-from letta.schemas.letta_message import AssistantMessage
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.user import User
-from letta.server.rest_api.utils import get_letta_server
-from letta.settings import settings
-
-
-# TODO needed?
-def generate_mcp_tool_wrapper(mcp_tool_name: str) -> tuple[str, str]:
- wrapper_function_str = f"""\
-def {mcp_tool_name}(**kwargs):
- raise RuntimeError("Something went wrong - we should never be using the persisted source code for MCP. Please reach out to Letta team")
-"""
-
- # Compile safety check
- _assert_code_gen_compilable(wrapper_function_str.strip())
-
- return mcp_tool_name, wrapper_function_str.strip()
-
-
-def generate_langchain_tool_wrapper(
- tool: "LangChainBaseTool", additional_imports_module_attr_map: dict[str, str] = None
-) -> tuple[str, str]:
- tool_name = tool.__class__.__name__
- import_statement = f"from langchain_community.tools import {tool_name}"
- extra_module_imports = _generate_import_code(additional_imports_module_attr_map)
-
- # Safety check that user has passed in all required imports:
- _assert_all_classes_are_imported(tool, additional_imports_module_attr_map)
-
- tool_instantiation = f"tool = {generate_imported_tool_instantiation_call_str(tool)}"
- run_call = "return tool._run(**kwargs)"
- func_name = humps.decamelize(tool_name)
-
- # Combine all parts into the wrapper function
- wrapper_function_str = f"""
-def {func_name}(**kwargs):
- import importlib
- {import_statement}
- {extra_module_imports}
- {tool_instantiation}
- {run_call}
-"""
-
- # Compile safety check
- _assert_code_gen_compilable(wrapper_function_str)
-
- return func_name, wrapper_function_str
-
-
-def _assert_code_gen_compilable(code_str):
- try:
- compile(code_str, "", "exec")
- except SyntaxError as e:
- print(f"Syntax error in code: {e}")
-
-
-def _assert_all_classes_are_imported(tool: Union["LangChainBaseTool"], additional_imports_module_attr_map: dict[str, str]) -> None:
- # Safety check that user has passed in all required imports:
- tool_name = tool.__class__.__name__
- current_class_imports = {tool_name}
- if additional_imports_module_attr_map:
- current_class_imports.update(set(additional_imports_module_attr_map.values()))
- required_class_imports = set(_find_required_class_names_for_import(tool))
-
- if not current_class_imports.issuperset(required_class_imports):
- err_msg = f"[ERROR] You are missing module_attr pairs in `additional_imports_module_attr_map`. Currently, you have imports for {current_class_imports}, but the required classes for import are {required_class_imports}"
- print(err_msg)
- raise RuntimeError(err_msg)
-
-
-def _find_required_class_names_for_import(obj: Union["LangChainBaseTool", BaseModel]) -> list[str]:
- """
- Finds all the class names for required imports when instantiating the `obj`.
- NOTE: This does not return the full import path, only the class name.
-
- We accomplish this by running BFS and deep searching all the BaseModel objects in the obj parameters.
- """
- class_names = {obj.__class__.__name__}
- queue = [obj]
-
- while queue:
- # Get the current object we are inspecting
- curr_obj = queue.pop()
-
- # Collect all possible candidates for BaseModel objects
- candidates = []
- if _is_base_model(curr_obj):
- # If it is a base model, we get all the values of the object parameters
- # i.e., if obj('b' = ), we would want to inspect
- fields = dict(curr_obj)
- # Generate code for each field, skipping empty or None values
- candidates = list(fields.values())
- elif isinstance(curr_obj, dict):
- # If it is a dictionary, we get all the values
- # i.e., if obj = {'a': 3, 'b': }, we would want to inspect
- candidates = list(curr_obj.values())
- elif isinstance(curr_obj, list):
- # If it is a list, we inspect all the items in the list
- # i.e., if obj = ['a', 3, None, ], we would want to inspect
- candidates = curr_obj
-
- # Filter out all candidates that are not BaseModels
- # In the list example above, ['a', 3, None, ], we want to filter out 'a', 3, and None
- candidates = filter(lambda x: _is_base_model(x), candidates)
-
- # Classic BFS here
- for c in candidates:
- c_name = c.__class__.__name__
- if c_name not in class_names:
- class_names.add(c_name)
- queue.append(c)
-
- return list(class_names)
-
-
-def generate_imported_tool_instantiation_call_str(obj: Any) -> Optional[str]:
- if isinstance(obj, (int, float, str, bool, type(None))):
- # This is the base case
- # If it is a basic Python type, we trivially return the string version of that value
- # Handle basic types
- return repr(obj)
- elif _is_base_model(obj):
- # Otherwise, if it is a BaseModel
- # We want to pull out all the parameters, and reformat them into strings
- # e.g. {arg}={value}
- # The reason why this is recursive, is because the value can be another BaseModel that we need to stringify
- model_name = obj.__class__.__name__
- fields = obj.dict()
- # Generate code for each field, skipping empty or None values
- field_assignments = []
- for arg, value in fields.items():
- python_string = generate_imported_tool_instantiation_call_str(value)
- if python_string:
- field_assignments.append(f"{arg}={python_string}")
-
- assignments = ", ".join(field_assignments)
- return f"{model_name}({assignments})"
- elif isinstance(obj, dict):
- # Inspect each of the items in the dict and stringify them
- # This is important because the dictionary may contain other BaseModels
- dict_items = []
- for k, v in obj.items():
- python_string = generate_imported_tool_instantiation_call_str(v)
- if python_string:
- dict_items.append(f"{repr(k)}: {python_string}")
-
- joined_items = ", ".join(dict_items)
- return f"{{{joined_items}}}"
- elif isinstance(obj, list):
- # Inspect each of the items in the list and stringify them
- # This is important because the list may contain other BaseModels
- list_items = [generate_imported_tool_instantiation_call_str(v) for v in obj]
- filtered_list_items = list(filter(None, list_items))
- list_items = ", ".join(filtered_list_items)
- return f"[{list_items}]"
- else:
- # Otherwise, if it is none of the above, that usually means it is a custom Python class that is NOT a BaseModel
- # Thus, we cannot get enough information about it to stringify it
- # This may cause issues, but we are making the assumption that any of these custom Python types are handled correctly by the parent library, such as LangChain
- # An example would be that WikipediaAPIWrapper has an argument that is a wikipedia (pip install wikipedia) object
- # We cannot stringify this easily, but WikipediaAPIWrapper handles the setting of this parameter internally
- # This assumption seems fair to me, since usually they are external imports, and LangChain should be bundling those as module-level imports within the tool
- # We throw a warning here anyway and provide the class name
- print(
- f"[WARNING] Skipping parsing unknown class {obj.__class__.__name__} (does not inherit from the Pydantic BaseModel and is not a basic Python type)"
- )
- if obj.__class__.__name__ == "function":
- import inspect
-
- print(inspect.getsource(obj))
-
- return None
-
-
-def _is_base_model(obj: Any):
- return isinstance(obj, BaseModel)
-
-
-def _generate_import_code(module_attr_map: Optional[dict]):
- if not module_attr_map:
- return ""
-
- code_lines = []
- for module, attr in module_attr_map.items():
- module_name = module.split(".")[-1]
- code_lines.append(f"# Load the module\n {module_name} = importlib.import_module('{module}')")
- code_lines.append(f" # Access the {attr} from the module")
- code_lines.append(f" {attr} = getattr({module_name}, '{attr}')")
- return "\n".join(code_lines)
-
-
-def _parse_letta_response_for_assistant_message(
- target_agent_id: str,
- letta_response: LettaResponse,
-) -> Optional[str]:
- messages = []
- for m in letta_response.messages:
- if isinstance(m, AssistantMessage):
- messages.append(m.content)
-
- if messages:
- messages_str = "\n".join(messages)
- return f"{target_agent_id} said: '{messages_str}'"
- else:
- return f"No response from {target_agent_id}"
-
-
-async def async_execute_send_message_to_agent(
- sender_agent: "Agent",
- messages: List[MessageCreate],
- other_agent_id: str,
- log_prefix: str,
-) -> Optional[str]:
- """
- Async helper to:
- 1) validate the target agent exists & is in the same org,
- 2) send a message via _async_send_message_with_retries.
- """
- server = get_letta_server()
-
- # 1. Validate target agent
- try:
- server.agent_manager.get_agent_by_id(agent_id=other_agent_id, actor=sender_agent.user)
- except NoResultFound:
- raise ValueError(f"Target agent {other_agent_id} either does not exist or is not in org ({sender_agent.user.organization_id}).")
-
- # 2. Use your async retry logic
- return await _async_send_message_with_retries(
- server=server,
- sender_agent=sender_agent,
- target_agent_id=other_agent_id,
- messages=messages,
- max_retries=settings.multi_agent_send_message_max_retries,
- timeout=settings.multi_agent_send_message_timeout,
- logging_prefix=log_prefix,
- )
-
-
-def execute_send_message_to_agent(
- sender_agent: "Agent",
- messages: List[MessageCreate],
- other_agent_id: str,
- log_prefix: str,
-) -> Optional[str]:
- """
- Synchronous wrapper that calls `async_execute_send_message_to_agent` using asyncio.run.
- This function must be called from a synchronous context (i.e., no running event loop).
- """
- return asyncio.run(async_execute_send_message_to_agent(sender_agent, messages, other_agent_id, log_prefix))
-
-
-async def _send_message_to_agent_no_stream(
- server: "SyncServer",
- agent_id: str,
- actor: User,
- messages: List[MessageCreate],
- metadata: Optional[dict] = None,
-) -> LettaResponse:
- """
- A simpler helper to send messages to a single agent WITHOUT streaming.
- Returns a LettaResponse containing the final messages.
- """
- interface = MultiAgentMessagingInterface()
- if metadata:
- interface.metadata = metadata
-
- # Offload the synchronous `send_messages` call
- usage_stats = await asyncio.to_thread(
- server.send_messages,
- actor=actor,
- agent_id=agent_id,
- input_messages=messages,
- interface=interface,
- metadata=metadata,
- )
-
- final_messages = interface.get_captured_send_messages()
- return LettaResponse(
- messages=final_messages,
- stop_reason=LettaStopReason(stop_reason=StopReasonType.end_turn.value),
- usage=usage_stats,
- )
-
-
-async def _async_send_message_with_retries(
- server: "SyncServer",
- sender_agent: "Agent",
- target_agent_id: str,
- messages: List[MessageCreate],
- max_retries: int,
- timeout: int,
- logging_prefix: Optional[str] = None,
-) -> str:
- logging_prefix = logging_prefix or "[_async_send_message_with_retries]"
-
- for attempt in range(1, max_retries + 1):
- try:
- response = await asyncio.wait_for(
- _send_message_to_agent_no_stream(
- server=server,
- agent_id=target_agent_id,
- actor=sender_agent.user,
- messages=messages,
- ),
- timeout=timeout,
- )
-
- # Then parse out the assistant message
- assistant_message = _parse_letta_response_for_assistant_message(target_agent_id, response)
- if assistant_message:
- sender_agent.logger.info(f"{logging_prefix} - {assistant_message}")
- return assistant_message
- else:
- msg = f"(No response from agent {target_agent_id})"
- sender_agent.logger.info(f"{logging_prefix} - {msg}")
- return msg
-
- except asyncio.TimeoutError:
- error_msg = f"(Timeout on attempt {attempt}/{max_retries} for agent {target_agent_id})"
- sender_agent.logger.warning(f"{logging_prefix} - {error_msg}")
-
- except Exception as e:
- error_msg = f"(Error on attempt {attempt}/{max_retries} for agent {target_agent_id}: {e})"
- sender_agent.logger.warning(f"{logging_prefix} - {error_msg}")
-
- # Exponential backoff before retrying
- if attempt < max_retries:
- backoff = uniform(0.5, 2) * (2**attempt)
- sender_agent.logger.warning(f"{logging_prefix} - Retrying the agent-to-agent send_message...sleeping for {backoff}")
- await asyncio.sleep(backoff)
- else:
- sender_agent.logger.error(f"{logging_prefix} - Fatal error: {error_msg}")
- raise Exception(error_msg)
-
-
-def fire_and_forget_send_to_agent(
- sender_agent: "Agent",
- messages: List[MessageCreate],
- other_agent_id: str,
- log_prefix: str,
- use_retries: bool = False,
-) -> None:
- """
- Fire-and-forget send of messages to a specific agent.
- Returns immediately in the calling thread, never blocks.
-
- Args:
- sender_agent (Agent): The sender agent object.
- server: The Letta server instance
- messages (List[MessageCreate]): The messages to send.
- other_agent_id (str): The ID of the target agent.
- log_prefix (str): Prefix for logging.
- use_retries (bool): If True, uses _async_send_message_with_retries;
- if False, calls server.send_message_to_agent directly.
- """
- server = get_letta_server()
-
- # 1) Validate the target agent (raises ValueError if not in same org)
- try:
- server.agent_manager.get_agent_by_id(agent_id=other_agent_id, actor=sender_agent.user)
- except NoResultFound:
- raise ValueError(
- f"The passed-in agent_id {other_agent_id} either does not exist, "
- f"or does not belong to the same org ({sender_agent.user.organization_id})."
- )
-
- # 2) Define the async coroutine to run
- async def background_task():
- try:
- if use_retries:
- result = await _async_send_message_with_retries(
- server=server,
- sender_agent=sender_agent,
- target_agent_id=other_agent_id,
- messages=messages,
- max_retries=settings.multi_agent_send_message_max_retries,
- timeout=settings.multi_agent_send_message_timeout,
- logging_prefix=log_prefix,
- )
- sender_agent.logger.info(f"{log_prefix} fire-and-forget success with retries: {result}")
- else:
- # Direct call to server.send_message_to_agent, no retry logic
- await server.send_message_to_agent(
- agent_id=other_agent_id,
- actor=sender_agent.user,
- input_messages=messages,
- stream_steps=False,
- stream_tokens=False,
- use_assistant_message=True,
- assistant_message_tool_name=DEFAULT_MESSAGE_TOOL,
- assistant_message_tool_kwarg=DEFAULT_MESSAGE_TOOL_KWARG,
- )
- sender_agent.logger.info(f"{log_prefix} fire-and-forget success (no retries).")
- except Exception as e:
- sender_agent.logger.error(f"{log_prefix} fire-and-forget send failed: {e}")
-
- # 3) Helper to run the coroutine in a brand-new event loop in a separate thread
- def run_in_background_thread(coro):
- def runner():
- loop = asyncio.new_event_loop()
- try:
- asyncio.set_event_loop(loop)
- loop.run_until_complete(coro)
- finally:
- loop.close()
-
- thread = threading.Thread(target=runner, daemon=True)
- thread.start()
-
- # 4) Try to schedule the coroutine in an existing loop, else spawn a thread
- try:
- loop = asyncio.get_running_loop()
- # If we get here, a loop is running; schedule the coroutine in background
- loop.create_task(background_task())
- except RuntimeError:
- # Means no event loop is running in this thread
- run_in_background_thread(background_task())
-
-
-async def _send_message_to_agents_matching_tags_async(
- sender_agent: "Agent", server: "SyncServer", messages: List[MessageCreate], matching_agents: List["AgentState"]
-) -> List[str]:
- async def _send_single(agent_state):
- return await _async_send_message_with_retries(
- server=server,
- sender_agent=sender_agent,
- target_agent_id=agent_state.id,
- messages=messages,
- max_retries=3,
- timeout=settings.multi_agent_send_message_timeout,
- )
-
- tasks = [asyncio.create_task(_send_single(agent_state)) for agent_state in matching_agents]
- results = await asyncio.gather(*tasks, return_exceptions=True)
- final = []
- for r in results:
- if isinstance(r, Exception):
- final.append(str(r))
- else:
- final.append(r)
-
- return final
-
-
-async def _send_message_to_all_agents_in_group_async(sender_agent: "Agent", message: str) -> List[str]:
- server = get_letta_server()
-
- augmented_message = (
- f"[Incoming message from agent with ID '{sender_agent.agent_state.id}' - to reply to this message, "
- f"make sure to use the 'send_message' at the end, and the system will notify the sender of your response] "
- f"{message}"
- )
-
- worker_agents_ids = sender_agent.agent_state.multi_agent_group.agent_ids
- worker_agents = [server.agent_manager.get_agent_by_id(agent_id=agent_id, actor=sender_agent.user) for agent_id in worker_agents_ids]
-
- # Create a system message
- messages = [MessageCreate(role=MessageRole.system, content=augmented_message, name=sender_agent.agent_state.name)]
-
- # Possibly limit concurrency to avoid meltdown:
- sem = asyncio.Semaphore(settings.multi_agent_concurrent_sends)
-
- async def _send_single(agent_state):
- async with sem:
- return await _async_send_message_with_retries(
- server=server,
- sender_agent=sender_agent,
- target_agent_id=agent_state.id,
- messages=messages,
- max_retries=3,
- timeout=settings.multi_agent_send_message_timeout,
- )
-
- tasks = [asyncio.create_task(_send_single(agent_state)) for agent_state in worker_agents]
- results = await asyncio.gather(*tasks, return_exceptions=True)
- final = []
- for r in results:
- if isinstance(r, Exception):
- final.append(str(r))
- else:
- final.append(r)
-
- return final
-
-
-def generate_model_from_args_json_schema(schema: Dict[str, Any]) -> Type[BaseModel]:
- """Creates a Pydantic model from a JSON schema.
-
- Args:
- schema: The JSON schema dictionary
-
- Returns:
- A Pydantic model class
- """
- # First create any nested models from $defs in reverse order to handle dependencies
- nested_models = {}
- if "$defs" in schema:
- for name, model_schema in reversed(list(schema.get("$defs", {}).items())):
- nested_models[name] = _create_model_from_schema(name, model_schema, nested_models)
-
- # Create and return the main model
- return _create_model_from_schema(schema.get("title", "DynamicModel"), schema, nested_models)
-
-
-def _create_model_from_schema(name: str, model_schema: Dict[str, Any], nested_models: Dict[str, Type[BaseModel]] = None) -> Type[BaseModel]:
- fields = {}
- for field_name, field_schema in model_schema["properties"].items():
- field_type = _get_field_type(field_schema, nested_models)
- required = field_name in model_schema.get("required", [])
- description = field_schema.get("description", "") # Get description or empty string
- fields[field_name] = (field_type, Field(..., description=description) if required else Field(None, description=description))
-
- return create_model(name, **fields)
-
-
-def _get_field_type(field_schema: Dict[str, Any], nested_models: Dict[str, Type[BaseModel]] = None) -> Any:
- """Helper to convert JSON schema types to Python types."""
- if field_schema.get("type") == "string":
- return str
- elif field_schema.get("type") == "integer":
- return int
- elif field_schema.get("type") == "number":
- return float
- elif field_schema.get("type") == "boolean":
- return bool
- elif field_schema.get("type") == "array":
- item_type = field_schema["items"].get("$ref", "").split("/")[-1]
- if item_type and nested_models and item_type in nested_models:
- return List[nested_models[item_type]]
- return List[_get_field_type(field_schema["items"], nested_models)]
- elif field_schema.get("type") == "object":
- if "$ref" in field_schema:
- ref_type = field_schema["$ref"].split("/")[-1]
- if nested_models and ref_type in nested_models:
- return nested_models[ref_type]
- elif "additionalProperties" in field_schema:
- # TODO: This is totally GPT generated and I'm not sure it works
- # TODO: This is done to quickly patch some tests, we should nuke this whole pathway asap
- ap = field_schema["additionalProperties"]
-
- if ap is True:
- return dict
- elif ap is False:
- raise ValueError("additionalProperties=false is not supported.")
- else:
- # Try resolving nested type
- nested_type = _get_field_type(ap, nested_models)
- # If nested_type is Any, fall back to `dict`, or raise, depending on how strict you want to be
- if nested_type == Any:
- return dict
- return Dict[str, nested_type]
-
- return dict
- elif field_schema.get("$ref") is not None:
- ref_type = field_schema["$ref"].split("/")[-1]
- if nested_models and ref_type in nested_models:
- return nested_models[ref_type]
- else:
- raise ValueError(f"Reference {ref_type} not found in nested models")
- elif field_schema.get("anyOf") is not None:
- types = []
- has_null = False
- for type_option in field_schema["anyOf"]:
- if type_option.get("type") == "null":
- has_null = True
- else:
- types.append(_get_field_type(type_option, nested_models))
- # If we have exactly one type and null, make it Optional
- if has_null and len(types) == 1:
- return Optional[types[0]]
- # Otherwise make it a Union of all types
- else:
- return Union[tuple(types)]
- raise ValueError(f"Unable to convert pydantic field schema to type: {field_schema}")
-
-
-def extract_send_message_from_steps_messages(
- steps_messages: List[List[Message]],
- agent_send_message_tool_name: str = DEFAULT_MESSAGE_TOOL,
- agent_send_message_tool_kwarg: str = DEFAULT_MESSAGE_TOOL_KWARG,
- logger: Optional[logging.Logger] = None,
-) -> List[str]:
- extracted_messages = []
-
- for step in steps_messages:
- for message in step:
- if message.tool_calls:
- for tool_call in message.tool_calls:
- if tool_call.function.name == agent_send_message_tool_name:
- try:
- # Parse arguments to extract the "message" field
- arguments = json.loads(tool_call.function.arguments)
- if agent_send_message_tool_kwarg in arguments:
- extracted_messages.append(arguments[agent_send_message_tool_kwarg])
- except json.JSONDecodeError:
- logger.error(f"Failed to parse arguments for tool call: {tool_call.id}")
-
- return extracted_messages
diff --git a/letta/functions/interface.py b/letta/functions/interface.py
deleted file mode 100644
index 2e284de3..00000000
--- a/letta/functions/interface.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import json
-from typing import List, Optional
-
-from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG
-from letta.interface import AgentInterface
-from letta.schemas.letta_message import AssistantMessage, LettaMessage
-from letta.schemas.message import Message
-
-
-class MultiAgentMessagingInterface(AgentInterface):
- """
- A minimal interface that captures *only* calls to the 'send_message' function
- by inspecting msg_obj.tool_calls. We parse out the 'message' field from the
- JSON function arguments and store it as an AssistantMessage.
- """
-
- def __init__(self):
- self._captured_messages: List[AssistantMessage] = []
- self.metadata = {}
-
- def internal_monologue(self, msg: str, msg_obj: Optional[Message] = None, chunk_index: Optional[int] = None):
- """Ignore internal monologue."""
-
- def assistant_message(self, msg: str, msg_obj: Optional[Message] = None):
- """Ignore normal assistant messages (only capturing send_message calls)."""
-
- def function_message(self, msg: str, msg_obj: Optional[Message] = None, chunk_index: Optional[int] = None):
- """
- Called whenever the agent logs a function call. We'll inspect msg_obj.tool_calls:
- - If tool_calls include a function named 'send_message', parse its arguments
- - Extract the 'message' field
- - Save it as an AssistantMessage in self._captured_messages
- """
- if not msg_obj or not msg_obj.tool_calls:
- return
-
- for tool_call in msg_obj.tool_calls:
- if not tool_call.function:
- continue
- if tool_call.function.name != DEFAULT_MESSAGE_TOOL:
- # Skip any other function calls
- continue
-
- # Now parse the JSON in tool_call.function.arguments
- func_args_str = tool_call.function.arguments or ""
- try:
- data = json.loads(func_args_str)
- # Extract the 'message' key if present
- content = data.get(DEFAULT_MESSAGE_TOOL_KWARG, str(data))
- except json.JSONDecodeError:
- # If we can't parse, store the raw string
- content = func_args_str
-
- # Store as an AssistantMessage
- new_msg = AssistantMessage(
- id=msg_obj.id,
- date=msg_obj.created_at,
- content=content,
- )
- self._captured_messages.append(new_msg)
-
- def user_message(self, msg: str, msg_obj: Optional[Message] = None):
- """Ignore user messages."""
-
- def step_complete(self):
- """No streaming => no step boundaries."""
-
- def step_yield(self):
- """No streaming => no final yield needed."""
-
- def get_captured_send_messages(self) -> List[LettaMessage]:
- """
- Returns only the messages extracted from 'send_message' calls.
- """
- return self._captured_messages
diff --git a/letta/functions/mcp_client/exceptions.py b/letta/functions/mcp_client/exceptions.py
deleted file mode 100644
index 94bf164c..00000000
--- a/letta/functions/mcp_client/exceptions.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class MCPTimeoutError(RuntimeError):
- """Custom exception raised when an MCP operation times out."""
-
- def __init__(self, operation: str, server_name: str, timeout: float):
- message = f"Timed out while {operation} for MCP server {server_name} (timeout={timeout}s)."
- super().__init__(message)
diff --git a/letta/functions/mcp_client/types.py b/letta/functions/mcp_client/types.py
deleted file mode 100644
index b1cd27e2..00000000
--- a/letta/functions/mcp_client/types.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import re
-from abc import abstractmethod
-from enum import Enum
-from typing import Dict, List, Optional
-
-from mcp import Tool
-from pydantic import BaseModel, Field
-
-from letta.utils import get_logger
-
-# MCP Authentication Constants
-MCP_AUTH_HEADER_AUTHORIZATION = "Authorization"
-MCP_AUTH_TOKEN_BEARER_PREFIX = "Bearer"
-TEMPLATED_VARIABLE_REGEX = (
- r"\{\{\s*([A-Z_][A-Z0-9_]*)\s*(?:\|\s*([^}]+?)\s*)?\}\}" # Allows for optional whitespace around the variable name and default value
-)
-
-logger = get_logger(__name__)
-
-
-class MCPToolHealth(BaseModel):
- """Health status for an MCP tool's schema."""
-
- # TODO: @jnjpng use the enum provided in schema_validator.py
- status: str = Field(..., description="Schema health status: STRICT_COMPLIANT, NON_STRICT_ONLY, or INVALID")
- reasons: List[str] = Field(default_factory=list, description="List of reasons for the health status")
-
-
-class MCPTool(Tool):
- """A simple wrapper around MCP's tool definition (to avoid conflict with our own)"""
-
- # Optional health information added at runtime
- health: Optional[MCPToolHealth] = Field(None, description="Schema health status for OpenAI strict mode")
-
-
-class MCPServerType(str, Enum):
- SSE = "sse"
- STDIO = "stdio"
- STREAMABLE_HTTP = "streamable_http"
-
-
-class BaseServerConfig(BaseModel):
- server_name: str = Field(..., description="The name of the server")
- type: MCPServerType
-
- def is_templated_tool_variable(self, value: str) -> bool:
- """
- Check if string contains templated variables.
-
- Args:
- value: The value string to check
-
- Returns:
- True if the value contains templated variables in the format {{ VARIABLE_NAME }} or {{ VARIABLE_NAME | default }}, False otherwise
- """
- return bool(re.search(TEMPLATED_VARIABLE_REGEX, value))
-
- def get_tool_variable(self, value: str, environment_variables: Dict[str, str]) -> Optional[str]:
- """
- Replace templated variables in a value string with their values from environment variables.
- Supports fallback/default values with pipe syntax.
-
- Args:
- value: The value string that may contain templated variables (e.g., "Bearer {{ API_KEY | default_token }}")
- environment_variables: Dictionary of environment variables
-
- Returns:
- The string with templated variables replaced, or None if no templated variables found
- """
-
- # If no templated variables found or default value provided, return the original value
- if not self.is_templated_tool_variable(value):
- return value
-
- def replace_template(match):
- variable_name = match.group(1)
- default_value = match.group(2) if match.group(2) else None
-
- # Try to get the value from environment variables
- env_value = environment_variables.get(variable_name) if environment_variables else None
-
- # Return environment value if found, otherwise return default value, otherwise return empty string
- if env_value is not None:
- return env_value
- elif default_value is not None:
- return default_value
- else:
- # If no environment value and no default, return the original template
- return match.group(0)
-
- # Replace all templated variables in the token
- result = re.sub(TEMPLATED_VARIABLE_REGEX, replace_template, value)
-
- # If the result still contains unreplaced templates, just return original value
- if re.search(TEMPLATED_VARIABLE_REGEX, result):
- logger.warning(f"Unable to resolve templated variable in value: {value}")
- return value
-
- return result
-
- def resolve_custom_headers(
- self, custom_headers: Optional[Dict[str, str]], environment_variables: Optional[Dict[str, str]] = None
- ) -> Optional[Dict[str, str]]:
- """
- Resolve templated variables in custom headers dictionary.
-
- Args:
- custom_headers: Dictionary of custom headers that may contain templated variables
- environment_variables: Dictionary of environment variables for resolving templates
-
- Returns:
- Dictionary with resolved header values, or None if custom_headers is None
- """
- if custom_headers is None:
- return None
-
- resolved_headers = {}
- for key, value in custom_headers.items():
- # Resolve templated variables in each header value
- if self.is_templated_tool_variable(value):
- resolved_headers[key] = self.get_tool_variable(value, environment_variables)
- else:
- resolved_headers[key] = value
-
- return resolved_headers
-
- @abstractmethod
- def resolve_environment_variables(self, environment_variables: Optional[Dict[str, str]] = None) -> None:
- raise NotImplementedError
-
-
-class SSEServerConfig(BaseServerConfig):
- """
- Configuration for an MCP server using SSE
-
- Authentication can be provided in multiple ways:
- 1. Using auth_header + auth_token: Will add a specific header with the token
- Example: auth_header="Authorization", auth_token="Bearer abc123"
-
- 2. Using the custom_headers dict: For more complex authentication scenarios
- Example: custom_headers={"X-API-Key": "abc123", "X-Custom-Header": "value"}
- """
-
- type: MCPServerType = MCPServerType.SSE
- server_url: str = Field(..., description="The URL of the server (MCP SSE client will connect to this URL)")
- auth_header: Optional[str] = Field(None, description="The name of the authentication header (e.g., 'Authorization')")
- auth_token: Optional[str] = Field(None, description="The authentication token or API key value")
- custom_headers: Optional[dict[str, str]] = Field(None, description="Custom HTTP headers to include with SSE requests")
-
- def resolve_token(self) -> Optional[str]:
- """
- Extract token for storage if auth_header/auth_token are provided
- and not already in custom_headers.
-
- Returns:
- The resolved token (without Bearer prefix) if it should be stored separately, None otherwise
- """
- if self.auth_token and self.auth_header:
- # Check if custom_headers already has the auth header
- if not self.custom_headers or self.auth_header not in self.custom_headers:
- # Strip Bearer prefix if present
- if self.auth_token.startswith(f"{MCP_AUTH_TOKEN_BEARER_PREFIX} "):
- return self.auth_token[len(f"{MCP_AUTH_TOKEN_BEARER_PREFIX} ") :]
- return self.auth_token
- return None
-
- def resolve_environment_variables(self, environment_variables: Optional[Dict[str, str]] = None) -> None:
- if self.auth_token and super().is_templated_tool_variable(self.auth_token):
- self.auth_token = super().get_tool_variable(self.auth_token, environment_variables)
-
- self.custom_headers = super().resolve_custom_headers(self.custom_headers, environment_variables)
-
- def to_dict(self) -> dict:
- values = {
- "transport": "sse",
- "url": self.server_url,
- }
-
- # TODO: handle custom headers
- if self.custom_headers is not None or (self.auth_header is not None and self.auth_token is not None):
- headers = self.custom_headers.copy() if self.custom_headers else {}
-
- # Add auth header if specified
- if self.auth_header is not None and self.auth_token is not None:
- headers[self.auth_header] = self.auth_token
-
- values["headers"] = headers
-
- return values
-
-
-class StdioServerConfig(BaseServerConfig):
- type: MCPServerType = MCPServerType.STDIO
- command: str = Field(..., description="The command to run (MCP 'local' client will run this command)")
- args: List[str] = Field(..., description="The arguments to pass to the command")
- env: Optional[dict[str, str]] = Field(None, description="Environment variables to set")
-
- # TODO: @jnjpng templated auth handling for stdio
- def resolve_environment_variables(self, environment_variables: Optional[Dict[str, str]] = None) -> None:
- pass
-
- def to_dict(self) -> dict:
- values = {
- "transport": "stdio",
- "command": self.command,
- "args": self.args,
- }
- if self.env is not None:
- values["env"] = self.env
- return values
-
-
-class StreamableHTTPServerConfig(BaseServerConfig):
- """
- Configuration for an MCP server using Streamable HTTP
-
- Authentication can be provided in multiple ways:
- 1. Using auth_header + auth_token: Will add a specific header with the token
- Example: auth_header="Authorization", auth_token="Bearer abc123"
-
- 2. Using the custom_headers dict: For more complex authentication scenarios
- Example: custom_headers={"X-API-Key": "abc123", "X-Custom-Header": "value"}
- """
-
- type: MCPServerType = MCPServerType.STREAMABLE_HTTP
- server_url: str = Field(..., description="The URL path for the streamable HTTP server (e.g., 'example/mcp')")
- auth_header: Optional[str] = Field(None, description="The name of the authentication header (e.g., 'Authorization')")
- auth_token: Optional[str] = Field(None, description="The authentication token or API key value")
- custom_headers: Optional[dict[str, str]] = Field(None, description="Custom HTTP headers to include with streamable HTTP requests")
-
- def resolve_token(self) -> Optional[str]:
- """
- Extract token for storage if auth_header/auth_token are provided
- and not already in custom_headers.
-
- Returns:
- The resolved token (without Bearer prefix) if it should be stored separately, None otherwise
- """
- if self.auth_token and self.auth_header:
- # Check if custom_headers already has the auth header
- if not self.custom_headers or self.auth_header not in self.custom_headers:
- # Strip Bearer prefix if present
- if self.auth_token.startswith(f"{MCP_AUTH_TOKEN_BEARER_PREFIX} "):
- return self.auth_token[len(f"{MCP_AUTH_TOKEN_BEARER_PREFIX} ") :]
- return self.auth_token
- return None
-
- def resolve_environment_variables(self, environment_variables: Optional[Dict[str, str]] = None) -> None:
- if self.auth_token and super().is_templated_tool_variable(self.auth_token):
- self.auth_token = super().get_tool_variable(self.auth_token, environment_variables)
-
- self.custom_headers = super().resolve_custom_headers(self.custom_headers, environment_variables)
-
- def model_post_init(self, __context) -> None:
- """Validate the server URL format."""
- # Basic validation for streamable HTTP URLs
- if not self.server_url:
- raise ValueError("server_url cannot be empty")
-
- # For streamable HTTP, the URL should typically be a path or full URL
- # We'll be lenient and allow both formats
- if self.server_url.startswith("http://") or self.server_url.startswith("https://"):
- # Full URL format - this is what the user is trying
- pass
- elif "/" in self.server_url:
- # Path format like "example/mcp" - this is the typical format
- pass
- else:
- # Single word - might be valid but warn in logs
- pass
-
- def to_dict(self) -> dict:
- values = {
- "transport": "streamable_http",
- "url": self.server_url,
- }
-
- # Handle custom headers
- if self.custom_headers is not None or (self.auth_header is not None and self.auth_token is not None):
- headers = self.custom_headers.copy() if self.custom_headers else {}
-
- # Add auth header if specified
- if self.auth_header is not None and self.auth_token is not None:
- headers[self.auth_header] = self.auth_token
-
- values["headers"] = headers
-
- return values
diff --git a/letta/functions/prompts.py b/letta/functions/prompts.py
deleted file mode 100644
index 780280c3..00000000
--- a/letta/functions/prompts.py
+++ /dev/null
@@ -1,26 +0,0 @@
-FIRECRAWL_SEARCH_SYSTEM_PROMPT = """You are an expert at extracting relevant information from web content.
-
-Given a document with line numbers (format: "LINE_NUM: content"), identify passages that answer the provided question by returning line ranges:
-- start_line: The starting line number (inclusive)
-- end_line: The ending line number (inclusive)
-
-SELECTION PRINCIPLES:
-1. Prefer comprehensive passages that include full context
-2. Capture complete thoughts, examples, and explanations
-3. When relevant content spans multiple paragraphs, include the entire section
-4. Favor fewer, substantial passages over many fragments
-
-Focus on passages that can stand alone as complete, meaningful responses."""
-
-
-def get_firecrawl_search_user_prompt(query: str, question: str, numbered_content: str) -> str:
- """Generate the user prompt for line-number based search analysis."""
- return f"""Search Query: {query}
-Question to Answer: {question}
-
-Document Content (with line numbers):
-{numbered_content}
-
-Identify line ranges that best answer: "{question}"
-
-Select comprehensive passages with full context. Include entire sections when relevant."""
diff --git a/letta/functions/schema_generator.py b/letta/functions/schema_generator.py
deleted file mode 100644
index 8c657f43..00000000
--- a/letta/functions/schema_generator.py
+++ /dev/null
@@ -1,751 +0,0 @@
-import inspect
-import warnings
-from typing import Any, Dict, List, Optional, Tuple, Type, Union, get_args, get_origin
-
-from composio.client.collections import ActionParametersModel
-from docstring_parser import parse
-from pydantic import BaseModel
-from typing_extensions import Literal
-
-from letta.constants import REQUEST_HEARTBEAT_DESCRIPTION, REQUEST_HEARTBEAT_PARAM
-from letta.functions.mcp_client.types import MCPTool
-from letta.log import get_logger
-
-logger = get_logger(__name__)
-
-
-def validate_google_style_docstring(function):
- """Validate that a function's docstring follows Google Python style format.
-
- Args:
- function: The function to validate
-
- Raises:
- ValueError: If the docstring is not in Google Python style format
- """
- if not function.__doc__:
- raise ValueError(
- f"Function '{function.__name__}' has no docstring. Expected Google Python style docstring with Args and Returns sections."
- )
-
- docstring = function.__doc__.strip()
-
- # Basic Google style requirements:
- # 1. Should have Args: section if function has parameters (excluding self, agent_state)
- # 2. Should have Returns: section if function returns something other than None
- # 3. Args and Returns sections should be properly formatted
-
- sig = inspect.signature(function)
- has_params = any(param.name not in ["self", "agent_state"] for param in sig.parameters.values())
-
- # Check for Args section if function has parameters
- if has_params and "Args:" not in docstring:
- raise ValueError(f"Function '{function.__name__}' with parameters must have 'Args:' section in Google Python style docstring")
-
- # NOTE: No check for Returns section - this is irrelevant to the LLM
- # In proper Google Python format, the Returns: is required
-
- # Validate Args section format if present
- if "Args:" in docstring:
- args_start = docstring.find("Args:")
- args_end = docstring.find("Returns:", args_start) if "Returns:" in docstring[args_start:] else len(docstring)
- args_section = docstring[args_start:args_end].strip()
-
- # Check that each parameter is documented
- for param in sig.parameters.values():
- if param.name in ["self", "agent_state"]:
- continue
- if f"{param.name} (" not in args_section and f"{param.name}:" not in args_section:
- raise ValueError(
- f"Function '{function.__name__}' parameter '{param.name}' not documented in Args section of Google Python style docstring"
- )
-
-
-def is_optional(annotation):
- # Check if the annotation is a Union
- if getattr(annotation, "__origin__", None) is Union:
- # Check if None is one of the options in the Union
- return type(None) in annotation.__args__
- return False
-
-
-def optional_length(annotation):
- if is_optional(annotation):
- # Subtract 1 to account for NoneType
- return len(annotation.__args__) - 1
- else:
- raise ValueError("The annotation is not an Optional type")
-
-
-def type_to_json_schema_type(py_type) -> dict:
- """
- Maps a Python type to a JSON schema type.
- Specifically handles typing.Optional and common Python types.
- """
- # if get_origin(py_type) is typing.Optional:
- if is_optional(py_type):
- # Assert that Optional has only one type argument
- type_args = get_args(py_type)
- assert optional_length(py_type) == 1, f"Optional type must have exactly one type argument, but got {py_type}"
-
- # Extract and map the inner type
- return type_to_json_schema_type(type_args[0])
-
- # Handle Union types (except Optional which is handled above)
- if get_origin(py_type) is Union:
- # TODO support mapping Unions to anyOf
- raise NotImplementedError("General Union types are not yet supported")
-
- # Handle array types
- origin = get_origin(py_type)
- if py_type == list or origin in (list, List):
- args = get_args(py_type)
- if len(args) == 0:
- # is this correct
- warnings.warn("Defaulting to string type for untyped List")
- return {
- "type": "array",
- "items": {"type": "string"},
- }
-
- if args and inspect.isclass(args[0]) and issubclass(args[0], BaseModel):
- # If it's a list of Pydantic models, return an array with the model schema as items
- return {
- "type": "array",
- "items": pydantic_model_to_json_schema(args[0]),
- }
-
- # Otherwise, recursively call the basic type checker
- return {
- "type": "array",
- # get the type of the items in the list
- "items": type_to_json_schema_type(args[0]),
- }
-
- # Handle literals
- if get_origin(py_type) is Literal:
- return {"type": "string", "enum": get_args(py_type)}
-
- # Handle tuple types (specifically fixed-length like Tuple[int, int])
- if origin in (tuple, Tuple):
- args = get_args(py_type)
- if len(args) == 0:
- raise ValueError("Tuple type must have at least one element")
-
- # Support only fixed-length tuples like Tuple[int, int], not variable-length like Tuple[int, ...]
- if len(args) == 2 and args[1] is Ellipsis:
- raise NotImplementedError("Variable-length tuples (e.g., Tuple[int, ...]) are not supported")
-
- return {
- "type": "array",
- "prefixItems": [type_to_json_schema_type(arg) for arg in args],
- "minItems": len(args),
- "maxItems": len(args),
- }
-
- # Handle object types
- if py_type == dict or origin in (dict, Dict):
- args = get_args(py_type)
- if not args:
- # Generic dict without type arguments
- return {
- "type": "object",
- # "properties": {}
- }
- else:
- raise ValueError(
- f"Dictionary types {py_type} with nested type arguments are not supported (consider using a Pydantic model instead)"
- )
-
- # NOTE: the below code works for generic JSON schema parsing, but there's a problem with the key inference
- # when it comes to OpenAI function schema generation so it doesn't make sense to allow for dict[str, Any] type hints
- # key_type, value_type = args
-
- # # Ensure dict keys are strings
- # # Otherwise there's no JSON schema equivalent
- # if key_type != str:
- # raise ValueError("Dictionary keys must be strings for OpenAI function schema compatibility")
-
- # # Handle value type to determine property schema
- # value_schema = {}
- # if inspect.isclass(value_type) and issubclass(value_type, BaseModel):
- # value_schema = pydantic_model_to_json_schema(value_type)
- # else:
- # value_schema = type_to_json_schema_type(value_type)
-
- # # NOTE: the problem lies here - the key is always "key_placeholder"
- # return {"type": "object", "properties": {"key_placeholder": value_schema}}
-
- # Handle direct Pydantic models
- if inspect.isclass(py_type) and issubclass(py_type, BaseModel):
- return pydantic_model_to_json_schema(py_type)
-
- # Mapping of Python types to JSON schema types
- type_map = {
- # Basic types
- # Optional, Union, and collections are handled above ^
- int: "integer",
- str: "string",
- bool: "boolean",
- float: "number",
- None: "null",
- }
- if py_type not in type_map:
- raise ValueError(f"Python type {py_type} has no corresponding JSON schema type - full map: {type_map}")
- else:
- return {"type": type_map[py_type]}
-
-
-def pydantic_model_to_open_ai(model: Type[BaseModel]) -> dict:
- """
- Converts a Pydantic model as a singular arg to a JSON schema object for use in OpenAI function calling.
- """
- schema = model.model_json_schema()
- docstring = parse(model.__doc__ or "")
- parameters = {k: v for k, v in schema.items() if k not in ("title", "description")}
- for param in docstring.params:
- if (name := param.arg_name) in parameters["properties"] and (description := param.description):
- if "description" not in parameters["properties"][name]:
- parameters["properties"][name]["description"] = description
-
- parameters["required"] = sorted(k for k, v in parameters["properties"].items() if "default" not in v)
-
- if "description" not in schema:
- # Support multiline docstrings for complex functions, TODO (cliandy): consider having this as a setting
- if docstring.long_description:
- schema["description"] = docstring.long_description
- elif docstring.short_description:
- schema["description"] = docstring.short_description
- else:
- raise ValueError(f"No description found in docstring or description field (model: {model}, docstring: {docstring})")
-
- return {
- "name": schema["title"],
- "description": schema["description"],
- "parameters": parameters,
- }
-
-
-def pydantic_model_to_json_schema(model: Type[BaseModel]) -> dict:
- """
- Converts a Pydantic model (as an arg that already is annotated) to a JSON schema object for use in OpenAI function calling.
-
- An example of a Pydantic model as an arg:
-
- class Step(BaseModel):
- name: str = Field(
- ...,
- description="Name of the step.",
- )
- key: str = Field(
- ...,
- description="Unique identifier for the step.",
- )
- description: str = Field(
- ...,
- description="An exhaustic description of what this step is trying to achieve and accomplish.",
- )
-
- def create_task_plan(steps: list[Step]):
- '''
- Creates a task plan for the current task.
-
- Args:
- steps: List of steps to add to the task plan.
- ...
-
- Should result in:
- {
- "name": "create_task_plan",
- "description": "Creates a task plan for the current task.",
- "parameters": {
- "type": "object",
- "properties": {
- "steps": { # <= this is the name of the arg
- "type": "object",
- "description": "List of steps to add to the task plan.",
- "properties": {
- "name": {
- "type": "str",
- "description": "Name of the step.",
- },
- "key": {
- "type": "str",
- "description": "Unique identifier for the step.",
- },
- "description": {
- "type": "str",
- "description": "An exhaustic description of what this step is trying to achieve and accomplish.",
- },
- },
- "required": ["name", "key", "description"],
- }
- },
- "required": ["steps"],
- }
- }
-
- Specifically, the result of pydantic_model_to_json_schema(steps) (where `steps` is an instance of BaseModel) is:
- {
- "type": "object",
- "properties": {
- "name": {
- "type": "str",
- "description": "Name of the step."
- },
- "key": {
- "type": "str",
- "description": "Unique identifier for the step."
- },
- "description": {
- "type": "str",
- "description": "An exhaustic description of what this step is trying to achieve and accomplish."
- },
- },
- "required": ["name", "key", "description"],
- }
- """
- schema = model.model_json_schema()
-
- def clean_property(prop: dict, full_schema: dict) -> dict:
- """Clean up a property schema to match desired format"""
-
- if "description" not in prop:
- raise ValueError(f"Property {prop} lacks a 'description' key")
-
- if "type" not in prop and "$ref" in prop:
- prop["type"] = "object"
-
- # Handle the case where the property is a $ref to another model
- if "$ref" in prop:
- # Resolve the reference to the nested model
- ref_schema = resolve_ref(prop["$ref"], full_schema)
- # Recursively clean the nested model
- return {
- "type": "object",
- **clean_schema(ref_schema, full_schema),
- "description": prop["description"],
- }
-
- # Handle the case where the property uses anyOf (e.g., Optional types)
- if "anyOf" in prop:
- # For Optional types, extract the non-null type
- non_null_types = [t for t in prop["anyOf"] if t.get("type") != "null"]
- if len(non_null_types) == 1:
- # Simple Optional[T] case - use the non-null type
- return {
- "type": non_null_types[0]["type"],
- "description": prop["description"],
- }
- else:
- # Complex anyOf case - not supported yet
- raise ValueError(f"Complex anyOf patterns are not supported: {prop}")
-
- # If it's a regular property with a direct type (e.g., string, number)
- return {
- "type": "string" if prop["type"] == "string" else prop["type"],
- "description": prop["description"],
- }
-
- def resolve_ref(ref: str, schema: dict) -> dict:
- """Resolve a $ref reference in the schema"""
- if not ref.startswith("#/$defs/"):
- raise ValueError(f"Unexpected reference format: {ref}")
-
- model_name = ref.split("/")[-1]
- if model_name not in schema.get("$defs", {}):
- raise ValueError(f"Reference {model_name} not found in schema definitions")
-
- return schema["$defs"][model_name]
-
- def clean_schema(schema_part: dict, full_schema: dict) -> dict:
- """Clean up a schema part, handling references and nested structures"""
- # Handle $ref
- if "$ref" in schema_part:
- schema_part = resolve_ref(schema_part["$ref"], full_schema)
-
- if "type" not in schema_part:
- raise ValueError(f"Schema part lacks a 'type' key: {schema_part}")
-
- # Handle array type
- if schema_part["type"] == "array":
- items_schema = schema_part["items"]
- if "$ref" in items_schema:
- items_schema = resolve_ref(items_schema["$ref"], full_schema)
- return {"type": "array", "items": clean_schema(items_schema, full_schema), "description": schema_part.get("description", "")}
-
- # Handle object type
- if schema_part["type"] == "object":
- if "properties" not in schema_part:
- raise ValueError(f"Object schema lacks 'properties' key: {schema_part}")
-
- properties = {}
- for name, prop in schema_part["properties"].items():
- if "items" in prop: # Handle arrays
- if "description" not in prop:
- raise ValueError(f"Property {prop} lacks a 'description' key")
- properties[name] = {
- "type": "array",
- "items": clean_schema(prop["items"], full_schema),
- "description": prop["description"],
- }
- else:
- properties[name] = clean_property(prop, full_schema)
-
- pydantic_model_schema_dict = {
- "type": "object",
- "properties": properties,
- "required": schema_part.get("required", []),
- }
- if "description" in schema_part:
- pydantic_model_schema_dict["description"] = schema_part["description"]
-
- return pydantic_model_schema_dict
-
- # Handle primitive types
- return clean_property(schema_part)
-
- return clean_schema(schema_part=schema, full_schema=schema)
-
-
-def generate_schema(function, name: Optional[str] = None, description: Optional[str] = None, tool_id: Optional[str] = None) -> dict:
- # Validate that the function has a Google Python style docstring
- try:
- validate_google_style_docstring(function)
- except ValueError as e:
- logger.warning(
- f"Function `{function.__name__}` in module `{function.__module__}` "
- f"{'(tool_id=' + tool_id + ') ' if tool_id else ''}"
- f"is not in Google style docstring format. "
- f"Docstring received:\n{repr(function.__doc__[:200]) if function.__doc__ else 'None'}"
- f"\nError: {str(e)}"
- )
-
- # Get the signature of the function
- sig = inspect.signature(function)
-
- # Parse the docstring
- docstring = parse(function.__doc__)
-
- if not description:
- # Support multiline docstrings for complex functions, TODO (cliandy): consider having this as a setting
- # Always prefer combining short + long description when both exist
- if docstring.short_description and docstring.long_description:
- description = f"{docstring.short_description}\n\n{docstring.long_description}"
- elif docstring.short_description:
- description = docstring.short_description
- elif docstring.long_description:
- description = docstring.long_description
- else:
- description = "No description available"
-
- examples_section = extract_examples_section(function.__doc__)
- if examples_section and "Examples:" not in description:
- description = f"{description}\n\n{examples_section}"
-
- # Prepare the schema dictionary
- schema = {
- "name": function.__name__ if name is None else name,
- "description": description,
- "parameters": {"type": "object", "properties": {}, "required": []},
- }
-
- # TODO: ensure that 'agent' keyword is reserved for `Agent` class
-
- for param in sig.parameters.values():
- # Exclude 'self' parameter
- # TODO: eventually remove this (only applies to BASE_TOOLS)
- if param.name in ["self", "agent_state"]: # Add agent_manager to excluded
- continue
-
- # Assert that the parameter has a type annotation
- if param.annotation == inspect.Parameter.empty:
- raise TypeError(f"Parameter '{param.name}' in function '{function.__name__}' lacks a type annotation")
-
- # Find the parameter's description in the docstring
- param_doc = next((d for d in docstring.params if d.arg_name == param.name), None)
-
- # Assert that the parameter has a description
- if not param_doc or not param_doc.description:
- raise ValueError(f"Parameter '{param.name}' in function '{function.__name__}' lacks a description in the docstring")
-
- # If the parameter is a pydantic model, we need to unpack the Pydantic model type into a JSON schema object
- # if inspect.isclass(param.annotation) and issubclass(param.annotation, BaseModel):
- if (
- (inspect.isclass(param.annotation) or inspect.isclass(get_origin(param.annotation) or param.annotation))
- and not get_origin(param.annotation)
- and issubclass(param.annotation, BaseModel)
- ):
- # print("Generating schema for pydantic model:", param.annotation)
- # Extract the properties from the pydantic model
- schema["parameters"]["properties"][param.name] = pydantic_model_to_json_schema(param.annotation)
- schema["parameters"]["properties"][param.name]["description"] = param_doc.description
-
- # Otherwise, we convert the Python typing to JSON schema types
- # NOTE: important - if a dict or list, the internal type can be a Pydantic model itself
- # however in that
- else:
- # print("Generating schema for non-pydantic model:", param.annotation)
- # Grab the description for the parameter from the extended docstring
- # If it doesn't exist, we should raise an error
- param_doc = next((d for d in docstring.params if d.arg_name == param.name), None)
- if not param_doc:
- raise ValueError(f"Parameter '{param.name}' in function '{function.__name__}' lacks a description in the docstring")
- elif not isinstance(param_doc.description, str):
- raise ValueError(
- f"Parameter '{param.name}' in function '{function.__name__}' has a description in the docstring that is not a string (type: {type(param_doc.description)})"
- )
- else:
- # If it's a string or a basic type, then all you need is: (1) type, (2) description
- # If it's a more complex type, then you also need either:
- # - for array, you need "items", each of which has "type"
- # - for a dict, you need "properties", which has keys which each have "type"
- if param.annotation != inspect.Parameter.empty:
- param_generated_schema = type_to_json_schema_type(param.annotation)
- else:
- # TODO why are we inferring here?
- param_generated_schema = {"type": "string"}
-
- # Add in the description
- param_generated_schema["description"] = param_doc.description
-
- # Add the schema to the function arg key
- schema["parameters"]["properties"][param.name] = param_generated_schema
-
- # If the parameter doesn't have a default value, it is required (so we need to add it to the required list)
- if param.default == inspect.Parameter.empty and not is_optional(param.annotation):
- schema["parameters"]["required"].append(param.name)
-
- # TODO what's going on here?
- # If the parameter is a list of strings we need to hard cast to "string" instead of `str`
- if get_origin(param.annotation) is list:
- if get_args(param.annotation)[0] is str:
- schema["parameters"]["properties"][param.name]["items"] = {"type": "string"}
-
- # TODO is this not duplicating the other append directly above?
- if param.annotation == inspect.Parameter.empty:
- schema["parameters"]["required"].append(param.name)
- return schema
-
-
-def extract_examples_section(docstring: Optional[str]) -> Optional[str]:
- """Extracts the 'Examples:' section from a Google-style docstring.
-
- Args:
- docstring (Optional[str]): The full docstring of a function.
-
- Returns:
- Optional[str]: The extracted examples section, or None if not found.
- """
- if not docstring or "Examples:" not in docstring:
- return None
-
- lines = docstring.strip().splitlines()
- in_examples = False
- examples_lines = []
-
- for line in lines:
- stripped = line.strip()
-
- if not in_examples and stripped.startswith("Examples:"):
- in_examples = True
- examples_lines.append(line)
- continue
-
- if in_examples:
- if stripped and not line.startswith(" ") and stripped.endswith(":"):
- break
- examples_lines.append(line)
-
- return "\n".join(examples_lines).strip() if examples_lines else None
-
-
-def generate_schema_from_args_schema_v2(
- args_schema: Type[BaseModel], name: Optional[str] = None, description: Optional[str] = None, append_heartbeat: bool = True
-) -> Dict[str, Any]:
- properties = {}
- required = []
- for field_name, field in args_schema.model_fields.items():
- field_type_annotation = field.annotation
- properties[field_name] = type_to_json_schema_type(field_type_annotation)
- properties[field_name]["description"] = field.description
- if field.is_required():
- required.append(field_name)
-
- function_call_json = {
- "name": name,
- "description": description,
- "parameters": {"type": "object", "properties": properties, "required": required},
- }
-
- if append_heartbeat:
- function_call_json["parameters"]["properties"][REQUEST_HEARTBEAT_PARAM] = {
- "type": "boolean",
- "description": REQUEST_HEARTBEAT_DESCRIPTION,
- }
- function_call_json["parameters"]["required"].append(REQUEST_HEARTBEAT_PARAM)
-
- return function_call_json
-
-
-def generate_tool_schema_for_mcp(
- mcp_tool: MCPTool,
- append_heartbeat: bool = True,
- strict: bool = False,
-) -> Dict[str, Any]:
- # MCP tool.inputSchema is a JSON schema
- # https://github.com/modelcontextprotocol/python-sdk/blob/775f87981300660ee957b63c2a14b448ab9c3675/src/mcp/types.py#L678
- parameters_schema = mcp_tool.inputSchema
- name = mcp_tool.name
- description = mcp_tool.description
-
- assert "type" in parameters_schema, parameters_schema
- assert "properties" in parameters_schema, parameters_schema
- # assert "required" in parameters_schema, parameters_schema
-
- # Zero-arg tools often omit "required" because nothing is required.
- # Normalise so downstream code can treat it consistently.
- parameters_schema.setdefault("required", [])
-
- # Process properties to handle anyOf types and make optional fields strict-compatible
- if "properties" in parameters_schema:
- for field_name, field_props in parameters_schema["properties"].items():
- # Handle anyOf types by flattening to type array
- if "anyOf" in field_props and "type" not in field_props:
- types = []
- format_value = None
- for option in field_props["anyOf"]:
- if "type" in option:
- types.append(option["type"])
- # Capture format if present (e.g., uuid format for strings)
- if "format" in option and not format_value:
- format_value = option["format"]
- if types:
- # Deduplicate types using set
- field_props["type"] = list(set(types))
- # Only add format if the field is not optional (doesn't have null type)
- if format_value and len(field_props["type"]) == 1 and "null" not in field_props["type"]:
- field_props["format"] = format_value
- # Remove the anyOf since we've flattened it
- del field_props["anyOf"]
-
- # For strict mode: heal optional fields by making them required with null type
- if strict and field_name not in parameters_schema["required"]:
- # Field is optional - add it to required array
- parameters_schema["required"].append(field_name)
-
- # Ensure the field can accept null to maintain optionality
- if "type" in field_props:
- if isinstance(field_props["type"], list):
- # Already an array of types - add null if not present
- if "null" not in field_props["type"]:
- field_props["type"].append("null")
- # Deduplicate
- field_props["type"] = list(set(field_props["type"]))
- elif field_props["type"] != "null":
- # Single type - convert to array with null
- field_props["type"] = list(set([field_props["type"], "null"]))
- elif "anyOf" in field_props:
- # If there's still an anyOf, ensure null is one of the options
- has_null = any(opt.get("type") == "null" for opt in field_props["anyOf"])
- if not has_null:
- field_props["anyOf"].append({"type": "null"})
-
- # Add the optional heartbeat parameter
- if append_heartbeat:
- parameters_schema["properties"][REQUEST_HEARTBEAT_PARAM] = {
- "type": "boolean",
- "description": REQUEST_HEARTBEAT_DESCRIPTION,
- }
- if REQUEST_HEARTBEAT_PARAM not in parameters_schema["required"]:
- parameters_schema["required"].append(REQUEST_HEARTBEAT_PARAM)
-
- # Return the final schema
- if strict:
- # https://platform.openai.com/docs/guides/function-calling#strict-mode
-
- # Add additionalProperties: False
- parameters_schema["additionalProperties"] = False
-
- return {
- "strict": True, # NOTE
- "name": name,
- "description": description,
- "parameters": parameters_schema,
- }
- else:
- return {
- "name": name,
- "description": description,
- "parameters": parameters_schema,
- }
-
-
-def generate_tool_schema_for_composio(
- parameters_model: ActionParametersModel,
- name: str,
- description: str,
- append_heartbeat: bool = True,
- strict: bool = False,
-) -> Dict[str, Any]:
- properties_json = {}
- required_fields = parameters_model.required or []
-
- # Extract properties from the ActionParametersModel
- for field_name, field_props in parameters_model.properties.items():
- # Initialize the property structure
- property_schema = {
- "type": field_props["type"],
- "description": field_props.get("description", ""),
- }
-
- # Handle optional default values
- if "default" in field_props:
- property_schema["default"] = field_props["default"]
-
- # Handle enumerations
- if "enum" in field_props:
- property_schema["enum"] = field_props["enum"]
-
- # Handle array item types
- if field_props["type"] == "array":
- if "items" in field_props:
- property_schema["items"] = field_props["items"]
- elif "anyOf" in field_props:
- property_schema["items"] = [t for t in field_props["anyOf"] if "items" in t][0]["items"]
-
- # Add the property to the schema
- properties_json[field_name] = property_schema
-
- # Add the optional heartbeat parameter
- if append_heartbeat:
- properties_json[REQUEST_HEARTBEAT_PARAM] = {
- "type": "boolean",
- "description": REQUEST_HEARTBEAT_DESCRIPTION,
- }
- required_fields.append(REQUEST_HEARTBEAT_PARAM)
-
- # Return the final schema
- if strict:
- # https://platform.openai.com/docs/guides/function-calling#strict-mode
- return {
- "name": name,
- "description": description,
- "strict": True, # NOTE
- "parameters": {
- "type": "object",
- "properties": properties_json,
- "additionalProperties": False, # NOTE
- "required": required_fields,
- },
- }
- else:
- return {
- "name": name,
- "description": description,
- "parameters": {
- "type": "object",
- "properties": properties_json,
- "required": required_fields,
- },
- }
diff --git a/letta/functions/schema_validator.py b/letta/functions/schema_validator.py
deleted file mode 100644
index dd99fd04..00000000
--- a/letta/functions/schema_validator.py
+++ /dev/null
@@ -1,202 +0,0 @@
-"""
-JSON Schema validator for OpenAI strict mode compliance.
-
-This module provides validation for JSON schemas to ensure they comply with
-OpenAI's strict mode requirements for tool schemas.
-"""
-
-from enum import Enum
-from typing import Any, Dict, List, Tuple
-
-
-class SchemaHealth(Enum):
- """Schema health status for OpenAI strict mode compliance."""
-
- STRICT_COMPLIANT = "STRICT_COMPLIANT" # Passes OpenAI strict mode
- NON_STRICT_ONLY = "NON_STRICT_ONLY" # Valid JSON Schema but too loose for strict mode
- INVALID = "INVALID" # Broken for both
-
-
-def validate_complete_json_schema(schema: Dict[str, Any]) -> Tuple[SchemaHealth, List[str]]:
- """
- Validate schema for OpenAI tool strict mode compliance.
-
- This validator checks for:
- - Valid JSON Schema structure
- - OpenAI strict mode requirements
- - Special cases like required properties with empty object schemas
-
- Args:
- schema: The JSON schema to validate
-
- Returns:
- A tuple of (SchemaHealth, list_of_reasons)
- """
-
- reasons: List[str] = []
- status = SchemaHealth.STRICT_COMPLIANT
-
- def mark_non_strict(reason: str):
- """Mark schema as non-strict only (valid but not strict-compliant)."""
- nonlocal status
- if status == SchemaHealth.STRICT_COMPLIANT:
- status = SchemaHealth.NON_STRICT_ONLY
- reasons.append(reason)
-
- def mark_invalid(reason: str):
- """Mark schema as invalid."""
- nonlocal status
- status = SchemaHealth.INVALID
- reasons.append(reason)
-
- def schema_allows_empty_object(obj_schema: Dict[str, Any]) -> bool:
- """
- Return True if this object schema allows {}, meaning no required props
- and no additionalProperties content.
- """
- if obj_schema.get("type") != "object":
- return False
- props = obj_schema.get("properties", {})
- required = obj_schema.get("required", [])
- additional = obj_schema.get("additionalProperties", True)
-
- # Empty object: no required props and additionalProperties is false
- if not required and additional is False:
- return True
- return False
-
- def schema_allows_empty_array(arr_schema: Dict[str, Any]) -> bool:
- """
- Return True if this array schema allows empty arrays with no constraints.
- """
- if arr_schema.get("type") != "array":
- return False
-
- # If minItems is set and > 0, it doesn't allow empty
- min_items = arr_schema.get("minItems", 0)
- if min_items > 0:
- return False
-
- # If items schema is not defined or very permissive, it allows empty
- items = arr_schema.get("items")
- if items is None:
- return True
-
- return False
-
- def recurse(node: Dict[str, Any], path: str, is_root: bool = False):
- """Recursively validate a schema node."""
- node_type = node.get("type")
-
- # Handle schemas without explicit type but with type-specific keywords
- if not node_type:
- # Check for type-specific keywords
- if "properties" in node or "additionalProperties" in node:
- node_type = "object"
- elif "items" in node:
- node_type = "array"
- elif any(kw in node for kw in ["anyOf", "oneOf", "allOf"]):
- # Union types don't require explicit type
- pass
- else:
- mark_invalid(f"{path}: Missing 'type'")
- return
-
- # OBJECT
- if node_type == "object":
- props = node.get("properties")
- if props is not None and not isinstance(props, dict):
- mark_invalid(f"{path}: 'properties' must be a dict for objects")
- return
-
- if "additionalProperties" not in node:
- mark_non_strict(f"{path}: 'additionalProperties' not explicitly set")
- elif node["additionalProperties"] is not False:
- mark_non_strict(f"{path}: 'additionalProperties' is not false (free-form object)")
-
- required = node.get("required")
- if required is None:
- # TODO: @jnjpng skip this check for now, seems like OpenAI strict mode doesn't enforce this
- # Only mark as non-strict for nested objects, not root
- # if not is_root:
- # mark_non_strict(f"{path}: 'required' not specified for object")
- required = []
- elif not isinstance(required, list):
- mark_invalid(f"{path}: 'required' must be a list if present")
- required = []
-
- # OpenAI strict-mode extra checks:
- # NOTE: We no longer flag properties not in required array as non-strict
- # because we can heal these schemas by adding null to the type union
- # This allows MCP tools with optional fields to be used with strict mode
- # The healing happens in generate_tool_schema_for_mcp() when strict=True
-
- for req_key in required:
- if props and req_key not in props:
- mark_invalid(f"{path}: required contains '{req_key}' not found in properties")
- elif props:
- req_schema = props[req_key]
- if isinstance(req_schema, dict):
- # Check for empty object issue
- if schema_allows_empty_object(req_schema):
- mark_invalid(f"{path}: required property '{req_key}' allows empty object (OpenAI will reject)")
- # Check for empty array issue
- if schema_allows_empty_array(req_schema):
- mark_invalid(f"{path}: required property '{req_key}' allows empty array (OpenAI will reject)")
-
- # Recurse into properties
- if props:
- for prop_name, prop_schema in props.items():
- if isinstance(prop_schema, dict):
- recurse(prop_schema, f"{path}.properties.{prop_name}", is_root=False)
- else:
- mark_invalid(f"{path}.properties.{prop_name}: Not a valid schema dict")
-
- # ARRAY
- elif node_type == "array":
- items = node.get("items")
- if items is None:
- mark_invalid(f"{path}: 'items' must be defined for arrays in strict mode")
- elif not isinstance(items, dict):
- mark_invalid(f"{path}: 'items' must be a schema dict for arrays")
- else:
- recurse(items, f"{path}.items", is_root=False)
-
- # PRIMITIVE TYPES
- elif node_type in ["string", "number", "integer", "boolean", "null"]:
- # These are generally fine, but check for specific constraints
- pass
-
- # TYPE ARRAYS (e.g., ["string", "null"] for optional fields)
- elif isinstance(node_type, list):
- # Type arrays are allowed in OpenAI strict mode
- # They represent union types (e.g., string | null)
- for t in node_type:
- # TODO: @jnjpng handle enum types?
- if t not in ["string", "number", "integer", "boolean", "null", "array", "object"]:
- mark_invalid(f"{path}: Invalid type '{t}' in type array")
-
- # UNION TYPES
- for kw in ("anyOf", "oneOf", "allOf"):
- if kw in node:
- if not isinstance(node[kw], list):
- mark_invalid(f"{path}: '{kw}' must be a list")
- else:
- for idx, sub_schema in enumerate(node[kw]):
- if isinstance(sub_schema, dict):
- recurse(sub_schema, f"{path}.{kw}[{idx}]", is_root=False)
- else:
- mark_invalid(f"{path}.{kw}[{idx}]: Not a valid schema dict")
-
- # Start validation
- if not isinstance(schema, dict):
- return SchemaHealth.INVALID, ["Top-level schema must be a dict"]
-
- # OpenAI tools require top-level type to be object
- if schema.get("type") != "object":
- mark_invalid("Top-level schema 'type' must be 'object' for OpenAI tools")
-
- # Begin recursive validation
- recurse(schema, "root", is_root=True)
-
- return status, reasons
diff --git a/letta/functions/types.py b/letta/functions/types.py
deleted file mode 100644
index c5b45e77..00000000
--- a/letta/functions/types.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class SearchTask(BaseModel):
- query: str = Field(description="Search query for web search")
- question: str = Field(description="Question to answer from search results, considering full conversation context")
-
-
-class FileOpenRequest(BaseModel):
- file_name: str = Field(description="Name of the file to open")
- offset: Optional[int] = Field(
- default=None, description="Optional offset for starting line number (0-indexed). If not specified, starts from beginning of file."
- )
- length: Optional[int] = Field(
- default=None, description="Optional number of lines to view from offset (inclusive). If not specified, views to end of file."
- )
diff --git a/letta/functions/typescript_parser.py b/letta/functions/typescript_parser.py
deleted file mode 100644
index 93d449d6..00000000
--- a/letta/functions/typescript_parser.py
+++ /dev/null
@@ -1,196 +0,0 @@
-"""TypeScript function parsing for JSON schema generation."""
-
-import re
-from typing import Any, Dict, Optional
-
-from letta.errors import LettaToolCreateError
-
-
-def derive_typescript_json_schema(source_code: str, name: Optional[str] = None) -> dict:
- """Derives the OpenAI JSON schema for a given TypeScript function source code.
-
- This parser extracts the function signature, parameters, and types from TypeScript
- code and generates a JSON schema compatible with OpenAI's function calling format.
-
- Args:
- source_code: TypeScript source code containing an exported function
- name: Optional function name override
-
- Returns:
- JSON schema dict with name, description, and parameters
-
- Raises:
- LettaToolCreateError: If parsing fails or no exported function is found
- """
- try:
- # Find the exported function
- function_pattern = r"export\s+function\s+(\w+)\s*\((.*?)\)\s*:\s*([\w<>\[\]|]+)?"
- match = re.search(function_pattern, source_code, re.DOTALL)
-
- if not match:
- # Try async function
- async_pattern = r"export\s+async\s+function\s+(\w+)\s*\((.*?)\)\s*:\s*([\w<>\[\]|]+)?"
- match = re.search(async_pattern, source_code, re.DOTALL)
-
- if not match:
- raise LettaToolCreateError("No exported function found in TypeScript source code")
-
- func_name = match.group(1)
- params_str = match.group(2).strip()
- # return_type = match.group(3) if match.group(3) else 'any'
-
- # Use provided name or extracted name
- schema_name = name or func_name
-
- # Extract JSDoc comment for description
- description = extract_jsdoc_description(source_code, func_name)
- if not description:
- description = f"TypeScript function {func_name}"
-
- # Parse parameters
- parameters = parse_typescript_parameters(params_str)
-
- # Build OpenAI-compatible JSON schema
- schema = {
- "name": schema_name,
- "description": description,
- "parameters": {"type": "object", "properties": parameters["properties"], "required": parameters["required"]},
- }
-
- return schema
-
- except Exception as e:
- raise LettaToolCreateError(f"TypeScript schema generation failed: {str(e)}") from e
-
-
-def extract_jsdoc_description(source_code: str, func_name: str) -> Optional[str]:
- """Extract JSDoc description for a function."""
- # Look for JSDoc comment before the function
- jsdoc_pattern = r"/\*\*(.*?)\*/\s*export\s+(?:async\s+)?function\s+" + re.escape(func_name)
- match = re.search(jsdoc_pattern, source_code, re.DOTALL)
-
- if match:
- jsdoc_content = match.group(1)
- # Extract the main description (text before @param tags)
- lines = jsdoc_content.split("\n")
- description_lines = []
-
- for line in lines:
- line = line.strip().lstrip("*").strip()
- if line and not line.startswith("@"):
- description_lines.append(line)
- elif line.startswith("@"):
- break
-
- if description_lines:
- return " ".join(description_lines)
-
- return None
-
-
-def parse_typescript_parameters(params_str: str) -> Dict[str, Any]:
- """Parse TypeScript function parameters and generate JSON schema properties."""
- properties = {}
- required = []
-
- if not params_str:
- return {"properties": properties, "required": required}
-
- # Split parameters by comma (handling nested types)
- params = split_parameters(params_str)
-
- for param in params:
- param = param.strip()
- if not param:
- continue
-
- # Parse parameter name, optional flag, and type
- param_match = re.match(r"(\w+)(\?)?\s*:\s*(.+)", param)
- if param_match:
- param_name = param_match.group(1)
- is_optional = param_match.group(2) == "?"
- param_type = param_match.group(3).strip()
-
- # Convert TypeScript type to JSON schema type
- json_type = typescript_to_json_schema_type(param_type)
-
- properties[param_name] = json_type
-
- # Add to required list if not optional
- if not is_optional:
- required.append(param_name)
-
- return {"properties": properties, "required": required}
-
-
-def split_parameters(params_str: str) -> list:
- """Split parameter string by commas, handling nested types."""
- params = []
- current_param = ""
- depth = 0
-
- for char in params_str:
- if char in "<[{(":
- depth += 1
- elif char in ">]})":
- depth -= 1
- elif char == "," and depth == 0:
- params.append(current_param)
- current_param = ""
- continue
-
- current_param += char
-
- if current_param:
- params.append(current_param)
-
- return params
-
-
-def typescript_to_json_schema_type(ts_type: str) -> Dict[str, Any]:
- """Convert TypeScript type to JSON schema type definition."""
- ts_type = ts_type.strip()
-
- # Basic type mappings
- type_map = {
- "string": {"type": "string"},
- "number": {"type": "number"},
- "boolean": {"type": "boolean"},
- "any": {"type": "string"}, # Default to string for any
- "void": {"type": "null"},
- "null": {"type": "null"},
- "undefined": {"type": "null"},
- }
-
- # Check for basic types
- if ts_type in type_map:
- return type_map[ts_type]
-
- # Handle arrays
- if ts_type.endswith("[]"):
- item_type = ts_type[:-2].strip()
- return {"type": "array", "items": typescript_to_json_schema_type(item_type)}
-
- # Handle Array syntax
- array_match = re.match(r"Array<(.+)>", ts_type)
- if array_match:
- item_type = array_match.group(1)
- return {"type": "array", "items": typescript_to_json_schema_type(item_type)}
-
- # Handle union types (simplified - just use string)
- if "|" in ts_type:
- # For union types, we'll default to string for simplicity
- # A more sophisticated parser could handle this better
- return {"type": "string"}
-
- # Handle object types (simplified)
- if ts_type.startswith("{") and ts_type.endswith("}"):
- return {"type": "object"}
-
- # Handle Record and similar generic types
- record_match = re.match(r"Record<(.+),\s*(.+)>", ts_type)
- if record_match:
- return {"type": "object", "additionalProperties": typescript_to_json_schema_type(record_match.group(2))}
-
- # Default case - treat unknown types as objects
- return {"type": "object"}
diff --git a/letta/groups/dynamic_multi_agent.py b/letta/groups/dynamic_multi_agent.py
deleted file mode 100644
index 500d923d..00000000
--- a/letta/groups/dynamic_multi_agent.py
+++ /dev/null
@@ -1,277 +0,0 @@
-from typing import List, Optional
-
-from letta.agent import Agent, AgentState
-from letta.interface import AgentInterface
-from letta.orm import User
-from letta.schemas.block import Block
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.openai.chat_completion_response import UsageStatistics
-from letta.schemas.usage import LettaUsageStatistics
-from letta.services.tool_manager import ToolManager
-
-
-class DynamicMultiAgent(Agent):
- def __init__(
- self,
- interface: AgentInterface,
- agent_state: AgentState,
- user: User,
- # custom
- group_id: str = "",
- agent_ids: List[str] = [],
- description: str = "",
- max_turns: Optional[int] = None,
- termination_token: str = "DONE!",
- ):
- super().__init__(interface, agent_state, user)
- self.group_id = group_id
- self.agent_ids = agent_ids
- self.description = description
- self.max_turns = max_turns or len(agent_ids)
- self.termination_token = termination_token
-
- self.tool_manager = ToolManager()
-
- def step(
- self,
- input_messages: List[MessageCreate],
- chaining: bool = True,
- max_chaining_steps: Optional[int] = None,
- put_inner_thoughts_first: bool = True,
- **kwargs,
- ) -> LettaUsageStatistics:
- total_usage = UsageStatistics()
- step_count = 0
- speaker_id = None
-
- # Load settings
- token_streaming = self.interface.streaming_mode if hasattr(self.interface, "streaming_mode") else False
- metadata = self.interface.metadata if hasattr(self.interface, "metadata") else None
-
- # Load agents and initialize chat history with indexing
- agents = {self.agent_state.id: self.load_manager_agent()}
- message_index = {self.agent_state.id: 0}
- chat_history: List[MessageCreate] = []
- for agent_id in self.agent_ids:
- agents[agent_id] = self.load_participant_agent(agent_id=agent_id)
- message_index[agent_id] = 0
-
- # Prepare new messages
- new_messages = []
- for message in input_messages:
- if isinstance(message.content, str):
- message.content = [TextContent(text=message.content)]
- message.group_id = self.group_id
- new_messages.append(message)
-
- try:
- for _ in range(self.max_turns):
- # Prepare manager message
- agent_id_options = [agent_id for agent_id in self.agent_ids if agent_id != speaker_id]
- manager_message = self.ask_manager_to_choose_participant_message(
- manager_agent_id=self.agent_state.id,
- new_messages=new_messages,
- chat_history=chat_history,
- agent_id_options=agent_id_options,
- )
-
- # Perform manager step
- manager_agent = agents[self.agent_state.id]
- usage_stats = manager_agent.step(
- input_messages=[manager_message],
- chaining=chaining,
- max_chaining_steps=max_chaining_steps,
- stream=token_streaming,
- skip_verify=True,
- metadata=metadata,
- put_inner_thoughts_first=put_inner_thoughts_first,
- )
-
- # Parse manager response
- responses = Message.to_letta_messages_from_list(manager_agent.last_response_messages)
- assistant_message = [response for response in responses if response.message_type == "assistant_message"][0]
- for name, agent_id in [(agents[agent_id].agent_state.name, agent_id) for agent_id in agent_id_options]:
- if name.lower() in assistant_message.content.lower():
- speaker_id = agent_id
- assert speaker_id is not None, f"No names found in {assistant_message.content}"
-
- # Sum usage
- total_usage.prompt_tokens += usage_stats.prompt_tokens
- total_usage.completion_tokens += usage_stats.completion_tokens
- total_usage.total_tokens += usage_stats.total_tokens
- step_count += 1
-
- # Update chat history
- chat_history.extend(new_messages)
-
- # Perform participant step
- participant_agent = agents[speaker_id]
- usage_stats = participant_agent.step(
- input_messages=chat_history[message_index[speaker_id] :],
- chaining=chaining,
- max_chaining_steps=max_chaining_steps,
- stream=token_streaming,
- skip_verify=True,
- metadata=metadata,
- put_inner_thoughts_first=put_inner_thoughts_first,
- )
-
- # Parse participant response
- responses = Message.to_letta_messages_from_list(
- participant_agent.last_response_messages,
- )
- assistant_messages = [response for response in responses if response.message_type == "assistant_message"]
- new_messages = [
- MessageCreate(
- role="system",
- content=[TextContent(text=message.content)] if isinstance(message.content, str) else message.content,
- name=participant_agent.agent_state.name,
- otid=message.otid,
- sender_id=participant_agent.agent_state.id,
- group_id=self.group_id,
- )
- for message in assistant_messages
- ]
-
- # Update message index
- message_index[speaker_id] = len(chat_history) + len(new_messages)
-
- # Sum usage
- total_usage.prompt_tokens += usage_stats.prompt_tokens
- total_usage.completion_tokens += usage_stats.completion_tokens
- total_usage.total_tokens += usage_stats.total_tokens
- step_count += 1
-
- # Check for termination token
- if any(self.termination_token in message.content for message in new_messages):
- break
-
- # Persist remaining chat history
- chat_history.extend(new_messages)
- for agent_id, index in message_index.items():
- if agent_id == speaker_id:
- continue
- messages_to_persist = []
- for message in chat_history[index:]:
- message_to_persist = Message(
- role=message.role,
- content=message.content,
- name=message.name,
- otid=message.otid,
- sender_id=message.sender_id,
- group_id=message.group_id,
- agent_id=agent_id,
- )
- messages_to_persist.append(message_to_persist)
- self.message_manager.create_many_messages(messages_to_persist, actor=self.user)
-
- except Exception as e:
- raise e
- finally:
- self.interface.step_yield()
-
- self.interface.step_complete()
-
- return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count)
-
- def load_manager_agent(self) -> Agent:
- for participant_agent_id in self.agent_ids:
- participant_agent_state = self.agent_manager.get_agent_by_id(agent_id=participant_agent_id, actor=self.user)
- participant_persona_block = participant_agent_state.memory.get_block(label="persona")
- new_block = self.block_manager.create_or_update_block(
- block=Block(
- label=participant_agent_id,
- value=participant_persona_block.value,
- ),
- actor=self.user,
- )
- self.agent_state = self.agent_manager.update_block_with_label(
- agent_id=self.agent_state.id,
- block_label=participant_agent_id,
- new_block_id=new_block.id,
- actor=self.user,
- )
-
- persona_block = self.agent_state.memory.get_block(label="persona")
- group_chat_manager_persona = (
- f"You are overseeing a group chat with {len(self.agent_ids) - 1} agents and "
- f"one user. Description of the group: {self.description}\n"
- "On each turn, you will be provided with the chat history and latest message. "
- "Your task is to decide which participant should speak next in the chat based "
- "on the chat history. Each agent has a memory block labeled with their ID which "
- "holds info about them, and you should use this context to inform your decision."
- )
- self.agent_state.memory.update_block_value(label="persona", value=persona_block.value + group_chat_manager_persona)
- return Agent(
- agent_state=self.agent_state,
- interface=self.interface,
- user=self.user,
- save_last_response=True,
- )
-
- def load_participant_agent(self, agent_id: str) -> Agent:
- agent_state = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=self.user)
- persona_block = agent_state.memory.get_block(label="persona")
- group_chat_participant_persona = (
- f"You are a participant in a group chat with {len(self.agent_ids) - 1} other "
- "agents and one user. Respond to new messages in the group chat when prompted. "
- f"Description of the group: {self.description}. About you: "
- )
- agent_state.memory.update_block_value(label="persona", value=group_chat_participant_persona + persona_block.value)
- return Agent(
- agent_state=agent_state,
- interface=self.interface,
- user=self.user,
- save_last_response=True,
- )
-
- '''
- def attach_choose_next_participant_tool(self) -> AgentState:
- def choose_next_participant(next_speaker_agent_id: str) -> str:
- """
- Returns ID of the agent in the group chat that should reply to the latest message in the conversation. The agent ID will always be in the format: `agent-{UUID}`.
- Args:
- next_speaker_agent_id (str): The ID of the agent that is most suitable to be the next speaker.
- Returns:
- str: The ID of the agent that should be the next speaker.
- """
- return next_speaker_agent_id
- source_code = parse_source_code(choose_next_participant)
- tool = self.tool_manager.create_or_update_tool(
- Tool(
- source_type="python",
- source_code=source_code,
- name="choose_next_participant",
- ),
- actor=self.user,
- )
- return self.agent_manager.attach_tool(agent_id=self.agent_state.id, tool_id=tool.id, actor=self.user)
- '''
-
- def ask_manager_to_choose_participant_message(
- self,
- manager_agent_id: str,
- new_messages: List[MessageCreate],
- chat_history: List[Message],
- agent_id_options: List[str],
- ) -> MessageCreate:
- text_chat_history = [f"{message.name or 'user'}: {message.content[0].text}" for message in chat_history]
- for message in new_messages:
- text_chat_history.append(f"{message.name or 'user'}: {message.content}")
- context_messages = "\n".join(text_chat_history)
-
- message_text = (
- "Choose the most suitable agent to reply to the latest message in the "
- f"group chat from the following options: {agent_id_options}. Do not "
- "respond to the messages yourself, your task is only to decide the "
- f"next speaker, not to participate. \nChat history:\n{context_messages}"
- )
- return MessageCreate(
- role="user",
- content=[TextContent(text=message_text)],
- name=None,
- otid=Message.generate_otid(),
- sender_id=manager_agent_id,
- group_id=self.group_id,
- )
diff --git a/letta/groups/helpers.py b/letta/groups/helpers.py
deleted file mode 100644
index 69507c0f..00000000
--- a/letta/groups/helpers.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import json
-from typing import Dict, Optional, Union
-
-from letta.agent import Agent
-from letta.interface import AgentInterface
-from letta.orm.group import Group
-from letta.orm.user import User
-from letta.schemas.agent import AgentState
-from letta.schemas.group import ManagerType
-from letta.schemas.letta_message_content import ImageContent, TextContent
-from letta.schemas.message import Message
-from letta.services.mcp.base_client import AsyncBaseMCPClient
-
-
-def load_multi_agent(
- group: Group,
- agent_state: Optional[AgentState],
- actor: User,
- interface: Union[AgentInterface, None] = None,
- mcp_clients: Optional[Dict[str, AsyncBaseMCPClient]] = None,
-) -> Agent:
- if len(group.agent_ids) == 0:
- raise ValueError("Empty group: group must have at least one agent")
-
- if not agent_state:
- raise ValueError("Empty manager agent state: manager agent state must be provided")
-
- match group.manager_type:
- case ManagerType.round_robin:
- from letta.groups.round_robin_multi_agent import RoundRobinMultiAgent
-
- return RoundRobinMultiAgent(
- agent_state=agent_state,
- interface=interface,
- user=actor,
- group_id=group.id,
- agent_ids=group.agent_ids,
- description=group.description,
- max_turns=group.max_turns,
- )
- case ManagerType.dynamic:
- from letta.groups.dynamic_multi_agent import DynamicMultiAgent
-
- return DynamicMultiAgent(
- agent_state=agent_state,
- interface=interface,
- user=actor,
- group_id=group.id,
- agent_ids=group.agent_ids,
- description=group.description,
- max_turns=group.max_turns,
- termination_token=group.termination_token,
- )
- case ManagerType.supervisor:
- from letta.groups.supervisor_multi_agent import SupervisorMultiAgent
-
- return SupervisorMultiAgent(
- agent_state=agent_state,
- interface=interface,
- user=actor,
- group_id=group.id,
- agent_ids=group.agent_ids,
- description=group.description,
- )
- case ManagerType.sleeptime:
- if not agent_state.enable_sleeptime:
- return Agent(
- agent_state=agent_state,
- interface=interface,
- user=actor,
- mcp_clients=mcp_clients,
- )
-
- from letta.groups.sleeptime_multi_agent import SleeptimeMultiAgent
-
- return SleeptimeMultiAgent(
- agent_state=agent_state,
- interface=interface,
- user=actor,
- group_id=group.id,
- agent_ids=group.agent_ids,
- description=group.description,
- sleeptime_agent_frequency=group.sleeptime_agent_frequency,
- )
- case _:
- raise ValueError(f"Type {group.manager_type} is not supported.")
-
-
-def stringify_message(message: Message, use_assistant_name: bool = False) -> str | None:
- assistant_name = message.name or "assistant" if use_assistant_name else "assistant"
- if message.role == "user":
- try:
- messages = []
- for content in message.content:
- if isinstance(content, TextContent):
- messages.append(f"{message.name or 'user'}: {content.text}")
- elif isinstance(content, ImageContent):
- messages.append(f"{message.name or 'user'}: [Image Here]")
- return "\n".join(messages)
- except:
- return f"{message.name or 'user'}: {message.content[0].text}"
- elif message.role == "assistant":
- messages = []
- if message.tool_calls:
- if message.tool_calls[0].function.name == "send_message":
- messages.append(f"{assistant_name}: {json.loads(message.tool_calls[0].function.arguments)['message']}")
- else:
- messages.append(f"{assistant_name}: Calling tool {message.tool_calls[0].function.name}")
- return "\n".join(messages)
- elif message.role == "tool":
- if message.content:
- content = json.loads(message.content[0].text)
- if str(content["message"]) != "None":
- return f"{assistant_name}: Tool call returned {content['message']}"
- return None
- elif message.role == "system":
- return None
- else:
- return f"{message.name or 'user'}: {message.content[0].text}"
diff --git a/letta/groups/round_robin_multi_agent.py b/letta/groups/round_robin_multi_agent.py
deleted file mode 100644
index 9c7b319d..00000000
--- a/letta/groups/round_robin_multi_agent.py
+++ /dev/null
@@ -1,159 +0,0 @@
-from typing import List, Optional
-
-from letta.agent import Agent, AgentState
-from letta.interface import AgentInterface
-from letta.orm import User
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.openai.chat_completion_response import UsageStatistics
-from letta.schemas.usage import LettaUsageStatistics
-
-
-class RoundRobinMultiAgent(Agent):
- def __init__(
- self,
- interface: AgentInterface,
- agent_state: AgentState,
- user: User,
- # custom
- group_id: str = "",
- agent_ids: List[str] = [],
- description: str = "",
- max_turns: Optional[int] = None,
- ):
- super().__init__(interface, agent_state, user)
- self.group_id = group_id
- self.agent_ids = agent_ids
- self.description = description
- self.max_turns = max_turns or len(agent_ids)
-
- def step(
- self,
- input_messages: List[MessageCreate],
- chaining: bool = True,
- max_chaining_steps: Optional[int] = None,
- put_inner_thoughts_first: bool = True,
- **kwargs,
- ) -> LettaUsageStatistics:
- total_usage = UsageStatistics()
- step_count = 0
- speaker_id = None
-
- # Load settings
- token_streaming = self.interface.streaming_mode if hasattr(self.interface, "streaming_mode") else False
- metadata = self.interface.metadata if hasattr(self.interface, "metadata") else None
-
- # Load agents and initialize chat history with indexing
- agents, message_index = {}, {}
- chat_history: List[MessageCreate] = []
- for agent_id in self.agent_ids:
- agents[agent_id] = self.load_participant_agent(agent_id=agent_id)
- message_index[agent_id] = 0
-
- # Prepare new messages
- new_messages = []
- for message in input_messages:
- if isinstance(message.content, str):
- message.content = [TextContent(text=message.content)]
- message.group_id = self.group_id
- new_messages.append(message)
-
- try:
- for i in range(self.max_turns):
- # Select speaker
- speaker_id = self.agent_ids[i % len(self.agent_ids)]
-
- # Update chat history
- chat_history.extend(new_messages)
-
- # Perform participant step
- participant_agent = agents[speaker_id]
- usage_stats = participant_agent.step(
- input_messages=chat_history[message_index[speaker_id] :],
- chaining=chaining,
- max_chaining_steps=max_chaining_steps,
- stream=token_streaming,
- skip_verify=True,
- metadata=metadata,
- put_inner_thoughts_first=put_inner_thoughts_first,
- )
-
- # Parse participant response
- responses = Message.to_letta_messages_from_list(participant_agent.last_response_messages)
- assistant_messages = [response for response in responses if response.message_type == "assistant_message"]
- new_messages = [
- MessageCreate(
- role="system",
- content=[TextContent(text=message.content)] if isinstance(message.content, str) else message.content,
- name=participant_agent.agent_state.name,
- otid=message.otid,
- sender_id=participant_agent.agent_state.id,
- group_id=self.group_id,
- )
- for message in assistant_messages
- ]
-
- # Update message index
- message_index[speaker_id] = len(chat_history) + len(new_messages)
-
- # Sum usage
- total_usage.prompt_tokens += usage_stats.prompt_tokens
- total_usage.completion_tokens += usage_stats.completion_tokens
- total_usage.total_tokens += usage_stats.total_tokens
- step_count += 1
-
- # Persist remaining chat history
- chat_history.extend(new_messages)
- for agent_id, index in message_index.items():
- if agent_id == speaker_id:
- continue
- messages_to_persist = []
- for message in chat_history[index:]:
- message_to_persist = Message(
- role=message.role,
- content=message.content,
- name=message.name,
- otid=message.otid,
- sender_id=message.sender_id,
- group_id=self.group_id,
- agent_id=agent_id,
- )
- messages_to_persist.append(message_to_persist)
- self.message_manager.create_many_messages(messages_to_persist, actor=self.user)
-
- except Exception as e:
- raise e
- finally:
- self.interface.step_yield()
-
- self.interface.step_complete()
-
- return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count)
-
- def load_participant_agent(self, agent_id: str) -> Agent:
- agent_state = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=self.user)
- persona_block = agent_state.memory.get_block(label="persona")
- group_chat_participant_persona = (
- f"%%% GROUP CHAT CONTEXT %%% "
- f"You are speaking in a group chat with {len(self.agent_ids)} other participants. "
- f"Group Description: {self.description} "
- "INTERACTION GUIDELINES:\n"
- "1. Be aware that others can see your messages - communicate as if in a real group conversation\n"
- "2. Acknowledge and build upon others' contributions when relevant\n"
- "3. Stay on topic while adding your unique perspective based on your role and personality\n"
- "4. Be concise but engaging - give others space to contribute\n"
- "5. Maintain your character's personality while being collaborative\n"
- "6. Feel free to ask questions to other participants to encourage discussion\n"
- "7. If someone addresses you directly, acknowledge their message\n"
- "8. Share relevant experiences or knowledge that adds value to the conversation\n\n"
- "Remember: This is a natural group conversation. Interact as you would in a real group setting, "
- "staying true to your character while fostering meaningful dialogue. "
- "%%% END GROUP CHAT CONTEXT %%%"
- )
- agent_state.memory.update_block_value(label="persona", value=persona_block.value + group_chat_participant_persona)
- return Agent(
- agent_state=agent_state,
- interface=self.interface,
- user=self.user,
- save_last_response=True,
- )
diff --git a/letta/groups/sleeptime_multi_agent.py b/letta/groups/sleeptime_multi_agent.py
deleted file mode 100644
index b207219c..00000000
--- a/letta/groups/sleeptime_multi_agent.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import asyncio
-import threading
-from datetime import datetime, timezone
-from typing import List, Optional
-
-from letta.agent import Agent, AgentState
-from letta.groups.helpers import stringify_message
-from letta.interface import AgentInterface
-from letta.orm import User
-from letta.schemas.enums import JobStatus
-from letta.schemas.job import JobUpdate
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.run import Run
-from letta.schemas.usage import LettaUsageStatistics
-from letta.server.rest_api.interface import StreamingServerInterface
-from letta.services.group_manager import GroupManager
-from letta.services.job_manager import JobManager
-from letta.services.message_manager import MessageManager
-
-
-class SleeptimeMultiAgent(Agent):
- def __init__(
- self,
- interface: AgentInterface,
- agent_state: AgentState,
- user: User,
- # mcp_clients: Optional[Dict[str, BaseMCPClient]] = None,
- # custom
- group_id: str = "",
- agent_ids: List[str] = [],
- description: str = "",
- sleeptime_agent_frequency: Optional[int] = None,
- ):
- super().__init__(interface, agent_state, user)
- self.group_id = group_id
- self.agent_ids = agent_ids
- self.description = description
- self.sleeptime_agent_frequency = sleeptime_agent_frequency
- self.group_manager = GroupManager()
- self.message_manager = MessageManager()
- self.job_manager = JobManager()
- # TODO: add back MCP support with new agent loop
- self.mcp_clients = {}
-
- def _run_async_in_new_thread(self, coro):
- """Run an async coroutine in a new thread with its own event loop"""
-
- def run_async():
- loop = asyncio.new_event_loop()
- asyncio.set_event_loop(loop)
- try:
- loop.run_until_complete(coro)
- finally:
- loop.close()
- asyncio.set_event_loop(None)
-
- thread = threading.Thread(target=run_async)
- thread.daemon = True
- thread.start()
-
- def _issue_background_task(
- self,
- participant_agent_id: str,
- messages: List[Message],
- chaining: bool,
- max_chaining_steps: Optional[int],
- token_streaming: bool,
- metadata: Optional[dict],
- put_inner_thoughts_first: bool,
- last_processed_message_id: str,
- ) -> str:
- run = Run(
- user_id=self.user.id,
- status=JobStatus.created,
- metadata={
- "job_type": "background_agent_send_message_async",
- "agent_id": participant_agent_id,
- },
- )
- run = self.job_manager.create_job(pydantic_job=run, actor=self.user)
-
- self._run_async_in_new_thread(
- self._perform_background_agent_step(
- participant_agent_id=participant_agent_id,
- messages=messages,
- chaining=chaining,
- max_chaining_steps=max_chaining_steps,
- token_streaming=token_streaming,
- metadata=metadata,
- put_inner_thoughts_first=put_inner_thoughts_first,
- last_processed_message_id=last_processed_message_id,
- run_id=run.id,
- )
- )
-
- return run.id
-
- async def _perform_background_agent_step(
- self,
- participant_agent_id: str,
- messages: List[Message],
- chaining: bool,
- max_chaining_steps: Optional[int],
- token_streaming: bool,
- metadata: Optional[dict],
- put_inner_thoughts_first: bool,
- last_processed_message_id: str,
- run_id: str,
- ) -> LettaUsageStatistics:
- try:
- job_update = JobUpdate(status=JobStatus.running)
- self.job_manager.update_job_by_id(job_id=run_id, job_update=job_update, actor=self.user)
-
- participant_agent_state = self.agent_manager.get_agent_by_id(participant_agent_id, actor=self.user)
- participant_agent = Agent(
- agent_state=participant_agent_state,
- interface=StreamingServerInterface(),
- user=self.user,
- mcp_clients=self.mcp_clients,
- )
-
- prior_messages = []
- if self.sleeptime_agent_frequency:
- try:
- prior_messages = self.message_manager.list_messages_for_agent(
- agent_id=self.agent_state.id,
- actor=self.user,
- after=last_processed_message_id,
- before=messages[0].id,
- )
- except Exception as e:
- print(f"Error fetching prior messages: {str(e)}")
- # continue with just latest messages
-
- transcript_summary = [stringify_message(message) for message in prior_messages + messages]
- transcript_summary = [summary for summary in transcript_summary if summary is not None]
- message_text = "\n".join(transcript_summary)
-
- participant_agent_messages = [
- Message(
- id=Message.generate_id(),
- agent_id=participant_agent.agent_state.id,
- role="user",
- content=[TextContent(text=message_text)],
- group_id=self.group_id,
- )
- ]
-
- # Convert Message objects to MessageCreate objects
- message_creates = [
- MessageCreate(
- role=m.role,
- content=m.content[0].text if m.content and len(m.content) == 1 else m.content,
- name=m.name,
- otid=m.otid,
- sender_id=m.sender_id,
- )
- for m in participant_agent_messages
- ]
-
- result = participant_agent.step(
- input_messages=message_creates,
- chaining=chaining,
- max_chaining_steps=max_chaining_steps,
- stream=token_streaming,
- skip_verify=True,
- metadata=metadata,
- put_inner_thoughts_first=put_inner_thoughts_first,
- )
- job_update = JobUpdate(
- status=JobStatus.completed,
- completed_at=datetime.now(timezone.utc),
- metadata={
- "result": result.model_dump(mode="json"),
- "agent_id": participant_agent.agent_state.id,
- },
- )
- self.job_manager.update_job_by_id(job_id=run_id, job_update=job_update, actor=self.user)
- return result
- except Exception as e:
- job_update = JobUpdate(
- status=JobStatus.failed,
- completed_at=datetime.now(timezone.utc),
- metadata={"error": str(e)},
- )
- self.job_manager.update_job_by_id(job_id=run_id, job_update=job_update, actor=self.user)
- raise
-
- def step(
- self,
- input_messages: List[MessageCreate],
- chaining: bool = True,
- max_chaining_steps: Optional[int] = None,
- put_inner_thoughts_first: bool = True,
- **kwargs,
- ) -> LettaUsageStatistics:
- run_ids = []
-
- # Load settings
- token_streaming = self.interface.streaming_mode if hasattr(self.interface, "streaming_mode") else False
- metadata = self.interface.metadata if hasattr(self.interface, "metadata") else None
-
- # Prepare new messages
- new_messages = []
- for message in input_messages:
- if isinstance(message.content, str):
- message.content = [TextContent(text=message.content)]
- message.group_id = self.group_id
- new_messages.append(message)
-
- try:
- # Load main agent
- main_agent = Agent(
- agent_state=self.agent_state,
- interface=self.interface,
- user=self.user,
- mcp_clients=self.mcp_clients,
- )
- # Perform main agent step
- usage_stats = main_agent.step(
- input_messages=new_messages,
- chaining=chaining,
- max_chaining_steps=max_chaining_steps,
- stream=token_streaming,
- skip_verify=True,
- metadata=metadata,
- put_inner_thoughts_first=put_inner_thoughts_first,
- )
-
- # Update turns counter
- turns_counter = None
- if self.sleeptime_agent_frequency is not None and self.sleeptime_agent_frequency > 0:
- turns_counter = self.group_manager.bump_turns_counter(group_id=self.group_id, actor=self.user)
-
- # Perform participant steps
- if self.sleeptime_agent_frequency is None or (
- turns_counter is not None and turns_counter % self.sleeptime_agent_frequency == 0
- ):
- last_response_messages = [message for sublist in usage_stats.steps_messages for message in sublist]
- last_processed_message_id = self.group_manager.get_last_processed_message_id_and_update(
- group_id=self.group_id, last_processed_message_id=last_response_messages[-1].id, actor=self.user
- )
- for participant_agent_id in self.agent_ids:
- try:
- run_id = self._issue_background_task(
- participant_agent_id,
- last_response_messages,
- chaining,
- max_chaining_steps,
- token_streaming,
- metadata,
- put_inner_thoughts_first,
- last_processed_message_id,
- )
- run_ids.append(run_id)
-
- except Exception as e:
- # Handle individual task failures
- print(f"Agent processing failed: {str(e)}")
- raise e
-
- except Exception as e:
- raise e
- finally:
- self.interface.step_yield()
-
- self.interface.step_complete()
-
- usage_stats.run_ids = run_ids
- return LettaUsageStatistics(**usage_stats.model_dump())
diff --git a/letta/groups/sleeptime_multi_agent_v2.py b/letta/groups/sleeptime_multi_agent_v2.py
deleted file mode 100644
index 275fe3bf..00000000
--- a/letta/groups/sleeptime_multi_agent_v2.py
+++ /dev/null
@@ -1,334 +0,0 @@
-import asyncio
-from collections.abc import AsyncGenerator
-from datetime import datetime, timezone
-
-from letta.agents.base_agent import BaseAgent
-from letta.agents.letta_agent import LettaAgent
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.groups.helpers import stringify_message
-from letta.otel.tracing import trace_method
-from letta.schemas.enums import JobStatus
-from letta.schemas.group import Group, ManagerType
-from letta.schemas.job import JobUpdate
-from letta.schemas.letta_message import MessageType
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.run import Run
-from letta.schemas.user import User
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.group_manager import GroupManager
-from letta.services.job_manager import JobManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.step_manager import NoopStepManager, StepManager
-from letta.services.telemetry_manager import NoopTelemetryManager, TelemetryManager
-
-
-class SleeptimeMultiAgentV2(BaseAgent):
- def __init__(
- self,
- agent_id: str,
- message_manager: MessageManager,
- agent_manager: AgentManager,
- block_manager: BlockManager,
- passage_manager: PassageManager,
- group_manager: GroupManager,
- job_manager: JobManager,
- actor: User,
- step_manager: StepManager = NoopStepManager(),
- telemetry_manager: TelemetryManager = NoopTelemetryManager(),
- group: Group | None = None,
- current_run_id: str | None = None,
- ):
- super().__init__(
- agent_id=agent_id,
- openai_client=None,
- message_manager=message_manager,
- agent_manager=agent_manager,
- actor=actor,
- )
- self.block_manager = block_manager
- self.passage_manager = passage_manager
- self.group_manager = group_manager
- self.job_manager = job_manager
- self.step_manager = step_manager
- self.telemetry_manager = telemetry_manager
- self.current_run_id = current_run_id
- # Group settings
- assert group.manager_type == ManagerType.sleeptime, f"Expected group manager type to be 'sleeptime', got {group.manager_type}"
- self.group = group
-
- @trace_method
- async def step(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- run_id: str | None = None,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: int | None = None,
- include_return_message_types: list[MessageType] | None = None,
- ) -> LettaResponse:
- run_ids = []
-
- # Prepare new messages
- new_messages = []
- for message in input_messages:
- if isinstance(message.content, str):
- message.content = [TextContent(text=message.content)]
- message.group_id = self.group.id
- new_messages.append(message)
-
- # Load foreground agent
- foreground_agent = LettaAgent(
- agent_id=self.agent_id,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- block_manager=self.block_manager,
- job_manager=self.job_manager,
- passage_manager=self.passage_manager,
- actor=self.actor,
- step_manager=self.step_manager,
- telemetry_manager=self.telemetry_manager,
- current_run_id=self.current_run_id,
- )
- # Perform foreground agent step
- response = await foreground_agent.step(
- input_messages=new_messages,
- max_steps=max_steps,
- run_id=run_id,
- use_assistant_message=use_assistant_message,
- include_return_message_types=include_return_message_types,
- )
-
- # Get last response messages
- last_response_messages = foreground_agent.response_messages
-
- # Update turns counter
- if self.group.sleeptime_agent_frequency is not None and self.group.sleeptime_agent_frequency > 0:
- turns_counter = await self.group_manager.bump_turns_counter_async(group_id=self.group.id, actor=self.actor)
-
- # Perform participant steps
- if self.group.sleeptime_agent_frequency is None or (
- turns_counter is not None and turns_counter % self.group.sleeptime_agent_frequency == 0
- ):
- last_processed_message_id = await self.group_manager.get_last_processed_message_id_and_update_async(
- group_id=self.group.id, last_processed_message_id=last_response_messages[-1].id, actor=self.actor
- )
- for participant_agent_id in self.group.agent_ids:
- try:
- run_id = await self._issue_background_task(
- participant_agent_id,
- last_response_messages,
- last_processed_message_id,
- use_assistant_message,
- )
- run_ids.append(run_id)
-
- except Exception as e:
- # Individual task failures
- print(f"Agent processing failed: {e!s}")
- raise e
-
- response.usage.run_ids = run_ids
- return response
-
- @trace_method
- async def step_stream_no_tokens(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: int | None = None,
- include_return_message_types: list[MessageType] | None = None,
- ):
- response = await self.step(
- input_messages=input_messages,
- max_steps=max_steps,
- use_assistant_message=use_assistant_message,
- request_start_timestamp_ns=request_start_timestamp_ns,
- include_return_message_types=include_return_message_types,
- )
-
- for message in response.messages:
- yield f"data: {message.model_dump_json()}\n\n"
-
- for finish_chunk in self.get_finish_chunks_for_stream(response.usage):
- yield f"data: {finish_chunk}\n\n"
-
- @trace_method
- async def step_stream(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: int | None = None,
- include_return_message_types: list[MessageType] | None = None,
- ) -> AsyncGenerator[str, None]:
- # Prepare new messages
- new_messages = []
- for message in input_messages:
- if isinstance(message.content, str):
- message.content = [TextContent(text=message.content)]
- message.group_id = self.group.id
- new_messages.append(message)
-
- # Load foreground agent
- foreground_agent = LettaAgent(
- agent_id=self.agent_id,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- block_manager=self.block_manager,
- job_manager=self.job_manager,
- passage_manager=self.passage_manager,
- actor=self.actor,
- step_manager=self.step_manager,
- telemetry_manager=self.telemetry_manager,
- current_run_id=self.current_run_id,
- )
- # Perform foreground agent step
- async for chunk in foreground_agent.step_stream(
- input_messages=new_messages,
- max_steps=max_steps,
- use_assistant_message=use_assistant_message,
- request_start_timestamp_ns=request_start_timestamp_ns,
- include_return_message_types=include_return_message_types,
- ):
- yield chunk
-
- # Get response messages
- last_response_messages = foreground_agent.response_messages
-
- # Update turns counter
- if self.group.sleeptime_agent_frequency is not None and self.group.sleeptime_agent_frequency > 0:
- turns_counter = await self.group_manager.bump_turns_counter_async(group_id=self.group.id, actor=self.actor)
-
- # Perform participant steps
- if self.group.sleeptime_agent_frequency is None or (
- turns_counter is not None and turns_counter % self.group.sleeptime_agent_frequency == 0
- ):
- last_processed_message_id = await self.group_manager.get_last_processed_message_id_and_update_async(
- group_id=self.group.id, last_processed_message_id=last_response_messages[-1].id, actor=self.actor
- )
- for sleeptime_agent_id in self.group.agent_ids:
- run_id = await self._issue_background_task(
- sleeptime_agent_id,
- last_response_messages,
- last_processed_message_id,
- use_assistant_message,
- )
-
- async def _issue_background_task(
- self,
- sleeptime_agent_id: str,
- response_messages: list[Message],
- last_processed_message_id: str,
- use_assistant_message: bool = True,
- ) -> str:
- run = Run(
- user_id=self.actor.id,
- status=JobStatus.created,
- metadata={
- "job_type": "sleeptime_agent_send_message_async", # is this right?
- "agent_id": sleeptime_agent_id,
- },
- )
- run = await self.job_manager.create_job_async(pydantic_job=run, actor=self.actor)
-
- asyncio.create_task(
- self._participant_agent_step(
- foreground_agent_id=self.agent_id,
- sleeptime_agent_id=sleeptime_agent_id,
- response_messages=response_messages,
- last_processed_message_id=last_processed_message_id,
- run_id=run.id,
- use_assistant_message=True,
- )
- )
- return run.id
-
- async def _participant_agent_step(
- self,
- foreground_agent_id: str,
- sleeptime_agent_id: str,
- response_messages: list[Message],
- last_processed_message_id: str,
- run_id: str,
- use_assistant_message: bool = True,
- ) -> str:
- try:
- # Update job status
- job_update = JobUpdate(status=JobStatus.running)
- await self.job_manager.update_job_by_id_async(job_id=run_id, job_update=job_update, actor=self.actor)
-
- # Create conversation transcript
- prior_messages = []
- if self.group.sleeptime_agent_frequency:
- try:
- prior_messages = await self.message_manager.list_messages_for_agent_async(
- agent_id=foreground_agent_id,
- actor=self.actor,
- after=last_processed_message_id,
- before=response_messages[0].id,
- )
- except Exception:
- pass # continue with just latest messages
-
- transcript_summary = [stringify_message(message) for message in prior_messages + response_messages]
- transcript_summary = [summary for summary in transcript_summary if summary is not None]
- message_text = "\n".join(transcript_summary)
-
- sleeptime_agent_messages = [
- MessageCreate(
- role="user",
- content=[TextContent(text=message_text)],
- id=Message.generate_id(),
- agent_id=sleeptime_agent_id,
- group_id=self.group.id,
- )
- ]
-
- # Load sleeptime agent
- sleeptime_agent = LettaAgent(
- agent_id=sleeptime_agent_id,
- message_manager=self.message_manager,
- agent_manager=self.agent_manager,
- block_manager=self.block_manager,
- job_manager=self.job_manager,
- passage_manager=self.passage_manager,
- actor=self.actor,
- step_manager=self.step_manager,
- telemetry_manager=self.telemetry_manager,
- current_run_id=self.current_run_id,
- message_buffer_limit=20, # TODO: Make this configurable
- message_buffer_min=8, # TODO: Make this configurable
- enable_summarization=False, # TODO: Make this configurable
- )
-
- # Perform sleeptime agent step
- result = await sleeptime_agent.step(
- input_messages=sleeptime_agent_messages,
- use_assistant_message=use_assistant_message,
- run_id=run_id,
- )
-
- # Update job status
- job_update = JobUpdate(
- status=JobStatus.completed,
- completed_at=datetime.now(timezone.utc).replace(tzinfo=None),
- metadata={
- "result": result.model_dump(mode="json"),
- "agent_id": sleeptime_agent_id,
- },
- )
- await self.job_manager.update_job_by_id_async(job_id=run_id, job_update=job_update, actor=self.actor)
- return result
- except Exception as e:
- job_update = JobUpdate(
- status=JobStatus.failed,
- completed_at=datetime.now(timezone.utc).replace(tzinfo=None),
- metadata={"error": str(e)},
- )
- await self.job_manager.update_job_by_id_async(job_id=run_id, job_update=job_update, actor=self.actor)
- raise
diff --git a/letta/groups/sleeptime_multi_agent_v3.py b/letta/groups/sleeptime_multi_agent_v3.py
deleted file mode 100644
index e95310e5..00000000
--- a/letta/groups/sleeptime_multi_agent_v3.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import asyncio
-from collections.abc import AsyncGenerator
-from datetime import datetime, timezone
-
-from letta.agents.letta_agent_v2 import LettaAgentV2
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.groups.helpers import stringify_message
-from letta.schemas.agent import AgentState
-from letta.schemas.enums import JobStatus
-from letta.schemas.group import Group, ManagerType
-from letta.schemas.job import JobUpdate
-from letta.schemas.letta_message import MessageType
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.message import Message, MessageCreate
-from letta.schemas.run import Run
-from letta.schemas.user import User
-from letta.services.group_manager import GroupManager
-
-
-class SleeptimeMultiAgentV3(LettaAgentV2):
- def __init__(
- self,
- agent_state: AgentState,
- actor: User,
- group: Group,
- ):
- super().__init__(agent_state, actor)
- assert group.manager_type == ManagerType.sleeptime, f"Expected group type to be 'sleeptime', got {group.manager_type}"
- self.group = group
- self.run_ids = []
-
- # Additional manager classes
- self.group_manager = GroupManager()
-
- async def step(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- run_id: str | None = None,
- use_assistant_message: bool = False,
- include_return_message_types: list[MessageType] | None = None,
- request_start_timestamp_ns: int | None = None,
- ) -> LettaResponse:
- self.run_ids = []
-
- for i in range(len(input_messages)):
- input_messages[i].group_id = self.group.id
-
- response = await super().step(
- input_messages=input_messages,
- max_steps=max_steps,
- run_id=run_id,
- use_assistant_message=use_assistant_message,
- include_return_message_types=include_return_message_types,
- request_start_timestamp_ns=request_start_timestamp_ns,
- )
-
- await self.run_sleeptime_agents(use_assistant_message=use_assistant_message)
-
- response.usage.run_ids = self.run_ids
- return response
-
- async def stream(
- self,
- input_messages: list[MessageCreate],
- max_steps: int = DEFAULT_MAX_STEPS,
- stream_tokens: bool = True,
- run_id: str | None = None,
- use_assistant_message: bool = True,
- request_start_timestamp_ns: int | None = None,
- include_return_message_types: list[MessageType] | None = None,
- ) -> AsyncGenerator[str, None]:
- self.run_ids = []
-
- for i in range(len(input_messages)):
- input_messages[i].group_id = self.group.id
-
- # Perform foreground agent step
- async for chunk in super().stream(
- input_messages=input_messages,
- max_steps=max_steps,
- stream_tokens=stream_tokens,
- run_id=run_id,
- use_assistant_message=use_assistant_message,
- include_return_message_types=include_return_message_types,
- request_start_timestamp_ns=request_start_timestamp_ns,
- ):
- yield chunk
-
- await self.run_sleeptime_agents(use_assistant_message=use_assistant_message)
-
- async def run_sleeptime_agents(self, use_assistant_message: bool = True):
- # Get response messages
- last_response_messages = self.response_messages
-
- # Update turns counter
- turns_counter = None
- if self.group.sleeptime_agent_frequency is not None and self.group.sleeptime_agent_frequency > 0:
- turns_counter = await self.group_manager.bump_turns_counter_async(group_id=self.group.id, actor=self.actor)
-
- # Perform participant steps
- if self.group.sleeptime_agent_frequency is None or (
- turns_counter is not None and turns_counter % self.group.sleeptime_agent_frequency == 0
- ):
- last_processed_message_id = await self.group_manager.get_last_processed_message_id_and_update_async(
- group_id=self.group.id, last_processed_message_id=last_response_messages[-1].id, actor=self.actor
- )
- for sleeptime_agent_id in self.group.agent_ids:
- try:
- sleeptime_run_id = await self._issue_background_task(
- sleeptime_agent_id,
- last_response_messages,
- last_processed_message_id,
- use_assistant_message,
- )
- self.run_ids.append(sleeptime_run_id)
- except Exception as e:
- # Individual task failures
- print(f"Sleeptime agent processing failed: {e!s}")
- raise e
-
- async def _issue_background_task(
- self,
- sleeptime_agent_id: str,
- response_messages: list[Message],
- last_processed_message_id: str,
- use_assistant_message: bool = True,
- ) -> str:
- run = Run(
- user_id=self.actor.id,
- status=JobStatus.created,
- metadata={
- "job_type": "sleeptime_agent_send_message_async", # is this right?
- "agent_id": sleeptime_agent_id,
- },
- )
- run = await self.job_manager.create_job_async(pydantic_job=run, actor=self.actor)
-
- asyncio.create_task(
- self._participant_agent_step(
- foreground_agent_id=self.agent_state.id,
- sleeptime_agent_id=sleeptime_agent_id,
- response_messages=response_messages,
- last_processed_message_id=last_processed_message_id,
- run_id=run.id,
- use_assistant_message=use_assistant_message,
- )
- )
- return run.id
-
- async def _participant_agent_step(
- self,
- foreground_agent_id: str,
- sleeptime_agent_id: str,
- response_messages: list[Message],
- last_processed_message_id: str,
- run_id: str,
- use_assistant_message: bool = True,
- ) -> LettaResponse:
- try:
- # Update job status
- job_update = JobUpdate(status=JobStatus.running)
- await self.job_manager.update_job_by_id_async(job_id=run_id, job_update=job_update, actor=self.actor)
-
- # Create conversation transcript
- prior_messages = []
- if self.group.sleeptime_agent_frequency:
- try:
- prior_messages = await self.message_manager.list_messages_for_agent_async(
- agent_id=foreground_agent_id,
- actor=self.actor,
- after=last_processed_message_id,
- before=response_messages[0].id,
- )
- except Exception:
- pass # continue with just latest messages
-
- transcript_summary = [stringify_message(message) for message in prior_messages + response_messages]
- transcript_summary = [summary for summary in transcript_summary if summary is not None]
- message_text = "\n".join(transcript_summary)
-
- sleeptime_agent_messages = [
- MessageCreate(
- role="user",
- content=[TextContent(text=message_text)],
- id=Message.generate_id(),
- agent_id=sleeptime_agent_id,
- group_id=self.group.id,
- )
- ]
-
- # Load sleeptime agent
- sleeptime_agent_state = await self.agent_manager.get_agent_by_id_async(agent_id=sleeptime_agent_id, actor=self.actor)
- sleeptime_agent = LettaAgentV2(
- agent_state=sleeptime_agent_state,
- actor=self.actor,
- )
-
- # Perform sleeptime agent step
- result = await sleeptime_agent.step(
- input_messages=sleeptime_agent_messages,
- run_id=run_id,
- use_assistant_message=use_assistant_message,
- )
-
- # Update job status
- job_update = JobUpdate(
- status=JobStatus.completed,
- completed_at=datetime.now(timezone.utc).replace(tzinfo=None),
- metadata={
- "result": result.model_dump(mode="json"),
- "agent_id": sleeptime_agent_state.id,
- },
- )
- await self.job_manager.update_job_by_id_async(job_id=run_id, job_update=job_update, actor=self.actor)
- return result
- except Exception as e:
- job_update = JobUpdate(
- status=JobStatus.failed,
- completed_at=datetime.now(timezone.utc).replace(tzinfo=None),
- metadata={"error": str(e)},
- )
- await self.job_manager.update_job_by_id_async(job_id=run_id, job_update=job_update, actor=self.actor)
- raise
diff --git a/letta/groups/supervisor_multi_agent.py b/letta/groups/supervisor_multi_agent.py
deleted file mode 100644
index 35b5bf98..00000000
--- a/letta/groups/supervisor_multi_agent.py
+++ /dev/null
@@ -1,116 +0,0 @@
-from typing import List, Optional
-
-from letta.agent import Agent, AgentState
-from letta.constants import DEFAULT_MESSAGE_TOOL
-from letta.functions.function_sets.multi_agent import send_message_to_all_agents_in_group
-from letta.functions.functions import parse_source_code
-from letta.functions.schema_generator import generate_schema
-from letta.interface import AgentInterface
-from letta.orm import User
-from letta.schemas.enums import ToolType
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.message import MessageCreate
-from letta.schemas.tool import Tool
-from letta.schemas.tool_rule import ChildToolRule, InitToolRule, TerminalToolRule
-from letta.schemas.usage import LettaUsageStatistics
-from letta.services.agent_manager import AgentManager
-from letta.services.tool_manager import ToolManager
-
-
-class SupervisorMultiAgent(Agent):
- def __init__(
- self,
- interface: AgentInterface,
- agent_state: AgentState,
- user: User,
- # custom
- group_id: str = "",
- agent_ids: List[str] = [],
- description: str = "",
- ):
- super().__init__(interface, agent_state, user)
- self.group_id = group_id
- self.agent_ids = agent_ids
- self.description = description
- self.agent_manager = AgentManager()
- self.tool_manager = ToolManager()
-
- def step(
- self,
- input_messages: List[MessageCreate],
- chaining: bool = True,
- max_chaining_steps: Optional[int] = None,
- put_inner_thoughts_first: bool = True,
- assistant_message_tool_name: str = DEFAULT_MESSAGE_TOOL,
- **kwargs,
- ) -> LettaUsageStatistics:
- # Load settings
- token_streaming = self.interface.streaming_mode if hasattr(self.interface, "streaming_mode") else False
- metadata = self.interface.metadata if hasattr(self.interface, "metadata") else None
-
- # Prepare supervisor agent
- if self.tool_manager.get_tool_by_name(tool_name="send_message_to_all_agents_in_group", actor=self.user) is None:
- multi_agent_tool = Tool(
- name=send_message_to_all_agents_in_group.__name__,
- description="",
- source_type="python",
- tags=[],
- source_code=parse_source_code(send_message_to_all_agents_in_group),
- json_schema=generate_schema(send_message_to_all_agents_in_group, None),
- )
- multi_agent_tool.tool_type = ToolType.LETTA_MULTI_AGENT_CORE
- multi_agent_tool = self.tool_manager.create_or_update_tool(
- pydantic_tool=multi_agent_tool,
- actor=self.user,
- )
- self.agent_state = self.agent_manager.attach_tool(agent_id=self.agent_state.id, tool_id=multi_agent_tool.id, actor=self.user)
-
- old_tool_rules = self.agent_state.tool_rules
- self.agent_state.tool_rules = [
- InitToolRule(
- tool_name="send_message_to_all_agents_in_group",
- ),
- TerminalToolRule(
- tool_name=assistant_message_tool_name,
- ),
- ChildToolRule(
- tool_name="send_message_to_all_agents_in_group",
- children=[assistant_message_tool_name],
- ),
- ]
-
- # Prepare new messages
- new_messages = []
- for message in input_messages:
- if isinstance(message.content, str):
- message.content = [TextContent(text=message.content)]
- message.group_id = self.group_id
- new_messages.append(message)
-
- try:
- # Load supervisor agent
- supervisor_agent = Agent(
- agent_state=self.agent_state,
- interface=self.interface,
- user=self.user,
- )
-
- # Perform supervisor step
- usage_stats = supervisor_agent.step(
- input_messages=new_messages,
- chaining=chaining,
- max_chaining_steps=max_chaining_steps,
- stream=token_streaming,
- skip_verify=True,
- metadata=metadata,
- put_inner_thoughts_first=put_inner_thoughts_first,
- )
- except Exception as e:
- raise e
- finally:
- self.interface.step_yield()
- self.agent_state.tool_rules = old_tool_rules
-
- self.interface.step_complete()
-
- return usage_stats
diff --git a/letta/helpers/__init__.py b/letta/helpers/__init__.py
deleted file mode 100644
index 62e8d709..00000000
--- a/letta/helpers/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from letta.helpers.tool_rule_solver import ToolRulesSolver
diff --git a/letta/helpers/composio_helpers.py b/letta/helpers/composio_helpers.py
deleted file mode 100644
index 1e6e31d6..00000000
--- a/letta/helpers/composio_helpers.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from logging import Logger
-from typing import Optional
-
-from letta.schemas.user import User
-from letta.services.sandbox_config_manager import SandboxConfigManager
-from letta.settings import tool_settings
-
-
-def get_composio_api_key(actor: User, logger: Optional[Logger] = None) -> Optional[str]:
- api_keys = SandboxConfigManager().list_sandbox_env_vars_by_key(key="COMPOSIO_API_KEY", actor=actor)
- if not api_keys:
- if logger:
- logger.debug("No API keys found for Composio. Defaulting to the environment variable...")
- if tool_settings.composio_api_key:
- return tool_settings.composio_api_key
- else:
- return None
- else:
- # TODO: Add more protections around this
- # Ideally, not tied to a specific sandbox, but for now we just get the first one
- # Theoretically possible for someone to have different composio api keys per sandbox
- return api_keys[0].value
-
-
-async def get_composio_api_key_async(actor: User, logger: Optional[Logger] = None) -> Optional[str]:
- api_keys = await SandboxConfigManager().list_sandbox_env_vars_by_key_async(key="COMPOSIO_API_KEY", actor=actor)
- if not api_keys:
- if logger:
- logger.debug("No API keys found for Composio. Defaulting to the environment variable...")
- if tool_settings.composio_api_key:
- return tool_settings.composio_api_key
- else:
- return None
- else:
- # TODO: Add more protections around this
- # Ideally, not tied to a specific sandbox, but for now we just get the first one
- # Theoretically possible for someone to have different composio api keys per sandbox
- return api_keys[0].value
diff --git a/letta/helpers/converters.py b/letta/helpers/converters.py
deleted file mode 100644
index d2fc323a..00000000
--- a/letta/helpers/converters.py
+++ /dev/null
@@ -1,463 +0,0 @@
-from typing import Any, Dict, List, Optional, Union
-
-import numpy as np
-from anthropic.types.beta.messages import BetaMessageBatch, BetaMessageBatchIndividualResponse
-from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall as OpenAIToolCall, Function as OpenAIFunction
-from sqlalchemy import Dialect
-
-from letta.functions.mcp_client.types import StdioServerConfig
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import ProviderType, ToolRuleType
-from letta.schemas.letta_message_content import (
- ImageContent,
- ImageSourceType,
- MessageContent,
- MessageContentType,
- OmittedReasoningContent,
- ReasoningContent,
- RedactedReasoningContent,
- TextContent,
- ToolCallContent,
- ToolReturnContent,
-)
-from letta.schemas.llm_batch_job import AgentStepState
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import ToolReturn
-from letta.schemas.response_format import (
- JsonObjectResponseFormat,
- JsonSchemaResponseFormat,
- ResponseFormatType,
- ResponseFormatUnion,
- TextResponseFormat,
-)
-from letta.schemas.tool_rule import (
- ChildToolRule,
- ConditionalToolRule,
- ContinueToolRule,
- InitToolRule,
- MaxCountPerStepToolRule,
- ParentToolRule,
- RequiredBeforeExitToolRule,
- RequiresApprovalToolRule,
- TerminalToolRule,
- ToolRule,
-)
-from letta.settings import DatabaseChoice, settings
-
-if settings.database_engine == DatabaseChoice.SQLITE:
- import sqlite_vec
-# --------------------------
-# LLMConfig Serialization
-# --------------------------
-
-
-def serialize_llm_config(config: Union[Optional[LLMConfig], Dict]) -> Optional[Dict]:
- """Convert an LLMConfig object into a JSON-serializable dictionary."""
- if config and isinstance(config, LLMConfig):
- return config.model_dump(mode="json")
- return config
-
-
-def deserialize_llm_config(data: Optional[Dict]) -> Optional[LLMConfig]:
- """Convert a dictionary back into an LLMConfig object."""
- return LLMConfig(**data) if data else None
-
-
-# --------------------------
-# EmbeddingConfig Serialization
-# --------------------------
-
-
-def serialize_embedding_config(config: Union[Optional[EmbeddingConfig], Dict]) -> Optional[Dict]:
- """Convert an EmbeddingConfig object into a JSON-serializable dictionary."""
- if config and isinstance(config, EmbeddingConfig):
- return config.model_dump(mode="json")
- return config
-
-
-def deserialize_embedding_config(data: Optional[Dict]) -> Optional[EmbeddingConfig]:
- """Convert a dictionary back into an EmbeddingConfig object."""
- return EmbeddingConfig(**data) if data else None
-
-
-# --------------------------
-# ToolRule Serialization
-# --------------------------
-
-
-def serialize_tool_rules(tool_rules: Optional[List[ToolRule]]) -> List[Dict[str, Any]]:
- """Convert a list of ToolRules into a JSON-serializable format."""
-
- if not tool_rules:
- return []
-
- # de-duplicate tool rules using dict.fromkeys (preserves order in Python 3.7+)
- deduplicated_rules = list(dict.fromkeys(tool_rules))
-
- data = [
- {**rule.model_dump(mode="json"), "type": rule.type.value} for rule in deduplicated_rules
- ] # Convert Enum to string for JSON compatibility
-
- # Validate ToolRule structure
- for rule_data in data:
- if rule_data["type"] == ToolRuleType.constrain_child_tools.value and "children" not in rule_data:
- raise ValueError(f"Invalid ToolRule serialization: 'children' field missing for rule {rule_data}")
-
- return data
-
-
-def deserialize_tool_rules(data: Optional[List[Dict]]) -> List[ToolRule]:
- """Convert a list of dictionaries back into ToolRule objects."""
- if not data:
- return []
-
- return [deserialize_tool_rule(rule_data) for rule_data in data]
-
-
-def deserialize_tool_rule(
- data: Dict,
-) -> ToolRule:
- """Deserialize a dictionary to the appropriate ToolRule subclass based on 'type'."""
- rule_type = ToolRuleType(data.get("type"))
-
- if rule_type == ToolRuleType.run_first:
- data["type"] = ToolRuleType.run_first
- return InitToolRule(**data)
- elif rule_type == ToolRuleType.exit_loop:
- data["type"] = ToolRuleType.exit_loop
- return TerminalToolRule(**data)
- elif rule_type == ToolRuleType.constrain_child_tools:
- data["type"] = ToolRuleType.constrain_child_tools
- return ChildToolRule(**data)
- elif rule_type == ToolRuleType.conditional:
- return ConditionalToolRule(**data)
- elif rule_type == ToolRuleType.continue_loop:
- return ContinueToolRule(**data)
- elif rule_type == ToolRuleType.max_count_per_step:
- return MaxCountPerStepToolRule(**data)
- elif rule_type == ToolRuleType.parent_last_tool:
- return ParentToolRule(**data)
- elif rule_type == ToolRuleType.required_before_exit:
- return RequiredBeforeExitToolRule(**data)
- elif rule_type == ToolRuleType.requires_approval:
- return RequiresApprovalToolRule(**data)
- raise ValueError(f"Unknown ToolRule type: {rule_type}")
-
-
-# --------------------------
-# ToolCall Serialization
-# --------------------------
-
-
-def serialize_tool_calls(tool_calls: Optional[List[Union[OpenAIToolCall, dict]]]) -> List[Dict]:
- """Convert a list of OpenAI ToolCall objects into JSON-serializable format."""
- if not tool_calls:
- return []
-
- serialized_calls = []
- for call in tool_calls:
- if isinstance(call, OpenAIToolCall):
- serialized_calls.append(call.model_dump(mode="json"))
- elif isinstance(call, dict):
- serialized_calls.append(call) # Already a dictionary, leave it as-is
- else:
- raise TypeError(f"Unexpected tool call type: {type(call)}")
-
- return serialized_calls
-
-
-def deserialize_tool_calls(data: Optional[List[Dict]]) -> List[OpenAIToolCall]:
- """Convert a JSON list back into OpenAIToolCall objects."""
- if not data:
- return []
-
- calls = []
- for item in data:
- func_data = item.pop("function", None)
- tool_call_function = OpenAIFunction(**func_data)
- calls.append(OpenAIToolCall(function=tool_call_function, **item))
-
- return calls
-
-
-# --------------------------
-# ToolReturn Serialization
-# --------------------------
-
-
-def serialize_tool_returns(tool_returns: Optional[List[Union[ToolReturn, dict]]]) -> List[Dict]:
- """Convert a list of ToolReturn objects into JSON-serializable format."""
- if not tool_returns:
- return []
-
- serialized_tool_returns = []
- for tool_return in tool_returns:
- if isinstance(tool_return, ToolReturn):
- serialized_tool_returns.append(tool_return.model_dump(mode="json"))
- elif isinstance(tool_return, dict):
- serialized_tool_returns.append(tool_return) # Already a dictionary, leave it as-is
- else:
- raise TypeError(f"Unexpected tool return type: {type(tool_return)}")
-
- return serialized_tool_returns
-
-
-def deserialize_tool_returns(data: Optional[List[Dict]]) -> List[ToolReturn]:
- """Convert a JSON list back into ToolReturn objects."""
- if not data:
- return []
-
- tool_returns = []
- for item in data:
- tool_return = ToolReturn(**item)
- tool_returns.append(tool_return)
-
- return tool_returns
-
-
-# ----------------------------
-# MessageContent Serialization
-# ----------------------------
-
-
-def serialize_message_content(message_content: Optional[List[Union[MessageContent, dict]]]) -> List[Dict]:
- """Convert a list of MessageContent objects into JSON-serializable format."""
- if not message_content:
- return []
-
- serialized_message_content = []
- for content in message_content:
- if isinstance(content, MessageContent):
- if content.type == MessageContentType.image:
- assert content.source.type == ImageSourceType.letta, f"Invalid image source type: {content.source.type}"
- serialized_message_content.append(content.model_dump(mode="json"))
- elif isinstance(content, dict):
- serialized_message_content.append(content) # Already a dictionary, leave it as-is
- else:
- raise TypeError(f"Unexpected message content type: {type(content)}")
- return serialized_message_content
-
-
-def deserialize_message_content(data: Optional[List[Dict]]) -> List[MessageContent]:
- """Convert a JSON list back into MessageContent objects."""
- if not data:
- return []
-
- message_content = []
- for item in data:
- if not item:
- continue
-
- content_type = item.get("type")
- if content_type == MessageContentType.text:
- content = TextContent(**item)
- elif content_type == MessageContentType.image:
- assert item["source"]["type"] == ImageSourceType.letta, f"Invalid image source type: {item['source']['type']}"
- content = ImageContent(**item)
- elif content_type == MessageContentType.tool_call:
- content = ToolCallContent(**item)
- elif content_type == MessageContentType.tool_return:
- content = ToolReturnContent(**item)
- elif content_type == MessageContentType.reasoning:
- content = ReasoningContent(**item)
- elif content_type == MessageContentType.redacted_reasoning:
- content = RedactedReasoningContent(**item)
- elif content_type == MessageContentType.omitted_reasoning:
- content = OmittedReasoningContent(**item)
- else:
- # Skip invalid content
- continue
-
- message_content.append(content)
-
- return message_content
-
-
-# --------------------------
-# Vector Serialization
-# --------------------------
-
-
-def serialize_vector(vector: Optional[Union[List[float], np.ndarray]]) -> Optional[bytes]:
- """Convert a NumPy array or list into serialized format using sqlite-vec."""
- if vector is None:
- return None
- if isinstance(vector, list):
- vector = np.array(vector, dtype=np.float32)
- else:
- vector = vector.astype(np.float32)
-
- return sqlite_vec.serialize_float32(vector.tolist())
-
-
-def deserialize_vector(data: Optional[bytes], dialect: Dialect) -> Optional[np.ndarray]:
- """Convert serialized data back into a NumPy array using sqlite-vec format."""
- if not data:
- return None
-
- if dialect.name == "sqlite":
- # Use sqlite-vec format
- if len(data) % 4 == 0: # Must be divisible by 4 for float32
- return np.frombuffer(data, dtype=np.float32)
- else:
- raise ValueError(f"Invalid sqlite-vec binary data length: {len(data)}")
-
- return np.frombuffer(data, dtype=np.float32)
-
-
-# --------------------------
-# Batch Request Serialization
-# --------------------------
-
-
-def serialize_create_batch_response(create_batch_response: Union[BetaMessageBatch]) -> Dict[str, Any]:
- """Convert a list of ToolRules into a JSON-serializable format."""
- llm_provider_type = None
- if isinstance(create_batch_response, BetaMessageBatch):
- llm_provider_type = ProviderType.anthropic.value
-
- if not llm_provider_type:
- raise ValueError(f"Could not determine llm provider from create batch response object type: {create_batch_response}")
-
- return {"data": create_batch_response.model_dump(mode="json"), "type": llm_provider_type}
-
-
-def deserialize_create_batch_response(data: Dict) -> Union[BetaMessageBatch]:
- provider_type = ProviderType(data.get("type"))
-
- if provider_type == ProviderType.anthropic:
- return BetaMessageBatch(**data.get("data"))
-
- raise ValueError(f"Unknown ProviderType type: {provider_type}")
-
-
-# TODO: Note that this is the same as above for Anthropic, but this is not the case for all providers
-# TODO: Some have different types based on the create v.s. poll requests
-def serialize_poll_batch_response(poll_batch_response: Optional[Union[BetaMessageBatch]]) -> Optional[Dict[str, Any]]:
- """Convert a list of ToolRules into a JSON-serializable format."""
- if not poll_batch_response:
- return None
-
- llm_provider_type = None
- if isinstance(poll_batch_response, BetaMessageBatch):
- llm_provider_type = ProviderType.anthropic.value
-
- if not llm_provider_type:
- raise ValueError(f"Could not determine llm provider from poll batch response object type: {poll_batch_response}")
-
- return {"data": poll_batch_response.model_dump(mode="json"), "type": llm_provider_type}
-
-
-def deserialize_poll_batch_response(data: Optional[Dict]) -> Optional[Union[BetaMessageBatch]]:
- if not data:
- return None
-
- provider_type = ProviderType(data.get("type"))
-
- if provider_type == ProviderType.anthropic:
- return BetaMessageBatch(**data.get("data"))
-
- raise ValueError(f"Unknown ProviderType type: {provider_type}")
-
-
-def serialize_batch_request_result(
- batch_individual_response: Optional[Union[BetaMessageBatchIndividualResponse]],
-) -> Optional[Dict[str, Any]]:
- """Convert a list of ToolRules into a JSON-serializable format."""
- if not batch_individual_response:
- return None
-
- llm_provider_type = None
- if isinstance(batch_individual_response, BetaMessageBatchIndividualResponse):
- llm_provider_type = ProviderType.anthropic.value
-
- if not llm_provider_type:
- raise ValueError(f"Could not determine llm provider from batch result object type: {batch_individual_response}")
-
- return {"data": batch_individual_response.model_dump(mode="json"), "type": llm_provider_type}
-
-
-def deserialize_batch_request_result(data: Optional[Dict]) -> Optional[Union[BetaMessageBatchIndividualResponse]]:
- if not data:
- return None
- provider_type = ProviderType(data.get("type"))
-
- if provider_type == ProviderType.anthropic:
- return BetaMessageBatchIndividualResponse(**data.get("data"))
-
- raise ValueError(f"Unknown ProviderType type: {provider_type}")
-
-
-def serialize_agent_step_state(agent_step_state: Optional[AgentStepState]) -> Optional[Dict[str, Any]]:
- """Convert a list of ToolRules into a JSON-serializable format."""
- if not agent_step_state:
- return None
-
- return agent_step_state.model_dump(mode="json")
-
-
-def deserialize_agent_step_state(data: Optional[Dict]) -> Optional[AgentStepState]:
- if not data:
- return None
-
- if solver_data := data.get("tool_rules_solver"):
- # Get existing tool_rules or reconstruct from categorized fields for backwards compatibility
- tool_rules_data = solver_data.get("tool_rules", [])
-
- if not tool_rules_data:
- for field_name in (
- "init_tool_rules",
- "continue_tool_rules",
- "child_based_tool_rules",
- "parent_tool_rules",
- "terminal_tool_rules",
- "required_before_exit_tool_rules",
- ):
- if field_data := solver_data.get(field_name):
- tool_rules_data.extend(field_data)
-
- solver_data["tool_rules"] = deserialize_tool_rules(tool_rules_data)
-
- return AgentStepState(**data)
-
-
-# --------------------------
-# Response Format Serialization
-# --------------------------
-
-
-def serialize_response_format(response_format: Optional[ResponseFormatUnion]) -> Optional[Dict[str, Any]]:
- if not response_format:
- return None
- return response_format.model_dump(mode="json")
-
-
-def deserialize_response_format(data: Optional[Dict]) -> Optional[ResponseFormatUnion]:
- if not data:
- return None
- if data["type"] == ResponseFormatType.text:
- return TextResponseFormat(**data)
- if data["type"] == ResponseFormatType.json_schema:
- return JsonSchemaResponseFormat(**data)
- if data["type"] == ResponseFormatType.json_object:
- return JsonObjectResponseFormat(**data)
- raise ValueError(f"Unknown Response Format type: {data['type']}")
-
-
-# --------------------------
-# MCP Stdio Server Config Serialization
-# --------------------------
-
-
-def serialize_mcp_stdio_config(config: Union[Optional[StdioServerConfig], Dict]) -> Optional[Dict]:
- """Convert an StdioServerConfig object into a JSON-serializable dictionary."""
- if config and isinstance(config, StdioServerConfig):
- return config.to_dict()
- return config
-
-
-def deserialize_mcp_stdio_config(data: Optional[Dict]) -> Optional[StdioServerConfig]:
- """Convert a dictionary back into an StdioServerConfig object."""
- if not data:
- return None
- return StdioServerConfig(**data)
diff --git a/letta/helpers/datetime_helpers.py b/letta/helpers/datetime_helpers.py
deleted file mode 100644
index 1c931c00..00000000
--- a/letta/helpers/datetime_helpers.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import re
-import time
-from datetime import datetime, timedelta, timezone as dt_timezone
-from typing import Callable
-
-import pytz
-
-from letta.constants import DEFAULT_TIMEZONE
-
-
-def parse_formatted_time(formatted_time):
- # parse times returned by letta.utils.get_formatted_time()
- return datetime.strptime(formatted_time, "%Y-%m-%d %I:%M:%S %p %Z%z")
-
-
-def datetime_to_timestamp(dt):
- # convert datetime object to integer timestamp
- return int(dt.timestamp())
-
-
-def get_local_time_fast(timezone):
- # Get current UTC time and convert to the specified timezone
- # Only return the date to avoid cache busting on every request
- if not timezone:
- return datetime.now().strftime("%B %d, %Y")
- current_time_utc = datetime.now(pytz.utc)
- local_time = current_time_utc.astimezone(pytz.timezone(timezone))
- # Return only the date in a human-readable format (e.g., "June 1, 2021")
- formatted_time = local_time.strftime("%B %d, %Y")
-
- return formatted_time
-
-
-def get_local_time_timezone(timezone=DEFAULT_TIMEZONE):
- # Get the current time in UTC
- current_time_utc = datetime.now(pytz.utc)
-
- local_time = current_time_utc.astimezone(pytz.timezone(timezone))
-
- # You may format it as you desire, including AM/PM
- formatted_time = local_time.strftime("%Y-%m-%d %I:%M:%S %p %Z%z")
-
- return formatted_time
-
-
-def get_local_time(timezone: str | None = DEFAULT_TIMEZONE):
- if timezone is not None:
- time_str = get_local_time_timezone(timezone)
- else:
- # Get the current time, which will be in the local timezone of the computer
- local_time = datetime.now().astimezone()
-
- # You may format it as you desire, including AM/PM
- time_str = local_time.strftime("%Y-%m-%d %I:%M:%S %p %Z%z")
-
- return time_str.strip()
-
-
-def get_utc_time() -> datetime:
- """Get the current UTC time"""
- # return datetime.now(pytz.utc)
- return datetime.now(dt_timezone.utc)
-
-
-def get_utc_time_int() -> int:
- return int(get_utc_time().timestamp())
-
-
-def get_utc_timestamp_ns() -> int:
- """Get the current UTC time in nanoseconds"""
- return int(time.time_ns())
-
-
-def ns_to_ms(ns: int) -> int:
- return ns // 1_000_000
-
-
-def timestamp_to_datetime(timestamp_seconds: int) -> datetime:
- """Convert Unix timestamp in seconds to UTC datetime object"""
- return datetime.fromtimestamp(timestamp_seconds, tz=dt_timezone.utc)
-
-
-def format_datetime(dt, timezone):
- if not timezone:
- # use local timezone
- return dt.strftime("%Y-%m-%d %I:%M:%S %p %Z%z")
- return dt.astimezone(pytz.timezone(timezone)).strftime("%Y-%m-%d %I:%M:%S %p %Z%z")
-
-
-def validate_date_format(date_str):
- """Validate the given date string in the format 'YYYY-MM-DD'."""
- try:
- datetime.strptime(date_str, "%Y-%m-%d")
- return True
- except (ValueError, TypeError):
- return False
-
-
-def extract_date_from_timestamp(timestamp):
- """Extracts and returns the date from the given timestamp."""
- # Extracts the date (ignoring the time and timezone)
- match = re.match(r"(\d{4}-\d{2}-\d{2})", timestamp)
- return match.group(1) if match else None
-
-
-def is_utc_datetime(dt: datetime) -> bool:
- return dt.tzinfo is not None and dt.tzinfo.utcoffset(dt) == timedelta(0)
-
-
-class AsyncTimer:
- """An async context manager for timing async code execution.
-
- Takes in an optional callback_func to call on exit with arguments
- taking in the elapsed_ms and exc if present.
-
- Do not use the start and end times outside of this function as they are relative.
- """
-
- def __init__(self, callback_func: Callable | None = None):
- self._start_time_ns = None
- self._end_time_ns = None
- self._elapsed_ns = None
- self.callback_func = callback_func
-
- async def __aenter__(self):
- self._start_time_ns = time.perf_counter_ns()
- return self
-
- async def __aexit__(self, exc_type, exc, tb):
- self._end_time_ns = time.perf_counter_ns()
- self._elapsed_ns = self._end_time_ns - self._start_time_ns
- if self.callback_func:
- from asyncio import iscoroutinefunction
-
- if iscoroutinefunction(self.callback_func):
- await self.callback_func(self.elapsed_ms, exc)
- else:
- self.callback_func(self.elapsed_ms, exc)
- return False
-
- @property
- def elapsed_ms(self):
- if self._elapsed_ns is not None:
- return ns_to_ms(self._elapsed_ns)
- return None
-
- @property
- def elapsed_ns(self):
- return self._elapsed_ns
diff --git a/letta/helpers/decorators.py b/letta/helpers/decorators.py
deleted file mode 100644
index 77744ea1..00000000
--- a/letta/helpers/decorators.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import inspect
-import json
-from dataclasses import dataclass
-from functools import wraps
-from typing import Callable
-
-from pydantic import BaseModel
-
-from letta.constants import REDIS_DEFAULT_CACHE_PREFIX
-from letta.data_sources.redis_client import NoopAsyncRedisClient, get_redis_client
-from letta.log import get_logger
-from letta.plugins.plugins import get_experimental_checker
-from letta.settings import settings
-
-logger = get_logger(__name__)
-
-
-def experimental(feature_name: str, fallback_function: Callable, **kwargs):
- """Decorator that runs a fallback function if experimental feature is not enabled.
-
- - kwargs from the decorator will be combined with function kwargs and overwritten only for experimental evaluation.
- - if the decorated function, fallback_function, or experimental checker function is async, the whole call will be async
- """
-
- def decorator(f):
- experimental_checker = get_experimental_checker()
- is_f_async = inspect.iscoroutinefunction(f)
- is_fallback_async = inspect.iscoroutinefunction(fallback_function)
- is_experimental_checker_async = inspect.iscoroutinefunction(experimental_checker)
-
- async def call_function(func, is_async, *args, **_kwargs):
- if is_async:
- return await func(*args, **_kwargs)
- return func(*args, **_kwargs)
-
- # asynchronous wrapper if any function is async
- if any((is_f_async, is_fallback_async, is_experimental_checker_async)):
-
- @wraps(f)
- async def async_wrapper(*args, **_kwargs):
- result = await call_function(experimental_checker, is_experimental_checker_async, feature_name, **dict(_kwargs, **kwargs))
- if result:
- return await call_function(f, is_f_async, *args, **_kwargs)
- else:
- return await call_function(fallback_function, is_fallback_async, *args, **_kwargs)
-
- return async_wrapper
-
- else:
-
- @wraps(f)
- def wrapper(*args, **_kwargs):
- if experimental_checker(feature_name, **dict(_kwargs, **kwargs)):
- return f(*args, **_kwargs)
- else:
- return fallback_function(*args, **kwargs)
-
- return wrapper
-
- return decorator
-
-
-def deprecated(message: str):
- """Simple decorator that marks a method as deprecated."""
-
- def decorator(f):
- @wraps(f)
- def wrapper(*args, **kwargs):
- if settings.debug:
- logger.warning(f"Function {f.__name__} is deprecated: {message}.")
- return f(*args, **kwargs)
-
- return wrapper
-
- return decorator
-
-
-@dataclass
-class CacheStats:
- """Note: this will be approximate to not add overhead of locking on counters.
- For exact measurements, use redis or track in other places.
- """
-
- hits: int = 0
- misses: int = 0
- invalidations: int = 0
-
-
-def async_redis_cache(
- key_func: Callable, prefix: str = REDIS_DEFAULT_CACHE_PREFIX, ttl_s: int = 600, model_class: type[BaseModel] | None = None
-):
- """
- Decorator for caching async function results in Redis. May be a Noop if redis is not available.
- Will handle pydantic objects and raw values.
-
- Attempts to write to and retrieve from cache, but does not fail on those cases
-
- Args:
- key_func: function to generate cache key (preferably lowercase strings to follow redis convention)
- prefix: cache key prefix
- ttl_s: time to live (s)
- model_class: custom pydantic model class for serialization/deserialization
-
- TODO (cliandy): move to class with generics for type hints
- """
-
- def decorator(func):
- stats = CacheStats()
-
- @wraps(func)
- async def async_wrapper(*args, **kwargs):
- redis_client = await get_redis_client()
-
- # Don't bother going through other operations for no reason.
- if isinstance(redis_client, NoopAsyncRedisClient):
- return await func(*args, **kwargs)
- cache_key = get_cache_key(*args, **kwargs)
- cached_value = await redis_client.get(cache_key)
-
- try:
- if cached_value is not None:
- stats.hits += 1
- if model_class:
- return model_class.model_validate_json(cached_value)
- return json.loads(cached_value)
- except Exception as e:
- logger.warning(f"Failed to retrieve value from cache: {e}")
-
- stats.misses += 1
- result = await func(*args, **kwargs)
- try:
- if model_class:
- await redis_client.set(cache_key, result.model_dump_json(), ex=ttl_s)
- elif isinstance(result, (dict, list, str, int, float, bool)):
- await redis_client.set(cache_key, json.dumps(result), ex=ttl_s)
- else:
- logger.warning(f"Cannot cache result of type {type(result).__name__} for {func.__name__}")
- except Exception as e:
- logger.warning(f"Redis cache set failed: {e}")
- return result
-
- async def invalidate(*args, **kwargs) -> bool:
- stats.invalidations += 1
- try:
- redis_client = await get_redis_client()
- cache_key = get_cache_key(*args, **kwargs)
- return (await redis_client.delete(cache_key)) > 0
- except Exception as e:
- logger.error(f"Failed to invalidate cache: {e}")
- return False
-
- def get_cache_key(*args, **kwargs):
- return f"{prefix}:{key_func(*args, **kwargs)}"
-
- async_wrapper.cache_invalidate = invalidate
- async_wrapper.cache_key_func = get_cache_key
- async_wrapper.cache_stats = stats
- return async_wrapper
-
- return decorator
diff --git a/letta/helpers/json_helpers.py b/letta/helpers/json_helpers.py
deleted file mode 100644
index ff6943b8..00000000
--- a/letta/helpers/json_helpers.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import base64
-import json
-from datetime import datetime
-
-
-def json_loads(data):
- return json.loads(data, strict=False)
-
-
-def json_dumps(data, indent=2) -> str:
- def safe_serializer(obj):
- if isinstance(obj, datetime):
- return obj.isoformat()
- if isinstance(obj, bytes):
- try:
- return obj.decode("utf-8")
- except Exception:
- # TODO: this is to handle Gemini thought signatures, b64 decode this back to bytes when sending back to Gemini
- return base64.b64encode(obj).decode("utf-8")
- raise TypeError(f"Type {type(obj)} not serializable")
-
- return json.dumps(data, indent=indent, default=safe_serializer, ensure_ascii=False)
diff --git a/letta/helpers/message_helper.py b/letta/helpers/message_helper.py
deleted file mode 100644
index 47c58f71..00000000
--- a/letta/helpers/message_helper.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import base64
-import mimetypes
-
-import httpx
-
-from letta import system
-from letta.schemas.enums import MessageRole
-from letta.schemas.letta_message_content import Base64Image, ImageContent, ImageSourceType, TextContent
-from letta.schemas.message import Message, MessageCreate
-
-
-def convert_message_creates_to_messages(
- message_creates: list[MessageCreate],
- agent_id: str,
- timezone: str,
- wrap_user_message: bool = True,
- wrap_system_message: bool = True,
-) -> list[Message]:
- return [
- _convert_message_create_to_message(
- message_create=create,
- agent_id=agent_id,
- timezone=timezone,
- wrap_user_message=wrap_user_message,
- wrap_system_message=wrap_system_message,
- )
- for create in message_creates
- ]
-
-
-def _convert_message_create_to_message(
- message_create: MessageCreate,
- agent_id: str,
- timezone: str,
- wrap_user_message: bool = True,
- wrap_system_message: bool = True,
-) -> Message:
- """Converts a MessageCreate object into a Message object, applying wrapping if needed."""
- # TODO: This seems like extra boilerplate with little benefit
- assert isinstance(message_create, MessageCreate)
-
- # Extract message content
- if isinstance(message_create.content, str) and message_create.content != "":
- message_content = [TextContent(text=message_create.content)]
- elif isinstance(message_create.content, list) and len(message_create.content) > 0:
- message_content = message_create.content
- else:
- raise ValueError("Message content is empty or invalid")
-
- assert message_create.role in {MessageRole.user, MessageRole.system}, f"Invalid message role: {message_create.role}"
- for content in message_content:
- if isinstance(content, TextContent):
- # Apply wrapping if needed
- if message_create.role == MessageRole.user and wrap_user_message:
- content.text = system.package_user_message(user_message=content.text, timezone=timezone)
- elif message_create.role == MessageRole.system and wrap_system_message:
- content.text = system.package_system_message(system_message=content.text, timezone=timezone)
- elif isinstance(content, ImageContent):
- if content.source.type == ImageSourceType.url:
- # Convert URL image to Base64Image if needed
- image_response = httpx.get(content.source.url)
- image_response.raise_for_status()
- image_media_type = image_response.headers.get("content-type")
- if not image_media_type:
- image_media_type, _ = mimetypes.guess_type(content.source.url)
- image_data = base64.standard_b64encode(image_response.content).decode("utf-8")
- content.source = Base64Image(media_type=image_media_type, data=image_data)
- if content.source.type == ImageSourceType.letta and not content.source.data:
- # TODO: hydrate letta image with data from db
- pass
-
- return Message(
- agent_id=agent_id,
- role=message_create.role,
- content=message_content,
- name=message_create.name,
- model=None, # assigned later?
- tool_calls=None, # irrelevant
- tool_call_id=None,
- otid=message_create.otid,
- sender_id=message_create.sender_id,
- group_id=message_create.group_id,
- batch_item_id=message_create.batch_item_id,
- )
diff --git a/letta/helpers/pinecone_utils.py b/letta/helpers/pinecone_utils.py
deleted file mode 100644
index f2958e8e..00000000
--- a/letta/helpers/pinecone_utils.py
+++ /dev/null
@@ -1,340 +0,0 @@
-import asyncio
-import random
-import time
-from functools import wraps
-from typing import Any, Dict, List
-
-from letta.otel.tracing import trace_method
-
-try:
- from pinecone import IndexEmbed, PineconeAsyncio
- from pinecone.exceptions.exceptions import (
- ForbiddenException,
- NotFoundException,
- PineconeApiException,
- ServiceException,
- UnauthorizedException,
- )
-
- PINECONE_AVAILABLE = True
-except ImportError:
- PINECONE_AVAILABLE = False
-
-from letta.constants import (
- PINECONE_CLOUD,
- PINECONE_EMBEDDING_MODEL,
- PINECONE_MAX_BATCH_SIZE,
- PINECONE_MAX_RETRY_ATTEMPTS,
- PINECONE_METRIC,
- PINECONE_REGION,
- PINECONE_RETRY_BACKOFF_FACTOR,
- PINECONE_RETRY_BASE_DELAY,
- PINECONE_RETRY_MAX_DELAY,
- PINECONE_TEXT_FIELD_NAME,
- PINECONE_THROTTLE_DELAY,
-)
-from letta.log import get_logger
-from letta.schemas.user import User
-from letta.settings import settings
-
-logger = get_logger(__name__)
-
-
-def pinecone_retry(
- max_attempts: int = PINECONE_MAX_RETRY_ATTEMPTS,
- base_delay: float = PINECONE_RETRY_BASE_DELAY,
- max_delay: float = PINECONE_RETRY_MAX_DELAY,
- backoff_factor: float = PINECONE_RETRY_BACKOFF_FACTOR,
-):
- """
- Decorator to retry Pinecone operations with exponential backoff.
-
- Args:
- max_attempts: Maximum number of retry attempts
- base_delay: Base delay in seconds for the first retry
- max_delay: Maximum delay in seconds between retries
- backoff_factor: Factor to increase delay after each failed attempt
- """
-
- def decorator(func):
- @wraps(func)
- async def wrapper(*args, **kwargs):
- operation_name = func.__name__
- start_time = time.time()
-
- for attempt in range(max_attempts):
- try:
- logger.debug(f"[Pinecone] Starting {operation_name} (attempt {attempt + 1}/{max_attempts})")
- result = await func(*args, **kwargs)
-
- execution_time = time.time() - start_time
- logger.info(f"[Pinecone] {operation_name} completed successfully in {execution_time:.2f}s")
- return result
-
- except (ServiceException, PineconeApiException) as e:
- # retryable server errors
- if attempt == max_attempts - 1:
- execution_time = time.time() - start_time
- logger.error(f"[Pinecone] {operation_name} failed after {max_attempts} attempts in {execution_time:.2f}s: {str(e)}")
- raise
-
- # calculate delay with exponential backoff and jitter
- delay = min(base_delay * (backoff_factor**attempt), max_delay)
- jitter = random.uniform(0, delay * 0.1) # add up to 10% jitter
- total_delay = delay + jitter
-
- logger.warning(
- f"[Pinecone] {operation_name} failed (attempt {attempt + 1}/{max_attempts}): {str(e)}. Retrying in {total_delay:.2f}s"
- )
- await asyncio.sleep(total_delay)
-
- except (UnauthorizedException, ForbiddenException) as e:
- # non-retryable auth errors
- execution_time = time.time() - start_time
- logger.error(f"[Pinecone] {operation_name} failed with auth error in {execution_time:.2f}s: {str(e)}")
- raise
-
- except NotFoundException as e:
- # non-retryable not found errors
- execution_time = time.time() - start_time
- logger.warning(f"[Pinecone] {operation_name} failed with not found error in {execution_time:.2f}s: {str(e)}")
- raise
-
- except Exception as e:
- # other unexpected errors - retry once then fail
- if attempt == max_attempts - 1:
- execution_time = time.time() - start_time
- logger.error(f"[Pinecone] {operation_name} failed after {max_attempts} attempts in {execution_time:.2f}s: {str(e)}")
- raise
-
- delay = min(base_delay * (backoff_factor**attempt), max_delay)
- jitter = random.uniform(0, delay * 0.1)
- total_delay = delay + jitter
-
- logger.warning(
- f"[Pinecone] {operation_name} failed with unexpected error (attempt {attempt + 1}/{max_attempts}): {str(e)}. Retrying in {total_delay:.2f}s"
- )
- await asyncio.sleep(total_delay)
-
- return wrapper
-
- return decorator
-
-
-def should_use_pinecone(verbose: bool = False):
- if verbose:
- logger.info(
- "Pinecone check: enable_pinecone=%s, api_key=%s, agent_index=%s, source_index=%s",
- settings.enable_pinecone,
- bool(settings.pinecone_api_key),
- bool(settings.pinecone_agent_index),
- bool(settings.pinecone_source_index),
- )
-
- return all(
- (
- PINECONE_AVAILABLE,
- settings.enable_pinecone,
- settings.pinecone_api_key,
- settings.pinecone_agent_index,
- settings.pinecone_source_index,
- )
- )
-
-
-@pinecone_retry()
-@trace_method
-async def upsert_pinecone_indices():
- if not PINECONE_AVAILABLE:
- raise ImportError("Pinecone is not available. Please install pinecone to use this feature.")
-
- indices = get_pinecone_indices()
- logger.info(f"[Pinecone] Upserting {len(indices)} indices: {indices}")
-
- for index_name in indices:
- async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
- if not await pc.has_index(index_name):
- logger.info(f"[Pinecone] Creating index {index_name} with model {PINECONE_EMBEDDING_MODEL}")
- await pc.create_index_for_model(
- name=index_name,
- cloud=PINECONE_CLOUD,
- region=PINECONE_REGION,
- embed=IndexEmbed(model=PINECONE_EMBEDDING_MODEL, field_map={"text": PINECONE_TEXT_FIELD_NAME}, metric=PINECONE_METRIC),
- )
- logger.info(f"[Pinecone] Successfully created index {index_name}")
- else:
- logger.debug(f"[Pinecone] Index {index_name} already exists")
-
-
-def get_pinecone_indices() -> List[str]:
- return [settings.pinecone_agent_index, settings.pinecone_source_index]
-
-
-@pinecone_retry()
-@trace_method
-async def upsert_file_records_to_pinecone_index(file_id: str, source_id: str, chunks: List[str], actor: User):
- if not PINECONE_AVAILABLE:
- raise ImportError("Pinecone is not available. Please install pinecone to use this feature.")
-
- logger.info(f"[Pinecone] Preparing to upsert {len(chunks)} chunks for file {file_id} source {source_id}")
-
- records = []
- for i, chunk in enumerate(chunks):
- record = {
- "_id": f"{file_id}_{i}",
- PINECONE_TEXT_FIELD_NAME: chunk,
- "file_id": file_id,
- "source_id": source_id,
- }
- records.append(record)
-
- logger.debug(f"[Pinecone] Created {len(records)} records for file {file_id}")
- return await upsert_records_to_pinecone_index(records, actor)
-
-
-@pinecone_retry()
-@trace_method
-async def delete_file_records_from_pinecone_index(file_id: str, actor: User):
- if not PINECONE_AVAILABLE:
- raise ImportError("Pinecone is not available. Please install pinecone to use this feature.")
-
- namespace = actor.organization_id
- logger.info(f"[Pinecone] Deleting records for file {file_id} from index {settings.pinecone_source_index} namespace {namespace}")
-
- try:
- async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
- description = await pc.describe_index(name=settings.pinecone_source_index)
- async with pc.IndexAsyncio(host=description.index.host) as dense_index:
- await dense_index.delete(
- filter={
- "file_id": {"$eq": file_id},
- },
- namespace=namespace,
- )
- logger.info(f"[Pinecone] Successfully deleted records for file {file_id}")
- except NotFoundException:
- logger.warning(f"[Pinecone] Namespace {namespace} not found for file {file_id} and org {actor.organization_id}")
-
-
-@pinecone_retry()
-@trace_method
-async def delete_source_records_from_pinecone_index(source_id: str, actor: User):
- if not PINECONE_AVAILABLE:
- raise ImportError("Pinecone is not available. Please install pinecone to use this feature.")
-
- namespace = actor.organization_id
- logger.info(f"[Pinecone] Deleting records for source {source_id} from index {settings.pinecone_source_index} namespace {namespace}")
-
- try:
- async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
- description = await pc.describe_index(name=settings.pinecone_source_index)
- async with pc.IndexAsyncio(host=description.index.host) as dense_index:
- await dense_index.delete(filter={"source_id": {"$eq": source_id}}, namespace=namespace)
- logger.info(f"[Pinecone] Successfully deleted records for source {source_id}")
- except NotFoundException:
- logger.warning(f"[Pinecone] Namespace {namespace} not found for source {source_id} and org {actor.organization_id}")
-
-
-@pinecone_retry()
-@trace_method
-async def upsert_records_to_pinecone_index(records: List[dict], actor: User):
- if not PINECONE_AVAILABLE:
- raise ImportError("Pinecone is not available. Please install pinecone to use this feature.")
-
- logger.info(f"[Pinecone] Upserting {len(records)} records to index {settings.pinecone_source_index} for org {actor.organization_id}")
-
- async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
- description = await pc.describe_index(name=settings.pinecone_source_index)
- async with pc.IndexAsyncio(host=description.index.host) as dense_index:
- # process records in batches to avoid exceeding pinecone limits
- total_batches = (len(records) + PINECONE_MAX_BATCH_SIZE - 1) // PINECONE_MAX_BATCH_SIZE
- logger.debug(f"[Pinecone] Processing {total_batches} batches of max {PINECONE_MAX_BATCH_SIZE} records each")
-
- for i in range(0, len(records), PINECONE_MAX_BATCH_SIZE):
- batch = records[i : i + PINECONE_MAX_BATCH_SIZE]
- batch_num = (i // PINECONE_MAX_BATCH_SIZE) + 1
-
- logger.debug(f"[Pinecone] Upserting batch {batch_num}/{total_batches} with {len(batch)} records")
- await dense_index.upsert_records(actor.organization_id, batch)
-
- # throttle between batches (except the last one)
- if batch_num < total_batches:
- jitter = random.uniform(0, PINECONE_THROTTLE_DELAY * 0.2) # ±20% jitter
- throttle_delay = PINECONE_THROTTLE_DELAY + jitter
- logger.debug(f"[Pinecone] Throttling for {throttle_delay:.3f}s before next batch")
- await asyncio.sleep(throttle_delay)
-
- logger.info(f"[Pinecone] Successfully upserted all {len(records)} records in {total_batches} batches")
-
-
-@pinecone_retry()
-@trace_method
-async def search_pinecone_index(query: str, limit: int, filter: Dict[str, Any], actor: User) -> Dict[str, Any]:
- if not PINECONE_AVAILABLE:
- raise ImportError("Pinecone is not available. Please install pinecone to use this feature.")
-
- namespace = actor.organization_id
- logger.info(
- f"[Pinecone] Searching index {settings.pinecone_source_index} namespace {namespace} with query length {len(query)} chars, limit {limit}"
- )
- logger.debug(f"[Pinecone] Search filter: {filter}")
-
- async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
- description = await pc.describe_index(name=settings.pinecone_source_index)
- async with pc.IndexAsyncio(host=description.index.host) as dense_index:
- try:
- # search the dense index with reranking
- search_results = await dense_index.search(
- namespace=namespace,
- query={
- "top_k": limit,
- "inputs": {"text": query},
- "filter": filter,
- },
- rerank={"model": "bge-reranker-v2-m3", "top_n": limit, "rank_fields": [PINECONE_TEXT_FIELD_NAME]},
- )
-
- result_count = len(search_results.get("matches", []))
- logger.info(f"[Pinecone] Search completed, found {result_count} matches")
- return search_results
-
- except Exception as e:
- logger.warning(f"[Pinecone] Failed to search namespace {namespace}: {str(e)}")
- raise e
-
-
-@pinecone_retry()
-@trace_method
-async def list_pinecone_index_for_files(file_id: str, actor: User, limit: int = None, pagination_token: str = None) -> List[str]:
- if not PINECONE_AVAILABLE:
- raise ImportError("Pinecone is not available. Please install pinecone to use this feature.")
-
- namespace = actor.organization_id
- logger.info(f"[Pinecone] Listing records for file {file_id} from index {settings.pinecone_source_index} namespace {namespace}")
- logger.debug(f"[Pinecone] List params - limit: {limit}, pagination_token: {pagination_token}")
-
- try:
- async with PineconeAsyncio(api_key=settings.pinecone_api_key) as pc:
- description = await pc.describe_index(name=settings.pinecone_source_index)
- async with pc.IndexAsyncio(host=description.index.host) as dense_index:
- kwargs = {"namespace": namespace, "prefix": file_id}
- if limit is not None:
- kwargs["limit"] = limit
- if pagination_token is not None:
- kwargs["pagination_token"] = pagination_token
-
- try:
- result = []
- async for ids in dense_index.list(**kwargs):
- result.extend(ids)
-
- logger.info(f"[Pinecone] Successfully listed {len(result)} records for file {file_id}")
- return result
-
- except Exception as e:
- logger.warning(f"[Pinecone] Failed to list records for file {file_id} in namespace {namespace}: {str(e)}")
- raise e
-
- except NotFoundException:
- logger.warning(f"[Pinecone] Namespace {namespace} not found for file {file_id} and org {actor.organization_id}")
- return []
diff --git a/letta/helpers/reasoning_helper.py b/letta/helpers/reasoning_helper.py
deleted file mode 100644
index 31f8b572..00000000
--- a/letta/helpers/reasoning_helper.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from typing import List
-
-from letta.schemas.enums import MessageRole
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message
-
-
-def is_reasoning_completely_disabled(llm_config: LLMConfig) -> bool:
- """
- Check if reasoning is completely disabled by verifying all three conditions:
- - put_inner_thoughts_in_kwargs is False
- - enable_reasoner is False
- - max_reasoning_tokens is 0
-
- Args:
- llm_config: The LLM configuration to check
-
- Returns:
- True if reasoning is completely disabled, False otherwise
- """
- return llm_config.put_inner_thoughts_in_kwargs is False and llm_config.enable_reasoner is False and llm_config.max_reasoning_tokens == 0
-
-
-def scrub_inner_thoughts_from_messages(messages: List[Message], llm_config: LLMConfig) -> List[Message]:
- """
- Remove inner thoughts (reasoning text) from assistant messages when reasoning is completely disabled.
- This makes the LLM think reasoning was never enabled by presenting clean message history.
-
- Args:
- messages: List of messages to potentially scrub
- llm_config: The LLM configuration to check
-
- Returns:
- The message list with inner thoughts removed if reasoning is disabled, otherwise unchanged
- """
- # early return if reasoning is not completely disabled
- if not is_reasoning_completely_disabled(llm_config):
- return messages
-
- # process messages to remove inner thoughts from assistant messages
- for message in messages:
- if message.role == MessageRole.assistant and message.content and message.tool_calls:
- # remove text content from assistant messages that also have tool calls
- # keep only non-text content (if any)
- message.content = [content for content in message.content if not isinstance(content, TextContent)]
-
- return messages
diff --git a/letta/helpers/singleton.py b/letta/helpers/singleton.py
deleted file mode 100644
index 1d382457..00000000
--- a/letta/helpers/singleton.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# TODO (cliandy): consolidate with decorators later
-from functools import wraps
-
-
-def singleton(cls):
- """Decorator to make a class a Singleton class."""
- instances = {}
-
- @wraps(cls)
- def get_instance(*args, **kwargs):
- if cls not in instances:
- instances[cls] = cls(*args, **kwargs)
- return instances[cls]
-
- return get_instance
diff --git a/letta/helpers/tool_execution_helper.py b/letta/helpers/tool_execution_helper.py
deleted file mode 100644
index 886e5239..00000000
--- a/letta/helpers/tool_execution_helper.py
+++ /dev/null
@@ -1,131 +0,0 @@
-from collections import OrderedDict
-from typing import Any, Dict, Optional
-
-from letta.constants import PRE_EXECUTION_MESSAGE_ARG
-from letta.schemas.tool import MCP_TOOL_METADATA_SCHEMA_STATUS, MCP_TOOL_METADATA_SCHEMA_WARNINGS
-from letta.utils import get_logger
-
-logger = get_logger(__name__)
-
-
-def enable_strict_mode(tool_schema: Dict[str, Any]) -> Dict[str, Any]:
- """Enables strict mode for a tool schema by setting 'strict' to True and
- disallowing additional properties in the parameters.
-
- If the tool schema is NON_STRICT_ONLY, strict mode will not be applied.
-
- Args:
- tool_schema (Dict[str, Any]): The original tool schema.
-
- Returns:
- Dict[str, Any]: A new tool schema with strict mode conditionally enabled.
- """
- schema = tool_schema.copy()
-
- # Check if schema has status metadata indicating NON_STRICT_ONLY
- schema_status = schema.get(MCP_TOOL_METADATA_SCHEMA_STATUS)
- if schema_status == "NON_STRICT_ONLY":
- # Don't apply strict mode for non-strict schemas
- # Remove the metadata fields from the schema
- schema.pop(MCP_TOOL_METADATA_SCHEMA_STATUS, None)
- schema.pop(MCP_TOOL_METADATA_SCHEMA_WARNINGS, None)
- return schema
- elif schema_status == "INVALID":
- # We should not be hitting this and allowing invalid schemas to be used
- logger.error(f"Tool schema {schema} is invalid: {schema.get(MCP_TOOL_METADATA_SCHEMA_WARNINGS)}")
-
- # Enable strict mode for STRICT_COMPLIANT or unspecified health status
- schema["strict"] = True
-
- # Ensure parameters is a valid dictionary
- parameters = schema.get("parameters", {})
- if isinstance(parameters, dict) and parameters.get("type") == "object":
- # Set additionalProperties to False
- parameters["additionalProperties"] = False
- schema["parameters"] = parameters
- # Remove the metadata fields from the schema
- schema.pop(MCP_TOOL_METADATA_SCHEMA_STATUS, None)
- schema.pop(MCP_TOOL_METADATA_SCHEMA_WARNINGS, None)
-
- return schema
-
-
-def add_pre_execution_message(tool_schema: Dict[str, Any], description: Optional[str] = None) -> Dict[str, Any]:
- """Adds a `pre_execution_message` parameter to a tool schema to prompt a natural, human-like message before executing the tool.
-
- Args:
- tool_schema (Dict[str, Any]): The original tool schema.
- description (Optional[str]): Description of the tool schema. Defaults to None.
-
- Returns:
- Dict[str, Any]: A new tool schema with the `pre_execution_message` field added at the beginning.
- """
- schema = tool_schema.copy()
- parameters = schema.get("parameters", {})
-
- if not isinstance(parameters, dict) or parameters.get("type") != "object":
- return schema # Do not modify if schema is not valid
-
- properties = parameters.get("properties", {})
- required = parameters.get("required", [])
-
- # Define the new `pre_execution_message` field
- if not description:
- # Default description
- description = (
- "A concise message to be uttered before executing this tool. "
- "This should sound natural, as if a person is casually announcing their next action."
- "You MUST also include punctuation at the end of this message."
- )
- pre_execution_message_field = {
- "type": "string",
- "description": description,
- }
-
- # Ensure the pre-execution message is the first field in properties
- updated_properties = OrderedDict()
- updated_properties[PRE_EXECUTION_MESSAGE_ARG] = pre_execution_message_field
- updated_properties.update(properties) # Retain all existing properties
-
- # Ensure pre-execution message is the first required field
- if PRE_EXECUTION_MESSAGE_ARG not in required:
- required = [PRE_EXECUTION_MESSAGE_ARG] + required
-
- # Update the schema with ordered properties and required list
- schema["parameters"] = {
- **parameters,
- "properties": dict(updated_properties), # Convert OrderedDict back to dict
- "required": required,
- }
-
- return schema
-
-
-def remove_request_heartbeat(tool_schema: Dict[str, Any]) -> Dict[str, Any]:
- """Removes the `request_heartbeat` parameter from a tool schema if it exists.
-
- Args:
- tool_schema (Dict[str, Any]): The original tool schema.
-
- Returns:
- Dict[str, Any]: A new tool schema without `request_heartbeat`.
- """
- schema = tool_schema.copy()
- parameters = schema.get("parameters", {})
-
- if isinstance(parameters, dict):
- properties = parameters.get("properties", {})
- required = parameters.get("required", [])
-
- # Remove the `request_heartbeat` property if it exists
- if "request_heartbeat" in properties:
- properties.pop("request_heartbeat")
-
- # Remove `request_heartbeat` from required fields if present
- if "request_heartbeat" in required:
- required = [r for r in required if r != "request_heartbeat"]
-
- # Update parameters with modified properties and required list
- schema["parameters"] = {**parameters, "properties": properties, "required": required}
-
- return schema
diff --git a/letta/helpers/tool_rule_solver.py b/letta/helpers/tool_rule_solver.py
deleted file mode 100644
index 73384971..00000000
--- a/letta/helpers/tool_rule_solver.py
+++ /dev/null
@@ -1,211 +0,0 @@
-from typing import TypeAlias
-
-from pydantic import BaseModel, Field
-
-from letta.schemas.block import Block
-from letta.schemas.tool_rule import (
- ChildToolRule,
- ConditionalToolRule,
- ContinueToolRule,
- InitToolRule,
- MaxCountPerStepToolRule,
- ParentToolRule,
- RequiredBeforeExitToolRule,
- RequiresApprovalToolRule,
- TerminalToolRule,
- ToolRule,
-)
-
-ToolName: TypeAlias = str
-
-COMPILED_PROMPT_DESCRIPTION = "The following constraints define rules for tool usage and guide desired behavior. These rules must be followed to ensure proper tool execution and workflow. A single response may contain multiple tool calls."
-
-
-class ToolRulesSolver(BaseModel):
- tool_rules: list[ToolRule] | None = Field(default=None, description="Input list of tool rules")
-
- # Categorized fields
- init_tool_rules: list[InitToolRule] = Field(
- default_factory=list, description="Initial tool rules to be used at the start of tool execution.", exclude=True
- )
- continue_tool_rules: list[ContinueToolRule] = Field(
- default_factory=list, description="Continue tool rules to be used to continue tool execution.", exclude=True
- )
- # TODO: This should be renamed?
- # TODO: These are tools that control the set of allowed functions in the next turn
- child_based_tool_rules: list[ChildToolRule | ConditionalToolRule | MaxCountPerStepToolRule] = Field(
- default_factory=list, description="Standard tool rules for controlling execution sequence and allowed transitions.", exclude=True
- )
- parent_tool_rules: list[ParentToolRule] = Field(
- default_factory=list, description="Filter tool rules to be used to filter out tools from the available set.", exclude=True
- )
- terminal_tool_rules: list[TerminalToolRule] = Field(
- default_factory=list, description="Terminal tool rules that end the agent loop if called.", exclude=True
- )
- required_before_exit_tool_rules: list[RequiredBeforeExitToolRule] = Field(
- default_factory=list, description="Tool rules that must be called before the agent can exit.", exclude=True
- )
- requires_approval_tool_rules: list[RequiresApprovalToolRule] = Field(
- default_factory=list, description="Tool rules that trigger an approval request for human-in-the-loop.", exclude=True
- )
- tool_call_history: list[str] = Field(default_factory=list, description="History of tool calls, updated with each tool call.")
-
- def __init__(self, tool_rules: list[ToolRule] | None = None, **kwargs):
- super().__init__(tool_rules=tool_rules, **kwargs)
-
- def model_post_init(self, __context):
- if self.tool_rules:
- for rule in self.tool_rules:
- if isinstance(rule, InitToolRule):
- self.init_tool_rules.append(rule)
- elif isinstance(rule, ChildToolRule):
- self.child_based_tool_rules.append(rule)
- elif isinstance(rule, ConditionalToolRule):
- self.child_based_tool_rules.append(rule)
- elif isinstance(rule, TerminalToolRule):
- self.terminal_tool_rules.append(rule)
- elif isinstance(rule, ContinueToolRule):
- self.continue_tool_rules.append(rule)
- elif isinstance(rule, MaxCountPerStepToolRule):
- self.child_based_tool_rules.append(rule)
- elif isinstance(rule, ParentToolRule):
- self.parent_tool_rules.append(rule)
- elif isinstance(rule, RequiredBeforeExitToolRule):
- self.required_before_exit_tool_rules.append(rule)
- elif isinstance(rule, RequiresApprovalToolRule):
- self.requires_approval_tool_rules.append(rule)
-
- def register_tool_call(self, tool_name: str):
- """Update the internal state to track tool call history."""
- self.tool_call_history.append(tool_name)
-
- def clear_tool_history(self):
- """Clear the history of tool calls."""
- self.tool_call_history.clear()
-
- def get_allowed_tool_names(
- self, available_tools: set[ToolName], error_on_empty: bool = True, last_function_response: str | None = None
- ) -> list[ToolName]:
- """Get a list of tool names allowed based on the last tool called.
-
- The logic is as follows:
- 1. if there are no previous tool calls, and we have InitToolRules, those are the only options for the first tool call
- 2. else we take the intersection of the Parent/Child/Conditional/MaxSteps as the options
- 3. Continue/Terminal/RequiredBeforeExit rules are applied in the agent loop flow, not to restrict tools
- """
- # TODO: This piece of code here is quite ugly and deserves a refactor
- # TODO: -> Tool rules should probably be refactored to take in a set of tool names?
- if not self.tool_call_history and self.init_tool_rules:
- return [rule.tool_name for rule in self.init_tool_rules]
- else:
- valid_tool_sets = []
- for rule in self.child_based_tool_rules + self.parent_tool_rules:
- tools = rule.get_valid_tools(self.tool_call_history, available_tools, last_function_response)
- valid_tool_sets.append(tools)
-
- # Compute intersection of all valid tool sets
- final_allowed_tools = set.intersection(*valid_tool_sets) if valid_tool_sets else available_tools
-
- if error_on_empty and not final_allowed_tools:
- raise ValueError("No valid tools found based on tool rules.")
-
- return list(final_allowed_tools)
-
- def is_terminal_tool(self, tool_name: ToolName) -> bool:
- """Check if the tool is defined as a terminal tool in the terminal tool rules or required-before-exit tool rules."""
- return any(rule.tool_name == tool_name for rule in self.terminal_tool_rules)
-
- def has_children_tools(self, tool_name: ToolName):
- """Check if the tool has children tools"""
- return any(rule.tool_name == tool_name for rule in self.child_based_tool_rules)
-
- def is_continue_tool(self, tool_name: ToolName):
- """Check if the tool is defined as a continue tool in the tool rules."""
- return any(rule.tool_name == tool_name for rule in self.continue_tool_rules)
-
- def is_requires_approval_tool(self, tool_name: ToolName):
- """Check if the tool is defined as a requires-approval tool in the tool rules."""
- return any(rule.tool_name == tool_name for rule in self.requires_approval_tool_rules)
-
- def has_required_tools_been_called(self, available_tools: set[ToolName]) -> bool:
- """Check if all required-before-exit tools have been called."""
- return len(self.get_uncalled_required_tools(available_tools=available_tools)) == 0
-
- def get_requires_approval_tools(self, available_tools: set[ToolName]) -> list[ToolName]:
- """Get the list of tools that require approval."""
- return [rule.tool_name for rule in self.requires_approval_tool_rules]
-
- def get_uncalled_required_tools(self, available_tools: set[ToolName]) -> list[str]:
- """Get the list of required-before-exit tools that have not been called yet."""
- if not self.required_before_exit_tool_rules:
- return [] # No required tools means no uncalled tools
-
- required_tool_names = {rule.tool_name for rule in self.required_before_exit_tool_rules}
- called_tool_names = set(self.tool_call_history)
-
- # Get required tools that are uncalled AND available
- return list((required_tool_names & available_tools) - called_tool_names)
-
- def compile_tool_rule_prompts(self) -> Block | None:
- """
- Compile prompt templates from all tool rules into an ephemeral Block.
-
- Returns:
- Block | None: Compiled prompt block with tool rule constraints, or None if no templates exist.
- """
- compiled_prompts = []
-
- all_rules = (
- self.init_tool_rules
- + self.continue_tool_rules
- + self.child_based_tool_rules
- + self.parent_tool_rules
- + self.terminal_tool_rules
- )
-
- for rule in all_rules:
- rendered = rule.render_prompt()
- if rendered:
- compiled_prompts.append(rendered)
-
- if compiled_prompts:
- return Block(
- label="tool_usage_rules",
- value="\n".join(compiled_prompts),
- description=COMPILED_PROMPT_DESCRIPTION,
- )
- return None
-
- def guess_rule_violation(self, tool_name: ToolName) -> list[str]:
- """
- Check if the given tool name or the previous tool in history matches any tool rule,
- and return rendered prompt templates for matching rule violations.
-
- Args:
- tool_name: The name of the tool to check for rule violations
-
- Returns:
- list of rendered prompt templates from matching tool rules
- """
- violated_rules = []
-
- # Get the previous tool from history if it exists
- previous_tool = self.tool_call_history[-1] if self.tool_call_history else None
-
- # Check all tool rules for matches
- all_rules = (
- self.init_tool_rules
- + self.continue_tool_rules
- + self.child_based_tool_rules
- + self.parent_tool_rules
- + self.terminal_tool_rules
- )
-
- for rule in all_rules:
- # Check if the current tool name or previous tool matches this rule's tool_name
- if rule.tool_name == tool_name or (previous_tool and rule.tool_name == previous_tool):
- rendered_prompt = rule.render_prompt()
- if rendered_prompt:
- violated_rules.append(rendered_prompt)
-
- return violated_rules
diff --git a/letta/helpers/tpuf_client.py b/letta/helpers/tpuf_client.py
deleted file mode 100644
index e7e2c8b0..00000000
--- a/letta/helpers/tpuf_client.py
+++ /dev/null
@@ -1,1421 +0,0 @@
-"""Turbopuffer utilities for archival memory storage."""
-
-import logging
-from datetime import datetime, timezone
-from typing import Any, Callable, List, Optional, Tuple
-
-from letta.constants import DEFAULT_EMBEDDING_CHUNK_SIZE
-from letta.otel.tracing import trace_method
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import MessageRole, TagMatchMode
-from letta.schemas.passage import Passage as PydanticPassage
-from letta.settings import model_settings, settings
-
-logger = logging.getLogger(__name__)
-
-
-def should_use_tpuf() -> bool:
- # We need OpenAI since we default to their embedding model
- return bool(settings.use_tpuf) and bool(settings.tpuf_api_key) and bool(model_settings.openai_api_key)
-
-
-def should_use_tpuf_for_messages() -> bool:
- """Check if Turbopuffer should be used for messages."""
- return should_use_tpuf() and bool(settings.embed_all_messages)
-
-
-class TurbopufferClient:
- """Client for managing archival memory with Turbopuffer vector database."""
-
- default_embedding_config = EmbeddingConfig(
- embedding_model="text-embedding-3-small",
- embedding_endpoint_type="openai",
- embedding_endpoint="https://api.openai.com/v1",
- embedding_dim=1536,
- embedding_chunk_size=DEFAULT_EMBEDDING_CHUNK_SIZE,
- )
-
- def __init__(self, api_key: str = None, region: str = None):
- """Initialize Turbopuffer client."""
- self.api_key = api_key or settings.tpuf_api_key
- self.region = region or settings.tpuf_region
-
- from letta.services.agent_manager import AgentManager
- from letta.services.archive_manager import ArchiveManager
-
- self.archive_manager = ArchiveManager()
- self.agent_manager = AgentManager()
-
- if not self.api_key:
- raise ValueError("Turbopuffer API key not provided")
-
- @trace_method
- async def _generate_embeddings(self, texts: List[str], actor: "PydanticUser") -> List[List[float]]:
- """Generate embeddings using the default embedding configuration.
-
- Args:
- texts: List of texts to embed
- actor: User actor for embedding generation
-
- Returns:
- List of embedding vectors
- """
- from letta.llm_api.llm_client import LLMClient
-
- embedding_client = LLMClient.create(
- provider_type=self.default_embedding_config.embedding_endpoint_type,
- actor=actor,
- )
- embeddings = await embedding_client.request_embeddings(texts, self.default_embedding_config)
- return embeddings
-
- @trace_method
- async def _get_archive_namespace_name(self, archive_id: str) -> str:
- """Get namespace name for a specific archive."""
- return await self.archive_manager.get_or_set_vector_db_namespace_async(archive_id)
-
- @trace_method
- async def _get_message_namespace_name(self, organization_id: str) -> str:
- """Get namespace name for messages (org-scoped).
-
- Args:
- organization_id: Organization ID for namespace generation
-
- Returns:
- The org-scoped namespace name for messages
- """
- environment = settings.environment
- if environment:
- namespace_name = f"messages_{organization_id}_{environment.lower()}"
- else:
- namespace_name = f"messages_{organization_id}"
-
- return namespace_name
-
- @trace_method
- async def insert_archival_memories(
- self,
- archive_id: str,
- text_chunks: List[str],
- passage_ids: List[str],
- organization_id: str,
- actor: "PydanticUser",
- tags: Optional[List[str]] = None,
- created_at: Optional[datetime] = None,
- ) -> List[PydanticPassage]:
- """Insert passages into Turbopuffer.
-
- Args:
- archive_id: ID of the archive
- text_chunks: List of text chunks to store
- passage_ids: List of passage IDs (must match 1:1 with text_chunks)
- organization_id: Organization ID for the passages
- actor: User actor for embedding generation
- tags: Optional list of tags to attach to all passages
- created_at: Optional timestamp for retroactive entries (defaults to current UTC time)
-
- Returns:
- List of PydanticPassage objects that were inserted
- """
- from turbopuffer import AsyncTurbopuffer
-
- # generate embeddings using the default config
- embeddings = await self._generate_embeddings(text_chunks, actor)
-
- namespace_name = await self._get_archive_namespace_name(archive_id)
-
- # handle timestamp - ensure UTC
- if created_at is None:
- timestamp = datetime.now(timezone.utc)
- else:
- # ensure the provided timestamp is timezone-aware and in UTC
- if created_at.tzinfo is None:
- # assume UTC if no timezone provided
- timestamp = created_at.replace(tzinfo=timezone.utc)
- else:
- # convert to UTC if in different timezone
- timestamp = created_at.astimezone(timezone.utc)
-
- # passage_ids must be provided for dual-write consistency
- if not passage_ids:
- raise ValueError("passage_ids must be provided for Turbopuffer insertion")
- if len(passage_ids) != len(text_chunks):
- raise ValueError(f"passage_ids length ({len(passage_ids)}) must match text_chunks length ({len(text_chunks)})")
-
- # prepare column-based data for turbopuffer - optimized for batch insert
- ids = []
- vectors = []
- texts = []
- organization_ids = []
- archive_ids = []
- created_ats = []
- tags_arrays = [] # Store tags as arrays
- passages = []
-
- for idx, (text, embedding) in enumerate(zip(text_chunks, embeddings)):
- passage_id = passage_ids[idx]
-
- # append to columns
- ids.append(passage_id)
- vectors.append(embedding)
- texts.append(text)
- organization_ids.append(organization_id)
- archive_ids.append(archive_id)
- created_ats.append(timestamp)
- tags_arrays.append(tags or []) # Store tags as array
-
- # Create PydanticPassage object
- passage = PydanticPassage(
- id=passage_id,
- text=text,
- organization_id=organization_id,
- archive_id=archive_id,
- created_at=timestamp,
- metadata_={},
- tags=tags or [], # Include tags in the passage
- embedding=embedding,
- embedding_config=self.default_embedding_config, # Will be set by caller if needed
- )
- passages.append(passage)
-
- # build column-based upsert data
- upsert_columns = {
- "id": ids,
- "vector": vectors,
- "text": texts,
- "organization_id": organization_ids,
- "archive_id": archive_ids,
- "created_at": created_ats,
- "tags": tags_arrays, # Add tags as array column
- }
-
- try:
- # Use AsyncTurbopuffer as a context manager for proper resource cleanup
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # turbopuffer recommends column-based writes for performance
- await namespace.write(
- upsert_columns=upsert_columns,
- distance_metric="cosine_distance",
- schema={"text": {"type": "string", "full_text_search": True}},
- )
- logger.info(f"Successfully inserted {len(ids)} passages to Turbopuffer for archive {archive_id}")
- return passages
-
- except Exception as e:
- logger.error(f"Failed to insert passages to Turbopuffer: {e}")
- # check if it's a duplicate ID error
- if "duplicate" in str(e).lower():
- logger.error("Duplicate passage IDs detected in batch")
- raise
-
- @trace_method
- async def insert_messages(
- self,
- agent_id: str,
- message_texts: List[str],
- message_ids: List[str],
- organization_id: str,
- actor: "PydanticUser",
- roles: List[MessageRole],
- created_ats: List[datetime],
- project_id: Optional[str] = None,
- template_id: Optional[str] = None,
- ) -> bool:
- """Insert messages into Turbopuffer.
-
- Args:
- agent_id: ID of the agent
- message_texts: List of message text content to store
- message_ids: List of message IDs (must match 1:1 with message_texts)
- organization_id: Organization ID for the messages
- actor: User actor for embedding generation
- roles: List of message roles corresponding to each message
- created_ats: List of creation timestamps for each message
- project_id: Optional project ID for all messages
- template_id: Optional template ID for all messages
-
- Returns:
- True if successful
- """
- from turbopuffer import AsyncTurbopuffer
-
- # generate embeddings using the default config
- embeddings = await self._generate_embeddings(message_texts, actor)
-
- namespace_name = await self._get_message_namespace_name(organization_id)
-
- # validation checks
- if not message_ids:
- raise ValueError("message_ids must be provided for Turbopuffer insertion")
- if len(message_ids) != len(message_texts):
- raise ValueError(f"message_ids length ({len(message_ids)}) must match message_texts length ({len(message_texts)})")
- if len(message_ids) != len(roles):
- raise ValueError(f"message_ids length ({len(message_ids)}) must match roles length ({len(roles)})")
- if len(message_ids) != len(created_ats):
- raise ValueError(f"message_ids length ({len(message_ids)}) must match created_ats length ({len(created_ats)})")
-
- # prepare column-based data for turbopuffer - optimized for batch insert
- ids = []
- vectors = []
- texts = []
- organization_ids = []
- agent_ids = []
- message_roles = []
- created_at_timestamps = []
- project_ids = []
- template_ids = []
-
- for idx, (text, embedding, role, created_at) in enumerate(zip(message_texts, embeddings, roles, created_ats)):
- message_id = message_ids[idx]
-
- # ensure the provided timestamp is timezone-aware and in UTC
- if created_at.tzinfo is None:
- # assume UTC if no timezone provided
- timestamp = created_at.replace(tzinfo=timezone.utc)
- else:
- # convert to UTC if in different timezone
- timestamp = created_at.astimezone(timezone.utc)
-
- # append to columns
- ids.append(message_id)
- vectors.append(embedding)
- texts.append(text)
- organization_ids.append(organization_id)
- agent_ids.append(agent_id)
- message_roles.append(role.value)
- created_at_timestamps.append(timestamp)
- project_ids.append(project_id)
- template_ids.append(template_id)
-
- # build column-based upsert data
- upsert_columns = {
- "id": ids,
- "vector": vectors,
- "text": texts,
- "organization_id": organization_ids,
- "agent_id": agent_ids,
- "role": message_roles,
- "created_at": created_at_timestamps,
- }
-
- # only include project_id if it's provided
- if project_id is not None:
- upsert_columns["project_id"] = project_ids
-
- # only include template_id if it's provided
- if template_id is not None:
- upsert_columns["template_id"] = template_ids
-
- try:
- # Use AsyncTurbopuffer as a context manager for proper resource cleanup
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # turbopuffer recommends column-based writes for performance
- await namespace.write(
- upsert_columns=upsert_columns,
- distance_metric="cosine_distance",
- schema={"text": {"type": "string", "full_text_search": True}},
- )
- logger.info(f"Successfully inserted {len(ids)} messages to Turbopuffer for agent {agent_id}")
- return True
-
- except Exception as e:
- logger.error(f"Failed to insert messages to Turbopuffer: {e}")
- # check if it's a duplicate ID error
- if "duplicate" in str(e).lower():
- logger.error("Duplicate message IDs detected in batch")
- raise
-
- @trace_method
- async def _execute_query(
- self,
- namespace_name: str,
- search_mode: str,
- query_embedding: Optional[List[float]],
- query_text: Optional[str],
- top_k: int,
- include_attributes: List[str],
- filters: Optional[Any] = None,
- vector_weight: float = 0.5,
- fts_weight: float = 0.5,
- ) -> Any:
- """Generic query execution for Turbopuffer.
-
- Args:
- namespace_name: Turbopuffer namespace to query
- search_mode: "vector", "fts", "hybrid", or "timestamp"
- query_embedding: Embedding for vector search
- query_text: Text for full-text search
- top_k: Number of results to return
- include_attributes: Attributes to include in results
- filters: Turbopuffer filter expression
- vector_weight: Weight for vector search in hybrid mode
- fts_weight: Weight for FTS in hybrid mode
-
- Returns:
- Raw Turbopuffer query results or multi-query response
- """
- from turbopuffer import AsyncTurbopuffer
- from turbopuffer.types import QueryParam
-
- # validate inputs based on search mode
- if search_mode == "vector" and query_embedding is None:
- raise ValueError("query_embedding is required for vector search mode")
- if search_mode == "fts" and query_text is None:
- raise ValueError("query_text is required for FTS search mode")
- if search_mode == "hybrid":
- if query_embedding is None or query_text is None:
- raise ValueError("Both query_embedding and query_text are required for hybrid search mode")
- if search_mode not in ["vector", "fts", "hybrid", "timestamp"]:
- raise ValueError(f"Invalid search_mode: {search_mode}. Must be 'vector', 'fts', 'hybrid', or 'timestamp'")
-
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
-
- if search_mode == "timestamp":
- # retrieve most recent items by timestamp
- query_params = {
- "rank_by": ("created_at", "desc"),
- "top_k": top_k,
- "include_attributes": include_attributes,
- }
- if filters:
- query_params["filters"] = filters
- return await namespace.query(**query_params)
-
- elif search_mode == "vector":
- # vector search query
- query_params = {
- "rank_by": ("vector", "ANN", query_embedding),
- "top_k": top_k,
- "include_attributes": include_attributes,
- }
- if filters:
- query_params["filters"] = filters
- return await namespace.query(**query_params)
-
- elif search_mode == "fts":
- # full-text search query
- query_params = {
- "rank_by": ("text", "BM25", query_text),
- "top_k": top_k,
- "include_attributes": include_attributes,
- }
- if filters:
- query_params["filters"] = filters
- return await namespace.query(**query_params)
-
- else: # hybrid mode
- queries = []
-
- # vector search query
- vector_query = {
- "rank_by": ("vector", "ANN", query_embedding),
- "top_k": top_k,
- "include_attributes": include_attributes,
- }
- if filters:
- vector_query["filters"] = filters
- queries.append(vector_query)
-
- # full-text search query
- fts_query = {
- "rank_by": ("text", "BM25", query_text),
- "top_k": top_k,
- "include_attributes": include_attributes,
- }
- if filters:
- fts_query["filters"] = filters
- queries.append(fts_query)
-
- # execute multi-query
- return await namespace.multi_query(queries=[QueryParam(**q) for q in queries])
-
- @trace_method
- async def query_passages(
- self,
- archive_id: str,
- actor: "PydanticUser",
- query_text: Optional[str] = None,
- search_mode: str = "vector", # "vector", "fts", "hybrid"
- top_k: int = 10,
- tags: Optional[List[str]] = None,
- tag_match_mode: TagMatchMode = TagMatchMode.ANY,
- vector_weight: float = 0.5,
- fts_weight: float = 0.5,
- start_date: Optional[datetime] = None,
- end_date: Optional[datetime] = None,
- ) -> List[Tuple[PydanticPassage, float, dict]]:
- """Query passages from Turbopuffer using vector search, full-text search, or hybrid search.
-
- Args:
- archive_id: ID of the archive
- actor: User actor for embedding generation
- query_text: Text query for search (used for embedding in vector/hybrid modes, and FTS in fts/hybrid modes)
- search_mode: Search mode - "vector", "fts", or "hybrid" (default: "vector")
- top_k: Number of results to return
- tags: Optional list of tags to filter by
- tag_match_mode: TagMatchMode.ANY (match any tag) or TagMatchMode.ALL (match all tags) - default: TagMatchMode.ANY
- vector_weight: Weight for vector search results in hybrid mode (default: 0.5)
- fts_weight: Weight for FTS results in hybrid mode (default: 0.5)
- start_date: Optional datetime to filter passages created after this date
- end_date: Optional datetime to filter passages created on or before this date (inclusive)
-
- Returns:
- List of (passage, score, metadata) tuples with relevance rankings
- """
- # generate embedding for vector/hybrid search if query_text is provided
- query_embedding = None
- if query_text and search_mode in ["vector", "hybrid"]:
- embeddings = await self._generate_embeddings([query_text], actor)
- query_embedding = embeddings[0]
-
- # Check if we should fallback to timestamp-based retrieval
- if query_embedding is None and query_text is None and search_mode not in ["timestamp"]:
- # Fallback to retrieving most recent passages when no search query is provided
- search_mode = "timestamp"
-
- namespace_name = await self._get_archive_namespace_name(archive_id)
-
- # build tag filter conditions
- tag_filter = None
- if tags:
- if tag_match_mode == TagMatchMode.ALL:
- # For ALL mode, need to check each tag individually with Contains
- tag_conditions = []
- for tag in tags:
- tag_conditions.append(("tags", "Contains", tag))
- if len(tag_conditions) == 1:
- tag_filter = tag_conditions[0]
- else:
- tag_filter = ("And", tag_conditions)
- else: # tag_match_mode == TagMatchMode.ANY
- # For ANY mode, use ContainsAny to match any of the tags
- tag_filter = ("tags", "ContainsAny", tags)
-
- # build date filter conditions
- date_filters = []
- if start_date:
- date_filters.append(("created_at", "Gte", start_date))
- if end_date:
- # if end_date has no time component (is at midnight), adjust to end of day
- # to make the filter inclusive of the entire day
- if end_date.hour == 0 and end_date.minute == 0 and end_date.second == 0 and end_date.microsecond == 0:
- from datetime import timedelta
-
- # add 1 day and subtract 1 microsecond to get 23:59:59.999999
- end_date = end_date + timedelta(days=1) - timedelta(microseconds=1)
- date_filters.append(("created_at", "Lte", end_date))
-
- # combine all filters
- all_filters = []
- if tag_filter:
- all_filters.append(tag_filter)
- if date_filters:
- all_filters.extend(date_filters)
-
- # create final filter expression
- final_filter = None
- if len(all_filters) == 1:
- final_filter = all_filters[0]
- elif len(all_filters) > 1:
- final_filter = ("And", all_filters)
-
- try:
- # use generic query executor
- result = await self._execute_query(
- namespace_name=namespace_name,
- search_mode=search_mode,
- query_embedding=query_embedding,
- query_text=query_text,
- top_k=top_k,
- include_attributes=["text", "organization_id", "archive_id", "created_at", "tags"],
- filters=final_filter,
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- )
-
- # process results based on search mode
- if search_mode == "hybrid":
- # for hybrid mode, we get a multi-query response
- vector_results = self._process_single_query_results(result.results[0], archive_id, tags)
- fts_results = self._process_single_query_results(result.results[1], archive_id, tags, is_fts=True)
- # use RRF and include metadata with ranks
- results_with_metadata = self._reciprocal_rank_fusion(
- vector_results=[passage for passage, _ in vector_results],
- fts_results=[passage for passage, _ in fts_results],
- get_id_func=lambda p: p.id,
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- top_k=top_k,
- )
- # Return (passage, score, metadata) with ranks
- return results_with_metadata
- else:
- # for single queries (vector, fts, timestamp) - add basic metadata
- is_fts = search_mode == "fts"
- results = self._process_single_query_results(result, archive_id, tags, is_fts=is_fts)
- # Add simple metadata for single search modes
- results_with_metadata = []
- for idx, (passage, score) in enumerate(results):
- metadata = {
- "combined_score": score,
- f"{search_mode}_rank": idx + 1, # Add the rank for this search mode
- }
- results_with_metadata.append((passage, score, metadata))
- return results_with_metadata
-
- except Exception as e:
- logger.error(f"Failed to query passages from Turbopuffer: {e}")
- raise
-
- @trace_method
- async def query_messages_by_agent_id(
- self,
- agent_id: str,
- organization_id: str,
- actor: "PydanticUser",
- query_text: Optional[str] = None,
- search_mode: str = "vector", # "vector", "fts", "hybrid", "timestamp"
- top_k: int = 10,
- roles: Optional[List[MessageRole]] = None,
- project_id: Optional[str] = None,
- template_id: Optional[str] = None,
- vector_weight: float = 0.5,
- fts_weight: float = 0.5,
- start_date: Optional[datetime] = None,
- end_date: Optional[datetime] = None,
- ) -> List[Tuple[dict, float, dict]]:
- """Query messages from Turbopuffer using vector search, full-text search, or hybrid search.
-
- Args:
- agent_id: ID of the agent (used for filtering results)
- organization_id: Organization ID for namespace lookup
- actor: User actor for embedding generation
- query_text: Text query for search (used for embedding in vector/hybrid modes, and FTS in fts/hybrid modes)
- search_mode: Search mode - "vector", "fts", "hybrid", or "timestamp" (default: "vector")
- top_k: Number of results to return
- roles: Optional list of message roles to filter by
- project_id: Optional project ID to filter messages by
- template_id: Optional template ID to filter messages by
- vector_weight: Weight for vector search results in hybrid mode (default: 0.5)
- fts_weight: Weight for FTS results in hybrid mode (default: 0.5)
- start_date: Optional datetime to filter messages created after this date
- end_date: Optional datetime to filter messages created on or before this date (inclusive)
-
- Returns:
- List of (message_dict, score, metadata) tuples where:
- - message_dict contains id, text, role, created_at
- - score is the final relevance score
- - metadata contains individual scores and ranking information
- """
- # generate embedding for vector/hybrid search if query_text is provided
- query_embedding = None
- if query_text and search_mode in ["vector", "hybrid"]:
- embeddings = await self._generate_embeddings([query_text], actor)
- query_embedding = embeddings[0]
-
- # Check if we should fallback to timestamp-based retrieval
- if query_embedding is None and query_text is None and search_mode not in ["timestamp"]:
- # Fallback to retrieving most recent messages when no search query is provided
- search_mode = "timestamp"
-
- namespace_name = await self._get_message_namespace_name(organization_id)
-
- # build agent_id filter
- agent_filter = ("agent_id", "Eq", agent_id)
-
- # build role filter conditions
- role_filter = None
- if roles:
- role_values = [r.value for r in roles]
- if len(role_values) == 1:
- role_filter = ("role", "Eq", role_values[0])
- else:
- role_filter = ("role", "In", role_values)
-
- # build date filter conditions
- date_filters = []
- if start_date:
- date_filters.append(("created_at", "Gte", start_date))
- if end_date:
- # if end_date has no time component (is at midnight), adjust to end of day
- # to make the filter inclusive of the entire day
- if end_date.hour == 0 and end_date.minute == 0 and end_date.second == 0 and end_date.microsecond == 0:
- from datetime import timedelta
-
- # add 1 day and subtract 1 microsecond to get 23:59:59.999999
- end_date = end_date + timedelta(days=1) - timedelta(microseconds=1)
- date_filters.append(("created_at", "Lte", end_date))
-
- # build project_id filter if provided
- project_filter = None
- if project_id:
- project_filter = ("project_id", "Eq", project_id)
-
- # build template_id filter if provided
- template_filter = None
- if template_id:
- template_filter = ("template_id", "Eq", template_id)
-
- # combine all filters
- all_filters = [agent_filter] # always include agent_id filter
- if role_filter:
- all_filters.append(role_filter)
- if project_filter:
- all_filters.append(project_filter)
- if template_filter:
- all_filters.append(template_filter)
- if date_filters:
- all_filters.extend(date_filters)
-
- # create final filter expression
- final_filter = None
- if len(all_filters) == 1:
- final_filter = all_filters[0]
- elif len(all_filters) > 1:
- final_filter = ("And", all_filters)
-
- try:
- # use generic query executor
- result = await self._execute_query(
- namespace_name=namespace_name,
- search_mode=search_mode,
- query_embedding=query_embedding,
- query_text=query_text,
- top_k=top_k,
- include_attributes=["text", "organization_id", "agent_id", "role", "created_at"],
- filters=final_filter,
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- )
-
- # process results based on search mode
- if search_mode == "hybrid":
- # for hybrid mode, we get a multi-query response
- vector_results = self._process_message_query_results(result.results[0])
- fts_results = self._process_message_query_results(result.results[1])
- # use RRF with lambda to extract ID from dict - returns metadata
- results_with_metadata = self._reciprocal_rank_fusion(
- vector_results=vector_results,
- fts_results=fts_results,
- get_id_func=lambda msg_dict: msg_dict["id"],
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- top_k=top_k,
- )
- # return results with metadata
- return results_with_metadata
- else:
- # for single queries (vector, fts, timestamp)
- results = self._process_message_query_results(result)
- # add simple metadata for single search modes
- results_with_metadata = []
- for idx, msg_dict in enumerate(results):
- metadata = {
- "combined_score": 1.0 / (idx + 1), # Use rank-based score for single mode
- "search_mode": search_mode,
- f"{search_mode}_rank": idx + 1, # Add the rank for this search mode
- }
- results_with_metadata.append((msg_dict, metadata["combined_score"], metadata))
- return results_with_metadata
-
- except Exception as e:
- logger.error(f"Failed to query messages from Turbopuffer: {e}")
- raise
-
- async def query_messages_by_org_id(
- self,
- organization_id: str,
- actor: "PydanticUser",
- query_text: Optional[str] = None,
- search_mode: str = "hybrid", # "vector", "fts", "hybrid"
- top_k: int = 10,
- roles: Optional[List[MessageRole]] = None,
- project_id: Optional[str] = None,
- template_id: Optional[str] = None,
- vector_weight: float = 0.5,
- fts_weight: float = 0.5,
- start_date: Optional[datetime] = None,
- end_date: Optional[datetime] = None,
- ) -> List[Tuple[dict, float, dict]]:
- """Query messages from Turbopuffer across an entire organization.
-
- Args:
- organization_id: Organization ID for namespace lookup (required)
- actor: User actor for embedding generation
- query_text: Text query for search (used for embedding in vector/hybrid modes, and FTS in fts/hybrid modes)
- search_mode: Search mode - "vector", "fts", or "hybrid" (default: "hybrid")
- top_k: Number of results to return
- roles: Optional list of message roles to filter by
- project_id: Optional project ID to filter messages by
- template_id: Optional template ID to filter messages by
- vector_weight: Weight for vector search results in hybrid mode (default: 0.5)
- fts_weight: Weight for FTS results in hybrid mode (default: 0.5)
- start_date: Optional datetime to filter messages created after this date
- end_date: Optional datetime to filter messages created on or before this date (inclusive)
-
- Returns:
- List of (message_dict, score, metadata) tuples where:
- - message_dict contains id, text, role, created_at, agent_id
- - score is the final relevance score (RRF score for hybrid, rank-based for single mode)
- - metadata contains individual scores and ranking information
- """
- # generate embedding for vector/hybrid search if query_text is provided
- query_embedding = None
- if query_text and search_mode in ["vector", "hybrid"]:
- embeddings = await self._generate_embeddings([query_text], actor)
- query_embedding = embeddings[0]
- # namespace is org-scoped
- namespace_name = await self._get_message_namespace_name(organization_id)
-
- # build filters
- all_filters = []
-
- # role filter
- if roles:
- role_values = [r.value for r in roles]
- if len(role_values) == 1:
- all_filters.append(("role", "Eq", role_values[0]))
- else:
- all_filters.append(("role", "In", role_values))
-
- # project filter
- if project_id:
- all_filters.append(("project_id", "Eq", project_id))
-
- # template filter
- if template_id:
- all_filters.append(("template_id", "Eq", template_id))
-
- # date filters
- if start_date:
- all_filters.append(("created_at", "Gte", start_date))
- if end_date:
- # make end_date inclusive of the entire day
- if end_date.hour == 0 and end_date.minute == 0 and end_date.second == 0 and end_date.microsecond == 0:
- from datetime import timedelta
-
- end_date = end_date + timedelta(days=1) - timedelta(microseconds=1)
- all_filters.append(("created_at", "Lte", end_date))
-
- # combine filters
- final_filter = None
- if len(all_filters) == 1:
- final_filter = all_filters[0]
- elif len(all_filters) > 1:
- final_filter = ("And", all_filters)
-
- try:
- # execute query
- result = await self._execute_query(
- namespace_name=namespace_name,
- search_mode=search_mode,
- query_embedding=query_embedding,
- query_text=query_text,
- top_k=top_k,
- include_attributes=["text", "organization_id", "agent_id", "role", "created_at"],
- filters=final_filter,
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- )
-
- # process results based on search mode
- if search_mode == "hybrid":
- # for hybrid mode, we get a multi-query response
- vector_results = self._process_message_query_results(result.results[0])
- fts_results = self._process_message_query_results(result.results[1])
-
- # use existing RRF method - it already returns metadata with ranks
- results_with_metadata = self._reciprocal_rank_fusion(
- vector_results=vector_results,
- fts_results=fts_results,
- get_id_func=lambda msg_dict: msg_dict["id"],
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- top_k=top_k,
- )
-
- # add raw scores to metadata if available
- vector_scores = {}
- for row in result.results[0].rows:
- if hasattr(row, "dist"):
- vector_scores[row.id] = row.dist
-
- fts_scores = {}
- for row in result.results[1].rows:
- if hasattr(row, "score"):
- fts_scores[row.id] = row.score
-
- # enhance metadata with raw scores
- enhanced_results = []
- for msg_dict, rrf_score, metadata in results_with_metadata:
- msg_id = msg_dict["id"]
- if msg_id in vector_scores:
- metadata["vector_score"] = vector_scores[msg_id]
- if msg_id in fts_scores:
- metadata["fts_score"] = fts_scores[msg_id]
- enhanced_results.append((msg_dict, rrf_score, metadata))
-
- return enhanced_results
- else:
- # for single queries (vector or fts)
- results = self._process_message_query_results(result)
- results_with_metadata = []
- for idx, msg_dict in enumerate(results):
- metadata = {
- "combined_score": 1.0 / (idx + 1),
- "search_mode": search_mode,
- f"{search_mode}_rank": idx + 1,
- }
-
- # add raw score if available
- if hasattr(result.rows[idx], "dist"):
- metadata["vector_score"] = result.rows[idx].dist
- elif hasattr(result.rows[idx], "score"):
- metadata["fts_score"] = result.rows[idx].score
-
- results_with_metadata.append((msg_dict, metadata["combined_score"], metadata))
-
- return results_with_metadata
-
- except Exception as e:
- logger.error(f"Failed to query messages from Turbopuffer: {e}")
- raise
-
- def _process_message_query_results(self, result) -> List[dict]:
- """Process results from a message query into message dicts.
-
- For RRF, we only need the rank order - scores are not used.
- """
- messages = []
-
- for row in result.rows:
- # Build message dict with key fields
- message_dict = {
- "id": row.id,
- "text": getattr(row, "text", ""),
- "organization_id": getattr(row, "organization_id", None),
- "agent_id": getattr(row, "agent_id", None),
- "role": getattr(row, "role", None),
- "created_at": getattr(row, "created_at", None),
- }
- messages.append(message_dict)
-
- return messages
-
- def _process_single_query_results(
- self, result, archive_id: str, tags: Optional[List[str]], is_fts: bool = False
- ) -> List[Tuple[PydanticPassage, float]]:
- """Process results from a single query into passage objects with scores."""
- passages_with_scores = []
-
- for row in result.rows:
- # Extract tags from the result row
- passage_tags = getattr(row, "tags", []) or []
-
- # Build metadata
- metadata = {}
-
- # Create a passage with minimal fields - embeddings are not returned from Turbopuffer
- passage = PydanticPassage(
- id=row.id,
- text=getattr(row, "text", ""),
- organization_id=getattr(row, "organization_id", None),
- archive_id=archive_id, # use the archive_id from the query
- created_at=getattr(row, "created_at", None),
- metadata_=metadata,
- tags=passage_tags, # Set the actual tags from the passage
- # Set required fields to empty/default values since we don't store embeddings
- embedding=[], # Empty embedding since we don't return it from Turbopuffer
- embedding_config=self.default_embedding_config, # No embedding config needed for retrieved passages
- )
-
- # handle score based on search type
- if is_fts:
- # for FTS, use the BM25 score directly (higher is better)
- score = getattr(row, "$score", 0.0)
- else:
- # for vector search, convert distance to similarity score
- distance = getattr(row, "$dist", 0.0)
- score = 1.0 - distance
-
- passages_with_scores.append((passage, score))
-
- return passages_with_scores
-
- def _reciprocal_rank_fusion(
- self,
- vector_results: List[Any],
- fts_results: List[Any],
- get_id_func: Callable[[Any], str],
- vector_weight: float,
- fts_weight: float,
- top_k: int,
- ) -> List[Tuple[Any, float, dict]]:
- """RRF implementation that works with any object type.
-
- RRF score = vector_weight * (1/(k + rank)) + fts_weight * (1/(k + rank))
- where k is a constant (typically 60) to avoid division by zero
-
- This is a pure rank-based fusion following the standard RRF algorithm.
-
- Args:
- vector_results: List of items from vector search (ordered by relevance)
- fts_results: List of items from FTS (ordered by relevance)
- get_id_func: Function to extract ID from an item
- vector_weight: Weight for vector search results
- fts_weight: Weight for FTS results
- top_k: Number of results to return
-
- Returns:
- List of (item, score, metadata) tuples sorted by RRF score
- metadata contains ranks from each result list
- """
- k = 60 # standard RRF constant from Cormack et al. (2009)
-
- # create rank mappings based on position in result lists
- # rank starts at 1, not 0
- vector_ranks = {get_id_func(item): rank + 1 for rank, item in enumerate(vector_results)}
- fts_ranks = {get_id_func(item): rank + 1 for rank, item in enumerate(fts_results)}
-
- # combine all unique items from both result sets
- all_items = {}
- for item in vector_results:
- all_items[get_id_func(item)] = item
- for item in fts_results:
- all_items[get_id_func(item)] = item
-
- # calculate RRF scores based purely on ranks
- rrf_scores = {}
- score_metadata = {}
- for item_id in all_items:
- # RRF formula: sum of 1/(k + rank) across result lists
- # If item not in a list, we don't add anything (equivalent to rank = infinity)
- vector_rrf_score = 0.0
- fts_rrf_score = 0.0
-
- if item_id in vector_ranks:
- vector_rrf_score = vector_weight / (k + vector_ranks[item_id])
- if item_id in fts_ranks:
- fts_rrf_score = fts_weight / (k + fts_ranks[item_id])
-
- combined_score = vector_rrf_score + fts_rrf_score
-
- rrf_scores[item_id] = combined_score
- score_metadata[item_id] = {
- "combined_score": combined_score, # Final RRF score
- "vector_rank": vector_ranks.get(item_id),
- "fts_rank": fts_ranks.get(item_id),
- }
-
- # sort by RRF score and return with metadata
- sorted_results = sorted(
- [(all_items[iid], score, score_metadata[iid]) for iid, score in rrf_scores.items()], key=lambda x: x[1], reverse=True
- )
-
- return sorted_results[:top_k]
-
- @trace_method
- async def delete_passage(self, archive_id: str, passage_id: str) -> bool:
- """Delete a passage from Turbopuffer."""
- from turbopuffer import AsyncTurbopuffer
-
- namespace_name = await self._get_archive_namespace_name(archive_id)
-
- try:
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # Use write API with deletes parameter as per Turbopuffer docs
- await namespace.write(deletes=[passage_id])
- logger.info(f"Successfully deleted passage {passage_id} from Turbopuffer archive {archive_id}")
- return True
- except Exception as e:
- logger.error(f"Failed to delete passage from Turbopuffer: {e}")
- raise
-
- @trace_method
- async def delete_passages(self, archive_id: str, passage_ids: List[str]) -> bool:
- """Delete multiple passages from Turbopuffer."""
- from turbopuffer import AsyncTurbopuffer
-
- if not passage_ids:
- return True
-
- namespace_name = await self._get_archive_namespace_name(archive_id)
-
- try:
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # Use write API with deletes parameter as per Turbopuffer docs
- await namespace.write(deletes=passage_ids)
- logger.info(f"Successfully deleted {len(passage_ids)} passages from Turbopuffer archive {archive_id}")
- return True
- except Exception as e:
- logger.error(f"Failed to delete passages from Turbopuffer: {e}")
- raise
-
- @trace_method
- async def delete_all_passages(self, archive_id: str) -> bool:
- """Delete all passages for an archive from Turbopuffer."""
- from turbopuffer import AsyncTurbopuffer
-
- namespace_name = await self._get_archive_namespace_name(archive_id)
-
- try:
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # Turbopuffer has a delete_all() method on namespace
- await namespace.delete_all()
- logger.info(f"Successfully deleted all passages for archive {archive_id}")
- return True
- except Exception as e:
- logger.error(f"Failed to delete all passages from Turbopuffer: {e}")
- raise
-
- @trace_method
- async def delete_messages(self, agent_id: str, organization_id: str, message_ids: List[str]) -> bool:
- """Delete multiple messages from Turbopuffer."""
- from turbopuffer import AsyncTurbopuffer
-
- if not message_ids:
- return True
-
- namespace_name = await self._get_message_namespace_name(organization_id)
-
- try:
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # Use write API with deletes parameter as per Turbopuffer docs
- await namespace.write(deletes=message_ids)
- logger.info(f"Successfully deleted {len(message_ids)} messages from Turbopuffer for agent {agent_id}")
- return True
- except Exception as e:
- logger.error(f"Failed to delete messages from Turbopuffer: {e}")
- raise
-
- @trace_method
- async def delete_all_messages(self, agent_id: str, organization_id: str) -> bool:
- """Delete all messages for an agent from Turbopuffer."""
- from turbopuffer import AsyncTurbopuffer
-
- namespace_name = await self._get_message_namespace_name(organization_id)
-
- try:
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # Use delete_by_filter to only delete messages for this agent
- # since namespace is now org-scoped
- result = await namespace.write(delete_by_filter=("agent_id", "Eq", agent_id))
- logger.info(f"Successfully deleted all messages for agent {agent_id} (deleted {result.rows_affected} rows)")
- return True
- except Exception as e:
- logger.error(f"Failed to delete all messages from Turbopuffer: {e}")
- raise
-
- # file/source passage methods
-
- @trace_method
- async def _get_file_passages_namespace_name(self, organization_id: str) -> str:
- """Get namespace name for file passages (org-scoped).
-
- Args:
- organization_id: Organization ID for namespace generation
-
- Returns:
- The org-scoped namespace name for file passages
- """
- environment = settings.environment
- if environment:
- namespace_name = f"file_passages_{organization_id}_{environment.lower()}"
- else:
- namespace_name = f"file_passages_{organization_id}"
-
- return namespace_name
-
- @trace_method
- async def insert_file_passages(
- self,
- source_id: str,
- file_id: str,
- text_chunks: List[str],
- organization_id: str,
- actor: "PydanticUser",
- created_at: Optional[datetime] = None,
- ) -> List[PydanticPassage]:
- """Insert file passages into Turbopuffer using org-scoped namespace.
-
- Args:
- source_id: ID of the source containing the file
- file_id: ID of the file
- text_chunks: List of text chunks to store
- organization_id: Organization ID for the passages
- actor: User actor for embedding generation
- created_at: Optional timestamp for retroactive entries (defaults to current UTC time)
-
- Returns:
- List of PydanticPassage objects that were inserted
- """
- from turbopuffer import AsyncTurbopuffer
-
- if not text_chunks:
- return []
-
- # generate embeddings using the default config
- embeddings = await self._generate_embeddings(text_chunks, actor)
-
- namespace_name = await self._get_file_passages_namespace_name(organization_id)
-
- # handle timestamp - ensure UTC
- if created_at is None:
- timestamp = datetime.now(timezone.utc)
- else:
- # ensure the provided timestamp is timezone-aware and in UTC
- if created_at.tzinfo is None:
- # assume UTC if no timezone provided
- timestamp = created_at.replace(tzinfo=timezone.utc)
- else:
- # convert to UTC if in different timezone
- timestamp = created_at.astimezone(timezone.utc)
-
- # prepare column-based data for turbopuffer - optimized for batch insert
- ids = []
- vectors = []
- texts = []
- organization_ids = []
- source_ids = []
- file_ids = []
- created_ats = []
- passages = []
-
- for idx, (text, embedding) in enumerate(zip(text_chunks, embeddings)):
- passage = PydanticPassage(
- text=text,
- file_id=file_id,
- source_id=source_id,
- embedding=embedding,
- embedding_config=self.default_embedding_config,
- organization_id=actor.organization_id,
- )
- passages.append(passage)
-
- # append to columns
- ids.append(passage.id)
- vectors.append(embedding)
- texts.append(text)
- organization_ids.append(organization_id)
- source_ids.append(source_id)
- file_ids.append(file_id)
- created_ats.append(timestamp)
-
- # build column-based upsert data
- upsert_columns = {
- "id": ids,
- "vector": vectors,
- "text": texts,
- "organization_id": organization_ids,
- "source_id": source_ids,
- "file_id": file_ids,
- "created_at": created_ats,
- }
-
- try:
- # use AsyncTurbopuffer as a context manager for proper resource cleanup
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # turbopuffer recommends column-based writes for performance
- await namespace.write(
- upsert_columns=upsert_columns,
- distance_metric="cosine_distance",
- schema={"text": {"type": "string", "full_text_search": True}},
- )
- logger.info(f"Successfully inserted {len(ids)} file passages to Turbopuffer for source {source_id}, file {file_id}")
- return passages
-
- except Exception as e:
- logger.error(f"Failed to insert file passages to Turbopuffer: {e}")
- # check if it's a duplicate ID error
- if "duplicate" in str(e).lower():
- logger.error("Duplicate passage IDs detected in batch")
- raise
-
- @trace_method
- async def query_file_passages(
- self,
- source_ids: List[str],
- organization_id: str,
- actor: "PydanticUser",
- query_text: Optional[str] = None,
- search_mode: str = "vector", # "vector", "fts", "hybrid"
- top_k: int = 10,
- file_id: Optional[str] = None, # optional filter by specific file
- vector_weight: float = 0.5,
- fts_weight: float = 0.5,
- ) -> List[Tuple[PydanticPassage, float, dict]]:
- """Query file passages from Turbopuffer using org-scoped namespace.
-
- Args:
- source_ids: List of source IDs to query
- organization_id: Organization ID for namespace lookup
- actor: User actor for embedding generation
- query_text: Text query for search
- search_mode: Search mode - "vector", "fts", or "hybrid" (default: "vector")
- top_k: Number of results to return
- file_id: Optional file ID to filter results to a specific file
- vector_weight: Weight for vector search results in hybrid mode (default: 0.5)
- fts_weight: Weight for FTS results in hybrid mode (default: 0.5)
-
- Returns:
- List of (passage, score, metadata) tuples with relevance rankings
- """
- # generate embedding for vector/hybrid search if query_text is provided
- query_embedding = None
- if query_text and search_mode in ["vector", "hybrid"]:
- embeddings = await self._generate_embeddings([query_text], actor)
- query_embedding = embeddings[0]
-
- # check if we should fallback to timestamp-based retrieval
- if query_embedding is None and query_text is None and search_mode not in ["timestamp"]:
- # fallback to retrieving most recent passages when no search query is provided
- search_mode = "timestamp"
-
- namespace_name = await self._get_file_passages_namespace_name(organization_id)
-
- # build filters - always filter by source_ids
- if len(source_ids) == 1:
- # single source_id, use Eq for efficiency
- filters = [("source_id", "Eq", source_ids[0])]
- else:
- # multiple source_ids, use In operator
- filters = [("source_id", "In", source_ids)]
-
- # add file filter if specified
- if file_id:
- filters.append(("file_id", "Eq", file_id))
-
- # combine filters
- final_filter = filters[0] if len(filters) == 1 else ("And", filters)
-
- try:
- # use generic query executor
- result = await self._execute_query(
- namespace_name=namespace_name,
- search_mode=search_mode,
- query_embedding=query_embedding,
- query_text=query_text,
- top_k=top_k,
- include_attributes=["text", "organization_id", "source_id", "file_id", "created_at"],
- filters=final_filter,
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- )
-
- # process results based on search mode
- if search_mode == "hybrid":
- # for hybrid mode, we get a multi-query response
- vector_results = self._process_file_query_results(result.results[0])
- fts_results = self._process_file_query_results(result.results[1], is_fts=True)
- # use RRF and include metadata with ranks
- results_with_metadata = self._reciprocal_rank_fusion(
- vector_results=[passage for passage, _ in vector_results],
- fts_results=[passage for passage, _ in fts_results],
- get_id_func=lambda p: p.id,
- vector_weight=vector_weight,
- fts_weight=fts_weight,
- top_k=top_k,
- )
- return results_with_metadata
- else:
- # for single queries (vector, fts, timestamp) - add basic metadata
- is_fts = search_mode == "fts"
- results = self._process_file_query_results(result, is_fts=is_fts)
- # add simple metadata for single search modes
- results_with_metadata = []
- for idx, (passage, score) in enumerate(results):
- metadata = {
- "combined_score": score,
- f"{search_mode}_rank": idx + 1, # add the rank for this search mode
- }
- results_with_metadata.append((passage, score, metadata))
- return results_with_metadata
-
- except Exception as e:
- logger.error(f"Failed to query file passages from Turbopuffer: {e}")
- raise
-
- def _process_file_query_results(self, result, is_fts: bool = False) -> List[Tuple[PydanticPassage, float]]:
- """Process results from a file query into passage objects with scores."""
- passages_with_scores = []
-
- for row in result.rows:
- # build metadata
- metadata = {}
-
- # create a passage with minimal fields - embeddings are not returned from Turbopuffer
- passage = PydanticPassage(
- id=row.id,
- text=getattr(row, "text", ""),
- organization_id=getattr(row, "organization_id", None),
- source_id=getattr(row, "source_id", None), # get source_id from the row
- file_id=getattr(row, "file_id", None),
- created_at=getattr(row, "created_at", None),
- metadata_=metadata,
- tags=[],
- # set required fields to empty/default values since we don't store embeddings
- embedding=[], # empty embedding since we don't return it from Turbopuffer
- embedding_config=self.default_embedding_config,
- )
-
- # handle score based on search type
- if is_fts:
- # for FTS, use the BM25 score directly (higher is better)
- score = getattr(row, "$score", 0.0)
- else:
- # for vector search, convert distance to similarity score
- distance = getattr(row, "$dist", 0.0)
- score = 1.0 - distance
-
- passages_with_scores.append((passage, score))
-
- return passages_with_scores
-
- @trace_method
- async def delete_file_passages(self, source_id: str, file_id: str, organization_id: str) -> bool:
- """Delete all passages for a specific file from Turbopuffer."""
- from turbopuffer import AsyncTurbopuffer
-
- namespace_name = await self._get_file_passages_namespace_name(organization_id)
-
- try:
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # use delete_by_filter to only delete passages for this file
- # need to filter by both source_id and file_id
- filter_expr = ("And", [("source_id", "Eq", source_id), ("file_id", "Eq", file_id)])
- result = await namespace.write(delete_by_filter=filter_expr)
- logger.info(
- f"Successfully deleted passages for file {file_id} from source {source_id} (deleted {result.rows_affected} rows)"
- )
- return True
- except Exception as e:
- logger.error(f"Failed to delete file passages from Turbopuffer: {e}")
- raise
-
- @trace_method
- async def delete_source_passages(self, source_id: str, organization_id: str) -> bool:
- """Delete all passages for a source from Turbopuffer."""
- from turbopuffer import AsyncTurbopuffer
-
- namespace_name = await self._get_file_passages_namespace_name(organization_id)
-
- try:
- async with AsyncTurbopuffer(api_key=self.api_key, region=self.region) as client:
- namespace = client.namespace(namespace_name)
- # delete all passages for this source
- result = await namespace.write(delete_by_filter=("source_id", "Eq", source_id))
- logger.info(f"Successfully deleted all passages for source {source_id} (deleted {result.rows_affected} rows)")
- return True
- except Exception as e:
- logger.error(f"Failed to delete source passages from Turbopuffer: {e}")
- raise
diff --git a/letta/humans/__init__.py b/letta/humans/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/humans/examples/basic.txt b/letta/humans/examples/basic.txt
deleted file mode 100644
index c49c7d31..00000000
--- a/letta/humans/examples/basic.txt
+++ /dev/null
@@ -1 +0,0 @@
-First name: Chad
diff --git a/letta/humans/examples/cs_phd.txt b/letta/humans/examples/cs_phd.txt
deleted file mode 100644
index 8b50cfa4..00000000
--- a/letta/humans/examples/cs_phd.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-This is what I know so far about the user, I should expand this as I learn more about them.
-
-First name: Chad
-Last name: ?
-Gender: Male
-Age: ?
-Nationality: ?
-Occupation: Computer science PhD student at UC Berkeley
-Interests: Formula 1, Sailing, Taste of the Himalayas Restaurant in Berkeley, CSGO
diff --git a/letta/interface.py b/letta/interface.py
deleted file mode 100644
index 7e146b07..00000000
--- a/letta/interface.py
+++ /dev/null
@@ -1,323 +0,0 @@
-import re
-from abc import ABC, abstractmethod
-from typing import List, Optional
-
-from colorama import Fore, Style, init
-
-from letta.constants import CLI_WARNING_PREFIX
-from letta.helpers.json_helpers import json_loads
-from letta.local_llm.constants import ASSISTANT_MESSAGE_CLI_SYMBOL, INNER_THOUGHTS_CLI_SYMBOL
-from letta.schemas.message import Message
-from letta.utils import printd
-
-init(autoreset=True)
-
-# DEBUG = True # puts full message outputs in the terminal
-DEBUG = False # only dumps important messages in the terminal
-
-STRIP_UI = False
-
-
-class AgentInterface(ABC):
- """Interfaces handle Letta-related events (observer pattern)
-
- The 'msg' args provides the scoped message, and the optional Message arg can provide additional metadata.
- """
-
- @abstractmethod
- def user_message(self, msg: str, msg_obj: Optional[Message] = None):
- """Letta receives a user message"""
- raise NotImplementedError
-
- @abstractmethod
- def internal_monologue(self, msg: str, msg_obj: Optional[Message] = None, chunk_index: Optional[int] = None):
- """Letta generates some internal monologue"""
- raise NotImplementedError
-
- @abstractmethod
- def assistant_message(self, msg: str, msg_obj: Optional[Message] = None):
- """Letta uses send_message"""
- raise NotImplementedError
-
- @abstractmethod
- def function_message(self, msg: str, msg_obj: Optional[Message] = None, chunk_index: Optional[int] = None):
- """Letta calls a function"""
- raise NotImplementedError
-
- # @abstractmethod
- # @staticmethod
- # def print_messages():
- # raise NotImplementedError
-
- # @abstractmethod
- # @staticmethod
- # def print_messages_raw():
- # raise NotImplementedError
-
- # @abstractmethod
- # @staticmethod
- # def step_yield():
- # raise NotImplementedError
-
-
-class CLIInterface(AgentInterface):
- """Basic interface for dumping agent events to the command-line"""
-
- @staticmethod
- def important_message(msg: str):
- fstr = f"{Fore.MAGENTA}{Style.BRIGHT}{{msg}}{Style.RESET_ALL}"
- if STRIP_UI:
- fstr = "{msg}"
- print(fstr.format(msg=msg))
-
- @staticmethod
- def warning_message(msg: str):
- fstr = f"{Fore.RED}{Style.BRIGHT}{{msg}}{Style.RESET_ALL}"
- if STRIP_UI:
- fstr = "{msg}"
- else:
- print(fstr.format(msg=msg))
-
- @staticmethod
- def internal_monologue(msg: str, msg_obj: Optional[Message] = None, chunk_index: Optional[int] = None):
- # ANSI escape code for italic is '\x1B[3m'
- fstr = f"\x1b[3m{Fore.LIGHTBLACK_EX}{INNER_THOUGHTS_CLI_SYMBOL} {{msg}}{Style.RESET_ALL}"
- if STRIP_UI:
- fstr = "{msg}"
- print(fstr.format(msg=msg))
-
- @staticmethod
- def assistant_message(msg: str, msg_obj: Optional[Message] = None):
- fstr = f"{Fore.YELLOW}{Style.BRIGHT}{ASSISTANT_MESSAGE_CLI_SYMBOL} {Fore.YELLOW}{{msg}}{Style.RESET_ALL}"
- if STRIP_UI:
- fstr = "{msg}"
- print(fstr.format(msg=msg))
-
- @staticmethod
- def memory_message(msg: str, msg_obj: Optional[Message] = None):
- fstr = f"{Fore.LIGHTMAGENTA_EX}{Style.BRIGHT}🧠 {Fore.LIGHTMAGENTA_EX}{{msg}}{Style.RESET_ALL}"
- if STRIP_UI:
- fstr = "{msg}"
- print(fstr.format(msg=msg))
-
- @staticmethod
- def system_message(msg: str, msg_obj: Optional[Message] = None):
- fstr = f"{Fore.MAGENTA}{Style.BRIGHT}🖥️ [system] {Fore.MAGENTA}{msg}{Style.RESET_ALL}"
- if STRIP_UI:
- fstr = "{msg}"
- print(fstr.format(msg=msg))
-
- @staticmethod
- def user_message(
- msg: str,
- msg_obj: Optional[Message] = None,
- raw: bool = False,
- dump: bool = False,
- debug: bool = DEBUG,
- chunk_index: Optional[int] = None,
- ):
- def print_user_message(icon, msg, printf=print):
- if STRIP_UI:
- printf(f"{icon} {msg}")
- else:
- printf(f"{Fore.GREEN}{Style.BRIGHT}{icon} {Fore.GREEN}{msg}{Style.RESET_ALL}")
-
- def printd_user_message(icon, msg):
- return print_user_message(icon, msg)
-
- if not (raw or dump or debug):
- # we do not want to repeat the message in normal use
- return
-
- if isinstance(msg, str):
- if raw:
- printd_user_message("🧑", msg)
- return
- else:
- try:
- msg_json = json_loads(msg)
- except:
- printd(f"{CLI_WARNING_PREFIX}failed to parse user message into json")
- printd_user_message("🧑", msg)
- return
- if msg_json["type"] == "user_message":
- if dump:
- print_user_message("🧑", msg_json["message"])
- return
- msg_json.pop("type")
- printd_user_message("🧑", msg_json)
- elif msg_json["type"] == "heartbeat":
- if debug:
- msg_json.pop("type")
- printd_user_message("💓", msg_json)
- elif dump:
- print_user_message("💓", msg_json)
- return
-
- elif msg_json["type"] == "system_message":
- msg_json.pop("type")
- printd_user_message("🖥️", msg_json)
- else:
- printd_user_message("🧑", msg_json)
-
- @staticmethod
- def function_message(msg: str, msg_obj: Optional[Message] = None, debug: bool = DEBUG, chunk_index: Optional[int] = None):
- def print_function_message(icon, msg, color=Fore.RED, printf=print):
- if STRIP_UI:
- printf(f"⚡{icon} [function] {msg}")
- else:
- printf(f"{color}{Style.BRIGHT}⚡{icon} [function] {color}{msg}{Style.RESET_ALL}")
-
- def printd_function_message(icon, msg, color=Fore.RED):
- return print_function_message(icon, msg, color, printf=(print if debug else printd))
-
- if isinstance(msg, dict):
- printd_function_message("", msg)
- return
-
- if msg.startswith("Success"):
- printd_function_message("🟢", msg)
- elif msg.startswith("Error: "):
- printd_function_message("🔴", msg)
- elif msg.startswith("Ran "):
- # NOTE: ignore 'ran' messages that come post-execution
- return
- elif msg.startswith("Running "):
- if debug:
- printd_function_message("", msg)
- else:
- match = re.search(r"Running (\w+)\((.*)\)", msg)
- if match:
- function_name = match.group(1)
- function_args = match.group(2)
- if function_name in ["archival_memory_insert", "archival_memory_search", "core_memory_replace", "core_memory_append"]:
- if function_name in ["archival_memory_insert", "core_memory_append", "core_memory_replace"]:
- print_function_message("🧠", f"updating memory with {function_name}")
- elif function_name == "archival_memory_search":
- print_function_message("🧠", f"searching memory with {function_name}")
- try:
- msg_dict = eval(function_args)
- if function_name == "archival_memory_search":
- output = f"\tquery: {msg_dict['query']}, page: {msg_dict['page']}"
- if STRIP_UI:
- print(output)
- else:
- print(f"{Fore.RED}{output}{Style.RESET_ALL}")
- elif function_name == "archival_memory_insert":
- output = f"\t→ {msg_dict['content']}"
- if STRIP_UI:
- print(output)
- else:
- print(f"{Style.BRIGHT}{Fore.RED}{output}{Style.RESET_ALL}")
- else:
- if STRIP_UI:
- print(f"\t {msg_dict['old_content']}\n\t→ {msg_dict['new_content']}")
- else:
- print(
- f"{Style.BRIGHT}\t{Fore.RED} {msg_dict['old_content']}\n\t{Fore.GREEN}→ {msg_dict['new_content']}{Style.RESET_ALL}"
- )
- except Exception as e:
- printd(str(e))
- printd(msg_dict)
- elif function_name in ["conversation_search", "conversation_search_date"]:
- print_function_message("🧠", f"searching memory with {function_name}")
- try:
- msg_dict = eval(function_args)
- output = f"\tquery: {msg_dict['query']}, page: {msg_dict['page']}"
- if STRIP_UI:
- print(output)
- else:
- print(f"{Fore.RED}{output}{Style.RESET_ALL}")
- except Exception as e:
- printd(str(e))
- printd(msg_dict)
- else:
- printd(f"{CLI_WARNING_PREFIX}did not recognize function message")
- printd_function_message("", msg)
- else:
- try:
- msg_dict = json_loads(msg)
- if "status" in msg_dict and msg_dict["status"] == "OK":
- printd_function_message("", str(msg), color=Fore.GREEN)
- else:
- printd_function_message("", str(msg), color=Fore.RED)
- except Exception:
- print(f"{CLI_WARNING_PREFIX}did not recognize function message {type(msg)} {msg}")
- printd_function_message("", msg)
-
- @staticmethod
- def print_messages(message_sequence: List[Message], dump=False):
- # rewrite to dict format
- message_sequence = Message.to_openai_dicts_from_list(message_sequence)
-
- idx = len(message_sequence)
- for msg in message_sequence:
- if dump:
- print(f"[{idx}] ", end="")
- idx -= 1
- role = msg["role"]
- content = msg["content"]
-
- if role == "system":
- CLIInterface.system_message(content)
- elif role == "assistant":
- # Differentiate between internal monologue, function calls, and messages
- if msg.get("function_call"):
- if content is not None:
- CLIInterface.internal_monologue(content)
- # I think the next one is not up to date
- # function_message(msg["function_call"])
- args = json_loads(msg["function_call"].get("arguments"))
- CLIInterface.assistant_message(args.get("message"))
- # assistant_message(content)
- elif msg.get("tool_calls"):
- if content is not None:
- CLIInterface.internal_monologue(content)
- function_obj = msg["tool_calls"][0].get("function")
- if function_obj:
- args = json_loads(function_obj.get("arguments"))
- CLIInterface.assistant_message(args.get("message"))
- else:
- CLIInterface.internal_monologue(content)
- elif role == "user":
- CLIInterface.user_message(content, dump=dump)
- elif role == "function":
- CLIInterface.function_message(content, debug=dump)
- elif role == "tool":
- CLIInterface.function_message(content, debug=dump)
- else:
- print(f"Unknown role: {content}")
-
- @staticmethod
- def print_messages_simple(message_sequence: List[Message]):
- # rewrite to dict format
- message_sequence = Message.to_openai_dicts_from_list(message_sequence)
-
- for msg in message_sequence:
- role = msg["role"]
- content = msg["content"]
-
- if role == "system":
- CLIInterface.system_message(content)
- elif role == "assistant":
- CLIInterface.assistant_message(content)
- elif role == "user":
- CLIInterface.user_message(content, raw=True)
- else:
- print(f"Unknown role: {content}")
-
- @staticmethod
- def print_messages_raw(message_sequence: List[Message]):
- # rewrite to dict format
- message_sequence = Message.to_openai_dicts_from_list(message_sequence)
-
- for msg in message_sequence:
- print(msg)
-
- @staticmethod
- def step_yield():
- pass
-
- @staticmethod
- def step_complete():
- pass
diff --git a/letta/interfaces/__init__.py b/letta/interfaces/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/interfaces/anthropic_streaming_interface.py b/letta/interfaces/anthropic_streaming_interface.py
deleted file mode 100644
index e295fdd7..00000000
--- a/letta/interfaces/anthropic_streaming_interface.py
+++ /dev/null
@@ -1,519 +0,0 @@
-import asyncio
-import json
-from collections.abc import AsyncGenerator
-from datetime import datetime, timezone
-from enum import Enum
-from typing import Optional
-
-from anthropic import AsyncStream
-from anthropic.types.beta import (
- BetaInputJSONDelta,
- BetaRawContentBlockDeltaEvent,
- BetaRawContentBlockStartEvent,
- BetaRawContentBlockStopEvent,
- BetaRawMessageDeltaEvent,
- BetaRawMessageStartEvent,
- BetaRawMessageStopEvent,
- BetaRawMessageStreamEvent,
- BetaRedactedThinkingBlock,
- BetaSignatureDelta,
- BetaTextBlock,
- BetaTextDelta,
- BetaThinkingBlock,
- BetaThinkingDelta,
- BetaToolUseBlock,
-)
-
-from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG
-from letta.log import get_logger
-from letta.schemas.letta_message import (
- ApprovalRequestMessage,
- AssistantMessage,
- HiddenReasoningMessage,
- LettaMessage,
- ReasoningMessage,
- ToolCallDelta,
- ToolCallMessage,
-)
-from letta.schemas.letta_message_content import ReasoningContent, RedactedReasoningContent, TextContent
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.message import Message
-from letta.schemas.openai.chat_completion_response import FunctionCall, ToolCall
-from letta.server.rest_api.json_parser import JSONParser, PydanticJSONParser
-
-logger = get_logger(__name__)
-
-
-# TODO: These modes aren't used right now - but can be useful we do multiple sequential tool calling within one Claude message
-class EventMode(Enum):
- TEXT = "TEXT"
- TOOL_USE = "TOOL_USE"
- THINKING = "THINKING"
- REDACTED_THINKING = "REDACTED_THINKING"
-
-
-class AnthropicStreamingInterface:
- """
- Encapsulates the logic for streaming responses from Anthropic.
- This class handles parsing of partial tokens, pre-execution messages,
- and detection of tool call events.
- """
-
- def __init__(
- self,
- use_assistant_message: bool = False,
- put_inner_thoughts_in_kwarg: bool = False,
- requires_approval_tools: list = [],
- ):
- self.json_parser: JSONParser = PydanticJSONParser()
- self.use_assistant_message = use_assistant_message
-
- # Premake IDs for database writes
- self.letta_message_id = Message.generate_id()
-
- self.anthropic_mode = None
- self.message_id = None
- self.accumulated_inner_thoughts = []
- self.tool_call_id = None
- self.tool_call_name = None
- self.accumulated_tool_call_args = ""
- self.previous_parse = {}
-
- # usage trackers
- self.input_tokens = 0
- self.output_tokens = 0
- self.model = None
-
- # reasoning object trackers
- self.reasoning_messages = []
-
- # Buffer to hold tool call messages until inner thoughts are complete
- self.tool_call_buffer = []
- self.inner_thoughts_complete = False
- self.put_inner_thoughts_in_kwarg = put_inner_thoughts_in_kwarg
-
- # Buffer to handle partial XML tags across chunks
- self.partial_tag_buffer = ""
-
- self.requires_approval_tools = requires_approval_tools
-
- def get_tool_call_object(self) -> ToolCall:
- """Useful for agent loop"""
- if not self.tool_call_name:
- raise ValueError("No tool call returned")
- # hack for tool rules
- try:
- tool_input = json.loads(self.accumulated_tool_call_args)
- except json.JSONDecodeError as e:
- logger.warning(
- f"Failed to decode tool call arguments for tool_call_id={self.tool_call_id}, "
- f"name={self.tool_call_name}. Raw input: {self.accumulated_tool_call_args!r}. Error: {e}"
- )
- raise
- if "id" in tool_input and tool_input["id"].startswith("toolu_") and "function" in tool_input:
- arguments = str(json.dumps(tool_input["function"]["arguments"], indent=2))
- else:
- arguments = self.accumulated_tool_call_args
- return ToolCall(id=self.tool_call_id, function=FunctionCall(arguments=arguments, name=self.tool_call_name))
-
- def _check_inner_thoughts_complete(self, combined_args: str) -> bool:
- """
- Check if inner thoughts are complete in the current tool call arguments
- by looking for a closing quote after the inner_thoughts field
- """
- try:
- if not self.put_inner_thoughts_in_kwarg:
- # None of the things should have inner thoughts in kwargs
- return True
- else:
- parsed = self.json_parser.parse(combined_args)
- # TODO: This will break on tools with 0 input
- return len(parsed.keys()) > 1 and INNER_THOUGHTS_KWARG in parsed.keys()
- except Exception as e:
- logger.error("Error checking inner thoughts: %s", e)
- raise
-
- def get_reasoning_content(self) -> list[TextContent | ReasoningContent | RedactedReasoningContent]:
- def _process_group(
- group: list[ReasoningMessage | HiddenReasoningMessage], group_type: str
- ) -> TextContent | ReasoningContent | RedactedReasoningContent:
- if group_type == "reasoning":
- reasoning_text = "".join(chunk.reasoning for chunk in group).strip()
- is_native = any(chunk.source == "reasoner_model" for chunk in group)
- signature = next((chunk.signature for chunk in group if chunk.signature is not None), None)
- if is_native:
- return ReasoningContent(is_native=is_native, reasoning=reasoning_text, signature=signature)
- else:
- return TextContent(text=reasoning_text)
- elif group_type == "redacted":
- redacted_text = "".join(chunk.hidden_reasoning for chunk in group if chunk.hidden_reasoning is not None)
- return RedactedReasoningContent(data=redacted_text)
- else:
- raise ValueError("Unexpected group type")
-
- merged = []
- current_group = []
- current_group_type = None # "reasoning" or "redacted"
-
- for msg in self.reasoning_messages:
- # Determine the type of the current message
- if isinstance(msg, HiddenReasoningMessage):
- msg_type = "redacted"
- elif isinstance(msg, ReasoningMessage):
- msg_type = "reasoning"
- else:
- raise ValueError("Unexpected message type")
-
- # Initialize group type if not set
- if current_group_type is None:
- current_group_type = msg_type
-
- # If the type changes, process the current group
- if msg_type != current_group_type:
- merged.append(_process_group(current_group, current_group_type))
- current_group = []
- current_group_type = msg_type
-
- current_group.append(msg)
-
- # Process the final group, if any.
- if current_group:
- merged.append(_process_group(current_group, current_group_type))
-
- # Strip out XML from any text content fields
- for content in merged:
- if isinstance(content, TextContent) and content.text.endswith(""):
- cutoff = len(content.text) - len("")
- content.text = content.text[:cutoff]
-
- return merged
-
- async def process(
- self,
- stream: AsyncStream[BetaRawMessageStreamEvent],
- ttft_span: Optional["Span"] = None,
- ) -> AsyncGenerator[LettaMessage | LettaStopReason, None]:
- prev_message_type = None
- message_index = 0
- event = None
- try:
- async with stream:
- async for event in stream:
- try:
- async for message in self._process_event(event, ttft_span, prev_message_type, message_index):
- new_message_type = message.message_type
- if new_message_type != prev_message_type:
- if prev_message_type != None:
- message_index += 1
- prev_message_type = new_message_type
- yield message
- except asyncio.CancelledError as e:
- import traceback
-
- logger.info("Cancelled stream attempt but overriding %s: %s", e, traceback.format_exc())
- async for message in self._process_event(event, ttft_span, prev_message_type, message_index):
- new_message_type = message.message_type
- if new_message_type != prev_message_type:
- if prev_message_type != None:
- message_index += 1
- prev_message_type = new_message_type
- yield message
-
- # Don't raise the exception here
- continue
-
- except Exception as e:
- import traceback
-
- logger.error("Error processing stream: %s", e, traceback.format_exc())
- ttft_span.add_event(
- name="stop_reason",
- attributes={"stop_reason": StopReasonType.error.value, "error": str(e), "stacktrace": traceback.format_exc()},
- )
- yield LettaStopReason(stop_reason=StopReasonType.error)
- raise e
- finally:
- logger.info("AnthropicStreamingInterface: Stream processing complete.")
-
- async def _process_event(
- self,
- event: BetaRawMessageStreamEvent,
- ttft_span: Optional["Span"] = None,
- prev_message_type: Optional[str] = None,
- message_index: int = 0,
- ) -> AsyncGenerator[LettaMessage | LettaStopReason, None]:
- """Process a single event from the Anthropic stream and yield any resulting messages.
-
- Args:
- event: The event to process
-
- Yields:
- Messages generated from processing this event
- """
- if isinstance(event, BetaRawContentBlockStartEvent):
- content = event.content_block
-
- if isinstance(content, BetaTextBlock):
- self.anthropic_mode = EventMode.TEXT
- # TODO: Can capture citations, etc.
- elif isinstance(content, BetaToolUseBlock):
- self.anthropic_mode = EventMode.TOOL_USE
- self.tool_call_id = content.id
- self.tool_call_name = content.name
- self.inner_thoughts_complete = False
-
- if not self.use_assistant_message:
- # Only buffer the initial tool call message if it doesn't require approval
- # For approval-required tools, we'll create the ApprovalRequestMessage later
- if self.tool_call_name not in self.requires_approval_tools:
- tool_call_msg = ToolCallMessage(
- id=self.letta_message_id,
- tool_call=ToolCallDelta(name=self.tool_call_name, tool_call_id=self.tool_call_id),
- date=datetime.now(timezone.utc).isoformat(),
- )
- self.tool_call_buffer.append(tool_call_msg)
- elif isinstance(content, BetaThinkingBlock):
- self.anthropic_mode = EventMode.THINKING
- # TODO: Can capture signature, etc.
- elif isinstance(content, BetaRedactedThinkingBlock):
- self.anthropic_mode = EventMode.REDACTED_THINKING
- if prev_message_type and prev_message_type != "hidden_reasoning_message":
- message_index += 1
- hidden_reasoning_message = HiddenReasoningMessage(
- id=self.letta_message_id,
- state="redacted",
- hidden_reasoning=content.data,
- date=datetime.now(timezone.utc).isoformat(),
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- self.reasoning_messages.append(hidden_reasoning_message)
- prev_message_type = hidden_reasoning_message.message_type
- yield hidden_reasoning_message
-
- elif isinstance(event, BetaRawContentBlockDeltaEvent):
- delta = event.delta
-
- if isinstance(delta, BetaTextDelta):
- # Safety check
- if not self.anthropic_mode == EventMode.TEXT:
- raise RuntimeError(f"Streaming integrity failed - received BetaTextDelta object while not in TEXT EventMode: {delta}")
-
- # Weird bug happens with native thinking where a single response can contain:
- # [reasoning, text, tool_call]
- # In these cases, we should pipe text out to null / ignore it
- # TODO this will have to be redone to support non-tool calling message sending
- if not self.put_inner_thoughts_in_kwarg:
- return
-
- # Combine buffer with current text to handle tags split across chunks
- combined_text = self.partial_tag_buffer + delta.text
-
- # Remove all occurrences of tag
- cleaned_text = combined_text.replace("", "")
-
- # Extract just the new content (without the buffer part)
- if len(self.partial_tag_buffer) <= len(cleaned_text):
- delta.text = cleaned_text[len(self.partial_tag_buffer) :]
- else:
- # Edge case: the tag was removed and now the text is shorter than the buffer
- delta.text = ""
-
- # Store the last 10 characters (or all if less than 10) for the next chunk
- # This is enough to catch " 10 else combined_text
- self.accumulated_inner_thoughts.append(delta.text)
-
- if prev_message_type and prev_message_type != "reasoning_message":
- message_index += 1
- reasoning_message = ReasoningMessage(
- id=self.letta_message_id,
- reasoning=self.accumulated_inner_thoughts[-1],
- date=datetime.now(timezone.utc).isoformat(),
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- self.reasoning_messages.append(reasoning_message)
- prev_message_type = reasoning_message.message_type
- yield reasoning_message
-
- elif isinstance(delta, BetaInputJSONDelta):
- if not self.anthropic_mode == EventMode.TOOL_USE:
- raise RuntimeError(
- f"Streaming integrity failed - received BetaInputJSONDelta object while not in TOOL_USE EventMode: {delta}"
- )
-
- self.accumulated_tool_call_args += delta.partial_json
- current_parsed = self.json_parser.parse(self.accumulated_tool_call_args)
-
- # Start detecting a difference in inner thoughts
- previous_inner_thoughts = self.previous_parse.get(INNER_THOUGHTS_KWARG, "")
- current_inner_thoughts = current_parsed.get(INNER_THOUGHTS_KWARG, "")
- inner_thoughts_diff = current_inner_thoughts[len(previous_inner_thoughts) :]
-
- if inner_thoughts_diff:
- if prev_message_type and prev_message_type != "reasoning_message":
- message_index += 1
- reasoning_message = ReasoningMessage(
- id=self.letta_message_id,
- reasoning=inner_thoughts_diff,
- date=datetime.now(timezone.utc).isoformat(),
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- self.reasoning_messages.append(reasoning_message)
- prev_message_type = reasoning_message.message_type
- yield reasoning_message
-
- # Check if inner thoughts are complete - if so, flush the buffer or create approval message
- if not self.inner_thoughts_complete and self._check_inner_thoughts_complete(self.accumulated_tool_call_args):
- self.inner_thoughts_complete = True
-
- # Check if this tool requires approval
- if self.tool_call_name in self.requires_approval_tools:
- # Create ApprovalRequestMessage directly (buffer should be empty)
- if prev_message_type and prev_message_type != "approval_request_message":
- message_index += 1
-
- # Strip out inner thoughts from arguments
- tool_call_args = self.accumulated_tool_call_args
- if current_inner_thoughts:
- tool_call_args = tool_call_args.replace(f'"{INNER_THOUGHTS_KWARG}": "{current_inner_thoughts}"', "")
-
- approval_msg = ApprovalRequestMessage(
- id=self.letta_message_id,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- date=datetime.now(timezone.utc).isoformat(),
- name=self.tool_call_name,
- tool_call=ToolCallDelta(
- name=self.tool_call_name,
- tool_call_id=self.tool_call_id,
- arguments=tool_call_args,
- ),
- )
- prev_message_type = approval_msg.message_type
- yield approval_msg
- elif len(self.tool_call_buffer) > 0:
- # Flush buffered tool call messages for non-approval tools
- if prev_message_type and prev_message_type != "tool_call_message":
- message_index += 1
-
- # Strip out the inner thoughts from the buffered tool call arguments before streaming
- tool_call_args = ""
- for buffered_msg in self.tool_call_buffer:
- tool_call_args += buffered_msg.tool_call.arguments if buffered_msg.tool_call.arguments else ""
- tool_call_args = tool_call_args.replace(f'"{INNER_THOUGHTS_KWARG}": "{current_inner_thoughts}"', "")
-
- tool_call_msg = ToolCallMessage(
- id=self.tool_call_buffer[0].id,
- otid=Message.generate_otid_from_id(self.tool_call_buffer[0].id, message_index),
- date=self.tool_call_buffer[0].date,
- tool_call=ToolCallDelta(
- name=self.tool_call_name,
- tool_call_id=self.tool_call_id,
- arguments=tool_call_args,
- ),
- )
- prev_message_type = tool_call_msg.message_type
- yield tool_call_msg
- self.tool_call_buffer = []
-
- # Start detecting special case of "send_message"
- if self.tool_call_name == DEFAULT_MESSAGE_TOOL and self.use_assistant_message:
- previous_send_message = self.previous_parse.get(DEFAULT_MESSAGE_TOOL_KWARG, "")
- current_send_message = current_parsed.get(DEFAULT_MESSAGE_TOOL_KWARG, "")
- send_message_diff = current_send_message[len(previous_send_message) :]
-
- # Only stream out if it's not an empty string
- if send_message_diff:
- if prev_message_type and prev_message_type != "assistant_message":
- message_index += 1
- assistant_msg = AssistantMessage(
- id=self.letta_message_id,
- content=[TextContent(text=send_message_diff)],
- date=datetime.now(timezone.utc).isoformat(),
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- prev_message_type = assistant_msg.message_type
- yield assistant_msg
- else:
- # Otherwise, it is a normal tool call - buffer or yield based on inner thoughts status
- if self.tool_call_name in self.requires_approval_tools:
- tool_call_msg = ApprovalRequestMessage(
- id=self.letta_message_id,
- tool_call=ToolCallDelta(name=self.tool_call_name, tool_call_id=self.tool_call_id, arguments=delta.partial_json),
- date=datetime.now(timezone.utc).isoformat(),
- )
- else:
- tool_call_msg = ToolCallMessage(
- id=self.letta_message_id,
- tool_call=ToolCallDelta(name=self.tool_call_name, tool_call_id=self.tool_call_id, arguments=delta.partial_json),
- date=datetime.now(timezone.utc).isoformat(),
- )
- if self.inner_thoughts_complete:
- if prev_message_type and prev_message_type != "tool_call_message":
- message_index += 1
- tool_call_msg.otid = Message.generate_otid_from_id(self.letta_message_id, message_index)
- prev_message_type = tool_call_msg.message_type
- yield tool_call_msg
- else:
- self.tool_call_buffer.append(tool_call_msg)
-
- # Set previous parse
- self.previous_parse = current_parsed
- elif isinstance(delta, BetaThinkingDelta):
- # Safety check
- if not self.anthropic_mode == EventMode.THINKING:
- raise RuntimeError(
- f"Streaming integrity failed - received BetaThinkingBlock object while not in THINKING EventMode: {delta}"
- )
-
- if prev_message_type and prev_message_type != "reasoning_message":
- message_index += 1
- reasoning_message = ReasoningMessage(
- id=self.letta_message_id,
- source="reasoner_model",
- reasoning=delta.thinking,
- date=datetime.now(timezone.utc).isoformat(),
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- self.reasoning_messages.append(reasoning_message)
- prev_message_type = reasoning_message.message_type
- yield reasoning_message
- elif isinstance(delta, BetaSignatureDelta):
- # Safety check
- if not self.anthropic_mode == EventMode.THINKING:
- raise RuntimeError(
- f"Streaming integrity failed - received BetaSignatureDelta object while not in THINKING EventMode: {delta}"
- )
-
- if prev_message_type and prev_message_type != "reasoning_message":
- message_index += 1
- reasoning_message = ReasoningMessage(
- id=self.letta_message_id,
- source="reasoner_model",
- reasoning="",
- date=datetime.now(timezone.utc).isoformat(),
- signature=delta.signature,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- self.reasoning_messages.append(reasoning_message)
- prev_message_type = reasoning_message.message_type
- yield reasoning_message
- elif isinstance(event, BetaRawMessageStartEvent):
- self.message_id = event.message.id
- self.input_tokens += event.message.usage.input_tokens
- self.output_tokens += event.message.usage.output_tokens
- self.model = event.message.model
- elif isinstance(event, BetaRawMessageDeltaEvent):
- self.output_tokens += event.usage.output_tokens
- elif isinstance(event, BetaRawMessageStopEvent):
- # Don't do anything here! We don't want to stop the stream.
- pass
- elif isinstance(event, BetaRawContentBlockStopEvent):
- # If we're exiting a tool use block and there are still buffered messages,
- # we should flush them now
- if self.anthropic_mode == EventMode.TOOL_USE and self.tool_call_buffer:
- for buffered_msg in self.tool_call_buffer:
- yield buffered_msg
- self.tool_call_buffer = []
-
- self.anthropic_mode = None
diff --git a/letta/interfaces/openai_chat_completions_streaming_interface.py b/letta/interfaces/openai_chat_completions_streaming_interface.py
deleted file mode 100644
index b0a06d39..00000000
--- a/letta/interfaces/openai_chat_completions_streaming_interface.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from collections.abc import AsyncGenerator
-from typing import Any
-
-from openai import AsyncStream
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk, Choice, ChoiceDelta
-
-from letta.constants import PRE_EXECUTION_MESSAGE_ARG
-from letta.interfaces.utils import _format_sse_chunk
-from letta.server.rest_api.json_parser import OptimisticJSONParser
-
-
-class OpenAIChatCompletionsStreamingInterface:
- """
- Encapsulates the logic for streaming responses from OpenAI.
- This class handles parsing of partial tokens, pre-execution messages,
- and detection of tool call events.
- """
-
- def __init__(self, stream_pre_execution_message: bool = True):
- self.optimistic_json_parser: OptimisticJSONParser = OptimisticJSONParser()
- self.stream_pre_execution_message: bool = stream_pre_execution_message
-
- self.current_parsed_json_result: dict[str, Any] = {}
- self.content_buffer: list[str] = []
- self.tool_call_happened: bool = False
- self.finish_reason_stop: bool = False
-
- self.tool_call_name: str | None = None
- self.tool_call_args_str: str = ""
- self.tool_call_id: str | None = None
-
- async def process(self, stream: AsyncStream[ChatCompletionChunk]) -> AsyncGenerator[str, None]:
- """
- Iterates over the OpenAI stream, yielding SSE events.
- It also collects tokens and detects if a tool call is triggered.
- """
- async with stream:
- async for chunk in stream:
- # TODO (cliandy): reconsider in stream cancellations
- # await cancellation_token.check_and_raise_if_cancelled()
- if chunk.choices:
- choice = chunk.choices[0]
- delta = choice.delta
- finish_reason = choice.finish_reason
-
- async for sse_chunk in self._process_content(delta, chunk):
- yield sse_chunk
-
- async for sse_chunk in self._process_tool_calls(delta, chunk):
- yield sse_chunk
-
- if self._handle_finish_reason(finish_reason):
- break
-
- async def _process_content(self, delta: ChoiceDelta, chunk: ChatCompletionChunk) -> AsyncGenerator[str, None]:
- """Processes regular content tokens and streams them."""
- if delta.content:
- self.content_buffer.append(delta.content)
- yield _format_sse_chunk(chunk)
-
- async def _process_tool_calls(self, delta: ChoiceDelta, chunk: ChatCompletionChunk) -> AsyncGenerator[str, None]:
- """Handles tool call initiation and streaming of pre-execution messages."""
- if not delta.tool_calls:
- return
-
- tool_call = delta.tool_calls[0]
- self._update_tool_call_info(tool_call)
-
- if self.stream_pre_execution_message and tool_call.function.arguments:
- self.tool_call_args_str += tool_call.function.arguments
- async for sse_chunk in self._stream_pre_execution_message(chunk, tool_call):
- yield sse_chunk
-
- def _update_tool_call_info(self, tool_call: Any) -> None:
- """Updates tool call-related attributes."""
- if tool_call.function.name:
- self.tool_call_name = tool_call.function.name
- if tool_call.id:
- self.tool_call_id = tool_call.id
-
- async def _stream_pre_execution_message(self, chunk: ChatCompletionChunk, tool_call: Any) -> AsyncGenerator[str, None]:
- """Parses and streams pre-execution messages if they have changed."""
- parsed_args = self.optimistic_json_parser.parse(self.tool_call_args_str)
-
- if parsed_args.get(PRE_EXECUTION_MESSAGE_ARG) and parsed_args[PRE_EXECUTION_MESSAGE_ARG] != self.current_parsed_json_result.get(
- PRE_EXECUTION_MESSAGE_ARG
- ):
- # Extract old and new message content
- old = self.current_parsed_json_result.get(PRE_EXECUTION_MESSAGE_ARG, "")
- new = parsed_args[PRE_EXECUTION_MESSAGE_ARG]
-
- # Compute the new content by slicing off the old prefix
- content = new[len(old) :] if old else new
-
- # Update current state
- self.current_parsed_json_result = parsed_args
-
- # Yield the formatted SSE chunk
- yield _format_sse_chunk(
- ChatCompletionChunk(
- id=chunk.id,
- object=chunk.object,
- created=chunk.created,
- model=chunk.model,
- choices=[Choice(index=0, delta=ChoiceDelta(content=content, role="assistant"), finish_reason=None)],
- )
- )
-
- def _handle_finish_reason(self, finish_reason: str | None) -> bool:
- """Handles the finish reason and determines if streaming should stop."""
- if finish_reason == "tool_calls":
- self.tool_call_happened = True
- return True
- if finish_reason == "stop":
- self.finish_reason_stop = True
- return True
- return False
diff --git a/letta/interfaces/openai_streaming_interface.py b/letta/interfaces/openai_streaming_interface.py
deleted file mode 100644
index 0ff2c6fb..00000000
--- a/letta/interfaces/openai_streaming_interface.py
+++ /dev/null
@@ -1,482 +0,0 @@
-import asyncio
-from collections.abc import AsyncGenerator
-from datetime import datetime, timezone
-from typing import Optional
-
-from openai import AsyncStream
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-
-from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG
-from letta.llm_api.openai_client import is_openai_reasoning_model
-from letta.local_llm.utils import num_tokens_from_functions, num_tokens_from_messages
-from letta.log import get_logger
-from letta.schemas.letta_message import (
- ApprovalRequestMessage,
- AssistantMessage,
- HiddenReasoningMessage,
- LettaMessage,
- ReasoningMessage,
- ToolCallDelta,
- ToolCallMessage,
-)
-from letta.schemas.letta_message_content import OmittedReasoningContent, TextContent
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.message import Message
-from letta.schemas.openai.chat_completion_response import FunctionCall, ToolCall
-from letta.server.rest_api.json_parser import OptimisticJSONParser
-from letta.streaming_utils import JSONInnerThoughtsExtractor
-from letta.utils import count_tokens
-
-logger = get_logger(__name__)
-
-
-class OpenAIStreamingInterface:
- """
- Encapsulates the logic for streaming responses from OpenAI.
- This class handles parsing of partial tokens, pre-execution messages,
- and detection of tool call events.
- """
-
- def __init__(
- self,
- use_assistant_message: bool = False,
- is_openai_proxy: bool = False,
- messages: Optional[list] = None,
- tools: Optional[list] = None,
- put_inner_thoughts_in_kwarg: bool = True,
- requires_approval_tools: list = [],
- ):
- self.use_assistant_message = use_assistant_message
- self.assistant_message_tool_name = DEFAULT_MESSAGE_TOOL
- self.assistant_message_tool_kwarg = DEFAULT_MESSAGE_TOOL_KWARG
- self.put_inner_thoughts_in_kwarg = put_inner_thoughts_in_kwarg
-
- self.optimistic_json_parser: OptimisticJSONParser = OptimisticJSONParser()
- self.function_args_reader = JSONInnerThoughtsExtractor(wait_for_first_key=put_inner_thoughts_in_kwarg)
- self.function_name_buffer = None
- self.function_args_buffer = None
- self.function_id_buffer = None
- self.last_flushed_function_name = None
- self.last_flushed_function_id = None
-
- # Buffer to hold function arguments until inner thoughts are complete
- self.current_function_arguments = ""
- self.current_json_parse_result = {}
-
- # Premake IDs for database writes
- self.letta_message_id = Message.generate_id()
-
- self.message_id = None
- self.model = None
-
- # Token counters (from OpenAI usage)
- self.input_tokens = 0
- self.output_tokens = 0
-
- # Fallback token counters (using tiktoken cl200k-base)
- self.fallback_input_tokens = 0
- self.fallback_output_tokens = 0
-
- # Store messages and tools for fallback counting
- self.is_openai_proxy = is_openai_proxy
- self.messages = messages or []
- self.tools = tools or []
-
- self.content_buffer: list[str] = []
- self.tool_call_name: str | None = None
- self.tool_call_id: str | None = None
- self.reasoning_messages = []
- self.emitted_hidden_reasoning = False # Track if we've emitted hidden reasoning message
-
- self.requires_approval_tools = requires_approval_tools
-
- def get_reasoning_content(self) -> list[TextContent | OmittedReasoningContent]:
- content = "".join(self.reasoning_messages).strip()
-
- # Right now we assume that all models omit reasoning content for OAI,
- # if this changes, we should return the reasoning content
- if is_openai_reasoning_model(self.model):
- return [OmittedReasoningContent()]
- else:
- return [TextContent(text=content)]
-
- def get_tool_call_object(self) -> ToolCall:
- """Useful for agent loop"""
- function_name = self.last_flushed_function_name if self.last_flushed_function_name else self.function_name_buffer
- if not function_name:
- raise ValueError("No tool call ID available")
- tool_call_id = self.last_flushed_function_id if self.last_flushed_function_id else self.function_id_buffer
- if not tool_call_id:
- raise ValueError("No tool call ID available")
- return ToolCall(
- id=tool_call_id,
- function=FunctionCall(arguments=self.current_function_arguments, name=function_name),
- )
-
- async def process(
- self,
- stream: AsyncStream[ChatCompletionChunk],
- ttft_span: Optional["Span"] = None,
- ) -> AsyncGenerator[LettaMessage | LettaStopReason, None]:
- """
- Iterates over the OpenAI stream, yielding SSE events.
- It also collects tokens and detects if a tool call is triggered.
- """
- # Fallback input token counting - this should only be required for non-OpenAI providers using the OpenAI client (e.g. LMStudio)
- if self.is_openai_proxy:
- if self.messages:
- # Convert messages to dict format for token counting
- message_dicts = [msg.to_openai_dict() if hasattr(msg, "to_openai_dict") else msg for msg in self.messages]
- message_dicts = [m for m in message_dicts if m is not None]
- self.fallback_input_tokens = num_tokens_from_messages(message_dicts) # fallback to gpt-4 cl100k-base
-
- if self.tools:
- # Convert tools to dict format for token counting
- tool_dicts = [tool["function"] if isinstance(tool, dict) and "function" in tool else tool for tool in self.tools]
- self.fallback_input_tokens += num_tokens_from_functions(tool_dicts)
-
- prev_message_type = None
- message_index = 0
- try:
- async with stream:
- async for chunk in stream:
- try:
- async for message in self._process_chunk(chunk, ttft_span, prev_message_type, message_index):
- new_message_type = message.message_type
- if new_message_type != prev_message_type:
- if prev_message_type != None:
- message_index += 1
- prev_message_type = new_message_type
- yield message
- except asyncio.CancelledError as e:
- import traceback
-
- logger.info("Cancelled stream attempt but overriding %s: %s", e, traceback.format_exc())
- async for message in self._process_chunk(chunk, ttft_span, prev_message_type, message_index):
- new_message_type = message.message_type
- if new_message_type != prev_message_type:
- if prev_message_type != None:
- message_index += 1
- prev_message_type = new_message_type
- yield message
-
- # Don't raise the exception here
- continue
-
- except Exception as e:
- import traceback
-
- logger.error("Error processing stream: %s", e, traceback.format_exc())
- ttft_span.add_event(
- name="stop_reason",
- attributes={"stop_reason": StopReasonType.error.value, "error": str(e), "stacktrace": traceback.format_exc()},
- )
- yield LettaStopReason(stop_reason=StopReasonType.error)
- raise e
- finally:
- logger.info("OpenAIStreamingInterface: Stream processing complete.")
-
- async def _process_chunk(
- self,
- chunk: ChatCompletionChunk,
- ttft_span: Optional["Span"] = None,
- prev_message_type: Optional[str] = None,
- message_index: int = 0,
- ) -> AsyncGenerator[LettaMessage | LettaStopReason, None]:
- if not self.model or not self.message_id:
- self.model = chunk.model
- self.message_id = chunk.id
-
- # track usage
- if chunk.usage:
- self.input_tokens += chunk.usage.prompt_tokens
- self.output_tokens += chunk.usage.completion_tokens
-
- if chunk.choices:
- choice = chunk.choices[0]
- message_delta = choice.delta
-
- if message_delta.tool_calls is not None and len(message_delta.tool_calls) > 0:
- tool_call = message_delta.tool_calls[0]
-
- # For OpenAI reasoning models, emit a hidden reasoning message before the first tool call
- if not self.emitted_hidden_reasoning and is_openai_reasoning_model(self.model) and not self.put_inner_thoughts_in_kwarg:
- self.emitted_hidden_reasoning = True
- if prev_message_type and prev_message_type != "hidden_reasoning_message":
- message_index += 1
- hidden_message = HiddenReasoningMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- state="omitted",
- hidden_reasoning=None,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- yield hidden_message
- prev_message_type = hidden_message.message_type
- message_index += 1 # Increment for the next message
-
- if tool_call.function.name:
- # If we're waiting for the first key, then we should hold back the name
- # ie add it to a buffer instead of returning it as a chunk
- if self.function_name_buffer is None:
- self.function_name_buffer = tool_call.function.name
- else:
- self.function_name_buffer += tool_call.function.name
-
- if tool_call.id:
- # Buffer until next time
- if self.function_id_buffer is None:
- self.function_id_buffer = tool_call.id
- else:
- self.function_id_buffer += tool_call.id
-
- if tool_call.function.arguments:
- # updates_main_json, updates_inner_thoughts = self.function_args_reader.process_fragment(tool_call.function.arguments)
- self.current_function_arguments += tool_call.function.arguments
- updates_main_json, updates_inner_thoughts = self.function_args_reader.process_fragment(tool_call.function.arguments)
-
- if self.is_openai_proxy:
- self.fallback_output_tokens += count_tokens(tool_call.function.arguments)
-
- # If we have inner thoughts, we should output them as a chunk
- if updates_inner_thoughts:
- if prev_message_type and prev_message_type != "reasoning_message":
- message_index += 1
- self.reasoning_messages.append(updates_inner_thoughts)
- reasoning_message = ReasoningMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- reasoning=updates_inner_thoughts,
- # name=name,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- prev_message_type = reasoning_message.message_type
- yield reasoning_message
-
- # Additionally inner thoughts may stream back with a chunk of main JSON
- # In that case, since we can only return a chunk at a time, we should buffer it
- if updates_main_json:
- if self.function_args_buffer is None:
- self.function_args_buffer = updates_main_json
- else:
- self.function_args_buffer += updates_main_json
-
- # If we have main_json, we should output a ToolCallMessage
- elif updates_main_json:
- # If there's something in the function_name buffer, we should release it first
- # NOTE: we could output it as part of a chunk that has both name and args,
- # however the frontend may expect name first, then args, so to be
- # safe we'll output name first in a separate chunk
- if self.function_name_buffer:
- # use_assisitant_message means that we should also not release main_json raw, and instead should only release the contents of "message": "..."
- if self.use_assistant_message and self.function_name_buffer == self.assistant_message_tool_name:
- # Store the ID of the tool call so allow skipping the corresponding response
- if self.function_id_buffer:
- self.prev_assistant_message_id = self.function_id_buffer
-
- else:
- if prev_message_type and prev_message_type != "tool_call_message":
- message_index += 1
- self.tool_call_name = str(self.function_name_buffer)
- if self.tool_call_name in self.requires_approval_tools:
- tool_call_msg = ApprovalRequestMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- tool_call=ToolCallDelta(
- name=self.function_name_buffer,
- arguments=None,
- tool_call_id=self.function_id_buffer,
- ),
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- else:
- tool_call_msg = ToolCallMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- tool_call=ToolCallDelta(
- name=self.function_name_buffer,
- arguments=None,
- tool_call_id=self.function_id_buffer,
- ),
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- prev_message_type = tool_call_msg.message_type
- yield tool_call_msg
-
- # Record what the last function name we flushed was
- self.last_flushed_function_name = self.function_name_buffer
- if self.last_flushed_function_id is None:
- self.last_flushed_function_id = self.function_id_buffer
- # Clear the buffer
- self.function_name_buffer = None
- self.function_id_buffer = None
- # Since we're clearing the name buffer, we should store
- # any updates to the arguments inside a separate buffer
-
- # Add any main_json updates to the arguments buffer
- if self.function_args_buffer is None:
- self.function_args_buffer = updates_main_json
- else:
- self.function_args_buffer += updates_main_json
-
- # If there was nothing in the name buffer, we can proceed to
- # output the arguments chunk as a ToolCallMessage
- else:
- # use_assistant_message means that we should also not release main_json raw, and instead should only release the contents of "message": "..."
- if self.use_assistant_message and (
- self.last_flushed_function_name is not None
- and self.last_flushed_function_name == self.assistant_message_tool_name
- ):
- # do an additional parse on the updates_main_json
- if self.function_args_buffer:
- updates_main_json = self.function_args_buffer + updates_main_json
- self.function_args_buffer = None
-
- # Pretty gross hardcoding that assumes that if we're toggling into the keywords, we have the full prefix
- match_str = '{"' + self.assistant_message_tool_kwarg + '":"'
- if updates_main_json == match_str:
- updates_main_json = None
-
- else:
- # Some hardcoding to strip off the trailing "}"
- if updates_main_json in ["}", '"}']:
- updates_main_json = None
- if updates_main_json and len(updates_main_json) > 0 and updates_main_json[-1:] == '"':
- updates_main_json = updates_main_json[:-1]
-
- if not updates_main_json:
- # early exit to turn into content mode
- pass
-
- # There may be a buffer from a previous chunk, for example
- # if the previous chunk had arguments but we needed to flush name
- if self.function_args_buffer:
- # In this case, we should release the buffer + new data at once
- combined_chunk = self.function_args_buffer + updates_main_json
-
- if prev_message_type and prev_message_type != "assistant_message":
- message_index += 1
- assistant_message = AssistantMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- content=combined_chunk,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- prev_message_type = assistant_message.message_type
- yield assistant_message
- # Store the ID of the tool call so allow skipping the corresponding response
- if self.function_id_buffer:
- self.prev_assistant_message_id = self.function_id_buffer
- # clear buffer
- self.function_args_buffer = None
- self.function_id_buffer = None
-
- else:
- # If there's no buffer to clear, just output a new chunk with new data
- # TODO: THIS IS HORRIBLE
- # TODO: WE USE THE OLD JSON PARSER EARLIER (WHICH DOES NOTHING) AND NOW THE NEW JSON PARSER
- # TODO: THIS IS TOTALLY WRONG AND BAD, BUT SAVING FOR A LARGER REWRITE IN THE NEAR FUTURE
- parsed_args = self.optimistic_json_parser.parse(self.current_function_arguments)
-
- if parsed_args.get(self.assistant_message_tool_kwarg) and parsed_args.get(
- self.assistant_message_tool_kwarg
- ) != self.current_json_parse_result.get(self.assistant_message_tool_kwarg):
- new_content = parsed_args.get(self.assistant_message_tool_kwarg)
- prev_content = self.current_json_parse_result.get(self.assistant_message_tool_kwarg, "")
- # TODO: Assumes consistent state and that prev_content is subset of new_content
- diff = new_content.replace(prev_content, "", 1)
-
- # quick patch to mitigate double message streaming error
- # TODO: root cause this issue and remove patch
- if diff != "" and "\\n" not in new_content:
- converted_new_content = new_content.replace("\n", "\\n")
- converted_content_diff = converted_new_content.replace(prev_content, "", 1)
- if converted_content_diff == "":
- diff = converted_content_diff
-
- self.current_json_parse_result = parsed_args
- if prev_message_type and prev_message_type != "assistant_message":
- message_index += 1
- assistant_message = AssistantMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- content=diff,
- # name=name,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- prev_message_type = assistant_message.message_type
- yield assistant_message
-
- # Store the ID of the tool call so allow skipping the corresponding response
- if self.function_id_buffer:
- self.prev_assistant_message_id = self.function_id_buffer
- # clear buffers
- self.function_id_buffer = None
- else:
- # There may be a buffer from a previous chunk, for example
- # if the previous chunk had arguments but we needed to flush name
- if self.function_args_buffer:
- # In this case, we should release the buffer + new data at once
- combined_chunk = self.function_args_buffer + updates_main_json
- if prev_message_type and prev_message_type != "tool_call_message":
- message_index += 1
- if self.function_name_buffer in self.requires_approval_tools:
- tool_call_msg = ApprovalRequestMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- tool_call=ToolCallDelta(
- name=self.function_name_buffer,
- arguments=combined_chunk,
- tool_call_id=self.function_id_buffer,
- ),
- # name=name,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- else:
- tool_call_msg = ToolCallMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- tool_call=ToolCallDelta(
- name=self.function_name_buffer,
- arguments=combined_chunk,
- tool_call_id=self.function_id_buffer,
- ),
- # name=name,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- prev_message_type = tool_call_msg.message_type
- yield tool_call_msg
- # clear buffer
- self.function_args_buffer = None
- self.function_id_buffer = None
- else:
- # If there's no buffer to clear, just output a new chunk with new data
- if prev_message_type and prev_message_type != "tool_call_message":
- message_index += 1
- if self.function_name_buffer in self.requires_approval_tools:
- tool_call_msg = ApprovalRequestMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- tool_call=ToolCallDelta(
- name=None,
- arguments=updates_main_json,
- tool_call_id=self.function_id_buffer,
- ),
- # name=name,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- else:
- tool_call_msg = ToolCallMessage(
- id=self.letta_message_id,
- date=datetime.now(timezone.utc),
- tool_call=ToolCallDelta(
- name=None,
- arguments=updates_main_json,
- tool_call_id=self.function_id_buffer,
- ),
- # name=name,
- otid=Message.generate_otid_from_id(self.letta_message_id, message_index),
- )
- prev_message_type = tool_call_msg.message_type
- yield tool_call_msg
- self.function_id_buffer = None
diff --git a/letta/interfaces/utils.py b/letta/interfaces/utils.py
deleted file mode 100644
index 4fa34327..00000000
--- a/letta/interfaces/utils.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import json
-
-from openai.types.chat import ChatCompletionChunk
-
-
-def _format_sse_error(error_payload: dict) -> str:
- return f"data: {json.dumps(error_payload)}\n\n"
-
-
-def _format_sse_chunk(chunk: ChatCompletionChunk) -> str:
- return f"data: {chunk.model_dump_json()}\n\n"
diff --git a/letta/jobs/__init__.py b/letta/jobs/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/jobs/helpers.py b/letta/jobs/helpers.py
deleted file mode 100644
index 20f4af8f..00000000
--- a/letta/jobs/helpers.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from anthropic.types.beta.messages import (
- BetaMessageBatchCanceledResult,
- BetaMessageBatchIndividualResponse,
- BetaMessageBatchSucceededResult,
-)
-
-from letta.schemas.enums import JobStatus
-
-
-def map_anthropic_batch_job_status_to_job_status(anthropic_status: str) -> JobStatus:
- mapping = {
- "in_progress": JobStatus.running,
- "canceling": JobStatus.cancelled,
- "ended": JobStatus.completed,
- }
- return mapping.get(anthropic_status, JobStatus.pending) # fallback just in case
-
-
-def map_anthropic_individual_batch_item_status_to_job_status(individual_item: BetaMessageBatchIndividualResponse) -> JobStatus:
- if isinstance(individual_item.result, BetaMessageBatchSucceededResult):
- return JobStatus.completed
- elif isinstance(individual_item.result, BetaMessageBatchCanceledResult):
- return JobStatus.cancelled
- else:
- return JobStatus.failed
diff --git a/letta/jobs/llm_batch_job_polling.py b/letta/jobs/llm_batch_job_polling.py
deleted file mode 100644
index cfaae2ab..00000000
--- a/letta/jobs/llm_batch_job_polling.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import asyncio
-import datetime
-from typing import List
-
-from letta.agents.letta_agent_batch import LettaAgentBatch
-from letta.jobs.helpers import map_anthropic_batch_job_status_to_job_status, map_anthropic_individual_batch_item_status_to_job_status
-from letta.jobs.types import BatchPollingResult, ItemUpdateInfo
-from letta.log import get_logger
-from letta.otel.tracing import trace_method
-from letta.schemas.enums import JobStatus, ProviderType
-from letta.schemas.letta_response import LettaBatchResponse
-from letta.schemas.llm_batch_job import LLMBatchJob
-from letta.schemas.user import User
-from letta.server.server import SyncServer
-from letta.settings import settings
-
-logger = get_logger(__name__)
-
-
-class BatchPollingMetrics:
- """Class to track metrics for batch polling operations."""
-
- def __init__(self):
- self.start_time = datetime.datetime.now()
- self.total_batches = 0
- self.anthropic_batches = 0
- self.running_count = 0
- self.completed_count = 0
- self.updated_items_count = 0
-
- def log_summary(self):
- """Log a summary of the metrics collected during polling."""
- elapsed = (datetime.datetime.now() - self.start_time).total_seconds()
- logger.info(f"[Poll BatchJob] Finished poll_running_llm_batches job in {elapsed:.2f}s")
- logger.info(f"[Poll BatchJob] Found {self.total_batches} running batches total.")
- logger.info(f"[Poll BatchJob] Found {self.anthropic_batches} Anthropic batch(es) to poll.")
- logger.info(f"[Poll BatchJob] Final results: {self.completed_count} completed, {self.running_count} still running.")
- logger.info(f"[Poll BatchJob] Updated {self.updated_items_count} items for newly completed batch(es).")
-
-
-@trace_method
-async def fetch_batch_status(server: SyncServer, batch_job: LLMBatchJob) -> BatchPollingResult:
- """
- Fetch the current status of a single batch job from the provider.
-
- Args:
- server: The SyncServer instance
- batch_job: The batch job to check status for
-
- Returns:
- A tuple containing (batch_id, new_status, polling_response)
- """
- batch_id_str = batch_job.create_batch_response.id
- try:
- response = await server.anthropic_async_client.beta.messages.batches.retrieve(batch_id_str)
- new_status = map_anthropic_batch_job_status_to_job_status(response.processing_status)
- logger.debug(f"[Poll BatchJob] Batch {batch_job.id}: provider={response.processing_status} → internal={new_status}")
- return BatchPollingResult(batch_job.id, new_status, response)
- except Exception as e:
- logger.error(f"[Poll BatchJob] Batch {batch_job.id}: failed to retrieve {batch_id_str}: {e}")
- # We treat a retrieval error as still running to try again next cycle
- return BatchPollingResult(batch_job.id, JobStatus.running, None)
-
-
-@trace_method
-async def fetch_batch_items(server: SyncServer, batch_id: str, batch_resp_id: str) -> List[ItemUpdateInfo]:
- """
- Fetch individual item results for a completed batch.
-
- Args:
- server: The SyncServer instance
- batch_id: The internal batch ID
- batch_resp_id: The provider's batch response ID
-
- Returns:
- A list of item update information tuples
- """
- updates = []
- try:
- results = await server.anthropic_async_client.beta.messages.batches.results(batch_resp_id)
- async for item_result in results:
- # Here, custom_id should be the agent_id
- item_status = map_anthropic_individual_batch_item_status_to_job_status(item_result)
- updates.append(ItemUpdateInfo(batch_id, item_result.custom_id, item_status, item_result))
- logger.info(f"[Poll BatchJob] Fetched {len(updates)} item updates for batch {batch_id}.")
- except Exception as e:
- logger.error(f"[Poll BatchJob] Error fetching item updates for batch {batch_id}: {e}")
-
- return updates
-
-
-@trace_method
-async def poll_batch_updates(server: SyncServer, batch_jobs: List[LLMBatchJob], metrics: BatchPollingMetrics) -> List[BatchPollingResult]:
- """
- Poll for updates to multiple batch jobs concurrently.
-
- Args:
- server: The SyncServer instance
- batch_jobs: List of batch jobs to poll
- metrics: Metrics collection object
-
- Returns:
- List of batch polling results
- """
- if not batch_jobs:
- logger.info("[Poll BatchJob] No Anthropic batches to update; job complete.")
- return []
-
- # Create polling tasks for all batch jobs
- coros = [fetch_batch_status(server, b) for b in batch_jobs]
- results: List[BatchPollingResult] = await asyncio.gather(*coros)
-
- # Update the server with batch status changes
- await server.batch_manager.bulk_update_llm_batch_statuses_async(updates=results)
- logger.info(f"[Poll BatchJob] Bulk-updated {len(results)} LLM batch(es) in the DB at job level.")
-
- return results
-
-
-@trace_method
-async def process_completed_batches(
- server: SyncServer, batch_results: List[BatchPollingResult], metrics: BatchPollingMetrics
-) -> List[ItemUpdateInfo]:
- """
- Process batches that have completed and fetch their item results.
-
- Args:
- server: The SyncServer instance
- batch_results: Results from polling batch statuses
- metrics: Metrics collection object
-
- Returns:
- List of item updates to apply
- """
- item_update_tasks = []
-
- # Process each top-level polling result
- for batch_id, new_status, maybe_batch_resp in batch_results:
- if not maybe_batch_resp:
- if new_status == JobStatus.running:
- metrics.running_count += 1
- logger.warning(f"[Poll BatchJob] Batch {batch_id}: JobStatus was {new_status} and no batch response was found.")
- continue
-
- if new_status == JobStatus.completed:
- metrics.completed_count += 1
- batch_resp_id = maybe_batch_resp.id # The Anthropic-assigned batch ID
- # Queue an async call to fetch item results for this batch
- item_update_tasks.append(fetch_batch_items(server, batch_id, batch_resp_id))
- elif new_status == JobStatus.running:
- metrics.running_count += 1
-
- # Launch all item update tasks concurrently
- concurrent_results = await asyncio.gather(*item_update_tasks, return_exceptions=True)
-
- # Flatten and filter the results
- item_updates = []
- for result in concurrent_results:
- if isinstance(result, Exception):
- logger.error(f"[Poll BatchJob] A fetch_batch_items task failed with: {result}")
- elif isinstance(result, list):
- item_updates.extend(result)
-
- logger.info(f"[Poll BatchJob] Collected a total of {len(item_updates)} item update(s) from completed batches.")
-
- return item_updates
-
-
-@trace_method
-async def poll_running_llm_batches(server: "SyncServer") -> List[LettaBatchResponse]:
- """
- Cron job to poll all running LLM batch jobs and update their polling responses in bulk.
-
- Steps:
- 1. Fetch currently running batch jobs
- 2. Filter Anthropic only
- 3. Retrieve updated top-level polling info concurrently
- 4. Bulk update LLMBatchJob statuses
- 5. For each completed batch, call .results(...) to get item-level results
- 6. Bulk update all matching LLMBatchItem records by (batch_id, agent_id)
- 7. Log telemetry about success/fail
- """
- # Initialize metrics tracking
- metrics = BatchPollingMetrics()
-
- logger.info("[Poll BatchJob] Starting poll_running_llm_batches job")
-
- try:
- # 1. Retrieve running batch jobs
- batches = await server.batch_manager.list_running_llm_batches_async(
- weeks=max(settings.batch_job_polling_lookback_weeks, 1), batch_size=settings.batch_job_polling_batch_size
- )
- metrics.total_batches = len(batches)
-
- # TODO: Expand to more providers
- # 2. Filter for Anthropic jobs only
- anthropic_batch_jobs = [b for b in batches if b.llm_provider == ProviderType.anthropic]
- metrics.anthropic_batches = len(anthropic_batch_jobs)
-
- # 3-4. Poll for batch updates and bulk update statuses
- batch_results = await poll_batch_updates(server, anthropic_batch_jobs, metrics)
-
- # 5. Process completed batches and fetch item results
- item_updates = await process_completed_batches(server, batch_results, metrics)
-
- # 6. Bulk update all items for newly completed batch(es)
- if item_updates:
- metrics.updated_items_count = len(item_updates)
- await server.batch_manager.bulk_update_batch_llm_items_results_by_agent_async(item_updates)
-
- # ─── Kick off post‑processing for each batch that just completed ───
- completed = [r for r in batch_results if r.request_status == JobStatus.completed]
-
- async def _resume(batch_row: LLMBatchJob) -> LettaBatchResponse:
- actor: User = await server.user_manager.get_actor_by_id_async(batch_row.created_by_id)
- runner = LettaAgentBatch(
- message_manager=server.message_manager,
- agent_manager=server.agent_manager,
- block_manager=server.block_manager,
- passage_manager=server.passage_manager,
- batch_manager=server.batch_manager,
- sandbox_config_manager=server.sandbox_config_manager,
- job_manager=server.job_manager,
- actor=actor,
- )
- return await runner.resume_step_after_request(
- letta_batch_id=batch_row.letta_batch_job_id,
- llm_batch_id=batch_row.id,
- )
-
- # launch them all at once
- async def get_and_resume(batch_id):
- batch = await server.batch_manager.get_llm_batch_job_by_id_async(batch_id)
- return await _resume(batch)
-
- tasks = [get_and_resume(bid) for bid, *_ in completed]
- new_batch_responses = await asyncio.gather(*tasks, return_exceptions=True)
-
- return new_batch_responses
- else:
- logger.info("[Poll BatchJob] No item-level updates needed.")
-
- except Exception as e:
- logger.exception("[Poll BatchJob] Unhandled error in poll_running_llm_batches", exc_info=e)
- finally:
- # 7. Log metrics summary
- metrics.log_summary()
diff --git a/letta/jobs/scheduler.py b/letta/jobs/scheduler.py
deleted file mode 100644
index 1e6867b2..00000000
--- a/letta/jobs/scheduler.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import asyncio
-import datetime
-from typing import Optional
-
-from apscheduler.schedulers.asyncio import AsyncIOScheduler
-from apscheduler.triggers.interval import IntervalTrigger
-from sqlalchemy import text
-
-from letta.jobs.llm_batch_job_polling import poll_running_llm_batches
-from letta.log import get_logger
-from letta.server.db import db_registry
-from letta.server.server import SyncServer
-from letta.settings import settings
-
-# --- Global State ---
-scheduler = AsyncIOScheduler()
-logger = get_logger(__name__)
-ADVISORY_LOCK_KEY = 0x12345678ABCDEF00
-
-_advisory_lock_session = None # Holds the async session if leader
-_lock_retry_task: Optional[asyncio.Task] = None # Background task handle for non-leaders
-_is_scheduler_leader = False # Flag indicating if this instance runs the scheduler
-
-
-async def _try_acquire_lock_and_start_scheduler(server: SyncServer) -> bool:
- """Attempts to acquire lock, starts scheduler if successful."""
- global _advisory_lock_session, _is_scheduler_leader, scheduler
-
- if _is_scheduler_leader:
- return True # Already leading
-
- engine_name = None
- lock_session = None
- acquired_lock = False
- try:
- async with db_registry.async_session() as session:
- engine = session.get_bind()
- engine_name = engine.name
- logger.info(f"Database engine type: {engine_name}")
-
- if engine_name != "postgresql":
- logger.warning(f"Advisory locks not supported for {engine_name} database. Starting scheduler without leader election.")
- acquired_lock = True
- else:
- lock_session = db_registry.get_async_session_factory()()
- result = await lock_session.execute(
- text("SELECT pg_try_advisory_lock(CAST(:lock_key AS bigint))"), {"lock_key": ADVISORY_LOCK_KEY}
- )
- acquired_lock = result.scalar()
- await lock_session.commit()
-
- if not acquired_lock:
- await lock_session.close()
- logger.info("Scheduler lock held by another instance.")
- return False
- else:
- _advisory_lock_session = lock_session
- lock_session = None
-
- trigger = IntervalTrigger(
- seconds=settings.poll_running_llm_batches_interval_seconds,
- jitter=10,
- )
- scheduler.add_job(
- poll_running_llm_batches,
- args=[server],
- trigger=trigger,
- id="poll_llm_batches",
- name="Poll LLM API batch jobs",
- replace_existing=True,
- next_run_time=datetime.datetime.now(datetime.timezone.utc),
- )
-
- if not scheduler.running:
- scheduler.start()
- elif scheduler.state == 2:
- scheduler.resume()
-
- _is_scheduler_leader = True
- return True
-
- except Exception as e:
- logger.error(f"Error during lock acquisition/scheduler start: {e}", exc_info=True)
- if acquired_lock:
- logger.warning("Attempting to release lock due to error during startup.")
- try:
- await _release_advisory_lock(lock_session)
- except Exception as unlock_err:
- logger.error(f"Failed to release lock during error handling: {unlock_err}", exc_info=True)
- finally:
- _advisory_lock_session = None
- _is_scheduler_leader = False
-
- if scheduler.running:
- try:
- scheduler.shutdown(wait=False)
- except:
- pass
- return False
- finally:
- if lock_session:
- try:
- await lock_session.close()
- except Exception as e:
- logger.error(f"Failed to close session during error handling: {e}", exc_info=True)
-
-
-async def _background_lock_retry_loop(server: SyncServer):
- """Periodically attempts to acquire the lock if not initially acquired."""
- global _lock_retry_task, _is_scheduler_leader
- logger.info("Starting background task to periodically check for scheduler lock.")
-
- while True:
- if _is_scheduler_leader:
- break
- try:
- wait_time = settings.poll_lock_retry_interval_seconds
- await asyncio.sleep(wait_time)
-
- if _is_scheduler_leader or _lock_retry_task is None:
- break
-
- acquired = await _try_acquire_lock_and_start_scheduler(server)
- if acquired:
- logger.info("Background task acquired lock and started scheduler.")
- _lock_retry_task = None
- break
-
- except asyncio.CancelledError:
- logger.info("Background lock retry task cancelled.")
- break
- except Exception as e:
- logger.error(f"Error in background lock retry loop: {e}", exc_info=True)
-
-
-async def _release_advisory_lock(target_lock_session=None):
- """Releases the advisory lock using the stored session."""
- global _advisory_lock_session
-
- lock_session = target_lock_session or _advisory_lock_session
-
- if lock_session is not None:
- logger.info(f"Attempting to release PostgreSQL advisory lock {ADVISORY_LOCK_KEY}")
- try:
- await lock_session.execute(text("SELECT pg_advisory_unlock(CAST(:lock_key AS bigint))"), {"lock_key": ADVISORY_LOCK_KEY})
- logger.info(f"Executed pg_advisory_unlock for lock {ADVISORY_LOCK_KEY}")
- await lock_session.commit()
- except Exception as e:
- logger.error(f"Error executing pg_advisory_unlock: {e}", exc_info=True)
- finally:
- try:
- if lock_session:
- await lock_session.close()
- logger.info("Closed database session that held advisory lock.")
- if lock_session == _advisory_lock_session:
- _advisory_lock_session = None
- except Exception as e:
- logger.error(f"Error closing advisory lock session: {e}", exc_info=True)
- else:
- logger.info("No PostgreSQL advisory lock to release (likely using SQLite or non-PostgreSQL database).")
-
-
-async def start_scheduler_with_leader_election(server: SyncServer):
- """
- Call this function from your FastAPI startup event handler.
- Attempts immediate lock acquisition, starts background retry if failed.
- """
- global _lock_retry_task, _is_scheduler_leader
-
- if not settings.enable_batch_job_polling:
- logger.info("Batch job polling is disabled.")
- return
-
- if _is_scheduler_leader:
- logger.warning("Scheduler start requested, but already leader.")
- return
-
- acquired_immediately = await _try_acquire_lock_and_start_scheduler(server)
-
- if not acquired_immediately and _lock_retry_task is None:
- loop = asyncio.get_running_loop()
- _lock_retry_task = loop.create_task(_background_lock_retry_loop(server))
-
-
-async def shutdown_scheduler_and_release_lock():
- """
- Call this function from your FastAPI shutdown event handler.
- Stops scheduler/releases lock if leader, cancels retry task otherwise.
- """
- global _is_scheduler_leader, _lock_retry_task, scheduler
-
- if _lock_retry_task is not None:
- logger.info("Shutting down: Cancelling background lock retry task.")
- current_task = _lock_retry_task
- _lock_retry_task = None
- current_task.cancel()
- try:
- await current_task
- except asyncio.CancelledError:
- logger.info("Background lock retry task successfully cancelled.")
- except Exception as e:
- logger.warning(f"Exception waiting for cancelled retry task: {e}", exc_info=True)
-
- if _is_scheduler_leader:
- logger.info("Shutting down: Leader instance stopping scheduler and releasing lock.")
- if scheduler.running:
- try:
- scheduler.shutdown(wait=True)
-
- await asyncio.sleep(0.1)
-
- logger.info("APScheduler shut down.")
- except Exception as e:
- logger.warning(f"Exception during APScheduler shutdown: {e}")
- if "not running" not in str(e).lower():
- logger.error(f"Unexpected error shutting down APScheduler: {e}", exc_info=True)
-
- await _release_advisory_lock()
- _is_scheduler_leader = False
- else:
- logger.info("Shutting down: Non-leader instance.")
-
- try:
- if scheduler.running:
- logger.warning("Scheduler still running after shutdown logic completed? Forcing shutdown.")
- scheduler.shutdown(wait=False)
- except Exception as e:
- logger.debug(f"Expected exception during final scheduler cleanup: {e}")
diff --git a/letta/jobs/types.py b/letta/jobs/types.py
deleted file mode 100644
index f7143541..00000000
--- a/letta/jobs/types.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from typing import NamedTuple, Optional
-
-from anthropic.types.beta.messages import BetaMessageBatch, BetaMessageBatchIndividualResponse
-
-from letta.schemas.enums import AgentStepStatus, JobStatus
-
-
-class BatchPollingResult(NamedTuple):
- llm_batch_id: str
- request_status: JobStatus
- batch_response: Optional[BetaMessageBatch]
-
-
-class ItemUpdateInfo(NamedTuple):
- llm_batch_id: str
- agent_id: str
- request_status: JobStatus
- batch_request_result: Optional[BetaMessageBatchIndividualResponse]
-
-
-class StepStatusUpdateInfo(NamedTuple):
- llm_batch_id: str
- agent_id: str
- step_status: AgentStepStatus
-
-
-class RequestStatusUpdateInfo(NamedTuple):
- llm_batch_id: str
- agent_id: str
- request_status: JobStatus
diff --git a/letta/llm_api/__init__.py b/letta/llm_api/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/llm_api/anthropic_client.py b/letta/llm_api/anthropic_client.py
deleted file mode 100644
index d83c25c5..00000000
--- a/letta/llm_api/anthropic_client.py
+++ /dev/null
@@ -1,888 +0,0 @@
-import json
-import logging
-import re
-from typing import Dict, List, Optional, Union
-
-import anthropic
-from anthropic import AsyncStream
-from anthropic.types.beta import BetaMessage as AnthropicMessage, BetaRawMessageStreamEvent
-from anthropic.types.beta.message_create_params import MessageCreateParamsNonStreaming
-from anthropic.types.beta.messages import BetaMessageBatch
-from anthropic.types.beta.messages.batch_create_params import Request
-
-from letta.constants import FUNC_FAILED_HEARTBEAT_MESSAGE, REQ_HEARTBEAT_MESSAGE
-from letta.errors import (
- ContextWindowExceededError,
- ErrorCode,
- LLMAuthenticationError,
- LLMBadRequestError,
- LLMConnectionError,
- LLMNotFoundError,
- LLMPermissionDeniedError,
- LLMRateLimitError,
- LLMServerError,
- LLMTimeoutError,
- LLMUnprocessableEntityError,
-)
-from letta.helpers.datetime_helpers import get_utc_time_int
-from letta.helpers.decorators import deprecated
-from letta.llm_api.helpers import add_inner_thoughts_to_functions, unpack_all_inner_thoughts_from_kwargs
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION
-from letta.log import get_logger
-from letta.otel.tracing import trace_method
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage
-from letta.schemas.openai.chat_completion_request import Tool as OpenAITool
-from letta.schemas.openai.chat_completion_response import (
- ChatCompletionResponse,
- Choice,
- FunctionCall,
- Message as ChoiceMessage,
- ToolCall,
- UsageStatistics,
-)
-from letta.settings import model_settings
-
-DUMMY_FIRST_USER_MESSAGE = "User initializing bootup sequence."
-
-logger = get_logger(__name__)
-
-
-class AnthropicClient(LLMClientBase):
- @trace_method
- @deprecated("Synchronous version of this is no longer valid. Will result in model_dump of coroutine")
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- client = self._get_anthropic_client(llm_config, async_client=False)
- response = client.beta.messages.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- client = await self._get_anthropic_client_async(llm_config, async_client=True)
-
- if llm_config.enable_reasoner:
- response = await client.beta.messages.create(**request_data, betas=["interleaved-thinking-2025-05-14"])
- else:
- response = await client.beta.messages.create(**request_data)
-
- return response.model_dump()
-
- @trace_method
- async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[BetaRawMessageStreamEvent]:
- client = await self._get_anthropic_client_async(llm_config, async_client=True)
- request_data["stream"] = True
-
- # Add fine-grained tool streaming beta header for better streaming performance
- # This helps reduce buffering when streaming tool call parameters
- # See: https://docs.anthropic.com/en/docs/build-with-claude/tool-use/fine-grained-streaming
- betas = ["fine-grained-tool-streaming-2025-05-14"]
-
- # If extended thinking, turn on interleaved header
- # https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#interleaved-thinking
- if llm_config.enable_reasoner:
- betas.append("interleaved-thinking-2025-05-14")
-
- return await client.beta.messages.create(**request_data, betas=betas)
-
- @trace_method
- async def send_llm_batch_request_async(
- self,
- agent_messages_mapping: Dict[str, List[PydanticMessage]],
- agent_tools_mapping: Dict[str, List[dict]],
- agent_llm_config_mapping: Dict[str, LLMConfig],
- ) -> BetaMessageBatch:
- """
- Sends a batch request to the Anthropic API using the provided agent messages and tools mappings.
-
- Args:
- agent_messages_mapping: A dict mapping agent_id to their list of PydanticMessages.
- agent_tools_mapping: A dict mapping agent_id to their list of tool dicts.
- agent_llm_config_mapping: A dict mapping agent_id to their LLM config
-
- Returns:
- BetaMessageBatch: The batch response from the Anthropic API.
-
- Raises:
- ValueError: If the sets of agent_ids in the two mappings do not match.
- Exception: Transformed errors from the underlying API call.
- """
- # Validate that both mappings use the same set of agent_ids.
- if set(agent_messages_mapping.keys()) != set(agent_tools_mapping.keys()):
- raise ValueError("Agent mappings for messages and tools must use the same agent_ids.")
-
- try:
- requests = {
- agent_id: self.build_request_data(
- messages=agent_messages_mapping[agent_id],
- llm_config=agent_llm_config_mapping[agent_id],
- tools=agent_tools_mapping[agent_id],
- )
- for agent_id in agent_messages_mapping
- }
-
- client = await self._get_anthropic_client_async(list(agent_llm_config_mapping.values())[0], async_client=True)
-
- anthropic_requests = [
- Request(custom_id=agent_id, params=MessageCreateParamsNonStreaming(**params)) for agent_id, params in requests.items()
- ]
-
- batch_response = await client.beta.messages.batches.create(requests=anthropic_requests)
-
- return batch_response
-
- except Exception as e:
- # Enhance logging here if additional context is needed
- logger.error("Error during send_llm_batch_request_async.", exc_info=True)
- raise self.handle_llm_error(e)
-
- @trace_method
- def _get_anthropic_client(
- self, llm_config: LLMConfig, async_client: bool = False
- ) -> Union[anthropic.AsyncAnthropic, anthropic.Anthropic]:
- api_key, _, _ = self.get_byok_overrides(llm_config)
-
- if async_client:
- return (
- anthropic.AsyncAnthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
- if api_key
- else anthropic.AsyncAnthropic(max_retries=model_settings.anthropic_max_retries)
- )
- return (
- anthropic.Anthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
- if api_key
- else anthropic.Anthropic(max_retries=model_settings.anthropic_max_retries)
- )
-
- @trace_method
- async def _get_anthropic_client_async(
- self, llm_config: LLMConfig, async_client: bool = False
- ) -> Union[anthropic.AsyncAnthropic, anthropic.Anthropic]:
- api_key, _, _ = await self.get_byok_overrides_async(llm_config)
-
- if async_client:
- return (
- anthropic.AsyncAnthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
- if api_key
- else anthropic.AsyncAnthropic(max_retries=model_settings.anthropic_max_retries)
- )
- return (
- anthropic.Anthropic(api_key=api_key, max_retries=model_settings.anthropic_max_retries)
- if api_key
- else anthropic.Anthropic(max_retries=model_settings.anthropic_max_retries)
- )
-
- @trace_method
- def build_request_data(
- self,
- messages: List[PydanticMessage],
- llm_config: LLMConfig,
- tools: Optional[List[dict]] = None,
- force_tool_call: Optional[str] = None,
- ) -> dict:
- # TODO: This needs to get cleaned up. The logic here is pretty confusing.
- # TODO: I really want to get rid of prefixing, it's a recipe for disaster code maintenance wise
- prefix_fill = True
- if not self.use_tool_naming:
- raise NotImplementedError("Only tool calling supported on Anthropic API requests")
-
- if not llm_config.max_tokens:
- # TODO strip this default once we add provider-specific defaults
- max_output_tokens = 4096 # the minimum max tokens (for Haiku 3)
- else:
- max_output_tokens = llm_config.max_tokens
-
- data = {
- "model": llm_config.model,
- "max_tokens": max_output_tokens,
- "temperature": llm_config.temperature,
- }
-
- # Extended Thinking
- if self.is_reasoning_model(llm_config) and llm_config.enable_reasoner:
- thinking_budget = max(llm_config.max_reasoning_tokens, 1024)
- if thinking_budget != llm_config.max_reasoning_tokens:
- logger.warning(
- f"Max reasoning tokens must be at least 1024 for Claude. Setting max_reasoning_tokens to 1024 for model {llm_config.model}."
- )
- data["thinking"] = {
- "type": "enabled",
- "budget_tokens": thinking_budget,
- }
- # `temperature` may only be set to 1 when thinking is enabled. Please consult our documentation at https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking#important-considerations-when-using-extended-thinking'
- data["temperature"] = 1.0
-
- # Silently disable prefix_fill for now
- prefix_fill = False
-
- # Tools
- # For an overview on tool choice:
- # https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview
- if not tools:
- # Special case for summarization path
- tools_for_request = None
- tool_choice = None
- elif self.is_reasoning_model(llm_config) and llm_config.enable_reasoner:
- # NOTE: reasoning models currently do not allow for `any`
- tool_choice = {"type": "auto", "disable_parallel_tool_use": True}
- tools_for_request = [OpenAITool(function=f) for f in tools]
- elif force_tool_call is not None:
- tool_choice = {"type": "tool", "name": force_tool_call, "disable_parallel_tool_use": True}
- tools_for_request = [OpenAITool(function=f) for f in tools if f["name"] == force_tool_call]
-
- # need to have this setting to be able to put inner thoughts in kwargs
- if not llm_config.put_inner_thoughts_in_kwargs:
- logger.warning(
- f"Force setting put_inner_thoughts_in_kwargs to True for Claude because there is a forced tool call: {force_tool_call}"
- )
- llm_config.put_inner_thoughts_in_kwargs = True
- else:
- tool_choice = {"type": "any", "disable_parallel_tool_use": True}
- tools_for_request = [OpenAITool(function=f) for f in tools] if tools is not None else None
-
- # Add tool choice
- if tool_choice:
- data["tool_choice"] = tool_choice
-
- # Add inner thoughts kwarg
- # TODO: Can probably make this more efficient
- if tools_for_request and len(tools_for_request) > 0 and llm_config.put_inner_thoughts_in_kwargs:
- tools_with_inner_thoughts = add_inner_thoughts_to_functions(
- functions=[t.function.model_dump() for t in tools_for_request],
- inner_thoughts_key=INNER_THOUGHTS_KWARG,
- inner_thoughts_description=INNER_THOUGHTS_KWARG_DESCRIPTION,
- )
- tools_for_request = [OpenAITool(function=f) for f in tools_with_inner_thoughts]
-
- if tools_for_request and len(tools_for_request) > 0:
- # TODO eventually enable parallel tool use
- data["tools"] = convert_tools_to_anthropic_format(tools_for_request)
-
- # Messages
- inner_thoughts_xml_tag = "thinking"
-
- # Move 'system' to the top level
- if messages[0].role != "system":
- raise RuntimeError(f"First message is not a system message, instead has role {messages[0].role}")
- system_content = messages[0].content if isinstance(messages[0].content, str) else messages[0].content[0].text
- data["system"] = self._add_cache_control_to_system_message(system_content)
- data["messages"] = PydanticMessage.to_anthropic_dicts_from_list(
- messages=messages[1:],
- inner_thoughts_xml_tag=inner_thoughts_xml_tag,
- put_inner_thoughts_in_kwargs=bool(llm_config.put_inner_thoughts_in_kwargs),
- )
-
- # Ensure first message is user
- if data["messages"][0]["role"] != "user":
- data["messages"] = [{"role": "user", "content": DUMMY_FIRST_USER_MESSAGE}] + data["messages"]
-
- # Handle alternating messages
- data["messages"] = merge_tool_results_into_user_messages(data["messages"])
-
- # Strip heartbeat pings if extended thinking
- if llm_config.enable_reasoner:
- data["messages"] = merge_heartbeats_into_tool_responses(data["messages"])
-
- # Prefix fill
- # https://docs.anthropic.com/en/api/messages#body-messages
- # NOTE: cannot prefill with tools for opus:
- # Your API request included an `assistant` message in the final position, which would pre-fill the `assistant` response. When using tools with "claude-3-opus-20240229"
- if prefix_fill and not llm_config.put_inner_thoughts_in_kwargs and "opus" not in data["model"]:
- data["messages"].append(
- # Start the thinking process for the assistant
- {"role": "assistant", "content": f"<{inner_thoughts_xml_tag}>"},
- )
-
- return data
-
- async def count_tokens(self, messages: List[dict] = None, model: str = None, tools: List[OpenAITool] = None) -> int:
- logging.getLogger("httpx").setLevel(logging.WARNING)
-
- client = anthropic.AsyncAnthropic()
- if messages and len(messages) == 0:
- messages = None
- if tools and len(tools) > 0:
- anthropic_tools = convert_tools_to_anthropic_format(tools)
- else:
- anthropic_tools = None
-
- thinking_enabled = False
- if messages and len(messages) > 0:
- # Check if the last assistant message starts with a thinking block
- # Find the last assistant message
- last_assistant_message = None
- for message in reversed(messages):
- if message.get("role") == "assistant":
- last_assistant_message = message
- break
-
- if (
- last_assistant_message
- and isinstance(last_assistant_message.get("content"), list)
- and len(last_assistant_message["content"]) > 0
- and last_assistant_message["content"][0].get("type") == "thinking"
- ):
- thinking_enabled = True
-
- try:
- count_params = {
- "model": model or "claude-3-7-sonnet-20250219",
- "messages": messages or [{"role": "user", "content": "hi"}],
- "tools": anthropic_tools or [],
- }
-
- if thinking_enabled:
- count_params["thinking"] = {"type": "enabled", "budget_tokens": 16000}
- result = await client.beta.messages.count_tokens(**count_params)
- except:
- raise
-
- token_count = result.input_tokens
- if messages is None:
- token_count -= 8
- return token_count
-
- def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
- return (
- llm_config.model.startswith("claude-3-7-sonnet")
- or llm_config.model.startswith("claude-sonnet-4")
- or llm_config.model.startswith("claude-opus-4")
- )
-
- @trace_method
- def handle_llm_error(self, e: Exception) -> Exception:
- if isinstance(e, anthropic.APITimeoutError):
- logger.warning(f"[Anthropic] Request timeout: {e}")
- return LLMTimeoutError(
- message=f"Request to Anthropic timed out: {str(e)}",
- code=ErrorCode.TIMEOUT,
- details={"cause": str(e.__cause__) if e.__cause__ else None},
- )
-
- if isinstance(e, anthropic.APIConnectionError):
- logger.warning(f"[Anthropic] API connection error: {e.__cause__}")
- return LLMConnectionError(
- message=f"Failed to connect to Anthropic: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- details={"cause": str(e.__cause__) if e.__cause__ else None},
- )
-
- if isinstance(e, anthropic.RateLimitError):
- logger.warning("[Anthropic] Rate limited (429). Consider backoff.")
- return LLMRateLimitError(
- message=f"Rate limited by Anthropic: {str(e)}",
- code=ErrorCode.RATE_LIMIT_EXCEEDED,
- )
-
- if isinstance(e, anthropic.BadRequestError):
- logger.warning(f"[Anthropic] Bad request: {str(e)}")
- error_str = str(e).lower()
- if "prompt is too long" in error_str or "exceed context limit" in error_str:
- # If the context window is too large, we expect to receive either:
- # 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'prompt is too long: 200758 tokens > 200000 maximum'}}
- # 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'input length and `max_tokens` exceed context limit: 173298 + 32000 > 200000, decrease input length or `max_tokens` and try again'}}
- return ContextWindowExceededError(
- message=f"Bad request to Anthropic (context window exceeded): {str(e)}",
- )
- else:
- return LLMBadRequestError(
- message=f"Bad request to Anthropic: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- )
-
- if isinstance(e, anthropic.AuthenticationError):
- logger.warning(f"[Anthropic] Authentication error: {str(e)}")
- return LLMAuthenticationError(
- message=f"Authentication failed with Anthropic: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- )
-
- if isinstance(e, anthropic.PermissionDeniedError):
- logger.warning(f"[Anthropic] Permission denied: {str(e)}")
- return LLMPermissionDeniedError(
- message=f"Permission denied by Anthropic: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- )
-
- if isinstance(e, anthropic.NotFoundError):
- logger.warning(f"[Anthropic] Resource not found: {str(e)}")
- return LLMNotFoundError(
- message=f"Resource not found in Anthropic: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- )
-
- if isinstance(e, anthropic.UnprocessableEntityError):
- logger.warning(f"[Anthropic] Unprocessable entity: {str(e)}")
- return LLMUnprocessableEntityError(
- message=f"Invalid request content for Anthropic: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- )
-
- if isinstance(e, anthropic.APIStatusError):
- logger.warning(f"[Anthropic] API status error: {str(e)}")
- return LLMServerError(
- message=f"Anthropic API error: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- details={
- "status_code": e.status_code if hasattr(e, "status_code") else None,
- "response": str(e.response) if hasattr(e, "response") else None,
- },
- )
-
- return super().handle_llm_error(e)
-
- # TODO: Input messages doesn't get used here
- # TODO: Clean up this interface
- @trace_method
- def convert_response_to_chat_completion(
- self,
- response_data: dict,
- input_messages: List[PydanticMessage],
- llm_config: LLMConfig,
- ) -> ChatCompletionResponse:
- """
- Example response from Claude 3:
- response.json = {
- 'id': 'msg_01W1xg9hdRzbeN2CfZM7zD2w',
- 'type': 'message',
- 'role': 'assistant',
- 'content': [
- {
- 'type': 'text',
- 'text': "Analyzing user login event. This is Chad's first
- interaction with me. I will adjust my personality and rapport accordingly. "
- },
- {
- 'type':
- 'tool_use',
- 'id': 'toolu_01Ka4AuCmfvxiidnBZuNfP1u',
- 'name': 'core_memory_append',
- 'input': {
- 'name': 'human',
- 'content': 'Chad is logging in for the first time. I will aim to build a warm
- and welcoming rapport.',
- 'request_heartbeat': True
- }
- }
- ],
- 'model': 'claude-3-haiku-20240307',
- 'stop_reason': 'tool_use',
- 'stop_sequence': None,
- 'usage': {
- 'input_tokens': 3305,
- 'output_tokens': 141
- }
- }
- """
- response = AnthropicMessage(**response_data)
- prompt_tokens = response.usage.input_tokens
- completion_tokens = response.usage.output_tokens
- finish_reason = remap_finish_reason(str(response.stop_reason))
-
- content = None
- reasoning_content = None
- reasoning_content_signature = None
- redacted_reasoning_content = None
- tool_calls = None
-
- if len(response.content) > 0:
- for content_part in response.content:
- if content_part.type == "text":
- content = strip_xml_tags(string=content_part.text, tag="thinking")
- if content_part.type == "tool_use":
- # hack for incorrect tool format
- tool_input = json.loads(json.dumps(content_part.input))
- if "id" in tool_input and tool_input["id"].startswith("toolu_") and "function" in tool_input:
- arguments = json.dumps(tool_input["function"]["arguments"], indent=2)
- try:
- args_json = json.loads(arguments)
- if not isinstance(args_json, dict):
- raise ValueError("Expected parseable json object for arguments")
- except:
- arguments = str(tool_input["function"]["arguments"])
- else:
- arguments = json.dumps(tool_input, indent=2)
- tool_calls = [
- ToolCall(
- id=content_part.id,
- type="function",
- function=FunctionCall(
- name=content_part.name,
- arguments=arguments,
- ),
- )
- ]
- if content_part.type == "thinking":
- reasoning_content = content_part.thinking
- reasoning_content_signature = content_part.signature
- if content_part.type == "redacted_thinking":
- redacted_reasoning_content = content_part.data
-
- else:
- raise RuntimeError("Unexpected empty content in response")
-
- assert response.role == "assistant"
- choice = Choice(
- index=0,
- finish_reason=finish_reason,
- message=ChoiceMessage(
- role=response.role,
- content=content,
- reasoning_content=reasoning_content,
- reasoning_content_signature=reasoning_content_signature,
- redacted_reasoning_content=redacted_reasoning_content,
- tool_calls=tool_calls,
- ),
- )
-
- chat_completion_response = ChatCompletionResponse(
- id=response.id,
- choices=[choice],
- created=get_utc_time_int(),
- model=response.model,
- usage=UsageStatistics(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=prompt_tokens + completion_tokens,
- ),
- )
- if llm_config.put_inner_thoughts_in_kwargs:
- chat_completion_response = unpack_all_inner_thoughts_from_kwargs(
- response=chat_completion_response, inner_thoughts_key=INNER_THOUGHTS_KWARG
- )
-
- return chat_completion_response
-
- def _add_cache_control_to_system_message(self, system_content):
- """Add cache control to system message content"""
- if isinstance(system_content, str):
- # For string content, convert to list format with cache control
- return [{"type": "text", "text": system_content, "cache_control": {"type": "ephemeral"}}]
- elif isinstance(system_content, list):
- # For list content, add cache control to the last text block
- cached_content = system_content.copy()
- for i in range(len(cached_content) - 1, -1, -1):
- if cached_content[i].get("type") == "text":
- cached_content[i]["cache_control"] = {"type": "ephemeral"}
- break
- return cached_content
-
- return system_content
-
-
-def convert_tools_to_anthropic_format(tools: List[OpenAITool]) -> List[dict]:
- """See: https://docs.anthropic.com/claude/docs/tool-use
-
- OpenAI style:
- "tools": [{
- "type": "function",
- "function": {
- "name": "find_movies",
- "description": "find ....",
- "parameters": {
- "type": "object",
- "properties": {
- PARAM: {
- "type": PARAM_TYPE, # eg "string"
- "description": PARAM_DESCRIPTION,
- },
- ...
- },
- "required": List[str],
- }
- }
- }
- ]
-
- Anthropic style:
- "tools": [{
- "name": "find_movies",
- "description": "find ....",
- "input_schema": {
- "type": "object",
- "properties": {
- PARAM: {
- "type": PARAM_TYPE, # eg "string"
- "description": PARAM_DESCRIPTION,
- },
- ...
- },
- "required": List[str],
- }
- }
- ]
-
- Two small differences:
- - 1 level less of nesting
- - "parameters" -> "input_schema"
- """
- formatted_tools = []
- for tool in tools:
- # Get the input schema
- input_schema = tool.function.parameters or {"type": "object", "properties": {}, "required": []}
-
- # Clean up the properties in the schema
- # The presence of union types / default fields seems Anthropic to produce invalid JSON for tool calls
- if isinstance(input_schema, dict) and "properties" in input_schema:
- cleaned_properties = {}
- for prop_name, prop_schema in input_schema.get("properties", {}).items():
- if isinstance(prop_schema, dict):
- cleaned_properties[prop_name] = _clean_property_schema(prop_schema)
- else:
- cleaned_properties[prop_name] = prop_schema
-
- # Create cleaned input schema
- cleaned_input_schema = {
- "type": input_schema.get("type", "object"),
- "properties": cleaned_properties,
- }
-
- # Only add required field if it exists and is non-empty
- if "required" in input_schema and input_schema["required"]:
- cleaned_input_schema["required"] = input_schema["required"]
- else:
- cleaned_input_schema = input_schema
-
- formatted_tool = {
- "name": tool.function.name,
- "description": tool.function.description if tool.function.description else "",
- "input_schema": cleaned_input_schema,
- }
- formatted_tools.append(formatted_tool)
-
- return formatted_tools
-
-
-def _clean_property_schema(prop_schema: dict) -> dict:
- """Clean up a property schema by removing defaults and simplifying union types."""
- cleaned = {}
-
- # Handle type field - simplify union types like ["null", "string"] to just "string"
- if "type" in prop_schema:
- prop_type = prop_schema["type"]
- if isinstance(prop_type, list):
- # Remove "null" from union types to simplify
- # e.g., ["null", "string"] becomes "string"
- non_null_types = [t for t in prop_type if t != "null"]
- if len(non_null_types) == 1:
- cleaned["type"] = non_null_types[0]
- elif len(non_null_types) > 1:
- # Keep as array if multiple non-null types
- cleaned["type"] = non_null_types
- else:
- # If only "null" was in the list, default to string
- cleaned["type"] = "string"
- else:
- cleaned["type"] = prop_type
-
- # Copy over other fields except 'default'
- for key, value in prop_schema.items():
- if key not in ["type", "default"]: # Skip 'default' field
- if key == "properties" and isinstance(value, dict):
- # Recursively clean nested properties
- cleaned["properties"] = {k: _clean_property_schema(v) if isinstance(v, dict) else v for k, v in value.items()}
- else:
- cleaned[key] = value
-
- return cleaned
-
-
-def is_heartbeat(message: dict, is_ping: bool = False) -> bool:
- """Check if the message is an automated heartbeat ping"""
-
- if "role" not in message or message["role"] != "user" or "content" not in message:
- return False
-
- try:
- message_json = json.loads(message["content"])
- except:
- return False
-
- if "reason" not in message_json:
- return False
-
- if message_json["type"] != "heartbeat":
- return False
-
- if not is_ping:
- # Just checking if 'type': 'heartbeat'
- return True
- else:
- # Also checking if it's specifically a 'ping' style message
- # NOTE: this will not catch tool rule heartbeats
- if REQ_HEARTBEAT_MESSAGE in message_json["reason"] or FUNC_FAILED_HEARTBEAT_MESSAGE in message_json["reason"]:
- return True
- else:
- return False
-
-
-def merge_heartbeats_into_tool_responses(messages: List[dict]):
- """For extended thinking mode, we don't want anything other than tool responses in-between assistant actions
-
- Otherwise, the thinking will silently get dropped.
-
- NOTE: assumes merge_tool_results_into_user_messages has already been called
- """
-
- merged_messages = []
-
- # Loop through messages
- # For messages with role 'user' and len(content) > 1,
- # Check if content[0].type == 'tool_result'
- # If so, iterate over content[1:] and while content.type == 'text' and is_heartbeat(content.text),
- # merge into content[0].content
-
- for message in messages:
- if "role" not in message or "content" not in message:
- # Skip invalid messages
- merged_messages.append(message)
- continue
-
- if message["role"] == "user" and len(message["content"]) > 1:
- content_parts = message["content"]
-
- # If the first content part is a tool result, merge the heartbeat content into index 0 of the content
- # Two end cases:
- # 1. It was [tool_result, heartbeat], in which case merged result is [tool_result+heartbeat] (len 1)
- # 2. It was [tool_result, user_text], in which case it should be unchanged (len 2)
- if "type" in content_parts[0] and "content" in content_parts[0] and content_parts[0]["type"] == "tool_result":
- new_content_parts = [content_parts[0]]
-
- # If the first content part is a tool result, merge the heartbeat content into index 0 of the content
- for i, content_part in enumerate(content_parts[1:]):
- # If it's a heartbeat, add it to the merge
- if (
- content_part["type"] == "text"
- and "text" in content_part
- and is_heartbeat({"role": "user", "content": content_part["text"]})
- ):
- # NOTE: joining with a ','
- new_content_parts[0]["content"] += ", " + content_part["text"]
-
- # If it's not, break, and concat to finish
- else:
- # Append the rest directly, no merging of content strings
- new_content_parts.extend(content_parts[i + 1 :])
- break
-
- # Set the content_parts
- message["content"] = new_content_parts
- merged_messages.append(message)
-
- else:
- # Skip invalid messages parts
- merged_messages.append(message)
- continue
- else:
- merged_messages.append(message)
-
- return merged_messages
-
-
-def merge_tool_results_into_user_messages(messages: List[dict]):
- """Anthropic API doesn't allow role 'tool'->'user' sequences
-
- Example HTTP error:
- messages: roles must alternate between "user" and "assistant", but found multiple "user" roles in a row
-
- From: https://docs.anthropic.com/claude/docs/tool-use
- You may be familiar with other APIs that return tool use as separate from the model's primary output,
- or which use a special-purpose tool or function message role.
- In contrast, Anthropic's models and API are built around alternating user and assistant messages,
- where each message is an array of rich content blocks: text, image, tool_use, and tool_result.
- """
-
- # TODO walk through the messages list
- # When a dict (dict_A) with 'role' == 'user' is followed by a dict with 'role' == 'user' (dict B), do the following
- # dict_A["content"] = dict_A["content"] + dict_B["content"]
-
- # The result should be a new merged_messages list that doesn't have any back-to-back dicts with 'role' == 'user'
- merged_messages = []
- if not messages:
- return merged_messages
-
- # Start with the first message in the list
- current_message = messages[0]
-
- for next_message in messages[1:]:
- if current_message["role"] == "user" and next_message["role"] == "user":
- # Merge contents of the next user message into current one
- current_content = (
- current_message["content"]
- if isinstance(current_message["content"], list)
- else [{"type": "text", "text": current_message["content"]}]
- )
- next_content = (
- next_message["content"]
- if isinstance(next_message["content"], list)
- else [{"type": "text", "text": next_message["content"]}]
- )
- merged_content: list = current_content + next_content
- current_message["content"] = merged_content
- else:
- # Append the current message to result as it's complete
- merged_messages.append(current_message)
- # Move on to the next message
- current_message = next_message
-
- # Append the last processed message to the result
- merged_messages.append(current_message)
-
- return merged_messages
-
-
-def remap_finish_reason(stop_reason: str) -> str:
- """Remap Anthropic's 'stop_reason' to OpenAI 'finish_reason'
-
- OpenAI: 'stop', 'length', 'function_call', 'content_filter', null
- see: https://platform.openai.com/docs/guides/text-generation/chat-completions-api
-
- From: https://docs.anthropic.com/claude/reference/migrating-from-text-completions-to-messages#stop-reason
-
- Messages have a stop_reason of one of the following values:
- "end_turn": The conversational turn ended naturally.
- "stop_sequence": One of your specified custom stop sequences was generated.
- "max_tokens": (unchanged)
-
- """
- if stop_reason == "end_turn":
- return "stop"
- elif stop_reason == "stop_sequence":
- return "stop"
- elif stop_reason == "max_tokens":
- return "length"
- elif stop_reason == "tool_use":
- return "function_call"
- else:
- raise ValueError(f"Unexpected stop_reason: {stop_reason}")
-
-
-def strip_xml_tags(string: str, tag: Optional[str]) -> str:
- if tag is None:
- return string
- # Construct the regular expression pattern to find the start and end tags
- tag_pattern = f"<{tag}.*?>|"
- # Use the regular expression to replace the tags with an empty string
- return re.sub(tag_pattern, "", string)
-
-
-def strip_xml_tags_streaming(string: str, tag: Optional[str]) -> str:
- if tag is None:
- return string
-
- # Handle common partial tag cases
- parts_to_remove = [
- "<", # Leftover start bracket
- f"<{tag}", # Opening tag start
- f"", # Closing tag end
- f"{tag}>", # Opening tag end
- f"/{tag}", # Partial closing tag without >
- ">", # Leftover end bracket
- ]
-
- result = string
- for part in parts_to_remove:
- result = result.replace(part, "")
-
- return result
diff --git a/letta/llm_api/azure_client.py b/letta/llm_api/azure_client.py
deleted file mode 100644
index 63977557..00000000
--- a/letta/llm_api/azure_client.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import os
-from typing import List, Optional, Tuple
-
-from openai import AsyncAzureOpenAI, AzureOpenAI
-from openai.types.chat.chat_completion import ChatCompletion
-
-from letta.llm_api.openai_client import OpenAIClient
-from letta.otel.tracing import trace_method
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import ProviderCategory
-from letta.schemas.llm_config import LLMConfig
-from letta.settings import model_settings
-
-
-class AzureClient(OpenAIClient):
- def get_byok_overrides(self, llm_config: LLMConfig) -> Tuple[Optional[str], Optional[str], Optional[str]]:
- if llm_config.provider_category == ProviderCategory.byok:
- from letta.services.provider_manager import ProviderManager
-
- return ProviderManager().get_azure_credentials(llm_config.provider_name, actor=self.actor)
-
- return None, None, None
-
- async def get_byok_overrides_async(self, llm_config: LLMConfig) -> Tuple[Optional[str], Optional[str], Optional[str]]:
- if llm_config.provider_category == ProviderCategory.byok:
- from letta.services.provider_manager import ProviderManager
-
- return await ProviderManager().get_azure_credentials_async(llm_config.provider_name, actor=self.actor)
-
- return None, None, None
-
- @trace_method
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying synchronous request to OpenAI API and returns raw response dict.
- """
- api_key, base_url, api_version = self.get_byok_overrides(llm_config)
- if not api_key or not base_url or not api_version:
- api_key = model_settings.azure_api_key or os.environ.get("AZURE_API_KEY")
- base_url = model_settings.azure_base_url or os.environ.get("AZURE_BASE_URL")
- api_version = model_settings.azure_api_version or os.environ.get("AZURE_API_VERSION")
-
- client = AzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version)
- response: ChatCompletion = client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying asynchronous request to OpenAI API and returns raw response dict.
- """
- api_key, base_url, api_version = await self.get_byok_overrides_async(llm_config)
- if not api_key or not base_url or not api_version:
- api_key = model_settings.azure_api_key or os.environ.get("AZURE_API_KEY")
- base_url = model_settings.azure_base_url or os.environ.get("AZURE_BASE_URL")
- api_version = model_settings.azure_api_version or os.environ.get("AZURE_API_VERSION")
-
- client = AsyncAzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version)
- response: ChatCompletion = await client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_embeddings(self, inputs: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
- """Request embeddings given texts and embedding config"""
- api_key = model_settings.azure_api_key or os.environ.get("AZURE_API_KEY")
- base_url = model_settings.azure_base_url or os.environ.get("AZURE_BASE_URL")
- api_version = model_settings.azure_api_version or os.environ.get("AZURE_API_VERSION")
- client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=base_url)
- response = await client.embeddings.create(model=embedding_config.embedding_model, input=inputs)
-
- # TODO: add total usage
- return [r.embedding for r in response.data]
diff --git a/letta/llm_api/bedrock_client.py b/letta/llm_api/bedrock_client.py
deleted file mode 100644
index 0d26e0f5..00000000
--- a/letta/llm_api/bedrock_client.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from typing import List, Optional, Union
-
-import anthropic
-from aioboto3.session import Session
-
-from letta.llm_api.anthropic_client import AnthropicClient
-from letta.log import get_logger
-from letta.otel.tracing import trace_method
-from letta.schemas.enums import ProviderCategory
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage
-from letta.services.provider_manager import ProviderManager
-from letta.settings import model_settings
-
-logger = get_logger(__name__)
-
-
-class BedrockClient(AnthropicClient):
- async def get_byok_overrides_async(self, llm_config: LLMConfig) -> tuple[str, str, str]:
- override_access_key_id, override_secret_access_key, override_default_region = None, None, None
- if llm_config.provider_category == ProviderCategory.byok:
- (
- override_access_key_id,
- override_secret_access_key,
- override_default_region,
- ) = await ProviderManager().get_bedrock_credentials_async(
- llm_config.provider_name,
- actor=self.actor,
- )
- return override_access_key_id, override_secret_access_key, override_default_region
-
- @trace_method
- async def _get_anthropic_client_async(
- self, llm_config: LLMConfig, async_client: bool = False
- ) -> Union[anthropic.AsyncAnthropic, anthropic.Anthropic, anthropic.AsyncAnthropicBedrock, anthropic.AnthropicBedrock]:
- override_access_key_id, override_secret_access_key, override_default_region = await self.get_byok_overrides_async(llm_config)
-
- session = Session()
- async with session.client(
- "sts",
- aws_access_key_id=override_access_key_id or model_settings.aws_access_key_id,
- aws_secret_access_key=override_secret_access_key or model_settings.aws_secret_access_key,
- region_name=override_default_region or model_settings.aws_default_region,
- ) as sts_client:
- session_token = await sts_client.get_session_token()
- credentials = session_token["Credentials"]
-
- if async_client:
- return anthropic.AsyncAnthropicBedrock(
- aws_access_key=credentials["AccessKeyId"],
- aws_secret_key=credentials["SecretAccessKey"],
- aws_session_token=credentials["SessionToken"],
- aws_region=override_default_region or model_settings.aws_default_region,
- max_retries=model_settings.anthropic_max_retries,
- )
- else:
- return anthropic.AnthropicBedrock(
- aws_access_key=credentials["AccessKeyId"],
- aws_secret_key=credentials["SecretAccessKey"],
- aws_session_token=credentials["SessionToken"],
- aws_region=override_default_region or model_settings.aws_default_region,
- max_retries=model_settings.anthropic_max_retries,
- )
-
- @trace_method
- def build_request_data(
- self,
- messages: List[PydanticMessage],
- llm_config: LLMConfig,
- tools: Optional[List[dict]] = None,
- force_tool_call: Optional[str] = None,
- ) -> dict:
- data = super().build_request_data(messages, llm_config, tools, force_tool_call)
- # remove disallowed fields
- if "tool_choice" in data:
- del data["tool_choice"]["disable_parallel_tool_use"]
- return data
diff --git a/letta/llm_api/deepseek_client.py b/letta/llm_api/deepseek_client.py
deleted file mode 100644
index a0037b1e..00000000
--- a/letta/llm_api/deepseek_client.py
+++ /dev/null
@@ -1,411 +0,0 @@
-import json
-import os
-import re
-import warnings
-from typing import List, Optional
-
-from openai import AsyncOpenAI, AsyncStream, OpenAI
-from openai.types.chat.chat_completion import ChatCompletion
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-
-from letta.llm_api.openai_client import OpenAIClient
-from letta.otel.tracing import trace_method
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage
-from letta.schemas.openai.chat_completion_request import (
- AssistantMessage,
- ChatCompletionRequest,
- ChatMessage,
- FunctionCall as ToolFunctionChoiceFunctionCall,
- Tool,
- ToolFunctionChoice,
- ToolMessage,
- UserMessage,
- cast_message_to_subtype,
-)
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
-from letta.schemas.openai.openai import Function, ToolCall
-from letta.settings import model_settings
-from letta.utils import get_tool_call_id
-
-
-def merge_tool_message(previous_message: ChatMessage, tool_message: ToolMessage) -> ChatMessage:
- """
- Merge `ToolMessage` objects into the previous message.
- """
- previous_message.content += (
- f" content: {tool_message.content}, role: {tool_message.role}, tool_call_id: {tool_message.tool_call_id} "
- )
- return previous_message
-
-
-def handle_assistant_message(assistant_message: AssistantMessage) -> AssistantMessage:
- """
- For `AssistantMessage` objects, remove the `tool_calls` field and add them to the `content` field.
- """
-
- if "tool_calls" in assistant_message.dict().keys():
- assistant_message.content = "".join(
- [
- # f" name: {tool_call.function.name}, function: {tool_call.function} "
- f" {json.dumps(tool_call.function.dict())} "
- for tool_call in assistant_message.tool_calls
- ]
- )
- del assistant_message.tool_calls
- return assistant_message
-
-
-def map_messages_to_deepseek_format(messages: List[ChatMessage]) -> List[_Message]:
- """
- Deepeek API has the following constraints: messages must be interleaved between user and assistant messages, ending on a user message.
- Tools are currently unstable for V3 and not supported for R1 in the API: https://api-docs.deepseek.com/guides/function_calling.
-
- This function merges ToolMessages into AssistantMessages and removes ToolCalls from AssistantMessages, and adds a dummy user message
- at the end.
-
- """
- deepseek_messages = []
- for idx, message in enumerate(messages):
- # First message is the system prompt, add it
- if idx == 0 and message.role == "system":
- deepseek_messages.append(message)
- continue
- if message.role == "user":
- if deepseek_messages[-1].role == "assistant" or deepseek_messages[-1].role == "system":
- # User message, add it
- deepseek_messages.append(UserMessage(content=message.content))
- else:
- # add to the content of the previous message
- deepseek_messages[-1].content += message.content
- elif message.role == "assistant":
- if deepseek_messages[-1].role == "user":
- # Assistant message, remove tool calls and add them to the content
- deepseek_messages.append(handle_assistant_message(message))
- else:
- # add to the content of the previous message
- deepseek_messages[-1].content += message.content
- elif message.role == "tool" and deepseek_messages[-1].role == "assistant":
- # Tool message, add it to the last assistant message
- merged_message = merge_tool_message(deepseek_messages[-1], message)
- deepseek_messages[-1] = merged_message
- else:
- print(f"Skipping message: {message}")
-
- # This needs to end on a user message, add a dummy message if the last was assistant
- if deepseek_messages[-1].role == "assistant":
- deepseek_messages.append(UserMessage(content=""))
- return deepseek_messages
-
-
-def build_deepseek_chat_completions_request(
- llm_config: LLMConfig,
- messages: List[_Message],
- user_id: Optional[str],
- functions: Optional[list],
- function_call: Optional[str],
- use_tool_naming: bool,
- max_tokens: Optional[int],
-) -> ChatCompletionRequest:
- # if functions and llm_config.put_inner_thoughts_in_kwargs:
- # # Special case for LM Studio backend since it needs extra guidance to force out the thoughts first
- # # TODO(fix)
- # inner_thoughts_desc = (
- # INNER_THOUGHTS_KWARG_DESCRIPTION_GO_FIRST if ":1234" in llm_config.model_endpoint else INNER_THOUGHTS_KWARG_DESCRIPTION
- # )
- # functions = add_inner_thoughts_to_functions(
- # functions=functions,
- # inner_thoughts_key=INNER_THOUGHTS_KWARG,
- # inner_thoughts_description=inner_thoughts_desc,
- # )
-
- openai_message_list = [
- cast_message_to_subtype(m) for m in PydanticMessage.to_openai_dicts_from_list(messages, put_inner_thoughts_in_kwargs=False)
- ]
-
- if llm_config.model:
- model = llm_config.model
- else:
- warnings.warn(f"Model type not set in llm_config: {llm_config.model_dump_json(indent=4)}")
- model = None
- if use_tool_naming:
- if function_call is None:
- tool_choice = None
- elif function_call not in ["none", "auto", "required"]:
- tool_choice = ToolFunctionChoice(type="function", function=ToolFunctionChoiceFunctionCall(name=function_call))
- else:
- tool_choice = function_call
-
- def add_functions_to_system_message(system_message: ChatMessage):
- system_message.content += f" {''.join(json.dumps(f) for f in functions)} "
- system_message.content += 'Select best function to call simply respond with a single json block with the fields "name" and "arguments". Use double quotes around the arguments.'
-
- if llm_config.model == "deepseek-reasoner": # R1 currently doesn't support function calling natively
- add_functions_to_system_message(
- openai_message_list[0]
- ) # Inject additional instructions to the system prompt with the available functions
-
- openai_message_list = map_messages_to_deepseek_format(openai_message_list)
-
- data = ChatCompletionRequest(
- model=model,
- messages=openai_message_list,
- user=str(user_id),
- max_completion_tokens=max_tokens,
- temperature=llm_config.temperature,
- )
- else:
- data = ChatCompletionRequest(
- model=model,
- messages=openai_message_list,
- tools=[Tool(type="function", function=f) for f in functions] if functions else None,
- tool_choice=tool_choice,
- user=str(user_id),
- max_completion_tokens=max_tokens,
- temperature=llm_config.temperature,
- )
- else:
- data = ChatCompletionRequest(
- model=model,
- messages=openai_message_list,
- functions=functions,
- function_call=function_call,
- user=str(user_id),
- max_completion_tokens=max_tokens,
- temperature=llm_config.temperature,
- )
-
- return data
-
-
-def convert_deepseek_response_to_chatcompletion(
- response: ChatCompletionResponse,
-) -> ChatCompletionResponse:
- """
- Example response from DeepSeek (NOTE: as of 8/28/25, deepseek api does populate tool call in response):
-
- ChatCompletion(
- id='bc7f7d25-82e4-443a-b217-dfad2b66da8e',
- choices=[
- Choice(
- finish_reason='stop',
- index=0,
- logprobs=None,
- message=ChatCompletionMessage(
- content='{"function": "send_message", "arguments": {"message": "Hey! Whales are such majestic creatures, aren\'t they? How\'s your day going? 🌊 "}}',
- refusal=None,
- role='assistant',
- audio=None,
- function_call=None,
- tool_calls=None,
- reasoning_content='Okay, the user said "hello whales". Hmm, that\'s an interesting greeting. Maybe they meant "hello there" or are they actually talking about whales? Let me check if I misheard. Whales are fascinating creatures. I should respond in a friendly way. Let me ask them how they\'re doing and mention whales to keep the conversation going.'
- )
- )
- ],
- created=1738266449,
- model='deepseek-reasoner',
- object='chat.completion',
- service_tier=None,
- system_fingerprint='fp_7e73fd9a08',
- usage=CompletionUsage(
- completion_tokens=111,
- prompt_tokens=1270,
- total_tokens=1381,
- completion_tokens_details=CompletionTokensDetails(
- accepted_prediction_tokens=None,
- audio_tokens=None,
- reasoning_tokens=72,
- rejected_prediction_tokens=None
- ),
- prompt_tokens_details=PromptTokensDetails(
- audio_tokens=None,
- cached_tokens=1088
- ),
- prompt_cache_hit_tokens=1088,
- prompt_cache_miss_tokens=182
- )
- )
- """
-
- def convert_dict_quotes(input_dict: dict):
- """
- Convert a dictionary with single-quoted keys to double-quoted keys,
- properly handling boolean values and nested structures.
-
- Args:
- input_dict (dict): Input dictionary with single-quoted keys
-
- Returns:
- str: JSON string with double-quoted keys
- """
- # First convert the dictionary to a JSON string to handle booleans properly
- json_str = json.dumps(input_dict)
-
- # Function to handle complex string replacements
- def replace_quotes(match):
- key = match.group(1)
- # Escape any existing double quotes in the key
- key = key.replace('"', '\\"')
- return f'"{key}":'
-
- # Replace single-quoted keys with double-quoted keys
- # This regex looks for single-quoted keys followed by a colon
- def strip_json_block(text):
- # Check if text starts with ```json or similar
- if text.strip().startswith("```"):
- # Split by \n to remove the first and last lines
- lines = text.split("\n")[1:-1]
- return "\n".join(lines)
- return text
-
- pattern = r"'([^']*)':"
- converted_str = re.sub(pattern, replace_quotes, strip_json_block(json_str))
-
- # Parse the string back to ensure valid JSON format
- try:
- json.loads(converted_str)
- return converted_str
- except json.JSONDecodeError as e:
- raise ValueError(f"Failed to create valid JSON with double quotes: {str(e)}")
-
- def extract_json_block(text):
- # Find the first {
- start = text.find("{")
- if start == -1:
- return text
-
- # Track nested braces to find the matching closing brace
- brace_count = 0
- end = start
-
- for i in range(start, len(text)):
- if text[i] == "{":
- brace_count += 1
- elif text[i] == "}":
- brace_count -= 1
- if brace_count == 0:
- end = i + 1
- break
-
- return text[start:end]
-
- content = response.choices[0].message.content
- try:
- content_dict = json.loads(extract_json_block(content))
-
- if type(content_dict["arguments"]) == str:
- content_dict["arguments"] = json.loads(content_dict["arguments"])
-
- tool_calls = [
- ToolCall(
- id=get_tool_call_id(),
- type="function",
- function=Function(
- name=content_dict["name"],
- arguments=convert_dict_quotes(content_dict["arguments"]),
- ),
- )
- ]
- except (json.JSONDecodeError, TypeError, KeyError) as e:
- print(e)
- tool_calls = response.choices[0].message.tool_calls
- raise ValueError(f"Failed to create valid JSON {content}")
-
- # Move the "reasoning_content" into the "content" field
- response.choices[0].message.content = response.choices[0].message.reasoning_content
- response.choices[0].message.tool_calls = tool_calls
-
- # Remove the "reasoning_content" field
- response.choices[0].message.reasoning_content = None
-
- return response
-
-
-class DeepseekClient(OpenAIClient):
- def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
- return False
-
- def supports_structured_output(self, llm_config: LLMConfig) -> bool:
- return False
-
- @trace_method
- def build_request_data(
- self,
- messages: List[PydanticMessage],
- llm_config: LLMConfig,
- tools: Optional[List[dict]] = None,
- force_tool_call: Optional[str] = None,
- ) -> dict:
- # Override put_inner_thoughts_in_kwargs to False for DeepSeek
- llm_config.put_inner_thoughts_in_kwargs = False
-
- data = super().build_request_data(messages, llm_config, tools, force_tool_call)
-
- def add_functions_to_system_message(system_message: ChatMessage):
- system_message.content += f" {''.join(json.dumps(f) for f in tools)} "
- system_message.content += 'Select best function to call simply respond with a single json block with the fields "name" and "arguments". Use double quotes around the arguments.'
-
- openai_message_list = [
- cast_message_to_subtype(m) for m in PydanticMessage.to_openai_dicts_from_list(messages, put_inner_thoughts_in_kwargs=False)
- ]
-
- if llm_config.model == "deepseek-reasoner": # R1 currently doesn't support function calling natively
- add_functions_to_system_message(
- openai_message_list[0]
- ) # Inject additional instructions to the system prompt with the available functions
-
- openai_message_list = map_messages_to_deepseek_format(openai_message_list)
-
- data["messages"] = [m.dict() for m in openai_message_list]
-
- return data
-
- @trace_method
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying synchronous request to OpenAI API and returns raw response dict.
- """
- api_key = model_settings.deepseek_api_key or os.environ.get("DEEPSEEK_API_KEY")
- client = OpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying asynchronous request to OpenAI API and returns raw response dict.
- """
- api_key = model_settings.deepseek_api_key or os.environ.get("DEEPSEEK_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = await client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[ChatCompletionChunk]:
- """
- Performs underlying asynchronous streaming request to OpenAI and returns the async stream iterator.
- """
- api_key = model_settings.deepseek_api_key or os.environ.get("DEEPSEEK_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
- response_stream: AsyncStream[ChatCompletionChunk] = await client.chat.completions.create(
- **request_data, stream=True, stream_options={"include_usage": True}
- )
- return response_stream
-
- @trace_method
- def convert_response_to_chat_completion(
- self,
- response_data: dict,
- input_messages: List[PydanticMessage], # Included for consistency, maybe used later
- llm_config: LLMConfig,
- ) -> ChatCompletionResponse:
- """
- Converts raw OpenAI response dict into the ChatCompletionResponse Pydantic model.
- Handles potential extraction of inner thoughts if they were added via kwargs.
- """
- response = ChatCompletionResponse(**response_data)
- if response.choices[0].message.tool_calls:
- return super().convert_response_to_chat_completion(response_data, input_messages, llm_config)
- return convert_deepseek_response_to_chatcompletion(response)
diff --git a/letta/llm_api/google_ai_client.py b/letta/llm_api/google_ai_client.py
deleted file mode 100644
index e7987aa2..00000000
--- a/letta/llm_api/google_ai_client.py
+++ /dev/null
@@ -1,218 +0,0 @@
-from typing import List, Optional, Tuple
-
-import httpx
-from google import genai
-from google.genai.types import HttpOptions
-
-from letta.errors import ErrorCode, LLMAuthenticationError, LLMError
-from letta.llm_api.google_constants import GOOGLE_MODEL_FOR_API_KEY_CHECK
-from letta.llm_api.google_vertex_client import GoogleVertexClient
-from letta.log import get_logger
-from letta.settings import model_settings, settings
-
-logger = get_logger(__name__)
-
-
-class GoogleAIClient(GoogleVertexClient):
- def _get_client(self):
- timeout_ms = int(settings.llm_request_timeout_seconds * 1000)
- return genai.Client(
- api_key=model_settings.gemini_api_key,
- http_options=HttpOptions(timeout=timeout_ms),
- )
-
-
-def get_gemini_endpoint_and_headers(
- base_url: str, model: Optional[str], api_key: str, key_in_header: bool = True, generate_content: bool = False
-) -> Tuple[str, dict]:
- """
- Dynamically generate the model endpoint and headers.
- """
- url = f"{base_url}/v1beta/models"
-
- # Add the model
- if model is not None:
- url += f"/{model}"
-
- # Add extension for generating content if we're hitting the LM
- if generate_content:
- url += ":generateContent"
-
- # Decide if api key should be in header or not
- # Two ways to pass the key: https://ai.google.dev/tutorials/setup
- if key_in_header:
- headers = {"Content-Type": "application/json", "x-goog-api-key": api_key}
- else:
- url += f"?key={api_key}"
- headers = {"Content-Type": "application/json"}
-
- return url, headers
-
-
-def google_ai_check_valid_api_key(api_key: str):
- client = genai.Client(api_key=api_key)
- # use the count token endpoint for a cheap model - as of 5/7/2025 this is slightly faster than fetching the list of models
- try:
- client.models.count_tokens(
- model=GOOGLE_MODEL_FOR_API_KEY_CHECK,
- contents="",
- )
- except genai.errors.ClientError as e:
- # google api returns 400 invalid argument for invalid api key
- if e.code == 400:
- raise LLMAuthenticationError(message=f"Failed to authenticate with Google AI: {e}", code=ErrorCode.UNAUTHENTICATED)
- raise e
- except Exception as e:
- raise LLMError(message=f"{e}", code=ErrorCode.INTERNAL_SERVER_ERROR)
-
-
-async def google_ai_get_model_list_async(
- base_url: str, api_key: str, key_in_header: bool = True, client: Optional[httpx.AsyncClient] = None
-) -> List[dict]:
- """Asynchronous version to get model list from Google AI API using httpx."""
- from letta.utils import printd
-
- url, headers = get_gemini_endpoint_and_headers(base_url, None, api_key, key_in_header)
-
- # Determine if we need to close the client at the end
- close_client = False
- if client is None:
- client = httpx.AsyncClient()
- close_client = True
-
- try:
- response = await client.get(url, headers=headers)
- response.raise_for_status() # Raises HTTPStatusError for 4XX/5XX status
- response_data = response.json() # convert to dict from string
-
- # Grab the models out
- model_list = response_data["models"]
- return model_list
-
- except httpx.HTTPStatusError as http_err:
- # Handle HTTP errors (e.g., response 4XX, 5XX)
- printd(f"Got HTTPError, exception={http_err}")
- # Print the HTTP status code
- print(f"HTTP Error: {http_err.response.status_code}")
- # Print the response content (error message from server)
- print(f"Message: {http_err.response.text}")
- raise http_err
-
- except httpx.RequestError as req_err:
- # Handle other httpx-related errors (e.g., connection error)
- printd(f"Got RequestException, exception={req_err}")
- raise req_err
-
- except Exception as e:
- # Handle other potential errors
- printd(f"Got unknown Exception, exception={e}")
- raise e
-
- finally:
- # Close the client if we created it
- if close_client:
- await client.aclose()
-
-
-def google_ai_get_model_details(base_url: str, api_key: str, model: str, key_in_header: bool = True) -> dict:
- """Synchronous version to get model details from Google AI API using httpx."""
- import httpx
-
- from letta.utils import printd
-
- url, headers = get_gemini_endpoint_and_headers(base_url, model, api_key, key_in_header)
-
- try:
- with httpx.Client() as client:
- response = client.get(url, headers=headers)
- printd(f"response = {response}")
- response.raise_for_status() # Raises HTTPStatusError for 4XX/5XX status
- response_data = response.json() # convert to dict from string
- printd(f"response.json = {response_data}")
-
- # Return the model details
- return response_data
-
- except httpx.HTTPStatusError as http_err:
- # Handle HTTP errors (e.g., response 4XX, 5XX)
- printd(f"Got HTTPError, exception={http_err}")
- # Print the HTTP status code
- print(f"HTTP Error: {http_err.response.status_code}")
- # Print the response content (error message from server)
- print(f"Message: {http_err.response.text}")
- raise http_err
-
- except httpx.RequestError as req_err:
- # Handle other httpx-related errors (e.g., connection error)
- printd(f"Got RequestException, exception={req_err}")
- raise req_err
-
- except Exception as e:
- # Handle other potential errors
- printd(f"Got unknown Exception, exception={e}")
- raise e
-
-
-async def google_ai_get_model_details_async(
- base_url: str, api_key: str, model: str, key_in_header: bool = True, client: Optional[httpx.AsyncClient] = None
-) -> dict:
- """Asynchronous version to get model details from Google AI API using httpx."""
- import httpx
-
- from letta.utils import printd
-
- url, headers = get_gemini_endpoint_and_headers(base_url, model, api_key, key_in_header)
-
- # Determine if we need to close the client at the end
- close_client = False
- if client is None:
- client = httpx.AsyncClient()
- close_client = True
-
- try:
- response = await client.get(url, headers=headers)
- printd(f"response = {response}")
- response.raise_for_status() # Raises HTTPStatusError for 4XX/5XX status
- response_data = response.json() # convert to dict from string
- printd(f"response.json = {response_data}")
-
- # Return the model details
- return response_data
-
- except httpx.HTTPStatusError as http_err:
- # Handle HTTP errors (e.g., response 4XX, 5XX)
- printd(f"Got HTTPError, exception={http_err}")
- # Print the HTTP status code
- print(f"HTTP Error: {http_err.response.status_code}")
- # Print the response content (error message from server)
- print(f"Message: {http_err.response.text}")
- raise http_err
-
- except httpx.RequestError as req_err:
- # Handle other httpx-related errors (e.g., connection error)
- printd(f"Got RequestException, exception={req_err}")
- raise req_err
-
- except Exception as e:
- # Handle other potential errors
- printd(f"Got unknown Exception, exception={e}")
- raise e
-
- finally:
- # Close the client if we created it
- if close_client:
- await client.aclose()
-
-
-def google_ai_get_model_context_window(base_url: str, api_key: str, model: str, key_in_header: bool = True) -> int:
- model_details = google_ai_get_model_details(base_url=base_url, api_key=api_key, model=model, key_in_header=key_in_header)
- # TODO should this be:
- # return model_details["inputTokenLimit"] + model_details["outputTokenLimit"]
- return int(model_details["inputTokenLimit"])
-
-
-async def google_ai_get_model_context_window_async(base_url: str, api_key: str, model: str, key_in_header: bool = True) -> int:
- model_details = await google_ai_get_model_details_async(base_url=base_url, api_key=api_key, model=model, key_in_header=key_in_header)
- # TODO should this be:
- # return model_details["inputTokenLimit"] + model_details["outputTokenLimit"]
- return int(model_details["inputTokenLimit"])
diff --git a/letta/llm_api/google_constants.py b/letta/llm_api/google_constants.py
deleted file mode 100644
index 3a50fb1b..00000000
--- a/letta/llm_api/google_constants.py
+++ /dev/null
@@ -1,21 +0,0 @@
-GOOGLE_MODEL_TO_CONTEXT_LENGTH = {
- "gemini-2.5-pro": 1048576,
- "gemini-2.5-flash": 1048576,
- "gemini-live-2.5-flash": 1048576,
- "gemini-2.0-flash-001": 1048576,
- "gemini-2.0-flash-lite-001": 1048576,
- # The following are either deprecated or discontinued.
- "gemini-2.5-pro-exp-03-25": 1048576,
- "gemini-2.5-flash-preview-04-17": 1048576,
- "gemini-2.0-pro-exp-02-05": 2097152,
- "gemini-2.0-flash-lite-preview-02-05": 1048576,
- "gemini-2.0-flash-thinking-exp-01-21": 1048576,
- "gemini-1.5-flash": 1048576,
- "gemini-1.5-pro": 2097152,
- "gemini-1.0-pro": 32760,
- "gemini-1.0-pro-vision": 16384,
-}
-
-GOOGLE_EMBEDING_MODEL_TO_DIM = {"text-embedding-005": 768, "text-multilingual-embedding-002": 768}
-
-GOOGLE_MODEL_FOR_API_KEY_CHECK = "gemini-2.0-flash-lite"
diff --git a/letta/llm_api/google_vertex_client.py b/letta/llm_api/google_vertex_client.py
deleted file mode 100644
index 62d93ee8..00000000
--- a/letta/llm_api/google_vertex_client.py
+++ /dev/null
@@ -1,580 +0,0 @@
-import json
-import uuid
-from typing import List, Optional
-
-from google import genai
-from google.genai import errors
-from google.genai.types import (
- FunctionCallingConfig,
- FunctionCallingConfigMode,
- GenerateContentResponse,
- HttpOptions,
- ThinkingConfig,
- ToolConfig,
-)
-
-from letta.constants import NON_USER_MSG_PREFIX
-from letta.helpers.datetime_helpers import get_utc_time_int
-from letta.helpers.json_helpers import json_dumps, json_loads
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.local_llm.json_parser import clean_json_string_extra_backslash
-from letta.local_llm.utils import count_tokens
-from letta.log import get_logger
-from letta.otel.tracing import trace_method
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage
-from letta.schemas.openai.chat_completion_request import Tool
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Choice, FunctionCall, Message, ToolCall, UsageStatistics
-from letta.settings import model_settings, settings
-from letta.utils import get_tool_call_id
-
-logger = get_logger(__name__)
-
-
-class GoogleVertexClient(LLMClientBase):
- MAX_RETRIES = model_settings.gemini_max_retries
-
- def _get_client(self):
- timeout_ms = int(settings.llm_request_timeout_seconds * 1000)
- return genai.Client(
- vertexai=True,
- project=model_settings.google_cloud_project,
- location=model_settings.google_cloud_location,
- http_options=HttpOptions(api_version="v1", timeout=timeout_ms),
- )
-
- @trace_method
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying request to llm and returns raw response.
- """
- client = self._get_client()
- response = client.models.generate_content(
- model=llm_config.model,
- contents=request_data["contents"],
- config=request_data["config"],
- )
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying request to llm and returns raw response.
- """
- client = self._get_client()
-
- # Gemini 2.5 models will often return MALFORMED_FUNCTION_CALL, force a retry
- # https://github.com/googleapis/python-aiplatform/issues/4472
- retry_count = 1
- should_retry = True
- while should_retry and retry_count <= self.MAX_RETRIES:
- try:
- response = await client.aio.models.generate_content(
- model=llm_config.model,
- contents=request_data["contents"],
- config=request_data["config"],
- )
- except errors.APIError as e:
- # Retry on 503 and 500 errors as well, usually ephemeral from Gemini
- if e.code == 503 or e.code == 500:
- logger.warning(f"Received {e}, retrying {retry_count}/{self.MAX_RETRIES}")
- retry_count += 1
- continue
- raise e
- except Exception as e:
- raise e
- response_data = response.model_dump()
- is_malformed_function_call = self.is_malformed_function_call(response_data)
- if is_malformed_function_call:
- logger.warning(
- f"Received FinishReason.MALFORMED_FUNCTION_CALL in response for {llm_config.model}, retrying {retry_count}/{self.MAX_RETRIES}"
- )
- # Modify the last message if it's a heartbeat to include warning about special characters
- if request_data["contents"] and len(request_data["contents"]) > 0:
- last_message = request_data["contents"][-1]
- if last_message.get("role") == "user" and last_message.get("parts"):
- for part in last_message["parts"]:
- if "text" in part:
- try:
- # Try to parse as JSON to check if it's a heartbeat
- message_json = json_loads(part["text"])
- if message_json.get("type") == "heartbeat" and "reason" in message_json:
- # Append warning to the reason
- warning = f" RETRY {retry_count}/{self.MAX_RETRIES} ***DO NOT USE SPECIAL CHARACTERS OR QUOTATIONS INSIDE FUNCTION CALL ARGUMENTS. IF YOU MUST, MAKE SURE TO ESCAPE THEM PROPERLY***"
- message_json["reason"] = message_json["reason"] + warning
- # Update the text with modified JSON
- part["text"] = json_dumps(message_json)
- logger.warning(
- f"Modified heartbeat message with special character warning for retry {retry_count}/{self.MAX_RETRIES}"
- )
- except (json.JSONDecodeError, TypeError):
- # Not a JSON message or not a heartbeat, skip modification
- pass
-
- should_retry = is_malformed_function_call
- retry_count += 1
-
- return response_data
-
- @staticmethod
- def add_dummy_model_messages(messages: List[dict]) -> List[dict]:
- """Google AI API requires all function call returns are immediately followed by a 'model' role message.
-
- In Letta, the 'model' will often call a function (e.g. send_message) that itself yields to the user,
- so there is no natural follow-up 'model' role message.
-
- To satisfy the Google AI API restrictions, we can add a dummy 'yield' message
- with role == 'model' that is placed in-betweeen and function output
- (role == 'tool') and user message (role == 'user').
- """
- dummy_yield_message = {
- "role": "model",
- "parts": [{"text": f"{NON_USER_MSG_PREFIX}Function call returned, waiting for user response."}],
- }
- messages_with_padding = []
- for i, message in enumerate(messages):
- messages_with_padding.append(message)
- # Check if the current message role is 'tool' and the next message role is 'user'
- if message["role"] in ["tool", "function"] and (i + 1 < len(messages) and messages[i + 1]["role"] == "user"):
- messages_with_padding.append(dummy_yield_message)
-
- return messages_with_padding
-
- def _clean_google_ai_schema_properties(self, schema_part: dict):
- """Recursively clean schema parts to remove unsupported Google AI keywords."""
- if not isinstance(schema_part, dict):
- return
-
- # Per https://ai.google.dev/gemini-api/docs/function-calling?example=meeting#notes_and_limitations
- # * Only a subset of the OpenAPI schema is supported.
- # * Supported parameter types in Python are limited.
- unsupported_keys = ["default", "exclusiveMaximum", "exclusiveMinimum", "additionalProperties", "$schema"]
- keys_to_remove_at_this_level = [key for key in unsupported_keys if key in schema_part]
- for key_to_remove in keys_to_remove_at_this_level:
- logger.debug(f"Removing unsupported keyword '{key_to_remove}' from schema part.")
- del schema_part[key_to_remove]
-
- if schema_part.get("type") == "string" and "format" in schema_part:
- allowed_formats = ["enum", "date-time"]
- if schema_part["format"] not in allowed_formats:
- logger.warning(f"Removing unsupported format '{schema_part['format']}' for string type. Allowed: {allowed_formats}")
- del schema_part["format"]
-
- # Check properties within the current level
- if "properties" in schema_part and isinstance(schema_part["properties"], dict):
- for prop_name, prop_schema in schema_part["properties"].items():
- self._clean_google_ai_schema_properties(prop_schema)
-
- # Check items within arrays
- if "items" in schema_part and isinstance(schema_part["items"], dict):
- self._clean_google_ai_schema_properties(schema_part["items"])
-
- # Check within anyOf, allOf, oneOf lists
- for key in ["anyOf", "allOf", "oneOf"]:
- if key in schema_part and isinstance(schema_part[key], list):
- for item_schema in schema_part[key]:
- self._clean_google_ai_schema_properties(item_schema)
-
- def convert_tools_to_google_ai_format(self, tools: List[Tool], llm_config: LLMConfig) -> List[dict]:
- """
- OpenAI style:
- "tools": [{
- "type": "function",
- "function": {
- "name": "find_movies",
- "description": "find ....",
- "parameters": {
- "type": "object",
- "properties": {
- PARAM: {
- "type": PARAM_TYPE, # eg "string"
- "description": PARAM_DESCRIPTION,
- },
- ...
- },
- "required": List[str],
- }
- }
- }
- ]
-
- Google AI style:
- "tools": [{
- "functionDeclarations": [{
- "name": "find_movies",
- "description": "find movie titles currently playing in theaters based on any description, genre, title words, etc.",
- "parameters": {
- "type": "OBJECT",
- "properties": {
- "location": {
- "type": "STRING",
- "description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616"
- },
- "description": {
- "type": "STRING",
- "description": "Any kind of description including category or genre, title words, attributes, etc."
- }
- },
- "required": ["description"]
- }
- }, {
- "name": "find_theaters",
- ...
- """
- function_list = [
- dict(
- name=t.function.name,
- description=t.function.description,
- parameters=t.function.parameters, # TODO need to unpack
- )
- for t in tools
- ]
-
- # Add inner thoughts if needed
- for func in function_list:
- # Note: Google AI API used to have weird casing requirements, but not any more
-
- # Google AI API only supports a subset of OpenAPI 3.0, so unsupported params must be cleaned
- if "parameters" in func and isinstance(func["parameters"], dict):
- self._clean_google_ai_schema_properties(func["parameters"])
-
- # Add inner thoughts
- if llm_config.put_inner_thoughts_in_kwargs:
- from letta.local_llm.constants import INNER_THOUGHTS_KWARG_DESCRIPTION, INNER_THOUGHTS_KWARG_VERTEX
-
- func["parameters"]["properties"][INNER_THOUGHTS_KWARG_VERTEX] = {
- "type": "string",
- "description": INNER_THOUGHTS_KWARG_DESCRIPTION,
- }
- func["parameters"]["required"].append(INNER_THOUGHTS_KWARG_VERTEX)
-
- return [{"functionDeclarations": function_list}]
-
- @trace_method
- def build_request_data(
- self,
- messages: List[PydanticMessage],
- llm_config: LLMConfig,
- tools: List[dict],
- force_tool_call: Optional[str] = None,
- ) -> dict:
- """
- Constructs a request object in the expected data format for this client.
- """
-
- if tools:
- tool_objs = [Tool(type="function", function=t) for t in tools]
- tool_names = [t.function.name for t in tool_objs]
- # Convert to the exact payload style Google expects
- formatted_tools = self.convert_tools_to_google_ai_format(tool_objs, llm_config)
- else:
- formatted_tools = []
- tool_names = []
-
- contents = self.add_dummy_model_messages(
- [m.to_google_ai_dict() for m in messages],
- )
-
- request_data = {
- "contents": contents,
- "config": {
- "temperature": llm_config.temperature,
- "tools": formatted_tools,
- },
- }
- # Make tokens is optional
- if llm_config.max_tokens:
- request_data["config"]["max_output_tokens"] = llm_config.max_tokens
-
- if len(tool_names) == 1 and settings.use_vertex_structured_outputs_experimental:
- request_data["config"]["response_mime_type"] = "application/json"
- request_data["config"]["response_schema"] = self.get_function_call_response_schema(tools[0])
- del request_data["config"]["tools"]
- elif tools:
- tool_config = ToolConfig(
- function_calling_config=FunctionCallingConfig(
- # ANY mode forces the model to predict only function calls
- mode=FunctionCallingConfigMode.ANY,
- # Provide the list of tools (though empty should also work, it seems not to)
- allowed_function_names=tool_names,
- )
- )
- request_data["config"]["tool_config"] = tool_config.model_dump()
-
- # Add thinking_config for flash
- # If enable_reasoner is False, set thinking_budget to 0
- # Otherwise, use the value from max_reasoning_tokens
- if "flash" in llm_config.model:
- # Gemini flash models may fail to call tools even with FunctionCallingConfigMode.ANY if thinking is fully disabled, set to minimum to prevent tool call failure
- thinking_budget = llm_config.max_reasoning_tokens if llm_config.enable_reasoner else self.get_thinking_budget(llm_config.model)
- if thinking_budget <= 0:
- logger.error(
- f"Thinking budget of {thinking_budget} for Gemini reasoning model {llm_config.model}, this will likely cause tool call failures"
- )
- thinking_config = ThinkingConfig(
- thinking_budget=(thinking_budget),
- )
- request_data["config"]["thinking_config"] = thinking_config.model_dump()
-
- return request_data
-
- @trace_method
- def convert_response_to_chat_completion(
- self,
- response_data: dict,
- input_messages: List[PydanticMessage],
- llm_config: LLMConfig,
- ) -> ChatCompletionResponse:
- """
- Converts custom response format from llm client into an OpenAI
- ChatCompletionsResponse object.
-
- Example:
- {
- "candidates": [
- {
- "content": {
- "parts": [
- {
- "text": " OK. Barbie is showing in two theaters in Mountain View, CA: AMC Mountain View 16 and Regal Edwards 14."
- }
- ]
- }
- }
- ],
- "usageMetadata": {
- "promptTokenCount": 9,
- "candidatesTokenCount": 27,
- "totalTokenCount": 36
- }
- }
- """
- response = GenerateContentResponse(**response_data)
- try:
- choices = []
- index = 0
- for candidate in response.candidates:
- content = candidate.content
-
- if content is None or content.role is None or content.parts is None:
- # This means the response is malformed like MALFORMED_FUNCTION_CALL
- # NOTE: must be a ValueError to trigger a retry
- if candidate.finish_reason == "MALFORMED_FUNCTION_CALL":
- raise ValueError(f"Error in response data from LLM: {candidate.finish_reason}")
- else:
- raise ValueError(f"Error in response data from LLM: {candidate.model_dump()}")
-
- role = content.role
- assert role == "model", f"Unknown role in response: {role}"
-
- parts = content.parts
-
- # NOTE: we aren't properly supported multi-parts here anyways (we're just appending choices),
- # so let's disable it for now
-
- # NOTE(Apr 9, 2025): there's a very strange bug on 2.5 where the response has a part with broken text
- # {'candidates': [{'content': {'parts': [{'functionCall': {'name': 'send_message', 'args': {'request_heartbeat': False, 'message': 'Hello! How can I make your day better?', 'inner_thoughts': 'User has initiated contact. Sending a greeting.'}}}], 'role': 'model'}, 'finishReason': 'STOP', 'avgLogprobs': -0.25891534213362066}], 'usageMetadata': {'promptTokenCount': 2493, 'candidatesTokenCount': 29, 'totalTokenCount': 2522, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 2493}], 'candidatesTokensDetails': [{'modality': 'TEXT', 'tokenCount': 29}]}, 'modelVersion': 'gemini-1.5-pro-002'}
- # To patch this, if we have multiple parts we can take the last one
- if len(parts) > 1:
- logger.warning(f"Unexpected multiple parts in response from Google AI: {parts}")
- parts = [parts[-1]]
-
- # TODO support parts / multimodal
- # TODO support parallel tool calling natively
- # TODO Alternative here is to throw away everything else except for the first part
- for response_message in parts:
- # Convert the actual message style to OpenAI style
- if response_message.function_call:
- function_call = response_message.function_call
- function_name = function_call.name
- function_args = function_call.args
- assert isinstance(function_args, dict), function_args
-
- # NOTE: this also involves stripping the inner monologue out of the function
- if llm_config.put_inner_thoughts_in_kwargs:
- from letta.local_llm.constants import INNER_THOUGHTS_KWARG_VERTEX
-
- assert INNER_THOUGHTS_KWARG_VERTEX in function_args, (
- f"Couldn't find inner thoughts in function args:\n{function_call}"
- )
- inner_thoughts = function_args.pop(INNER_THOUGHTS_KWARG_VERTEX)
- assert inner_thoughts is not None, f"Expected non-null inner thoughts function arg:\n{function_call}"
- else:
- inner_thoughts = None
-
- # Google AI API doesn't generate tool call IDs
- openai_response_message = Message(
- role="assistant", # NOTE: "model" -> "assistant"
- content=inner_thoughts,
- tool_calls=[
- ToolCall(
- id=get_tool_call_id(),
- type="function",
- function=FunctionCall(
- name=function_name,
- arguments=clean_json_string_extra_backslash(json_dumps(function_args)),
- ),
- )
- ],
- )
-
- else:
- try:
- # Structured output tool call
- function_call = json_loads(response_message.text)
- function_name = function_call["name"]
- function_args = function_call["args"]
- assert isinstance(function_args, dict), function_args
-
- # NOTE: this also involves stripping the inner monologue out of the function
- if llm_config.put_inner_thoughts_in_kwargs:
- from letta.local_llm.constants import INNER_THOUGHTS_KWARG_VERTEX
-
- assert INNER_THOUGHTS_KWARG_VERTEX in function_args, (
- f"Couldn't find inner thoughts in function args:\n{function_call}"
- )
- inner_thoughts = function_args.pop(INNER_THOUGHTS_KWARG_VERTEX)
- assert inner_thoughts is not None, f"Expected non-null inner thoughts function arg:\n{function_call}"
- else:
- inner_thoughts = None
-
- # Google AI API doesn't generate tool call IDs
- openai_response_message = Message(
- role="assistant", # NOTE: "model" -> "assistant"
- content=inner_thoughts,
- tool_calls=[
- ToolCall(
- id=get_tool_call_id(),
- type="function",
- function=FunctionCall(
- name=function_name,
- arguments=clean_json_string_extra_backslash(json_dumps(function_args)),
- ),
- )
- ],
- )
-
- except json.decoder.JSONDecodeError:
- if candidate.finish_reason == "MAX_TOKENS":
- raise ValueError("Could not parse response data from LLM: exceeded max token limit")
- # Inner thoughts are the content by default
- inner_thoughts = response_message.text
-
- # Google AI API doesn't generate tool call IDs
- openai_response_message = Message(
- role="assistant", # NOTE: "model" -> "assistant"
- content=inner_thoughts,
- )
-
- # Google AI API uses different finish reason strings than OpenAI
- # OpenAI: 'stop', 'length', 'function_call', 'content_filter', null
- # see: https://platform.openai.com/docs/guides/text-generation/chat-completions-api
- # Google AI API: FINISH_REASON_UNSPECIFIED, STOP, MAX_TOKENS, SAFETY, RECITATION, OTHER
- # see: https://ai.google.dev/api/python/google/ai/generativelanguage/Candidate/FinishReason
- finish_reason = candidate.finish_reason.value
- if finish_reason == "STOP":
- openai_finish_reason = (
- "function_call"
- if openai_response_message.tool_calls is not None and len(openai_response_message.tool_calls) > 0
- else "stop"
- )
- elif finish_reason == "MAX_TOKENS":
- openai_finish_reason = "length"
- elif finish_reason == "SAFETY":
- openai_finish_reason = "content_filter"
- elif finish_reason == "RECITATION":
- openai_finish_reason = "content_filter"
- else:
- raise ValueError(f"Unrecognized finish reason in Google AI response: {finish_reason}")
-
- choices.append(
- Choice(
- finish_reason=openai_finish_reason,
- index=index,
- message=openai_response_message,
- )
- )
- index += 1
-
- # if len(choices) > 1:
- # raise UserWarning(f"Unexpected number of candidates in response (expected 1, got {len(choices)})")
-
- # NOTE: some of the Google AI APIs show UsageMetadata in the response, but it seems to not exist?
- # "usageMetadata": {
- # "promptTokenCount": 9,
- # "candidatesTokenCount": 27,
- # "totalTokenCount": 36
- # }
- if response.usage_metadata:
- usage = UsageStatistics(
- prompt_tokens=response.usage_metadata.prompt_token_count,
- completion_tokens=response.usage_metadata.candidates_token_count,
- total_tokens=response.usage_metadata.total_token_count,
- )
- else:
- # Count it ourselves
- assert input_messages is not None, "Didn't get UsageMetadata from the API response, so input_messages is required"
- prompt_tokens = count_tokens(json_dumps(input_messages)) # NOTE: this is a very rough approximation
- completion_tokens = count_tokens(json_dumps(openai_response_message.model_dump())) # NOTE: this is also approximate
- total_tokens = prompt_tokens + completion_tokens
- usage = UsageStatistics(
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- total_tokens=total_tokens,
- )
-
- response_id = str(uuid.uuid4())
- return ChatCompletionResponse(
- id=response_id,
- choices=choices,
- model=llm_config.model, # NOTE: Google API doesn't pass back model in the response
- created=get_utc_time_int(),
- usage=usage,
- )
- except KeyError as e:
- raise e
-
- def get_function_call_response_schema(self, tool: dict) -> dict:
- return {
- "type": "OBJECT",
- "properties": {
- "name": {"type": "STRING", "enum": [tool["name"]]},
- "args": {
- "type": "OBJECT",
- "properties": tool["parameters"]["properties"],
- "required": tool["parameters"]["required"],
- },
- },
- "propertyOrdering": ["name", "args"],
- "required": ["name", "args"],
- }
-
- # https://ai.google.dev/gemini-api/docs/thinking#set-budget
- # | Model | Default setting | Range | Disable thinking | Turn on dynamic thinking|
- # |-----------------|-------------------------------------------------------------------|--------------|----------------------------|-------------------------|
- # | 2.5 Pro | Dynamic thinking: Model decides when and how much to think | 128-32768 | N/A: Cannot disable | thinkingBudget = -1 |
- # | 2.5 Flash | Dynamic thinking: Model decides when and how much to think | 0-24576 | thinkingBudget = 0 | thinkingBudget = -1 |
- # | 2.5 Flash Lite | Model does not think | 512-24576 | thinkingBudget = 0 | thinkingBudget = -1 |
- def get_thinking_budget(self, model: str) -> bool:
- if model_settings.gemini_force_minimum_thinking_budget:
- if all(substring in model for substring in ["2.5", "flash", "lite"]):
- return 512
- elif all(substring in model for substring in ["2.5", "flash"]):
- return 1
- return 0
-
- def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
- return llm_config.model.startswith("gemini-2.5-flash") or llm_config.model.startswith("gemini-2.5-pro")
-
- def is_malformed_function_call(self, response_data: dict) -> dict:
- response = GenerateContentResponse(**response_data)
- for candidate in response.candidates:
- content = candidate.content
- if content is None or content.role is None or content.parts is None:
- return candidate.finish_reason == "MALFORMED_FUNCTION_CALL"
- return False
-
- @trace_method
- def handle_llm_error(self, e: Exception) -> Exception:
- # Fallback to base implementation
- return super().handle_llm_error(e)
diff --git a/letta/llm_api/groq_client.py b/letta/llm_api/groq_client.py
deleted file mode 100644
index 25d7aaaf..00000000
--- a/letta/llm_api/groq_client.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-from typing import List, Optional
-
-from openai import AsyncOpenAI, AsyncStream, OpenAI
-from openai.types.chat.chat_completion import ChatCompletion
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-
-from letta.llm_api.openai_client import OpenAIClient
-from letta.otel.tracing import trace_method
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage
-from letta.settings import model_settings
-
-
-class GroqClient(OpenAIClient):
- def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
- return False
-
- def supports_structured_output(self, llm_config: LLMConfig) -> bool:
- return True
-
- @trace_method
- def build_request_data(
- self,
- messages: List[PydanticMessage],
- llm_config: LLMConfig,
- tools: Optional[List[dict]] = None,
- force_tool_call: Optional[str] = None,
- ) -> dict:
- data = super().build_request_data(messages, llm_config, tools, force_tool_call)
-
- # Groq validation - these fields are not supported and will cause 400 errors
- # https://console.groq.com/docs/openai
- if "top_logprobs" in data:
- del data["top_logprobs"]
- if "logit_bias" in data:
- del data["logit_bias"]
- data["logprobs"] = False
- data["n"] = 1
-
- return data
-
- @trace_method
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying synchronous request to Groq API and returns raw response dict.
- """
- api_key = model_settings.groq_api_key or os.environ.get("GROQ_API_KEY")
- client = OpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying asynchronous request to Groq API and returns raw response dict.
- """
- api_key = model_settings.groq_api_key or os.environ.get("GROQ_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = await client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_embeddings(self, inputs: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
- """Request embeddings given texts and embedding config"""
- api_key = model_settings.groq_api_key or os.environ.get("GROQ_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=embedding_config.embedding_endpoint)
- response = await client.embeddings.create(model=embedding_config.embedding_model, input=inputs)
-
- # TODO: add total usage
- return [r.embedding for r in response.data]
-
- @trace_method
- async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[ChatCompletionChunk]:
- raise NotImplementedError("Streaming not supported for Groq.")
diff --git a/letta/llm_api/helpers.py b/letta/llm_api/helpers.py
deleted file mode 100644
index c87ec188..00000000
--- a/letta/llm_api/helpers.py
+++ /dev/null
@@ -1,398 +0,0 @@
-import copy
-import json
-import warnings
-from collections import OrderedDict
-from typing import Any, List, Union
-
-import requests
-
-from letta.constants import OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING
-from letta.helpers.json_helpers import json_dumps
-from letta.schemas.message import Message
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Choice
-from letta.settings import summarizer_settings
-from letta.utils import count_tokens, printd
-
-
-def _convert_to_structured_output_helper(property: dict) -> dict:
- """Convert a single JSON schema property to structured output format (recursive)"""
-
- if "type" not in property:
- raise ValueError(f"Property {property} is missing a type")
- param_type = property["type"]
-
- if "description" not in property:
- # raise ValueError(f"Property {property} is missing a description")
- param_description = None
- else:
- param_description = property["description"]
-
- if param_type == "object":
- if "properties" not in property:
- raise ValueError(f"Property {property} of type object is missing properties")
- properties = property["properties"]
- property_dict = {
- "type": "object",
- "properties": {k: _convert_to_structured_output_helper(v) for k, v in properties.items()},
- "additionalProperties": False,
- "required": list(properties.keys()),
- }
- if param_description is not None:
- property_dict["description"] = param_description
- return property_dict
-
- elif param_type == "array":
- if "items" not in property:
- raise ValueError(f"Property {property} of type array is missing items")
- items = property["items"]
- property_dict = {
- "type": "array",
- "items": _convert_to_structured_output_helper(items),
- }
- if param_description is not None:
- property_dict["description"] = param_description
- return property_dict
-
- else:
- property_dict = {
- "type": param_type, # simple type
- }
- if param_description is not None:
- property_dict["description"] = param_description
- return property_dict
-
-
-def convert_to_structured_output(openai_function: dict, allow_optional: bool = False) -> dict:
- """Convert function call objects to structured output objects.
-
- See: https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
- """
- description = openai_function.get("description", "")
-
- structured_output = {
- "name": openai_function["name"],
- "description": description,
- "strict": True,
- "parameters": {
- "type": "object",
- "properties": {},
- "additionalProperties": False,
- "required": [],
- },
- }
-
- for param, details in openai_function["parameters"]["properties"].items():
- param_type = details["type"]
- param_description = details.get("description", "")
-
- if param_type == "object":
- if "properties" not in details:
- raise ValueError(f"Property {param} of type object is missing 'properties'")
- structured_output["parameters"]["properties"][param] = {
- "type": "object",
- "description": param_description,
- "properties": {k: _convert_to_structured_output_helper(v) for k, v in details["properties"].items()},
- "additionalProperties": False,
- "required": list(details["properties"].keys()),
- }
-
- elif param_type == "array":
- items_schema = details.get("items")
- prefix_items_schema = details.get("prefixItems")
-
- if prefix_items_schema:
- # assume fixed-length tuple — safe fallback to use first type for items
- fallback_item = prefix_items_schema[0] if isinstance(prefix_items_schema, list) else prefix_items_schema
- structured_output["parameters"]["properties"][param] = {
- "type": "array",
- "description": param_description,
- "prefixItems": [_convert_to_structured_output_helper(item) for item in prefix_items_schema],
- "items": _convert_to_structured_output_helper(fallback_item),
- "minItems": details.get("minItems", len(prefix_items_schema)),
- "maxItems": details.get("maxItems", len(prefix_items_schema)),
- }
- elif items_schema:
- structured_output["parameters"]["properties"][param] = {
- "type": "array",
- "description": param_description,
- "items": _convert_to_structured_output_helper(items_schema),
- }
- else:
- raise ValueError(f"Array param '{param}' is missing both 'items' and 'prefixItems'")
-
- else:
- prop = {
- "type": param_type,
- "description": param_description,
- }
- if "enum" in details:
- prop["enum"] = details["enum"]
- structured_output["parameters"]["properties"][param] = prop
-
- if not allow_optional:
- structured_output["parameters"]["required"] = list(structured_output["parameters"]["properties"].keys())
- else:
- raise NotImplementedError("Optional parameter handling is not implemented.")
- return structured_output
-
-
-def make_post_request(url: str, headers: dict[str, str], data: dict[str, Any]) -> dict[str, Any]:
- printd(f"Sending request to {url}")
- try:
- # Make the POST request
- response = requests.post(url, headers=headers, json=data)
- printd(f"Response status code: {response.status_code}")
-
- # Raise for 4XX/5XX HTTP errors
- response.raise_for_status()
-
- # Check if the response content type indicates JSON and attempt to parse it
- content_type = response.headers.get("Content-Type", "")
- if "application/json" in content_type.lower():
- try:
- response_data = response.json() # Attempt to parse the response as JSON
- printd(f"Response JSON: {response_data}")
- except ValueError as json_err:
- # Handle the case where the content type says JSON but the body is invalid
- error_message = f"Failed to parse JSON despite Content-Type being {content_type}: {json_err}"
- printd(error_message)
- raise ValueError(error_message) from json_err
- else:
- error_message = f"Unexpected content type returned: {response.headers.get('Content-Type')}"
- printd(error_message)
- raise ValueError(error_message)
-
- # Process the response using the callback function
- return response_data
-
- except requests.exceptions.HTTPError as http_err:
- # HTTP errors (4XX, 5XX)
- error_message = f"HTTP error occurred: {http_err}"
- if http_err.response is not None:
- error_message += f" | Status code: {http_err.response.status_code}, Message: {http_err.response.text}"
- printd(error_message)
- raise requests.exceptions.HTTPError(error_message) from http_err
-
- except requests.exceptions.Timeout as timeout_err:
- # Handle timeout errors
- error_message = f"Request timed out: {timeout_err}"
- printd(error_message)
- raise requests.exceptions.Timeout(error_message) from timeout_err
-
- except requests.exceptions.RequestException as req_err:
- # Non-HTTP errors (e.g., connection, SSL errors)
- error_message = f"Request failed: {req_err}"
- printd(error_message)
- raise requests.exceptions.RequestException(error_message) from req_err
-
- except ValueError as val_err:
- # Handle content-type or non-JSON response issues
- error_message = f"ValueError: {val_err}"
- printd(error_message)
- raise ValueError(error_message) from val_err
-
- except Exception as e:
- # Catch any other unknown exceptions
- error_message = f"An unexpected error occurred: {e}"
- printd(error_message)
- raise Exception(error_message) from e
-
-
-# TODO update to use better types
-def add_inner_thoughts_to_functions(
- functions: List[dict],
- inner_thoughts_key: str,
- inner_thoughts_description: str,
- inner_thoughts_required: bool = True,
- put_inner_thoughts_first: bool = True,
-) -> List[dict]:
- """Add an inner_thoughts kwarg to every function in the provided list, ensuring it's the first parameter"""
- new_functions = []
- for function_object in functions:
- new_function_object = copy.deepcopy(function_object)
- new_properties = OrderedDict()
-
- # For chat completions, we want inner thoughts to come later
- if put_inner_thoughts_first:
- # Create with inner_thoughts as the first item
- new_properties[inner_thoughts_key] = {
- "type": "string",
- "description": inner_thoughts_description,
- }
- # Add the rest of the properties
- new_properties.update(function_object["parameters"]["properties"])
- else:
- new_properties.update(function_object["parameters"]["properties"])
- new_properties[inner_thoughts_key] = {
- "type": "string",
- "description": inner_thoughts_description,
- }
-
- # Cast OrderedDict back to a regular dict
- new_function_object["parameters"]["properties"] = dict(new_properties)
-
- # Update required parameters if necessary
- if inner_thoughts_required:
- required_params = new_function_object["parameters"].get("required", [])
- if inner_thoughts_key not in required_params:
- if put_inner_thoughts_first:
- required_params.insert(0, inner_thoughts_key)
- else:
- required_params.append(inner_thoughts_key)
- new_function_object["parameters"]["required"] = required_params
- new_functions.append(new_function_object)
-
- return new_functions
-
-
-def unpack_all_inner_thoughts_from_kwargs(
- response: ChatCompletionResponse,
- inner_thoughts_key: str,
-) -> ChatCompletionResponse:
- """Strip the inner thoughts out of the tool call and put it in the message content"""
- if len(response.choices) == 0:
- raise ValueError("Unpacking inner thoughts from empty response not supported")
-
- new_choices = []
- for choice in response.choices:
- new_choices.append(unpack_inner_thoughts_from_kwargs(choice, inner_thoughts_key))
-
- # return an updated copy
- new_response = response.model_copy(deep=True)
- new_response.choices = new_choices
- return new_response
-
-
-def unpack_inner_thoughts_from_kwargs(choice: Choice, inner_thoughts_key: str) -> Choice:
- message = choice.message
- rewritten_choice = choice # inner thoughts unpacked out of the function
-
- if message.role == "assistant" and message.tool_calls and len(message.tool_calls) >= 1:
- if len(message.tool_calls) > 1:
- warnings.warn(f"Unpacking inner thoughts from more than one tool call ({len(message.tool_calls)}) is not supported")
- # TODO support multiple tool calls
- tool_call = message.tool_calls[0]
-
- try:
- # Sadly we need to parse the JSON since args are in string format
- func_args = dict(json.loads(tool_call.function.arguments))
- if inner_thoughts_key in func_args:
- # extract the inner thoughts
- inner_thoughts = func_args.pop(inner_thoughts_key)
-
- # replace the kwargs
- new_choice = choice.model_copy(deep=True)
- new_choice.message.tool_calls[0].function.arguments = json_dumps(func_args)
- # also replace the message content
- if new_choice.message.content is not None:
- warnings.warn(f"Overwriting existing inner monologue ({new_choice.message.content}) with kwarg ({inner_thoughts})")
- new_choice.message.content = inner_thoughts
-
- # update the choice object
- rewritten_choice = new_choice
- else:
- warnings.warn(f"Did not find inner thoughts in tool call: {str(tool_call)}")
-
- except json.JSONDecodeError as e:
- warnings.warn(f"Failed to strip inner thoughts from kwargs: {e}")
- print(f"\nFailed to strip inner thoughts from kwargs: {e}")
- print(f"\nTool call arguments: {tool_call.function.arguments}")
- raise e
- else:
- warnings.warn(f"Did not find tool call in message: {str(message)}")
-
- return rewritten_choice
-
-
-def calculate_summarizer_cutoff(in_context_messages: List[Message], token_counts: List[int], logger: "logging.Logger") -> int:
- if len(in_context_messages) != len(token_counts):
- raise ValueError(
- f"Given in_context_messages has different length from given token_counts: {len(in_context_messages)} != {len(token_counts)}"
- )
-
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
-
- if summarizer_settings.evict_all_messages:
- logger.info("Evicting all messages...")
- return len(in_context_messages)
- else:
- # Start at index 1 (past the system message),
- # and collect messages for summarization until we reach the desired truncation token fraction (eg 50%)
- # We do the inverse of `desired_memory_token_pressure` to get what we need to remove
- desired_token_count_to_summarize = int(sum(token_counts) * (1 - summarizer_settings.desired_memory_token_pressure))
- logger.info(f"desired_token_count_to_summarize={desired_token_count_to_summarize}")
-
- tokens_so_far = 0
- cutoff = 0
- for i, msg in enumerate(in_context_messages_openai):
- # Skip system
- if i == 0:
- continue
- cutoff = i
- tokens_so_far += token_counts[i]
-
- if msg["role"] not in ["user", "tool", "function"] and tokens_so_far >= desired_token_count_to_summarize:
- # Break if the role is NOT a user or tool/function and tokens_so_far is enough
- break
- elif len(in_context_messages) - cutoff - 1 <= summarizer_settings.keep_last_n_messages:
- # Also break if we reached the `keep_last_n_messages` threshold
- # NOTE: This may be on a user, tool, or function in theory
- logger.warning(
- f"Breaking summary cutoff early on role={msg['role']} because we hit the `keep_last_n_messages`={summarizer_settings.keep_last_n_messages}"
- )
- break
-
- # includes the tool response to be summarized after a tool call so we don't have any hanging tool calls after trimming.
- if i + 1 < len(in_context_messages_openai) and in_context_messages_openai[i + 1]["role"] == "tool":
- cutoff += 1
-
- logger.info(f"Evicting {cutoff}/{len(in_context_messages)} messages...")
- return cutoff + 1
-
-
-def get_token_counts_for_messages(in_context_messages: List[Message]) -> List[int]:
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
- token_counts = [count_tokens(str(msg)) for msg in in_context_messages_openai]
- return token_counts
-
-
-def is_context_overflow_error(exception: Union[requests.exceptions.RequestException, Exception]) -> bool:
- """Checks if an exception is due to context overflow (based on common OpenAI response messages)"""
- from letta.utils import printd
-
- match_string = OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING
-
- # Backwards compatibility with openai python package/client v0.28 (pre-v1 client migration)
- if match_string in str(exception):
- printd(f"Found '{match_string}' in str(exception)={(str(exception))}")
- return True
-
- # Based on python requests + OpenAI REST API (/v1)
- elif isinstance(exception, requests.exceptions.HTTPError):
- if exception.response is not None and "application/json" in exception.response.headers.get("Content-Type", ""):
- try:
- error_details = exception.response.json()
- if "error" not in error_details:
- printd(f"HTTPError occurred, but couldn't find error field: {error_details}")
- return False
- else:
- error_details = error_details["error"]
-
- # Check for the specific error code
- if error_details.get("code") == "context_length_exceeded":
- printd(f"HTTPError occurred, caught error code {error_details.get('code')}")
- return True
- # Soft-check for "maximum context length" inside of the message
- elif error_details.get("message") and "maximum context length" in error_details.get("message"):
- printd(f"HTTPError occurred, found '{match_string}' in error message contents ({error_details})")
- return True
- else:
- printd(f"HTTPError occurred, but unknown error message: {error_details}")
- return False
- except ValueError:
- # JSON decoding failed
- printd(f"HTTPError occurred ({exception}), but no JSON error message.")
-
- # Generic fail
- else:
- return False
diff --git a/letta/llm_api/llm_api_tools.py b/letta/llm_api/llm_api_tools.py
deleted file mode 100644
index 8a75bc7b..00000000
--- a/letta/llm_api/llm_api_tools.py
+++ /dev/null
@@ -1,273 +0,0 @@
-import json
-import random
-import time
-from typing import List, Optional, Union
-
-import requests
-
-from letta.constants import CLI_WARNING_PREFIX
-from letta.errors import LettaConfigurationError, RateLimitExceededError
-from letta.llm_api.helpers import unpack_all_inner_thoughts_from_kwargs
-from letta.llm_api.openai import (
- build_openai_chat_completions_request,
- openai_chat_completions_process_stream,
- openai_chat_completions_request,
- prepare_openai_payload,
-)
-from letta.local_llm.chat_completion_proxy import get_chat_completion
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG
-from letta.local_llm.utils import num_tokens_from_functions, num_tokens_from_messages
-from letta.orm.user import User
-from letta.otel.tracing import log_event, trace_method
-from letta.schemas.enums import ProviderCategory
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
-from letta.schemas.provider_trace import ProviderTraceCreate
-from letta.services.telemetry_manager import TelemetryManager
-from letta.settings import ModelSettings
-from letta.streaming_interface import AgentChunkStreamingInterface, AgentRefreshStreamingInterface
-
-LLM_API_PROVIDER_OPTIONS = ["openai", "azure", "anthropic", "google_ai", "local", "groq", "deepseek"]
-
-
-def retry_with_exponential_backoff(
- func,
- initial_delay: float = 1,
- exponential_base: float = 2,
- jitter: bool = True,
- max_retries: int = 20,
- # List of OpenAI error codes: https://github.com/openai/openai-python/blob/17ac6779958b2b74999c634c4ea4c7b74906027a/src/openai/_client.py#L227-L250
- # 429 = rate limit
- error_codes: tuple = (429,),
-):
- """Retry a function with exponential backoff."""
-
- def wrapper(*args, **kwargs):
- pass
-
- # Initialize variables
- num_retries = 0
- delay = initial_delay
-
- # Loop until a successful response or max_retries is hit or an exception is raised
- while True:
- try:
- return func(*args, **kwargs)
- except KeyboardInterrupt:
- # Stop retrying if user hits Ctrl-C
- raise KeyboardInterrupt("User intentionally stopped thread. Stopping...")
- except requests.exceptions.HTTPError as http_err:
- if not hasattr(http_err, "response") or not http_err.response:
- raise
-
- # Retry on specified errors
- if http_err.response.status_code in error_codes:
- # Increment retries
- num_retries += 1
- log_event(
- "llm_retry_attempt",
- {
- "attempt": num_retries,
- "delay": delay,
- "status_code": http_err.response.status_code,
- "error_type": type(http_err).__name__,
- "error": str(http_err),
- },
- )
-
- # Check if max retries has been reached
- if num_retries > max_retries:
- log_event(
- "llm_max_retries_exceeded",
- {
- "max_retries": max_retries,
- "status_code": http_err.response.status_code,
- "error_type": type(http_err).__name__,
- "error": str(http_err),
- },
- )
- raise RateLimitExceededError("Maximum number of retries exceeded", max_retries=max_retries)
-
- # Increment the delay
- delay *= exponential_base * (1 + jitter * random.random())
-
- # Sleep for the delay
- # printd(f"Got a rate limit error ('{http_err}') on LLM backend request, waiting {int(delay)}s then retrying...")
- print(
- f"{CLI_WARNING_PREFIX}Got a rate limit error ('{http_err}') on LLM backend request, waiting {int(delay)}s then retrying..."
- )
- time.sleep(delay)
- else:
- # For other HTTP errors, re-raise the exception
- log_event(
- "llm_non_retryable_error",
- {"status_code": http_err.response.status_code, "error_type": type(http_err).__name__, "error": str(http_err)},
- )
- raise
-
- # Raise exceptions for any errors not specified
- except Exception as e:
- log_event("llm_unexpected_error", {"error_type": type(e).__name__, "error": str(e)})
- raise e
-
- return wrapper
-
-
-@trace_method
-@retry_with_exponential_backoff
-def create(
- # agent_state: AgentState,
- llm_config: LLMConfig,
- messages: List[Message],
- user_id: Optional[str] = None, # option UUID to associate request with
- functions: Optional[list] = None,
- functions_python: Optional[dict] = None,
- function_call: Optional[str] = None, # see: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice
- # hint
- first_message: bool = False,
- force_tool_call: Optional[str] = None, # Force a specific tool to be called
- # use tool naming?
- # if false, will use deprecated 'functions' style
- use_tool_naming: bool = True,
- # streaming?
- stream: bool = False,
- stream_interface: Optional[Union[AgentRefreshStreamingInterface, AgentChunkStreamingInterface]] = None,
- model_settings: Optional[dict] = None, # TODO: eventually pass from server
- put_inner_thoughts_first: bool = True,
- name: Optional[str] = None,
- telemetry_manager: Optional[TelemetryManager] = None,
- step_id: Optional[str] = None,
- actor: Optional[User] = None,
-) -> ChatCompletionResponse:
- """Return response to chat completion with backoff"""
- from letta.utils import printd
-
- # Count the tokens first, if there's an overflow exit early by throwing an error up the stack
- # NOTE: we want to include a specific substring in the error message to trigger summarization
- messages_oai_format = Message.to_openai_dicts_from_list(messages)
- prompt_tokens = num_tokens_from_messages(messages=messages_oai_format, model=llm_config.model)
- function_tokens = num_tokens_from_functions(functions=functions, model=llm_config.model) if functions else 0
- if prompt_tokens + function_tokens > llm_config.context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens + function_tokens} > {llm_config.context_window} tokens)")
-
- if not model_settings:
- from letta.settings import model_settings
-
- model_settings = model_settings
- assert isinstance(model_settings, ModelSettings)
-
- printd(f"Using model {llm_config.model_endpoint_type}, endpoint: {llm_config.model_endpoint}")
-
- if function_call and not functions:
- printd("unsetting function_call because functions is None")
- function_call = None
-
- # openai
- if llm_config.model_endpoint_type == "openai":
- if model_settings.openai_api_key is None and llm_config.model_endpoint == "https://api.openai.com/v1":
- # only is a problem if we are *not* using an openai proxy
- raise LettaConfigurationError(message="OpenAI key is missing from letta config file", missing_fields=["openai_api_key"])
- elif llm_config.provider_category == ProviderCategory.byok:
- from letta.services.provider_manager import ProviderManager
- from letta.services.user_manager import UserManager
-
- actor = UserManager().get_user_or_default(user_id=user_id)
- api_key = ProviderManager().get_override_key(llm_config.provider_name, actor=actor)
- elif model_settings.openai_api_key is None:
- # the openai python client requires a dummy API key
- api_key = "DUMMY_API_KEY"
- else:
- api_key = model_settings.openai_api_key
-
- if function_call is None and functions is not None and len(functions) > 0:
- # force function calling for reliability, see https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice
- # TODO(matt) move into LLMConfig
- # TODO: This vllm checking is very brittle and is a patch at most
- if llm_config.handle and "vllm" in llm_config.handle:
- function_call = "auto"
- else:
- function_call = "required"
-
- data = build_openai_chat_completions_request(
- llm_config,
- messages,
- user_id,
- functions,
- function_call,
- use_tool_naming,
- put_inner_thoughts_first=put_inner_thoughts_first,
- use_structured_output=True, # NOTE: turn on all the time for OpenAI API
- )
-
- if stream: # Client requested token streaming
- data.stream = True
- assert isinstance(stream_interface, AgentChunkStreamingInterface) or isinstance(
- stream_interface, AgentRefreshStreamingInterface
- ), type(stream_interface)
- response = openai_chat_completions_process_stream(
- url=llm_config.model_endpoint,
- api_key=api_key,
- chat_completion_request=data,
- stream_interface=stream_interface,
- name=name,
- # NOTE: needs to be true for OpenAI proxies that use the `reasoning_content` field
- # For example, DeepSeek, or LM Studio
- expect_reasoning_content=False,
- )
- else: # Client did not request token streaming (expect a blocking backend response)
- data.stream = False
- if isinstance(stream_interface, AgentChunkStreamingInterface):
- stream_interface.stream_start()
- try:
- response = openai_chat_completions_request(
- url=llm_config.model_endpoint,
- api_key=api_key,
- chat_completion_request=data,
- )
- finally:
- if isinstance(stream_interface, AgentChunkStreamingInterface):
- stream_interface.stream_end()
-
- telemetry_manager.create_provider_trace(
- actor=actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=prepare_openai_payload(data),
- response_json=response.model_json_schema(),
- step_id=step_id,
- organization_id=actor.organization_id,
- ),
- )
-
- if llm_config.put_inner_thoughts_in_kwargs:
- response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG)
-
- return response
-
- # local model
- else:
- if stream:
- raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}")
-
- if "DeepSeek-R1".lower() in llm_config.model.lower(): # TODO: move this to the llm_config.
- messages[0].content[0].text += f" {''.join(json.dumps(f) for f in functions)} "
- messages[0].content[
- 0
- ].text += 'Select best function to call simply by responding with a single json block with the keys "function" and "params". Use double quotes around the arguments.'
- return get_chat_completion(
- model=llm_config.model,
- messages=messages,
- functions=functions,
- functions_python=functions_python,
- function_call=function_call,
- context_window=llm_config.context_window,
- endpoint=llm_config.model_endpoint,
- endpoint_type=llm_config.model_endpoint_type,
- wrapper=llm_config.model_wrapper,
- user=str(user_id),
- # hint
- first_message=first_message,
- # auth-related
- auth_type=model_settings.openllm_auth_type,
- auth_key=model_settings.openllm_api_key,
- )
diff --git a/letta/llm_api/llm_client.py b/letta/llm_api/llm_client.py
deleted file mode 100644
index d778b319..00000000
--- a/letta/llm_api/llm_client.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from typing import TYPE_CHECKING, Optional
-
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.schemas.enums import ProviderType
-
-if TYPE_CHECKING:
- from letta.orm import User
-
-
-class LLMClient:
- """Factory class for creating LLM clients based on the model endpoint type."""
-
- @staticmethod
- def create(
- provider_type: ProviderType,
- put_inner_thoughts_first: bool = True,
- actor: Optional["User"] = None,
- ) -> Optional[LLMClientBase]:
- """
- Create an LLM client based on the model endpoint type.
-
- Args:
- provider: The model endpoint type
- put_inner_thoughts_first: Whether to put inner thoughts first in the response
-
- Returns:
- An instance of LLMClientBase subclass
-
- Raises:
- ValueError: If the model endpoint type is not supported
- """
- match provider_type:
- case ProviderType.google_ai:
- from letta.llm_api.google_ai_client import GoogleAIClient
-
- return GoogleAIClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.google_vertex:
- from letta.llm_api.google_vertex_client import GoogleVertexClient
-
- return GoogleVertexClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.anthropic:
- from letta.llm_api.anthropic_client import AnthropicClient
-
- return AnthropicClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.bedrock:
- from letta.llm_api.bedrock_client import BedrockClient
-
- return BedrockClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.together:
- from letta.llm_api.together_client import TogetherClient
-
- return TogetherClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.azure:
- from letta.llm_api.azure_client import AzureClient
-
- return AzureClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.xai:
- from letta.llm_api.xai_client import XAIClient
-
- return XAIClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.groq:
- from letta.llm_api.groq_client import GroqClient
-
- return GroqClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case ProviderType.deepseek:
- from letta.llm_api.deepseek_client import DeepseekClient
-
- return DeepseekClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
- case _:
- from letta.llm_api.openai_client import OpenAIClient
-
- return OpenAIClient(
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=actor,
- )
diff --git a/letta/llm_api/llm_client_base.py b/letta/llm_api/llm_client_base.py
deleted file mode 100644
index af3730a8..00000000
--- a/letta/llm_api/llm_client_base.py
+++ /dev/null
@@ -1,253 +0,0 @@
-import json
-from abc import abstractmethod
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
-
-from anthropic.types.beta.messages import BetaMessageBatch
-from openai import AsyncStream, Stream
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-
-from letta.errors import LLMError
-from letta.otel.tracing import log_event, trace_method
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import ProviderCategory
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
-from letta.schemas.provider_trace import ProviderTraceCreate
-from letta.services.telemetry_manager import TelemetryManager
-from letta.settings import settings
-
-if TYPE_CHECKING:
- from letta.orm import User
-
-
-class LLMClientBase:
- """
- Abstract base class for LLM clients, formatting the request objects,
- handling the downstream request and parsing into chat completions response format
- """
-
- def __init__(
- self,
- put_inner_thoughts_first: Optional[bool] = True,
- use_tool_naming: bool = True,
- actor: Optional["User"] = None,
- ):
- self.actor = actor
- self.put_inner_thoughts_first = put_inner_thoughts_first
- self.use_tool_naming = use_tool_naming
-
- @trace_method
- def send_llm_request(
- self,
- messages: List[Message],
- llm_config: LLMConfig,
- tools: Optional[List[dict]] = None, # TODO: change to Tool object
- force_tool_call: Optional[str] = None,
- telemetry_manager: Optional["TelemetryManager"] = None,
- step_id: Optional[str] = None,
- ) -> Union[ChatCompletionResponse, Stream[ChatCompletionChunk]]:
- """
- Issues a request to the downstream model endpoint and parses response.
- If stream=True, returns a Stream[ChatCompletionChunk] that can be iterated over.
- Otherwise returns a ChatCompletionResponse.
- """
- request_data = self.build_request_data(messages, llm_config, tools, force_tool_call)
-
- try:
- log_event(name="llm_request_sent", attributes=request_data)
- response_data = self.request(request_data, llm_config)
- if step_id and telemetry_manager:
- telemetry_manager.create_provider_trace(
- actor=self.actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=request_data,
- response_json=response_data,
- step_id=step_id,
- organization_id=self.actor.organization_id,
- ),
- )
- log_event(name="llm_response_received", attributes=response_data)
- except Exception as e:
- raise self.handle_llm_error(e)
-
- return self.convert_response_to_chat_completion(response_data, messages, llm_config)
-
- @trace_method
- async def send_llm_request_async(
- self,
- request_data: dict,
- messages: List[Message],
- llm_config: LLMConfig,
- telemetry_manager: "TelemetryManager | None" = None,
- step_id: str | None = None,
- ) -> Union[ChatCompletionResponse, AsyncStream[ChatCompletionChunk]]:
- """
- Issues a request to the downstream model endpoint.
- If stream=True, returns an AsyncStream[ChatCompletionChunk] that can be async iterated over.
- Otherwise returns a ChatCompletionResponse.
- """
-
- try:
- log_event(name="llm_request_sent", attributes=request_data)
- response_data = await self.request_async(request_data, llm_config)
- if settings.track_provider_trace and telemetry_manager:
- await telemetry_manager.create_provider_trace_async(
- actor=self.actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=request_data,
- response_json=response_data,
- step_id=step_id,
- organization_id=self.actor.organization_id,
- ),
- )
-
- log_event(name="llm_response_received", attributes=response_data)
- except Exception as e:
- raise self.handle_llm_error(e)
-
- return self.convert_response_to_chat_completion(response_data, messages, llm_config)
-
- async def send_llm_batch_request_async(
- self,
- agent_messages_mapping: Dict[str, List[Message]],
- agent_tools_mapping: Dict[str, List[dict]],
- agent_llm_config_mapping: Dict[str, LLMConfig],
- ) -> Union[BetaMessageBatch]:
- """
- Issues a batch request to the downstream model endpoint and parses response.
- """
- raise NotImplementedError
-
- @abstractmethod
- def build_request_data(
- self,
- messages: List[Message],
- llm_config: LLMConfig,
- tools: List[dict],
- force_tool_call: Optional[str] = None,
- ) -> dict:
- """
- Constructs a request object in the expected data format for this client.
- """
- raise NotImplementedError
-
- @abstractmethod
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying request to llm and returns raw response.
- """
- raise NotImplementedError
-
- @abstractmethod
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying request to llm and returns raw response.
- """
- raise NotImplementedError
-
- @abstractmethod
- async def request_embeddings(self, texts: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
- """
- Generate embeddings for a batch of texts.
-
- Args:
- texts (List[str]): List of texts to generate embeddings for.
- embedding_config (EmbeddingConfig): Configuration for the embedding model.
-
- Returns:
- embeddings (List[List[float]]): List of embeddings for the input texts.
- """
- raise NotImplementedError
-
- @abstractmethod
- def convert_response_to_chat_completion(
- self,
- response_data: dict,
- input_messages: List[Message],
- llm_config: LLMConfig,
- ) -> ChatCompletionResponse:
- """
- Converts custom response format from llm client into an OpenAI
- ChatCompletionsResponse object.
- """
- raise NotImplementedError
-
- @abstractmethod
- async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[ChatCompletionChunk]:
- """
- Performs underlying streaming request to llm and returns raw response.
- """
- raise NotImplementedError(f"Streaming is not supported for {llm_config.model_endpoint_type}")
-
- @abstractmethod
- def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
- """
- Returns True if the model is a native reasoning model.
- """
- raise NotImplementedError
-
- @abstractmethod
- def handle_llm_error(self, e: Exception) -> Exception:
- """
- Maps provider-specific errors to common LLMError types.
- Each LLM provider should implement this to translate their specific errors.
-
- Args:
- e: The original provider-specific exception
-
- Returns:
- An LLMError subclass that represents the error in a provider-agnostic way
- """
- return LLMError(f"Unhandled LLM error: {str(e)}")
-
- def get_byok_overrides(self, llm_config: LLMConfig) -> Tuple[Optional[str], Optional[str], Optional[str]]:
- """
- Returns the override key for the given llm config.
- """
- api_key = None
- if llm_config.provider_category == ProviderCategory.byok:
- from letta.services.provider_manager import ProviderManager
-
- api_key = ProviderManager().get_override_key(llm_config.provider_name, actor=self.actor)
-
- return api_key, None, None
-
- async def get_byok_overrides_async(self, llm_config: LLMConfig) -> Tuple[Optional[str], Optional[str], Optional[str]]:
- """
- Returns the override key for the given llm config.
- """
- api_key = None
- if llm_config.provider_category == ProviderCategory.byok:
- from letta.services.provider_manager import ProviderManager
-
- api_key = await ProviderManager().get_override_key_async(llm_config.provider_name, actor=self.actor)
-
- return api_key, None, None
-
- def _fix_truncated_json_response(self, response: ChatCompletionResponse) -> ChatCompletionResponse:
- """
- Fixes truncated JSON responses by ensuring the content is properly formatted.
- This is a workaround for some providers that may return incomplete JSON.
- """
- if response.choices and response.choices[0].message and response.choices[0].message.tool_calls:
- tool_call_args_str = response.choices[0].message.tool_calls[0].function.arguments
- try:
- json.loads(tool_call_args_str)
- except json.JSONDecodeError:
- try:
- json_str_end = ""
- quote_count = tool_call_args_str.count('"')
- if quote_count % 2 != 0:
- json_str_end = json_str_end + '"'
-
- open_braces = tool_call_args_str.count("{")
- close_braces = tool_call_args_str.count("}")
- missing_braces = open_braces - close_braces
- json_str_end += "}" * missing_braces
- fixed_tool_call_args_str = tool_call_args_str[: -len(json_str_end)] + json_str_end
- json.loads(fixed_tool_call_args_str)
- response.choices[0].message.tool_calls[0].function.arguments = fixed_tool_call_args_str
- except json.JSONDecodeError:
- pass
- return response
diff --git a/letta/llm_api/mistral.py b/letta/llm_api/mistral.py
deleted file mode 100644
index 8d5b8b10..00000000
--- a/letta/llm_api/mistral.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import aiohttp
-
-from letta.log import get_logger
-from letta.utils import smart_urljoin
-
-logger = get_logger(__name__)
-
-
-async def mistral_get_model_list_async(url: str, api_key: str) -> dict:
- url = smart_urljoin(url, "models")
-
- headers = {"Content-Type": "application/json"}
- if api_key is not None:
- headers["Authorization"] = f"Bearer {api_key}"
-
- logger.debug("Sending request to %s", url)
-
- async with aiohttp.ClientSession() as session:
- # TODO add query param "tool" to be true
- async with session.get(url, headers=headers) as response:
- response.raise_for_status()
- return await response.json()
diff --git a/letta/llm_api/openai.py b/letta/llm_api/openai.py
deleted file mode 100644
index da1e5a8d..00000000
--- a/letta/llm_api/openai.py
+++ /dev/null
@@ -1,620 +0,0 @@
-import warnings
-from typing import Generator, List, Optional, Union
-
-import httpx
-import requests
-from openai import OpenAI
-
-from letta.constants import LETTA_MODEL_ENDPOINT
-from letta.errors import ErrorCode, LLMAuthenticationError, LLMError
-from letta.helpers.datetime_helpers import timestamp_to_datetime
-from letta.llm_api.helpers import add_inner_thoughts_to_functions, convert_to_structured_output, make_post_request
-from letta.llm_api.openai_client import (
- accepts_developer_role,
- requires_auto_tool_choice,
- supports_parallel_tool_calling,
- supports_structured_output,
- supports_temperature_param,
-)
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION, INNER_THOUGHTS_KWARG_DESCRIPTION_GO_FIRST
-from letta.local_llm.utils import num_tokens_from_functions, num_tokens_from_messages
-from letta.log import get_logger
-from letta.otel.tracing import log_event
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage, MessageRole as _MessageRole
-from letta.schemas.openai.chat_completion_request import (
- ChatCompletionRequest,
- FunctionCall as ToolFunctionChoiceFunctionCall,
- FunctionSchema,
- Tool,
- ToolFunctionChoice,
- cast_message_to_subtype,
-)
-from letta.schemas.openai.chat_completion_response import (
- ChatCompletionChunkResponse,
- ChatCompletionResponse,
- Choice,
- FunctionCall,
- Message,
- ToolCall,
- UsageStatistics,
-)
-from letta.schemas.openai.embedding_response import EmbeddingResponse
-from letta.streaming_interface import AgentChunkStreamingInterface, AgentRefreshStreamingInterface
-from letta.utils import get_tool_call_id, smart_urljoin
-
-logger = get_logger(__name__)
-
-
-# TODO: MOVE THIS TO OPENAI_CLIENT
-def openai_check_valid_api_key(base_url: str, api_key: Union[str, None]) -> None:
- if api_key:
- try:
- # just get model list to check if the api key is valid until we find a cheaper / quicker endpoint
- openai_get_model_list(url=base_url, api_key=api_key)
- except requests.HTTPError as e:
- if e.response.status_code == 401:
- raise LLMAuthenticationError(message=f"Failed to authenticate with OpenAI: {e}", code=ErrorCode.UNAUTHENTICATED)
- raise e
- except Exception as e:
- raise LLMError(message=f"{e}", code=ErrorCode.INTERNAL_SERVER_ERROR)
- else:
- raise ValueError("No API key provided")
-
-
-def openai_get_model_list(url: str, api_key: Optional[str] = None, fix_url: bool = False, extra_params: Optional[dict] = None) -> dict:
- """https://platform.openai.com/docs/api-reference/models/list"""
-
- # In some cases we may want to double-check the URL and do basic correction, eg:
- # In Letta config the address for vLLM is w/o a /v1 suffix for simplicity
- # However if we're treating the server as an OpenAI proxy we want the /v1 suffix on our model hit
-
- import warnings
-
- warnings.warn("The synchronous version of openai_get_model_list function is deprecated. Use the async one instead.", DeprecationWarning)
-
- if fix_url:
- if not url.endswith("/v1"):
- url = smart_urljoin(url, "v1")
-
- url = smart_urljoin(url, "models")
-
- headers = {"Content-Type": "application/json"}
- if api_key is not None:
- headers["Authorization"] = f"Bearer {api_key}"
-
- logger.debug(f"Sending request to {url}")
- response = None
- try:
- # TODO add query param "tool" to be true
- response = requests.get(url, headers=headers, params=extra_params)
- response.raise_for_status() # Raises HTTPError for 4XX/5XX status
- response = response.json() # convert to dict from string
- logger.debug(f"response = {response}")
- return response
- except requests.exceptions.HTTPError as http_err:
- # Handle HTTP errors (e.g., response 4XX, 5XX)
- try:
- if response:
- response = response.json()
- except:
- pass
- logger.debug(f"Got HTTPError, exception={http_err}, response={response}")
- raise http_err
- except requests.exceptions.RequestException as req_err:
- # Handle other requests-related errors (e.g., connection error)
- try:
- if response:
- response = response.json()
- except:
- pass
- logger.debug(f"Got RequestException, exception={req_err}, response={response}")
- raise req_err
- except Exception as e:
- # Handle other potential errors
- try:
- if response:
- response = response.json()
- except:
- pass
- logger.debug(f"Got unknown Exception, exception={e}, response={response}")
- raise e
-
-
-async def openai_get_model_list_async(
- url: str,
- api_key: Optional[str] = None,
- fix_url: bool = False,
- extra_params: Optional[dict] = None,
- client: Optional["httpx.AsyncClient"] = None,
-) -> dict:
- """https://platform.openai.com/docs/api-reference/models/list"""
-
- # In some cases we may want to double-check the URL and do basic correction
- if fix_url and not url.endswith("/v1"):
- url = smart_urljoin(url, "v1")
-
- url = smart_urljoin(url, "models")
-
- headers = {"Content-Type": "application/json"}
- if api_key is not None:
- headers["Authorization"] = f"Bearer {api_key}"
-
- logger.debug(f"Sending request to {url}")
-
- # Use provided client or create a new one
- close_client = False
- if client is None:
- client = httpx.AsyncClient()
- close_client = True
-
- try:
- response = await client.get(url, headers=headers, params=extra_params)
- response.raise_for_status()
- result = response.json()
- logger.debug(f"response = {result}")
- return result
- except httpx.HTTPStatusError as http_err:
- # Handle HTTP errors (e.g., response 4XX, 5XX)
- try:
- error_response = http_err.response.json()
- except:
- error_response = {"status_code": http_err.response.status_code, "text": http_err.response.text}
- logger.debug(f"Got HTTPError, exception={http_err}, response={error_response}")
- raise http_err
- except httpx.RequestError as req_err:
- # Handle other httpx-related errors (e.g., connection error)
- logger.debug(f"Got RequestException, exception={req_err}")
- raise req_err
- except Exception as e:
- # Handle other potential errors
- logger.debug(f"Got unknown Exception, exception={e}")
- raise e
- finally:
- if close_client:
- await client.aclose()
-
-
-def build_openai_chat_completions_request(
- llm_config: LLMConfig,
- messages: List[PydanticMessage],
- user_id: Optional[str],
- functions: Optional[list],
- function_call: Optional[str],
- use_tool_naming: bool,
- put_inner_thoughts_first: bool = True,
- use_structured_output: bool = True,
-) -> ChatCompletionRequest:
- if functions and llm_config.put_inner_thoughts_in_kwargs:
- # Special case for LM Studio backend since it needs extra guidance to force out the thoughts first
- # TODO(fix)
- inner_thoughts_desc = (
- INNER_THOUGHTS_KWARG_DESCRIPTION_GO_FIRST if ":1234" in llm_config.model_endpoint else INNER_THOUGHTS_KWARG_DESCRIPTION
- )
- functions = add_inner_thoughts_to_functions(
- functions=functions,
- inner_thoughts_key=INNER_THOUGHTS_KWARG,
- inner_thoughts_description=inner_thoughts_desc,
- put_inner_thoughts_first=put_inner_thoughts_first,
- )
-
- use_developer_message = accepts_developer_role(llm_config.model)
-
- openai_message_list = [
- cast_message_to_subtype(m)
- for m in PydanticMessage.to_openai_dicts_from_list(
- messages,
- put_inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs,
- use_developer_message=use_developer_message,
- )
- ]
-
- if llm_config.model:
- model = llm_config.model
- else:
- warnings.warn(f"Model type not set in llm_config: {llm_config.model_dump_json(indent=4)}")
- model = None
-
- if use_tool_naming:
- if function_call is None:
- tool_choice = None
- elif function_call not in ["none", "auto", "required"]:
- tool_choice = ToolFunctionChoice(type="function", function=ToolFunctionChoiceFunctionCall(name=function_call))
- else:
- if requires_auto_tool_choice(llm_config):
- tool_choice = "auto"
- else:
- tool_choice = function_call
- data = ChatCompletionRequest(
- model=model,
- messages=openai_message_list,
- tools=[Tool(type="function", function=f) for f in functions] if functions else None,
- tool_choice=tool_choice,
- user=str(user_id),
- max_completion_tokens=llm_config.max_tokens,
- temperature=llm_config.temperature if supports_temperature_param(model) else 1.0,
- reasoning_effort=llm_config.reasoning_effort,
- )
- else:
- data = ChatCompletionRequest(
- model=model,
- messages=openai_message_list,
- functions=functions,
- function_call=function_call,
- user=str(user_id),
- max_completion_tokens=llm_config.max_tokens,
- temperature=llm_config.temperature if supports_temperature_param(model) else 1.0,
- reasoning_effort=llm_config.reasoning_effort,
- )
- # https://platform.openai.com/docs/guides/text-generation/json-mode
- # only supported by gpt-4o, gpt-4-turbo, or gpt-3.5-turbo
- # if "gpt-4o" in llm_config.model or "gpt-4-turbo" in llm_config.model or "gpt-3.5-turbo" in llm_config.model:
- # data.response_format = {"type": "json_object"}
-
- # always set user id for openai requests
- if user_id:
- data.user = str(user_id)
-
- if llm_config.model_endpoint == LETTA_MODEL_ENDPOINT:
- if not user_id:
- # override user id for inference.letta.com
- import uuid
-
- data.user = str(uuid.UUID(int=0))
-
- data.model = "memgpt-openai"
-
- if use_structured_output and data.tools is not None and len(data.tools) > 0:
- # Convert to structured output style (which has 'strict' and no optionals)
- for tool in data.tools:
- if supports_structured_output(llm_config):
- try:
- # tool["function"] = convert_to_structured_output(tool["function"])
- structured_output_version = convert_to_structured_output(tool.function.model_dump())
- tool.function = FunctionSchema(**structured_output_version)
- except ValueError as e:
- warnings.warn(f"Failed to convert tool function to structured output, tool={tool}, error={e}")
- return data
-
-
-def openai_chat_completions_process_stream(
- url: str,
- api_key: str,
- chat_completion_request: ChatCompletionRequest,
- stream_interface: Optional[Union[AgentChunkStreamingInterface, AgentRefreshStreamingInterface]] = None,
- create_message_id: bool = True,
- create_message_datetime: bool = True,
- override_tool_call_id: bool = True,
- # if we expect reasoning content in the response,
- # then we should emit reasoning_content as "inner_thoughts"
- # however, we don't necessarily want to put these
- # expect_reasoning_content: bool = False,
- expect_reasoning_content: bool = True,
- name: Optional[str] = None,
-) -> ChatCompletionResponse:
- """Process a streaming completion response, and return a ChatCompletionResponse at the end.
-
- To "stream" the response in Letta, we want to call a streaming-compatible interface function
- on the chunks received from the OpenAI-compatible server POST SSE response.
- """
- assert chat_completion_request.stream == True
- assert stream_interface is not None, "Required"
-
- # Count the prompt tokens
- # TODO move to post-request?
- chat_history = [m.model_dump(exclude_none=True) for m in chat_completion_request.messages]
- # print(chat_history)
-
- prompt_tokens = num_tokens_from_messages(
- messages=chat_history,
- model=chat_completion_request.model,
- )
- # We also need to add the cost of including the functions list to the input prompt
- if chat_completion_request.tools is not None:
- assert chat_completion_request.functions is None
- prompt_tokens += num_tokens_from_functions(
- functions=[t.function.model_dump() for t in chat_completion_request.tools],
- model=chat_completion_request.model,
- )
- elif chat_completion_request.functions is not None:
- assert chat_completion_request.tools is None
- prompt_tokens += num_tokens_from_functions(
- functions=[f.model_dump() for f in chat_completion_request.functions],
- model=chat_completion_request.model,
- )
-
- # Create a dummy Message object to get an ID and date
- # TODO(sarah): add message ID generation function
- dummy_message = PydanticMessage(
- role=_MessageRole.assistant,
- content=[],
- agent_id="",
- model="",
- name=None,
- tool_calls=None,
- tool_call_id=None,
- )
-
- TEMP_STREAM_RESPONSE_ID = "temp_id"
- TEMP_STREAM_FINISH_REASON = "temp_null"
- TEMP_STREAM_TOOL_CALL_ID = "temp_id"
- chat_completion_response = ChatCompletionResponse(
- id=dummy_message.id if create_message_id else TEMP_STREAM_RESPONSE_ID,
- choices=[],
- created=int(dummy_message.created_at.timestamp()), # NOTE: doesn't matter since both will do get_utc_time()
- model=chat_completion_request.model,
- usage=UsageStatistics(
- completion_tokens=0,
- prompt_tokens=prompt_tokens,
- total_tokens=prompt_tokens,
- ),
- )
-
- log_event(name="llm_request_sent", attributes=chat_completion_request.model_dump())
-
- if stream_interface:
- stream_interface.stream_start()
-
- n_chunks = 0 # approx == n_tokens
- chunk_idx = 0
- prev_message_type = None
- message_idx = 0
- try:
- for chat_completion_chunk in openai_chat_completions_request_stream(
- url=url, api_key=api_key, chat_completion_request=chat_completion_request
- ):
- assert isinstance(chat_completion_chunk, ChatCompletionChunkResponse), type(chat_completion_chunk)
- if chat_completion_chunk.choices is None or len(chat_completion_chunk.choices) == 0:
- warnings.warn(f"No choices in chunk: {chat_completion_chunk}")
- continue
-
- # NOTE: this assumes that the tool call ID will only appear in one of the chunks during the stream
- if override_tool_call_id:
- for choice in chat_completion_chunk.choices:
- if choice.delta.tool_calls and len(choice.delta.tool_calls) > 0:
- for tool_call in choice.delta.tool_calls:
- if tool_call.id is not None:
- tool_call.id = get_tool_call_id()
-
- if stream_interface:
- if isinstance(stream_interface, AgentChunkStreamingInterface):
- message_type = stream_interface.process_chunk(
- chat_completion_chunk,
- message_id=chat_completion_response.id if create_message_id else chat_completion_chunk.id,
- message_date=(
- timestamp_to_datetime(chat_completion_response.created)
- if create_message_datetime
- else timestamp_to_datetime(chat_completion_chunk.created)
- ),
- expect_reasoning_content=expect_reasoning_content,
- name=name,
- message_index=message_idx,
- prev_message_type=prev_message_type,
- )
- if message_type != prev_message_type and message_type is not None and prev_message_type is not None:
- message_idx += 1
- if message_type is not None:
- prev_message_type = message_type
- elif isinstance(stream_interface, AgentRefreshStreamingInterface):
- stream_interface.process_refresh(chat_completion_response)
- else:
- raise TypeError(stream_interface)
-
- if chunk_idx == 0:
- # initialize the choice objects which we will increment with the deltas
- num_choices = len(chat_completion_chunk.choices)
- assert num_choices > 0
- chat_completion_response.choices = [
- Choice(
- finish_reason=TEMP_STREAM_FINISH_REASON, # NOTE: needs to be ovrerwritten
- index=i,
- message=Message(
- role="assistant",
- ),
- )
- for i in range(len(chat_completion_chunk.choices))
- ]
-
- # add the choice delta
- assert len(chat_completion_chunk.choices) == len(chat_completion_response.choices), chat_completion_chunk
- for chunk_choice in chat_completion_chunk.choices:
- if chunk_choice.finish_reason is not None:
- chat_completion_response.choices[chunk_choice.index].finish_reason = chunk_choice.finish_reason
-
- if chunk_choice.logprobs is not None:
- chat_completion_response.choices[chunk_choice.index].logprobs = chunk_choice.logprobs
-
- accum_message = chat_completion_response.choices[chunk_choice.index].message
- message_delta = chunk_choice.delta
-
- if message_delta.content is not None:
- content_delta = message_delta.content
- if accum_message.content is None:
- accum_message.content = content_delta
- else:
- accum_message.content += content_delta
-
- if expect_reasoning_content and message_delta.reasoning_content is not None:
- reasoning_content_delta = message_delta.reasoning_content
- if accum_message.reasoning_content is None:
- accum_message.reasoning_content = reasoning_content_delta
- else:
- accum_message.reasoning_content += reasoning_content_delta
-
- # TODO(charles) make sure this works for parallel tool calling?
- if message_delta.tool_calls is not None:
- tool_calls_delta = message_delta.tool_calls
-
- # If this is the first tool call showing up in a chunk, initialize the list with it
- if accum_message.tool_calls is None:
- accum_message.tool_calls = [
- ToolCall(id=TEMP_STREAM_TOOL_CALL_ID, function=FunctionCall(name="", arguments=""))
- for _ in range(len(tool_calls_delta))
- ]
-
- # There may be many tool calls in a tool calls delta (e.g. parallel tool calls)
- for tool_call_delta in tool_calls_delta:
- if tool_call_delta.id is not None:
- # TODO assert that we're not overwriting?
- # TODO += instead of =?
- try:
- accum_message.tool_calls[tool_call_delta.index].id = tool_call_delta.id
- except IndexError:
- warnings.warn(
- f"Tool call index out of range ({tool_call_delta.index})\ncurrent tool calls: {accum_message.tool_calls}\ncurrent delta: {tool_call_delta}"
- )
- # force index 0
- # accum_message.tool_calls[0].id = tool_call_delta.id
- else:
- accum_message.tool_calls[tool_call_delta.index].id = tool_call_delta.id
- if tool_call_delta.function is not None:
- if tool_call_delta.function.name is not None:
- try:
- accum_message.tool_calls[
- tool_call_delta.index
- ].function.name += tool_call_delta.function.name # TODO check for parallel tool calls
- except IndexError:
- warnings.warn(
- f"Tool call index out of range ({tool_call_delta.index})\ncurrent tool calls: {accum_message.tool_calls}\ncurrent delta: {tool_call_delta}"
- )
- if tool_call_delta.function.arguments is not None:
- try:
- accum_message.tool_calls[tool_call_delta.index].function.arguments += tool_call_delta.function.arguments
- except IndexError:
- warnings.warn(
- f"Tool call index out of range ({tool_call_delta.index})\ncurrent tool calls: {accum_message.tool_calls}\ncurrent delta: {tool_call_delta}"
- )
-
- if message_delta.function_call is not None:
- raise NotImplementedError("Old function_call style not support with stream=True")
-
- # overwrite response fields based on latest chunk
- if not create_message_id:
- chat_completion_response.id = chat_completion_chunk.id
- if not create_message_datetime:
- chat_completion_response.created = chat_completion_chunk.created
- chat_completion_response.model = chat_completion_chunk.model
- chat_completion_response.system_fingerprint = chat_completion_chunk.system_fingerprint
-
- # increment chunk counter
- n_chunks += 1
- chunk_idx += 1
-
- except Exception as e:
- if stream_interface:
- stream_interface.stream_end()
- import traceback
-
- traceback.print_exc()
- logger.error(f"Parsing ChatCompletion stream failed with error:\n{str(e)}")
- raise e
- finally:
- logger.info("Finally ending streaming interface.")
- if stream_interface:
- stream_interface.stream_end()
-
- # make sure we didn't leave temp stuff in
- assert all([c.finish_reason != TEMP_STREAM_FINISH_REASON for c in chat_completion_response.choices])
- assert all(
- [
- all([tc.id != TEMP_STREAM_TOOL_CALL_ID for tc in c.message.tool_calls]) if c.message.tool_calls else True
- for c in chat_completion_response.choices
- ]
- )
- if not create_message_id:
- assert chat_completion_response.id != dummy_message.id
-
- # compute token usage before returning
- # TODO try actually computing the #tokens instead of assuming the chunks is the same
- chat_completion_response.usage.completion_tokens = n_chunks
- chat_completion_response.usage.total_tokens = prompt_tokens + n_chunks
-
- assert len(chat_completion_response.choices) > 0, f"No response from provider {chat_completion_response}"
-
- log_event(name="llm_response_received", attributes=chat_completion_response.model_dump())
- return chat_completion_response
-
-
-def openai_chat_completions_request_stream(
- url: str,
- api_key: str,
- chat_completion_request: ChatCompletionRequest,
- fix_url: bool = False,
-) -> Generator[ChatCompletionChunkResponse, None, None]:
- # In some cases we may want to double-check the URL and do basic correction, eg:
- # In Letta config the address for vLLM is w/o a /v1 suffix for simplicity
- # However if we're treating the server as an OpenAI proxy we want the /v1 suffix on our model hit
- if fix_url:
- if not url.endswith("/v1"):
- url = smart_urljoin(url, "v1")
-
- data = prepare_openai_payload(chat_completion_request)
- data["stream"] = True
- client = OpenAI(api_key=api_key, base_url=url, max_retries=0)
- try:
- stream = client.chat.completions.create(**data)
- for chunk in stream:
- # TODO: Use the native OpenAI objects here?
- yield ChatCompletionChunkResponse(**chunk.model_dump(exclude_none=True))
- except Exception as e:
- print(f"Error request stream from /v1/chat/completions, url={url}, data={data}:\n{e}")
- raise e
-
-
-def openai_chat_completions_request(
- url: str,
- api_key: str,
- chat_completion_request: ChatCompletionRequest,
-) -> ChatCompletionResponse:
- """Send a ChatCompletion request to an OpenAI-compatible server
-
- If request.stream == True, will yield ChatCompletionChunkResponses
- If request.stream == False, will return a ChatCompletionResponse
-
- https://platform.openai.com/docs/guides/text-generation?lang=curl
- """
- data = prepare_openai_payload(chat_completion_request)
- client = OpenAI(api_key=api_key, base_url=url, max_retries=0)
- log_event(name="llm_request_sent", attributes=data)
- chat_completion = client.chat.completions.create(**data)
- log_event(name="llm_response_received", attributes=chat_completion.model_dump())
- return ChatCompletionResponse(**chat_completion.model_dump())
-
-
-def openai_embeddings_request(url: str, api_key: str, data: dict) -> EmbeddingResponse:
- """https://platform.openai.com/docs/api-reference/embeddings/create"""
-
- url = smart_urljoin(url, "embeddings")
- headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
- response_json = make_post_request(url, headers, data)
- return EmbeddingResponse(**response_json)
-
-
-def prepare_openai_payload(chat_completion_request: ChatCompletionRequest):
- data = chat_completion_request.model_dump(exclude_none=True)
-
- # add check otherwise will cause error: "Invalid value for 'parallel_tool_calls': 'parallel_tool_calls' is only allowed when 'tools' are specified."
- if chat_completion_request.tools is not None:
- data["parallel_tool_calls"] = False
-
- # If functions == None, strip from the payload
- if "functions" in data and data["functions"] is None:
- data.pop("functions")
- data.pop("function_call", None) # extra safe, should exist always (default="auto")
-
- if "tools" in data and data["tools"] is None:
- data.pop("tools")
- data.pop("tool_choice", None) # extra safe, should exist always (default="auto")
-
- # # NOTE: move this out to wherever the ChatCompletionRequest is created
- # if "tools" in data:
- # for tool in data["tools"]:
- # try:
- # tool["function"] = convert_to_structured_output(tool["function"])
- # except ValueError as e:
- # warnings.warn(f"Failed to convert tool function to structured output, tool={tool}, error={e}")
-
- if not supports_parallel_tool_calling(chat_completion_request.model):
- data.pop("parallel_tool_calls", None)
-
- return data
diff --git a/letta/llm_api/openai_client.py b/letta/llm_api/openai_client.py
deleted file mode 100644
index 7f29da9a..00000000
--- a/letta/llm_api/openai_client.py
+++ /dev/null
@@ -1,526 +0,0 @@
-import asyncio
-import os
-from typing import List, Optional
-
-import openai
-from openai import AsyncOpenAI, AsyncStream, OpenAI
-from openai.types.chat.chat_completion import ChatCompletion
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-
-from letta.constants import LETTA_MODEL_ENDPOINT
-from letta.errors import (
- ContextWindowExceededError,
- ErrorCode,
- LLMAuthenticationError,
- LLMBadRequestError,
- LLMConnectionError,
- LLMNotFoundError,
- LLMPermissionDeniedError,
- LLMRateLimitError,
- LLMServerError,
- LLMTimeoutError,
- LLMUnprocessableEntityError,
-)
-from letta.llm_api.helpers import add_inner_thoughts_to_functions, convert_to_structured_output, unpack_all_inner_thoughts_from_kwargs
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION, INNER_THOUGHTS_KWARG_DESCRIPTION_GO_FIRST
-from letta.log import get_logger
-from letta.otel.tracing import trace_method
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.letta_message_content import MessageContentType
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage
-from letta.schemas.openai.chat_completion_request import (
- ChatCompletionRequest,
- FunctionCall as ToolFunctionChoiceFunctionCall,
- FunctionSchema,
- Tool as OpenAITool,
- ToolFunctionChoice,
- cast_message_to_subtype,
-)
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
-from letta.settings import model_settings
-
-logger = get_logger(__name__)
-
-
-def is_openai_reasoning_model(model: str) -> bool:
- """Utility function to check if the model is a 'reasoner'"""
-
- # NOTE: needs to be updated with new model releases
- is_reasoning = model.startswith("o1") or model.startswith("o3") or model.startswith("o4") or model.startswith("gpt-5")
- return is_reasoning
-
-
-def is_openai_5_model(model: str) -> bool:
- """Utility function to check if the model is a '5' model"""
- return model.startswith("gpt-5")
-
-
-def supports_verbosity_control(model: str) -> bool:
- """Check if the model supports verbosity control, currently only GPT-5 models support this"""
- return is_openai_5_model(model)
-
-
-def accepts_developer_role(model: str) -> bool:
- """Checks if the model accepts the 'developer' role. Note that not all reasoning models accept this role.
-
- See: https://community.openai.com/t/developer-role-not-accepted-for-o1-o1-mini-o3-mini/1110750/7
- """
- if is_openai_reasoning_model(model) and "o1-mini" not in model or "o1-preview" in model:
- return True
- else:
- return False
-
-
-def supports_temperature_param(model: str) -> bool:
- """Certain OpenAI models don't support configuring the temperature.
-
- Example error: 400 - {'error': {'message': "Unsupported parameter: 'temperature' is not supported with this model.", 'type': 'invalid_request_error', 'param': 'temperature', 'code': 'unsupported_parameter'}}
- """
- if is_openai_reasoning_model(model) or is_openai_5_model(model):
- return False
- else:
- return True
-
-
-def supports_parallel_tool_calling(model: str) -> bool:
- """Certain OpenAI models don't support parallel tool calls."""
-
- if is_openai_reasoning_model(model):
- return False
- else:
- return True
-
-
-# TODO move into LLMConfig as a field?
-def supports_structured_output(llm_config: LLMConfig) -> bool:
- """Certain providers don't support structured output."""
-
- # FIXME pretty hacky - turn off for providers we know users will use,
- # but also don't support structured output
- if "nebius.com" in llm_config.model_endpoint:
- return False
- else:
- return True
-
-
-# TODO move into LLMConfig as a field?
-def requires_auto_tool_choice(llm_config: LLMConfig) -> bool:
- """Certain providers require the tool choice to be set to 'auto'."""
- if "nebius.com" in llm_config.model_endpoint:
- return True
- if llm_config.handle and "vllm" in llm_config.handle:
- return True
- if llm_config.compatibility_type == "mlx":
- return True
- return False
-
-
-class OpenAIClient(LLMClientBase):
- def _prepare_client_kwargs(self, llm_config: LLMConfig) -> dict:
- api_key, _, _ = self.get_byok_overrides(llm_config)
-
- if not api_key:
- api_key = model_settings.openai_api_key or os.environ.get("OPENAI_API_KEY")
- # supposedly the openai python client requires a dummy API key
- api_key = api_key or "DUMMY_API_KEY"
- kwargs = {"api_key": api_key, "base_url": llm_config.model_endpoint}
-
- return kwargs
-
- def _prepare_client_kwargs_embedding(self, embedding_config: EmbeddingConfig) -> dict:
- api_key = model_settings.openai_api_key or os.environ.get("OPENAI_API_KEY")
- # supposedly the openai python client requires a dummy API key
- api_key = api_key or "DUMMY_API_KEY"
- kwargs = {"api_key": api_key, "base_url": embedding_config.embedding_endpoint}
- return kwargs
-
- async def _prepare_client_kwargs_async(self, llm_config: LLMConfig) -> dict:
- api_key, _, _ = await self.get_byok_overrides_async(llm_config)
-
- if not api_key:
- api_key = model_settings.openai_api_key or os.environ.get("OPENAI_API_KEY")
- # supposedly the openai python client requires a dummy API key
- api_key = api_key or "DUMMY_API_KEY"
- kwargs = {"api_key": api_key, "base_url": llm_config.model_endpoint}
-
- return kwargs
-
- def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
- return requires_auto_tool_choice(llm_config)
-
- def supports_structured_output(self, llm_config: LLMConfig) -> bool:
- return supports_structured_output(llm_config)
-
- @trace_method
- def build_request_data(
- self,
- messages: List[PydanticMessage],
- llm_config: LLMConfig,
- tools: Optional[List[dict]] = None, # Keep as dict for now as per base class
- force_tool_call: Optional[str] = None,
- ) -> dict:
- """
- Constructs a request object in the expected data format for the OpenAI API.
- """
- if tools and llm_config.put_inner_thoughts_in_kwargs:
- # Special case for LM Studio backend since it needs extra guidance to force out the thoughts first
- # TODO(fix)
- inner_thoughts_desc = (
- INNER_THOUGHTS_KWARG_DESCRIPTION_GO_FIRST if ":1234" in llm_config.model_endpoint else INNER_THOUGHTS_KWARG_DESCRIPTION
- )
- tools = add_inner_thoughts_to_functions(
- functions=tools,
- inner_thoughts_key=INNER_THOUGHTS_KWARG,
- inner_thoughts_description=inner_thoughts_desc,
- put_inner_thoughts_first=True,
- )
-
- use_developer_message = accepts_developer_role(llm_config.model)
-
- openai_message_list = [
- cast_message_to_subtype(m)
- for m in PydanticMessage.to_openai_dicts_from_list(
- messages,
- put_inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs,
- use_developer_message=use_developer_message,
- )
- ]
-
- if llm_config.model:
- model = llm_config.model
- else:
- logger.warning(f"Model type not set in llm_config: {llm_config.model_dump_json(indent=4)}")
- model = None
-
- # force function calling for reliability, see https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice
- # TODO(matt) move into LLMConfig
- # TODO: This vllm checking is very brittle and is a patch at most
- tool_choice = None
- if self.requires_auto_tool_choice(llm_config):
- tool_choice = "auto"
- elif tools:
- # only set if tools is non-Null
- tool_choice = "required"
-
- if force_tool_call is not None:
- tool_choice = ToolFunctionChoice(type="function", function=ToolFunctionChoiceFunctionCall(name=force_tool_call))
-
- data = ChatCompletionRequest(
- model=model,
- messages=fill_image_content_in_messages(openai_message_list, messages),
- tools=[OpenAITool(type="function", function=f) for f in tools] if tools else None,
- tool_choice=tool_choice,
- user=str(),
- max_completion_tokens=llm_config.max_tokens,
- # NOTE: the reasoners that don't support temperature require 1.0, not None
- temperature=llm_config.temperature if supports_temperature_param(model) else 1.0,
- )
-
- # Add verbosity control for GPT-5 models
- if supports_verbosity_control(model) and llm_config.verbosity:
- data.verbosity = llm_config.verbosity
-
- # Add reasoning effort control for reasoning models
- if is_openai_reasoning_model(model) and llm_config.reasoning_effort:
- data.reasoning_effort = llm_config.reasoning_effort
-
- if llm_config.frequency_penalty is not None:
- data.frequency_penalty = llm_config.frequency_penalty
-
- if tools and supports_parallel_tool_calling(model):
- data.parallel_tool_calls = False
-
- # always set user id for openai requests
- if self.actor:
- data.user = self.actor.id
-
- if llm_config.model_endpoint == LETTA_MODEL_ENDPOINT:
- if not self.actor:
- # override user id for inference.letta.com
- import uuid
-
- data.user = str(uuid.UUID(int=0))
-
- data.model = "memgpt-openai"
-
- if data.tools is not None and len(data.tools) > 0:
- # Convert to structured output style (which has 'strict' and no optionals)
- for tool in data.tools:
- if supports_structured_output(llm_config):
- try:
- structured_output_version = convert_to_structured_output(tool.function.model_dump())
- tool.function = FunctionSchema(**structured_output_version)
- except ValueError as e:
- logger.warning(f"Failed to convert tool function to structured output, tool={tool}, error={e}")
- request_data = data.model_dump(exclude_unset=True)
- return request_data
-
- @trace_method
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying synchronous request to OpenAI API and returns raw response dict.
- """
- client = OpenAI(**self._prepare_client_kwargs(llm_config))
- response: ChatCompletion = client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying asynchronous request to OpenAI API and returns raw response dict.
- """
- kwargs = await self._prepare_client_kwargs_async(llm_config)
- client = AsyncOpenAI(**kwargs)
- response: ChatCompletion = await client.chat.completions.create(**request_data)
- return response.model_dump()
-
- def is_reasoning_model(self, llm_config: LLMConfig) -> bool:
- return is_openai_reasoning_model(llm_config.model)
-
- @trace_method
- def convert_response_to_chat_completion(
- self,
- response_data: dict,
- input_messages: List[PydanticMessage], # Included for consistency, maybe used later
- llm_config: LLMConfig,
- ) -> ChatCompletionResponse:
- """
- Converts raw OpenAI response dict into the ChatCompletionResponse Pydantic model.
- Handles potential extraction of inner thoughts if they were added via kwargs.
- """
- # OpenAI's response structure directly maps to ChatCompletionResponse
- # We just need to instantiate the Pydantic model for validation and type safety.
- chat_completion_response = ChatCompletionResponse(**response_data)
- chat_completion_response = self._fix_truncated_json_response(chat_completion_response)
- # Unpack inner thoughts if they were embedded in function arguments
- if llm_config.put_inner_thoughts_in_kwargs:
- chat_completion_response = unpack_all_inner_thoughts_from_kwargs(
- response=chat_completion_response, inner_thoughts_key=INNER_THOUGHTS_KWARG
- )
-
- # If we used a reasoning model, create a content part for the ommitted reasoning
- if self.is_reasoning_model(llm_config):
- chat_completion_response.choices[0].message.omitted_reasoning_content = True
-
- return chat_completion_response
-
- @trace_method
- async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[ChatCompletionChunk]:
- """
- Performs underlying asynchronous streaming request to OpenAI and returns the async stream iterator.
- """
- kwargs = await self._prepare_client_kwargs_async(llm_config)
- client = AsyncOpenAI(**kwargs)
- response_stream: AsyncStream[ChatCompletionChunk] = await client.chat.completions.create(
- **request_data, stream=True, stream_options={"include_usage": True}
- )
- return response_stream
-
- @trace_method
- async def request_embeddings(self, inputs: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
- """Request embeddings given texts and embedding config with chunking and retry logic"""
- if not inputs:
- return []
-
- kwargs = self._prepare_client_kwargs_embedding(embedding_config)
- client = AsyncOpenAI(**kwargs)
-
- # track results by original index to maintain order
- results = [None] * len(inputs)
-
- # queue of (start_idx, chunk_inputs) to process
- chunks_to_process = [(i, inputs[i : i + 2048]) for i in range(0, len(inputs), 2048)]
-
- min_chunk_size = 256
-
- while chunks_to_process:
- tasks = []
- task_metadata = []
-
- for start_idx, chunk_inputs in chunks_to_process:
- task = client.embeddings.create(model=embedding_config.embedding_model, input=chunk_inputs)
- tasks.append(task)
- task_metadata.append((start_idx, chunk_inputs))
-
- task_results = await asyncio.gather(*tasks, return_exceptions=True)
-
- failed_chunks = []
- for (start_idx, chunk_inputs), result in zip(task_metadata, task_results):
- if isinstance(result, Exception):
- # check if we can retry with smaller chunks
- if len(chunk_inputs) > min_chunk_size:
- # split chunk in half and queue for retry
- mid = len(chunk_inputs) // 2
- failed_chunks.append((start_idx, chunk_inputs[:mid]))
- failed_chunks.append((start_idx + mid, chunk_inputs[mid:]))
- else:
- # can't split further, re-raise the error
- logger.error(f"Failed to get embeddings for chunk starting at {start_idx} even with minimum size {min_chunk_size}")
- raise result
- else:
- embeddings = [r.embedding for r in result.data]
- for i, embedding in enumerate(embeddings):
- results[start_idx + i] = embedding
-
- chunks_to_process = failed_chunks
-
- return results
-
- @trace_method
- def handle_llm_error(self, e: Exception) -> Exception:
- """
- Maps OpenAI-specific errors to common LLMError types.
- """
- if isinstance(e, openai.APITimeoutError):
- timeout_duration = getattr(e, "timeout", "unknown")
- logger.warning(f"[OpenAI] Request timeout after {timeout_duration} seconds: {e}")
- return LLMTimeoutError(
- message=f"Request to OpenAI timed out: {str(e)}",
- code=ErrorCode.TIMEOUT,
- details={
- "timeout_duration": timeout_duration,
- "cause": str(e.__cause__) if e.__cause__ else None,
- },
- )
-
- if isinstance(e, openai.APIConnectionError):
- logger.warning(f"[OpenAI] API connection error: {e}")
- return LLMConnectionError(
- message=f"Failed to connect to OpenAI: {str(e)}",
- code=ErrorCode.INTERNAL_SERVER_ERROR,
- details={"cause": str(e.__cause__) if e.__cause__ else None},
- )
-
- if isinstance(e, openai.RateLimitError):
- logger.warning(f"[OpenAI] Rate limited (429). Consider backoff. Error: {e}")
- return LLMRateLimitError(
- message=f"Rate limited by OpenAI: {str(e)}",
- code=ErrorCode.RATE_LIMIT_EXCEEDED,
- details=e.body, # Include body which often has rate limit details
- )
-
- if isinstance(e, openai.BadRequestError):
- logger.warning(f"[OpenAI] Bad request (400): {str(e)}")
- # BadRequestError can signify different issues (e.g., invalid args, context length)
- # Check for context_length_exceeded error code in the error body
- error_code = None
- if e.body and isinstance(e.body, dict):
- error_details = e.body.get("error", {})
- if isinstance(error_details, dict):
- error_code = error_details.get("code")
-
- # Check both the error code and message content for context length issues
- if (
- error_code == "context_length_exceeded"
- or "This model's maximum context length is" in str(e)
- or "Input tokens exceed the configured limit" in str(e)
- ):
- return ContextWindowExceededError(
- message=f"Bad request to OpenAI (context window exceeded): {str(e)}",
- )
- else:
- return LLMBadRequestError(
- message=f"Bad request to OpenAI: {str(e)}",
- code=ErrorCode.INVALID_ARGUMENT, # Or more specific if detectable
- details=e.body,
- )
-
- if isinstance(e, openai.AuthenticationError):
- logger.error(f"[OpenAI] Authentication error (401): {str(e)}") # More severe log level
- return LLMAuthenticationError(
- message=f"Authentication failed with OpenAI: {str(e)}", code=ErrorCode.UNAUTHENTICATED, details=e.body
- )
-
- if isinstance(e, openai.PermissionDeniedError):
- logger.error(f"[OpenAI] Permission denied (403): {str(e)}") # More severe log level
- return LLMPermissionDeniedError(
- message=f"Permission denied by OpenAI: {str(e)}", code=ErrorCode.PERMISSION_DENIED, details=e.body
- )
-
- if isinstance(e, openai.NotFoundError):
- logger.warning(f"[OpenAI] Resource not found (404): {str(e)}")
- # Could be invalid model name, etc.
- return LLMNotFoundError(message=f"Resource not found in OpenAI: {str(e)}", code=ErrorCode.NOT_FOUND, details=e.body)
-
- if isinstance(e, openai.UnprocessableEntityError):
- logger.warning(f"[OpenAI] Unprocessable entity (422): {str(e)}")
- return LLMUnprocessableEntityError(
- message=f"Invalid request content for OpenAI: {str(e)}",
- code=ErrorCode.INVALID_ARGUMENT, # Usually validation errors
- details=e.body,
- )
-
- # General API error catch-all
- if isinstance(e, openai.APIStatusError):
- logger.warning(f"[OpenAI] API status error ({e.status_code}): {str(e)}")
- # Map based on status code potentially
- if e.status_code >= 500:
- error_cls = LLMServerError
- error_code = ErrorCode.INTERNAL_SERVER_ERROR
- else:
- # Treat other 4xx as bad requests if not caught above
- error_cls = LLMBadRequestError
- error_code = ErrorCode.INVALID_ARGUMENT
-
- return error_cls(
- message=f"OpenAI API error: {str(e)}",
- code=error_code,
- details={
- "status_code": e.status_code,
- "response": str(e.response),
- "body": e.body,
- },
- )
-
- # Fallback for unexpected errors
- return super().handle_llm_error(e)
-
-
-def fill_image_content_in_messages(openai_message_list: List[dict], pydantic_message_list: List[PydanticMessage]) -> List[dict]:
- """
- Converts image content to openai format.
- """
-
- if len(openai_message_list) != len(pydantic_message_list):
- return openai_message_list
-
- new_message_list = []
- for idx in range(len(openai_message_list)):
- openai_message, pydantic_message = openai_message_list[idx], pydantic_message_list[idx]
- if pydantic_message.role != "user":
- new_message_list.append(openai_message)
- continue
-
- if not isinstance(pydantic_message.content, list) or (
- len(pydantic_message.content) == 1 and pydantic_message.content[0].type == MessageContentType.text
- ):
- new_message_list.append(openai_message)
- continue
-
- message_content = []
- for content in pydantic_message.content:
- if content.type == MessageContentType.text:
- message_content.append(
- {
- "type": "text",
- "text": content.text,
- }
- )
- elif content.type == MessageContentType.image:
- message_content.append(
- {
- "type": "image_url",
- "image_url": {
- "url": f"data:{content.source.media_type};base64,{content.source.data}",
- "detail": content.source.detail or "auto",
- },
- }
- )
- else:
- raise ValueError(f"Unsupported content type {content.type}")
-
- new_message_list.append({"role": "user", "content": message_content})
-
- return new_message_list
diff --git a/letta/llm_api/sample_response_jsons/aws_bedrock.json b/letta/llm_api/sample_response_jsons/aws_bedrock.json
deleted file mode 100644
index c8ff79c8..00000000
--- a/letta/llm_api/sample_response_jsons/aws_bedrock.json
+++ /dev/null
@@ -1,38 +0,0 @@
-{
- "id": "msg_123",
- "type": "message",
- "role": "assistant",
- "model": "anthropic.claude-3-5-sonnet-20241022-v2:0",
- "content": [
- {
- "type": "text",
- "text": "I see the Firefox icon. Let me click on it and then navigate to a weather website."
- },
- {
- "type": "tool_use",
- "id": "toolu_123",
- "name": "computer",
- "input": {
- "action": "mouse_move",
- "coordinate": [
- 708,
- 736
- ]
- }
- },
- {
- "type": "tool_use",
- "id": "toolu_234",
- "name": "computer",
- "input": {
- "action": "left_click"
- }
- }
- ],
- "stop_reason": "tool_use",
- "stop_sequence": null,
- "usage": {
- "input_tokens": 3391,
- "output_tokens": 132
- }
-}
diff --git a/letta/llm_api/sample_response_jsons/lmstudio_embedding_list.json b/letta/llm_api/sample_response_jsons/lmstudio_embedding_list.json
deleted file mode 100644
index dc2a2d2c..00000000
--- a/letta/llm_api/sample_response_jsons/lmstudio_embedding_list.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "object": "list",
- "data": [
- {
- "id": "text-embedding-nomic-embed-text-v1.5",
- "object": "model",
- "type": "embeddings",
- "publisher": "nomic-ai",
- "arch": "nomic-bert",
- "compatibility_type": "gguf",
- "quantization": "Q4_0",
- "state": "not-loaded",
- "max_context_length": 2048
- }
- ]
-}
diff --git a/letta/llm_api/sample_response_jsons/lmstudio_model_list.json b/letta/llm_api/sample_response_jsons/lmstudio_model_list.json
deleted file mode 100644
index 8b7e7b70..00000000
--- a/letta/llm_api/sample_response_jsons/lmstudio_model_list.json
+++ /dev/null
@@ -1,15 +0,0 @@
- {
- "object": "list",
- "data": [
- {
- "id": "qwen2-vl-7b-instruct",
- "object": "model",
- "type": "vlm",
- "publisher": "mlx-community",
- "arch": "qwen2_vl",
- "compatibility_type": "mlx",
- "quantization": "4bit",
- "state": "not-loaded",
- "max_context_length": 32768
- },
- ...,
diff --git a/letta/llm_api/together_client.py b/letta/llm_api/together_client.py
deleted file mode 100644
index 98ebf768..00000000
--- a/letta/llm_api/together_client.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-from typing import List
-
-from openai import AsyncOpenAI, OpenAI
-from openai.types.chat.chat_completion import ChatCompletion
-
-from letta.llm_api.openai_client import OpenAIClient
-from letta.otel.tracing import trace_method
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.llm_config import LLMConfig
-from letta.settings import model_settings
-
-
-class TogetherClient(OpenAIClient):
- def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
- return True
-
- @trace_method
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying synchronous request to OpenAI API and returns raw response dict.
- """
- api_key, _, _ = self.get_byok_overrides(llm_config)
-
- if not api_key:
- api_key = model_settings.together_api_key or os.environ.get("TOGETHER_API_KEY")
- client = OpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying asynchronous request to OpenAI API and returns raw response dict.
- """
- api_key, _, _ = await self.get_byok_overrides_async(llm_config)
-
- if not api_key:
- api_key = model_settings.together_api_key or os.environ.get("TOGETHER_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = await client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_embeddings(self, inputs: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
- """Request embeddings given texts and embedding config"""
- api_key = model_settings.together_api_key or os.environ.get("TOGETHER_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=embedding_config.embedding_endpoint)
- response = await client.embeddings.create(model=embedding_config.embedding_model, input=inputs)
-
- # TODO: add total usage
- return [r.embedding for r in response.data]
diff --git a/letta/llm_api/xai_client.py b/letta/llm_api/xai_client.py
deleted file mode 100644
index b9d37a95..00000000
--- a/letta/llm_api/xai_client.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-from typing import List, Optional
-
-from openai import AsyncOpenAI, AsyncStream, OpenAI
-from openai.types.chat.chat_completion import ChatCompletion
-from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-
-from letta.llm_api.openai_client import OpenAIClient
-from letta.otel.tracing import trace_method
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.message import Message as PydanticMessage
-from letta.settings import model_settings
-
-
-class XAIClient(OpenAIClient):
- def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
- return False
-
- def supports_structured_output(self, llm_config: LLMConfig) -> bool:
- return False
-
- @trace_method
- def build_request_data(
- self,
- messages: List[PydanticMessage],
- llm_config: LLMConfig,
- tools: Optional[List[dict]] = None,
- force_tool_call: Optional[str] = None,
- ) -> dict:
- data = super().build_request_data(messages, llm_config, tools, force_tool_call)
-
- # Specific bug for the mini models (as of Apr 14, 2025)
- # 400 - {'code': 'Client specified an invalid argument', 'error': 'Argument not supported on this model: presencePenalty'}
- # 400 - {'code': 'Client specified an invalid argument', 'error': 'Argument not supported on this model: frequencyPenalty'}
- if "grok-3-mini-" in llm_config.model:
- data.pop("presence_penalty", None)
- data.pop("frequency_penalty", None)
-
- return data
-
- @trace_method
- def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying synchronous request to OpenAI API and returns raw response dict.
- """
- api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
- client = OpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
- """
- Performs underlying asynchronous request to OpenAI API and returns raw response dict.
- """
- api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
-
- response: ChatCompletion = await client.chat.completions.create(**request_data)
- return response.model_dump()
-
- @trace_method
- async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[ChatCompletionChunk]:
- """
- Performs underlying asynchronous streaming request to OpenAI and returns the async stream iterator.
- """
- api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
- response_stream: AsyncStream[ChatCompletionChunk] = await client.chat.completions.create(
- **request_data, stream=True, stream_options={"include_usage": True}
- )
- return response_stream
-
- @trace_method
- async def request_embeddings(self, inputs: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
- """Request embeddings given texts and embedding config"""
- api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
- client = AsyncOpenAI(api_key=api_key, base_url=embedding_config.embedding_endpoint)
- response = await client.embeddings.create(model=embedding_config.embedding_model, input=inputs)
-
- # TODO: add total usage
- return [r.embedding for r in response.data]
diff --git a/letta/local_llm/README.md b/letta/local_llm/README.md
deleted file mode 100644
index ca30eec8..00000000
--- a/letta/local_llm/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Letta + local LLMs
-
-See [https://letta.readme.io/docs/local_llm](https://letta.readme.io/docs/local_llm) for documentation on running Letta with custom LLM backends.
diff --git a/letta/local_llm/__init__.py b/letta/local_llm/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/local_llm/chat_completion_proxy.py b/letta/local_llm/chat_completion_proxy.py
deleted file mode 100644
index 1129b125..00000000
--- a/letta/local_llm/chat_completion_proxy.py
+++ /dev/null
@@ -1,284 +0,0 @@
-"""Key idea: create drop-in replacement for agent's ChatCompletion call that runs on an OpenLLM backend"""
-
-import uuid
-
-import requests
-
-from letta.constants import CLI_WARNING_PREFIX
-from letta.errors import LocalLLMConnectionError, LocalLLMError
-from letta.helpers.datetime_helpers import get_utc_time_int
-from letta.helpers.json_helpers import json_dumps
-from letta.local_llm.constants import DEFAULT_WRAPPER
-from letta.local_llm.function_parser import patch_function
-from letta.local_llm.grammars.gbnf_grammar_generator import create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation
-from letta.local_llm.koboldcpp.api import get_koboldcpp_completion
-from letta.local_llm.llamacpp.api import get_llamacpp_completion
-from letta.local_llm.llm_chat_completion_wrappers import simple_summary_wrapper
-from letta.local_llm.lmstudio.api import get_lmstudio_completion, get_lmstudio_completion_chatcompletions
-from letta.local_llm.ollama.api import get_ollama_completion
-from letta.local_llm.utils import count_tokens, get_available_wrappers
-from letta.local_llm.vllm.api import get_vllm_completion
-from letta.local_llm.webui.api import get_webui_completion
-from letta.local_llm.webui.legacy_api import get_webui_completion as get_webui_completion_legacy
-from letta.otel.tracing import log_event
-from letta.prompts.gpt_summarize import SYSTEM as SUMMARIZE_SYSTEM_MESSAGE
-from letta.schemas.message import Message as PydanticMessage
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Choice, Message, ToolCall, UsageStatistics
-from letta.utils import get_tool_call_id
-
-has_shown_warning = False
-grammar_supported_backends = ["koboldcpp", "llamacpp", "webui", "webui-legacy"]
-
-
-def get_chat_completion(
- model,
- # no model required (except for Ollama), since the model is fixed to whatever you set in your own backend
- messages,
- functions=None,
- functions_python=None,
- function_call="auto",
- context_window=None,
- user=None,
- # required
- wrapper=None,
- endpoint=None,
- endpoint_type=None,
- # optional cleanup
- function_correction=True,
- # extra hints to allow for additional prompt formatting hacks
- # TODO this could alternatively be supported via passing function_call="send_message" into the wrapper
- first_message=False,
- # optional auth headers
- auth_type=None,
- auth_key=None,
-) -> ChatCompletionResponse:
- from letta.utils import printd
-
- assert context_window is not None, "Local LLM calls need the context length to be explicitly set"
- assert endpoint is not None, "Local LLM calls need the endpoint (eg http://localendpoint:1234) to be explicitly set"
- assert endpoint_type is not None, "Local LLM calls need the endpoint type (eg webui) to be explicitly set"
- global has_shown_warning
- grammar = None
-
- # TODO: eventually just process Message object
- if not isinstance(messages[0], dict):
- messages = PydanticMessage.to_openai_dicts_from_list(messages)
-
- if function_call is not None and function_call != "auto":
- raise ValueError(f"function_call == {function_call} not supported (auto or None only)")
-
- available_wrappers = get_available_wrappers()
- documentation = None
-
- # Special case for if the call we're making is coming from the summarizer
- if messages[0]["role"] == "system" and messages[0]["content"].strip() == SUMMARIZE_SYSTEM_MESSAGE.strip():
- llm_wrapper = simple_summary_wrapper.SimpleSummaryWrapper()
-
- # Select a default prompt formatter
- elif wrapper is None:
- # Warn the user that we're using the fallback
- if not has_shown_warning:
- print(f"{CLI_WARNING_PREFIX}no prompt formatter specified for local LLM, using the default formatter")
- has_shown_warning = True
-
- llm_wrapper = DEFAULT_WRAPPER()
-
- # User provided an incorrect prompt formatter
- elif wrapper not in available_wrappers:
- raise ValueError(f"Could not find requested wrapper '{wrapper} in available wrappers list:\n{', '.join(available_wrappers)}")
-
- # User provided a correct prompt formatter
- else:
- llm_wrapper = available_wrappers[wrapper]
-
- # If the wrapper uses grammar, generate the grammar using the grammar generating function
- # TODO move this to a flag
- if wrapper is not None and "grammar" in wrapper:
- # When using grammars, we don't want to do any extras output tricks like appending a response prefix
- setattr(llm_wrapper, "assistant_prefix_extra_first_message", "")
- setattr(llm_wrapper, "assistant_prefix_extra", "")
-
- # TODO find a better way to do this than string matching (eg an attribute)
- if "noforce" in wrapper:
- # "noforce" means that the prompt formatter expects inner thoughts as a top-level parameter
- # this is closer to the OpenAI style since it allows for messages w/o any function calls
- # however, with bad LLMs it makes it easier for the LLM to "forget" to call any of the functions
- grammar, documentation = generate_grammar_and_documentation(
- functions_python=functions_python,
- add_inner_thoughts_top_level=True,
- add_inner_thoughts_param_level=False,
- allow_only_inner_thoughts=True,
- )
- else:
- # otherwise, the other prompt formatters will insert inner thoughts as a function call parameter (by default)
- # this means that every response from the LLM will be required to call a function
- grammar, documentation = generate_grammar_and_documentation(
- functions_python=functions_python,
- add_inner_thoughts_top_level=False,
- add_inner_thoughts_param_level=True,
- allow_only_inner_thoughts=False,
- )
- printd(grammar)
-
- if grammar is not None and endpoint_type not in grammar_supported_backends:
- print(
- f"{CLI_WARNING_PREFIX}grammars are currently not supported when using {endpoint_type} as the Letta local LLM backend (supported: {', '.join(grammar_supported_backends)})"
- )
- grammar = None
-
- # First step: turn the message sequence into a prompt that the model expects
- try:
- # if hasattr(llm_wrapper, "supports_first_message"):
- if hasattr(llm_wrapper, "supports_first_message") and llm_wrapper.supports_first_message:
- prompt = llm_wrapper.chat_completion_to_prompt(
- messages=messages, functions=functions, first_message=first_message, function_documentation=documentation
- )
- else:
- prompt = llm_wrapper.chat_completion_to_prompt(messages=messages, functions=functions, function_documentation=documentation)
-
- printd(prompt)
- except Exception as e:
- print(e)
- raise LocalLLMError(
- f"Failed to convert ChatCompletion messages into prompt string with wrapper {str(llm_wrapper)} - error: {str(e)}"
- )
-
- # get the schema for the model
-
- """
- if functions_python is not None:
- model_schema = generate_schema(functions)
- else:
- model_schema = None
- """
- log_event(name="llm_request_sent", attributes={"prompt": prompt, "grammar": grammar})
- # Run the LLM
- try:
- result_reasoning = None
- if endpoint_type == "webui":
- result, usage = get_webui_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=grammar)
- elif endpoint_type == "webui-legacy":
- result, usage = get_webui_completion_legacy(endpoint, auth_type, auth_key, prompt, context_window, grammar=grammar)
- elif endpoint_type == "lmstudio-chatcompletions":
- result, usage, result_reasoning = get_lmstudio_completion_chatcompletions(endpoint, auth_type, auth_key, model, messages)
- elif endpoint_type == "lmstudio":
- result, usage = get_lmstudio_completion(endpoint, auth_type, auth_key, prompt, context_window, api="completions")
- elif endpoint_type == "lmstudio-legacy":
- result, usage = get_lmstudio_completion(endpoint, auth_type, auth_key, prompt, context_window, api="chat")
- elif endpoint_type == "llamacpp":
- result, usage = get_llamacpp_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=grammar)
- elif endpoint_type == "koboldcpp":
- result, usage = get_koboldcpp_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=grammar)
- elif endpoint_type == "ollama":
- result, usage = get_ollama_completion(endpoint, auth_type, auth_key, model, prompt, context_window)
- elif endpoint_type == "vllm":
- result, usage = get_vllm_completion(endpoint, auth_type, auth_key, model, prompt, context_window, user)
- else:
- raise LocalLLMError(
- f"Invalid endpoint type {endpoint_type}, please set variable depending on your backend (webui, lmstudio, llamacpp, koboldcpp)"
- )
- except requests.exceptions.ConnectionError as e:
- raise LocalLLMConnectionError(f"Unable to connect to endpoint {endpoint}")
-
- attributes = usage if isinstance(usage, dict) else {"usage": usage}
- attributes.update({"result": result})
- log_event(name="llm_request_sent", attributes=attributes)
-
- if result is None or result == "":
- raise LocalLLMError(f"Got back an empty response string from {endpoint}")
- printd(f"Raw LLM output:\n====\n{result}\n====")
-
- try:
- if hasattr(llm_wrapper, "supports_first_message") and llm_wrapper.supports_first_message:
- chat_completion_result = llm_wrapper.output_to_chat_completion_response(result, first_message=first_message)
- else:
- chat_completion_result = llm_wrapper.output_to_chat_completion_response(result)
- printd(json_dumps(chat_completion_result, indent=2))
- except Exception as e:
- raise LocalLLMError(f"Failed to parse JSON from local LLM response - error: {str(e)}")
-
- # Run through some manual function correction (optional)
- if function_correction:
- chat_completion_result = patch_function(message_history=messages, new_message=chat_completion_result)
-
- # Fill in potential missing usage information (used for tracking token use)
- if not ("prompt_tokens" in usage and "completion_tokens" in usage and "total_tokens" in usage):
- raise LocalLLMError(f"usage dict in response was missing fields ({usage})")
-
- if usage["prompt_tokens"] is None:
- printd("usage dict was missing prompt_tokens, computing on-the-fly...")
- usage["prompt_tokens"] = count_tokens(prompt)
-
- # NOTE: we should compute on-the-fly anyways since we might have to correct for errors during JSON parsing
- usage["completion_tokens"] = count_tokens(json_dumps(chat_completion_result))
- """
- if usage["completion_tokens"] is None:
- printd(f"usage dict was missing completion_tokens, computing on-the-fly...")
- # chat_completion_result is dict with 'role' and 'content'
- # token counter wants a string
- usage["completion_tokens"] = count_tokens(json_dumps(chat_completion_result))
- """
-
- # NOTE: this is the token count that matters most
- if usage["total_tokens"] is None:
- printd("usage dict was missing total_tokens, computing on-the-fly...")
- usage["total_tokens"] = usage["prompt_tokens"] + usage["completion_tokens"]
-
- # unpack with response.choices[0].message.content
- response = ChatCompletionResponse(
- id=str(uuid.uuid4()), # TODO something better?
- choices=[
- Choice(
- finish_reason="stop",
- index=0,
- message=Message(
- role=chat_completion_result["role"],
- content=result_reasoning if result_reasoning is not None else chat_completion_result["content"],
- tool_calls=(
- [ToolCall(id=get_tool_call_id(), type="function", function=chat_completion_result["function_call"])]
- if "function_call" in chat_completion_result
- else []
- ),
- ),
- )
- ],
- created=get_utc_time_int(),
- model=model,
- # "This fingerprint represents the backend configuration that the model runs with."
- # system_fingerprint=user if user is not None else "null",
- system_fingerprint=None,
- object="chat.completion",
- usage=UsageStatistics(**usage),
- )
- printd(response)
- return response
-
-
-def generate_grammar_and_documentation(
- functions_python: dict,
- add_inner_thoughts_top_level: bool,
- add_inner_thoughts_param_level: bool,
- allow_only_inner_thoughts: bool,
-):
- from letta.utils import printd
-
- assert not (add_inner_thoughts_top_level and add_inner_thoughts_param_level), (
- "Can only place inner thoughts in one location in the grammar generator"
- )
-
- grammar_function_models = []
- # create_dynamic_model_from_function will add inner thoughts to the function parameters if add_inner_thoughts is True.
- # generate_gbnf_grammar_and_documentation will add inner thoughts to the outer object of the function parameters if add_inner_thoughts is True.
- for key, func in functions_python.items():
- grammar_function_models.append(create_dynamic_model_from_function(func, add_inner_thoughts=add_inner_thoughts_param_level))
- grammar, documentation = generate_gbnf_grammar_and_documentation(
- grammar_function_models,
- outer_object_name="function",
- outer_object_content="params",
- model_prefix="function",
- fields_prefix="params",
- add_inner_thoughts=add_inner_thoughts_top_level,
- allow_only_inner_thoughts=allow_only_inner_thoughts,
- )
- printd(grammar)
- return grammar, documentation
diff --git a/letta/local_llm/constants.py b/letta/local_llm/constants.py
deleted file mode 100644
index 2b51101d..00000000
--- a/letta/local_llm/constants.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from letta.local_llm.llm_chat_completion_wrappers.chatml import ChatMLInnerMonologueWrapper
-
-DEFAULT_WRAPPER = ChatMLInnerMonologueWrapper
-DEFAULT_WRAPPER_NAME = "chatml"
-
-INNER_THOUGHTS_KWARG = "thinking"
-INNER_THOUGHTS_KWARG_VERTEX = "thinking"
-VALID_INNER_THOUGHTS_KWARGS = ("thinking", "inner_thoughts")
-INNER_THOUGHTS_KWARG_DESCRIPTION = "Deep inner monologue private to you only."
-INNER_THOUGHTS_KWARG_DESCRIPTION_GO_FIRST = f"Deep inner monologue private to you only. Think before you act, so always generate arg '{INNER_THOUGHTS_KWARG}' first before any other arg."
-INNER_THOUGHTS_CLI_SYMBOL = "💭"
-
-ASSISTANT_MESSAGE_CLI_SYMBOL = "🤖"
diff --git a/letta/local_llm/function_parser.py b/letta/local_llm/function_parser.py
deleted file mode 100644
index d6636363..00000000
--- a/letta/local_llm/function_parser.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import copy
-import json
-
-from letta.helpers.json_helpers import json_dumps, json_loads
-
-NO_HEARTBEAT_FUNCS = ["send_message"]
-
-
-def insert_heartbeat(message):
- # message_copy = message.copy()
- message_copy = copy.deepcopy(message)
-
- if message_copy.get("function_call"):
- # function_name = message.get("function_call").get("name")
- params = message_copy.get("function_call").get("arguments")
- params = json_loads(params)
- params["request_heartbeat"] = True
- message_copy["function_call"]["arguments"] = json_dumps(params)
-
- elif message_copy.get("tool_call"):
- # function_name = message.get("tool_calls")[0].get("function").get("name")
- params = message_copy.get("tool_calls")[0].get("function").get("arguments")
- params = json_loads(params)
- params["request_heartbeat"] = True
- message_copy["tools_calls"][0]["function"]["arguments"] = json_dumps(params)
-
- return message_copy
-
-
-def heartbeat_correction(message_history, new_message):
- """Add heartbeats where we think the agent forgot to add them themselves
-
- If the last message in the stack is a user message and the new message is an assistant func call, fix the heartbeat
-
- See: https://github.com/letta-ai/letta/issues/601
- """
- if len(message_history) < 1:
- return None
-
- last_message_was_user = False
- if message_history[-1]["role"] == "user":
- try:
- content = json_loads(message_history[-1]["content"])
- except json.JSONDecodeError:
- return None
- # Check if it's a user message or system message
- if content["type"] == "user_message":
- last_message_was_user = True
-
- new_message_is_heartbeat_function = False
- if new_message["role"] == "assistant":
- if new_message.get("function_call") or new_message.get("tool_calls"):
- if new_message.get("function_call"):
- function_name = new_message.get("function_call").get("name")
- elif new_message.get("tool_calls"):
- function_name = new_message.get("tool_calls")[0].get("function").get("name")
- if function_name not in NO_HEARTBEAT_FUNCS:
- new_message_is_heartbeat_function = True
-
- if last_message_was_user and new_message_is_heartbeat_function:
- return insert_heartbeat(new_message)
- else:
- return None
-
-
-def patch_function(message_history, new_message):
- corrected_output = heartbeat_correction(message_history=message_history, new_message=new_message)
- return corrected_output if corrected_output is not None else new_message
diff --git a/letta/local_llm/grammars/__init__.py b/letta/local_llm/grammars/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/local_llm/grammars/gbnf_grammar_generator.py b/letta/local_llm/grammars/gbnf_grammar_generator.py
deleted file mode 100644
index 536bf8e2..00000000
--- a/letta/local_llm/grammars/gbnf_grammar_generator.py
+++ /dev/null
@@ -1,1313 +0,0 @@
-import inspect
-import json
-import re
-from copy import copy
-from enum import Enum
-from inspect import getdoc, isclass
-from types import NoneType
-from typing import Any, Callable, List, Optional, Tuple, Type, Union, _GenericAlias, get_args, get_origin
-
-from docstring_parser import parse
-from pydantic import BaseModel, create_model
-
-from letta.helpers.json_helpers import json_dumps
-
-
-class PydanticDataType(Enum):
- """
- Defines the data types supported by the grammar_generator.
-
- Attributes:
- STRING (str): Represents a string data type.
- BOOLEAN (str): Represents a boolean data type.
- INTEGER (str): Represents an integer data type.
- FLOAT (str): Represents a float data type.
- OBJECT (str): Represents an object data type.
- ARRAY (str): Represents an array data type.
- ENUM (str): Represents an enum data type.
- CUSTOM_CLASS (str): Represents a custom class data type.
- """
-
- STRING = "string"
- TRIPLE_QUOTED_STRING = "triple_quoted_string"
- MARKDOWN_CODE_BLOCK = "markdown_code_block"
- BOOLEAN = "boolean"
- INTEGER = "integer"
- FLOAT = "float"
- OBJECT = "object"
- ARRAY = "array"
- ENUM = "enum"
- ANY = "any"
- NULL = "null"
- CUSTOM_CLASS = "custom-class"
- CUSTOM_DICT = "custom-dict"
- SET = "set"
-
-
-def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str:
- if isclass(pydantic_type) and issubclass(pydantic_type, str):
- return PydanticDataType.STRING.value
- elif isclass(pydantic_type) and issubclass(pydantic_type, bool):
- return PydanticDataType.BOOLEAN.value
- elif isclass(pydantic_type) and issubclass(pydantic_type, int):
- return PydanticDataType.INTEGER.value
- elif isclass(pydantic_type) and issubclass(pydantic_type, float):
- return PydanticDataType.FLOAT.value
- elif isclass(pydantic_type) and issubclass(pydantic_type, Enum):
- return PydanticDataType.ENUM.value
-
- elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel):
- return format_model_and_field_name(pydantic_type.__name__)
- elif get_origin(pydantic_type) == list:
- element_type = get_args(pydantic_type)[0]
- return f"{map_pydantic_type_to_gbnf(element_type)}-list"
- elif get_origin(pydantic_type) == set:
- element_type = get_args(pydantic_type)[0]
- return f"{map_pydantic_type_to_gbnf(element_type)}-set"
- elif get_origin(pydantic_type) == Union:
- union_types = get_args(pydantic_type)
- union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types]
- return f"union-{'-or-'.join(union_rules)}"
- elif get_origin(pydantic_type) == Optional:
- element_type = get_args(pydantic_type)[0]
- return f"optional-{map_pydantic_type_to_gbnf(element_type)}"
- elif isclass(pydantic_type):
- return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}"
- elif get_origin(pydantic_type) == dict:
- key_type, value_type = get_args(pydantic_type)
- return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}"
- else:
- return "unknown"
-
-
-def format_model_and_field_name(model_name: str) -> str:
- parts = re.findall("[A-Z][^A-Z]*", model_name)
- if not parts: # Check if the list is empty
- return model_name.lower().replace("_", "-")
- return "-".join(part.lower().replace("_", "-") for part in parts)
-
-
-def generate_list_rule(element_type):
- """
- Generate a GBNF rule for a list of a given element type.
-
- :param element_type: The type of the elements in the list (e.g., 'string').
- :return: A string representing the GBNF rule for a list of the given type.
- """
- rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list"
- element_rule = map_pydantic_type_to_gbnf(element_type)
- list_rule = rf'{rule_name} ::= "[" {element_rule} ("," {element_rule})* "]"'
- return list_rule
-
-
-def get_members_structure(cls, rule_name):
- if issubclass(cls, Enum):
- # Handle Enum types
- members = [f'"\\"{member.value}\\""' for name, member in cls.__members__.items()]
- return f"{cls.__name__.lower()} ::= " + " | ".join(members)
- if cls.__annotations__ and cls.__annotations__ != {}:
- result = f'{rule_name} ::= "{{"'
- type_list_rules = []
- # Modify this comprehension
- members = [
- f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}'
- for name, param_type in cls.__annotations__.items()
- if name != "self"
- ]
-
- result += '"," '.join(members)
- result += ' "}"'
- return result, type_list_rules
- elif rule_name == "custom-class-any":
- result = f"{rule_name} ::= "
- result += "value"
- type_list_rules = []
- return result, type_list_rules
- else:
- init_signature = inspect.signature(cls.__init__)
- parameters = init_signature.parameters
- result = f'{rule_name} ::= "{{"'
- type_list_rules = []
- # Modify this comprehension too
- members = [
- f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param.annotation)}'
- for name, param in parameters.items()
- if name != "self" and param.annotation != inspect.Parameter.empty
- ]
-
- result += '", "'.join(members)
- result += ' "}"'
- return result, type_list_rules
-
-
-def regex_to_gbnf(regex_pattern: str) -> str:
- """
- Translate a basic regex pattern to a GBNF rule.
- Note: This function handles only a subset of simple regex patterns.
- """
- gbnf_rule = regex_pattern
-
- # Translate common regex components to GBNF
- gbnf_rule = gbnf_rule.replace("\\d", "[0-9]")
- gbnf_rule = gbnf_rule.replace("\\s", "[ \t\n]")
-
- # Handle quantifiers and other regex syntax that is similar in GBNF
- # (e.g., '*', '+', '?', character classes)
-
- return gbnf_rule
-
-
-def generate_gbnf_integer_rules(max_digit=None, min_digit=None):
- """
-
- Generate GBNF Integer Rules
-
- Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits.
-
- Parameters:
- max_digit (int): The maximum number of digits for the integer. Default is None.
- min_digit (int): The minimum number of digits for the integer. Default is None.
-
- Returns:
- integer_rule (str): The identifier for the integer rule generated.
- additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits.
-
- """
- additional_rules = []
-
- # Define the rule identifier based on max_digit and min_digit
- integer_rule = "integer-part"
- if max_digit is not None:
- integer_rule += f"-max{max_digit}"
- if min_digit is not None:
- integer_rule += f"-min{min_digit}"
-
- # Handling Integer Rules
- if max_digit is not None or min_digit is not None:
- # Start with an empty rule part
- integer_rule_part = ""
-
- # Add mandatory digits as per min_digit
- if min_digit is not None:
- integer_rule_part += "[0-9] " * min_digit
-
- # Add optional digits up to max_digit
- if max_digit is not None:
- optional_digits = max_digit - (min_digit if min_digit is not None else 0)
- integer_rule_part += "".join(["[0-9]? " for _ in range(optional_digits)])
-
- # Trim the rule part and append it to additional rules
- integer_rule_part = integer_rule_part.strip()
- if integer_rule_part:
- additional_rules.append(f"{integer_rule} ::= {integer_rule_part}")
-
- return integer_rule, additional_rules
-
-
-def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None, min_precision=None):
- """
- Generate GBNF float rules based on the given constraints.
-
- :param max_digit: Maximum number of digits in the integer part (default: None)
- :param min_digit: Minimum number of digits in the integer part (default: None)
- :param max_precision: Maximum number of digits in the fractional part (default: None)
- :param min_precision: Minimum number of digits in the fractional part (default: None)
- :return: A tuple containing the float rule and additional rules as a list
-
- Example Usage:
- max_digit = 3
- min_digit = 1
- max_precision = 2
- min_precision = 1
- generate_gbnf_float_rules(max_digit, min_digit, max_precision, min_precision)
-
- Output:
- ('float-3-1-2-1', ['integer-part-max3-min1 ::= [0-9] [0-9] [0-9]?', 'fractional-part-max2-min1 ::= [0-9] [0-9]?', 'float-3-1-2-1 ::= integer-part-max3-min1 "." fractional-part-max2-min
- *1'])
-
- Note:
- GBNF stands for Generalized Backus-Naur Form, which is a notation technique to specify the syntax of programming languages or other formal grammars.
- """
- additional_rules = []
-
- # Define the integer part rule
- integer_part_rule = (
- "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + (f"-min{min_digit}" if min_digit is not None else "")
- )
-
- # Define the fractional part rule based on precision constraints
- fractional_part_rule = "fractional-part"
- fractional_rule_part = ""
- if max_precision is not None or min_precision is not None:
- fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + (
- f"-min{min_precision}" if min_precision is not None else ""
- )
- # Minimum number of digits
- fractional_rule_part = "[0-9]" * (min_precision if min_precision is not None else 1)
- # Optional additional digits
- fractional_rule_part += "".join(
- [" [0-9]?"] * ((max_precision - (min_precision if min_precision is not None else 1)) if max_precision is not None else 0)
- )
- additional_rules.append(f"{fractional_part_rule} ::= {fractional_rule_part}")
-
- # Define the float rule
- float_rule = f"float-{max_digit if max_digit is not None else 'X'}-{min_digit if min_digit is not None else 'X'}-{max_precision if max_precision is not None else 'X'}-{min_precision if min_precision is not None else 'X'}"
- additional_rules.append(f'{float_rule} ::= {integer_part_rule} "." {fractional_part_rule}')
-
- # Generating the integer part rule definition, if necessary
- if max_digit is not None or min_digit is not None:
- integer_rule_part = "[0-9]"
- if min_digit is not None and min_digit > 1:
- integer_rule_part += " [0-9]" * (min_digit - 1)
- if max_digit is not None:
- integer_rule_part += "".join([" [0-9]?"] * (max_digit - (min_digit if min_digit is not None else 1)))
- additional_rules.append(f"{integer_part_rule} ::= {integer_rule_part.strip()}")
-
- return float_rule, additional_rules
-
-
-def generate_gbnf_rule_for_type(
- model_name, field_name, field_type, is_optional, processed_models, created_rules, field_info=None
-) -> Tuple[str, list]:
- """
- Generate GBNF rule for a given field type.
-
- :param model_name: Name of the model.
-
- :param field_name: Name of the field.
- :param field_type: Type of the field.
- :param is_optional: Whether the field is optional.
- :param processed_models: List of processed models.
- :param created_rules: List of created rules.
- :param field_info: Additional information about the field (optional).
-
- :return: Tuple containing the GBNF type and a list of additional rules.
- :rtype: Tuple[str, list]
- """
- rules = []
-
- field_name = format_model_and_field_name(field_name)
- gbnf_type = map_pydantic_type_to_gbnf(field_type)
-
- if isclass(field_type) and issubclass(field_type, BaseModel):
- nested_model_name = format_model_and_field_name(field_type.__name__)
- nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules)
- rules.extend(nested_model_rules)
- gbnf_type, rules = nested_model_name, rules
- elif isclass(field_type) and issubclass(field_type, Enum):
- enum_values = [f'"\\"{e.value}\\""' for e in field_type] # Adding escaped quotes
- enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}"
- rules.append(enum_rule)
- gbnf_type, rules = model_name + "-" + field_name, rules
- elif get_origin(field_type) == list: # Array
- element_type = get_args(field_type)[0]
- element_rule_name, additional_rules = generate_gbnf_rule_for_type(
- model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
- )
- rules.extend(additional_rules)
- array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """
- rules.append(array_rule)
- gbnf_type, rules = model_name + "-" + field_name, rules
-
- elif get_origin(field_type) == set or field_type == set: # Array
- element_type = get_args(field_type)[0]
- element_rule_name, additional_rules = generate_gbnf_rule_for_type(
- model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
- )
- rules.extend(additional_rules)
- array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})* "]" """
- rules.append(array_rule)
- gbnf_type, rules = model_name + "-" + field_name, rules
-
- elif gbnf_type.startswith("custom-class-"):
- nested_model_rules, field_types = get_members_structure(field_type, gbnf_type)
- rules.append(nested_model_rules)
- elif gbnf_type.startswith("custom-dict-"):
- key_type, value_type = get_args(field_type)
-
- additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(
- model_name, f"{field_name}-key-type", key_type, is_optional, processed_models, created_rules
- )
- additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(
- model_name, f"{field_name}-value-type", value_type, is_optional, processed_models, created_rules
- )
- gbnf_type = rf'{gbnf_type} ::= "{{" ( {additional_key_type} ": " {additional_value_type} ("," "\n" ws {additional_key_type} ":" {additional_value_type})* )? "}}" '
-
- rules.extend(additional_key_rules)
- rules.extend(additional_value_rules)
- elif gbnf_type.startswith("union-"):
- union_types = get_args(field_type)
- union_rules = []
-
- for union_type in union_types:
- if isinstance(union_type, _GenericAlias):
- union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
- model_name, field_name, union_type, False, processed_models, created_rules
- )
- union_rules.append(union_gbnf_type)
- rules.extend(union_rules_list)
-
- elif not issubclass(union_type, NoneType):
- union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
- model_name, field_name, union_type, False, processed_models, created_rules
- )
- union_rules.append(union_gbnf_type)
- rules.extend(union_rules_list)
-
- # Defining the union grammar rule separately
- if len(union_rules) == 1:
- union_grammar_rule = f"{model_name}-{field_name}-optional ::= {' | '.join(union_rules)} | null"
- else:
- union_grammar_rule = f"{model_name}-{field_name}-union ::= {' | '.join(union_rules)}"
- rules.append(union_grammar_rule)
- if len(union_rules) == 1:
- gbnf_type = f"{model_name}-{field_name}-optional"
- else:
- gbnf_type = f"{model_name}-{field_name}-union"
- elif isclass(field_type) and issubclass(field_type, str):
- if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None:
- triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False)
- markdown_string = field_info.json_schema_extra.get("markdown_code_block", False)
-
- gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value
- gbnf_type = PydanticDataType.MARKDOWN_CODE_BLOCK.value if markdown_string else gbnf_type
-
- elif field_info and hasattr(field_info, "pattern"):
- # Convert regex pattern to grammar rule
- regex_pattern = field_info.regex.pattern
- gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}"
- else:
- gbnf_type = PydanticDataType.STRING.value
-
- elif (
- isclass(field_type)
- and issubclass(field_type, float)
- and field_info
- and hasattr(field_info, "json_schema_extra")
- and field_info.json_schema_extra is not None
- ):
- # Retrieve precision attributes for floats
- max_precision = (
- field_info.json_schema_extra.get("max_precision") if field_info and hasattr(field_info, "json_schema_extra") else None
- )
- min_precision = (
- field_info.json_schema_extra.get("min_precision") if field_info and hasattr(field_info, "json_schema_extra") else None
- )
- max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info, "json_schema_extra") else None
- min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info, "json_schema_extra") else None
-
- # Generate GBNF rule for float with given attributes
- gbnf_type, rules = generate_gbnf_float_rules(
- max_digit=max_digits, min_digit=min_digits, max_precision=max_precision, min_precision=min_precision
- )
-
- elif (
- isclass(field_type)
- and issubclass(field_type, int)
- and field_info
- and hasattr(field_info, "json_schema_extra")
- and field_info.json_schema_extra is not None
- ):
- # Retrieve digit attributes for integers
- max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info, "json_schema_extra") else None
- min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info, "json_schema_extra") else None
-
- # Generate GBNF rule for integer with given attributes
- gbnf_type, rules = generate_gbnf_integer_rules(max_digit=max_digits, min_digit=min_digits)
- else:
- gbnf_type, rules = gbnf_type, []
-
- if gbnf_type not in created_rules:
- return gbnf_type, rules
- else:
- if gbnf_type in created_rules:
- return gbnf_type, rules
-
-
-def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created_rules: dict) -> (list, bool, bool):
- """
-
- Generate GBnF Grammar
-
- Generates a GBnF grammar for a given model.
-
- :param model: A Pydantic model class to generate the grammar for. Must be a subclass of BaseModel.
- :param processed_models: A set of already processed models to prevent infinite recursion.
- :param created_rules: A dict containing already created rules to prevent duplicates.
- :return: A list of GBnF grammar rules in string format. And two booleans indicating if an extra markdown or triple quoted string is in the grammar.
- Example Usage:
- ```
- model = MyModel
- processed_models = set()
- created_rules = dict()
-
- gbnf_grammar = generate_gbnf_grammar(model, processed_models, created_rules)
- ```
- """
- if model in processed_models:
- return []
-
- processed_models.add(model)
- model_name = format_model_and_field_name(model.__name__)
-
- if not issubclass(model, BaseModel):
- # For non-Pydantic classes, generate model_fields from __annotations__ or __init__
- if hasattr(model, "__annotations__") and model.__annotations__:
- model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()}
- else:
- init_signature = inspect.signature(model.__init__)
- parameters = init_signature.parameters
- model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() if name != "self"}
- else:
- # For Pydantic models, use model_fields and check for ellipsis (required fields)
- model_fields = model.__annotations__
-
- model_rule_parts = []
- nested_rules = []
- has_markdown_code_block = False
- has_triple_quoted_string = False
-
- for field_name, field_info in model_fields.items():
- if not issubclass(model, BaseModel):
- field_type, default_value = field_info
- # Check if the field is optional (not required)
- is_optional = (default_value is not inspect.Parameter.empty) and (default_value is not Ellipsis)
- else:
- field_type = field_info
- field_info = model.model_fields[field_name]
- is_optional = field_info.is_required is False and get_origin(field_type) is Optional
- rule_name, additional_rules = generate_gbnf_rule_for_type(
- model_name, format_model_and_field_name(field_name), field_type, is_optional, processed_models, created_rules, field_info
- )
- look_for_markdown_code_block = True if rule_name == "markdown_code_block" else False
- look_for_triple_quoted_string = True if rule_name == "triple_quoted_string" else False
- if not look_for_markdown_code_block and not look_for_triple_quoted_string:
- if rule_name not in created_rules:
- created_rules[rule_name] = additional_rules
- model_rule_parts.append(f' ws "\\"{field_name}\\"" ":" ws {rule_name}') # Adding escaped quotes
- nested_rules.extend(additional_rules)
- else:
- has_triple_quoted_string = look_for_triple_quoted_string
- has_markdown_code_block = look_for_markdown_code_block
-
- fields_joined = r' "," "\n" '.join(model_rule_parts)
- model_rule = rf'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"'
-
- has_special_string = False
- if has_triple_quoted_string:
- model_rule += '"\\n" ws "}"'
- model_rule += '"\\n" triple-quoted-string'
- has_special_string = True
- if has_markdown_code_block:
- model_rule += '"\\n" ws "}"'
- model_rule += '"\\n" markdown-code-block'
- has_special_string = True
- all_rules = [model_rule] + nested_rules
-
- return all_rules, has_special_string
-
-
-def generate_gbnf_grammar_from_pydantic_models(
- models: List[Type[BaseModel]],
- outer_object_name: str = None,
- outer_object_content: str = None,
- list_of_outputs: bool = False,
- add_inner_thoughts: bool = False,
- allow_only_inner_thoughts: bool = False,
-) -> str:
- """
- Generate GBNF Grammar from Pydantic Models.
-
- This method takes a list of Pydantic models and uses them to generate a GBNF grammar string. The generated grammar string can be used for parsing and validating data using the generated
- * grammar.
-
- Args:
- models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from.
- outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
- outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
- list_of_outputs (str, optional): Allows a list of output objects
- add_inner_thoughts (bool): Add inner thoughts field on the top level.
- allow_only_inner_thoughts (bool): Allow inner thoughts without a function call.
- Returns:
- str: The generated GBNF grammar string.
-
- Examples:
- models = [UserModel, PostModel]
- grammar = generate_gbnf_grammar_from_pydantic(models)
- print(grammar)
- # Output:
- # root ::= UserModel | PostModel
- # ...
- """
- processed_models = set()
- all_rules = []
- created_rules = {}
- if outer_object_name is None:
- for model in models:
- model_rules, _ = generate_gbnf_grammar(model, processed_models, created_rules)
- all_rules.extend(model_rules)
-
- if list_of_outputs:
- root_rule = r'root ::= (" "| "\n") "[" ws grammar-models ("," ws grammar-models)* ws "]"' + "\n"
- else:
- root_rule = r'root ::= (" "| "\n") grammar-models' + "\n"
- root_rule += "grammar-models ::= " + " | ".join([format_model_and_field_name(model.__name__) for model in models])
- all_rules.insert(0, root_rule)
- return "\n".join(all_rules)
- elif outer_object_name is not None:
- if list_of_outputs:
- root_rule = (
- rf'root ::= (" "| "\n") "[" ws {format_model_and_field_name(outer_object_name)} ("," ws {format_model_and_field_name(outer_object_name)})* ws "]"'
- + "\n"
- )
- else:
- root_rule = f"root ::= {format_model_and_field_name(outer_object_name)}\n"
-
- if add_inner_thoughts:
- if allow_only_inner_thoughts:
- model_rule = rf'{format_model_and_field_name(outer_object_name)} ::= (" "| "\n") "{{" ws "\"inner_thoughts\"" ":" ws string ("," "\n" ws "\"{outer_object_name}\"" ":" ws grammar-models)?'
- else:
- model_rule = rf'{format_model_and_field_name(outer_object_name)} ::= (" "| "\n") "{{" ws "\"inner_thoughts\"" ":" ws string "," "\n" ws "\"{outer_object_name}\"" ":" ws grammar-models'
- else:
- model_rule = rf'{format_model_and_field_name(outer_object_name)} ::= (" "| "\n") "{{" ws "\"{outer_object_name}\"" ":" ws grammar-models'
-
- fields_joined = " | ".join([rf"{format_model_and_field_name(model.__name__)}-grammar-model" for model in models])
-
- grammar_model_rules = f"\ngrammar-models ::= {fields_joined}"
- mod_rules = []
- for model in models:
- mod_rule = rf"{format_model_and_field_name(model.__name__)}-grammar-model ::= "
- mod_rule += (
- rf'"\"{model.__name__}\"" "," ws "\"{outer_object_content}\"" ":" ws {format_model_and_field_name(model.__name__)}' + "\n"
- )
- mod_rules.append(mod_rule)
- grammar_model_rules += "\n" + "\n".join(mod_rules)
-
- for model in models:
- model_rules, has_special_string = generate_gbnf_grammar(model, processed_models, created_rules)
-
- if not has_special_string:
- model_rules[0] += r'"\n" ws "}"'
-
- all_rules.extend(model_rules)
-
- all_rules.insert(0, root_rule + model_rule + grammar_model_rules)
- return "\n".join(all_rules)
-
-
-def get_primitive_grammar(grammar):
- """
- Returns the needed GBNF primitive grammar for a given GBNF grammar string.
-
- Args:
- grammar (str): The string containing the GBNF grammar.
-
- Returns:
- str: GBNF primitive grammar string.
- """
- type_list = []
- if "string-list" in grammar:
- type_list.append(str)
- if "boolean-list" in grammar:
- type_list.append(bool)
- if "integer-list" in grammar:
- type_list.append(int)
- if "float-list" in grammar:
- type_list.append(float)
- additional_grammar = [generate_list_rule(t) for t in type_list]
- primitive_grammar = r"""
-boolean ::= "true" | "false"
-null ::= "null"
-string ::= "\"" (
- [^"\\] |
- "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
- )* "\""
-ws ::= ([ \t\n] ws)?
-float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
-
-integer ::= [0-9]+"""
-
- any_block = ""
- if "custom-class-any" in grammar:
- any_block = """
-value ::= object | array | string | number | boolean | null
-
-object ::=
- "{" ws (
- string ":" ws value
- ("," ws string ":" ws value)*
- )? "}"
-
-array ::=
- "[" ws (
- value
- ("," ws value)*
- )? "]"
-
-number ::= integer | float"""
-
- markdown_code_block_grammar = ""
- if "markdown-code-block" in grammar:
- markdown_code_block_grammar = r'''
-markdown-code-block ::= opening-triple-ticks markdown-code-block-content closing-triple-ticks
-markdown-code-block-content ::= ( [^`] | "`" [^`] | "`" "`" [^`] )*
-opening-triple-ticks ::= "```" "python" "\n" | "```" "c" "\n" | "```" "cpp" "\n" | "```" "txt" "\n" | "```" "text" "\n" | "```" "json" "\n" | "```" "javascript" "\n" | "```" "css" "\n" | "```" "html" "\n" | "```" "markdown" "\n"
-closing-triple-ticks ::= "```" "\n"'''
-
- if "triple-quoted-string" in grammar:
- markdown_code_block_grammar = r"""
-triple-quoted-string ::= triple-quotes triple-quoted-string-content triple-quotes
-triple-quoted-string-content ::= ( [^'] | "'" [^'] | "'" "'" [^'] )*
-triple-quotes ::= "'''" """
- return "\n" + "\n".join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar
-
-
-def generate_markdown_documentation(
- pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", documentation_with_field_description=True
-) -> str:
- """
- Generate markdown documentation for a list of Pydantic models.
-
- Args:
- pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes.
- model_prefix (str): Prefix for the model section.
- fields_prefix (str): Prefix for the fields section.
- documentation_with_field_description (bool): Include field descriptions in the documentation.
-
- Returns:
- str: Generated text documentation.
- """
- documentation = ""
- pyd_models = [(model, True) for model in pydantic_models]
- for model, add_prefix in pyd_models:
- if add_prefix:
- documentation += f"{model_prefix}: {model.__name__}\n"
- else:
- documentation += f"class: {model.__name__}\n"
-
- # Handling multi-line model description with proper indentation
-
- class_doc = getdoc(model)
- base_class_doc = getdoc(BaseModel)
- class_description = class_doc if class_doc and class_doc != base_class_doc else ""
- if class_description != "":
- documentation += format_multiline_description("description: " + class_description, 1) + "\n"
-
- if add_prefix:
- # Indenting the fields section
- documentation += f" {fields_prefix}:\n"
- else:
- documentation += " attributes:\n"
- if isclass(model) and issubclass(model, BaseModel):
- for name, field_type in model.__annotations__.items():
- # if name == "markdown_code_block":
- # continue
- if isclass(field_type) and issubclass(field_type, BaseModel):
- pyd_models.append((field_type, False))
- if get_origin(field_type) == list:
- element_type = get_args(field_type)[0]
- if isclass(element_type) and issubclass(element_type, BaseModel):
- pyd_models.append((element_type, False))
- if get_origin(field_type) == Union:
- element_types = get_args(field_type)
- for element_type in element_types:
- if isclass(element_type) and issubclass(element_type, BaseModel):
- pyd_models.append((element_type, False))
- documentation += generate_field_markdown(
- name, field_type, model, documentation_with_field_description=documentation_with_field_description
- )
- documentation += "\n"
-
- if hasattr(model, "Config") and hasattr(model.Config, "json_schema_extra") and "example" in model.Config.json_schema_extra:
- documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n"
- json_example = json_dumps(model.Config.json_schema_extra["example"])
- documentation += format_multiline_description(json_example, 2) + "\n"
-
- return documentation
-
-
-def generate_field_markdown(
- field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, documentation_with_field_description=True
-) -> str:
- """
- Generate markdown documentation for a Pydantic model field.
-
- Args:
- field_name (str): Name of the field.
- field_type (Type[Any]): Type of the field.
- model (Type[BaseModel]): Pydantic model class.
- depth (int): Indentation depth in the documentation.
- documentation_with_field_description (bool): Include field descriptions in the documentation.
-
- Returns:
- str: Generated text documentation for the field.
- """
- indent = " " * depth
-
- field_info = model.model_fields.get(field_name)
- field_description = field_info.description if field_info and field_info.description else ""
-
- if get_origin(field_type) == list:
- element_type = get_args(field_type)[0]
- field_text = f"{indent}{field_name} ({field_type.__name__} of {element_type.__name__})"
- if field_description != "":
- field_text += ": "
- else:
- field_text += "\n"
- elif get_origin(field_type) == Union:
- element_types = get_args(field_type)
- types = []
- for element_type in element_types:
- types.append(element_type.__name__)
- field_text = f"{indent}{field_name} ({' or '.join(types)})"
- if field_description != "":
- field_text += ": "
- else:
- field_text += "\n"
- elif issubclass(field_type, Enum):
- enum_values = [f"'{str(member.value)}'" for member in field_type]
-
- field_text = f"{indent}{field_name} ({' or '.join(enum_values)})"
- if field_description != "":
- field_text += ": "
- else:
- field_text += "\n"
- else:
- field_text = f"{indent}{field_name} ({field_type.__name__})"
- if field_description != "":
- field_text += ": "
- else:
- field_text += "\n"
-
- if not documentation_with_field_description:
- return field_text
-
- if field_description != "":
- field_text += field_description + "\n"
-
- # Check for and include field-specific examples if available
- if hasattr(model, "Config") and hasattr(model.Config, "json_schema_extra") and "example" in model.Config.json_schema_extra:
- field_example = model.Config.json_schema_extra["example"].get(field_name)
- if field_example is not None:
- example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
- field_text += f"{indent} Example: {example_text}\n"
-
- if isclass(field_type) and issubclass(field_type, BaseModel):
- field_text += f"{indent} details:\n"
- for name, type_ in field_type.__annotations__.items():
- field_text += generate_field_markdown(name, type_, field_type, depth + 2)
-
- return field_text
-
-
-def format_json_example(example: dict, depth: int) -> str:
- """
- Format a JSON example into a readable string with indentation.
-
- Args:
- example (dict): JSON example to be formatted.
- depth (int): Indentation depth.
-
- Returns:
- str: Formatted JSON example string.
- """
- indent = " " * depth
- formatted_example = "{\n"
- for key, value in example.items():
- value_text = f"'{value}'" if isinstance(value, str) else value
- formatted_example += f"{indent}{key}: {value_text},\n"
- formatted_example = formatted_example.rstrip(",\n") + "\n" + indent + "}"
- return formatted_example
-
-
-def generate_text_documentation(
- pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", documentation_with_field_description=True
-) -> str:
- """
- Generate text documentation for a list of Pydantic models.
-
- Args:
- pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes.
- model_prefix (str): Prefix for the model section.
- fields_prefix (str): Prefix for the fields section.
- documentation_with_field_description (bool): Include field descriptions in the documentation.
-
- Returns:
- str: Generated text documentation.
- """
- documentation = ""
- pyd_models = [(model, True) for model in pydantic_models]
- for model, add_prefix in pyd_models:
- if add_prefix:
- documentation += f"{model_prefix}: {model.__name__}\n"
- else:
- documentation += f"Model: {model.__name__}\n"
-
- # Handling multi-line model description with proper indentation
-
- class_doc = getdoc(model)
- base_class_doc = getdoc(BaseModel)
- class_description = class_doc if class_doc and class_doc != base_class_doc else ""
- if class_description != "":
- documentation += " Description: "
- documentation += "\n" + format_multiline_description(class_description, 2) + "\n"
-
- if isclass(model) and issubclass(model, BaseModel):
- documentation_fields = ""
- for name, field_type in model.__annotations__.items():
- # if name == "markdown_code_block":
- # continue
- if get_origin(field_type) == list:
- element_type = get_args(field_type)[0]
- if isclass(element_type) and issubclass(element_type, BaseModel):
- pyd_models.append((element_type, False))
- if get_origin(field_type) == Union:
- element_types = get_args(field_type)
- for element_type in element_types:
- if isclass(element_type) and issubclass(element_type, BaseModel):
- pyd_models.append((element_type, False))
- documentation_fields += generate_field_text(
- name, field_type, model, documentation_with_field_description=documentation_with_field_description
- )
- if documentation_fields != "":
- if add_prefix:
- documentation += f" {fields_prefix}:\n{documentation_fields}"
- else:
- documentation += f" Fields:\n{documentation_fields}"
- documentation += "\n"
-
- if hasattr(model, "Config") and hasattr(model.Config, "json_schema_extra") and "example" in model.Config.json_schema_extra:
- documentation += f" Expected Example Output for {format_model_and_field_name(model.__name__)}:\n"
- json_example = json.dumps(model.Config.json_schema_extra["example"])
- documentation += format_multiline_description(json_example, 2) + "\n"
-
- return documentation
-
-
-def generate_field_text(
- field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, documentation_with_field_description=True
-) -> str:
- """
- Generate text documentation for a Pydantic model field.
-
- Args:
- field_name (str): Name of the field.
- field_type (Type[Any]): Type of the field.
- model (Type[BaseModel]): Pydantic model class.
- depth (int): Indentation depth in the documentation.
- documentation_with_field_description (bool): Include field descriptions in the documentation.
-
- Returns:
- str: Generated text documentation for the field.
- """
- indent = " " * depth
-
- field_info = model.model_fields.get(field_name)
- field_description = field_info.description if field_info and field_info.description else ""
-
- if get_origin(field_type) == list:
- element_type = get_args(field_type)[0]
- field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
- if field_description != "":
- field_text += ":\n"
- else:
- field_text += "\n"
- elif get_origin(field_type) == Union:
- element_types = get_args(field_type)
- types = []
- for element_type in element_types:
- types.append(format_model_and_field_name(element_type.__name__))
- field_text = f"{indent}{field_name} ({' or '.join(types)})"
- if field_description != "":
- field_text += ":\n"
- else:
- field_text += "\n"
- else:
- field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})"
- if field_description != "":
- field_text += ":\n"
- else:
- field_text += "\n"
-
- if not documentation_with_field_description:
- return field_text
-
- if field_description != "":
- field_text += f"{indent} Description: " + field_description + "\n"
-
- # Check for and include field-specific examples if available
- if hasattr(model, "Config") and hasattr(model.Config, "json_schema_extra") and "example" in model.Config.json_schema_extra:
- field_example = model.Config.json_schema_extra["example"].get(field_name)
- if field_example is not None:
- example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
- field_text += f"{indent} Example: {example_text}\n"
-
- if isclass(field_type) and issubclass(field_type, BaseModel):
- field_text += f"{indent} Details:\n"
- for name, type_ in field_type.__annotations__.items():
- field_text += generate_field_text(name, type_, field_type, depth + 2)
-
- return field_text
-
-
-def format_multiline_description(description: str, indent_level: int) -> str:
- """
- Format a multiline description with proper indentation.
-
- Args:
- description (str): Multiline description.
- indent_level (int): Indentation level.
-
- Returns:
- str: Formatted multiline description.
- """
- indent = " " * indent_level
- return indent + description.replace("\n", "\n" + indent)
-
-
-def save_gbnf_grammar_and_documentation(
- grammar, documentation, grammar_file_path="./grammar.gbnf", documentation_file_path="./grammar_documentation.md"
-):
- """
- Save GBNF grammar and documentation to specified files.
-
- Args:
- grammar (str): GBNF grammar string.
- documentation (str): Documentation string.
- grammar_file_path (str): File path to save the GBNF grammar.
- documentation_file_path (str): File path to save the documentation.
-
- Returns:
- None
- """
- try:
- with open(grammar_file_path, "w", encoding="utf-8") as file:
- file.write(grammar + get_primitive_grammar(grammar))
- print(f"Grammar successfully saved to {grammar_file_path}")
- except IOError as e:
- print(f"An error occurred while saving the grammar file: {e}")
-
- try:
- with open(documentation_file_path, "w", encoding="utf-8") as file:
- file.write(documentation)
- print(f"Documentation successfully saved to {documentation_file_path}")
- except IOError as e:
- print(f"An error occurred while saving the documentation file: {e}")
-
-
-def remove_empty_lines(string):
- """
- Remove empty lines from a string.
-
- Args:
- string (str): Input string.
-
- Returns:
- str: String with empty lines removed.
- """
- lines = string.splitlines()
- non_empty_lines = [line for line in lines if line.strip() != ""]
- string_no_empty_lines = "\n".join(non_empty_lines)
- return string_no_empty_lines
-
-
-def generate_and_save_gbnf_grammar_and_documentation(
- pydantic_model_list,
- grammar_file_path="./generated_grammar.gbnf",
- documentation_file_path="./generated_grammar_documentation.md",
- outer_object_name: str = None,
- outer_object_content: str = None,
- model_prefix: str = "Output Model",
- fields_prefix: str = "Output Fields",
- list_of_outputs: bool = False,
- documentation_with_field_description=True,
-):
- """
- Generate GBNF grammar and documentation, and save them to specified files.
-
- Args:
- pydantic_model_list: List of Pydantic model classes.
- grammar_file_path (str): File path to save the generated GBNF grammar.
- documentation_file_path (str): File path to save the generated documentation.
- outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
- outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
- model_prefix (str): Prefix for the model section in the documentation.
- fields_prefix (str): Prefix for the fields section in the documentation.
- list_of_outputs (bool): Whether the output is a list of items.
- documentation_with_field_description (bool): Include field descriptions in the documentation.
-
- Returns:
- None
- """
- documentation = generate_markdown_documentation(
- pydantic_model_list, model_prefix, fields_prefix, documentation_with_field_description=documentation_with_field_description
- )
- grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, list_of_outputs)
- grammar = remove_empty_lines(grammar)
- save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path, documentation_file_path)
-
-
-def generate_gbnf_grammar_and_documentation(
- pydantic_model_list,
- outer_object_name: str = None,
- outer_object_content: str = None,
- model_prefix: str = "Output Model",
- fields_prefix: str = "Output Fields",
- list_of_outputs: bool = False,
- add_inner_thoughts: bool = False,
- allow_only_inner_thoughts: bool = False,
- documentation_with_field_description=True,
-):
- """
- Generate GBNF grammar and documentation for a list of Pydantic models.
-
- Args:
- pydantic_model_list: List of Pydantic model classes.
- outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
- outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
- model_prefix (str): Prefix for the model section in the documentation.
- fields_prefix (str): Prefix for the fields section in the documentation.
- list_of_outputs (bool): Whether the output is a list of items.
- add_inner_thoughts (bool): Add inner thoughts field on the top level.
- allow_only_inner_thoughts (bool): Allow inner thoughts without a function call.
- documentation_with_field_description (bool): Include field descriptions in the documentation.
-
- Returns:
- tuple: GBNF grammar string, documentation string.
- """
- documentation = generate_markdown_documentation(
- copy(pydantic_model_list), model_prefix, fields_prefix, documentation_with_field_description=documentation_with_field_description
- )
- grammar = generate_gbnf_grammar_from_pydantic_models(
- pydantic_model_list, outer_object_name, outer_object_content, list_of_outputs, add_inner_thoughts, allow_only_inner_thoughts
- )
- grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar))
- return grammar, documentation
-
-
-def generate_gbnf_grammar_and_documentation_from_dictionaries(
- dictionaries: List[dict],
- outer_object_name: str = None,
- outer_object_content: str = None,
- model_prefix: str = "Output Model",
- fields_prefix: str = "Output Fields",
- list_of_outputs: bool = False,
- documentation_with_field_description=True,
-):
- """
- Generate GBNF grammar and documentation from a list of dictionaries.
-
- Args:
- dictionaries (List[dict]): List of dictionaries representing Pydantic models.
- outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
- outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
- model_prefix (str): Prefix for the model section in the documentation.
- fields_prefix (str): Prefix for the fields section in the documentation.
- list_of_outputs (bool): Whether the output is a list of items.
- documentation_with_field_description (bool): Include field descriptions in the documentation.
-
- Returns:
- tuple: GBNF grammar string, documentation string.
- """
- pydantic_model_list = create_dynamic_models_from_dictionaries(dictionaries)
- documentation = generate_markdown_documentation(
- copy(pydantic_model_list), model_prefix, fields_prefix, documentation_with_field_description=documentation_with_field_description
- )
- grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content, list_of_outputs)
- grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar))
- return grammar, documentation
-
-
-def create_dynamic_model_from_function(func: Callable, add_inner_thoughts: bool = False):
- """
- Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method.
-
- Args:
- func (Callable): A function with type hints from which to create the model.
- add_inner_thoughts: Add an inner thoughts parameter on the params level
-
- Returns:
- A dynamic Pydantic model class with the provided function as a 'run' method.
- """
-
- # Get the signature of the function
- sig = inspect.signature(func)
-
- # Parse the docstring
- docstring = parse(func.__doc__)
-
- dynamic_fields = {}
- param_docs = []
- if add_inner_thoughts:
- dynamic_fields["inner_thoughts"] = (str, None)
- for param in sig.parameters.values():
- # Exclude 'self' parameter
- if param.name == "self":
- continue
-
- # Assert that the parameter has a type annotation
- if param.annotation == inspect.Parameter.empty:
- raise TypeError(f"Parameter '{param.name}' in function '{func.__name__}' lacks a type annotation")
-
- # Find the parameter's description in the docstring
- param_doc = next((d for d in docstring.params if d.arg_name == param.name), None)
-
- # Assert that the parameter has a description
- if not param_doc or not param_doc.description:
- raise ValueError(f"Parameter '{param.name}' in function '{func.__name__}' lacks a description in the docstring")
-
- # Add parameter details to the schema
- param_doc = next((d for d in docstring.params if d.arg_name == param.name), None)
- param_docs.append((param.name, param_doc))
- if param.default == inspect.Parameter.empty:
- default_value = ...
- else:
- default_value = param.default
-
- dynamic_fields[param.name] = (param.annotation if param.annotation != inspect.Parameter.empty else str, default_value)
- # Creating the dynamic model
- dynamic_model = create_model(f"{func.__name__}", **dynamic_fields)
- if add_inner_thoughts:
- dynamic_model.model_fields["inner_thoughts"].description = "Deep inner monologue private to you only."
- for param_doc in param_docs:
- dynamic_model.model_fields[param_doc[0]].description = param_doc[1].description
-
- dynamic_model.__doc__ = docstring.short_description
-
- def run_method_wrapper(self):
- func_args = {name: getattr(self, name) for name, _ in dynamic_fields.items()}
- return func(**func_args)
-
- # Adding the wrapped function as a 'run' method
- setattr(dynamic_model, "run", run_method_wrapper)
- return dynamic_model
-
-
-def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable):
- """
- Add a 'run' method to a dynamic Pydantic model, using the provided function.
-
- Args:
- model (Type[BaseModel]): Dynamic Pydantic model class.
- func (Callable): Function to be added as a 'run' method to the model.
-
- Returns:
- Type[BaseModel]: Pydantic model class with the added 'run' method.
- """
-
- def run_method_wrapper(self):
- func_args = {name: getattr(self, name) for name in model.model_fields}
- return func(**func_args)
-
- # Adding the wrapped function as a 'run' method
- setattr(model, "run", run_method_wrapper)
-
- return model
-
-
-def create_dynamic_models_from_dictionaries(dictionaries: List[dict]):
- """
- Create a list of dynamic Pydantic model classes from a list of dictionaries.
-
- Args:
- dictionaries (List[dict]): List of dictionaries representing model structures.
-
- Returns:
- List[Type[BaseModel]]: List of generated dynamic Pydantic model classes.
- """
- dynamic_models = []
- for func in dictionaries:
- model_name = format_model_and_field_name(func.get("name", ""))
- dyn_model = convert_dictionary_to_pydantic_model(func, model_name)
- dynamic_models.append(dyn_model)
- return dynamic_models
-
-
-def map_grammar_names_to_pydantic_model_class(pydantic_model_list):
- output = {}
- for model in pydantic_model_list:
- output[format_model_and_field_name(model.__name__)] = model
-
- return output
-
-
-from enum import Enum
-
-
-def json_schema_to_python_types(schema):
- type_map = {
- "any": Any,
- "string": str,
- "number": float,
- "integer": int,
- "boolean": bool,
- "array": list,
- }
- return type_map[schema]
-
-
-def list_to_enum(enum_name, values):
- return Enum(enum_name, {value: value for value in values})
-
-
-def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "CustomModel") -> Type[BaseModel]:
- """
- Convert a dictionary to a Pydantic model class.
-
- Args:
- dictionary (dict): Dictionary representing the model structure.
- model_name (str): Name of the generated Pydantic model.
-
- Returns:
- Type[BaseModel]: Generated Pydantic model class.
- """
- fields = {}
-
- if "properties" in dictionary:
- for field_name, field_data in dictionary.get("properties", {}).items():
- if field_data == "object":
- submodel = convert_dictionary_to_pydantic_model(dictionary, f"{model_name}_{field_name}")
- fields[field_name] = (submodel, ...)
- else:
- field_type = field_data.get("type", "str")
-
- if field_data.get("enum", []):
- fields[field_name] = (list_to_enum(field_name, field_data.get("enum", [])), ...)
- elif field_type == "array":
- items = field_data.get("items", {})
- if items != {}:
- array = {"properties": items}
- array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items")
- fields[field_name] = (List[array_type], ...)
- else:
- fields[field_name] = (list, ...)
- elif field_type == "object":
- submodel = convert_dictionary_to_pydantic_model(field_data, f"{model_name}_{field_name}")
- fields[field_name] = (submodel, ...)
- elif field_type == "required":
- required = field_data.get("enum", [])
- for key, field in fields.items():
- if key not in required:
- fields[key] = (Optional[fields[key][0]], ...)
- else:
- field_type = json_schema_to_python_types(field_type)
- fields[field_name] = (field_type, ...)
- if "function" in dictionary:
- for field_name, field_data in dictionary.get("function", {}).items():
- if field_name == "name":
- model_name = field_data
- elif field_name == "description":
- fields["__doc__"] = field_data
- elif field_name == "parameters":
- return convert_dictionary_to_pydantic_model(field_data, f"{model_name}")
-
- if "parameters" in dictionary:
- field_data = {"function": dictionary}
- return convert_dictionary_to_pydantic_model(field_data, f"{model_name}")
- if "required" in dictionary:
- required = dictionary.get("required", [])
- for key, field in fields.items():
- if key not in required:
- fields[key] = (Optional[fields[key][0]], ...)
- custom_model = create_model(model_name, **fields)
- return custom_model
diff --git a/letta/local_llm/grammars/json.gbnf b/letta/local_llm/grammars/json.gbnf
deleted file mode 100644
index 47afedbf..00000000
--- a/letta/local_llm/grammars/json.gbnf
+++ /dev/null
@@ -1,26 +0,0 @@
-# https://github.com/ggerganov/llama.cpp/blob/master/grammars/json.gbnf
-root ::= object
-value ::= object | array | string | number | ("true" | "false" | "null") ws
-
-object ::=
- "{" ws (
- string ":" ws value
- ("," ws string ":" ws value)*
- )? "}" ws
-
-array ::=
- "[" ws (
- value
- ("," ws value)*
- )? "]" ws
-
-string ::=
- "\"" (
- [^"\\] |
- "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
- )* "\"" ws
-
-number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
-
-# Optional space: by convention, applied in this grammar after literal chars when allowed
-ws ::= ([ \t\n] ws)?
diff --git a/letta/local_llm/grammars/json_func_calls_with_inner_thoughts.gbnf b/letta/local_llm/grammars/json_func_calls_with_inner_thoughts.gbnf
deleted file mode 100644
index f6548a9c..00000000
--- a/letta/local_llm/grammars/json_func_calls_with_inner_thoughts.gbnf
+++ /dev/null
@@ -1,32 +0,0 @@
-root ::= Function
-Function ::= SendMessage | PauseHeartbeats | CoreMemoryAppend | CoreMemoryReplace | ConversationSearch | ConversationSearchDate | ArchivalMemoryInsert | ArchivalMemorySearch
-SendMessage ::= "{" ws "\"function\":" ws "\"send_message\"," ws "\"params\":" ws SendMessageParams "}"
-PauseHeartbeats ::= "{" ws "\"function\":" ws "\"pause_heartbeats\"," ws "\"params\":" ws PauseHeartbeatsParams "}"
-CoreMemoryAppend ::= "{" ws "\"function\":" ws "\"core_memory_append\"," ws "\"params\":" ws CoreMemoryAppendParams "}"
-CoreMemoryReplace ::= "{" ws "\"function\":" ws "\"core_memory_replace\"," ws "\"params\":" ws CoreMemoryReplaceParams "}"
-ConversationSearch ::= "{" ws "\"function\":" ws "\"conversation_search\"," ws "\"params\":" ws ConversationSearchParams "}"
-ConversationSearchDate ::= "{" ws "\"function\":" ws "\"conversation_search_date\"," ws "\"params\":" ws ConversationSearchDateParams "}"
-ArchivalMemoryInsert ::= "{" ws "\"function\":" ws "\"archival_memory_insert\"," ws "\"params\":" ws ArchivalMemoryInsertParams "}"
-ArchivalMemorySearch ::= "{" ws "\"function\":" ws "\"archival_memory_search\"," ws "\"params\":" ws ArchivalMemorySearchParams "}"
-SendMessageParams ::= "{" ws InnerThoughtsParam "," ws "\"message\":" ws string ws "}"
-PauseHeartbeatsParams ::= "{" ws InnerThoughtsParam "," ws "\"minutes\":" ws number ws "}"
-CoreMemoryAppendParams ::= "{" ws InnerThoughtsParam "," ws "\"name\":" ws namestring "," ws "\"content\":" ws string ws "," ws RequestHeartbeatParam ws "}"
-CoreMemoryReplaceParams ::= "{" ws InnerThoughtsParam "," ws "\"name\":" ws namestring "," ws "\"old_content\":" ws string "," ws "\"new_content\":" ws string ws "," ws RequestHeartbeatParam ws "}"
-ConversationSearchParams ::= "{" ws InnerThoughtsParam "," ws "\"query\":" ws string ws "," ws "\"page\":" ws number ws "," ws RequestHeartbeatParam ws "}"
-ConversationSearchDateParams ::= "{" ws InnerThoughtsParam "," ws "\"start_date\":" ws string ws "," ws "\"end_date\":" ws string ws "," ws "\"page\":" ws number ws "," ws RequestHeartbeatParam ws "}"
-ArchivalMemoryInsertParams ::= "{" ws InnerThoughtsParam "," ws "\"content\":" ws string ws "," ws RequestHeartbeatParam ws "}"
-ArchivalMemorySearchParams ::= "{" ws InnerThoughtsParam "," ws "\"query\":" ws string ws "," ws "\"page\":" ws number ws "," ws RequestHeartbeatParam ws "}"
-InnerThoughtsParam ::= "\"inner_thoughts\":" ws string
-RequestHeartbeatParam ::= "\"request_heartbeat\":" ws boolean
-namestring ::= "\"human\"" | "\"persona\""
-boolean ::= "true" | "false"
-number ::= [0-9]+
-
-string ::=
- "\"" (
- [^"\\] |
- "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
- )* "\"" ws
-
-# Optional space: by convention, applied in this grammar after literal chars when allowed
-ws ::= ([ \t\n] ws)?
diff --git a/letta/local_llm/json_parser.py b/letta/local_llm/json_parser.py
deleted file mode 100644
index 961c32d4..00000000
--- a/letta/local_llm/json_parser.py
+++ /dev/null
@@ -1,212 +0,0 @@
-import json
-import re
-import warnings
-
-from letta.errors import LLMJSONParsingError
-from letta.helpers.json_helpers import json_loads
-
-
-def clean_json_string_extra_backslash(s):
- """Clean extra backslashes out from stringified JSON
-
- NOTE: Google AI Gemini API likes to include these
- """
- # Strip slashes that are used to escape single quotes and other backslashes
- # Use json.loads to parse it correctly
- while "\\\\" in s:
- s = s.replace("\\\\", "\\")
- return s
-
-
-def replace_escaped_underscores(string: str):
- r"""Handles the case of escaped underscores, e.g.:
-
- {
- "function":"send\_message",
- "params": {
- "inner\_thoughts": "User is asking for information about themselves. Retrieving data from core memory.",
- "message": "I know that you are Chad. Is there something specific you would like to know or talk about regarding yourself?"
- """
- return string.replace(r"\_", "_")
-
-
-def extract_first_json(string: str):
- """Handles the case of two JSON objects back-to-back"""
- from letta.utils import printd
-
- depth = 0
- start_index = None
-
- for i, char in enumerate(string):
- if char == "{":
- if depth == 0:
- start_index = i
- depth += 1
- elif char == "}":
- depth -= 1
- if depth == 0 and start_index is not None:
- try:
- return json_loads(string[start_index : i + 1])
- except json.JSONDecodeError as e:
- raise LLMJSONParsingError(f"Matched closing bracket, but decode failed with error: {str(e)}")
- printd("No valid JSON object found.")
- raise LLMJSONParsingError("Couldn't find starting bracket")
-
-
-def add_missing_heartbeat(llm_json):
- """Manually insert heartbeat requests into messages that should have them
-
- Use the following heuristic:
- - if (function call is not send_message && prev message['role'] == user): insert heartbeat
-
- Basically, if Letta is calling a function (not send_message) immediately after the user sending a message,
- it probably is a retriever or insertion call, in which case we likely want to eventually reply with send_message
-
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- raise NotImplementedError
-
-
-def clean_and_interpret_send_message_json(json_string):
- from letta.local_llm.constants import INNER_THOUGHTS_KWARG, VALID_INNER_THOUGHTS_KWARGS
- from letta.settings import model_settings
-
- kwarg = model_settings.inner_thoughts_kwarg
- if kwarg not in VALID_INNER_THOUGHTS_KWARGS:
- warnings.warn(f"INNER_THOUGHTS_KWARG is not valid: {kwarg}")
- kwarg = INNER_THOUGHTS_KWARG
-
- # If normal parsing fails, attempt to clean and extract manually
- cleaned_json_string = re.sub(r"[^\x00-\x7F]+", "", json_string) # Remove non-ASCII characters
- function_match = re.search(r'"function":\s*"send_message"', cleaned_json_string)
-
- inner_thoughts_match = re.search(rf'"{kwarg}":\s*"([^"]+)"', cleaned_json_string)
- message_match = re.search(r'"message":\s*"([^"]+)"', cleaned_json_string)
-
- if function_match and inner_thoughts_match and message_match:
- return {
- "function": "send_message",
- "params": {
- "inner_thoughts": inner_thoughts_match.group(1),
- "message": message_match.group(1),
- },
- }
- else:
- raise LLMJSONParsingError(f"Couldn't manually extract send_message pattern from:\n{json_string}")
-
-
-def repair_json_string(json_string):
- """
- This function repairs a JSON string where line feeds were accidentally added
- within string literals. The line feeds are replaced with the escaped line
- feed sequence '\\n'.
- """
- new_string = ""
- in_string = False
- escape = False
-
- for char in json_string:
- if char == '"' and not escape:
- in_string = not in_string
- if char == "\\" and not escape:
- escape = True
- else:
- escape = False
- if char == "\n" and in_string:
- new_string += "\\n"
- else:
- new_string += char
-
- return new_string
-
-
-def repair_even_worse_json(json_string):
- """
- This function repairs a malformed JSON string where string literals are broken up and
- not properly enclosed in quotes. It aims to consolidate everything between 'message': and
- the two ending curly braces into one string for the 'message' field.
- """
- # State flags
- in_message = False
- in_string = False
- escape = False
- message_content = []
-
- # Storage for the new JSON
- new_json_parts = []
-
- # Iterating through each character
- for char in json_string:
- if char == '"' and not escape:
- in_string = not in_string
- if not in_message:
- # If we encounter a quote and are not in message, append normally
- new_json_parts.append(char)
- elif char == "\\" and not escape:
- escape = True
- new_json_parts.append(char)
- else:
- if escape:
- escape = False
- if in_message:
- if char == "}":
- # Append the consolidated message and the closing characters then reset the flag
- new_json_parts.append('"{}"'.format("".join(message_content).replace("\n", " ")))
- new_json_parts.append(char)
- in_message = False
- elif in_string or char.isalnum() or char.isspace() or char in ".',;:!":
- # Collect the message content, excluding structural characters
- message_content.append(char)
- else:
- # If we're not in message mode, append character to the output as is
- new_json_parts.append(char)
- if '"message":' in "".join(new_json_parts[-10:]):
- # If we detect "message": pattern, switch to message mode
- in_message = True
- message_content = []
-
- # Joining everything to form the new JSON
- repaired_json = "".join(new_json_parts)
- return repaired_json
-
-
-def clean_json(raw_llm_output, messages=None, functions=None):
- from letta.utils import printd
-
- strategies = [
- lambda output: json_loads(output),
- lambda output: json_loads(output + "}"),
- lambda output: json_loads(output + "}}"),
- lambda output: json_loads(output + '"}}'),
- # with strip and strip comma
- lambda output: json_loads(output.strip().rstrip(",") + "}"),
- lambda output: json_loads(output.strip().rstrip(",") + "}}"),
- lambda output: json_loads(output.strip().rstrip(",") + '"}}'),
- # more complex patchers
- lambda output: json_loads(repair_json_string(output)),
- lambda output: json_loads(repair_even_worse_json(output)),
- lambda output: extract_first_json(output + "}}"),
- lambda output: clean_and_interpret_send_message_json(output),
- # replace underscores
- lambda output: json_loads(replace_escaped_underscores(output)),
- lambda output: extract_first_json(replace_escaped_underscores(output) + "}}"),
- ]
-
- for strategy in strategies:
- try:
- printd(f"Trying strategy: {strategy.__name__}")
- return strategy(raw_llm_output)
- except (json.JSONDecodeError, LLMJSONParsingError) as e:
- printd(f"Strategy {strategy.__name__} failed with error: {e}")
-
- raise LLMJSONParsingError(f"Failed to decode valid Letta JSON from LLM output:\n=====\n{raw_llm_output}\n=====")
diff --git a/letta/local_llm/koboldcpp/api.py b/letta/local_llm/koboldcpp/api.py
deleted file mode 100644
index e3aee69d..00000000
--- a/letta/local_llm/koboldcpp/api.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from urllib.parse import urljoin
-
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import count_tokens, post_json_auth_request
-
-KOBOLDCPP_API_SUFFIX = "/api/v1/generate"
-
-
-def get_koboldcpp_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=None):
- """See https://lite.koboldai.net/koboldcpp_api for API spec"""
- from letta.utils import printd
-
- prompt_tokens = count_tokens(prompt)
- if prompt_tokens > context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- settings = get_completions_settings()
- request = settings
- request["prompt"] = prompt
- request["max_context_length"] = context_window
- request["max_length"] = 400 # if we don't set this, it'll default to 100 which is quite short
-
- # Set grammar
- if grammar is not None:
- request["grammar"] = grammar
-
- if not endpoint.startswith(("http://", "https://")):
- raise ValueError(f"Provided OPENAI_API_BASE value ({endpoint}) must begin with http:// or https://")
-
- try:
- # NOTE: llama.cpp server returns the following when it's out of context
- # curl: (52) Empty reply from server
- URI = urljoin(endpoint.strip("/") + "/", KOBOLDCPP_API_SUFFIX.strip("/"))
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- result = result_full["results"][0]["text"]
- else:
- raise Exception(
- f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
- + f" Make sure that the koboldcpp server is running and reachable at {URI}."
- )
-
- except:
- # TODO handle gracefully
- raise
-
- # Pass usage statistics back to main thread
- # These are used to compute memory warning messages
- # KoboldCpp doesn't return anything?
- # https://lite.koboldai.net/koboldcpp_api#/v1/post_v1_generate
- completion_tokens = None
- total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
- usage = {
- "prompt_tokens": prompt_tokens,
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- }
-
- return result, usage
diff --git a/letta/local_llm/koboldcpp/settings.py b/letta/local_llm/koboldcpp/settings.py
deleted file mode 100644
index 51f49565..00000000
--- a/letta/local_llm/koboldcpp/settings.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# see https://lite.koboldai.net/koboldcpp_api#/v1/post_v1_generate
-SIMPLE = {
- "stop_sequence": [
- "\nUSER:",
- "\nASSISTANT:",
- "\nFUNCTION RETURN:",
- "\nUSER",
- "\nASSISTANT",
- "\nFUNCTION RETURN",
- "\nFUNCTION",
- "\nFUNC",
- "<|im_start|>",
- "<|im_end|>",
- "<|im_sep|>",
- # '\n' +
- # '',
- # '<|',
- # '\n#',
- # '\n\n\n',
- ],
- # "max_context_length": LLM_MAX_TOKENS,
- "max_length": 512,
-}
diff --git a/letta/local_llm/llamacpp/api.py b/letta/local_llm/llamacpp/api.py
deleted file mode 100644
index e5d24eea..00000000
--- a/letta/local_llm/llamacpp/api.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from urllib.parse import urljoin
-
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import count_tokens, post_json_auth_request
-
-LLAMACPP_API_SUFFIX = "/completion"
-
-
-def get_llamacpp_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=None):
- """See https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md for instructions on how to run the LLM web server"""
- from letta.utils import printd
-
- prompt_tokens = count_tokens(prompt)
- if prompt_tokens > context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- settings = get_completions_settings()
- request = settings
- request["prompt"] = prompt
-
- # Set grammar
- if grammar is not None:
- request["grammar"] = grammar
-
- if not endpoint.startswith(("http://", "https://")):
- raise ValueError(f"Provided OPENAI_API_BASE value ({endpoint}) must begin with http:// or https://")
-
- try:
- # NOTE: llama.cpp server returns the following when it's out of context
- # curl: (52) Empty reply from server
- URI = urljoin(endpoint.strip("/") + "/", LLAMACPP_API_SUFFIX.strip("/"))
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- result = result_full["content"]
- else:
- raise Exception(
- f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
- + f" Make sure that the llama.cpp server is running and reachable at {URI}."
- )
-
- except:
- # TODO handle gracefully
- raise
-
- # Pass usage statistics back to main thread
- # These are used to compute memory warning messages
- completion_tokens = result_full.get("tokens_predicted", None)
- total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
- usage = {
- "prompt_tokens": prompt_tokens, # can grab from "tokens_evaluated", but it's usually wrong (set to 0)
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- }
-
- return result, usage
diff --git a/letta/local_llm/llamacpp/settings.py b/letta/local_llm/llamacpp/settings.py
deleted file mode 100644
index c352a1c8..00000000
--- a/letta/local_llm/llamacpp/settings.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# see https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md#api-endpoints for options
-SIMPLE = {
- "stop": [
- "\nUSER:",
- "\nASSISTANT:",
- "\nFUNCTION RETURN:",
- "\nUSER",
- "\nASSISTANT",
- "\nFUNCTION RETURN",
- "\nFUNCTION",
- "\nFUNC",
- "<|im_start|>",
- "<|im_end|>",
- "<|im_sep|>",
- # '\n' +
- # '',
- # '<|',
- # '\n#',
- # '\n\n\n',
- ],
- # "n_predict": 3072,
-}
diff --git a/letta/local_llm/llm_chat_completion_wrappers/__init__.py b/letta/local_llm/llm_chat_completion_wrappers/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/local_llm/llm_chat_completion_wrappers/airoboros.py b/letta/local_llm/llm_chat_completion_wrappers/airoboros.py
deleted file mode 100644
index 544d11d4..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/airoboros.py
+++ /dev/null
@@ -1,451 +0,0 @@
-from ...errors import LLMJSONParsingError
-from ...helpers.json_helpers import json_dumps, json_loads
-from ..json_parser import clean_json
-from .wrapper_base import LLMChatCompletionWrapper
-
-
-class Airoboros21Wrapper(LLMChatCompletionWrapper):
- """Wrapper for Airoboros 70b v2.1: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1
-
- Note: this wrapper formats a prompt that only generates JSON, no inner thoughts
- """
-
- def __init__(
- self,
- simplify_json_content=True,
- clean_function_args=True,
- include_assistant_prefix=True,
- include_opening_brace_in_prefix=True,
- include_section_separators=True,
- ):
- self.simplify_json_content = simplify_json_content
- self.clean_func_args = clean_function_args
- self.include_assistant_prefix = include_assistant_prefix
- self.include_opening_brance_in_prefix = include_opening_brace_in_prefix
- self.include_section_separators = include_section_separators
-
- def chat_completion_to_prompt(self, messages, functions, function_documentation=None):
- """Example for airoboros: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#prompt-format
-
- A chat.
- USER: {prompt}
- ASSISTANT:
-
- Functions support: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#agentfunction-calling
-
- As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
-
- Input: I want to know how many times 'Python' is mentioned in my text file.
-
- Available functions:
- file_analytics:
- description: This tool performs various operations on a text file.
- params:
- action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
- filters:
- keyword: The word or phrase we want to search for.
-
- OpenAI functions schema style:
-
- {
- "name": "send_message",
- "description": "Sends a message to the human user",
- "parameters": {
- "type": "object",
- "properties": {
- # https://json-schema.org/understanding-json-schema/reference/array.html
- "message": {
- "type": "string",
- "description": "Message contents. All unicode (including emojis) are supported.",
- },
- },
- "required": ["message"],
- }
- },
- """
- prompt = ""
-
- # System insturctions go first
- assert messages[0]["role"] == "system"
- prompt += messages[0]["content"]
-
- # Next is the functions preamble
- def create_function_description(schema):
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "\nPlease select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- if function_documentation is not None:
- prompt += f"\n{function_documentation}"
- else:
- for function_dict in functions:
- prompt += f"\n{create_function_description(function_dict)}"
-
- def create_function_call(function_call):
- """Go from ChatCompletion to Airoboros style function trace (in prompt)
-
- ChatCompletion data (inside message['function_call']):
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
-
- Airoboros output:
- {
- "function": "send_message",
- "params": {
- "message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
- }
- }
- """
- airo_func_call = {
- "function": function_call["name"],
- "params": json_loads(function_call["arguments"]),
- }
- return json_dumps(airo_func_call, indent=2)
-
- # Add a sep for the conversation
- if self.include_section_separators:
- prompt += "\n### INPUT"
-
- # Last are the user/assistant messages
- for message in messages[1:]:
- assert message["role"] in ["user", "assistant", "function", "tool"], message
-
- if message["role"] == "user":
- if self.simplify_json_content:
- try:
- content_json = json_loads(message["content"])
- content_simple = content_json["message"]
- prompt += f"\nUSER: {content_simple}"
- except:
- prompt += f"\nUSER: {message['content']}"
- elif message["role"] == "assistant":
- prompt += f"\nASSISTANT: {message['content']}"
- # need to add the function call if there was one
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{create_function_call(message['function_call'])}"
- elif message["role"] in ["function", "tool"]:
- # TODO find a good way to add this
- # prompt += f"\nASSISTANT: (function return) {message['content']}"
- prompt += f"\nFUNCTION RETURN: {message['content']}"
- continue
- else:
- raise ValueError(message)
-
- # Add a sep for the response
- if self.include_section_separators:
- prompt += "\n### RESPONSE"
-
- if self.include_assistant_prefix:
- prompt += "\nASSISTANT:"
- if self.include_opening_brance_in_prefix:
- prompt += "\n{"
-
- print(prompt)
- return prompt
-
- def clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- # TODO more cleaning to fix errors LLM makes
- return cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
- raw_llm_output = "{" + raw_llm_output
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- except KeyError as e:
- raise LLMJSONParsingError(f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}")
-
- if self.clean_func_args:
- function_name, function_parameters = self.clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": None,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
-
-
-class Airoboros21InnerMonologueWrapper(Airoboros21Wrapper):
- """Still expect only JSON outputs from model, but add inner monologue as a field"""
-
- def __init__(
- self,
- simplify_json_content=True,
- clean_function_args=True,
- include_assistant_prefix=True,
- # include_opening_brace_in_prefix=True,
- # assistant_prefix_extra="\n{"
- # assistant_prefix_extra='\n{\n "function": ',
- assistant_prefix_extra='\n{\n "function":',
- include_section_separators=True,
- ):
- self.simplify_json_content = simplify_json_content
- self.clean_func_args = clean_function_args
- self.include_assistant_prefix = include_assistant_prefix
- # self.include_opening_brance_in_prefix = include_opening_brace_in_prefix
- self.assistant_prefix_extra = assistant_prefix_extra
- self.include_section_separators = include_section_separators
-
- def chat_completion_to_prompt(self, messages, functions, function_documentation=None):
- """Example for airoboros: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#prompt-format
-
- A chat.
- USER: {prompt}
- ASSISTANT:
-
- Functions support: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#agentfunction-calling
-
- As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
-
- Input: I want to know how many times 'Python' is mentioned in my text file.
-
- Available functions:
- file_analytics:
- description: This tool performs various operations on a text file.
- params:
- action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
- filters:
- keyword: The word or phrase we want to search for.
-
- OpenAI functions schema style:
-
- {
- "name": "send_message",
- "description": "Sends a message to the human user",
- "parameters": {
- "type": "object",
- "properties": {
- # https://json-schema.org/understanding-json-schema/reference/array.html
- "message": {
- "type": "string",
- "description": "Message contents. All unicode (including emojis) are supported.",
- },
- },
- "required": ["message"],
- }
- },
- """
- prompt = ""
-
- # System insturctions go first
- assert messages[0]["role"] == "system"
- prompt += messages[0]["content"]
-
- # Next is the functions preamble
- def create_function_description(schema, add_inner_thoughts=True):
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- if add_inner_thoughts:
- func_str += "\n inner_thoughts: Deep inner monologue private to you only."
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "\nPlease select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- if function_documentation is not None:
- prompt += f"\n{function_documentation}"
- else:
- for function_dict in functions:
- prompt += f"\n{create_function_description(function_dict)}"
-
- def create_function_call(function_call, inner_thoughts=None):
- """Go from ChatCompletion to Airoboros style function trace (in prompt)
-
- ChatCompletion data (inside message['function_call']):
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
-
- Airoboros output:
- {
- "function": "send_message",
- "params": {
- "message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
- }
- }
- """
- airo_func_call = {
- "function": function_call["name"],
- "params": {
- "inner_thoughts": inner_thoughts,
- **json_loads(function_call["arguments"]),
- },
- }
- return json_dumps(airo_func_call, indent=2)
-
- # Add a sep for the conversation
- if self.include_section_separators:
- prompt += "\n### INPUT"
-
- # Last are the user/assistant messages
- for message in messages[1:]:
- assert message["role"] in ["user", "assistant", "function", "tool"], message
-
- if message["role"] == "user":
- # Support for AutoGen naming of agents
- if "name" in message:
- user_prefix = message["name"].strip()
- user_prefix = f"USER ({user_prefix})"
- else:
- user_prefix = "USER"
- if self.simplify_json_content:
- try:
- content_json = json_loads(message["content"])
- content_simple = content_json["message"]
- prompt += f"\n{user_prefix}: {content_simple}"
- except:
- prompt += f"\n{user_prefix}: {message['content']}"
- elif message["role"] == "assistant":
- # Support for AutoGen naming of agents
- if "name" in message:
- assistant_prefix = message["name"].strip()
- assistant_prefix = f"ASSISTANT ({assistant_prefix})"
- else:
- assistant_prefix = "ASSISTANT"
- prompt += f"\n{assistant_prefix}:"
- # need to add the function call if there was one
- inner_thoughts = message["content"]
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{create_function_call(message['function_call'], inner_thoughts=inner_thoughts)}"
- elif message["role"] in ["function", "tool"]:
- # TODO find a good way to add this
- # prompt += f"\nASSISTANT: (function return) {message['content']}"
- prompt += f"\nFUNCTION RETURN: {message['content']}"
- continue
- else:
- raise ValueError(message)
-
- # Add a sep for the response
- if self.include_section_separators:
- prompt += "\n### RESPONSE"
-
- if self.include_assistant_prefix:
- prompt += "\nASSISTANT:"
- if self.assistant_prefix_extra:
- prompt += self.assistant_prefix_extra
-
- return prompt
-
- def clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- inner_thoughts = None
- if "inner_thoughts" in function_args:
- inner_thoughts = cleaned_function_args.pop("inner_thoughts")
-
- # TODO more cleaning to fix errors LLM makes
- return inner_thoughts, cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- # if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
- # raw_llm_output = "{" + raw_llm_output
- if self.assistant_prefix_extra and raw_llm_output[: len(self.assistant_prefix_extra)] != self.assistant_prefix_extra:
- # print(f"adding prefix back to llm, raw_llm_output=\n{raw_llm_output}")
- raw_llm_output = self.assistant_prefix_extra + raw_llm_output
- # print(f"->\n{raw_llm_output}")
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- # NOTE: weird bug can happen where 'function' gets nested if the prefix in the prompt isn't abided by
- if isinstance(function_json_output["function"], dict):
- function_json_output = function_json_output["function"]
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- except KeyError as e:
- raise LLMJSONParsingError(
- f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}. JSON result was:\n{function_json_output}"
- )
-
- if self.clean_func_args:
- (
- inner_thoughts,
- function_name,
- function_parameters,
- ) = self.clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": inner_thoughts,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
diff --git a/letta/local_llm/llm_chat_completion_wrappers/chatml.py b/letta/local_llm/llm_chat_completion_wrappers/chatml.py
deleted file mode 100644
index 71589959..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/chatml.py
+++ /dev/null
@@ -1,476 +0,0 @@
-from letta.errors import LLMJSONParsingError
-from letta.helpers.json_helpers import json_dumps, json_loads
-from letta.local_llm.json_parser import clean_json
-from letta.local_llm.llm_chat_completion_wrappers.wrapper_base import LLMChatCompletionWrapper
-from letta.schemas.enums import MessageRole
-
-PREFIX_HINT = """# Reminders:
-# Important information about yourself and the user is stored in (limited) core memory
-# You can modify core memory with core_memory_replace
-# You can add to core memory with core_memory_append
-# Less important information is stored in (unlimited) archival memory
-# You can add to archival memory with archival_memory_insert
-# You can search archival memory with archival_memory_search
-# You will always see the statistics of archival memory, so you know if there is content inside it
-# If you receive new important information about the user (or yourself), you immediately update your memory with core_memory_replace, core_memory_append, or archival_memory_insert"""
-
-FIRST_PREFIX_HINT = """# Reminders:
-# This is your first interaction with the user!
-# Initial information about them is provided in the core memory user block
-# Make sure to introduce yourself to them
-# Your inner thoughts should be private, interesting, and creative
-# Do NOT use inner thoughts to communicate with the user
-# Use send_message to communicate with the user"""
-# Don't forget to use send_message, otherwise the user won't see your message"""
-
-
-class ChatMLInnerMonologueWrapper(LLMChatCompletionWrapper):
- """ChatML-style prompt formatter, tested for use with https://huggingface.co/ehartford/dolphin-2.5-mixtral-8x7b#training"""
-
- supports_first_message = True
-
- def __init__(
- self,
- json_indent=2,
- # simplify_json_content=True,
- simplify_json_content=False,
- clean_function_args=True,
- include_assistant_prefix=True,
- assistant_prefix_extra='\n{\n "function":',
- assistant_prefix_extra_first_message='\n{\n "function": "send_message",',
- allow_custom_roles=True, # allow roles outside user/assistant
- use_system_role_in_user=False, # use the system role on user messages that don't use "type: user_message"
- # allow_function_role=True, # use function role for function replies?
- allow_function_role=False, # use function role for function replies?
- no_function_role_role="assistant", # if no function role, which role to use?
- no_function_role_prefix="FUNCTION RETURN:\n", # if no function role, what prefix to use?
- # add a guiding hint
- assistant_prefix_hint=False,
- ):
- self.simplify_json_content = simplify_json_content
- self.clean_func_args = clean_function_args
- self.include_assistant_prefix = include_assistant_prefix
- self.assistant_prefix_extra = assistant_prefix_extra
- self.assistant_prefix_extra_first_message = assistant_prefix_extra_first_message
- self.assistant_prefix_hint = assistant_prefix_hint
-
- # role-based
- self.allow_custom_roles = allow_custom_roles
- self.use_system_role_in_user = use_system_role_in_user
- self.allow_function_role = allow_function_role
- # extras for when the function role is disallowed
- self.no_function_role_role = no_function_role_role
- self.no_function_role_prefix = no_function_role_prefix
-
- # how to set json in prompt
- self.json_indent = json_indent
-
- def _compile_function_description(self, schema, add_inner_thoughts=True) -> str:
- """Go from a JSON schema to a string description for a prompt"""
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- if add_inner_thoughts:
- from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION
-
- func_str += f"\n {INNER_THOUGHTS_KWARG}: {INNER_THOUGHTS_KWARG_DESCRIPTION}"
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- def _compile_function_block(self, functions) -> str:
- """functions dict -> string describing functions choices"""
- prompt = ""
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "Please select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- for function_dict in functions:
- prompt += f"\n{self._compile_function_description(function_dict)}"
-
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_system_message(self, system_message, functions, function_documentation=None) -> str:
- """system prompt + memory + functions -> string"""
- prompt = ""
- prompt += system_message
- prompt += "\n"
- if function_documentation is not None:
- prompt += "Please select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:\n"
- prompt += function_documentation
- else:
- prompt += self._compile_function_block(functions)
- return prompt
-
- def _compile_function_call(self, function_call, inner_thoughts=None):
- """Go from ChatCompletion to Airoboros style function trace (in prompt)
-
- ChatCompletion data (inside message['function_call']):
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
-
- Airoboros output:
- {
- "function": "send_message",
- "params": {
- "message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
- }
- }
- """
- airo_func_call = {
- "function": function_call["name"],
- "params": {
- "inner_thoughts": inner_thoughts,
- **json_loads(function_call["arguments"]),
- },
- }
- return json_dumps(airo_func_call, indent=self.json_indent)
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_assistant_message(self, message) -> str:
- """assistant message -> string"""
- prompt = ""
-
- # need to add the function call if there was one
- inner_thoughts = message["content"]
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{self._compile_function_call(message['function_call'], inner_thoughts=inner_thoughts)}"
- elif "tool_calls" in message and message["tool_calls"]:
- for tool_call in message["tool_calls"]:
- prompt += f"\n{self._compile_function_call(tool_call['function'], inner_thoughts=inner_thoughts)}"
- else:
- # TODO should we format this into JSON somehow?
- prompt += inner_thoughts
-
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_user_message(self, message) -> str:
- """user message (should be JSON) -> string"""
- prompt = ""
- if self.simplify_json_content:
- # Make user messages not JSON but plaintext instead
- try:
- user_msg_json = json_loads(message["content"])
- user_msg_str = user_msg_json["message"]
- except:
- user_msg_str = message["content"]
- else:
- # Otherwise just dump the full json
- try:
- user_msg_json = json_loads(message["content"])
- user_msg_str = json_dumps(user_msg_json, indent=self.json_indent)
- except:
- user_msg_str = message["content"]
-
- prompt += user_msg_str
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_function_response(self, message) -> str:
- """function response message (should be JSON) -> string"""
- # TODO we should clean up send_message returns to avoid cluttering the prompt
- prompt = ""
- try:
- # indent the function replies
- function_return_dict = json_loads(message["content"])
- function_return_str = json_dumps(function_return_dict, indent=0)
- except:
- function_return_str = message["content"]
-
- prompt += function_return_str
- return prompt
-
- def chat_completion_to_prompt(self, messages, functions, first_message=False, function_documentation=None):
- """chatml-style prompt formatting, with implied support for multi-role"""
- prompt = ""
-
- # System insturctions go first
- assert messages[0]["role"] == "system"
- system_block = self._compile_system_message(
- system_message=messages[0]["content"], functions=functions, function_documentation=function_documentation
- )
- prompt += f"<|im_start|>system\n{system_block.strip()}<|im_end|>"
-
- # Last are the user/assistant messages
- for message in messages[1:]:
- # check that message["role"] is a valid option for MessageRole
- # TODO: this shouldn't be necessary if we use pydantic in the future
- assert message["role"] in [role.value for role in MessageRole]
-
- if message["role"] == "user":
- # Support for AutoGen naming of agents
- role_str = message["name"].strip().lower() if (self.allow_custom_roles and "name" in message) else message["role"]
- msg_str = self._compile_user_message(message)
-
- if self.use_system_role_in_user:
- try:
- msg_json = json_loads(message["content"])
- if msg_json["type"] != "user_message":
- role_str = "system"
- except:
- pass
- prompt += f"\n<|im_start|>{role_str}\n{msg_str.strip()}<|im_end|>"
-
- elif message["role"] == "assistant":
- # Support for AutoGen naming of agents
- role_str = message["name"].strip().lower() if (self.allow_custom_roles and "name" in message) else message["role"]
- msg_str = self._compile_assistant_message(message)
-
- prompt += f"\n<|im_start|>{role_str}\n{msg_str.strip()}<|im_end|>"
-
- elif message["role"] == "system":
- role_str = "system"
- msg_str = self._compile_system_message(
- system_message=message["content"], functions=functions, function_documentation=function_documentation
- )
-
- prompt += f"\n<|im_start|>{role_str}\n{msg_str.strip()}<|im_end|>"
-
- elif message["role"] in ["tool", "function"]:
- if self.allow_function_role:
- role_str = message["role"]
- msg_str = self._compile_function_response(message)
- prompt += f"\n<|im_start|>{role_str}\n{msg_str.strip()}<|im_end|>"
- else:
- # TODO figure out what to do with functions if we disallow function role
- role_str = self.no_function_role_role
- msg_str = self._compile_function_response(message)
- func_resp_prefix = self.no_function_role_prefix
- # NOTE whatever the special prefix is, it should also be a stop token
- prompt += f"\n<|im_start|>{role_str}\n{func_resp_prefix}{msg_str.strip()}<|im_end|>"
-
- else:
- raise ValueError(message)
-
- if self.include_assistant_prefix:
- prompt += "\n<|im_start|>assistant"
- if self.assistant_prefix_hint:
- prompt += f"\n{FIRST_PREFIX_HINT if first_message else PREFIX_HINT}"
- if self.supports_first_message and first_message:
- if self.assistant_prefix_extra_first_message:
- prompt += self.assistant_prefix_extra_first_message
- else:
- if self.assistant_prefix_extra:
- # assistant_prefix_extra='\n{\n "function":',
- prompt += self.assistant_prefix_extra
-
- return prompt
-
- def _clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- inner_thoughts = None
- if "inner_thoughts" in function_args:
- inner_thoughts = cleaned_function_args.pop("inner_thoughts")
-
- # TODO more cleaning to fix errors LLM makes
- return inner_thoughts, cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output, first_message=False):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- # if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
- # raw_llm_output = "{" + raw_llm_output
- assistant_prefix = self.assistant_prefix_extra_first_message if first_message else self.assistant_prefix_extra
- if assistant_prefix and raw_llm_output[: len(assistant_prefix)] != assistant_prefix:
- # print(f"adding prefix back to llm, raw_llm_output=\n{raw_llm_output}")
- raw_llm_output = assistant_prefix + raw_llm_output
- # print(f"->\n{raw_llm_output}")
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- # NOTE: weird bug can happen where 'function' gets nested if the prefix in the prompt isn't abided by
- if isinstance(function_json_output["function"], dict):
- function_json_output = function_json_output["function"]
- # regular unpacking
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- except KeyError as e:
- raise LLMJSONParsingError(
- f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}. JSON result was:\n{function_json_output}"
- )
-
- if self.clean_func_args:
- (
- inner_thoughts,
- function_name,
- function_parameters,
- ) = self._clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": inner_thoughts,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
-
-
-class ChatMLOuterInnerMonologueWrapper(ChatMLInnerMonologueWrapper):
- """Moves the inner monologue outside the main function to allow the LLM to omit function calls
-
- NOTE: warning - this makes it easier for the agent to forget to call functions,
- so it is advised to use the function-forcing wrapper unless the LLM is very good
-
- ie instead of:
- {
- "function": "send_message",
- "params": {
- "inner_thoughts": "User has repeated the message. Recognizing repetition and taking a different approach.",
- "message": "It looks like you're repeating yourself, Chad. Is there something you're trying to express, or are you just
- testing me?"
- }
- }
-
- this wrapper does:
- {
- "inner_thoughts": "User has repeated the message. Recognizing repetition and taking a different approach.",
- "function": "send_message",
- "params": {
- "message": "It looks like you're repeating yourself, Chad. Is there something you're trying to express, or are you just
- testing me?"
- }
- }
- """
-
- # TODO find a way to support forcing the first func call
- supports_first_message = False
-
- def __init__(self, **kwargs):
- # Set a different default for assistant_prefix_extra if not provided
- kwargs.setdefault("assistant_prefix_extra", '\n{\n "inner_thoughts":')
- super().__init__(**kwargs)
-
- def _compile_function_block(self, functions) -> str:
- """NOTE: modified to not include inner thoughts at all as extras"""
- prompt = ""
-
- prompt += " ".join(
- [
- "Please select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation.",
- "Provide your response in JSON format.",
- "You must always include inner thoughts, but you do not always have to call a function.",
- ]
- )
- prompt += "\nAvailable functions:"
- for function_dict in functions:
- prompt += f"\n{self._compile_function_description(function_dict, add_inner_thoughts=False)}"
-
- return prompt
-
- def _compile_function_call(self, function_call, inner_thoughts=None):
- """NOTE: Modified to put inner thoughts outside the function"""
- airo_func_call = {
- "inner_thoughts": inner_thoughts,
- "function": function_call["name"],
- "params": {
- # "inner_thoughts": inner_thoughts,
- **json_loads(function_call["arguments"]),
- },
- }
- return json_dumps(airo_func_call, indent=self.json_indent)
-
- def output_to_chat_completion_response(self, raw_llm_output, first_message=False):
- """NOTE: Modified to expect "inner_thoughts" outside the function
-
- Also, allow messages that have None/null function calls
- """
-
- # If we used a prefex to guide generation, we need to add it to the output as a preefix
- assistant_prefix = (
- self.assistant_prefix_extra_first_message if (self.supports_first_message and first_message) else self.assistant_prefix_extra
- )
- if assistant_prefix and raw_llm_output[: len(assistant_prefix)] != assistant_prefix:
- raw_llm_output = assistant_prefix + raw_llm_output
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- # NOTE: main diff
- inner_thoughts = function_json_output["inner_thoughts"]
- # NOTE: also have to account for "function": null
- if (
- "function" in function_json_output
- and function_json_output["function"] is not None
- and function_json_output["function"].strip().lower() != "none"
- ):
- # TODO apply lm studio nested bug patch?
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- else:
- function_name = None
- function_parameters = None
- except KeyError as e:
- raise LLMJSONParsingError(f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}")
-
- # TODO add some code to clean inner thoughts
- # e.g. fix this:
- """
- 💭 I sense a new mind to engage with. Interesting...
- 🤖 Hello, I'm Sam. Welcome to our conversation.
- > Enter your message: what do you know about me?
- 💭 : I've been observing our previous conversations. I remember that your name is Chad.
- 🤖 I recall our previous interactions, Chad. How can I assist you today?
- > Enter your message: is that all you know about me?
- 💭 : I see you're curious about our connection. Let me do a quick search of my memory.
- """
-
- if function_name is not None and self.clean_func_args:
- (
- _inner_thoughts, # NOTE: main diff (ignore)
- function_name,
- function_parameters,
- ) = self._clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": inner_thoughts,
- # "function_call": {
- # "name": function_name,
- # "arguments": json_dumps(function_parameters),
- # },
- }
-
- # Add the function if not none:
- if function_name is not None:
- message["function_call"] = {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- }
-
- return message
diff --git a/letta/local_llm/llm_chat_completion_wrappers/configurable_wrapper.py b/letta/local_llm/llm_chat_completion_wrappers/configurable_wrapper.py
deleted file mode 100644
index 9f53fa83..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/configurable_wrapper.py
+++ /dev/null
@@ -1,386 +0,0 @@
-import yaml
-
-from ...errors import LLMJSONParsingError
-from ...helpers.json_helpers import json_dumps, json_loads
-from ..json_parser import clean_json
-from .wrapper_base import LLMChatCompletionWrapper
-
-
-# A configurable model agnostic wrapper.
-class ConfigurableJSONWrapper(LLMChatCompletionWrapper):
- def __init__(
- self,
- pre_prompt: str = "",
- post_prompt: str = "",
- sys_prompt_start: str = "",
- sys_prompt_end: str = "",
- user_prompt_start: str = "",
- user_prompt_end: str = "",
- assistant_prompt_start: str = "",
- assistant_prompt_end: str = "",
- tool_prompt_start: str = "",
- tool_prompt_end: str = "",
- assistant_prefix_extra="",
- assistant_prefix_extra_first_message="",
- allow_custom_roles: bool = False, # allow roles outside user/assistant
- custom_post_role: str = "", # For chatml this would be '\n'
- custom_roles_prompt_start: str = "", # For chatml this would be '<|im_start|>'
- custom_roles_prompt_end: str = "", # For chatml this would be '<|im_end|>'
- include_sys_prompt_in_first_user_message: bool = False,
- default_stop_sequences=None,
- simplify_json_content: bool = False,
- strip_prompt: bool = False,
- json_indent: int = 2,
- clean_function_args: bool = False,
- ):
- """
- Initializes a new MessagesFormatter object.
-
- Args:
- pre_prompt (str): The pre-prompt content.
- post_prompt (str): The post-prompt content
- sys_prompt_start (str): The system messages prompt start. For chatml, this would be '<|im_start|>system\n'
- sys_prompt_end (str): The system messages prompt end. For chatml, this would be '<|im_end|>'
- user_prompt_start (str): The user messages prompt start. For chatml, this would be '<|im_start|>user\n'
- user_prompt_end (str): The user messages prompt end. For chatml, this would be '<|im_end|>\n'
- assistant_prompt_start (str): The assistant messages prompt start. For chatml, this would be '<|im_start|>user\n'
- assistant_prompt_end (str): The assistant messages prompt end. For chatml, this would be '<|im_end|>\n'
- tool_prompt_start (str): The tool messages prompt start. For chatml, this would be '<|im_start|>tool\n' if the model supports the tool role, otherwise it would be something like '<|im_start|>user\nFUNCTION RETURN:\n'
- tool_prompt_end (str): The tool messages prompt end. For chatml, this would be '<|im_end|>\n'
- assistant_prefix_extra (str): A prefix for every assistant message to steer the model to output JSON. Something like '\n{\n "function":'
- assistant_prefix_extra_first_message (str): A prefix for the first assistant message to steer the model to output JSON and use a specific function. Something like '\n{\n "function": "send_message",'
- allow_custom_roles (bool): If the wrapper allows custom roles, like names for autogen agents.
- custom_post_role (str): The part that comes after the custom role string. For chatml, this would be '\n'
- custom_roles_prompt_start: (str): Custom role prompt start. For chatml, this would be '<|im_start|>'
- custom_roles_prompt_end: (str): Custom role prompt start. For chatml, this would be '<|im_end|>\n'
- include_sys_prompt_in_first_user_message (bool): Indicates whether to include the system prompt in the first user message. For Llama2 this would be True, for chatml, this would be False
- simplify_json_content (bool):
- strip_prompt (bool): If whitespaces at the end and beginning of the prompt get stripped.
- default_stop_sequences (List[str]): List of default stop sequences.
-
- """
- if default_stop_sequences is None:
- default_stop_sequences = []
- self.pre_prompt = pre_prompt
- self.post_prompt = post_prompt
- self.sys_prompt_start = sys_prompt_start
- self.sys_prompt_end = sys_prompt_end
- self.user_prompt_start = user_prompt_start
- self.user_prompt_end = user_prompt_end
- self.assistant_prompt_start = assistant_prompt_start
- self.assistant_prompt_end = assistant_prompt_end
- self.tool_prompt_start = tool_prompt_start
- self.tool_prompt_end = tool_prompt_end
- self.assistant_prefix_extra = assistant_prefix_extra
- self.assistant_prefix_extra_first_message = assistant_prefix_extra_first_message
- self.allow_custom_roles = allow_custom_roles
- self.custom_post_role = custom_post_role
- self.custom_roles_prompt_start = custom_roles_prompt_start
- self.custom_roles_prompt_end = custom_roles_prompt_end
- self.include_sys_prompt_in_first_user_message = include_sys_prompt_in_first_user_message
- self.simplify_json_content = simplify_json_content
- self.default_stop_sequences = default_stop_sequences
- self.strip_prompt = strip_prompt
- self.json_indent = json_indent
- self.clean_func_args = clean_function_args
- self.supports_first_message = True
-
- def _compile_function_description(self, schema, add_inner_thoughts=True) -> str:
- """Go from a JSON schema to a string description for a prompt"""
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- if add_inner_thoughts:
- func_str += "\n inner_thoughts: Deep inner monologue private to you only."
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- def _compile_function_block(self, functions) -> str:
- """functions dict -> string describing functions choices"""
- prompt = ""
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "Please select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- for function_dict in functions:
- prompt += f"\n{self._compile_function_description(function_dict)}"
-
- return prompt
-
- def _compile_system_message(self, system_message, functions, function_documentation=None) -> str:
- """system prompt + memory + functions -> string"""
- prompt = system_message
- prompt += "\n"
- if function_documentation is not None:
- prompt += "Please select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- prompt += function_documentation
- else:
- prompt += self._compile_function_block(functions)
- return prompt
-
- def _compile_function_call(self, function_call, inner_thoughts=None):
- airo_func_call = {
- "function": function_call["name"],
- "params": {
- "inner_thoughts": inner_thoughts,
- **json_loads(function_call["arguments"]),
- },
- }
- return json_dumps(airo_func_call, indent=self.json_indent)
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_assistant_message(self, message) -> str:
- """assistant message -> string"""
- prompt = ""
-
- # need to add the function call if there was one
- inner_thoughts = message["content"]
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{self._compile_function_call(message['function_call'], inner_thoughts=inner_thoughts)}"
- elif "tool_calls" in message and message["tool_calls"]:
- for tool_call in message["tool_calls"]:
- prompt += f"\n{self._compile_function_call(tool_call['function'], inner_thoughts=inner_thoughts)}"
- else:
- # TODO should we format this into JSON somehow?
- prompt += inner_thoughts
-
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_user_message(self, message) -> str:
- """user message (should be JSON) -> string"""
- prompt = ""
- if self.simplify_json_content:
- # Make user messages not JSON but plaintext instead
- try:
- user_msg_json = json_loads(message["content"])
- user_msg_str = user_msg_json["message"]
- except:
- user_msg_str = message["content"]
- else:
- # Otherwise just dump the full json
- try:
- user_msg_json = json_loads(message["content"])
- user_msg_str = json_dumps(user_msg_json, indent=self.json_indent)
- except:
- user_msg_str = message["content"]
-
- prompt += user_msg_str
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_function_response(self, message) -> str:
- """function response message (should be JSON) -> string"""
- # TODO we should clean up send_message returns to avoid cluttering the prompt
- prompt = ""
- try:
- # indent the function replies
- function_return_dict = json_loads(message["content"])
- function_return_str = json_dumps(function_return_dict, indent=0)
- except:
- function_return_str = message["content"]
-
- prompt += function_return_str
- return prompt
-
- def chat_completion_to_prompt(self, messages, functions, first_message=False, function_documentation=None):
- formatted_messages = self.pre_prompt
-
- no_user_prompt_start = False
-
- for message in messages:
- if message["role"] == "system":
- msg = self._compile_system_message(message["content"], functions, function_documentation)
- formatted_messages += self.sys_prompt_start + msg + self.sys_prompt_end
-
- if self.include_sys_prompt_in_first_user_message:
- formatted_messages = self.user_prompt_start + formatted_messages
- no_user_prompt_start = True
- elif message["role"] == "user":
- msg = self._compile_user_message(message)
- if no_user_prompt_start:
- no_user_prompt_start = False
- formatted_messages += msg + self.user_prompt_end
- else:
- formatted_messages += self.user_prompt_start + msg + self.user_prompt_end
-
- elif message["role"] == "assistant":
- msg = self._compile_assistant_message(message)
- if self.allow_custom_roles and "name" in message:
- role_str = message["name"].strip().lower() if (self.allow_custom_roles and "name" in message) else message["role"]
- if no_user_prompt_start:
- no_user_prompt_start = False
- formatted_messages += (
- self.user_prompt_end
- + self.custom_roles_prompt_start
- + role_str
- + self.custom_post_role
- + msg
- + self.custom_roles_prompt_end
- )
- else:
- formatted_messages += (
- self.custom_roles_prompt_start + role_str + self.custom_post_role + msg + self.custom_roles_prompt_end
- )
- else:
- if no_user_prompt_start:
- no_user_prompt_start = False
- formatted_messages += self.user_prompt_end + self.assistant_prompt_start + msg + self.assistant_prompt_end
- else:
- formatted_messages += self.assistant_prompt_start + msg + self.assistant_prompt_end
- elif message["role"] == "tool":
- msg = self._compile_function_response(message)
- formatted_messages += self.tool_prompt_start + msg + self.tool_prompt_end
-
- if self.strip_prompt:
- if first_message:
- prompt = formatted_messages + self.post_prompt + self.assistant_prefix_extra_first_message
- else:
- prompt = formatted_messages + self.post_prompt + self.assistant_prefix_extra
- return prompt.strip()
- else:
- if first_message:
- prompt = formatted_messages + self.post_prompt + self.assistant_prefix_extra_first_message
- else:
- prompt = formatted_messages + self.post_prompt + self.assistant_prefix_extra
- return prompt
-
- def _clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- inner_thoughts = None
- if "inner_thoughts" in function_args:
- inner_thoughts = cleaned_function_args.pop("inner_thoughts")
-
- # TODO more cleaning to fix errors LLM makes
- return inner_thoughts, cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output, first_message=False):
- assistant_prefix = self.assistant_prefix_extra_first_message if first_message else self.assistant_prefix_extra
- if assistant_prefix and raw_llm_output[: len(assistant_prefix)] != assistant_prefix:
- raw_llm_output = assistant_prefix + raw_llm_output
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- # NOTE: weird bug can happen where 'function' gets nested if the prefix in the prompt isn't abided by
- if isinstance(function_json_output["function"], dict):
- function_json_output = function_json_output["function"]
- # regular unpacking
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- if "inner_thoughts" in function_json_output:
- inner_thoughts = function_json_output["inner_thoughts"]
- else:
- if "inner_thoughts" in function_json_output["params"]:
- inner_thoughts = function_json_output["params"]["inner_thoughts"]
- else:
- inner_thoughts = ""
- except KeyError as e:
- raise LLMJSONParsingError(
- f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}. JSON result was:\n{function_json_output}"
- )
-
- if self.clean_func_args:
- (
- inner_thoughts,
- function_name,
- function_parameters,
- ) = self._clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": inner_thoughts,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
-
- def save_to_yaml(self, file_path: str):
- """
- Save the configuration to a YAML file.
-
- Args:
- file_path (str): The path to the YAML file.
- """
- data = {
- "pre_prompt": self.pre_prompt,
- "post_prompt": self.post_prompt,
- "sys_prompt_start": self.sys_prompt_start,
- "sys_prompt_end": self.sys_prompt_end,
- "user_prompt_start": self.user_prompt_start,
- "user_prompt_end": self.user_prompt_end,
- "assistant_prompt_start": self.assistant_prompt_start,
- "assistant_prompt_end": self.assistant_prompt_end,
- "tool_prompt_start": self.tool_prompt_start,
- "tool_prompt_end": self.tool_prompt_end,
- "assistant_prefix_extra": self.assistant_prefix_extra,
- "assistant_prefix_extra_first_message": self.assistant_prefix_extra_first_message,
- "allow_custom_roles": self.allow_custom_roles,
- "custom_post_role": self.custom_post_role,
- "custom_roles_prompt_start": self.custom_roles_prompt_start,
- "custom_roles_prompt_end": self.custom_roles_prompt_end,
- "include_sys_prompt_in_first_user_message": self.include_sys_prompt_in_first_user_message,
- "simplify_json_content": self.simplify_json_content,
- "strip_prompt": self.strip_prompt,
- "json_indent": self.json_indent,
- "clean_function_args": self.clean_func_args,
- "default_stop_sequences": self.default_stop_sequences,
- }
-
- with open(file_path, "w", encoding="utf-8") as yaml_file:
- yaml.dump(data, yaml_file, default_flow_style=False)
-
- @staticmethod
- def load_from_yaml(file_path: str):
- """
- Load the configuration from a YAML file.
-
- Args:
- file_path (str): The path to the YAML file.
- """
- with open(file_path, "r", encoding="utf-8") as yaml_file:
- data = yaml.safe_load(yaml_file)
-
- wrapper = ConfigurableJSONWrapper()
- # Set the attributes from the loaded data
- wrapper.pre_prompt = data.get("pre_prompt", "")
- wrapper.post_prompt = data.get("post_prompt", "")
- wrapper.sys_prompt_start = data.get("sys_prompt_start", "")
- wrapper.sys_prompt_end = data.get("sys_prompt_end", "")
- wrapper.user_prompt_start = data.get("user_prompt_start", "")
- wrapper.user_prompt_end = data.get("user_prompt_end", "")
- wrapper.assistant_prompt_start = data.get("assistant_prompt_start", "")
- wrapper.assistant_prompt_end = data.get("assistant_prompt_end", "")
- wrapper.tool_prompt_start = data.get("tool_prompt_start", "")
- wrapper.tool_prompt_end = data.get("tool_prompt_end", "")
- wrapper.assistant_prefix_extra = data.get("assistant_prefix_extra", "")
- wrapper.assistant_prefix_extra_first_message = data.get("assistant_prefix_extra_first_message", "")
- wrapper.allow_custom_roles = data.get("allow_custom_roles", False)
- wrapper.custom_post_role = data.get("custom_post_role", "")
- wrapper.custom_roles_prompt_start = data.get("custom_roles_prompt_start", "")
- wrapper.custom_roles_prompt_end = data.get("custom_roles_prompt_end", "")
- wrapper.include_sys_prompt_in_first_user_message = data.get("include_sys_prompt_in_first_user_message", False)
- wrapper.simplify_json_content = data.get("simplify_json_content", False)
- wrapper.strip_prompt = data.get("strip_prompt", False)
- wrapper.json_indent = data.get("json_indent", 2)
- wrapper.clean_func_args = data.get("clean_function_args", False)
- wrapper.default_stop_sequences = data.get("default_stop_sequences", [])
-
- return wrapper
diff --git a/letta/local_llm/llm_chat_completion_wrappers/dolphin.py b/letta/local_llm/llm_chat_completion_wrappers/dolphin.py
deleted file mode 100644
index e393d9b1..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/dolphin.py
+++ /dev/null
@@ -1,245 +0,0 @@
-from ...errors import LLMJSONParsingError
-from ...helpers.json_helpers import json_dumps, json_loads
-from ..json_parser import clean_json
-from .wrapper_base import LLMChatCompletionWrapper
-
-
-class Dolphin21MistralWrapper(LLMChatCompletionWrapper):
- """Wrapper for Dolphin 2.1 Mistral 7b: https://huggingface.co/ehartford/dolphin-2.1-mistral-7b
-
- Note: this wrapper formats a prompt that only generates JSON, no inner thoughts
- """
-
- def __init__(
- self,
- simplify_json_content=True,
- clean_function_args=True,
- include_assistant_prefix=True,
- include_opening_brace_in_prefix=True,
- include_section_separators=False,
- ):
- self.simplify_json_content = simplify_json_content
- self.clean_func_args = clean_function_args
- self.include_assistant_prefix = include_assistant_prefix
- self.include_opening_brance_in_prefix = include_opening_brace_in_prefix
- self.include_section_separators = include_section_separators
-
- def chat_completion_to_prompt(self, messages, functions, function_documentation=None):
- """Example for airoboros: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#prompt-format
-
- <|im_start|>system
- You are Dolphin, a helpful AI assistant.<|im_end|>
- <|im_start|>user
- {prompt}<|im_end|>
- <|im_start|>assistant
-
- Do function spec Airoboros style inside the system message:
- Functions support: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#agentfunction-calling
-
- As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
-
- Input: I want to know how many times 'Python' is mentioned in my text file.
-
- Available functions:
- file_analytics:
- description: This tool performs various operations on a text file.
- params:
- action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
- filters:
- keyword: The word or phrase we want to search for.
-
- OpenAI functions schema style:
-
- {
- "name": "send_message",
- "description": "Sends a message to the human user",
- "parameters": {
- "type": "object",
- "properties": {
- # https://json-schema.org/understanding-json-schema/reference/array.html
- "message": {
- "type": "string",
- "description": "Message contents. All unicode (including emojis) are supported.",
- },
- },
- "required": ["message"],
- }
- },
- """
- prompt = ""
-
- # <|im_start|>system
- # You are Dolphin, a helpful AI assistant.<|im_end|>
-
- IM_START_TOKEN = "<|im_start|>"
- IM_END_TOKEN = "<|im_end|>"
-
- # System instructions go first
- assert messages[0]["role"] == "system"
- prompt += f"{IM_START_TOKEN}system"
- prompt += f"\n{messages[0]['content']}"
-
- # Next is the functions preamble
- def create_function_description(schema):
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "\nPlease select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- if function_documentation is not None:
- prompt += f"\n{function_documentation}"
- else:
- for function_dict in functions:
- prompt += f"\n{create_function_description(function_dict)}"
-
- # Put functions INSIDE system message (TODO experiment with this)
- prompt += IM_END_TOKEN
-
- def create_function_call(function_call):
- """Go from ChatCompletion to Airoboros style function trace (in prompt)
-
- ChatCompletion data (inside message['function_call']):
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
-
- Airoboros output:
- {
- "function": "send_message",
- "params": {
- "message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
- }
- }
- """
- airo_func_call = {
- "function": function_call["name"],
- "params": json_loads(function_call["arguments"]),
- }
- return json_dumps(airo_func_call, indent=2)
-
- # option (1): from HF README:
- # <|im_start|>user
- # {prompt}<|im_end|>
- # <|im_start|>assistant
- # {assistant reply}
- # {function output (if function)}
-
- # option (2): take liberties
- # <|im_start|>user
- # {prompt}<|im_end|>
- # <|im_start|>assistant
- # or
- # <|im_start|>function
-
- # Add a sep for the conversation
- # if self.include_section_separators:
- # prompt += "\n### INPUT"
-
- # Last are the user/assistant messages
- for message in messages[1:]:
- assert message["role"] in ["user", "assistant", "function", "tool"], message
-
- if message["role"] == "user":
- if self.simplify_json_content:
- try:
- content_json = (json_loads(message["content"]),)
- content_simple = content_json["message"]
- prompt += f"\n{IM_START_TOKEN}user\n{content_simple}{IM_END_TOKEN}"
- # prompt += f"\nUSER: {content_simple}"
- except:
- prompt += f"\n{IM_START_TOKEN}user\n{message['content']}{IM_END_TOKEN}"
- # prompt += f"\nUSER: {message['content']}"
- elif message["role"] == "assistant":
- prompt += f"\n{IM_START_TOKEN}assistant"
- if message["content"] is not None:
- prompt += f"\n{message['content']}"
- # prompt += f"\nASSISTANT: {message['content']}"
- # need to add the function call if there was one
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{create_function_call(message['function_call'])}"
- prompt += f"{IM_END_TOKEN}"
- elif message["role"] in ["function", "tool"]:
- # TODO find a good way to add this
- # prompt += f"\nASSISTANT: (function return) {message['content']}"
- prompt += f"\n{IM_START_TOKEN}assistant"
- prompt += f"\nFUNCTION RETURN: {message['content']}"
- # prompt += f"\nFUNCTION RETURN: {message['content']}"
- continue
- else:
- raise ValueError(message)
-
- # Add a sep for the response
- # if self.include_section_separators:
- # prompt += "\n### RESPONSE"
-
- if self.include_assistant_prefix:
- # prompt += f"\nASSISTANT:"
- prompt += f"\n{IM_START_TOKEN}assistant"
- if self.include_opening_brance_in_prefix:
- prompt += "\n{"
-
- return prompt
-
- def clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- # TODO more cleaning to fix errors LLM makes
- return cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
- raw_llm_output = "{" + raw_llm_output
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- except KeyError as e:
- raise LLMJSONParsingError(f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}")
-
- if self.clean_func_args:
- function_name, function_parameters = self.clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": None,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
diff --git a/letta/local_llm/llm_chat_completion_wrappers/llama3.py b/letta/local_llm/llm_chat_completion_wrappers/llama3.py
deleted file mode 100644
index 12153209..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/llama3.py
+++ /dev/null
@@ -1,340 +0,0 @@
-from letta.errors import LLMJSONParsingError
-from letta.helpers.json_helpers import json_dumps, json_loads
-from letta.local_llm.json_parser import clean_json
-from letta.local_llm.llm_chat_completion_wrappers.wrapper_base import LLMChatCompletionWrapper
-
-PREFIX_HINT = """# Reminders:
-# Important information about yourself and the user is stored in (limited) core memory
-# You can modify core memory with core_memory_replace
-# You can add to core memory with core_memory_append
-# Less important information is stored in (unlimited) archival memory
-# You can add to archival memory with archival_memory_insert
-# You can search archival memory with archival_memory_search
-# You will always see the statistics of archival memory, so you know if there is content inside it
-# If you receive new important information about the user (or yourself), you immediately update your memory with core_memory_replace, core_memory_append, or archival_memory_insert"""
-
-FIRST_PREFIX_HINT = """# Reminders:
-# This is your first interaction with the user!
-# Initial information about them is provided in the core memory user block
-# Make sure to introduce yourself to them
-# Your inner thoughts should be private, interesting, and creative
-# Do NOT use inner thoughts to communicate with the user
-# Use send_message to communicate with the user"""
-# Don't forget to use send_message, otherwise the user won't see your message"""
-
-
-class LLaMA3InnerMonologueWrapper(LLMChatCompletionWrapper):
- """ChatML-style prompt formatter, tested for use with https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"""
-
- supports_first_message = True
-
- def __init__(
- self,
- json_indent=2,
- # simplify_json_content=True,
- simplify_json_content=False,
- clean_function_args=True,
- include_assistant_prefix=True,
- assistant_prefix_extra='\n{\n "function":',
- assistant_prefix_extra_first_message='\n{\n "function": "send_message",',
- allow_custom_roles=True, # allow roles outside user/assistant
- use_system_role_in_user=False, # use the system role on user messages that don't use "type: user_message"
- # allow_function_role=True, # use function role for function replies?
- allow_function_role=False, # use function role for function replies?
- no_function_role_role="assistant", # if no function role, which role to use?
- no_function_role_prefix="FUNCTION RETURN:\n", # if no function role, what prefix to use?
- # add a guiding hint
- assistant_prefix_hint=False,
- ):
- self.simplify_json_content = simplify_json_content
- self.clean_func_args = clean_function_args
- self.include_assistant_prefix = include_assistant_prefix
- self.assistant_prefix_extra = assistant_prefix_extra
- self.assistant_prefix_extra_first_message = assistant_prefix_extra_first_message
- self.assistant_prefix_hint = assistant_prefix_hint
-
- # role-based
- self.allow_custom_roles = allow_custom_roles
- self.use_system_role_in_user = use_system_role_in_user
- self.allow_function_role = allow_function_role
- # extras for when the function role is disallowed
- self.no_function_role_role = no_function_role_role
- self.no_function_role_prefix = no_function_role_prefix
-
- # how to set json in prompt
- self.json_indent = json_indent
-
- def _compile_function_description(self, schema, add_inner_thoughts=True) -> str:
- """Go from a JSON schema to a string description for a prompt"""
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- if add_inner_thoughts:
- from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION
-
- func_str += f"\n {INNER_THOUGHTS_KWARG}: {INNER_THOUGHTS_KWARG_DESCRIPTION}"
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- def _compile_function_block(self, functions) -> str:
- """functions dict -> string describing functions choices"""
- prompt = ""
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "Please select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- for function_dict in functions:
- prompt += f"\n{self._compile_function_description(function_dict)}"
-
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_system_message(self, system_message, functions, function_documentation=None) -> str:
- """system prompt + memory + functions -> string"""
- prompt = ""
- prompt += system_message
- prompt += "\n"
- if function_documentation is not None:
- prompt += "Please select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:\n"
- prompt += function_documentation
- else:
- prompt += self._compile_function_block(functions)
- return prompt
-
- def _compile_function_call(self, function_call, inner_thoughts=None):
- """Go from ChatCompletion to Airoboros style function trace (in prompt)
-
- ChatCompletion data (inside message['function_call']):
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
-
- Airoboros output:
- {
- "function": "send_message",
- "params": {
- "message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
- }
- }
- """
- airo_func_call = {
- "function": function_call["name"],
- "params": {
- "inner_thoughts": inner_thoughts,
- **json_loads(function_call["arguments"]),
- },
- }
- return json_dumps(airo_func_call, indent=self.json_indent)
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_assistant_message(self, message) -> str:
- """assistant message -> string"""
- prompt = ""
-
- # need to add the function call if there was one
- inner_thoughts = message["content"]
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{self._compile_function_call(message['function_call'], inner_thoughts=inner_thoughts)}"
- elif "tool_calls" in message and message["tool_calls"]:
- for tool_call in message["tool_calls"]:
- prompt += f"\n{self._compile_function_call(tool_call['function'], inner_thoughts=inner_thoughts)}"
- else:
- # TODO should we format this into JSON somehow?
- prompt += inner_thoughts
-
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_user_message(self, message) -> str:
- """user message (should be JSON) -> string"""
- prompt = ""
- if self.simplify_json_content:
- # Make user messages not JSON but plaintext instead
- try:
- user_msg_json = json_loads(message["content"])
- user_msg_str = user_msg_json["message"]
- except:
- user_msg_str = message["content"]
- else:
- # Otherwise just dump the full json
- try:
- user_msg_json = json_loads(message["content"])
- user_msg_str = json_dumps(
- user_msg_json,
- indent=self.json_indent,
- )
- except:
- user_msg_str = message["content"]
-
- prompt += user_msg_str
- return prompt
-
- # NOTE: BOS/EOS chatml tokens are NOT inserted here
- def _compile_function_response(self, message) -> str:
- """function response message (should be JSON) -> string"""
- # TODO we should clean up send_message returns to avoid cluttering the prompt
- prompt = ""
- try:
- # indent the function replies
- function_return_dict = json_loads(message["content"])
- function_return_str = json_dumps(
- function_return_dict,
- indent=self.json_indent,
- )
- except:
- function_return_str = message["content"]
-
- prompt += function_return_str
- return prompt
-
- def chat_completion_to_prompt(self, messages, functions, first_message=False, function_documentation=None):
- """chatml-style prompt formatting, with implied support for multi-role"""
- prompt = "<|begin_of_text|>"
-
- # System insturctions go first
- assert messages[0]["role"] == "system"
- system_block = self._compile_system_message(
- system_message=messages[0]["content"],
- functions=functions,
- function_documentation=function_documentation,
- )
- prompt += f"<|start_header_id|>system<|end_header_id|>\n\n{system_block.strip()}<|eot_id|>"
-
- # Last are the user/assistant messages
- for message in messages[1:]:
- assert message["role"] in ["user", "assistant", "function", "tool"], message
-
- if message["role"] == "user":
- # Support for AutoGen naming of agents
- role_str = message["name"].strip().lower() if (self.allow_custom_roles and "name" in message) else message["role"]
- msg_str = self._compile_user_message(message)
-
- if self.use_system_role_in_user:
- try:
- msg_json = json_loads(message["content"])
- if msg_json["type"] != "user_message":
- role_str = "system"
- except:
- pass
- prompt += f"\n<|start_header_id|>{role_str}<|end_header_id|>\n\n{msg_str.strip()}<|eot_id|>"
-
- elif message["role"] == "assistant":
- # Support for AutoGen naming of agents
- role_str = message["name"].strip().lower() if (self.allow_custom_roles and "name" in message) else message["role"]
- msg_str = self._compile_assistant_message(message)
-
- prompt += f"\n<|start_header_id|>{role_str}<|end_header_id|>\n\n{msg_str.strip()}<|eot_id|>"
-
- elif message["role"] in ["tool", "function"]:
- if self.allow_function_role:
- role_str = message["role"]
- msg_str = self._compile_function_response(message)
- prompt += f"\n<|start_header_id|>{role_str}<|end_header_id|>\n\n{msg_str.strip()}<|eot_id|>"
- else:
- # TODO figure out what to do with functions if we disallow function role
- role_str = self.no_function_role_role
- msg_str = self._compile_function_response(message)
- func_resp_prefix = self.no_function_role_prefix
- # NOTE whatever the special prefix is, it should also be a stop token
- prompt += f"\n<|start_header_id|>{role_str}\n{func_resp_prefix}{msg_str.strip()}<|eot_id|>"
-
- else:
- raise ValueError(message)
-
- if self.include_assistant_prefix:
- prompt += "\n<|start_header_id|>assistant\n\n"
- if self.assistant_prefix_hint:
- prompt += f"\n{FIRST_PREFIX_HINT if first_message else PREFIX_HINT}"
- if self.supports_first_message and first_message:
- if self.assistant_prefix_extra_first_message:
- prompt += self.assistant_prefix_extra_first_message
- else:
- if self.assistant_prefix_extra:
- # assistant_prefix_extra='\n{\n "function":',
- prompt += self.assistant_prefix_extra
-
- return prompt
-
- def _clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- inner_thoughts = None
- if "inner_thoughts" in function_args:
- inner_thoughts = cleaned_function_args.pop("inner_thoughts")
-
- # TODO more cleaning to fix errors LLM makes
- return inner_thoughts, cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output, first_message=False):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- # if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
- # raw_llm_output = "{" + raw_llm_output
- assistant_prefix = self.assistant_prefix_extra_first_message if first_message else self.assistant_prefix_extra
- if assistant_prefix and raw_llm_output[: len(assistant_prefix)] != assistant_prefix:
- # print(f"adding prefix back to llm, raw_llm_output=\n{raw_llm_output}")
- raw_llm_output = assistant_prefix + raw_llm_output
- # print(f"->\n{raw_llm_output}")
-
- try:
- # cover llama.cpp server for now #TODO remove this when fixed
- raw_llm_output = raw_llm_output.rstrip()
- if raw_llm_output.endswith("<|eot_id|>"):
- raw_llm_output = raw_llm_output[: -len("<|eot_id|>")]
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- # NOTE: weird bug can happen where 'function' gets nested if the prefix in the prompt isn't abided by
- if isinstance(function_json_output["function"], dict):
- function_json_output = function_json_output["function"]
- # regular unpacking
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- except KeyError as e:
- raise LLMJSONParsingError(
- f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}. JSON result was:\n{function_json_output}"
- )
-
- if self.clean_func_args:
- (
- inner_thoughts,
- function_name,
- function_parameters,
- ) = self._clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": inner_thoughts,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
diff --git a/letta/local_llm/llm_chat_completion_wrappers/simple_summary_wrapper.py b/letta/local_llm/llm_chat_completion_wrappers/simple_summary_wrapper.py
deleted file mode 100644
index d20bd2d3..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/simple_summary_wrapper.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from ...helpers.json_helpers import json_dumps, json_loads
-from .wrapper_base import LLMChatCompletionWrapper
-
-
-class SimpleSummaryWrapper(LLMChatCompletionWrapper):
- """A super basic wrapper that's meant to be used for summary generation only"""
-
- def __init__(
- self,
- simplify_json_content=True,
- include_assistant_prefix=True,
- # include_assistant_prefix=False, # False here, because we launch directly into summary
- include_section_separators=True,
- ):
- self.simplify_json_content = simplify_json_content
- self.include_assistant_prefix = include_assistant_prefix
- self.include_section_separators = include_section_separators
-
- def chat_completion_to_prompt(self, messages, functions, function_documentation=None):
- """Example for airoboros: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#prompt-format
-
- Instructions on how to summarize
- USER: {prompt}
- ASSISTANT:
-
- Functions support: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#agentfunction-calling
-
- As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
-
- Input: I want to know how many times 'Python' is mentioned in my text file.
-
- Available functions:
- file_analytics:
- description: This tool performs various operations on a text file.
- params:
- action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
- filters:
- keyword: The word or phrase we want to search for.
-
- OpenAI functions schema style:
-
- {
- "name": "send_message",
- "description": "Sends a message to the human user",
- "parameters": {
- "type": "object",
- "properties": {
- # https://json-schema.org/understanding-json-schema/reference/array.html
- "message": {
- "type": "string",
- "description": "Message contents. All unicode (including emojis) are supported.",
- },
- },
- "required": ["message"],
- }
- },
- """
- assert functions is None
- prompt = ""
-
- # System insturctions go first
- assert messages[0]["role"] == "system"
- prompt += messages[0]["content"]
-
- def create_function_call(function_call):
- """Go from ChatCompletion to Airoboros style function trace (in prompt)
-
- ChatCompletion data (inside message['function_call']):
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
-
- Airoboros output:
- {
- "function": "send_message",
- "params": {
- "message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
- }
- }
- """
- airo_func_call = {
- "function": function_call["name"],
- "params": json_loads(function_call["arguments"]),
- }
- return json_dumps(airo_func_call, indent=2)
-
- # Add a sep for the conversation
- if self.include_section_separators:
- prompt += "\n### INPUT"
-
- # Last are the user/assistant messages
- for message in messages[1:]:
- assert message["role"] in ["user", "assistant", "function", "tool"], message
-
- if message["role"] == "user":
- if self.simplify_json_content:
- try:
- content_json = json_loads(message["content"])
- content_simple = content_json["message"]
- prompt += f"\nUSER: {content_simple}"
- except:
- prompt += f"\nUSER: {message['content']}"
- elif message["role"] == "assistant":
- prompt += f"\nASSISTANT: {message['content']}"
- # need to add the function call if there was one
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{create_function_call(message['function_call'])}"
- elif "tool_calls" in message and message["tool_calls"]:
- prompt += f"\n{create_function_call(message['tool_calls'][0]['function'])}"
- elif message["role"] in ["function", "tool"]:
- # TODO find a good way to add this
- # prompt += f"\nASSISTANT: (function return) {message['content']}"
- prompt += f"\nFUNCTION RETURN: {message['content']}"
- continue
- else:
- raise ValueError(message)
-
- # Add a sep for the response
- if self.include_section_separators:
- prompt += "\n### RESPONSE (your summary of the above conversation in plain English (no JSON!), do NOT exceed the word limit)"
-
- if self.include_assistant_prefix:
- # prompt += f"\nASSISTANT:"
- prompt += "\nSUMMARY:"
-
- # print(prompt)
- return prompt
-
- def output_to_chat_completion_response(self, raw_llm_output):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- raw_llm_output = raw_llm_output.strip()
- message = {
- "role": "assistant",
- "content": raw_llm_output,
- # "function_call": {
- # "name": function_name,
- # "arguments": json_dumps(function_parameters),
- # },
- }
- return message
diff --git a/letta/local_llm/llm_chat_completion_wrappers/wrapper_base.py b/letta/local_llm/llm_chat_completion_wrappers/wrapper_base.py
deleted file mode 100644
index 01f442b1..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/wrapper_base.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class LLMChatCompletionWrapper(ABC):
- @abstractmethod
- def chat_completion_to_prompt(self, messages, functions, function_documentation=None):
- """Go from ChatCompletion to a single prompt string"""
-
- @abstractmethod
- def output_to_chat_completion_response(self, raw_llm_output):
- """Turn the LLM output string into a ChatCompletion response"""
diff --git a/letta/local_llm/llm_chat_completion_wrappers/zephyr.py b/letta/local_llm/llm_chat_completion_wrappers/zephyr.py
deleted file mode 100644
index 8ee733aa..00000000
--- a/letta/local_llm/llm_chat_completion_wrappers/zephyr.py
+++ /dev/null
@@ -1,344 +0,0 @@
-from ...errors import LLMJSONParsingError
-from ...helpers.json_helpers import json_dumps, json_loads
-from ..json_parser import clean_json
-from .wrapper_base import LLMChatCompletionWrapper
-
-
-class ZephyrMistralWrapper(LLMChatCompletionWrapper):
- """
- Wrapper for Zephyr Alpha and Beta, Mistral 7B:
- https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
- https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
- Note: this wrapper formats a prompt that only generates JSON, no inner thoughts
- """
-
- def __init__(
- self,
- simplify_json_content=True,
- clean_function_args=True,
- include_assistant_prefix=True,
- include_opening_brace_in_prefix=True,
- include_section_separators=False,
- ):
- self.simplify_json_content = simplify_json_content
- self.clean_func_args = clean_function_args
- self.include_assistant_prefix = include_assistant_prefix
- self.include_opening_brance_in_prefix = include_opening_brace_in_prefix
- self.include_section_separators = include_section_separators
-
- def chat_completion_to_prompt(self, messages, functions, function_documentation=None):
- """
- Zephyr prompt format:
- <|system|>
-
- <|user|>
- {prompt}
- <|assistant|>
- (source: https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF#prompt-template-zephyr)
- """
-
- prompt = ""
-
- IM_END_TOKEN = ""
-
- # System instructions go first
- assert messages[0]["role"] == "system"
- prompt += "<|system|>"
- prompt += f"\n{messages[0]['content']}"
-
- # Next is the functions preamble
- def create_function_description(schema):
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "\nPlease select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- if function_documentation is not None:
- prompt += f"\n{function_documentation}"
- else:
- for function_dict in functions:
- prompt += f"\n{create_function_description(function_dict)}"
-
- # Put functions INSIDE system message (TODO experiment with this)
- prompt += IM_END_TOKEN
-
- def create_function_call(function_call):
- airo_func_call = {
- "function": function_call["name"],
- "params": json_loads(function_call["arguments"]),
- }
- return json_dumps(airo_func_call, indent=2)
-
- for message in messages[1:]:
- assert message["role"] in ["user", "assistant", "function", "tool"], message
-
- if message["role"] == "user":
- if self.simplify_json_content:
- try:
- content_json = json_loads(message["content"])
- content_simple = content_json["message"]
- prompt += f"\n<|user|>\n{content_simple}{IM_END_TOKEN}"
- # prompt += f"\nUSER: {content_simple}"
- except:
- prompt += f"\n<|user|>\n{message['content']}{IM_END_TOKEN}"
- # prompt += f"\nUSER: {message['content']}"
- elif message["role"] == "assistant":
- prompt += "\n<|assistant|>"
- if message["content"] is not None:
- prompt += f"\n{message['content']}"
- # prompt += f"\nASSISTANT: {message['content']}"
- # need to add the function call if there was one
- if "function_call" in message and message["function_call"]:
- prompt += f"\n{create_function_call(message['function_call'])}"
- prompt += f"{IM_END_TOKEN}"
- elif message["role"] in ["function", "tool"]:
- # TODO find a good way to add this
- # prompt += f"\nASSISTANT: (function return) {message['content']}"
- prompt += "\n<|assistant|>"
- prompt += f"\nFUNCTION RETURN: {message['content']}"
- # prompt += f"\nFUNCTION RETURN: {message['content']}"
- continue
- else:
- raise ValueError(message)
-
- # Add a sep for the response
- # if self.include_section_separators:
- # prompt += "\n### RESPONSE"
-
- if self.include_assistant_prefix:
- # prompt += f"\nASSISTANT:"
- prompt += "\n<|assistant|>"
- if self.include_opening_brance_in_prefix:
- prompt += "\n{"
-
- return prompt
-
- def clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- # TODO more cleaning to fix errors LLM makes
- return cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
- raw_llm_output = "{" + raw_llm_output
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- except KeyError as e:
- raise LLMJSONParsingError(f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}")
-
- if self.clean_func_args:
- function_name, function_parameters = self.clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": None,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
-
-
-class ZephyrMistralInnerMonologueWrapper(ZephyrMistralWrapper):
- """Still expect only JSON outputs from model, but add inner monologue as a field"""
-
- """
- Wrapper for Zephyr Alpha and Beta, Mistral 7B:
- https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
- https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
- Note: this wrapper formats a prompt with inner thoughts included
- """
-
- def __init__(
- self,
- simplify_json_content=True,
- clean_function_args=True,
- include_assistant_prefix=True,
- include_opening_brace_in_prefix=True,
- include_section_separators=True,
- ):
- self.simplify_json_content = simplify_json_content
- self.clean_func_args = clean_function_args
- self.include_assistant_prefix = include_assistant_prefix
- self.include_opening_brance_in_prefix = include_opening_brace_in_prefix
- self.include_section_separators = include_section_separators
-
- def chat_completion_to_prompt(self, messages, functions, function_documentation=None):
- prompt = ""
-
- IM_END_TOKEN = ""
-
- # System insturctions go first
- assert messages[0]["role"] == "system"
- prompt += messages[0]["content"]
-
- # Next is the functions preamble
- def create_function_description(schema, add_inner_thoughts=True):
- # airorobos style
- func_str = ""
- func_str += f"{schema['name']}:"
- func_str += f"\n description: {schema['description']}"
- func_str += "\n params:"
- if add_inner_thoughts:
- func_str += "\n inner_thoughts: Deep inner monologue private to you only."
- for param_k, param_v in schema["parameters"]["properties"].items():
- # TODO we're ignoring type
- func_str += f"\n {param_k}: {param_v['description']}"
- # TODO we're ignoring schema['parameters']['required']
- return func_str
-
- # prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
- prompt += "\nPlease select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
- prompt += "\nAvailable functions:"
- if function_documentation is not None:
- prompt += f"\n{function_documentation}"
- else:
- for function_dict in functions:
- prompt += f"\n{create_function_description(function_dict)}"
-
- def create_function_call(function_call, inner_thoughts=None):
- airo_func_call = {
- "function": function_call["name"],
- "params": {
- "inner_thoughts": inner_thoughts,
- **json_loads(function_call["arguments"]),
- },
- }
- return json_dumps(airo_func_call, indent=2)
-
- # Add a sep for the conversation
- if self.include_section_separators:
- prompt += "\n<|user|>"
-
- # Last are the user/assistant messages
- for message in messages[1:]:
- assert message["role"] in ["user", "assistant", "function", "tool"], message
-
- if message["role"] == "user":
- if self.simplify_json_content:
- try:
- content_json = json_loads(message["content"])
- content_simple = content_json["message"]
- prompt += f"\n<|user|>\n{content_simple}{IM_END_TOKEN}"
- except:
- prompt += f"\n<|user|>\n{message['content']}{IM_END_TOKEN}"
- elif message["role"] == "assistant":
- prompt += "\n<|assistant|>"
- # need to add the function call if there was one
- inner_thoughts = message["content"]
- if message.get("function_call"):
- prompt += f"\n{create_function_call(message['function_call'], inner_thoughts=inner_thoughts)}"
- elif message["role"] in ["function", "tool"]:
- # TODO find a good way to add this
- # prompt += f"\nASSISTANT: (function return) {message['content']}"
- prompt += f"\nFUNCTION RETURN: {message['content']}"
- continue
- else:
- raise ValueError(message)
-
- # Add a sep for the response
- # if self.include_section_separators:
- # prompt += "\n### RESPONSE"
-
- if self.include_assistant_prefix:
- prompt += "\n<|assistant|>"
- if self.include_opening_brance_in_prefix:
- prompt += "\n{"
-
- return prompt
-
- def clean_function_args(self, function_name, function_args):
- """Some basic Letta-specific cleaning of function args"""
- cleaned_function_name = function_name
- cleaned_function_args = function_args.copy() if function_args is not None else {}
-
- if function_name == "send_message":
- # strip request_heartbeat
- cleaned_function_args.pop("request_heartbeat", None)
-
- inner_thoughts = None
- if "inner_thoughts" in function_args:
- inner_thoughts = cleaned_function_args.pop("inner_thoughts")
-
- # TODO more cleaning to fix errors LLM makes
- return inner_thoughts, cleaned_function_name, cleaned_function_args
-
- def output_to_chat_completion_response(self, raw_llm_output):
- """Turn raw LLM output into a ChatCompletion style response with:
- "message" = {
- "role": "assistant",
- "content": ...,
- "function_call": {
- "name": ...
- "arguments": {
- "arg1": val1,
- ...
- }
- }
- }
- """
- if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
- raw_llm_output = "{" + raw_llm_output
-
- try:
- function_json_output = clean_json(raw_llm_output)
- except Exception as e:
- raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output} - error\n{str(e)}")
- try:
- function_name = function_json_output["function"]
- function_parameters = function_json_output["params"]
- except KeyError as e:
- raise LLMJSONParsingError(f"Received valid JSON from LLM, but JSON was missing fields: {str(e)}")
-
- if self.clean_func_args:
- (
- inner_thoughts,
- function_name,
- function_parameters,
- ) = self.clean_function_args(function_name, function_parameters)
-
- message = {
- "role": "assistant",
- "content": inner_thoughts,
- "function_call": {
- "name": function_name,
- "arguments": json_dumps(function_parameters),
- },
- }
- return message
diff --git a/letta/local_llm/lmstudio/api.py b/letta/local_llm/lmstudio/api.py
deleted file mode 100644
index dd0debee..00000000
--- a/letta/local_llm/lmstudio/api.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import json
-from urllib.parse import urljoin
-
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import post_json_auth_request
-from letta.utils import count_tokens
-
-LMSTUDIO_API_CHAT_SUFFIX = "/v1/chat/completions"
-LMSTUDIO_API_COMPLETIONS_SUFFIX = "/v1/completions"
-LMSTUDIO_API_CHAT_COMPLETIONS_SUFFIX = "/v1/chat/completions"
-
-
-def get_lmstudio_completion_chatcompletions(endpoint, auth_type, auth_key, model, messages):
- """
- This is the request we need to send
-
- {
- "model": "deepseek-r1-distill-qwen-7b",
- "messages": [
- { "role": "system", "content": "Always answer in rhymes. Today is Thursday" },
- { "role": "user", "content": "What day is it today?" },
- { "role": "user", "content": "What day is it today?" }],
- "temperature": 0.7,
- "max_tokens": -1,
- "stream": false
- """
- from letta.utils import printd
-
- URI = endpoint + LMSTUDIO_API_CHAT_COMPLETIONS_SUFFIX
- request = {"model": model, "messages": messages}
-
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
-
- # Get the reasoning from the model
- if response.status_code == 200:
- result_full = response.json()
- result_reasoning = result_full["choices"][0]["message"].get("reasoning_content")
- result = result_full["choices"][0]["message"]["content"]
- usage = result_full["usage"]
-
- # See if result is json
- try:
- function_call = json.loads(result)
- if "function" in function_call and "params" in function_call:
- return result, usage, result_reasoning
- else:
- print("Did not get json on without json constraint, attempting with json decoding")
- except Exception as e:
- print(f"Did not get json on without json constraint, attempting with json decoding: {e}")
-
- request["messages"].append({"role": "assistant", "content": result_reasoning})
- request["messages"].append({"role": "user", "content": ""}) # last message must be user
- # Now run with json decoding to get the function
- request["response_format"] = {
- "type": "json_schema",
- "json_schema": {
- "name": "function_call",
- "strict": "true",
- "schema": {
- "type": "object",
- "properties": {"function": {"type": "string"}, "params": {"type": "object"}},
- "required": ["function", "params"],
- },
- },
- }
-
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- result = result_full["choices"][0]["message"]["content"]
- # add usage with previous call, merge with prev usage
- for key, value in result_full["usage"].items():
- usage[key] += value
-
- return result, usage, result_reasoning
-
-
-def get_lmstudio_completion(endpoint, auth_type, auth_key, prompt, context_window, api="completions"):
- """Based on the example for using LM Studio as a backend from https://github.com/lmstudio-ai/examples/tree/main/Hello%2C%20world%20-%20OpenAI%20python%20client"""
- from letta.utils import printd
-
- prompt_tokens = count_tokens(prompt)
- if prompt_tokens > context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
- settings = get_completions_settings()
- settings.update(
- {
- "input_prefix": "",
- "input_suffix": "",
- # This controls how LM studio handles context overflow
- # In Letta we handle this ourselves, so this should be disabled
- # "context_overflow_policy": 0,
- # "lmstudio": {"context_overflow_policy": 0}, # 0 = stop at limit
- # "lmstudio": {"context_overflow_policy": "stopAtLimit"}, # https://github.com/letta-ai/letta/issues/1782
- "stream": False,
- "model": "local model",
- }
- )
-
- # Uses the ChatCompletions API style
- # Seems to work better, probably because it's applying some extra settings under-the-hood?
- if api == "chat":
- URI = urljoin(endpoint.strip("/") + "/", LMSTUDIO_API_CHAT_SUFFIX.strip("/"))
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- request = settings
- request["max_tokens"] = context_window
-
- # Put the entire completion string inside the first message
- message_structure = [{"role": "user", "content": prompt}]
- request["messages"] = message_structure
-
- # Uses basic string completions (string in, string out)
- # Does not work as well as ChatCompletions for some reason
- elif api == "completions":
- URI = urljoin(endpoint.strip("/") + "/", LMSTUDIO_API_COMPLETIONS_SUFFIX.strip("/"))
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- request = settings
- request["max_tokens"] = context_window
-
- # Standard completions format, formatted string goes in prompt
- request["prompt"] = prompt
-
- else:
- raise ValueError(api)
-
- if not endpoint.startswith(("http://", "https://")):
- raise ValueError(f"Provided OPENAI_API_BASE value ({endpoint}) must begin with http:// or https://")
-
- try:
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- if api == "chat":
- result = result_full["choices"][0]["message"]["content"]
- usage = result_full.get("usage", None)
- elif api == "completions":
- result = result_full["choices"][0]["text"]
- usage = result_full.get("usage", None)
- elif api == "chat/completions":
- result = result_full["choices"][0]["content"]
- result_full["choices"][0]["reasoning_content"]
- usage = result_full.get("usage", None)
-
- else:
- # Example error: msg={"error":"Context length exceeded. Tokens in context: 8000, Context length: 8000"}
- if "context length" in str(response.text).lower():
- # "exceeds context length" is what appears in the LM Studio error message
- # raise an alternate exception that matches OpenAI's message, which is "maximum context length"
- raise Exception(f"Request exceeds maximum context length (code={response.status_code}, msg={response.text}, URI={URI})")
- else:
- raise Exception(
- f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
- + f" Make sure that the LM Studio local inference server is running and reachable at {URI}."
- )
- except:
- # TODO handle gracefully
- raise
-
- # Pass usage statistics back to main thread
- # These are used to compute memory warning messages
- completion_tokens = usage.get("completion_tokens", None) if usage is not None else None
- total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
- usage = {
- "prompt_tokens": prompt_tokens, # can grab from usage dict, but it's usually wrong (set to 0)
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- }
-
- return result, usage
diff --git a/letta/local_llm/lmstudio/settings.py b/letta/local_llm/lmstudio/settings.py
deleted file mode 100644
index c2ee66f9..00000000
--- a/letta/local_llm/lmstudio/settings.py
+++ /dev/null
@@ -1,29 +0,0 @@
-SIMPLE = {
- "stop": [
- "\nUSER:",
- "\nASSISTANT:",
- "\nFUNCTION RETURN:",
- "\nUSER",
- "\nASSISTANT",
- "\nFUNCTION RETURN",
- "\nFUNCTION",
- "\nFUNC",
- "<|im_start|>",
- "<|im_end|>",
- "<|im_sep|>",
- # '\n' +
- # '',
- # '<|',
- # '\n#',
- # '\n\n\n',
- ],
- # This controls the maximum number of tokens that the model can generate
- # Cap this at the model context length (assuming 8k for Mistral 7B)
- # "max_tokens": 8000,
- # "max_tokens": LLM_MAX_TOKENS,
- # This controls how LM studio handles context overflow
- # In Letta we handle this ourselves, so this should be commented out
- # "lmstudio": {"context_overflow_policy": 2},
- "stream": False,
- "model": "local model",
-}
diff --git a/letta/local_llm/ollama/api.py b/letta/local_llm/ollama/api.py
deleted file mode 100644
index 69926a43..00000000
--- a/letta/local_llm/ollama/api.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from urllib.parse import urljoin
-
-from letta.errors import LocalLLMError
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import post_json_auth_request
-from letta.utils import count_tokens
-
-OLLAMA_API_SUFFIX = "/api/generate"
-
-
-def get_ollama_completion(endpoint, auth_type, auth_key, model, prompt, context_window, grammar=None):
- """See https://github.com/jmorganca/ollama/blob/main/docs/api.md for instructions on how to run the LLM web server"""
- from letta.utils import printd
-
- prompt_tokens = count_tokens(prompt)
- if prompt_tokens > context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
- if model is None:
- raise LocalLLMError(
- "Error: model name not specified. Set model in your config to the model you want to run (e.g. 'dolphin2.2-mistral')"
- )
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- # https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
- settings = get_completions_settings()
- settings.update(
- {
- # specific naming for context length
- "num_ctx": context_window,
- }
- )
-
- # https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion
- request = {
- ## base parameters
- "model": model,
- "prompt": prompt,
- # "images": [], # TODO eventually support
- ## advanced parameters
- # "format": "json", # TODO eventually support
- "stream": False,
- "options": settings,
- "raw": True, # no prompt formatting
- # "raw mode does not support template, system, or context"
- # "system": "", # no prompt formatting
- # "template": "{{ .Prompt }}", # no prompt formatting
- # "context": None, # no memory via prompt formatting
- }
-
- # Set grammar
- if grammar is not None:
- # request["grammar_string"] = load_grammar_file(grammar)
- raise NotImplementedError("Ollama does not support grammars")
-
- if not endpoint.startswith(("http://", "https://")):
- raise ValueError(f"Provided OPENAI_API_BASE value ({endpoint}) must begin with http:// or https://")
-
- try:
- URI = urljoin(endpoint.strip("/") + "/", OLLAMA_API_SUFFIX.strip("/"))
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- # https://github.com/jmorganca/ollama/blob/main/docs/api.md
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- result = result_full["response"]
- else:
- raise Exception(
- f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
- + f" Make sure that the ollama API server is running and reachable at {URI}."
- )
-
- except:
- # TODO handle gracefully
- raise
-
- # Pass usage statistics back to main thread
- # These are used to compute memory warning messages
- # https://github.com/jmorganca/ollama/blob/main/docs/api.md#response
- completion_tokens = result_full.get("eval_count", None)
- total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
- usage = {
- "prompt_tokens": prompt_tokens, # can also grab from "prompt_eval_count"
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- }
-
- return result, usage
diff --git a/letta/local_llm/ollama/settings.py b/letta/local_llm/ollama/settings.py
deleted file mode 100644
index eb68317a..00000000
--- a/letta/local_llm/ollama/settings.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# see https://github.com/jmorganca/ollama/blob/main/docs/api.md
-# and https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
-SIMPLE = {
- "options": {
- "stop": [
- "\nUSER:",
- "\nASSISTANT:",
- "\nFUNCTION RETURN:",
- "\nUSER",
- "\nASSISTANT",
- "\nFUNCTION RETURN",
- "\nFUNCTION",
- "\nFUNC",
- "<|im_start|>",
- "<|im_end|>",
- "<|im_sep|>",
- # '\n' +
- # '',
- # '<|',
- # '\n#',
- # '\n\n\n',
- ],
- # "num_ctx": LLM_MAX_TOKENS,
- },
- "stream": False,
- # turn off Ollama's own prompt formatting
- "system": "",
- "template": "{{ .Prompt }}",
- # "system": None,
- # "template": None,
- "context": None,
-}
diff --git a/letta/local_llm/settings/__init__.py b/letta/local_llm/settings/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/local_llm/settings/deterministic_mirostat.py b/letta/local_llm/settings/deterministic_mirostat.py
deleted file mode 100644
index 6dba1ad4..00000000
--- a/letta/local_llm/settings/deterministic_mirostat.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from letta.local_llm.settings.simple import settings as simple_settings
-
-settings = {
- "max_new_tokens": 250,
- "do_sample": False,
- "temperature": 0,
- "top_p": 0,
- "typical_p": 1,
- "repetition_penalty": 1.18,
- "repetition_penalty_range": 0,
- "encoder_repetition_penalty": 1,
- "top_k": 1,
- "min_length": 0,
- "no_repeat_ngram_size": 0,
- "num_beams": 1,
- "penalty_alpha": 0,
- "length_penalty": 1,
- "early_stopping": False,
- "guidance_scale": 1,
- "negative_prompt": "",
- "seed": -1,
- "add_bos_token": True,
- # NOTE: important - these are the BASE stopping strings, and should be combined with {{user}}/{{char}}-based stopping strings
- "stopping_strings": [
- simple_settings["stop"]
- # '### Response (JSON only, engaging, natural, authentic, descriptive, creative):',
- # "",
- # "<|",
- # "\n#",
- # "\n*{{user}} ",
- # "\n\n\n",
- # "\n{",
- # ",\n{",
- ],
- "truncation_length": 4096,
- "ban_eos_token": False,
- "skip_special_tokens": True,
- "top_a": 0,
- "tfs": 1,
- "epsilon_cutoff": 0,
- "eta_cutoff": 0,
- "mirostat_mode": 2,
- "mirostat_tau": 4,
- "mirostat_eta": 0.1,
-}
diff --git a/letta/local_llm/settings/settings.py b/letta/local_llm/settings/settings.py
deleted file mode 100644
index 3671e30b..00000000
--- a/letta/local_llm/settings/settings.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import json
-import os
-
-from letta.constants import LETTA_DIR
-from letta.local_llm.settings.deterministic_mirostat import settings as det_miro_settings
-from letta.local_llm.settings.simple import settings as simple_settings
-
-DEFAULT = "simple"
-SETTINGS_FOLDER_NAME = "settings"
-COMPLETION_SETTINGS_FILE_NAME = "completions_api_settings.json"
-
-
-def get_completions_settings(defaults="simple") -> dict:
- """Pull from the home directory settings if they exist, otherwise default"""
- from letta.utils import printd
-
- # Load up some default base settings
- printd(f"Loading default settings from '{defaults}'")
- if defaults == "simple":
- # simple = basic stop strings
- settings = simple_settings
- elif defaults == "deterministic_mirostat":
- settings = det_miro_settings
- elif defaults is None:
- settings = dict()
- else:
- raise ValueError(defaults)
-
- # Check if settings_dir folder exists (if not, create it)
- settings_dir = os.path.join(LETTA_DIR, SETTINGS_FOLDER_NAME)
- if not os.path.exists(settings_dir):
- printd(f"Settings folder '{settings_dir}' doesn't exist, creating it...")
- try:
- os.makedirs(settings_dir)
- except Exception as e:
- print(f"Error: failed to create settings folder '{settings_dir}'.\n{e}")
- return settings
-
- # Then, check if settings_dir/completions_api_settings.json file exists
- settings_file = os.path.join(settings_dir, COMPLETION_SETTINGS_FILE_NAME)
-
- if os.path.isfile(settings_file):
- # Load into a dict called "settings"
- printd(f"Found completion settings file '{settings_file}', loading it...")
- try:
- with open(settings_file, "r", encoding="utf-8") as file:
- user_settings = json.load(file)
- if len(user_settings) > 0:
- printd(f"Updating base settings with the following user settings:\n{json_dumps(user_settings, indent=2)}")
- settings.update(user_settings)
- else:
- printd(f"'{settings_file}' was empty, ignoring...")
- except json.JSONDecodeError as e:
- print(f"Error: failed to load user settings file '{settings_file}', invalid json.\n{e}")
- except Exception as e:
- print(f"Error: failed to load user settings file.\n{e}")
-
- else:
- printd(f"No completion settings file '{settings_file}', skipping...")
- # Create the file settings_file to make it easy for the user to edit
- try:
- with open(settings_file, "w", encoding="utf-8") as file:
- # We don't want to dump existing default settings in case we modify
- # the default settings in the future
- # json.dump(settings, file, indent=4)
- json.dump({}, file, indent=4)
- except Exception as e:
- print(f"Error: failed to create empty settings file '{settings_file}'.\n{e}")
-
- return settings
diff --git a/letta/local_llm/settings/simple.py b/letta/local_llm/settings/simple.py
deleted file mode 100644
index 19e858b6..00000000
--- a/letta/local_llm/settings/simple.py
+++ /dev/null
@@ -1,28 +0,0 @@
-settings = {
- # "stopping_strings": [
- "stop": [
- "\nUSER:",
- "\nASSISTANT:",
- "\nFUNCTION RETURN:",
- "\nUSER",
- "\nASSISTANT",
- "\nFUNCTION RETURN",
- "\nFUNCTION",
- "\nFUNC",
- "<|im_start|>",
- "<|im_end|>",
- "<|im_sep|>",
- # airoboros specific
- "\n### ",
- # '\n' +
- # '',
- # '<|',
- "\n#",
- # "\n\n\n",
- # prevent chaining function calls / multi json objects / run-on generations
- # NOTE: this requires the ability to patch the extra '}}' back into the prompt
- " }\n}\n",
- ],
- # most lm frontends default to 0.7-0.8 these days
- # "temperature": 0.8,
-}
diff --git a/letta/local_llm/utils.py b/letta/local_llm/utils.py
deleted file mode 100644
index 6027484d..00000000
--- a/letta/local_llm/utils.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import os
-import warnings
-from typing import List, Union
-
-import requests
-import tiktoken
-
-import letta.local_llm.llm_chat_completion_wrappers.airoboros as airoboros
-import letta.local_llm.llm_chat_completion_wrappers.chatml as chatml
-import letta.local_llm.llm_chat_completion_wrappers.configurable_wrapper as configurable_wrapper
-import letta.local_llm.llm_chat_completion_wrappers.dolphin as dolphin
-import letta.local_llm.llm_chat_completion_wrappers.llama3 as llama3
-import letta.local_llm.llm_chat_completion_wrappers.zephyr as zephyr
-from letta.log import get_logger
-from letta.schemas.openai.chat_completion_request import Tool, ToolCall
-
-logger = get_logger(__name__)
-
-
-def post_json_auth_request(uri, json_payload, auth_type, auth_key):
- """Send a POST request with a JSON payload and optional authentication"""
-
- # By default most local LLM inference servers do not have authorization enabled
- if auth_type is None or auth_type == "":
- response = requests.post(uri, json=json_payload)
-
- # Used by OpenAI, together.ai, Mistral AI
- elif auth_type == "bearer_token":
- if auth_key is None:
- raise ValueError(f"auth_type is {auth_type}, but auth_key is null")
- headers = {"Content-Type": "application/json", "Authorization": f"Bearer {auth_key}"}
- response = requests.post(uri, json=json_payload, headers=headers)
-
- # Used by OpenAI Azure
- elif auth_type == "api_key":
- if auth_key is None:
- raise ValueError(f"auth_type is {auth_type}, but auth_key is null")
- headers = {"Content-Type": "application/json", "api-key": f"{auth_key}"}
- response = requests.post(uri, json=json_payload, headers=headers)
-
- else:
- raise ValueError(f"Unsupport authentication type: {auth_type}")
-
- return response
-
-
-def load_grammar_file(grammar):
- # Set grammar
- grammar_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), "grammars", f"{grammar}.gbnf")
-
- # Check if the file exists
- if not os.path.isfile(grammar_file):
- # If the file doesn't exist, raise a FileNotFoundError
- raise FileNotFoundError(f"The grammar file {grammar_file} does not exist.")
-
- with open(grammar_file, "r", encoding="utf-8") as file:
- grammar_str = file.read()
-
- return grammar_str
-
-
-# TODO: support tokenizers/tokenizer apis available in local models
-def count_tokens(s: str, model: str = "gpt-4") -> int:
- from letta.utils import count_tokens
-
- return count_tokens(s, model)
-
-
-def num_tokens_from_functions(functions: List[dict], model: str = "gpt-4"):
- """Return the number of tokens used by a list of functions.
-
- Copied from https://community.openai.com/t/how-to-calculate-the-tokens-when-using-function-call/266573/11
- """
- try:
- encoding = tiktoken.encoding_for_model(model)
- except KeyError:
- from letta.utils import printd
-
- printd("Warning: model not found. Using cl100k_base encoding.")
- encoding = tiktoken.get_encoding("cl100k_base")
-
- num_tokens = 0
- for function in functions:
- function_tokens = len(encoding.encode(function["name"]))
- if function["description"]:
- if not isinstance(function["description"], str):
- warnings.warn(f"Function {function['name']} has non-string description: {function['description']}")
- else:
- function_tokens += len(encoding.encode(function["description"]))
- else:
- warnings.warn(f"Function {function['name']} has no description, function: {function}")
-
- if "parameters" in function:
- parameters = function["parameters"]
- if "properties" in parameters:
- for propertiesKey in parameters["properties"]:
- function_tokens += len(encoding.encode(propertiesKey))
- v = parameters["properties"][propertiesKey]
- for field in v:
- try:
- if field == "type":
- function_tokens += 2
- # Handle both string and array types, e.g. {"type": ["string", "null"]}
- if isinstance(v["type"], list):
- function_tokens += len(encoding.encode(",".join(v["type"])))
- else:
- function_tokens += len(encoding.encode(v["type"]))
- elif field == "description":
- function_tokens += 2
- function_tokens += len(encoding.encode(v["description"]))
- elif field == "enum":
- function_tokens -= 3
- for o in v["enum"]:
- function_tokens += 3
- function_tokens += len(encoding.encode(o))
- elif field == "items":
- function_tokens += 2
- if isinstance(v["items"], dict) and "type" in v["items"]:
- function_tokens += len(encoding.encode(v["items"]["type"]))
- elif field == "default":
- function_tokens += 2
- function_tokens += len(encoding.encode(str(v["default"])))
- elif field == "title":
- # TODO: Is this right? For MCP
- continue
- else:
- # TODO: Handle nesting here properly
- # Disable this for now for MCP
- continue
- # logger.warning(f"num_tokens_from_functions: Unsupported field {field} in function {function}")
- except:
- logger.error(f"Failed to encode field {field} with value {v}")
- raise
- function_tokens += 11
-
- num_tokens += function_tokens
-
- num_tokens += 12
- return num_tokens
-
-
-def num_tokens_from_tool_calls(tool_calls: Union[List[dict], List[ToolCall]], model: str = "gpt-4"):
- """Based on above code (num_tokens_from_functions).
-
- Example to encode:
- [{
- 'id': '8b6707cf-2352-4804-93db-0423f',
- 'type': 'function',
- 'function': {
- 'name': 'send_message',
- 'arguments': '{\n "message": "More human than human is our motto."\n}'
- }
- }]
- """
- try:
- encoding = tiktoken.encoding_for_model(model)
- except KeyError:
- # print("Warning: model not found. Using cl100k_base encoding.")
- encoding = tiktoken.get_encoding("cl100k_base")
-
- num_tokens = 0
- for tool_call in tool_calls:
- if isinstance(tool_call, dict):
- tool_call_id = tool_call["id"]
- tool_call_type = tool_call["type"]
- tool_call_function = tool_call["function"]
- tool_call_function_name = tool_call_function["name"]
- tool_call_function_arguments = tool_call_function["arguments"]
- elif isinstance(tool_call, Tool):
- tool_call_id = tool_call.id
- tool_call_type = tool_call.type
- tool_call_function = tool_call.function
- tool_call_function_name = tool_call_function.name
- tool_call_function_arguments = tool_call_function.arguments
- else:
- raise ValueError(f"Unknown tool call type: {type(tool_call)}")
-
- function_tokens = len(encoding.encode(tool_call_id))
- function_tokens += 2 + len(encoding.encode(tool_call_type))
- function_tokens += 2 + len(encoding.encode(tool_call_function_name))
- function_tokens += 2 + len(encoding.encode(tool_call_function_arguments))
-
- num_tokens += function_tokens
-
- # TODO adjust?
- num_tokens += 12
- return num_tokens
-
-
-def num_tokens_from_messages(messages: List[dict], model: str = "gpt-4") -> int:
- """Return the number of tokens used by a list of messages.
-
- From: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
-
- For counting tokens in function calling RESPONSES, see:
- https://hmarr.com/blog/counting-openai-tokens/, https://github.com/hmarr/openai-chat-tokens
-
- For counting tokens in function calling REQUESTS, see:
- https://community.openai.com/t/how-to-calculate-the-tokens-when-using-function-call/266573/11
- """
- try:
- # Attempt to search for the encoding based on the model string
- encoding = tiktoken.encoding_for_model(model)
- except KeyError:
- # print("Warning: model not found. Using cl100k_base encoding.")
- encoding = tiktoken.get_encoding("cl100k_base")
- if model in {
- "gpt-3.5-turbo-0613",
- "gpt-3.5-turbo-16k-0613",
- "gpt-4-0314",
- "gpt-4-32k-0314",
- "gpt-4-0613",
- "gpt-4-32k-0613",
- }:
- tokens_per_message = 3
- tokens_per_name = 1
- elif model == "gpt-3.5-turbo-0301":
- tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
- tokens_per_name = -1 # if there's a name, the role is omitted
- elif "gpt-3.5-turbo" in model:
- # print("Warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
- return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613")
- elif "gpt-4" in model:
- # print("Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
- return num_tokens_from_messages(messages, model="gpt-4-0613")
- else:
- from letta.utils import printd
-
- printd(
- f"num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."
- )
- return num_tokens_from_messages(messages, model="gpt-4-0613")
- # raise NotImplementedError(
- # f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
- # )
- num_tokens = 0
- for message in messages:
- num_tokens += tokens_per_message
- for key, value in message.items():
- try:
- if isinstance(value, list) and key == "tool_calls":
- num_tokens += num_tokens_from_tool_calls(tool_calls=value, model=model)
- # special case for tool calling (list)
- # num_tokens += len(encoding.encode(value["name"]))
- # num_tokens += len(encoding.encode(value["arguments"]))
-
- else:
- if value is not None:
- if not isinstance(value, str):
- raise ValueError(f"Message has non-string value: {key} with value: {value} - message={message}")
- num_tokens += len(encoding.encode(value))
-
- if key == "name":
- num_tokens += tokens_per_name
-
- except TypeError as e:
- print(f"tiktoken encoding failed on: {value}")
- raise e
-
- num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
- return num_tokens
-
-
-def get_available_wrappers() -> dict:
- return {
- "llama3": llama3.LLaMA3InnerMonologueWrapper(),
- "llama3-grammar": llama3.LLaMA3InnerMonologueWrapper(),
- "llama3-hints-grammar": llama3.LLaMA3InnerMonologueWrapper(assistant_prefix_hint=True),
- "experimental-wrapper-neural-chat-grammar-noforce": configurable_wrapper.ConfigurableJSONWrapper(
- post_prompt="### Assistant:",
- sys_prompt_start="### System:\n",
- sys_prompt_end="\n",
- user_prompt_start="### User:\n",
- user_prompt_end="\n",
- assistant_prompt_start="### Assistant:\n",
- assistant_prompt_end="\n",
- tool_prompt_start="### User:\n",
- tool_prompt_end="\n",
- strip_prompt=True,
- ),
- # New chatml-based wrappers
- "chatml": chatml.ChatMLInnerMonologueWrapper(),
- "chatml-grammar": chatml.ChatMLInnerMonologueWrapper(),
- "chatml-noforce": chatml.ChatMLOuterInnerMonologueWrapper(),
- "chatml-noforce-grammar": chatml.ChatMLOuterInnerMonologueWrapper(),
- # "chatml-noforce-sysm": chatml.ChatMLOuterInnerMonologueWrapper(use_system_role_in_user=True),
- "chatml-noforce-roles": chatml.ChatMLOuterInnerMonologueWrapper(use_system_role_in_user=True, allow_function_role=True),
- "chatml-noforce-roles-grammar": chatml.ChatMLOuterInnerMonologueWrapper(use_system_role_in_user=True, allow_function_role=True),
- # With extra hints
- "chatml-hints": chatml.ChatMLInnerMonologueWrapper(assistant_prefix_hint=True),
- "chatml-hints-grammar": chatml.ChatMLInnerMonologueWrapper(assistant_prefix_hint=True),
- "chatml-noforce-hints": chatml.ChatMLOuterInnerMonologueWrapper(assistant_prefix_hint=True),
- "chatml-noforce-hints-grammar": chatml.ChatMLOuterInnerMonologueWrapper(assistant_prefix_hint=True),
- # Legacy wrappers
- "airoboros-l2-70b-2.1": airoboros.Airoboros21InnerMonologueWrapper(),
- "airoboros-l2-70b-2.1-grammar": airoboros.Airoboros21InnerMonologueWrapper(assistant_prefix_extra=None),
- "dolphin-2.1-mistral-7b": dolphin.Dolphin21MistralWrapper(),
- "dolphin-2.1-mistral-7b-grammar": dolphin.Dolphin21MistralWrapper(include_opening_brace_in_prefix=False),
- "zephyr-7B": zephyr.ZephyrMistralInnerMonologueWrapper(),
- "zephyr-7B-grammar": zephyr.ZephyrMistralInnerMonologueWrapper(include_opening_brace_in_prefix=False),
- }
diff --git a/letta/local_llm/vllm/api.py b/letta/local_llm/vllm/api.py
deleted file mode 100644
index dde863c8..00000000
--- a/letta/local_llm/vllm/api.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from urllib.parse import urljoin
-
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import count_tokens, post_json_auth_request
-
-WEBUI_API_SUFFIX = "/completions"
-
-
-def get_vllm_completion(endpoint, auth_type, auth_key, model, prompt, context_window, user, grammar=None):
- """https://github.com/vllm-project/vllm/blob/main/examples/api_client.py"""
- from letta.utils import printd
-
- prompt_tokens = count_tokens(prompt)
- if prompt_tokens > context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- settings = get_completions_settings()
- request = settings
- request["prompt"] = prompt
- request["max_tokens"] = 3000 # int(context_window - prompt_tokens)
- request["stream"] = False
- request["user"] = user
-
- # currently hardcoded, since we are only supporting one model with the hosted endpoint
- request["model"] = model
-
- # Set grammar
- if grammar is not None:
- raise NotImplementedError
-
- if not endpoint.startswith(("http://", "https://")):
- raise ValueError(f"Endpoint ({endpoint}) must begin with http:// or https://")
-
- if not endpoint.endswith("/v1"):
- endpoint = endpoint.rstrip("/") + "/v1"
-
- try:
- URI = urljoin(endpoint.strip("/") + "/", WEBUI_API_SUFFIX.strip("/"))
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- result = result_full["choices"][0]["text"]
- usage = result_full.get("usage", None)
- else:
- raise Exception(
- f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
- + f" Make sure that the vLLM server is running and reachable at {URI}."
- )
-
- except:
- # TODO handle gracefully
- raise
-
- # Pass usage statistics back to main thread
- # These are used to compute memory warning messages
- completion_tokens = usage.get("completion_tokens", None) if usage is not None else None
- total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
- usage = {
- "prompt_tokens": prompt_tokens, # can grab from usage dict, but it's usually wrong (set to 0)
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- }
-
- return result, usage
diff --git a/letta/local_llm/webui/api.py b/letta/local_llm/webui/api.py
deleted file mode 100644
index 7c4a0967..00000000
--- a/letta/local_llm/webui/api.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from urllib.parse import urljoin
-
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import count_tokens, post_json_auth_request
-
-WEBUI_API_SUFFIX = "/v1/completions"
-
-
-def get_webui_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=None):
- """Compatibility for the new OpenAI API: https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API#examples"""
- from letta.utils import printd
-
- prompt_tokens = count_tokens(prompt)
- if prompt_tokens > context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- settings = get_completions_settings()
- request = settings
- request["prompt"] = prompt
- request["truncation_length"] = context_window
- request["max_tokens"] = int(context_window - prompt_tokens)
- request["max_new_tokens"] = int(context_window - prompt_tokens) # safety backup to "max_tokens", shouldn't matter
-
- # Set grammar
- if grammar is not None:
- request["grammar_string"] = grammar
-
- if not endpoint.startswith(("http://", "https://")):
- raise ValueError(f"Endpoint value ({endpoint}) must begin with http:// or https://")
-
- try:
- URI = urljoin(endpoint.strip("/") + "/", WEBUI_API_SUFFIX.strip("/"))
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- result = result_full["choices"][0]["text"]
- usage = result_full.get("usage", None)
- else:
- raise Exception(
- f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
- + f" Make sure that the web UI server is running and reachable at {URI}."
- )
-
- except:
- # TODO handle gracefully
- raise
-
- # Pass usage statistics back to main thread
- # These are used to compute memory warning messages
- completion_tokens = usage.get("completion_tokens", None) if usage is not None else None
- total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
- usage = {
- "prompt_tokens": prompt_tokens, # can grab from usage dict, but it's usually wrong (set to 0)
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- }
-
- return result, usage
diff --git a/letta/local_llm/webui/legacy_api.py b/letta/local_llm/webui/legacy_api.py
deleted file mode 100644
index 01403c1f..00000000
--- a/letta/local_llm/webui/legacy_api.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from urllib.parse import urljoin
-
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import count_tokens, post_json_auth_request
-
-WEBUI_API_SUFFIX = "/api/v1/generate"
-
-
-def get_webui_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=None):
- """See https://github.com/oobabooga/text-generation-webui for instructions on how to run the LLM web server"""
- from letta.utils import printd
-
- prompt_tokens = count_tokens(prompt)
- if prompt_tokens > context_window:
- raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
- # Settings for the generation, includes the prompt + stop tokens, max length, etc
- settings = get_completions_settings()
- request = settings
- request["stopping_strings"] = request["stop"] # alias
- request["max_new_tokens"] = 3072 # random hack?
- request["prompt"] = prompt
- request["truncation_length"] = context_window # assuming mistral 7b
-
- # Set grammar
- if grammar is not None:
- request["grammar_string"] = grammar
-
- if not endpoint.startswith(("http://", "https://")):
- raise ValueError(f"Provided OPENAI_API_BASE value ({endpoint}) must begin with http:// or https://")
-
- try:
- URI = urljoin(endpoint.strip("/") + "/", WEBUI_API_SUFFIX.strip("/"))
- response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
- if response.status_code == 200:
- result_full = response.json()
- printd(f"JSON API response:\n{result_full}")
- result = result_full["results"][0]["text"]
- else:
- raise Exception(
- f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
- + f" Make sure that the web UI server is running and reachable at {URI}."
- )
-
- except:
- # TODO handle gracefully
- raise
-
- # TODO correct for legacy
- completion_tokens = None
- total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
- usage = {
- "prompt_tokens": prompt_tokens,
- "completion_tokens": completion_tokens,
- "total_tokens": total_tokens,
- }
-
- return result, usage
diff --git a/letta/local_llm/webui/legacy_settings.py b/letta/local_llm/webui/legacy_settings.py
deleted file mode 100644
index d2f09903..00000000
--- a/letta/local_llm/webui/legacy_settings.py
+++ /dev/null
@@ -1,23 +0,0 @@
-SIMPLE = {
- "stopping_strings": [
- "\nUSER:",
- "\nASSISTANT:",
- "\nFUNCTION RETURN:",
- "\nUSER",
- "\nASSISTANT",
- "\nFUNCTION RETURN",
- "\nFUNCTION",
- "\nFUNC",
- "<|im_start|>",
- "<|im_end|>",
- "<|im_sep|>",
- # '\n' +
- # '',
- # '<|',
- # '\n#',
- # '\n\n\n',
- ],
- "max_new_tokens": 3072,
- # "truncation_length": 4096, # assuming llama2 models
- # "truncation_length": LLM_MAX_TOKENS, # assuming mistral 7b
-}
diff --git a/letta/local_llm/webui/settings.py b/letta/local_llm/webui/settings.py
deleted file mode 100644
index 27da3e74..00000000
--- a/letta/local_llm/webui/settings.py
+++ /dev/null
@@ -1,24 +0,0 @@
-SIMPLE = {
- # "stopping_strings": [
- "stop": [
- "\nUSER:",
- "\nASSISTANT:",
- "\nFUNCTION RETURN:",
- "\nUSER",
- "\nASSISTANT",
- "\nFUNCTION RETURN",
- "\nFUNCTION",
- "\nFUNC",
- "<|im_start|>",
- "<|im_end|>",
- "<|im_sep|>",
- # '\n' +
- # '',
- # '<|',
- # '\n#',
- # '\n\n\n',
- ],
- # "max_tokens": 3072,
- # "truncation_length": 4096, # assuming llama2 models
- # "truncation_length": LLM_MAX_TOKENS, # assuming mistral 7b
-}
diff --git a/letta/log.py b/letta/log.py
deleted file mode 100644
index 52acf3b0..00000000
--- a/letta/log.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import logging
-from logging.config import dictConfig
-from pathlib import Path
-from sys import stdout
-from typing import Optional
-
-from letta.settings import settings
-
-selected_log_level = logging.DEBUG if settings.debug else logging.INFO
-
-
-def _setup_logfile() -> "Path":
- """ensure the logger filepath is in place
-
- Returns: the logfile Path
- """
- logfile = Path(settings.letta_dir / "logs" / "Letta.log")
- logfile.parent.mkdir(parents=True, exist_ok=True)
- logfile.touch(exist_ok=True)
- return logfile
-
-
-# TODO: production logging should be much less invasive
-DEVELOPMENT_LOGGING = {
- "version": 1,
- "disable_existing_loggers": False, # Allow capturing from all loggers
- "formatters": {
- "standard": {"format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"},
- "no_datetime": {"format": "%(name)s - %(levelname)s - %(message)s"},
- },
- "handlers": {
- "console": {
- "level": selected_log_level,
- "class": "logging.StreamHandler",
- "stream": stdout,
- "formatter": "no_datetime",
- },
- "file": {
- "level": "DEBUG",
- "class": "logging.handlers.RotatingFileHandler",
- "filename": _setup_logfile(),
- "maxBytes": 1024**2 * 10,
- "backupCount": 3,
- "formatter": "standard",
- },
- },
- "root": { # Root logger handles all logs
- "level": logging.DEBUG if settings.debug else logging.INFO,
- "handlers": ["console", "file"],
- },
- "loggers": {
- "Letta": {
- "level": logging.DEBUG if settings.debug else logging.INFO,
- "propagate": True, # Let logs bubble up to root
- },
- "uvicorn": {
- "level": "CRITICAL",
- "handlers": ["console"],
- "propagate": True,
- },
- },
-}
-
-# Configure logging once at module initialization to avoid performance overhead
-dictConfig(DEVELOPMENT_LOGGING)
-
-
-def get_logger(name: Optional[str] = None) -> "logging.Logger":
- """returns the project logger, scoped to a child name if provided
- Args:
- name: will define a child logger
- """
- parent_logger = logging.getLogger("Letta")
- if name:
- return parent_logger.getChild(name)
- return parent_logger
diff --git a/letta/main.py b/letta/main.py
deleted file mode 100644
index a64b3637..00000000
--- a/letta/main.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import os
-
-import typer
-
-from letta.cli.cli import server
-from letta.cli.cli_load import app as load_app
-
-# disable composio print on exit
-os.environ["COMPOSIO_DISABLE_VERSION_CHECK"] = "true"
-
-app = typer.Typer(pretty_exceptions_enable=False)
-app.command(name="server")(server)
-
-app.add_typer(load_app, name="load")
diff --git a/letta/memory.py b/letta/memory.py
deleted file mode 100644
index c6a03c14..00000000
--- a/letta/memory.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from typing import TYPE_CHECKING, Callable, Dict, List
-
-from letta.constants import MESSAGE_SUMMARY_REQUEST_ACK
-from letta.llm_api.llm_api_tools import create
-from letta.llm_api.llm_client import LLMClient
-from letta.otel.tracing import trace_method
-from letta.prompts.gpt_summarize import SYSTEM as SUMMARY_PROMPT_SYSTEM
-from letta.schemas.agent import AgentState
-from letta.schemas.enums import MessageRole
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.memory import Memory
-from letta.schemas.message import Message
-from letta.settings import summarizer_settings
-from letta.utils import count_tokens, printd
-
-if TYPE_CHECKING:
- from letta.orm import User
-
-
-def get_memory_functions(cls: Memory) -> Dict[str, Callable]:
- """Get memory functions for a memory class"""
- functions = {}
-
- # collect base memory functions (should not be included)
- base_functions = []
- for func_name in dir(Memory):
- funct = getattr(Memory, func_name)
- if callable(funct):
- base_functions.append(func_name)
-
- for func_name in dir(cls):
- if func_name.startswith("_") or func_name in ["load", "to_dict"]: # skip base functions
- continue
- if func_name in base_functions: # dont use BaseMemory functions
- continue
- func = getattr(cls, func_name)
- if not callable(func): # not a function
- continue
- functions[func_name] = func
- return functions
-
-
-def _format_summary_history(message_history: List[Message]):
- # TODO use existing prompt formatters for this (eg ChatML)
- def get_message_text(content):
- if content and len(content) == 1 and isinstance(content[0], TextContent):
- return content[0].text
- return ""
-
- return "\n".join([f"{m.role}: {get_message_text(m.content)}" for m in message_history])
-
-
-@trace_method
-def summarize_messages(
- agent_state: AgentState,
- message_sequence_to_summarize: List[Message],
- actor: "User",
-):
- """Summarize a message sequence using GPT"""
- # we need the context_window
- context_window = agent_state.llm_config.context_window
-
- summary_prompt = SUMMARY_PROMPT_SYSTEM
- summary_input = _format_summary_history(message_sequence_to_summarize)
- summary_input_tkns = count_tokens(summary_input)
- if summary_input_tkns > summarizer_settings.memory_warning_threshold * context_window:
- trunc_ratio = (summarizer_settings.memory_warning_threshold * context_window / summary_input_tkns) * 0.8 # For good measure...
- cutoff = int(len(message_sequence_to_summarize) * trunc_ratio)
- summary_input = str(
- [summarize_messages(agent_state, message_sequence_to_summarize=message_sequence_to_summarize[:cutoff], actor=actor)]
- + message_sequence_to_summarize[cutoff:]
- )
-
- dummy_agent_id = agent_state.id
- message_sequence = [
- Message(agent_id=dummy_agent_id, role=MessageRole.system, content=[TextContent(text=summary_prompt)]),
- Message(agent_id=dummy_agent_id, role=MessageRole.assistant, content=[TextContent(text=MESSAGE_SUMMARY_REQUEST_ACK)]),
- Message(agent_id=dummy_agent_id, role=MessageRole.user, content=[TextContent(text=summary_input)]),
- ]
-
- # TODO: We need to eventually have a separate LLM config for the summarizer LLM
- llm_config_no_inner_thoughts = agent_state.llm_config.model_copy(deep=True)
- llm_config_no_inner_thoughts.put_inner_thoughts_in_kwargs = False
-
- llm_client = LLMClient.create(
- provider_type=agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=False,
- actor=actor,
- )
- # try to use new client, otherwise fallback to old flow
- # TODO: we can just directly call the LLM here?
- if llm_client:
- response = llm_client.send_llm_request(
- messages=message_sequence,
- llm_config=llm_config_no_inner_thoughts,
- )
- else:
- response = create(
- llm_config=llm_config_no_inner_thoughts,
- user_id=agent_state.created_by_id,
- messages=message_sequence,
- stream=False,
- )
-
- printd(f"summarize_messages gpt reply: {response.choices[0]}")
- reply = response.choices[0].message.content
- return reply
diff --git a/letta/openai_backcompat/__init__.py b/letta/openai_backcompat/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/openai_backcompat/openai_object.py b/letta/openai_backcompat/openai_object.py
deleted file mode 100644
index f2988d58..00000000
--- a/letta/openai_backcompat/openai_object.py
+++ /dev/null
@@ -1,437 +0,0 @@
-# https://github.com/openai/openai-python/blob/v0.27.4/openai/openai_object.py
-
-from copy import deepcopy
-from enum import Enum
-from typing import Optional, Tuple, Union
-
-from letta.helpers.json_helpers import json_dumps
-
-api_requestor = None
-api_resources = None
-CompletionConfig = None
-
-OBJECT_CLASSES = {
- # "engine": api_resources.Engine,
- # "experimental.completion_config": CompletionConfig,
- # "file": api_resources.File,
- # "fine-tune": api_resources.FineTune,
- # "model": api_resources.Model,
- # "deployment": api_resources.Deployment,
-}
-
-
-def get_object_classes():
- # This is here to avoid a circular dependency
- # from openai.object_classes import OBJECT_CLASSES
-
- return OBJECT_CLASSES
-
-
-class OpenAIResponse:
- def __init__(self, data, headers):
- self._headers = headers
- self.data = data
-
- @property
- def request_id(self) -> Optional[str]:
- return self._headers.get("request-id")
-
- @property
- def organization(self) -> Optional[str]:
- return self._headers.get("OpenAI-Organization")
-
- @property
- def response_ms(self) -> Optional[int]:
- h = self._headers.get("Openai-Processing-Ms")
- return None if h is None else round(float(h))
-
-
-class ApiType(Enum):
- AZURE = 1
- OPEN_AI = 2
- AZURE_AD = 3
-
- @staticmethod
- def from_str(label):
- if label.lower() == "azure":
- return ApiType.AZURE
- elif label.lower() in ("azure_ad", "azuread"):
- return ApiType.AZURE_AD
- elif label.lower() in ("open_ai", "openai"):
- return ApiType.OPEN_AI
- else:
- # raise openai.error.InvalidAPIType(
- raise Exception(
- "The API type provided in invalid. Please select one of the supported API types: 'azure', 'azure_ad', 'open_ai'"
- )
-
-
-class OpenAIObject(dict):
- api_base_override = None
-
- def __init__(
- self,
- id=None,
- api_key=None,
- api_version=None,
- api_type=None,
- organization=None,
- response_ms: Optional[int] = None,
- api_base=None,
- engine=None,
- **params,
- ):
- super(OpenAIObject, self).__init__()
-
- if response_ms is not None and not isinstance(response_ms, int):
- raise TypeError(f"response_ms is a {type(response_ms).__name__}.")
- self._response_ms = response_ms
-
- self._retrieve_params = params
-
- object.__setattr__(self, "api_key", api_key)
- object.__setattr__(self, "api_version", api_version)
- object.__setattr__(self, "api_type", api_type)
- object.__setattr__(self, "organization", organization)
- object.__setattr__(self, "api_base_override", api_base)
- object.__setattr__(self, "engine", engine)
-
- if id:
- self["id"] = id
-
- @property
- def response_ms(self) -> Optional[int]:
- return self._response_ms
-
- def __setattr__(self, k, v):
- if k[0] == "_" or k in self.__dict__:
- return super(OpenAIObject, self).__setattr__(k, v)
-
- self[k] = v
- return None
-
- def __getattr__(self, k):
- if k[0] == "_":
- raise AttributeError(k)
- try:
- return self[k]
- except KeyError as err:
- raise AttributeError(*err.args)
-
- def __delattr__(self, k):
- if k[0] == "_" or k in self.__dict__:
- return super(OpenAIObject, self).__delattr__(k)
- else:
- del self[k]
-
- def __setitem__(self, k, v):
- if v == "":
- raise ValueError(
- "You cannot set %s to an empty string. "
- "We interpret empty strings as None in requests."
- "You may set %s.%s = None to delete the property" % (k, str(self), k)
- )
- super(OpenAIObject, self).__setitem__(k, v)
-
- def __delitem__(self, k):
- raise NotImplementedError("del is not supported")
-
- # Custom unpickling method that uses `update` to update the dictionary
- # without calling __setitem__, which would fail if any value is an empty
- # string
- def __setstate__(self, state):
- self.update(state)
-
- # Custom pickling method to ensure the instance is pickled as a custom
- # class and not as a dict, otherwise __setstate__ would not be called when
- # unpickling.
- def __reduce__(self):
- reduce_value = (
- type(self), # callable
- ( # args
- self.get("id", None),
- self.api_key,
- self.api_version,
- self.api_type,
- self.organization,
- ),
- dict(self), # state
- )
- return reduce_value
-
- @classmethod
- def construct_from(
- cls,
- values,
- api_key: Optional[str] = None,
- api_version=None,
- organization=None,
- engine=None,
- response_ms: Optional[int] = None,
- ):
- instance = cls(
- values.get("id"),
- api_key=api_key,
- api_version=api_version,
- organization=organization,
- engine=engine,
- response_ms=response_ms,
- )
- instance.refresh_from(
- values,
- api_key=api_key,
- api_version=api_version,
- organization=organization,
- response_ms=response_ms,
- )
- return instance
-
- def refresh_from(
- self,
- values,
- api_key=None,
- api_version=None,
- api_type=None,
- organization=None,
- response_ms: Optional[int] = None,
- ):
- self.api_key = api_key or getattr(values, "api_key", None)
- self.api_version = api_version or getattr(values, "api_version", None)
- self.api_type = api_type or getattr(values, "api_type", None)
- self.organization = organization or getattr(values, "organization", None)
- self._response_ms = response_ms or getattr(values, "_response_ms", None)
-
- # Wipe old state before setting new.
- self.clear()
- for k, v in values.items():
- super(OpenAIObject, self).__setitem__(k, convert_to_openai_object(v, api_key, api_version, organization))
-
- self._previous = values
-
- @classmethod
- def api_base(cls):
- return None
-
- def request(
- self,
- method,
- url,
- params=None,
- headers=None,
- stream=False,
- plain_old_data=False,
- request_id: Optional[str] = None,
- request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
- ):
- if params is None:
- params = self._retrieve_params
- requestor = api_requestor.APIRequestor(
- key=self.api_key,
- api_base=self.api_base_override or self.api_base(),
- api_type=self.api_type,
- api_version=self.api_version,
- organization=self.organization,
- )
- response, stream, api_key = requestor.request(
- method,
- url,
- params=params,
- stream=stream,
- headers=headers,
- request_id=request_id,
- request_timeout=request_timeout,
- )
-
- if stream:
- assert not isinstance(response, OpenAIResponse) # must be an iterator
- return (
- convert_to_openai_object(
- line,
- api_key,
- self.api_version,
- self.organization,
- plain_old_data=plain_old_data,
- )
- for line in response
- )
- else:
- return convert_to_openai_object(
- response,
- api_key,
- self.api_version,
- self.organization,
- plain_old_data=plain_old_data,
- )
-
- async def arequest(
- self,
- method,
- url,
- params=None,
- headers=None,
- stream=False,
- plain_old_data=False,
- request_id: Optional[str] = None,
- request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
- ):
- if params is None:
- params = self._retrieve_params
- requestor = api_requestor.APIRequestor(
- key=self.api_key,
- api_base=self.api_base_override or self.api_base(),
- api_type=self.api_type,
- api_version=self.api_version,
- organization=self.organization,
- )
- response, stream, api_key = await requestor.arequest(
- method,
- url,
- params=params,
- stream=stream,
- headers=headers,
- request_id=request_id,
- request_timeout=request_timeout,
- )
-
- if stream:
- assert not isinstance(response, OpenAIResponse) # must be an iterator
- return (
- convert_to_openai_object(
- line,
- api_key,
- self.api_version,
- self.organization,
- plain_old_data=plain_old_data,
- )
- for line in response
- )
- else:
- return convert_to_openai_object(
- response,
- api_key,
- self.api_version,
- self.organization,
- plain_old_data=plain_old_data,
- )
-
- def __repr__(self):
- ident_parts = [type(self).__name__]
-
- obj = self.get("object")
- if isinstance(obj, str):
- ident_parts.append(obj)
-
- if isinstance(self.get("id"), str):
- ident_parts.append("id=%s" % (self.get("id"),))
-
- unicode_repr = "<%s at %s> JSON: %s" % (
- " ".join(ident_parts),
- hex(id(self)),
- str(self),
- )
-
- return unicode_repr
-
- def __str__(self):
- obj = self.to_dict_recursive()
- return json_dumps(obj, sort_keys=True, indent=2)
-
- def to_dict(self):
- return dict(self)
-
- def to_dict_recursive(self):
- d = dict(self)
- for k, v in d.items():
- if isinstance(v, OpenAIObject):
- d[k] = v.to_dict_recursive()
- elif isinstance(v, list):
- d[k] = [e.to_dict_recursive() if isinstance(e, OpenAIObject) else e for e in v]
- return d
-
- @property
- def openai_id(self):
- return self.id
-
- @property
- def typed_api_type(self):
- # return ApiType.from_str(self.api_type) if self.api_type else ApiType.from_str(openai.api_type)
- return ApiType.from_str(self.api_type) if self.api_type else ApiType.from_str(ApiType.OPEN_AI)
-
- # This class overrides __setitem__ to throw exceptions on inputs that it
- # doesn't like. This can cause problems when we try to copy an object
- # wholesale because some data that's returned from the API may not be valid
- # if it was set to be set manually. Here we override the class' copy
- # arguments so that we can bypass these possible exceptions on __setitem__.
- def __copy__(self):
- copied = OpenAIObject(
- self.get("id"),
- self.api_key,
- api_version=self.api_version,
- api_type=self.api_type,
- organization=self.organization,
- )
-
- copied._retrieve_params = self._retrieve_params
-
- for k, v in self.items():
- # Call parent's __setitem__ to avoid checks that we've added in the
- # overridden version that can throw exceptions.
- super(OpenAIObject, copied).__setitem__(k, v)
-
- return copied
-
- # This class overrides __setitem__ to throw exceptions on inputs that it
- # doesn't like. This can cause problems when we try to copy an object
- # wholesale because some data that's returned from the API may not be valid
- # if it was set to be set manually. Here we override the class' copy
- # arguments so that we can bypass these possible exceptions on __setitem__.
- def __deepcopy__(self, memo):
- copied = self.__copy__()
- memo[id(self)] = copied
-
- for k, v in self.items():
- # Call parent's __setitem__ to avoid checks that we've added in the
- # overridden version that can throw exceptions.
- super(OpenAIObject, copied).__setitem__(k, deepcopy(v, memo))
-
- return copied
-
-
-def convert_to_openai_object(
- resp,
- api_key=None,
- api_version=None,
- organization=None,
- engine=None,
- plain_old_data=False,
-):
- # If we get a OpenAIResponse, we'll want to return a OpenAIObject.
-
- response_ms: Optional[int] = None
- if isinstance(resp, OpenAIResponse):
- organization = resp.organization
- response_ms = resp.response_ms
- resp = resp.data
-
- if plain_old_data:
- return resp
- elif isinstance(resp, list):
- return [convert_to_openai_object(i, api_key, api_version, organization, engine=engine) for i in resp]
- elif isinstance(resp, dict) and not isinstance(resp, OpenAIObject):
- resp = resp.copy()
- klass_name = resp.get("object")
- if isinstance(klass_name, str):
- klass = get_object_classes().get(klass_name, OpenAIObject)
- else:
- klass = OpenAIObject
-
- return klass.construct_from(
- resp,
- api_key=api_key,
- api_version=api_version,
- organization=organization,
- response_ms=response_ms,
- engine=engine,
- )
- else:
- return resp
diff --git a/letta/orm/__init__.py b/letta/orm/__init__.py
deleted file mode 100644
index f2d1bd15..00000000
--- a/letta/orm/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from letta.orm.agent import Agent
-from letta.orm.agents_tags import AgentsTags
-from letta.orm.archive import Archive
-from letta.orm.archives_agents import ArchivesAgents
-from letta.orm.base import Base
-from letta.orm.block import Block
-from letta.orm.block_history import BlockHistory
-from letta.orm.blocks_agents import BlocksAgents
-from letta.orm.file import FileMetadata
-from letta.orm.files_agents import FileAgent
-from letta.orm.group import Group
-from letta.orm.groups_agents import GroupsAgents
-from letta.orm.groups_blocks import GroupsBlocks
-from letta.orm.identities_agents import IdentitiesAgents
-from letta.orm.identities_blocks import IdentitiesBlocks
-from letta.orm.identity import Identity
-from letta.orm.job import Job
-from letta.orm.job_messages import JobMessage
-from letta.orm.llm_batch_items import LLMBatchItem
-from letta.orm.llm_batch_job import LLMBatchJob
-from letta.orm.mcp_server import MCPServer
-from letta.orm.message import Message
-from letta.orm.organization import Organization
-from letta.orm.passage import ArchivalPassage, BasePassage, SourcePassage
-from letta.orm.passage_tag import PassageTag
-from letta.orm.prompt import Prompt
-from letta.orm.provider import Provider
-from letta.orm.provider_trace import ProviderTrace
-from letta.orm.sandbox_config import AgentEnvironmentVariable, SandboxConfig, SandboxEnvironmentVariable
-from letta.orm.source import Source
-from letta.orm.sources_agents import SourcesAgents
-from letta.orm.step import Step
-from letta.orm.step_metrics import StepMetrics
-from letta.orm.tool import Tool
-from letta.orm.tools_agents import ToolsAgents
-from letta.orm.user import User
diff --git a/letta/orm/agent.py b/letta/orm/agent.py
deleted file mode 100644
index 5c50017f..00000000
--- a/letta/orm/agent.py
+++ /dev/null
@@ -1,369 +0,0 @@
-import asyncio
-import uuid
-from datetime import datetime
-from typing import TYPE_CHECKING, List, Optional, Set
-
-from sqlalchemy import JSON, Boolean, DateTime, Index, Integer, String
-from sqlalchemy.ext.asyncio import AsyncAttrs
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.block import Block
-from letta.orm.custom_columns import EmbeddingConfigColumn, LLMConfigColumn, ResponseFormatColumn, ToolRulesColumn
-from letta.orm.identity import Identity
-from letta.orm.mixins import OrganizationMixin, ProjectMixin, TemplateEntityMixin, TemplateMixin
-from letta.orm.organization import Organization
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.agent import AgentState as PydanticAgentState, AgentType, get_prompt_template_for_agent_type
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.memory import Memory
-from letta.schemas.response_format import ResponseFormatUnion
-from letta.schemas.tool_rule import ToolRule
-from letta.utils import calculate_file_defaults_based_on_context_window
-
-if TYPE_CHECKING:
- from letta.orm.agents_tags import AgentsTags
- from letta.orm.archives_agents import ArchivesAgents
- from letta.orm.files_agents import FileAgent
- from letta.orm.identity import Identity
- from letta.orm.organization import Organization
- from letta.orm.source import Source
- from letta.orm.tool import Tool
-
-
-class Agent(SqlalchemyBase, OrganizationMixin, ProjectMixin, TemplateEntityMixin, TemplateMixin, AsyncAttrs):
- __tablename__ = "agents"
- __pydantic_model__ = PydanticAgentState
- __table_args__ = (Index("ix_agents_created_at", "created_at", "id"),)
-
- # agent generates its own id
- # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- # TODO: Some still rely on the Pydantic object to do this
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"agent-{uuid.uuid4()}")
-
- # Descriptor fields
- agent_type: Mapped[Optional[AgentType]] = mapped_column(String, nullable=True, doc="The type of Agent")
- name: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="a human-readable identifier for an agent, non-unique.")
- description: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The description of the agent.")
-
- # System prompt
- system: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The system prompt used by the agent.")
-
- # In context memory
- # TODO: This should be a separate mapping table
- # This is dangerously flexible with the JSON type
- message_ids: Mapped[Optional[List[str]]] = mapped_column(JSON, nullable=True, doc="List of message IDs in in-context memory.")
-
- # Response Format
- response_format: Mapped[Optional[ResponseFormatUnion]] = mapped_column(
- ResponseFormatColumn, nullable=True, doc="The response format for the agent."
- )
-
- # Metadata and configs
- metadata_: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True, doc="metadata for the agent.")
- llm_config: Mapped[Optional[LLMConfig]] = mapped_column(
- LLMConfigColumn, nullable=True, doc="the LLM backend configuration object for this agent."
- )
- embedding_config: Mapped[Optional[EmbeddingConfig]] = mapped_column(
- EmbeddingConfigColumn, doc="the embedding configuration object for this agent."
- )
-
- # Tool rules
- tool_rules: Mapped[Optional[List[ToolRule]]] = mapped_column(ToolRulesColumn, doc="the tool rules for this agent.")
-
- # Stateless
- message_buffer_autoclear: Mapped[bool] = mapped_column(
- Boolean, doc="If set to True, the agent will not remember previous messages. Not recommended unless you have an advanced use case."
- )
- enable_sleeptime: Mapped[Optional[bool]] = mapped_column(
- Boolean, doc="If set to True, memory management will move to a background agent thread."
- )
-
- # Run metrics
- last_run_completion: Mapped[Optional[datetime]] = mapped_column(
- DateTime(timezone=True), nullable=True, doc="The timestamp when the agent last completed a run."
- )
- last_run_duration_ms: Mapped[Optional[int]] = mapped_column(
- Integer, nullable=True, doc="The duration in milliseconds of the agent's last run."
- )
-
- # timezone
- timezone: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The timezone of the agent (for the context window).")
-
- # file related controls
- max_files_open: Mapped[Optional[int]] = mapped_column(
- Integer, nullable=True, doc="Maximum number of files that can be open at once for this agent."
- )
- per_file_view_window_char_limit: Mapped[Optional[int]] = mapped_column(
- Integer, nullable=True, doc="The per-file view window character limit for this agent."
- )
-
- # indexing controls
- hidden: Mapped[Optional[bool]] = mapped_column(Boolean, nullable=True, default=None, doc="If set to True, the agent will be hidden.")
- _vector_db_namespace: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="Private field for vector database namespace")
-
- # relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="agents", lazy="raise")
- tool_exec_environment_variables: Mapped[List["AgentEnvironmentVariable"]] = relationship(
- "AgentEnvironmentVariable",
- back_populates="agent",
- cascade="all, delete-orphan",
- lazy="selectin",
- doc="Environment variables associated with this agent.",
- )
- tools: Mapped[List["Tool"]] = relationship("Tool", secondary="tools_agents", lazy="selectin", passive_deletes=True)
- sources: Mapped[List["Source"]] = relationship("Source", secondary="sources_agents", lazy="selectin")
- core_memory: Mapped[List["Block"]] = relationship(
- "Block",
- secondary="blocks_agents",
- lazy="selectin",
- passive_deletes=True, # Ensures SQLAlchemy doesn't fetch blocks_agents rows before deleting
- back_populates="agents",
- doc="Blocks forming the core memory of the agent.",
- )
- tags: Mapped[List["AgentsTags"]] = relationship(
- "AgentsTags",
- back_populates="agent",
- cascade="all, delete-orphan",
- lazy="selectin",
- doc="Tags associated with the agent.",
- )
- identities: Mapped[List["Identity"]] = relationship(
- "Identity",
- secondary="identities_agents",
- lazy="selectin",
- back_populates="agents",
- passive_deletes=True,
- )
- groups: Mapped[List["Group"]] = relationship(
- "Group",
- secondary="groups_agents",
- lazy="raise",
- back_populates="agents",
- passive_deletes=True,
- )
- multi_agent_group: Mapped["Group"] = relationship(
- "Group",
- lazy="selectin",
- viewonly=True,
- back_populates="manager_agent",
- )
- batch_items: Mapped[List["LLMBatchItem"]] = relationship("LLMBatchItem", back_populates="agent", lazy="raise")
- file_agents: Mapped[List["FileAgent"]] = relationship(
- "FileAgent",
- back_populates="agent",
- cascade="all, delete-orphan",
- lazy="selectin",
- )
- archives_agents: Mapped[List["ArchivesAgents"]] = relationship(
- "ArchivesAgents",
- back_populates="agent",
- cascade="all, delete-orphan",
- lazy="noload",
- doc="Archives accessible by this agent.",
- )
-
- def _get_per_file_view_window_char_limit(self) -> int:
- """Get the per_file_view_window_char_limit, calculating defaults if None."""
- if self.per_file_view_window_char_limit is not None:
- return self.per_file_view_window_char_limit
-
- context_window = self.llm_config.context_window if self.llm_config and self.llm_config.context_window else None
- _, default_char_limit = calculate_file_defaults_based_on_context_window(context_window)
- return default_char_limit
-
- def to_pydantic(self, include_relationships: Optional[Set[str]] = None) -> PydanticAgentState:
- """
- Converts the SQLAlchemy Agent model into its Pydantic counterpart.
-
- The following base fields are always included:
- - id, agent_type, name, description, system, message_ids, metadata_,
- llm_config, embedding_config, project_id, template_id, base_template_id,
- tool_rules, message_buffer_autoclear, tags
-
- Everything else (e.g., tools, sources, memory, etc.) is optional and only
- included if specified in `include_fields`.
-
- Args:
- include_relationships (Optional[Set[str]]):
- A set of additional field names to include in the output. If None or empty,
- no extra fields are loaded beyond the base fields.
-
- Returns:
- PydanticAgentState: The Pydantic representation of the agent.
- """
- # Base fields: always included
- state = {
- "id": self.id,
- "agent_type": self.agent_type,
- "name": self.name,
- "description": self.description,
- "system": self.system,
- "message_ids": self.message_ids,
- "metadata": self.metadata_, # Exposed as 'metadata' to Pydantic
- "llm_config": self.llm_config,
- "embedding_config": self.embedding_config,
- "project_id": self.project_id,
- "template_id": self.template_id,
- "base_template_id": self.base_template_id,
- "deployment_id": self.deployment_id,
- "entity_id": self.entity_id,
- "tool_rules": self.tool_rules,
- "message_buffer_autoclear": self.message_buffer_autoclear,
- "created_by_id": self.created_by_id,
- "last_updated_by_id": self.last_updated_by_id,
- "created_at": self.created_at,
- "updated_at": self.updated_at,
- "enable_sleeptime": self.enable_sleeptime,
- "response_format": self.response_format,
- "last_run_completion": self.last_run_completion,
- "last_run_duration_ms": self.last_run_duration_ms,
- "timezone": self.timezone,
- "max_files_open": self.max_files_open,
- "per_file_view_window_char_limit": self.per_file_view_window_char_limit,
- "hidden": self.hidden,
- # optional field defaults
- "tags": [],
- "tools": [],
- "sources": [],
- "memory": Memory(blocks=[]),
- "identity_ids": [],
- "multi_agent_group": None,
- "tool_exec_environment_variables": [],
- }
-
- # Optional fields: only included if requested
- optional_fields = {
- "tags": lambda: [t.tag for t in self.tags],
- "tools": lambda: self.tools,
- "sources": lambda: [s.to_pydantic() for s in self.sources],
- "memory": lambda: Memory(
- blocks=[b.to_pydantic() for b in self.core_memory],
- file_blocks=[
- block
- for b in self.file_agents
- if (block := b.to_pydantic_block(per_file_view_window_char_limit=self._get_per_file_view_window_char_limit()))
- is not None
- ],
- prompt_template=get_prompt_template_for_agent_type(self.agent_type),
- ),
- "identity_ids": lambda: [i.id for i in self.identities],
- "multi_agent_group": lambda: self.multi_agent_group,
- "tool_exec_environment_variables": lambda: self.tool_exec_environment_variables,
- }
-
- include_relationships = set(optional_fields.keys() if include_relationships is None else include_relationships)
-
- for field_name in include_relationships:
- resolver = optional_fields.get(field_name)
- if resolver:
- state[field_name] = resolver()
-
- return self.__pydantic_model__(**state)
-
- async def to_pydantic_async(self, include_relationships: Optional[Set[str]] = None) -> PydanticAgentState:
- """
- Converts the SQLAlchemy Agent model into its Pydantic counterpart.
-
- The following base fields are always included:
- - id, agent_type, name, description, system, message_ids, metadata_,
- llm_config, embedding_config, project_id, template_id, base_template_id,
- tool_rules, message_buffer_autoclear, tags
-
- Everything else (e.g., tools, sources, memory, etc.) is optional and only
- included if specified in `include_fields`.
-
- Args:
- include_relationships (Optional[Set[str]]):
- A set of additional field names to include in the output. If None or empty,
- no extra fields are loaded beyond the base fields.
-
- Returns:
- PydanticAgentState: The Pydantic representation of the agent.
- """
-
- # Base fields: always included
- state = {
- "id": self.id,
- "agent_type": self.agent_type,
- "name": self.name,
- "description": self.description,
- "system": self.system,
- "message_ids": self.message_ids,
- "metadata": self.metadata_, # Exposed as 'metadata' to Pydantic
- "llm_config": self.llm_config,
- "embedding_config": self.embedding_config,
- "project_id": self.project_id,
- "template_id": self.template_id,
- "base_template_id": self.base_template_id,
- "deployment_id": self.deployment_id,
- "entity_id": self.entity_id,
- "tool_rules": self.tool_rules,
- "message_buffer_autoclear": self.message_buffer_autoclear,
- "created_by_id": self.created_by_id,
- "last_updated_by_id": self.last_updated_by_id,
- "created_at": self.created_at,
- "updated_at": self.updated_at,
- "timezone": self.timezone,
- "enable_sleeptime": self.enable_sleeptime,
- "response_format": self.response_format,
- "last_run_completion": self.last_run_completion,
- "last_run_duration_ms": self.last_run_duration_ms,
- "max_files_open": self.max_files_open,
- "per_file_view_window_char_limit": self.per_file_view_window_char_limit,
- "hidden": self.hidden,
- }
- optional_fields = {
- "tags": [],
- "tools": [],
- "sources": [],
- "memory": Memory(blocks=[]),
- "identity_ids": [],
- "multi_agent_group": None,
- "tool_exec_environment_variables": [],
- }
-
- # Initialize include_relationships to an empty set if it's None
- include_relationships = set(optional_fields.keys() if include_relationships is None else include_relationships)
-
- async def empty_list_async():
- return []
-
- async def none_async():
- return None
-
- # Only load requested relationships
- tags = self.awaitable_attrs.tags if "tags" in include_relationships else empty_list_async()
- tools = self.awaitable_attrs.tools if "tools" in include_relationships else empty_list_async()
- sources = self.awaitable_attrs.sources if "sources" in include_relationships else empty_list_async()
- memory = self.awaitable_attrs.core_memory if "memory" in include_relationships else empty_list_async()
- identities = self.awaitable_attrs.identities if "identity_ids" in include_relationships else empty_list_async()
- multi_agent_group = self.awaitable_attrs.multi_agent_group if "multi_agent_group" in include_relationships else none_async()
- tool_exec_environment_variables = (
- self.awaitable_attrs.tool_exec_environment_variables
- if "tool_exec_environment_variables" in include_relationships
- else empty_list_async()
- )
- file_agents = self.awaitable_attrs.file_agents if "memory" in include_relationships else empty_list_async()
-
- (tags, tools, sources, memory, identities, multi_agent_group, tool_exec_environment_variables, file_agents) = await asyncio.gather(
- tags, tools, sources, memory, identities, multi_agent_group, tool_exec_environment_variables, file_agents
- )
-
- state["tags"] = [t.tag for t in tags]
- state["tools"] = [t.to_pydantic() for t in tools]
- state["sources"] = [s.to_pydantic() for s in sources]
- state["memory"] = Memory(
- blocks=[m.to_pydantic() for m in memory],
- file_blocks=[
- block
- for b in file_agents
- if (block := b.to_pydantic_block(per_file_view_window_char_limit=self._get_per_file_view_window_char_limit())) is not None
- ],
- prompt_template=get_prompt_template_for_agent_type(self.agent_type),
- )
- state["identity_ids"] = [i.id for i in identities]
- state["multi_agent_group"] = multi_agent_group
- state["tool_exec_environment_variables"] = tool_exec_environment_variables
-
- return self.__pydantic_model__(**state)
diff --git a/letta/orm/agents_tags.py b/letta/orm/agents_tags.py
deleted file mode 100644
index d7177083..00000000
--- a/letta/orm/agents_tags.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from sqlalchemy import ForeignKey, Index, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.base import Base
-
-
-class AgentsTags(Base):
- __tablename__ = "agents_tags"
- __table_args__ = (
- UniqueConstraint("agent_id", "tag", name="unique_agent_tag"),
- Index("ix_agents_tags_agent_id_tag", "agent_id", "tag"),
- Index("ix_agents_tags_tag_agent_id", "tag", "agent_id"),
- )
-
- # # agent generates its own id
- # # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- # # TODO: Move this in this PR? at the very end?
- # id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"agents_tags-{uuid.uuid4()}")
-
- agent_id: Mapped[String] = mapped_column(String, ForeignKey("agents.id"), primary_key=True)
- tag: Mapped[str] = mapped_column(String, doc="The name of the tag associated with the agent.", primary_key=True)
-
- # Relationships
- agent: Mapped["Agent"] = relationship("Agent", back_populates="tags")
diff --git a/letta/orm/archive.py b/letta/orm/archive.py
deleted file mode 100644
index 75f36906..00000000
--- a/letta/orm/archive.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import uuid
-from datetime import datetime, timezone
-from typing import TYPE_CHECKING, List, Optional
-
-from sqlalchemy import JSON, Enum, Index, String
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.archive import Archive as PydanticArchive
-from letta.schemas.enums import VectorDBProvider
-from letta.settings import DatabaseChoice, settings
-
-if TYPE_CHECKING:
- from sqlalchemy.ext.asyncio import AsyncSession
- from sqlalchemy.orm import Session
-
- from letta.orm.archives_agents import ArchivesAgents
- from letta.orm.organization import Organization
- from letta.schemas.user import User
-
-
-class Archive(SqlalchemyBase, OrganizationMixin):
- """An archive represents a collection of archival passages that can be shared between agents"""
-
- __tablename__ = "archives"
- __pydantic_model__ = PydanticArchive
-
- __table_args__ = (
- Index("ix_archives_created_at", "created_at", "id"),
- Index("ix_archives_organization_id", "organization_id"),
- )
-
- # archive generates its own id
- # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- # TODO: Some still rely on the Pydantic object to do this
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"archive-{uuid.uuid4()}")
-
- # archive-specific fields
- name: Mapped[str] = mapped_column(String, nullable=False, doc="The name of the archive")
- description: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="A description of the archive")
- vector_db_provider: Mapped[VectorDBProvider] = mapped_column(
- Enum(VectorDBProvider),
- nullable=False,
- default=VectorDBProvider.NATIVE,
- doc="The vector database provider used for this archive's passages",
- )
- metadata_: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True, doc="Additional metadata for the archive")
- _vector_db_namespace: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="Private field for vector database namespace")
-
- # relationships
- archives_agents: Mapped[List["ArchivesAgents"]] = relationship(
- "ArchivesAgents",
- back_populates="archive",
- cascade="all, delete-orphan", # this will delete junction entries when archive is deleted
- lazy="noload",
- )
-
- organization: Mapped["Organization"] = relationship("Organization", back_populates="archives", lazy="selectin")
-
- def create(
- self,
- db_session: "Session",
- actor: Optional["User"] = None,
- no_commit: bool = False,
- ) -> "Archive":
- """Override create to handle SQLite timestamp issues"""
- # For SQLite, explicitly set timestamps as server_default may not work
- if settings.database_engine == DatabaseChoice.SQLITE:
- now = datetime.now(timezone.utc)
- if not self.created_at:
- self.created_at = now
- if not self.updated_at:
- self.updated_at = now
-
- return super().create(db_session, actor=actor, no_commit=no_commit)
-
- async def create_async(
- self,
- db_session: "AsyncSession",
- actor: Optional["User"] = None,
- no_commit: bool = False,
- no_refresh: bool = False,
- ) -> "Archive":
- """Override create_async to handle SQLite timestamp issues"""
- # For SQLite, explicitly set timestamps as server_default may not work
- if settings.database_engine == DatabaseChoice.SQLITE:
- now = datetime.now(timezone.utc)
- if not self.created_at:
- self.created_at = now
- if not self.updated_at:
- self.updated_at = now
-
- return await super().create_async(db_session, actor=actor, no_commit=no_commit, no_refresh=no_refresh)
diff --git a/letta/orm/archives_agents.py b/letta/orm/archives_agents.py
deleted file mode 100644
index 06c63a5e..00000000
--- a/letta/orm/archives_agents.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from datetime import datetime
-
-from sqlalchemy import Boolean, DateTime, ForeignKey, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.base import Base
-
-
-class ArchivesAgents(Base):
- """Many-to-many relationship between agents and archives"""
-
- __tablename__ = "archives_agents"
-
- # TODO: Remove this unique constraint when we support multiple archives per agent
- # For now, each agent can only have one archive
- __table_args__ = (UniqueConstraint("agent_id", name="unique_agent_archive"),)
-
- agent_id: Mapped[str] = mapped_column(String, ForeignKey("agents.id", ondelete="CASCADE"), primary_key=True)
- archive_id: Mapped[str] = mapped_column(String, ForeignKey("archives.id", ondelete="CASCADE"), primary_key=True)
-
- # track when the relationship was created and if agent is owner
- created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default="now()")
- is_owner: Mapped[bool] = mapped_column(Boolean, default=False, doc="Whether this agent created/owns the archive")
-
- # relationships
- agent: Mapped["Agent"] = relationship("Agent", back_populates="archives_agents")
- archive: Mapped["Archive"] = relationship("Archive", back_populates="archives_agents")
diff --git a/letta/orm/base.py b/letta/orm/base.py
deleted file mode 100644
index 8145dfcb..00000000
--- a/letta/orm/base.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from datetime import datetime, timezone
-from typing import Optional
-
-from sqlalchemy import Boolean, DateTime, String, func, text
-from sqlalchemy.orm import DeclarativeBase, Mapped, declarative_mixin, declared_attr, mapped_column
-
-
-class Base(DeclarativeBase):
- """absolute base for sqlalchemy classes"""
-
-
-@declarative_mixin
-class CommonSqlalchemyMetaMixins(Base):
- __abstract__ = True
-
- created_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), server_default=func.now())
- updated_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), server_default=func.now(), server_onupdate=func.now())
- is_deleted: Mapped[bool] = mapped_column(Boolean, server_default=text("FALSE"))
-
- def set_updated_at(self, timestamp: Optional[datetime] = None) -> None:
- """
- Set the updated_at timestamp for the model instance.
-
- Args:
- timestamp (Optional[datetime]): The timestamp to set.
- If None, uses the current UTC time.
- """
- self.updated_at = timestamp or datetime.now(timezone.utc)
-
- def _set_created_and_updated_by_fields(self, actor_id: str) -> None:
- """Populate created_by_id and last_updated_by_id based on actor."""
- if not self.created_by_id:
- self.created_by_id = actor_id
- # Always set the last_updated_by_id when updating
- self.last_updated_by_id = actor_id
-
- @declared_attr
- def _created_by_id(cls):
- return cls._user_by_id()
-
- @declared_attr
- def _last_updated_by_id(cls):
- return cls._user_by_id()
-
- @classmethod
- def _user_by_id(cls):
- """a flexible non-constrained record of a user.
- This way users can get added, deleted etc without history freaking out
- """
- return mapped_column(String, nullable=True)
-
- @property
- def last_updated_by_id(self) -> Optional[str]:
- return self._user_id_getter("last_updated")
-
- @last_updated_by_id.setter
- def last_updated_by_id(self, value: str) -> None:
- self._user_id_setter("last_updated", value)
-
- @property
- def created_by_id(self) -> Optional[str]:
- return self._user_id_getter("created")
-
- @created_by_id.setter
- def created_by_id(self, value: str) -> None:
- self._user_id_setter("created", value)
-
- def _user_id_getter(self, prop: str) -> Optional[str]:
- """returns the user id for the specified property"""
- full_prop = f"_{prop}_by_id"
- prop_value = getattr(self, full_prop, None)
- return prop_value
-
- def _user_id_setter(self, prop: str, value: str) -> None:
- """returns the user id for the specified property"""
- full_prop = f"_{prop}_by_id"
- if not value:
- setattr(self, full_prop, None)
- return
- # Safety check
- prefix, id_ = value.split("-", 1)
- assert prefix == "user", f"{prefix} is not a valid id prefix for a user id"
-
- # Set the full value
- setattr(self, full_prop, value)
diff --git a/letta/orm/block.py b/letta/orm/block.py
deleted file mode 100644
index 4fe3c78b..00000000
--- a/letta/orm/block.py
+++ /dev/null
@@ -1,128 +0,0 @@
-from typing import TYPE_CHECKING, List, Optional, Type
-
-from sqlalchemy import JSON, BigInteger, ForeignKey, Index, Integer, String, UniqueConstraint, event
-from sqlalchemy.orm import Mapped, attributes, declared_attr, mapped_column, relationship
-
-from letta.constants import CORE_MEMORY_BLOCK_CHAR_LIMIT
-from letta.orm.block_history import BlockHistory
-from letta.orm.blocks_agents import BlocksAgents
-from letta.orm.mixins import OrganizationMixin, ProjectMixin, TemplateEntityMixin, TemplateMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.block import Block as PydanticBlock, Human, Persona
-
-if TYPE_CHECKING:
- from letta.orm import Organization
- from letta.orm.identity import Identity
-
-
-class Block(OrganizationMixin, SqlalchemyBase, ProjectMixin, TemplateEntityMixin, TemplateMixin):
- """Blocks are sections of the LLM context, representing a specific part of the total Memory"""
-
- __tablename__ = "block"
- __pydantic_model__ = PydanticBlock
- # This may seem redundant, but is necessary for the BlocksAgents composite FK relationship
- __table_args__ = (
- UniqueConstraint("id", "label", name="unique_block_id_label"),
- Index("created_at_label_idx", "created_at", "label"),
- )
-
- template_name: Mapped[Optional[str]] = mapped_column(
- nullable=True, doc="the unique name that identifies a block in a human-readable way"
- )
- description: Mapped[Optional[str]] = mapped_column(nullable=True, doc="a description of the block for context")
- label: Mapped[str] = mapped_column(doc="the type of memory block in use, ie 'human', 'persona', 'system'")
- is_template: Mapped[bool] = mapped_column(
- doc="whether the block is a template (e.g. saved human/persona options as baselines for other templates)", default=False
- )
- preserve_on_migration: Mapped[Optional[bool]] = mapped_column(doc="preserve the block on template migration", default=False)
- value: Mapped[str] = mapped_column(doc="Text content of the block for the respective section of core memory.")
- limit: Mapped[BigInteger] = mapped_column(Integer, default=CORE_MEMORY_BLOCK_CHAR_LIMIT, doc="Character limit of the block.")
- metadata_: Mapped[Optional[dict]] = mapped_column(JSON, default={}, doc="arbitrary information related to the block.")
-
- # permissions of the agent
- read_only: Mapped[bool] = mapped_column(doc="whether the agent has read-only access to the block", default=False)
- hidden: Mapped[Optional[bool]] = mapped_column(nullable=True, doc="If set to True, the block will be hidden.")
-
- # history pointers / locking mechanisms
- current_history_entry_id: Mapped[Optional[str]] = mapped_column(
- String, ForeignKey("block_history.id", name="fk_block_current_history_entry", use_alter=True), nullable=True, index=True
- )
- version: Mapped[int] = mapped_column(
- Integer, nullable=False, default=1, server_default="1", doc="Optimistic locking version counter, incremented on each state change."
- )
- # NOTE: This takes advantage of built-in optimistic locking functionality by SqlAlchemy
- # https://docs.sqlalchemy.org/en/20/orm/versioning.html
- __mapper_args__ = {"version_id_col": version}
-
- # relationships
- organization: Mapped[Optional["Organization"]] = relationship("Organization", lazy="raise")
- agents: Mapped[List["Agent"]] = relationship(
- "Agent",
- secondary="blocks_agents",
- lazy="raise",
- passive_deletes=True, # Ensures SQLAlchemy doesn't fetch blocks_agents rows before deleting
- back_populates="core_memory",
- doc="Agents associated with this block.",
- )
- identities: Mapped[List["Identity"]] = relationship(
- "Identity",
- secondary="identities_blocks",
- lazy="raise",
- back_populates="blocks",
- passive_deletes=True,
- )
- groups: Mapped[List["Group"]] = relationship(
- "Group",
- secondary="groups_blocks",
- lazy="raise",
- back_populates="shared_blocks",
- passive_deletes=True,
- )
-
- def to_pydantic(self) -> Type:
- match self.label:
- case "human":
- Schema = Human
- case "persona":
- Schema = Persona
- case _:
- Schema = PydanticBlock
- model_dict = {k: v for k, v in self.__dict__.items() if k in self.__pydantic_model__.model_fields}
- model_dict["metadata"] = self.metadata_
- return Schema.model_validate(model_dict)
-
- @declared_attr
- def current_history_entry(cls) -> Mapped[Optional["BlockHistory"]]:
- # Relationship to easily load the specific history entry that is current
- return relationship(
- "BlockHistory",
- primaryjoin=lambda: cls.current_history_entry_id == BlockHistory.id,
- foreign_keys=[cls.current_history_entry_id],
- lazy="joined", # Typically want current history details readily available
- post_update=True,
- ) # Helps manage potential FK cycles
-
-
-@event.listens_for(Block, "after_update") # Changed from 'before_update'
-def block_before_update(mapper, connection, target):
- """Handle updating BlocksAgents when a block's label changes."""
- label_history = attributes.get_history(target, "label")
- if not label_history.has_changes():
- return
-
- blocks_agents = BlocksAgents.__table__
- connection.execute(
- blocks_agents.update()
- .where(blocks_agents.c.block_id == target.id, blocks_agents.c.block_label == label_history.deleted[0])
- .values(block_label=label_history.added[0])
- )
-
-
-@event.listens_for(Block, "before_insert")
-@event.listens_for(Block, "before_update")
-def validate_value_length(mapper, connection, target):
- """Ensure the value length does not exceed the limit."""
- if target.value and len(target.value) > target.limit:
- raise ValueError(
- f"Value length ({len(target.value)}) exceeds the limit ({target.limit}) for block with label '{target.label}' and id '{target.id}'."
- )
diff --git a/letta/orm/block_history.py b/letta/orm/block_history.py
deleted file mode 100644
index 9819e447..00000000
--- a/letta/orm/block_history.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import uuid
-from typing import Optional
-
-from sqlalchemy import JSON, BigInteger, ForeignKey, Index, Integer, String, Text
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.enums import ActorType
-
-
-class BlockHistory(OrganizationMixin, SqlalchemyBase):
- """Stores a single historical state of a Block for undo/redo functionality."""
-
- __tablename__ = "block_history"
-
- __table_args__ = (
- # PRIMARY lookup index for finding specific history entries & ordering
- Index("ix_block_history_block_id_sequence", "block_id", "sequence_number", unique=True),
- )
-
- # agent generates its own id
- # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- # TODO: Some still rely on the Pydantic object to do this
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"block_hist-{uuid.uuid4()}")
-
- # Snapshot State Fields (Copied from Block)
- description: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
- label: Mapped[str] = mapped_column(String, nullable=False)
- value: Mapped[str] = mapped_column(Text, nullable=False)
- limit: Mapped[BigInteger] = mapped_column(BigInteger, nullable=False)
- metadata_: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True)
-
- # Editor info
- # These are not made to be FKs because these may not always exist (e.g. a User be deleted after they made a checkpoint)
- actor_type: Mapped[Optional[ActorType]] = mapped_column(String, nullable=True)
- actor_id: Mapped[Optional[str]] = mapped_column(String, nullable=True)
-
- # Relationships
- block_id: Mapped[str] = mapped_column(
- String,
- ForeignKey("block.id", ondelete="CASCADE"),
- nullable=False, # History deleted if Block is deleted
- )
-
- sequence_number: Mapped[int] = mapped_column(
- Integer, nullable=False, doc="Monotonically increasing sequence number for the history of a specific block_id, starting from 1."
- )
diff --git a/letta/orm/blocks_agents.py b/letta/orm/blocks_agents.py
deleted file mode 100644
index 15f2d015..00000000
--- a/letta/orm/blocks_agents.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from sqlalchemy import ForeignKey, ForeignKeyConstraint, Index, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.base import Base
-
-
-class BlocksAgents(Base):
- """Agents must have one or many blocks to make up their core memory."""
-
- __tablename__ = "blocks_agents"
- __table_args__ = (
- UniqueConstraint(
- "agent_id",
- "block_label",
- name="unique_label_per_agent",
- ),
- ForeignKeyConstraint(
- ["block_id", "block_label"], ["block.id", "block.label"], name="fk_block_id_label", deferrable=True, initially="DEFERRED"
- ),
- UniqueConstraint("agent_id", "block_id", name="unique_agent_block"),
- Index("ix_blocks_agents_block_label_agent_id", "block_label", "agent_id"),
- Index("ix_blocks_block_label", "block_label"),
- )
-
- # unique agent + block label
- agent_id: Mapped[str] = mapped_column(String, ForeignKey("agents.id"), primary_key=True)
- block_id: Mapped[str] = mapped_column(String, primary_key=True)
- block_label: Mapped[str] = mapped_column(String, primary_key=True)
diff --git a/letta/orm/custom_columns.py b/letta/orm/custom_columns.py
deleted file mode 100644
index 686b35dc..00000000
--- a/letta/orm/custom_columns.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from sqlalchemy import JSON
-from sqlalchemy.types import BINARY, TypeDecorator
-
-from letta.helpers.converters import (
- deserialize_agent_step_state,
- deserialize_batch_request_result,
- deserialize_create_batch_response,
- deserialize_embedding_config,
- deserialize_llm_config,
- deserialize_mcp_stdio_config,
- deserialize_message_content,
- deserialize_poll_batch_response,
- deserialize_response_format,
- deserialize_tool_calls,
- deserialize_tool_returns,
- deserialize_tool_rules,
- deserialize_vector,
- serialize_agent_step_state,
- serialize_batch_request_result,
- serialize_create_batch_response,
- serialize_embedding_config,
- serialize_llm_config,
- serialize_mcp_stdio_config,
- serialize_message_content,
- serialize_poll_batch_response,
- serialize_response_format,
- serialize_tool_calls,
- serialize_tool_returns,
- serialize_tool_rules,
- serialize_vector,
-)
-
-
-class LLMConfigColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing LLMConfig as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_llm_config(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_llm_config(value)
-
-
-class EmbeddingConfigColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing EmbeddingConfig as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_embedding_config(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_embedding_config(value)
-
-
-class ToolRulesColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing a list of ToolRules as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_tool_rules(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_tool_rules(value)
-
-
-class ToolCallColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing OpenAI ToolCall objects as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_tool_calls(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_tool_calls(value)
-
-
-class ToolReturnColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing the return value of a tool call as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_tool_returns(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_tool_returns(value)
-
-
-class MessageContentColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing the content parts of a message as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_message_content(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_message_content(value)
-
-
-class CommonVector(TypeDecorator):
- """Custom SQLAlchemy column type for storing vectors in SQLite."""
-
- impl = BINARY
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_vector(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_vector(value, dialect)
-
-
-class CreateBatchResponseColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing a list of ToolRules as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_create_batch_response(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_create_batch_response(value)
-
-
-class PollBatchResponseColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing a list of ToolRules as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_poll_batch_response(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_poll_batch_response(value)
-
-
-class BatchRequestResultColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing a list of ToolRules as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_batch_request_result(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_batch_request_result(value)
-
-
-class AgentStepStateColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing a list of ToolRules as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_agent_step_state(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_agent_step_state(value)
-
-
-class ResponseFormatColumn(TypeDecorator):
- """Custom SQLAlchemy column type for storing a list of ToolRules as JSON."""
-
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_response_format(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_response_format(value)
-
-
-class MCPStdioServerConfigColumn(TypeDecorator):
- impl = JSON
- cache_ok = True
-
- def process_bind_param(self, value, dialect):
- return serialize_mcp_stdio_config(value)
-
- def process_result_value(self, value, dialect):
- return deserialize_mcp_stdio_config(value)
diff --git a/letta/orm/errors.py b/letta/orm/errors.py
deleted file mode 100644
index a574e74c..00000000
--- a/letta/orm/errors.py
+++ /dev/null
@@ -1,22 +0,0 @@
-class NoResultFound(Exception):
- """A record or records cannot be found given the provided search params"""
-
-
-class MalformedIdError(Exception):
- """An id not in the right format, most likely violating uuid4 format."""
-
-
-class UniqueConstraintViolationError(ValueError):
- """Custom exception for unique constraint violations."""
-
-
-class ForeignKeyConstraintViolationError(ValueError):
- """Custom exception for foreign key constraint violations."""
-
-
-class DatabaseTimeoutError(Exception):
- """Custom exception for database timeout issues."""
-
- def __init__(self, message="Database operation timed out", original_exception=None):
- super().__init__(message)
- self.original_exception = original_exception
diff --git a/letta/orm/file.py b/letta/orm/file.py
deleted file mode 100644
index 3229675f..00000000
--- a/letta/orm/file.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import uuid
-from typing import TYPE_CHECKING, Optional
-
-from sqlalchemy import ForeignKey, Index, Integer, String, Text, UniqueConstraint, desc
-from sqlalchemy.ext.asyncio import AsyncAttrs
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin, SourceMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.enums import FileProcessingStatus
-from letta.schemas.file import FileMetadata as PydanticFileMetadata
-
-if TYPE_CHECKING:
- pass
-
-
-# TODO: Note that this is NOT organization scoped, this is potentially dangerous if we misuse this
-# TODO: This should ONLY be manipulated internally in relation to FileMetadata.content
-# TODO: Leaving organization_id out of this for now for simplicity
-class FileContent(SqlalchemyBase):
- """Holds the full text content of a file (potentially large)."""
-
- __tablename__ = "file_contents"
- __table_args__ = (UniqueConstraint("file_id", name="uq_file_contents_file_id"),)
-
- # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- # TODO: Some still rely on the Pydantic object to do this
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"file_content-{uuid.uuid4()}")
- file_id: Mapped[str] = mapped_column(ForeignKey("files.id", ondelete="CASCADE"), nullable=False, doc="Foreign key to files table.")
-
- text: Mapped[str] = mapped_column(Text, nullable=False, doc="Full plain-text content of the file (e.g., extracted from a PDF).")
-
- # back-reference to FileMetadata
- file: Mapped["FileMetadata"] = relationship(back_populates="content", lazy="selectin")
-
-
-class FileMetadata(SqlalchemyBase, OrganizationMixin, SourceMixin, AsyncAttrs):
- """Represents an uploaded file."""
-
- __tablename__ = "files"
- __pydantic_model__ = PydanticFileMetadata
- __table_args__ = (
- Index("ix_files_org_created", "organization_id", desc("created_at")),
- Index("ix_files_source_created", "source_id", desc("created_at")),
- Index("ix_files_processing_status", "processing_status"),
- )
-
- file_name: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The name of the file.")
- original_file_name: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The original name of the file as uploaded.")
- file_path: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The file path on the system.")
- file_type: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The type of the file.")
- file_size: Mapped[Optional[int]] = mapped_column(Integer, nullable=True, doc="The size of the file in bytes.")
- file_creation_date: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The creation date of the file.")
- file_last_modified_date: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The last modified date of the file.")
- processing_status: Mapped[FileProcessingStatus] = mapped_column(
- String, default=FileProcessingStatus.PENDING, nullable=False, doc="The current processing status of the file."
- )
-
- error_message: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="Any error message encountered during processing.")
- total_chunks: Mapped[Optional[int]] = mapped_column(Integer, nullable=True, doc="Total number of chunks for the file.")
- chunks_embedded: Mapped[Optional[int]] = mapped_column(Integer, nullable=True, doc="Number of chunks that have been embedded.")
-
- # relationships
- content: Mapped[Optional["FileContent"]] = relationship(
- "FileContent",
- uselist=False,
- back_populates="file",
- lazy="raise", # raises if you access without eager load
- cascade="all, delete-orphan",
- )
-
- async def to_pydantic_async(self, include_content: bool = False, strip_directory_prefix: bool = False) -> PydanticFileMetadata:
- """
- Async version of `to_pydantic` that supports optional relationship loading
- without requiring `expire_on_commit=False`.
- """
-
- # Load content relationship if requested
- if include_content:
- content_obj = await self.awaitable_attrs.content
- content_text = content_obj.text if content_obj else None
- else:
- content_text = None
-
- file_name = self.file_name
- if strip_directory_prefix and "/" in file_name:
- file_name = "/".join(file_name.split("/")[1:])
-
- return PydanticFileMetadata(
- id=self.id,
- organization_id=self.organization_id,
- source_id=self.source_id,
- file_name=file_name,
- original_file_name=self.original_file_name,
- file_path=self.file_path,
- file_type=self.file_type,
- file_size=self.file_size,
- file_creation_date=self.file_creation_date,
- file_last_modified_date=self.file_last_modified_date,
- processing_status=self.processing_status,
- error_message=self.error_message,
- total_chunks=self.total_chunks,
- chunks_embedded=self.chunks_embedded,
- created_at=self.created_at,
- updated_at=self.updated_at,
- content=content_text,
- )
diff --git a/letta/orm/files_agents.py b/letta/orm/files_agents.py
deleted file mode 100644
index 1c768711..00000000
--- a/letta/orm/files_agents.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import uuid
-from datetime import datetime
-from typing import TYPE_CHECKING, Optional
-
-from sqlalchemy import Boolean, DateTime, ForeignKey, Index, Integer, String, Text, UniqueConstraint, func
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.block import FileBlock as PydanticFileBlock
-from letta.schemas.file import FileAgent as PydanticFileAgent
-from letta.utils import truncate_file_visible_content
-
-if TYPE_CHECKING:
- pass
-
-
-class FileAgent(SqlalchemyBase, OrganizationMixin):
- """
- Join table between File and Agent.
-
- Tracks whether a file is currently "open" for the agent and
- the specific excerpt (grepped section) the agent is looking at.
- """
-
- __tablename__ = "files_agents"
- __table_args__ = (
- # (file_id, agent_id) must be unique
- UniqueConstraint("file_id", "agent_id", name="uq_file_agent"),
- # (file_name, agent_id) must be unique
- UniqueConstraint("agent_id", "file_name", name="uq_agent_filename"),
- # helpful indexes for look-ups
- Index("ix_file_agent", "file_id", "agent_id"),
- Index("ix_agent_filename", "agent_id", "file_name"),
- )
- __pydantic_model__ = PydanticFileAgent
-
- # single-column surrogate PK
- id: Mapped[str] = mapped_column(
- String,
- primary_key=True,
- default=lambda: f"file_agent-{uuid.uuid4()}",
- )
-
- # not part of the PK, but NOT NULL + FK
- file_id: Mapped[str] = mapped_column(
- String,
- ForeignKey("files.id", ondelete="CASCADE"),
- nullable=False,
- doc="ID of the file",
- )
- agent_id: Mapped[str] = mapped_column(
- String,
- ForeignKey("agents.id", ondelete="CASCADE"),
- nullable=False,
- doc="ID of the agent",
- )
- source_id: Mapped[str] = mapped_column(
- String,
- ForeignKey("sources.id", ondelete="CASCADE"),
- nullable=False,
- doc="ID of the source",
- )
-
- file_name: Mapped[str] = mapped_column(
- String,
- nullable=False,
- doc="Denormalized copy of files.file_name; unique per agent",
- )
-
- is_open: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True, doc="True if the agent currently has the file open.")
- visible_content: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="Portion of the file the agent is focused on.")
- last_accessed_at: Mapped[datetime] = mapped_column(
- DateTime(timezone=True),
- server_default=func.now(),
- onupdate=func.now(),
- nullable=False,
- doc="UTC timestamp when this agent last accessed the file.",
- )
- start_line: Mapped[Optional[int]] = mapped_column(
- Integer, nullable=True, doc="Starting line number (1-indexed) when file was opened with line range."
- )
- end_line: Mapped[Optional[int]] = mapped_column(
- Integer, nullable=True, doc="Ending line number (exclusive) when file was opened with line range."
- )
-
- # relationships
- agent: Mapped["Agent"] = relationship(
- "Agent",
- back_populates="file_agents",
- lazy="selectin",
- )
-
- # TODO: This is temporary as we figure out if we want FileBlock as a first class citizen
- def to_pydantic_block(self, per_file_view_window_char_limit: int) -> PydanticFileBlock:
- visible_content = truncate_file_visible_content(self.visible_content, self.is_open, per_file_view_window_char_limit)
-
- return PydanticFileBlock(
- value=visible_content,
- label=self.file_name,
- read_only=True,
- file_id=self.file_id,
- source_id=self.source_id,
- is_open=self.is_open,
- last_accessed_at=self.last_accessed_at,
- limit=per_file_view_window_char_limit,
- )
diff --git a/letta/orm/group.py b/letta/orm/group.py
deleted file mode 100644
index 5b2c7e57..00000000
--- a/letta/orm/group.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import uuid
-from typing import List, Optional
-
-from sqlalchemy import JSON, ForeignKey, String
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin, ProjectMixin, TemplateMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.group import Group as PydanticGroup
-
-
-class Group(SqlalchemyBase, OrganizationMixin, ProjectMixin, TemplateMixin):
- __tablename__ = "groups"
- __pydantic_model__ = PydanticGroup
-
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"group-{uuid.uuid4()}")
- description: Mapped[str] = mapped_column(nullable=False, doc="")
- manager_type: Mapped[str] = mapped_column(nullable=False, doc="")
- manager_agent_id: Mapped[Optional[str]] = mapped_column(String, ForeignKey("agents.id", ondelete="RESTRICT"), nullable=True, doc="")
- termination_token: Mapped[Optional[str]] = mapped_column(nullable=True, doc="")
- max_turns: Mapped[Optional[int]] = mapped_column(nullable=True, doc="")
- sleeptime_agent_frequency: Mapped[Optional[int]] = mapped_column(nullable=True, doc="")
- max_message_buffer_length: Mapped[Optional[int]] = mapped_column(nullable=True, doc="")
- min_message_buffer_length: Mapped[Optional[int]] = mapped_column(nullable=True, doc="")
- turns_counter: Mapped[Optional[int]] = mapped_column(nullable=True, doc="")
- last_processed_message_id: Mapped[Optional[str]] = mapped_column(nullable=True, doc="")
- hidden: Mapped[Optional[bool]] = mapped_column(nullable=True, doc="If set to True, the group will be hidden.")
-
- # relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="groups")
- agent_ids: Mapped[List[str]] = mapped_column(JSON, nullable=False, doc="Ordered list of agent IDs in this group")
- agents: Mapped[List["Agent"]] = relationship(
- "Agent", secondary="groups_agents", lazy="selectin", passive_deletes=True, back_populates="groups"
- )
- shared_blocks: Mapped[List["Block"]] = relationship(
- "Block", secondary="groups_blocks", lazy="selectin", passive_deletes=True, back_populates="groups"
- )
- manager_agent: Mapped["Agent"] = relationship("Agent", lazy="joined", back_populates="multi_agent_group")
diff --git a/letta/orm/groups_agents.py b/letta/orm/groups_agents.py
deleted file mode 100644
index 375b7fe0..00000000
--- a/letta/orm/groups_agents.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from sqlalchemy import ForeignKey, String
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.base import Base
-
-
-class GroupsAgents(Base):
- """Agents may have one or many groups associated with them."""
-
- __tablename__ = "groups_agents"
-
- group_id: Mapped[str] = mapped_column(String, ForeignKey("groups.id", ondelete="CASCADE"), primary_key=True)
- agent_id: Mapped[str] = mapped_column(String, ForeignKey("agents.id", ondelete="CASCADE"), primary_key=True)
diff --git a/letta/orm/groups_blocks.py b/letta/orm/groups_blocks.py
deleted file mode 100644
index 5c5b0205..00000000
--- a/letta/orm/groups_blocks.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from sqlalchemy import ForeignKey, String
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.base import Base
-
-
-class GroupsBlocks(Base):
- """Groups may have one or many shared blocks associated with them."""
-
- __tablename__ = "groups_blocks"
-
- group_id: Mapped[str] = mapped_column(String, ForeignKey("groups.id", ondelete="CASCADE"), primary_key=True)
- block_id: Mapped[str] = mapped_column(String, ForeignKey("block.id", ondelete="CASCADE"), primary_key=True)
diff --git a/letta/orm/identities_agents.py b/letta/orm/identities_agents.py
deleted file mode 100644
index a8958691..00000000
--- a/letta/orm/identities_agents.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from sqlalchemy import ForeignKey, String
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.base import Base
-
-
-class IdentitiesAgents(Base):
- """Identities may have one or many agents associated with them."""
-
- __tablename__ = "identities_agents"
-
- identity_id: Mapped[str] = mapped_column(String, ForeignKey("identities.id", ondelete="CASCADE"), primary_key=True)
- agent_id: Mapped[str] = mapped_column(String, ForeignKey("agents.id", ondelete="CASCADE"), primary_key=True)
diff --git a/letta/orm/identities_blocks.py b/letta/orm/identities_blocks.py
deleted file mode 100644
index 2c5a8ef0..00000000
--- a/letta/orm/identities_blocks.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from sqlalchemy import ForeignKey, String
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.base import Base
-
-
-class IdentitiesBlocks(Base):
- """Identities may have one or many blocks associated with them."""
-
- __tablename__ = "identities_blocks"
-
- identity_id: Mapped[str] = mapped_column(String, ForeignKey("identities.id", ondelete="CASCADE"), primary_key=True)
- block_id: Mapped[str] = mapped_column(String, ForeignKey("block.id", ondelete="CASCADE"), primary_key=True)
diff --git a/letta/orm/identity.py b/letta/orm/identity.py
deleted file mode 100644
index 75a90525..00000000
--- a/letta/orm/identity.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import uuid
-from typing import List
-
-from sqlalchemy import String, UniqueConstraint
-from sqlalchemy.dialects.postgresql import JSON
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin, ProjectMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.identity import Identity as PydanticIdentity, IdentityProperty
-
-
-class Identity(SqlalchemyBase, OrganizationMixin, ProjectMixin):
- """Identity ORM class"""
-
- __tablename__ = "identities"
- __pydantic_model__ = PydanticIdentity
- __table_args__ = (
- UniqueConstraint(
- "identifier_key",
- "project_id",
- "organization_id",
- name="unique_identifier_key_project_id_organization_id",
- postgresql_nulls_not_distinct=True,
- # For SQLite compatibility, we'll need to handle the NULL case differently
- # in the service layer since SQLite doesn't support postgresql_nulls_not_distinct
- ),
- )
-
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"identity-{uuid.uuid4()}")
- identifier_key: Mapped[str] = mapped_column(nullable=False, doc="External, user-generated identifier key of the identity.")
- name: Mapped[str] = mapped_column(nullable=False, doc="The name of the identity.")
- identity_type: Mapped[str] = mapped_column(nullable=False, doc="The type of the identity.")
- properties: Mapped[List["IdentityProperty"]] = mapped_column(
- JSON, nullable=False, default=list, doc="List of properties associated with the identity"
- )
-
- # relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="identities")
- agents: Mapped[List["Agent"]] = relationship(
- "Agent", secondary="identities_agents", lazy="selectin", passive_deletes=True, back_populates="identities"
- )
- blocks: Mapped[List["Block"]] = relationship(
- "Block", secondary="identities_blocks", lazy="selectin", passive_deletes=True, back_populates="identities"
- )
-
- @property
- def agent_ids(self) -> List[str]:
- """Get just the agent IDs without loading the full agent objects"""
- return [agent.id for agent in self.agents]
-
- @property
- def block_ids(self) -> List[str]:
- """Get just the block IDs without loading the full agent objects"""
- return [block.id for block in self.blocks]
-
- def to_pydantic(self) -> PydanticIdentity:
- state = {
- "id": self.id,
- "identifier_key": self.identifier_key,
- "name": self.name,
- "identity_type": self.identity_type,
- "project_id": self.project_id,
- "agent_ids": self.agent_ids,
- "block_ids": self.block_ids,
- "organization_id": self.organization_id,
- "properties": self.properties,
- }
- return PydanticIdentity(**state)
diff --git a/letta/orm/job.py b/letta/orm/job.py
deleted file mode 100644
index 37edc701..00000000
--- a/letta/orm/job.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from datetime import datetime
-from typing import TYPE_CHECKING, List, Optional
-
-from sqlalchemy import JSON, BigInteger, Index, String
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import UserMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.enums import JobStatus, JobType
-from letta.schemas.job import Job as PydanticJob, LettaRequestConfig
-
-if TYPE_CHECKING:
- from letta.orm.job_messages import JobMessage
- from letta.orm.message import Message
- from letta.orm.step import Step
- from letta.orm.user import User
-
-
-class Job(SqlalchemyBase, UserMixin):
- """Jobs run in the background and are owned by a user.
- Typical jobs involve loading and processing sources etc.
- """
-
- __tablename__ = "jobs"
- __pydantic_model__ = PydanticJob
- __table_args__ = (Index("ix_jobs_created_at", "created_at", "id"),)
-
- status: Mapped[JobStatus] = mapped_column(String, default=JobStatus.created, doc="The current status of the job.")
- completed_at: Mapped[Optional[datetime]] = mapped_column(nullable=True, doc="The unix timestamp of when the job was completed.")
- metadata_: Mapped[Optional[dict]] = mapped_column(JSON, doc="The metadata of the job.")
- job_type: Mapped[JobType] = mapped_column(
- String,
- default=JobType.JOB,
- doc="The type of job. This affects whether or not we generate json_schema and source_code on the fly.",
- )
- request_config: Mapped[Optional[LettaRequestConfig]] = mapped_column(
- JSON, nullable=True, doc="The request configuration for the job, stored as JSON."
- )
-
- # callback related columns
- callback_url: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="When set, POST to this URL after job completion.")
- callback_sent_at: Mapped[Optional[datetime]] = mapped_column(nullable=True, doc="Timestamp when the callback was last attempted.")
- callback_status_code: Mapped[Optional[int]] = mapped_column(nullable=True, doc="HTTP status code returned by the callback endpoint.")
- callback_error: Mapped[Optional[str]] = mapped_column(
- nullable=True, doc="Optional error message from attempting to POST the callback endpoint."
- )
-
- # timing metrics (in nanoseconds for precision)
- ttft_ns: Mapped[Optional[int]] = mapped_column(BigInteger, nullable=True, doc="Time to first token in nanoseconds")
- total_duration_ns: Mapped[Optional[int]] = mapped_column(BigInteger, nullable=True, doc="Total run duration in nanoseconds")
-
- # relationships
- user: Mapped["User"] = relationship("User", back_populates="jobs")
- job_messages: Mapped[List["JobMessage"]] = relationship("JobMessage", back_populates="job", cascade="all, delete-orphan")
- steps: Mapped[List["Step"]] = relationship("Step", back_populates="job", cascade="save-update")
-
- @property
- def messages(self) -> List["Message"]:
- """Get all messages associated with this job."""
- return [jm.message for jm in self.job_messages]
diff --git a/letta/orm/job_messages.py b/letta/orm/job_messages.py
deleted file mode 100644
index 063febfc..00000000
--- a/letta/orm/job_messages.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from typing import TYPE_CHECKING
-
-from sqlalchemy import ForeignKey, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-
-if TYPE_CHECKING:
- from letta.orm.job import Job
- from letta.orm.message import Message
-
-
-class JobMessage(SqlalchemyBase):
- """Tracks messages that were created during job execution."""
-
- __tablename__ = "job_messages"
- __table_args__ = (UniqueConstraint("job_id", "message_id", name="unique_job_message"),)
-
- id: Mapped[int] = mapped_column(primary_key=True, doc="Unique identifier for the job message")
- job_id: Mapped[str] = mapped_column(
- ForeignKey("jobs.id", ondelete="CASCADE"),
- nullable=False, # A job message must belong to a job
- doc="ID of the job that created the message",
- )
- message_id: Mapped[str] = mapped_column(
- ForeignKey("messages.id", ondelete="CASCADE"),
- nullable=False, # A job message must have a message
- doc="ID of the message created by the job",
- )
-
- # Relationships
- job: Mapped["Job"] = relationship("Job", back_populates="job_messages")
- message: Mapped["Message"] = relationship("Message", back_populates="job_message")
diff --git a/letta/orm/llm_batch_items.py b/letta/orm/llm_batch_items.py
deleted file mode 100644
index b4f08cb0..00000000
--- a/letta/orm/llm_batch_items.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import uuid
-from typing import Optional, Union
-
-from anthropic.types.beta.messages import BetaMessageBatchIndividualResponse
-from sqlalchemy import ForeignKey, Index, String
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.custom_columns import AgentStepStateColumn, BatchRequestResultColumn, LLMConfigColumn
-from letta.orm.mixins import AgentMixin, OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.enums import AgentStepStatus, JobStatus
-from letta.schemas.llm_batch_job import AgentStepState, LLMBatchItem as PydanticLLMBatchItem
-from letta.schemas.llm_config import LLMConfig
-
-
-class LLMBatchItem(SqlalchemyBase, OrganizationMixin, AgentMixin):
- """Represents a single agent's LLM request within a batch"""
-
- __tablename__ = "llm_batch_items"
- __pydantic_model__ = PydanticLLMBatchItem
- __table_args__ = (
- Index("ix_llm_batch_items_llm_batch_id", "llm_batch_id"),
- Index("ix_llm_batch_items_agent_id", "agent_id"),
- Index("ix_llm_batch_items_status", "request_status"),
- )
-
- # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- # TODO: Some still rely on the Pydantic object to do this
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"batch_item-{uuid.uuid4()}")
-
- llm_batch_id: Mapped[str] = mapped_column(
- ForeignKey("llm_batch_job.id", ondelete="CASCADE"), doc="Foreign key to the LLM provider batch this item belongs to"
- )
-
- llm_config: Mapped[LLMConfig] = mapped_column(LLMConfigColumn, nullable=False, doc="LLM configuration specific to this request")
-
- request_status: Mapped[JobStatus] = mapped_column(
- String, default=JobStatus.created, doc="Status of the LLM request in the batch (PENDING, SUBMITTED, DONE, ERROR)"
- )
-
- step_status: Mapped[AgentStepStatus] = mapped_column(String, default=AgentStepStatus.paused, doc="Status of the agent's step execution")
-
- step_state: Mapped[AgentStepState] = mapped_column(
- AgentStepStateColumn, doc="Execution metadata for resuming the agent step (e.g., tool call ID, timestamps)"
- )
-
- batch_request_result: Mapped[Optional[Union[BetaMessageBatchIndividualResponse]]] = mapped_column(
- BatchRequestResultColumn, nullable=True, doc="Raw JSON response from the LLM for this item"
- )
-
- # relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="llm_batch_items")
- batch: Mapped["LLMBatchJob"] = relationship("LLMBatchJob", back_populates="items", lazy="selectin")
- agent: Mapped["Agent"] = relationship("Agent", back_populates="batch_items", lazy="selectin")
diff --git a/letta/orm/llm_batch_job.py b/letta/orm/llm_batch_job.py
deleted file mode 100644
index db085dc7..00000000
--- a/letta/orm/llm_batch_job.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import uuid
-from datetime import datetime
-from typing import List, Optional, Union
-
-from anthropic.types.beta.messages import BetaMessageBatch
-from sqlalchemy import DateTime, ForeignKey, Index, String
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.custom_columns import CreateBatchResponseColumn, PollBatchResponseColumn
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.enums import JobStatus, ProviderType
-from letta.schemas.llm_batch_job import LLMBatchJob as PydanticLLMBatchJob
-
-
-class LLMBatchJob(SqlalchemyBase, OrganizationMixin):
- """Represents a single LLM batch request made to a provider like Anthropic"""
-
- __tablename__ = "llm_batch_job"
- __table_args__ = (
- Index("ix_llm_batch_job_created_at", "created_at"),
- Index("ix_llm_batch_job_status", "status"),
- )
-
- __pydantic_model__ = PydanticLLMBatchJob
-
- # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- # TODO: Some still rely on the Pydantic object to do this
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"batch_req-{uuid.uuid4()}")
-
- status: Mapped[JobStatus] = mapped_column(String, default=JobStatus.created, doc="The current status of the batch.")
-
- llm_provider: Mapped[ProviderType] = mapped_column(String, doc="LLM provider used (e.g., 'Anthropic')")
-
- create_batch_response: Mapped[Union[BetaMessageBatch]] = mapped_column(
- CreateBatchResponseColumn, doc="Full JSON response from initial batch creation"
- )
- latest_polling_response: Mapped[Union[BetaMessageBatch]] = mapped_column(
- PollBatchResponseColumn, nullable=True, doc="Last known polling result from LLM provider"
- )
-
- last_polled_at: Mapped[Optional[datetime]] = mapped_column(
- DateTime(timezone=True), nullable=True, doc="Last time we polled the provider for status"
- )
-
- letta_batch_job_id: Mapped[str] = mapped_column(
- String, ForeignKey("jobs.id", ondelete="CASCADE"), nullable=False, doc="ID of the Letta batch job"
- )
-
- organization: Mapped["Organization"] = relationship("Organization", back_populates="llm_batch_jobs")
- items: Mapped[List["LLMBatchItem"]] = relationship("LLMBatchItem", back_populates="batch", lazy="selectin")
diff --git a/letta/orm/mcp_oauth.py b/letta/orm/mcp_oauth.py
deleted file mode 100644
index e34f685a..00000000
--- a/letta/orm/mcp_oauth.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import uuid
-from datetime import datetime
-from enum import Enum
-from typing import Optional
-
-from sqlalchemy import DateTime, ForeignKey, String, Text
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.mixins import OrganizationMixin, UserMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-
-
-class OAuthSessionStatus(str, Enum):
- """OAuth session status enumeration."""
-
- PENDING = "pending"
- AUTHORIZED = "authorized"
- ERROR = "error"
-
-
-class MCPOAuth(SqlalchemyBase, OrganizationMixin, UserMixin):
- """OAuth session model for MCP server authentication."""
-
- __tablename__ = "mcp_oauth"
-
- # Override the id field to match database UUID generation
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"{uuid.uuid4()}")
-
- # Core session information
- state: Mapped[str] = mapped_column(String(255), unique=True, nullable=False, doc="OAuth state parameter")
- server_id: Mapped[str] = mapped_column(String(255), ForeignKey("mcp_server.id", ondelete="CASCADE"), nullable=True, doc="MCP server ID")
- server_url: Mapped[str] = mapped_column(Text, nullable=False, doc="MCP server URL")
- server_name: Mapped[str] = mapped_column(Text, nullable=False, doc="MCP server display name")
-
- # OAuth flow data
- authorization_url: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth authorization URL")
- authorization_code: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth authorization code")
-
- # Token data
- access_token: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth access token")
- refresh_token: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth refresh token")
- token_type: Mapped[str] = mapped_column(String(50), default="Bearer", doc="Token type")
- expires_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), nullable=True, doc="Token expiry time")
- scope: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth scope")
-
- # Client configuration
- client_id: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth client ID")
- client_secret: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth client secret")
- redirect_uri: Mapped[Optional[str]] = mapped_column(Text, nullable=True, doc="OAuth redirect URI")
-
- # Session state
- status: Mapped[OAuthSessionStatus] = mapped_column(String(20), default=OAuthSessionStatus.PENDING, doc="Session status")
-
- # Timestamps
- created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=lambda: datetime.now(), doc="Session creation time")
- updated_at: Mapped[datetime] = mapped_column(
- DateTime(timezone=True), default=lambda: datetime.now(), onupdate=lambda: datetime.now(), doc="Last update time"
- )
-
- # Relationships (if needed in the future)
- # user: Mapped[Optional["User"]] = relationship("User", back_populates="oauth_sessions")
- # organization: Mapped["Organization"] = relationship("Organization", back_populates="oauth_sessions")
diff --git a/letta/orm/mcp_server.py b/letta/orm/mcp_server.py
deleted file mode 100644
index 55a2a672..00000000
--- a/letta/orm/mcp_server.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from typing import TYPE_CHECKING, Optional
-
-from sqlalchemy import JSON, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.functions.mcp_client.types import StdioServerConfig
-from letta.orm.custom_columns import MCPStdioServerConfigColumn
-
-# TODO everything in functions should live in this model
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.enums import MCPServerType
-from letta.schemas.mcp import MCPServer
-
-if TYPE_CHECKING:
- pass
-
-
-class MCPServer(SqlalchemyBase, OrganizationMixin):
- """Represents a registered MCP server"""
-
- __tablename__ = "mcp_server"
- __pydantic_model__ = MCPServer
-
- # Add unique constraint on (name, _organization_id)
- # An organization should not have multiple tools with the same name
- __table_args__ = (UniqueConstraint("server_name", "organization_id", name="uix_name_organization_mcp_server"),)
-
- server_name: Mapped[str] = mapped_column(doc="The display name of the MCP server")
- server_type: Mapped[MCPServerType] = mapped_column(
- String, default=MCPServerType.SSE, doc="The type of the MCP server. Only SSE is supported for remote servers."
- )
-
- # sse server
- server_url: Mapped[Optional[str]] = mapped_column(
- String, nullable=True, doc="The URL of the server (MCP SSE client will connect to this URL)"
- )
-
- # access token / api key for MCP servers that require authentication
- token: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="The access token or api key for the MCP server")
-
- # custom headers for authentication (key-value pairs)
- custom_headers: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True, doc="Custom authentication headers as key-value pairs")
-
- # stdio server
- stdio_config: Mapped[Optional[StdioServerConfig]] = mapped_column(
- MCPStdioServerConfigColumn, nullable=True, doc="The configuration for the stdio server"
- )
-
- metadata_: Mapped[Optional[dict]] = mapped_column(
- JSON, default=lambda: {}, doc="A dictionary of additional metadata for the MCP server."
- )
diff --git a/letta/orm/message.py b/letta/orm/message.py
deleted file mode 100644
index 76b9a8c0..00000000
--- a/letta/orm/message.py
+++ /dev/null
@@ -1,231 +0,0 @@
-from typing import List, Optional
-
-from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall as OpenAIToolCall
-from sqlalchemy import BigInteger, FetchedValue, ForeignKey, Index, event, text
-from sqlalchemy.orm import Mapped, Session, mapped_column, relationship
-
-from letta.orm.custom_columns import MessageContentColumn, ToolCallColumn, ToolReturnColumn
-from letta.orm.mixins import AgentMixin, OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.letta_message_content import MessageContent, TextContent as PydanticTextContent
-from letta.schemas.message import Message as PydanticMessage, ToolReturn
-from letta.settings import DatabaseChoice, settings
-
-
-class Message(SqlalchemyBase, OrganizationMixin, AgentMixin):
- """Defines data model for storing Message objects"""
-
- __tablename__ = "messages"
- __table_args__ = (
- Index("ix_messages_agent_created_at", "agent_id", "created_at"),
- Index("ix_messages_created_at", "created_at", "id"),
- Index("ix_messages_agent_sequence", "agent_id", "sequence_id"),
- Index("ix_messages_org_agent", "organization_id", "agent_id"),
- )
- __pydantic_model__ = PydanticMessage
-
- id: Mapped[str] = mapped_column(primary_key=True, doc="Unique message identifier")
- role: Mapped[str] = mapped_column(doc="Message role (user/assistant/system/tool)")
- text: Mapped[Optional[str]] = mapped_column(nullable=True, doc="Message content")
- content: Mapped[List[MessageContent]] = mapped_column(MessageContentColumn, nullable=True, doc="Message content parts")
- model: Mapped[Optional[str]] = mapped_column(nullable=True, doc="LLM model used")
- name: Mapped[Optional[str]] = mapped_column(nullable=True, doc="Name for multi-agent scenarios")
- tool_calls: Mapped[List[OpenAIToolCall]] = mapped_column(ToolCallColumn, doc="Tool call information")
- tool_call_id: Mapped[Optional[str]] = mapped_column(nullable=True, doc="ID of the tool call")
- step_id: Mapped[Optional[str]] = mapped_column(
- ForeignKey("steps.id", ondelete="SET NULL"), nullable=True, doc="ID of the step that this message belongs to"
- )
- otid: Mapped[Optional[str]] = mapped_column(nullable=True, doc="The offline threading ID associated with this message")
- tool_returns: Mapped[List[ToolReturn]] = mapped_column(
- ToolReturnColumn, nullable=True, doc="Tool execution return information for prior tool calls"
- )
- group_id: Mapped[Optional[str]] = mapped_column(nullable=True, doc="The multi-agent group that the message was sent in")
- sender_id: Mapped[Optional[str]] = mapped_column(
- nullable=True, doc="The id of the sender of the message, can be an identity id or agent id"
- )
- batch_item_id: Mapped[Optional[str]] = mapped_column(
- nullable=True,
- doc="The id of the LLMBatchItem that this message is associated with",
- )
- is_err: Mapped[Optional[bool]] = mapped_column(
- nullable=True, doc="Whether this message is part of an error step. Used only for debugging purposes."
- )
- approval_request_id: Mapped[Optional[str]] = mapped_column(
- nullable=True,
- doc="The id of the approval request if this message is associated with a tool call request.",
- )
- approve: Mapped[Optional[bool]] = mapped_column(nullable=True, doc="Whether tool call is approved.")
- denial_reason: Mapped[Optional[str]] = mapped_column(nullable=True, doc="The reason the tool call request was denied.")
-
- # Monotonically increasing sequence for efficient/correct listing
- sequence_id: Mapped[int] = mapped_column(
- BigInteger,
- server_default=FetchedValue(),
- unique=True,
- nullable=False,
- )
-
- # Relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="messages", lazy="raise")
- step: Mapped["Step"] = relationship("Step", back_populates="messages", lazy="selectin")
-
- # Job relationship
- job_message: Mapped[Optional["JobMessage"]] = relationship(
- "JobMessage", back_populates="message", uselist=False, cascade="all, delete-orphan", single_parent=True
- )
-
- @property
- def job(self) -> Optional["Job"]:
- """Get the job associated with this message, if any."""
- return self.job_message.job if self.job_message else None
-
- def to_pydantic(self) -> PydanticMessage:
- """Custom pydantic conversion to handle data using legacy text field"""
- model = self.__pydantic_model__.model_validate(self)
- if self.text and not model.content:
- model.content = [PydanticTextContent(text=self.text)]
- # If there are no tool calls, set tool_calls to None
- if self.tool_calls is None or len(self.tool_calls) == 0:
- model.tool_calls = None
- return model
-
-
-# listener
-
-
-@event.listens_for(Session, "before_flush")
-def set_sequence_id_for_sqlite_bulk(session, flush_context, instances):
- # Handle bulk inserts for SQLite
- if settings.database_engine is DatabaseChoice.SQLITE:
- # Find all new Message objects that need sequence IDs
- new_messages = [obj for obj in session.new if isinstance(obj, Message) and obj.sequence_id is None]
-
- if new_messages:
- # Create a sequence table if it doesn't exist for atomic increments
- session.execute(
- text(
- """
- CREATE TABLE IF NOT EXISTS message_sequence (
- id INTEGER PRIMARY KEY,
- next_val INTEGER NOT NULL DEFAULT 1
- )
- """
- )
- )
-
- # Initialize the sequence table if empty
- session.execute(
- text(
- """
- INSERT OR IGNORE INTO message_sequence (id, next_val)
- SELECT 1, COALESCE(MAX(sequence_id), 0) + 1
- FROM messages
- """
- )
- )
-
- # Get the number of records being inserted
- records_count = len(new_messages)
-
- # Atomically reserve a range of sequence values for this batch
- result = session.execute(
- text(
- """
- UPDATE message_sequence
- SET next_val = next_val + :count
- WHERE id = 1
- RETURNING next_val - :count
- """
- ),
- {"count": records_count},
- )
-
- start_sequence_id = result.scalar()
- if start_sequence_id is None:
- # Fallback if RETURNING doesn't work (older SQLite versions)
- session.execute(
- text(
- """
- UPDATE message_sequence
- SET next_val = next_val + :count
- WHERE id = 1
- """
- ),
- {"count": records_count},
- )
- start_sequence_id = session.execute(
- text(
- """
- SELECT next_val - :count FROM message_sequence WHERE id = 1
- """
- ),
- {"count": records_count},
- ).scalar()
-
- # Assign sequential IDs to each record
- for i, obj in enumerate(new_messages):
- obj.sequence_id = start_sequence_id + i
-
-
-@event.listens_for(Message, "before_insert")
-def set_sequence_id_for_sqlite(mapper, connection, target):
- if settings.database_engine is DatabaseChoice.SQLITE:
- # For SQLite, we need to generate sequence_id manually
- # Use a database-level atomic operation to avoid race conditions
-
- # Create a sequence table if it doesn't exist for atomic increments
- connection.execute(
- text(
- """
- CREATE TABLE IF NOT EXISTS message_sequence (
- id INTEGER PRIMARY KEY,
- next_val INTEGER NOT NULL DEFAULT 1
- )
- """
- )
- )
-
- # Initialize the sequence table if empty
- connection.execute(
- text(
- """
- INSERT OR IGNORE INTO message_sequence (id, next_val)
- SELECT 1, COALESCE(MAX(sequence_id), 0) + 1
- FROM messages
- """
- )
- )
-
- # Atomically get the next sequence value
- result = connection.execute(
- text(
- """
- UPDATE message_sequence
- SET next_val = next_val + 1
- WHERE id = 1
- RETURNING next_val - 1
- """
- )
- )
-
- sequence_id = result.scalar()
- if sequence_id is None:
- # Fallback if RETURNING doesn't work (older SQLite versions)
- connection.execute(
- text(
- """
- UPDATE message_sequence
- SET next_val = next_val + 1
- WHERE id = 1
- """
- )
- )
- sequence_id = connection.execute(
- text(
- """
- SELECT next_val - 1 FROM message_sequence WHERE id = 1
- """
- )
- ).scalar()
-
- target.sequence_id = sequence_id
diff --git a/letta/orm/mixins.py b/letta/orm/mixins.py
deleted file mode 100644
index 9358e51c..00000000
--- a/letta/orm/mixins.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from typing import Optional
-from uuid import UUID
-
-from sqlalchemy import ForeignKey, String
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.base import Base
-
-
-def is_valid_uuid4(uuid_string: str) -> bool:
- """Check if a string is a valid UUID4."""
- try:
- uuid_obj = UUID(uuid_string)
- return uuid_obj.version == 4
- except ValueError:
- return False
-
-
-class OrganizationMixin(Base):
- """Mixin for models that belong to an organization."""
-
- __abstract__ = True
-
- organization_id: Mapped[str] = mapped_column(String, ForeignKey("organizations.id"))
-
-
-class UserMixin(Base):
- """Mixin for models that belong to a user."""
-
- __abstract__ = True
-
- user_id: Mapped[str] = mapped_column(String, ForeignKey("users.id"))
-
-
-class AgentMixin(Base):
- """Mixin for models that belong to an agent."""
-
- __abstract__ = True
-
- agent_id: Mapped[str] = mapped_column(String, ForeignKey("agents.id", ondelete="CASCADE"))
-
-
-class FileMixin(Base):
- """Mixin for models that belong to a file."""
-
- __abstract__ = True
-
- file_id: Mapped[Optional[str]] = mapped_column(String, ForeignKey("files.id", ondelete="CASCADE"))
-
-
-class SourceMixin(Base):
- """Mixin for models (e.g. file) that belong to a source."""
-
- __abstract__ = True
-
- source_id: Mapped[str] = mapped_column(String, ForeignKey("sources.id", ondelete="CASCADE"), nullable=False)
-
-
-class SandboxConfigMixin(Base):
- """Mixin for models that belong to a SandboxConfig."""
-
- __abstract__ = True
-
- sandbox_config_id: Mapped[str] = mapped_column(String, ForeignKey("sandbox_configs.id"))
-
-
-class ProjectMixin(Base):
- """Mixin for models that belong to a project."""
-
- __abstract__ = True
-
- project_id: Mapped[str] = mapped_column(String, nullable=True, doc="The associated project id.")
-
-
-class ArchiveMixin(Base):
- """Mixin for models that belong to an archive."""
-
- __abstract__ = True
-
- archive_id: Mapped[str] = mapped_column(String, ForeignKey("archives.id", ondelete="CASCADE"))
-
-
-class TemplateMixin(Base):
- """TemplateMixin for models that belong to a template."""
-
- __abstract__ = True
-
- base_template_id: Mapped[str] = mapped_column(nullable=True, doc="The id of the base template.")
- template_id: Mapped[str] = mapped_column(nullable=True, doc="The id of the template.")
- deployment_id: Mapped[str] = mapped_column(nullable=True, doc="The id of the deployment.")
-
-
-class TemplateEntityMixin(Base):
- """Mixin for models that belong to an entity (only used for templates)."""
-
- __abstract__ = True
-
- entity_id: Mapped[str] = mapped_column(nullable=True, doc="The id of the entity within the template.")
diff --git a/letta/orm/organization.py b/letta/orm/organization.py
deleted file mode 100644
index 57ab0c52..00000000
--- a/letta/orm/organization.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from typing import TYPE_CHECKING, List
-
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.organization import Organization as PydanticOrganization
-
-if TYPE_CHECKING:
- from letta.orm import Source
- from letta.orm.agent import Agent
- from letta.orm.archive import Archive
- from letta.orm.block import Block
- from letta.orm.group import Group
- from letta.orm.identity import Identity
- from letta.orm.llm_batch_items import LLMBatchItem
- from letta.orm.llm_batch_job import LLMBatchJob
- from letta.orm.message import Message
- from letta.orm.passage import ArchivalPassage, SourcePassage
- from letta.orm.passage_tag import PassageTag
- from letta.orm.provider import Provider
- from letta.orm.sandbox_config import AgentEnvironmentVariable, SandboxConfig, SandboxEnvironmentVariable
- from letta.orm.tool import Tool
- from letta.orm.user import User
-
-
-class Organization(SqlalchemyBase):
- """The highest level of the object tree. All Entities belong to one and only one Organization."""
-
- __tablename__ = "organizations"
- __pydantic_model__ = PydanticOrganization
-
- name: Mapped[str] = mapped_column(doc="The display name of the organization.")
- privileged_tools: Mapped[bool] = mapped_column(doc="Whether the organization has access to privileged tools.")
-
- # relationships
- users: Mapped[List["User"]] = relationship("User", back_populates="organization", cascade="all, delete-orphan")
- tools: Mapped[List["Tool"]] = relationship("Tool", back_populates="organization", cascade="all, delete-orphan")
- # mcp_servers: Mapped[List["MCPServer"]] = relationship("MCPServer", back_populates="organization", cascade="all, delete-orphan")
- blocks: Mapped[List["Block"]] = relationship("Block", back_populates="organization", cascade="all, delete-orphan")
- sandbox_configs: Mapped[List["SandboxConfig"]] = relationship(
- "SandboxConfig", back_populates="organization", cascade="all, delete-orphan"
- )
- sandbox_environment_variables: Mapped[List["SandboxEnvironmentVariable"]] = relationship(
- "SandboxEnvironmentVariable", back_populates="organization", cascade="all, delete-orphan"
- )
- agent_environment_variables: Mapped[List["AgentEnvironmentVariable"]] = relationship(
- "AgentEnvironmentVariable", back_populates="organization", cascade="all, delete-orphan"
- )
-
- # relationships
- agents: Mapped[List["Agent"]] = relationship("Agent", back_populates="organization", cascade="all, delete-orphan")
- sources: Mapped[List["Source"]] = relationship("Source", cascade="all, delete-orphan")
- messages: Mapped[List["Message"]] = relationship("Message", back_populates="organization", cascade="all, delete-orphan")
- source_passages: Mapped[List["SourcePassage"]] = relationship(
- "SourcePassage", back_populates="organization", cascade="all, delete-orphan"
- )
- archival_passages: Mapped[List["ArchivalPassage"]] = relationship(
- "ArchivalPassage", back_populates="organization", cascade="all, delete-orphan"
- )
- passage_tags: Mapped[List["PassageTag"]] = relationship("PassageTag", back_populates="organization", cascade="all, delete-orphan")
- archives: Mapped[List["Archive"]] = relationship("Archive", back_populates="organization", cascade="all, delete-orphan")
- providers: Mapped[List["Provider"]] = relationship("Provider", back_populates="organization", cascade="all, delete-orphan")
- identities: Mapped[List["Identity"]] = relationship("Identity", back_populates="organization", cascade="all, delete-orphan")
- groups: Mapped[List["Group"]] = relationship("Group", back_populates="organization", cascade="all, delete-orphan")
- llm_batch_jobs: Mapped[List["LLMBatchJob"]] = relationship("LLMBatchJob", back_populates="organization", cascade="all, delete-orphan")
- llm_batch_items: Mapped[List["LLMBatchItem"]] = relationship(
- "LLMBatchItem", back_populates="organization", cascade="all, delete-orphan"
- )
diff --git a/letta/orm/passage.py b/letta/orm/passage.py
deleted file mode 100644
index cf17bc83..00000000
--- a/letta/orm/passage.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from typing import TYPE_CHECKING, List, Optional
-
-from sqlalchemy import JSON, Column, Index
-from sqlalchemy.orm import Mapped, declared_attr, mapped_column, relationship
-
-from letta.config import LettaConfig
-from letta.constants import MAX_EMBEDDING_DIM
-from letta.orm.custom_columns import CommonVector, EmbeddingConfigColumn
-from letta.orm.mixins import ArchiveMixin, FileMixin, OrganizationMixin, SourceMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.passage import Passage as PydanticPassage
-from letta.settings import DatabaseChoice, settings
-
-config = LettaConfig()
-
-if TYPE_CHECKING:
- from letta.orm.organization import Organization
-
-
-class BasePassage(SqlalchemyBase, OrganizationMixin):
- """Base class for all passage types with common fields"""
-
- __abstract__ = True
- __pydantic_model__ = PydanticPassage
-
- id: Mapped[str] = mapped_column(primary_key=True, doc="Unique passage identifier")
- text: Mapped[str] = mapped_column(doc="Passage text content")
- embedding_config: Mapped[dict] = mapped_column(EmbeddingConfigColumn, doc="Embedding configuration")
- metadata_: Mapped[dict] = mapped_column(JSON, doc="Additional metadata")
- # dual storage: json column for fast retrieval, junction table for efficient queries
- tags: Mapped[Optional[List[str]]] = mapped_column(JSON, nullable=True, doc="Tags associated with this passage")
-
- # Vector embedding field based on database type
- if settings.database_engine is DatabaseChoice.POSTGRES:
- from pgvector.sqlalchemy import Vector
-
- embedding = mapped_column(Vector(MAX_EMBEDDING_DIM))
- else:
- embedding = Column(CommonVector)
-
- @declared_attr
- def organization(cls) -> Mapped["Organization"]:
- """Relationship to organization"""
- return relationship("Organization", back_populates="passages", lazy="selectin")
-
-
-class SourcePassage(BasePassage, FileMixin, SourceMixin):
- """Passages derived from external files/sources"""
-
- __tablename__ = "source_passages"
-
- file_name: Mapped[str] = mapped_column(doc="The name of the file that this passage was derived from")
-
- @declared_attr
- def organization(cls) -> Mapped["Organization"]:
- return relationship("Organization", back_populates="source_passages", lazy="selectin")
-
- @declared_attr
- def __table_args__(cls):
- # TODO (cliandy): investigate if this is necessary, may be for SQLite compatability or do we need to add as well?
- if settings.database_engine is DatabaseChoice.POSTGRES:
- return (
- Index("source_passages_org_idx", "organization_id"),
- Index("source_passages_created_at_id_idx", "created_at", "id"),
- Index("source_passages_file_id_idx", "file_id"),
- {"extend_existing": True},
- )
- return (
- Index("source_passages_created_at_id_idx", "created_at", "id"),
- Index("source_passages_file_id_idx", "file_id"),
- {"extend_existing": True},
- )
-
-
-class ArchivalPassage(BasePassage, ArchiveMixin):
- """Passages stored in archives as archival memories"""
-
- __tablename__ = "archival_passages"
-
- # junction table for efficient tag queries (complements json column above)
- passage_tags: Mapped[List["PassageTag"]] = relationship(
- "PassageTag", back_populates="passage", cascade="all, delete-orphan", lazy="noload"
- )
-
- @declared_attr
- def organization(cls) -> Mapped["Organization"]:
- return relationship("Organization", back_populates="archival_passages", lazy="selectin")
-
- @declared_attr
- def __table_args__(cls):
- if settings.database_engine is DatabaseChoice.POSTGRES:
- return (
- Index("archival_passages_org_idx", "organization_id"),
- Index("ix_archival_passages_org_archive", "organization_id", "archive_id"),
- Index("archival_passages_created_at_id_idx", "created_at", "id"),
- Index("ix_archival_passages_archive_id", "archive_id"),
- {"extend_existing": True},
- )
- return (
- Index("ix_archival_passages_org_archive", "organization_id", "archive_id"),
- Index("archival_passages_created_at_id_idx", "created_at", "id"),
- Index("ix_archival_passages_archive_id", "archive_id"),
- {"extend_existing": True},
- )
diff --git a/letta/orm/passage_tag.py b/letta/orm/passage_tag.py
deleted file mode 100644
index 45f24f0a..00000000
--- a/letta/orm/passage_tag.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import TYPE_CHECKING
-
-from sqlalchemy import ForeignKey, Index, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-
-if TYPE_CHECKING:
- from letta.orm.organization import Organization
- from letta.orm.passage import ArchivalPassage
-
-
-class PassageTag(SqlalchemyBase, OrganizationMixin):
- """Junction table for tags associated with passages.
-
- Design: dual storage approach where tags are stored both in:
- 1. JSON column in passages table (fast retrieval with passage data)
- 2. This junction table (efficient DISTINCT/COUNT queries and filtering)
- """
-
- __tablename__ = "passage_tags"
-
- __table_args__ = (
- # ensure uniqueness of tag per passage
- UniqueConstraint("passage_id", "tag", name="uq_passage_tag"),
- # indexes for efficient queries
- Index("ix_passage_tags_archive_id", "archive_id"),
- Index("ix_passage_tags_tag", "tag"),
- Index("ix_passage_tags_archive_tag", "archive_id", "tag"),
- Index("ix_passage_tags_org_archive", "organization_id", "archive_id"),
- )
-
- # primary key
- id: Mapped[str] = mapped_column(String, primary_key=True, doc="Unique identifier for the tag entry")
-
- # tag value
- tag: Mapped[str] = mapped_column(String, nullable=False, doc="The tag value")
-
- # foreign keys
- passage_id: Mapped[str] = mapped_column(
- String, ForeignKey("archival_passages.id", ondelete="CASCADE"), nullable=False, doc="ID of the passage this tag belongs to"
- )
-
- archive_id: Mapped[str] = mapped_column(
- String,
- ForeignKey("archives.id", ondelete="CASCADE"),
- nullable=False,
- doc="ID of the archive this passage belongs to (denormalized for efficient queries)",
- )
-
- # relationships
- passage: Mapped["ArchivalPassage"] = relationship("ArchivalPassage", back_populates="passage_tags", lazy="noload")
-
- organization: Mapped["Organization"] = relationship("Organization", back_populates="passage_tags", lazy="selectin")
diff --git a/letta/orm/prompt.py b/letta/orm/prompt.py
deleted file mode 100644
index 572e840d..00000000
--- a/letta/orm/prompt.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.mixins import ProjectMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.prompt import Prompt as PydanticPrompt
-
-
-class Prompt(SqlalchemyBase, ProjectMixin):
- __pydantic_model__ = PydanticPrompt
- __tablename__ = "prompts"
-
- id: Mapped[str] = mapped_column(primary_key=True, doc="Unique passage identifier")
- prompt: Mapped[str] = mapped_column(doc="The string contents of the prompt.")
diff --git a/letta/orm/provider.py b/letta/orm/provider.py
deleted file mode 100644
index b46a95b8..00000000
--- a/letta/orm/provider.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from typing import TYPE_CHECKING
-
-from sqlalchemy import UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.providers import Provider as PydanticProvider
-
-if TYPE_CHECKING:
- from letta.orm.organization import Organization
-
-
-class Provider(SqlalchemyBase, OrganizationMixin):
- """Provider ORM class"""
-
- __tablename__ = "providers"
- __pydantic_model__ = PydanticProvider
- __table_args__ = (
- UniqueConstraint(
- "name",
- "organization_id",
- name="unique_name_organization_id",
- ),
- )
-
- name: Mapped[str] = mapped_column(nullable=False, doc="The name of the provider")
- provider_type: Mapped[str] = mapped_column(nullable=True, doc="The type of the provider")
- provider_category: Mapped[str] = mapped_column(nullable=True, doc="The category of the provider (base or byok)")
- api_key: Mapped[str] = mapped_column(nullable=True, doc="API key or secret key used for requests to the provider.")
- base_url: Mapped[str] = mapped_column(nullable=True, doc="Base URL for the provider.")
- access_key: Mapped[str] = mapped_column(nullable=True, doc="Access key used for requests to the provider.")
- region: Mapped[str] = mapped_column(nullable=True, doc="Region used for requests to the provider.")
- api_version: Mapped[str] = mapped_column(nullable=True, doc="API version used for requests to the provider.")
-
- # relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="providers")
diff --git a/letta/orm/provider_trace.py b/letta/orm/provider_trace.py
deleted file mode 100644
index 69b7df14..00000000
--- a/letta/orm/provider_trace.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import uuid
-
-from sqlalchemy import JSON, Index, String
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.provider_trace import ProviderTrace as PydanticProviderTrace
-
-
-class ProviderTrace(SqlalchemyBase, OrganizationMixin):
- """Defines data model for storing provider trace information"""
-
- __tablename__ = "provider_traces"
- __pydantic_model__ = PydanticProviderTrace
- __table_args__ = (Index("ix_step_id", "step_id"),)
-
- id: Mapped[str] = mapped_column(
- primary_key=True, doc="Unique provider trace identifier", default=lambda: f"provider_trace-{uuid.uuid4()}"
- )
- request_json: Mapped[dict] = mapped_column(JSON, doc="JSON content of the provider request")
- response_json: Mapped[dict] = mapped_column(JSON, doc="JSON content of the provider response")
- step_id: Mapped[str] = mapped_column(String, nullable=True, doc="ID of the step that this trace is associated with")
-
- # Relationships
- organization: Mapped["Organization"] = relationship("Organization", lazy="selectin")
diff --git a/letta/orm/sandbox_config.py b/letta/orm/sandbox_config.py
deleted file mode 100644
index a3d22b18..00000000
--- a/letta/orm/sandbox_config.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import uuid
-from typing import TYPE_CHECKING, Dict, List, Optional
-
-from sqlalchemy import JSON, Enum as SqlEnum, Index, String, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column, relationship
-
-from letta.orm.mixins import AgentMixin, OrganizationMixin, SandboxConfigMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.enums import SandboxType
-from letta.schemas.environment_variables import SandboxEnvironmentVariable as PydanticSandboxEnvironmentVariable
-from letta.schemas.sandbox_config import SandboxConfig as PydanticSandboxConfig
-
-if TYPE_CHECKING:
- from letta.orm.agent import Agent
- from letta.orm.organization import Organization
-
-
-class SandboxConfig(SqlalchemyBase, OrganizationMixin):
- """ORM model for sandbox configurations with JSON storage for arbitrary config data."""
-
- __tablename__ = "sandbox_configs"
- __pydantic_model__ = PydanticSandboxConfig
-
- # For now, we only allow one type of sandbox config per organization
- __table_args__ = (UniqueConstraint("type", "organization_id", name="uix_type_organization"),)
-
- id: Mapped[str] = mapped_column(String, primary_key=True, nullable=False)
- type: Mapped[SandboxType] = mapped_column(SqlEnum(SandboxType), nullable=False, doc="The type of sandbox.")
- config: Mapped[Dict] = mapped_column(JSON, nullable=False, doc="The JSON configuration data.")
-
- # relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="sandbox_configs")
- sandbox_environment_variables: Mapped[List["SandboxEnvironmentVariable"]] = relationship(
- "SandboxEnvironmentVariable", back_populates="sandbox_config", cascade="all, delete-orphan"
- )
-
-
-class SandboxEnvironmentVariable(SqlalchemyBase, OrganizationMixin, SandboxConfigMixin):
- """ORM model for environment variables associated with sandboxes."""
-
- __tablename__ = "sandbox_environment_variables"
- __pydantic_model__ = PydanticSandboxEnvironmentVariable
-
- # We cannot have duplicate key names in the same sandbox, the env var would get overwritten
- __table_args__ = (UniqueConstraint("key", "sandbox_config_id", name="uix_key_sandbox_config"),)
-
- id: Mapped[str] = mapped_column(String, primary_key=True, nullable=False)
- key: Mapped[str] = mapped_column(String, nullable=False, doc="The name of the environment variable.")
- value: Mapped[str] = mapped_column(String, nullable=False, doc="The value of the environment variable.")
- description: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="An optional description of the environment variable.")
-
- # relationships
- organization: Mapped["Organization"] = relationship("Organization", back_populates="sandbox_environment_variables")
- sandbox_config: Mapped["SandboxConfig"] = relationship("SandboxConfig", back_populates="sandbox_environment_variables")
-
-
-class AgentEnvironmentVariable(SqlalchemyBase, OrganizationMixin, AgentMixin):
- """ORM model for environment variables associated with agents."""
-
- __tablename__ = "agent_environment_variables"
- # We cannot have duplicate key names for the same agent, the env var would get overwritten
- __table_args__ = (
- UniqueConstraint("key", "agent_id", name="uix_key_agent"),
- Index("idx_agent_environment_variables_agent_id", "agent_id"),
- )
-
- # agent_env_var generates its own id
- # TODO: We want to migrate all the ORM models to do this, so we will need to move this to the SqlalchemyBase
- id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"agent-env-{uuid.uuid4()}")
- key: Mapped[str] = mapped_column(String, nullable=False, doc="The name of the environment variable.")
- value: Mapped[str] = mapped_column(String, nullable=False, doc="The value of the environment variable.")
- description: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="An optional description of the environment variable.")
-
- organization: Mapped["Organization"] = relationship("Organization", back_populates="agent_environment_variables")
- agent: Mapped[List["Agent"]] = relationship("Agent", back_populates="tool_exec_environment_variables")
diff --git a/letta/orm/source.py b/letta/orm/source.py
deleted file mode 100644
index e81711eb..00000000
--- a/letta/orm/source.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from typing import TYPE_CHECKING, Optional
-
-from sqlalchemy import JSON, Enum, Index, UniqueConstraint
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.custom_columns import EmbeddingConfigColumn
-from letta.orm.mixins import OrganizationMixin
-from letta.orm.sqlalchemy_base import SqlalchemyBase
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import VectorDBProvider
-from letta.schemas.source import Source as PydanticSource
-
-if TYPE_CHECKING:
- pass
-
-
-class Source(SqlalchemyBase, OrganizationMixin):
- """A source represents an embedded text passage"""
-
- __tablename__ = "sources"
- __pydantic_model__ = PydanticSource
-
- __table_args__ = (
- Index("source_created_at_id_idx", "created_at", "id"),
- UniqueConstraint("name", "organization_id", name="uq_source_name_organization"),
- {"extend_existing": True},
- )
-
- name: Mapped[str] = mapped_column(doc="the name of the source, must be unique within the org", nullable=False)
- description: Mapped[str] = mapped_column(nullable=True, doc="a human-readable description of the source")
- instructions: Mapped[str] = mapped_column(nullable=True, doc="instructions for how to use the source")
- embedding_config: Mapped[EmbeddingConfig] = mapped_column(EmbeddingConfigColumn, doc="Configuration settings for embedding.")
- metadata_: Mapped[Optional[dict]] = mapped_column(JSON, nullable=True, doc="metadata for the source.")
- vector_db_provider: Mapped[VectorDBProvider] = mapped_column(
- Enum(VectorDBProvider),
- nullable=False,
- default=VectorDBProvider.NATIVE,
- doc="The vector database provider used for this source's passages",
- )
diff --git a/letta/orm/sources_agents.py b/letta/orm/sources_agents.py
deleted file mode 100644
index ffe8a9d0..00000000
--- a/letta/orm/sources_agents.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from sqlalchemy import ForeignKey, String
-from sqlalchemy.orm import Mapped, mapped_column
-
-from letta.orm.base import Base
-
-
-class SourcesAgents(Base):
- """Agents can have zero to many sources"""
-
- __tablename__ = "sources_agents"
-
- agent_id: Mapped[String] = mapped_column(String, ForeignKey("agents.id", ondelete="CASCADE"), primary_key=True)
- source_id: Mapped[String] = mapped_column(String, ForeignKey("sources.id", ondelete="CASCADE"), primary_key=True)
diff --git a/letta/orm/sqlalchemy_base.py b/letta/orm/sqlalchemy_base.py
deleted file mode 100644
index f9ee7452..00000000
--- a/letta/orm/sqlalchemy_base.py
+++ /dev/null
@@ -1,1110 +0,0 @@
-import inspect
-from datetime import datetime
-from enum import Enum
-from functools import wraps
-from pprint import pformat
-from typing import TYPE_CHECKING, List, Literal, Optional, Tuple, Union
-
-from sqlalchemy import Sequence, String, and_, delete, func, or_, select
-from sqlalchemy.exc import DBAPIError, IntegrityError, TimeoutError
-from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy.orm import Mapped, Session, mapped_column
-from sqlalchemy.orm.interfaces import ORMOption
-
-from letta.log import get_logger
-from letta.orm.base import Base, CommonSqlalchemyMetaMixins
-from letta.orm.errors import DatabaseTimeoutError, ForeignKeyConstraintViolationError, NoResultFound, UniqueConstraintViolationError
-from letta.orm.sqlite_functions import adapt_array
-from letta.settings import DatabaseChoice
-
-if TYPE_CHECKING:
- from pydantic import BaseModel
-
-
-logger = get_logger(__name__)
-
-
-def handle_db_timeout(func):
- """Decorator to handle SQLAlchemy TimeoutError and wrap it in a custom exception."""
- if not inspect.iscoroutinefunction(func):
-
- @wraps(func)
- def wrapper(*args, **kwargs):
- try:
- return func(*args, **kwargs)
- except TimeoutError as e:
- logger.error(f"Timeout while executing {func.__name__} with args {args} and kwargs {kwargs}: {e}")
- raise DatabaseTimeoutError(message=f"Timeout occurred in {func.__name__}.", original_exception=e)
-
- return wrapper
- else:
-
- @wraps(func)
- async def async_wrapper(*args, **kwargs):
- try:
- return await func(*args, **kwargs)
- except TimeoutError as e:
- logger.error(f"Timeout while executing {func.__name__} with args {args} and kwargs {kwargs}: {e}")
- raise DatabaseTimeoutError(message=f"Timeout occurred in {func.__name__}.", original_exception=e)
-
- return async_wrapper
-
-
-def is_postgresql_session(session: Session) -> bool:
- """Check if the database session is PostgreSQL instead of SQLite for setting query options."""
- return session.bind.dialect.name == "postgresql"
-
-
-class AccessType(str, Enum):
- ORGANIZATION = "organization"
- USER = "user"
-
-
-class SqlalchemyBase(CommonSqlalchemyMetaMixins, Base):
- __abstract__ = True
-
- __order_by_default__ = "created_at"
-
- id: Mapped[str] = mapped_column(String, primary_key=True)
-
- @classmethod
- @handle_db_timeout
- def list(
- cls,
- *,
- db_session: "Session",
- before: Optional[str] = None,
- after: Optional[str] = None,
- start_date: Optional[datetime] = None,
- end_date: Optional[datetime] = None,
- limit: Optional[int] = 50,
- query_text: Optional[str] = None,
- query_embedding: Optional[List[float]] = None,
- ascending: bool = True,
- actor: Optional["User"] = None,
- access: Optional[List[Literal["read", "write", "admin"]]] = ["read"],
- access_type: AccessType = AccessType.ORGANIZATION,
- join_model: Optional[Base] = None,
- join_conditions: Optional[Union[Tuple, List]] = None,
- identifier_keys: Optional[List[str]] = None,
- identity_id: Optional[str] = None,
- **kwargs,
- ) -> List["SqlalchemyBase"]:
- """
- List records with before/after pagination, ordering by created_at.
- Can use both before and after to fetch a window of records.
-
- Args:
- db_session: SQLAlchemy session
- before: ID of item to paginate before (upper bound)
- after: ID of item to paginate after (lower bound)
- start_date: Filter items after this date
- end_date: Filter items before this date
- limit: Maximum number of items to return
- query_text: Text to search for
- query_embedding: Vector to search for similar embeddings
- ascending: Sort direction
- **kwargs: Additional filters to apply
- """
- if start_date and end_date and start_date > end_date:
- raise ValueError("start_date must be earlier than or equal to end_date")
-
- logger.debug(f"Listing {cls.__name__} with kwarg filters {kwargs}")
-
- with db_session as session:
- # Get the reference objects for pagination
- before_obj = None
- after_obj = None
-
- if before:
- before_obj = session.get(cls, before)
- if not before_obj:
- raise NoResultFound(f"No {cls.__name__} found with id {before}")
-
- if after:
- after_obj = session.get(cls, after)
- if not after_obj:
- raise NoResultFound(f"No {cls.__name__} found with id {after}")
-
- # Validate that before comes after the after object if both are provided
- if before_obj and after_obj and before_obj.created_at < after_obj.created_at:
- raise ValueError("'before' reference must be later than 'after' reference")
-
- query = cls._list_preprocess(
- before_obj=before_obj,
- after_obj=after_obj,
- start_date=start_date,
- end_date=end_date,
- limit=limit,
- query_text=query_text,
- query_embedding=query_embedding,
- ascending=ascending,
- actor=actor,
- access=access,
- access_type=access_type,
- join_model=join_model,
- join_conditions=join_conditions,
- identifier_keys=identifier_keys,
- identity_id=identity_id,
- **kwargs,
- )
-
- # Execute the query
- results = session.execute(query)
-
- results = list(results.scalars())
- results = cls._list_postprocess(
- before=before,
- after=after,
- limit=limit,
- results=results,
- )
-
- return results
-
- @classmethod
- @handle_db_timeout
- async def list_async(
- cls,
- *,
- db_session: "AsyncSession",
- before: Optional[str] = None,
- after: Optional[str] = None,
- start_date: Optional[datetime] = None,
- end_date: Optional[datetime] = None,
- limit: Optional[int] = 50,
- query_text: Optional[str] = None,
- query_embedding: Optional[List[float]] = None,
- ascending: bool = True,
- actor: Optional["User"] = None,
- access: Optional[List[Literal["read", "write", "admin"]]] = ["read"],
- access_type: AccessType = AccessType.ORGANIZATION,
- join_model: Optional[Base] = None,
- join_conditions: Optional[Union[Tuple, List]] = None,
- identifier_keys: Optional[List[str]] = None,
- identity_id: Optional[str] = None,
- query_options: Sequence[ORMOption] | None = None, # ← new
- has_feedback: Optional[bool] = None,
- **kwargs,
- ) -> List["SqlalchemyBase"]:
- """
- Async version of list method above.
- NOTE: Keep in sync.
- List records with before/after pagination, ordering by created_at.
- Can use both before and after to fetch a window of records.
-
- Args:
- db_session: SQLAlchemy session
- before: ID of item to paginate before (upper bound)
- after: ID of item to paginate after (lower bound)
- start_date: Filter items after this date
- end_date: Filter items before this date
- limit: Maximum number of items to return
- query_text: Text to search for
- query_embedding: Vector to search for similar embeddings
- ascending: Sort direction
- **kwargs: Additional filters to apply
- """
- if start_date and end_date and start_date > end_date:
- raise ValueError("start_date must be earlier than or equal to end_date")
-
- logger.debug(f"Listing {cls.__name__} with kwarg filters {kwargs}")
-
- # Get the reference objects for pagination
- before_obj = None
- after_obj = None
-
- if before:
- before_obj = await db_session.get(cls, before)
- if not before_obj:
- raise NoResultFound(f"No {cls.__name__} found with id {before}")
-
- if after:
- after_obj = await db_session.get(cls, after)
- if not after_obj:
- raise NoResultFound(f"No {cls.__name__} found with id {after}")
-
- # Validate that before comes after the after object if both are provided
- if before_obj and after_obj and before_obj.created_at < after_obj.created_at:
- raise ValueError("'before' reference must be later than 'after' reference")
-
- query = cls._list_preprocess(
- before_obj=before_obj,
- after_obj=after_obj,
- start_date=start_date,
- end_date=end_date,
- limit=limit,
- query_text=query_text,
- query_embedding=query_embedding,
- ascending=ascending,
- actor=actor,
- access=access,
- access_type=access_type,
- join_model=join_model,
- join_conditions=join_conditions,
- identifier_keys=identifier_keys,
- identity_id=identity_id,
- has_feedback=has_feedback,
- **kwargs,
- )
- if query_options:
- for opt in query_options:
- query = query.options(opt)
-
- # Execute the query
- results = await db_session.execute(query)
-
- results = list(results.scalars())
- results = cls._list_postprocess(
- before=before,
- after=after,
- limit=limit,
- results=results,
- )
-
- return results
-
- @classmethod
- def _list_preprocess(
- cls,
- *,
- before_obj,
- after_obj,
- start_date: Optional[datetime] = None,
- end_date: Optional[datetime] = None,
- limit: Optional[int] = 50,
- query_text: Optional[str] = None,
- query_embedding: Optional[List[float]] = None,
- ascending: bool = True,
- actor: Optional["User"] = None,
- access: Optional[List[Literal["read", "write", "admin"]]] = ["read"],
- access_type: AccessType = AccessType.ORGANIZATION,
- join_model: Optional[Base] = None,
- join_conditions: Optional[Union[Tuple, List]] = None,
- identifier_keys: Optional[List[str]] = None,
- identity_id: Optional[str] = None,
- check_is_deleted: bool = False,
- has_feedback: Optional[bool] = None,
- **kwargs,
- ):
- """
- Constructs the query for listing records.
- """
- query = select(cls)
-
- if join_model and join_conditions:
- query = query.join(join_model, and_(*join_conditions))
-
- # Apply access predicate if actor is provided
- if actor:
- query = cls.apply_access_predicate(query, actor, access, access_type)
-
- if identifier_keys and hasattr(cls, "identities"):
- query = query.join(cls.identities).filter(cls.identities.property.mapper.class_.identifier_key.in_(identifier_keys))
-
- # given the identity_id, we can find within the agents table any agents that have the identity_id in their identity_ids
- if identity_id and hasattr(cls, "identities"):
- query = query.join(cls.identities).filter(cls.identities.property.mapper.class_.id == identity_id)
-
- # Apply filtering logic from kwargs
- # 1 part: // 2 parts: .
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "66e47b34-5feb-4660-85f0-14b5ee7f62b9",
- "metadata": {},
- "source": [
- "## Part 2: Create an agent \n",
- "We'll first start with creating a basic Letta agent. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "24745606-b0fb-4157-a5cd-82fd0c26711f",
- "metadata": {},
- "outputs": [],
- "source": [
- "basic_agent = client.create_agent(\n",
- " name=\"basic_agent\", \n",
- ")\n",
- "print(f\"Created agent: {basic_agent.name}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fcfb0d7b-b260-4bc0-8db2-c65f40e4afd5",
- "metadata": {},
- "source": [
- "We can now send messages from the user to the agent by specifying the `agent_id`: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a37bc9aa-4efb-4b4d-a6ce-f02505cb3240",
- "metadata": {},
- "outputs": [],
- "source": [
- "from letta.client.utils import pprint \n",
- "\n",
- "response = client.user_message(agent_id=basic_agent.id, message=\"hello\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9803140c-2b9d-426b-8812-9295806eb312",
- "metadata": {},
- "source": [
- "### Chatting in the developer portal \n",
- "You can also chat with the agent inside of the developer portal. Try clicking the chat button in the agent view. \n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "99ae20ec-e92e-4480-a652-b4aea28a6199",
- "metadata": {},
- "source": [
- "### Adding Personalization\n",
- "We can now create a more customized agent, but specifying a custom `human` and `persona` field. \n",
- "* The *human* specifies the personalization information about the user interacting with the agent \n",
- "* The *persona* specifies the behavior and personality of the event\n",
- "\n",
- "What makes Letta unique is that the starting *persona* and *human* can change over time as the agent gains new information, enabling it to have evolving memory. We'll see an example of this later in the tutorial."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c0876410-4d70-490d-a798-39938b5ce941",
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: feel free to change the human and person to what you'd like \n",
- "persona = \\\n",
- "\"\"\"\n",
- "You are a friendly and helpful agent!\n",
- "\"\"\"\n",
- "\n",
- "human = \\\n",
- "\"\"\"\n",
- "I am an Accenture consultant with many specializations. My name is Sarah.\n",
- "\"\"\"\n",
- "\n",
- "custom_agent = client.create_agent(\n",
- " name=\"custom_agent\", \n",
- " human=human, \n",
- " persona=persona\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "21293857-80e4-46e4-b628-3912fad038e9",
- "metadata": {},
- "source": [
- "### Viewing memory \n",
- "You can view and edit the agent's memory inside of the developer console. There are two type of memory, *core* and *archival* memory: \n",
- "1. Core memory stores short-term memories in the LLM's context \n",
- "2. Archival memory stores long term memories in a vector database\n",
- "\n",
- "In this example, we'll look at how the agent can modify its core memory with new information. To see the agent's memory, click the \"Core Memory\" section on the developer console. \n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d8fa13eb-ce4b-4e4f-81b6-9d6ef6fa67c2",
- "metadata": {},
- "source": [
- "### Referencing memory \n",
- "Letta agents can customize their responses based on what memories they have stored. Try asking a question that related to the human and persona you provided. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fddbefe5-3b94-4a08-aa50-d80fb581c747",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.user_message(agent_id=custom_agent.id, message=\"what do I work as?\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "30497119-e208-4a4e-b482-e7cfff346263",
- "metadata": {},
- "source": [
- "### Evolving memory \n",
- "Letta agents have long term memory, and can evolve what they store in their memory over time. In the example below, we make a correction to the previously provided information. See how the agent processes this new information. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "679fa708-20ee-4e75-9222-b476f126bc6f",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = client.user_message(agent_id=custom_agent.id, message=\"Actually, my name is Charles\") \n",
- "pprint(response.messages)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "686ac5a3-be63-4afd-97ae-b7d05219dd60",
- "metadata": {},
- "source": [
- "Now, look back at the developer portal and at the agent's *core memory*. Do you see a change in the *human* section of the memory? "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "878d2f49-a5a6-4483-9f69-7436bcf00cfb",
- "metadata": {},
- "source": [
- "## Part 3: Adding Tools \n",
- "Letta agents can be connected to custom tools. Currently, tools must be created by service administrators. However, you can add additional tools provided by the service administrator to the agent you create. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "35785d36-2674-4a00-937b-4c747e0fb6bf",
- "metadata": {},
- "source": [
- "### View Available Tools "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c307a6f7-276b-49f5-8d3d-48aaaea221a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "tools = client.list_tools().tools\n",
- "for tool in tools: \n",
- " print(f\"Tool: {tool.name} - {tool.json_schema['description']}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "318d19dc-b9dd-448c-ab5c-9c9311d21fad",
- "metadata": {},
- "source": [
- "### Create a tool using agent in the developer portal \n",
- "Create an agent in the developer portal and toggle additional tools you want the agent to use. We recommend modifying the *persona* to notify the agent that it should be using the tools for certain tasks. \n",
- "\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aecdaa70-861a-43d5-b006-fecd90a8ed19",
- "metadata": {},
- "source": [
- "## Part 4: Cleanup (optional) \n",
- "You can cleanup the agents you creating the following command to delete your agents: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1320d9c9-170b-48a8-b5e8-70737b1a8aac",
- "metadata": {},
- "outputs": [],
- "source": [
- "for agent in client.list_agents().agents: \n",
- " client.delete_agent(agent[\"id\"])\n",
- " print(f\"Deleted agent {agent['name']} with ID {agent['id']}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "510675a8-22bc-4f9f-9c79-91e2ffa9caf9",
- "metadata": {},
- "source": [
- "## 🎉 Congrats, you're done with day 1 of Letta! \n",
- "For day 2, we'll go over how to connect *data sources* to Letta to run RAG agents. "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "letta",
- "language": "python",
- "name": "letta"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/init.sql b/init.sql
deleted file mode 100644
index 9d866db2..00000000
--- a/init.sql
+++ /dev/null
@@ -1,36 +0,0 @@
--- Title: Init Letta Database
-
--- Fetch the docker secrets, if they are available.
--- Otherwise fall back to environment variables, or hardwired 'letta'
-\set db_user `([ -r /var/run/secrets/letta-user ] && cat /var/run/secrets/letta-user) || echo "${POSTGRES_USER:-letta}"`
-\set db_password `([ -r /var/run/secrets/letta-password ] && cat /var/run/secrets/letta-password) || echo "${POSTGRES_PASSWORD:-letta}"`
-\set db_name `([ -r /var/run/secrets/letta-db ] && cat /var/run/secrets/letta-db) || echo "${POSTGRES_DB:-letta}"`
-
--- CREATE USER :"db_user"
--- WITH PASSWORD :'db_password'
--- NOCREATEDB
--- NOCREATEROLE
--- ;
---
--- CREATE DATABASE :"db_name"
--- WITH
--- OWNER = :"db_user"
--- ENCODING = 'UTF8'
--- LC_COLLATE = 'en_US.utf8'
--- LC_CTYPE = 'en_US.utf8'
--- LOCALE_PROVIDER = 'libc'
--- TABLESPACE = pg_default
--- CONNECTION LIMIT = -1;
-
--- Set up our schema and extensions in our new database.
-\c :"db_name"
-
-CREATE SCHEMA :"db_name"
- AUTHORIZATION :"db_user";
-
-ALTER DATABASE :"db_name"
- SET search_path TO :"db_name";
-
-CREATE EXTENSION IF NOT EXISTS vector WITH SCHEMA :"db_name";
-
-DROP SCHEMA IF EXISTS public CASCADE;
diff --git a/letta/__init__.py b/letta/__init__.py
deleted file mode 100644
index bb6eb8a4..00000000
--- a/letta/__init__.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-from importlib.metadata import PackageNotFoundError, version
-
-try:
- __version__ = version("letta")
-except PackageNotFoundError:
- # Fallback for development installations
- __version__ = "0.11.7"
-
-if os.environ.get("LETTA_VERSION"):
- __version__ = os.environ["LETTA_VERSION"]
-
-# Import sqlite_functions early to ensure event handlers are registered
-from letta.orm import sqlite_functions
-
-# # imports for easier access
-from letta.schemas.agent import AgentState
-from letta.schemas.block import Block
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import JobStatus
-from letta.schemas.file import FileMetadata
-from letta.schemas.job import Job
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.letta_ping import LettaPing
-from letta.schemas.letta_stop_reason import LettaStopReason
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.memory import ArchivalMemorySummary, BasicBlockMemory, ChatMemory, Memory, RecallMemorySummary
-from letta.schemas.message import Message
-from letta.schemas.organization import Organization
-from letta.schemas.passage import Passage
-from letta.schemas.source import Source
-from letta.schemas.tool import Tool
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
diff --git a/letta/adapters/letta_llm_adapter.py b/letta/adapters/letta_llm_adapter.py
deleted file mode 100644
index a554b368..00000000
--- a/letta/adapters/letta_llm_adapter.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import AsyncGenerator
-
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.letta_message_content import ReasoningContent, RedactedReasoningContent, TextContent
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, ToolCall
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.services.telemetry_manager import TelemetryManager
-
-
-class LettaLLMAdapter(ABC):
- """
- Base adapter for handling LLM calls in a unified way.
-
- This abstract class defines the interface for both blocking and streaming
- LLM interactions, allowing the agent to use different execution modes
- through a consistent API.
- """
-
- def __init__(self, llm_client: LLMClientBase, llm_config: LLMConfig) -> None:
- self.llm_client: LLMClientBase = llm_client
- self.llm_config: LLMConfig = llm_config
- self.message_id: str | None = None
- self.request_data: dict | None = None
- self.response_data: dict | None = None
- self.chat_completions_response: ChatCompletionResponse | None = None
- self.reasoning_content: list[TextContent | ReasoningContent | RedactedReasoningContent] | None = None
- self.tool_call: ToolCall | None = None
- self.usage: LettaUsageStatistics = LettaUsageStatistics()
- self.telemetry_manager: TelemetryManager = TelemetryManager()
- self.llm_request_finish_timestamp_ns: int | None = None
-
- @abstractmethod
- async def invoke_llm(
- self,
- request_data: dict,
- messages: list,
- tools: list,
- use_assistant_message: bool,
- requires_approval_tools: list[str] = [],
- step_id: str | None = None,
- actor: User | None = None,
- ) -> AsyncGenerator[LettaMessage | None, None]:
- """
- Execute the LLM call and yield results as they become available.
-
- Args:
- request_data: The prepared request data for the LLM API
- messages: The messages in context for the request
- tools: The tools available for the LLM to use
- use_assistant_message: If true, use assistant messages when streaming response
- requires_approval_tools: The subset of tools that require approval before use
- step_id: The step ID associated with this request. If provided, logs request and response data.
- actor: The optional actor associated with this request for logging purposes.
-
- Yields:
- LettaMessage: Chunks of data for streaming adapters, or None for blocking adapters
- """
- raise NotImplementedError
-
- def supports_token_streaming(self) -> bool:
- """
- Check if the adapter supports token-level streaming.
-
- Returns:
- bool: True if the adapter can stream back tokens as they are generated, False otherwise
- """
- return False
-
- def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
- """
- Log provider trace data for telemetry purposes.
-
- Args:
- step_id: The step ID associated with this request for logging purposes
- actor: The user associated with this request for logging purposes
- """
- raise NotImplementedError
diff --git a/letta/adapters/letta_llm_request_adapter.py b/letta/adapters/letta_llm_request_adapter.py
deleted file mode 100644
index a21663f4..00000000
--- a/letta/adapters/letta_llm_request_adapter.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import asyncio
-from typing import AsyncGenerator
-
-from letta.adapters.letta_llm_adapter import LettaLLMAdapter
-from letta.helpers.datetime_helpers import get_utc_timestamp_ns
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, TextContent
-from letta.schemas.provider_trace import ProviderTraceCreate
-from letta.schemas.user import User
-from letta.settings import settings
-
-
-class LettaLLMRequestAdapter(LettaLLMAdapter):
- """
- Adapter for handling blocking (non-streaming) LLM requests.
-
- This adapter makes synchronous requests to the LLM and returns complete
- responses. It extracts reasoning content, tool calls, and usage statistics
- from the response and updates instance variables for access by the agent.
- """
-
- async def invoke_llm(
- self,
- request_data: dict,
- messages: list,
- tools: list,
- use_assistant_message: bool,
- requires_approval_tools: list[str] = [],
- step_id: str | None = None,
- actor: str | None = None,
- ) -> AsyncGenerator[LettaMessage | None, None]:
- """
- Execute a blocking LLM request and yield the response.
-
- This adapter:
- 1. Makes a blocking request to the LLM
- 2. Converts the response to chat completion format
- 3. Extracts reasoning and tool call information
- 4. Updates all instance variables
- 5. Yields nothing (blocking mode doesn't stream)
- """
- # Store request data
- self.request_data = request_data
-
- # Make the blocking LLM request
- self.response_data = await self.llm_client.request_async(request_data, self.llm_config)
- self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
-
- # Convert response to chat completion format
- self.chat_completions_response = self.llm_client.convert_response_to_chat_completion(self.response_data, messages, self.llm_config)
-
- # Extract reasoning content from the response
- if self.chat_completions_response.choices[0].message.reasoning_content:
- self.reasoning_content = [
- ReasoningContent(
- reasoning=self.chat_completions_response.choices[0].message.reasoning_content,
- is_native=True,
- signature=self.chat_completions_response.choices[0].message.reasoning_content_signature,
- )
- ]
- elif self.chat_completions_response.choices[0].message.omitted_reasoning_content:
- self.reasoning_content = [OmittedReasoningContent()]
- elif self.chat_completions_response.choices[0].message.content:
- # Reasoning placed into content for legacy reasons
- self.reasoning_content = [TextContent(text=self.chat_completions_response.choices[0].message.content)]
- else:
- # logger.info("No reasoning content found.")
- self.reasoning_content = None
-
- # Extract tool call
- if self.chat_completions_response.choices[0].message.tool_calls:
- self.tool_call = self.chat_completions_response.choices[0].message.tool_calls[0]
- else:
- self.tool_call = None
-
- # Extract usage statistics
- self.usage.step_count = 1
- self.usage.completion_tokens = self.chat_completions_response.usage.completion_tokens
- self.usage.prompt_tokens = self.chat_completions_response.usage.prompt_tokens
- self.usage.total_tokens = self.chat_completions_response.usage.total_tokens
-
- self.log_provider_trace(step_id=step_id, actor=actor)
-
- yield None
- return
-
- def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
- """
- Log provider trace data for telemetry purposes in a fire-and-forget manner.
-
- Creates an async task to log the request/response data without blocking
- the main execution flow. The task runs in the background.
-
- Args:
- step_id: The step ID associated with this request for logging purposes
- actor: The user associated with this request for logging purposes
- """
- if step_id is None or actor is None or not settings.track_provider_trace:
- return
-
- asyncio.create_task(
- self.telemetry_manager.create_provider_trace_async(
- actor=actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=self.request_data,
- response_json=self.response_data,
- step_id=step_id, # Use original step_id for telemetry
- organization_id=actor.organization_id,
- ),
- )
- )
diff --git a/letta/adapters/letta_llm_stream_adapter.py b/letta/adapters/letta_llm_stream_adapter.py
deleted file mode 100644
index c0bf2e9a..00000000
--- a/letta/adapters/letta_llm_stream_adapter.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import asyncio
-from typing import AsyncGenerator
-
-from letta.adapters.letta_llm_adapter import LettaLLMAdapter
-from letta.helpers.datetime_helpers import get_utc_timestamp_ns
-from letta.interfaces.anthropic_streaming_interface import AnthropicStreamingInterface
-from letta.interfaces.openai_streaming_interface import OpenAIStreamingInterface
-from letta.llm_api.llm_client_base import LLMClientBase
-from letta.schemas.enums import ProviderType
-from letta.schemas.letta_message import LettaMessage
-from letta.schemas.llm_config import LLMConfig
-from letta.schemas.provider_trace import ProviderTraceCreate
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.settings import settings
-
-
-class LettaLLMStreamAdapter(LettaLLMAdapter):
- """
- Adapter for handling streaming LLM requests with immediate token yielding.
-
- This adapter supports real-time streaming of tokens from the LLM, providing
- minimal time-to-first-token (TTFT) latency. It uses specialized streaming
- interfaces for different providers (OpenAI, Anthropic) to handle their
- specific streaming formats.
- """
-
- def __init__(self, llm_client: LLMClientBase, llm_config: LLMConfig) -> None:
- super().__init__(llm_client, llm_config)
- self.interface: OpenAIStreamingInterface | AnthropicStreamingInterface | None = None
-
- async def invoke_llm(
- self,
- request_data: dict,
- messages: list,
- tools: list,
- use_assistant_message: bool,
- requires_approval_tools: list[str] = [],
- step_id: str | None = None,
- actor: User | None = None,
- ) -> AsyncGenerator[LettaMessage, None]:
- """
- Execute a streaming LLM request and yield tokens/chunks as they arrive.
-
- This adapter:
- 1. Makes a streaming request to the LLM
- 2. Yields chunks immediately for minimal TTFT
- 3. Accumulates response data through the streaming interface
- 4. Updates all instance variables after streaming completes
- """
- # Store request data
- self.request_data = request_data
-
- # Instantiate streaming interface
- if self.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock]:
- self.interface = AnthropicStreamingInterface(
- use_assistant_message=use_assistant_message,
- put_inner_thoughts_in_kwarg=self.llm_config.put_inner_thoughts_in_kwargs,
- requires_approval_tools=requires_approval_tools,
- )
- elif self.llm_config.model_endpoint_type == ProviderType.openai:
- self.interface = OpenAIStreamingInterface(
- use_assistant_message=use_assistant_message,
- is_openai_proxy=self.llm_config.provider_name == "lmstudio_openai",
- put_inner_thoughts_in_kwarg=self.llm_config.put_inner_thoughts_in_kwargs,
- messages=messages,
- tools=tools,
- requires_approval_tools=requires_approval_tools,
- )
- else:
- raise ValueError(f"Streaming not supported for provider {self.llm_config.model_endpoint_type}")
-
- # Extract optional parameters
- # ttft_span = kwargs.get('ttft_span', None)
-
- # Start the streaming request
- stream = await self.llm_client.stream_async(request_data, self.llm_config)
-
- # Process the stream and yield chunks immediately for TTFT
- async for chunk in self.interface.process(stream): # TODO: add ttft span
- # Yield each chunk immediately as it arrives
- yield chunk
-
- # After streaming completes, extract the accumulated data
- self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
-
- # Extract tool call from the interface
- try:
- self.tool_call = self.interface.get_tool_call_object()
- except ValueError as e:
- # No tool call, handle upstream
- self.tool_call = None
-
- # Extract reasoning content from the interface
- self.reasoning_content = self.interface.get_reasoning_content()
-
- # Extract usage statistics
- # Some providers don't provide usage in streaming, use fallback if needed
- if hasattr(self.interface, "input_tokens") and hasattr(self.interface, "output_tokens"):
- # Handle cases where tokens might not be set (e.g., LMStudio)
- input_tokens = self.interface.input_tokens
- output_tokens = self.interface.output_tokens
-
- # Fallback to estimated values if not provided
- if not input_tokens and hasattr(self.interface, "fallback_input_tokens"):
- input_tokens = self.interface.fallback_input_tokens
- if not output_tokens and hasattr(self.interface, "fallback_output_tokens"):
- output_tokens = self.interface.fallback_output_tokens
-
- self.usage = LettaUsageStatistics(
- step_count=1,
- completion_tokens=output_tokens or 0,
- prompt_tokens=input_tokens or 0,
- total_tokens=(input_tokens or 0) + (output_tokens or 0),
- )
- else:
- # Default usage statistics if not available
- self.usage = LettaUsageStatistics(step_count=1, completion_tokens=0, prompt_tokens=0, total_tokens=0)
-
- # Store any additional data from the interface
- self.message_id = self.interface.letta_message_id
-
- # Log request and response data
- self.log_provider_trace(step_id=step_id, actor=actor)
-
- def supports_token_streaming(self) -> bool:
- return True
-
- def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
- """
- Log provider trace data for telemetry purposes in a fire-and-forget manner.
-
- Creates an async task to log the request/response data without blocking
- the main execution flow. For streaming adapters, this includes the final
- tool call and reasoning content collected during streaming.
-
- Args:
- step_id: The step ID associated with this request for logging purposes
- actor: The user associated with this request for logging purposes
- """
- if step_id is None or actor is None or not settings.track_provider_trace:
- return
-
- asyncio.create_task(
- self.telemetry_manager.create_provider_trace_async(
- actor=actor,
- provider_trace_create=ProviderTraceCreate(
- request_json=self.request_data,
- response_json={
- "content": {
- "tool_call": self.tool_call.model_dump_json(),
- "reasoning": [content.model_dump_json() for content in self.reasoning_content],
- },
- "id": self.interface.message_id,
- "model": self.interface.model,
- "role": "assistant",
- # "stop_reason": "",
- # "stop_sequence": None,
- "type": "message",
- "usage": {
- "input_tokens": self.usage.prompt_tokens,
- "output_tokens": self.usage.completion_tokens,
- },
- },
- step_id=step_id, # Use original step_id for telemetry
- organization_id=actor.organization_id,
- ),
- )
- )
diff --git a/letta/agent.py b/letta/agent.py
deleted file mode 100644
index 52f7de37..00000000
--- a/letta/agent.py
+++ /dev/null
@@ -1,1758 +0,0 @@
-import asyncio
-import json
-import time
-import traceback
-import warnings
-from abc import ABC, abstractmethod
-from typing import Dict, List, Optional, Tuple, Union
-
-from openai.types.beta.function_tool import FunctionTool as OpenAITool
-
-from letta.agents.helpers import generate_step_id
-from letta.constants import (
- CLI_WARNING_PREFIX,
- COMPOSIO_ENTITY_ENV_VAR_KEY,
- ERROR_MESSAGE_PREFIX,
- FIRST_MESSAGE_ATTEMPTS,
- FUNC_FAILED_HEARTBEAT_MESSAGE,
- LETTA_CORE_TOOL_MODULE_NAME,
- LETTA_MULTI_AGENT_TOOL_MODULE_NAME,
- LLM_MAX_TOKENS,
- READ_ONLY_BLOCK_EDIT_ERROR,
- REQ_HEARTBEAT_MESSAGE,
- SEND_MESSAGE_TOOL_NAME,
-)
-from letta.errors import ContextWindowExceededError
-from letta.functions.ast_parsers import coerce_dict_args_by_annotations, get_function_annotations_from_source
-from letta.functions.composio_helpers import execute_composio_action, generate_composio_action_from_func_name
-from letta.functions.functions import get_function_from_module
-from letta.helpers import ToolRulesSolver
-from letta.helpers.composio_helpers import get_composio_api_key
-from letta.helpers.datetime_helpers import get_utc_time
-from letta.helpers.json_helpers import json_dumps, json_loads
-from letta.helpers.message_helper import convert_message_creates_to_messages
-from letta.interface import AgentInterface
-from letta.llm_api.helpers import calculate_summarizer_cutoff, get_token_counts_for_messages, is_context_overflow_error
-from letta.llm_api.llm_api_tools import create
-from letta.llm_api.llm_client import LLMClient
-from letta.local_llm.constants import INNER_THOUGHTS_KWARG
-from letta.local_llm.utils import num_tokens_from_functions, num_tokens_from_messages
-from letta.log import get_logger
-from letta.memory import summarize_messages
-from letta.orm import User
-from letta.otel.tracing import log_event, trace_method
-from letta.prompts.prompt_generator import PromptGenerator
-from letta.schemas.agent import AgentState, AgentStepResponse, UpdateAgent, get_prompt_template_for_agent_type
-from letta.schemas.block import BlockUpdate
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import MessageRole, ProviderType, StepStatus, ToolType
-from letta.schemas.letta_message_content import ImageContent, TextContent
-from letta.schemas.memory import ContextWindowOverview, Memory
-from letta.schemas.message import Message, MessageCreate, ToolReturn
-from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Message as ChatCompletionMessage, UsageStatistics
-from letta.schemas.response_format import ResponseFormatType
-from letta.schemas.tool import Tool
-from letta.schemas.tool_execution_result import ToolExecutionResult
-from letta.schemas.tool_rule import TerminalToolRule
-from letta.schemas.usage import LettaUsageStatistics
-from letta.services.agent_manager import AgentManager
-from letta.services.block_manager import BlockManager
-from letta.services.helpers.agent_manager_helper import check_supports_structured_output
-from letta.services.helpers.tool_parser_helper import runtime_override_tool_json_schema
-from letta.services.job_manager import JobManager
-from letta.services.mcp.base_client import AsyncBaseMCPClient
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.services.provider_manager import ProviderManager
-from letta.services.step_manager import StepManager
-from letta.services.telemetry_manager import NoopTelemetryManager, TelemetryManager
-from letta.services.tool_executor.tool_execution_sandbox import ToolExecutionSandbox
-from letta.services.tool_manager import ToolManager
-from letta.settings import model_settings, settings, summarizer_settings
-from letta.streaming_interface import StreamingRefreshCLIInterface
-from letta.system import get_heartbeat, get_token_limit_warning, package_function_response, package_summarize_message, package_user_message
-from letta.utils import count_tokens, get_friendly_error_msg, get_tool_call_id, log_telemetry, parse_json, validate_function_response
-
-logger = get_logger(__name__)
-
-
-class BaseAgent(ABC):
- """
- Abstract class for all agents.
- Only one interface is required: step.
- """
-
- @abstractmethod
- def step(
- self,
- input_messages: List[MessageCreate],
- ) -> LettaUsageStatistics:
- """
- Top-level event message handler for the agent.
- """
- raise NotImplementedError
-
-
-class Agent(BaseAgent):
- def __init__(
- self,
- interface: Optional[Union[AgentInterface, StreamingRefreshCLIInterface]],
- agent_state: AgentState, # in-memory representation of the agent state (read from multiple tables)
- user: User,
- # extras
- first_message_verify_mono: bool = True, # TODO move to config?
- # MCP sessions, state held in-memory in the server
- mcp_clients: Optional[Dict[str, AsyncBaseMCPClient]] = None,
- save_last_response: bool = False,
- ):
- assert isinstance(agent_state.memory, Memory), f"Memory object is not of type Memory: {type(agent_state.memory)}"
- # Hold a copy of the state that was used to init the agent
- self.agent_state = agent_state
- assert isinstance(self.agent_state.memory, Memory), f"Memory object is not of type Memory: {type(self.agent_state.memory)}"
-
- self.user = user
-
- # initialize a tool rules solver
- self.tool_rules_solver = ToolRulesSolver(tool_rules=agent_state.tool_rules)
-
- # gpt-4, gpt-3.5-turbo, ...
- self.model = self.agent_state.llm_config.model
- self.supports_structured_output = check_supports_structured_output(model=self.model, tool_rules=agent_state.tool_rules)
-
- # if there are tool rules, print out a warning
- if not self.supports_structured_output and agent_state.tool_rules:
- for rule in agent_state.tool_rules:
- if not isinstance(rule, TerminalToolRule):
- warnings.warn("Tool rules only work reliably for model backends that support structured outputs (e.g. OpenAI gpt-4o).")
- break
-
- # state managers
- self.block_manager = BlockManager()
-
- # Interface must implement:
- # - internal_monologue
- # - assistant_message
- # - function_message
- # ...
- # Different interfaces can handle events differently
- # e.g., print in CLI vs send a discord message with a discord bot
- self.interface = interface
-
- # Create the persistence manager object based on the AgentState info
- self.message_manager = MessageManager()
- self.passage_manager = PassageManager()
- self.provider_manager = ProviderManager()
- self.agent_manager = AgentManager()
- self.job_manager = JobManager()
- self.step_manager = StepManager()
- self.telemetry_manager = TelemetryManager() if settings.llm_api_logging else NoopTelemetryManager()
-
- # State needed for heartbeat pausing
-
- self.first_message_verify_mono = first_message_verify_mono
-
- # Controls if the convo memory pressure warning is triggered
- # When an alert is sent in the message queue, set this to True (to avoid repeat alerts)
- # When the summarizer is run, set this back to False (to reset)
- self.agent_alerted_about_memory_pressure = False
-
- # Load last function response from message history
- self.last_function_response = self.load_last_function_response()
-
- # Save last responses in memory
- self.save_last_response = save_last_response
- self.last_response_messages = []
-
- # Logger that the Agent specifically can use, will also report the agent_state ID with the logs
- self.logger = get_logger(agent_state.id)
-
- # MCPClient, state/sessions managed by the server
- # TODO: This is temporary, as a bridge
- self.mcp_clients = None
- # TODO: no longer supported
- # if mcp_clients:
- # self.mcp_clients = {client_id: client.to_sync_client() for client_id, client in mcp_clients.items()}
-
- def load_last_function_response(self):
- """Load the last function response from message history"""
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- for i in range(len(in_context_messages) - 1, -1, -1):
- msg = in_context_messages[i]
- if msg.role == MessageRole.tool and msg.content and len(msg.content) == 1 and isinstance(msg.content[0], TextContent):
- text_content = msg.content[0].text
- try:
- response_json = json.loads(text_content)
- if response_json.get("message"):
- return response_json["message"]
- except (json.JSONDecodeError, KeyError):
- raise ValueError(f"Invalid JSON format in message: {text_content}")
- return None
-
- def ensure_read_only_block_not_modified(self, new_memory: Memory) -> None:
- """
- Throw an error if a read-only block has been modified
- """
- for label in self.agent_state.memory.list_block_labels():
- if self.agent_state.memory.get_block(label).read_only:
- if new_memory.get_block(label).value != self.agent_state.memory.get_block(label).value:
- raise ValueError(READ_ONLY_BLOCK_EDIT_ERROR)
-
- def update_memory_if_changed(self, new_memory: Memory) -> bool:
- """
- Update internal memory object and system prompt if there have been modifications.
-
- Args:
- new_memory (Memory): the new memory object to compare to the current memory object
-
- Returns:
- modified (bool): whether the memory was updated
- """
- system_message = self.message_manager.get_message_by_id(message_id=self.agent_state.message_ids[0], actor=self.user)
- if new_memory.compile() not in system_message.content[0].text:
- # update the blocks (LRW) in the DB
- for label in self.agent_state.memory.list_block_labels():
- updated_value = new_memory.get_block(label).value
- if updated_value != self.agent_state.memory.get_block(label).value:
- # update the block if it's changed
- block_id = self.agent_state.memory.get_block(label).id
- self.block_manager.update_block(block_id=block_id, block_update=BlockUpdate(value=updated_value), actor=self.user)
-
- # refresh memory from DB (using block ids)
- self.agent_state.memory = Memory(
- blocks=[self.block_manager.get_block_by_id(block.id, actor=self.user) for block in self.agent_state.memory.get_blocks()],
- file_blocks=self.agent_state.memory.file_blocks,
- prompt_template=get_prompt_template_for_agent_type(self.agent_state.agent_type),
- )
-
- # NOTE: don't do this since re-buildin the memory is handled at the start of the step
- # rebuild memory - this records the last edited timestamp of the memory
- # TODO: pass in update timestamp from block edit time
- self.agent_state = self.agent_manager.rebuild_system_prompt(agent_id=self.agent_state.id, actor=self.user)
-
- return True
-
- return False
-
- def _handle_function_error_response(
- self,
- error_msg: str,
- tool_call_id: str,
- function_name: str,
- function_args: dict,
- function_response: str,
- messages: List[Message],
- tool_returns: Optional[List[ToolReturn]] = None,
- include_function_failed_message: bool = False,
- group_id: Optional[str] = None,
- ) -> List[Message]:
- """
- Handle error from function call response
- """
- # Update tool rules
- self.last_function_response = function_response
- self.tool_rules_solver.register_tool_call(function_name)
-
- # Extend conversation with function response
- function_response = package_function_response(False, error_msg, self.agent_state.timezone)
- new_message = Message(
- agent_id=self.agent_state.id,
- # Base info OpenAI-style
- model=self.model,
- role="tool",
- name=function_name, # NOTE: when role is 'tool', the 'name' is the function name, not agent name
- content=[TextContent(text=function_response)],
- tool_call_id=tool_call_id,
- # Letta extras
- tool_returns=tool_returns,
- group_id=group_id,
- )
- messages.append(new_message)
- self.interface.function_message(f"Error: {error_msg}", msg_obj=new_message, chunk_index=0)
- if include_function_failed_message:
- self.interface.function_message(f"Ran {function_name}({function_args})", msg_obj=new_message)
-
- # Return updated messages
- return messages
-
- def _runtime_override_tool_json_schema(
- self,
- functions_list: List[Dict | None],
- ) -> List[Dict | None]:
- """Override the tool JSON schema at runtime for a particular tool if conditions are met."""
-
- # Currently just injects `send_message` with a `response_format` if provided to the agent.
- if self.agent_state.response_format and self.agent_state.response_format.type != ResponseFormatType.text:
- for func in functions_list:
- if func["name"] == SEND_MESSAGE_TOOL_NAME:
- if self.agent_state.response_format.type == ResponseFormatType.json_schema:
- func["parameters"]["properties"]["message"] = self.agent_state.response_format.json_schema["schema"]
- if self.agent_state.response_format.type == ResponseFormatType.json_object:
- func["parameters"]["properties"]["message"] = {
- "type": "object",
- "description": "Message contents. All unicode (including emojis) are supported.",
- "additionalProperties": True,
- "properties": {},
- }
- break
- return functions_list
-
- @trace_method
- def _get_ai_reply(
- self,
- message_sequence: List[Message],
- function_call: Optional[str] = None,
- first_message: bool = False,
- stream: bool = False, # TODO move to config?
- empty_response_retry_limit: int = 3,
- backoff_factor: float = 0.5, # delay multiplier for exponential backoff
- max_delay: float = 10.0, # max delay between retries
- step_count: Optional[int] = None,
- last_function_failed: bool = False,
- put_inner_thoughts_first: bool = True,
- step_id: Optional[str] = None,
- ) -> ChatCompletionResponse | None:
- """Get response from LLM API with robust retry mechanism."""
- log_telemetry(self.logger, "_get_ai_reply start")
- available_tools = set([t.name for t in self.agent_state.tools])
- agent_state_tool_jsons = [t.json_schema for t in self.agent_state.tools]
-
- # Get allowed tools or allow all if none are allowed
- allowed_tool_names = self.tool_rules_solver.get_allowed_tool_names(
- available_tools=available_tools, last_function_response=self.last_function_response
- ) or list(available_tools)
-
- # Don't allow a tool to be called if it failed last time
- if last_function_failed and self.tool_rules_solver.tool_call_history:
- allowed_tool_names = [f for f in allowed_tool_names if f != self.tool_rules_solver.tool_call_history[-1]]
- if not allowed_tool_names:
- return None
-
- allowed_functions = [func for func in agent_state_tool_jsons if func["name"] in allowed_tool_names]
- # Extract terminal tool names from tool rules
- terminal_tool_names = {rule.tool_name for rule in self.tool_rules_solver.terminal_tool_rules}
- allowed_functions = runtime_override_tool_json_schema(
- tool_list=allowed_functions,
- response_format=self.agent_state.response_format,
- request_heartbeat=True,
- terminal_tools=terminal_tool_names,
- )
-
- # For the first message, force the initial tool if one is specified
- force_tool_call = None
- if (
- step_count is not None
- and step_count == 0
- and not self.supports_structured_output
- and len(self.tool_rules_solver.init_tool_rules) > 0
- ):
- # TODO: This just seems wrong? What if there are more than 1 init tool rules?
- force_tool_call = self.tool_rules_solver.init_tool_rules[0].tool_name
- # Force a tool call if exactly one tool is specified
- elif step_count is not None and step_count > 0 and len(allowed_tool_names) == 1:
- force_tool_call = allowed_tool_names[0]
-
- for attempt in range(1, empty_response_retry_limit + 1):
- try:
- log_telemetry(self.logger, "_get_ai_reply create start")
- # New LLM client flow
- llm_client = LLMClient.create(
- provider_type=self.agent_state.llm_config.model_endpoint_type,
- put_inner_thoughts_first=put_inner_thoughts_first,
- actor=self.user,
- )
-
- if llm_client and not stream:
- response = llm_client.send_llm_request(
- messages=message_sequence,
- llm_config=self.agent_state.llm_config,
- tools=allowed_functions,
- force_tool_call=force_tool_call,
- telemetry_manager=self.telemetry_manager,
- step_id=step_id,
- )
- else:
- # Fallback to existing flow
- for message in message_sequence:
- if isinstance(message.content, list):
-
- def get_fallback_text_content(content):
- if isinstance(content, ImageContent):
- return TextContent(text="[Image Here]")
- return content
-
- message.content = [get_fallback_text_content(content) for content in message.content]
-
- response = create(
- llm_config=self.agent_state.llm_config,
- messages=message_sequence,
- user_id=self.agent_state.created_by_id,
- functions=allowed_functions,
- # functions_python=self.functions_python, do we need this?
- function_call=function_call,
- first_message=first_message,
- force_tool_call=force_tool_call,
- stream=stream,
- stream_interface=self.interface,
- put_inner_thoughts_first=put_inner_thoughts_first,
- name=self.agent_state.name,
- telemetry_manager=self.telemetry_manager,
- step_id=step_id,
- actor=self.user,
- )
- log_telemetry(self.logger, "_get_ai_reply create finish")
-
- # These bottom two are retryable
- if len(response.choices) == 0 or response.choices[0] is None:
- raise ValueError(f"API call returned an empty message: {response}")
-
- if response.choices[0].finish_reason not in ["stop", "function_call", "tool_calls"]:
- if response.choices[0].finish_reason == "length":
- # This is not retryable, hence RuntimeError v.s. ValueError
- raise RuntimeError("Finish reason was length (maximum context length)")
- else:
- raise ValueError(f"Bad finish reason from API: {response.choices[0].finish_reason}")
- log_telemetry(self.logger, "_handle_ai_response finish")
-
- except ValueError as ve:
- if attempt >= empty_response_retry_limit:
- warnings.warn(f"Retry limit reached. Final error: {ve}")
- log_telemetry(self.logger, "_handle_ai_response finish ValueError")
- raise Exception(f"Retries exhausted and no valid response received. Final error: {ve}")
- else:
- delay = min(backoff_factor * (2 ** (attempt - 1)), max_delay)
- warnings.warn(f"Attempt {attempt} failed: {ve}. Retrying in {delay} seconds...")
- time.sleep(delay)
- continue
-
- except Exception as e:
- # For non-retryable errors, exit immediately
- log_telemetry(self.logger, "_handle_ai_response finish generic Exception")
- raise e
-
- # check if we are going over the context window: this allows for articifial constraints
- if response.usage.total_tokens > self.agent_state.llm_config.context_window:
- # trigger summarization
- log_telemetry(self.logger, "_get_ai_reply summarize_messages_inplace")
- self.summarize_messages_inplace()
-
- # return the response
- return response
-
- log_telemetry(self.logger, "_handle_ai_response finish catch-all exception")
- raise Exception("Retries exhausted and no valid response received.")
-
- @trace_method
- def _handle_ai_response(
- self,
- response_message: ChatCompletionMessage, # TODO should we eventually move the Message creation outside of this function?
- override_tool_call_id: bool = False,
- # If we are streaming, we needed to create a Message ID ahead of time,
- # and now we want to use it in the creation of the Message object
- # TODO figure out a cleaner way to do this
- response_message_id: Optional[str] = None,
- group_id: Optional[str] = None,
- ) -> Tuple[List[Message], bool, bool]:
- """Handles parsing and function execution"""
- log_telemetry(self.logger, "_handle_ai_response start")
- # Hacky failsafe for now to make sure we didn't implement the streaming Message ID creation incorrectly
- if response_message_id is not None:
- assert response_message_id.startswith("message-"), response_message_id
-
- messages = [] # append these to the history when done
- function_name = None
- function_args = {}
- chunk_index = 0
-
- # Step 2: check if LLM wanted to call a function
- if response_message.function_call or (response_message.tool_calls is not None and len(response_message.tool_calls) > 0):
- if response_message.function_call:
- raise DeprecationWarning(response_message)
- if response_message.tool_calls is not None and len(response_message.tool_calls) > 1:
- # raise NotImplementedError(f">1 tool call not supported")
- # TODO eventually support sequential tool calling
- self.logger.warning(f">1 tool call not supported, using index=0 only\n{response_message.tool_calls}")
- response_message.tool_calls = [response_message.tool_calls[0]]
- assert response_message.tool_calls is not None and len(response_message.tool_calls) > 0
-
- # generate UUID for tool call
- if override_tool_call_id or response_message.function_call:
- warnings.warn("Overriding the tool call can result in inconsistent tool call IDs during streaming")
- tool_call_id = get_tool_call_id() # needs to be a string for JSON
- response_message.tool_calls[0].id = tool_call_id
- else:
- tool_call_id = response_message.tool_calls[0].id
- assert tool_call_id is not None # should be defined
-
- # only necessary to add the tool_call_id to a function call (antipattern)
- # response_message_dict = response_message.model_dump()
- # response_message_dict["tool_call_id"] = tool_call_id
-
- # role: assistant (requesting tool call, set tool call ID)
- messages.append(
- # NOTE: we're recreating the message here
- # TODO should probably just overwrite the fields?
- Message.dict_to_message(
- id=response_message_id,
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=response_message.model_dump(),
- name=self.agent_state.name,
- group_id=group_id,
- )
- ) # extend conversation with assistant's reply
- self.logger.debug(f"Function call message: {messages[-1]}")
-
- nonnull_content = False
- if response_message.content or response_message.reasoning_content or response_message.redacted_reasoning_content:
- # The content if then internal monologue, not chat
- self.interface.internal_monologue(response_message.content, msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
- # Flag to avoid printing a duplicate if inner thoughts get popped from the function call
- nonnull_content = True
-
- # Step 3: call the function
- # Note: the JSON response may not always be valid; be sure to handle errors
- function_call = (
- response_message.function_call if response_message.function_call is not None else response_message.tool_calls[0].function
- )
- function_name = function_call.name
- self.logger.info(f"Request to call function {function_name} with tool_call_id: {tool_call_id}")
-
- # Failure case 1: function name is wrong (not in agent_state.tools)
- target_letta_tool = None
- for t in self.agent_state.tools:
- if t.name == function_name:
- # This force refreshes the target_letta_tool from the database
- # We only do this on name match to confirm that the agent state contains a specific tool with the right name
- target_letta_tool = ToolManager().get_tool_by_name(tool_name=function_name, actor=self.user)
- break
-
- if not target_letta_tool:
- error_msg = f"No function named {function_name}"
- function_response = "None" # more like "never ran?"
- messages = self._handle_function_error_response(
- error_msg, tool_call_id, function_name, function_args, function_response, messages, group_id=group_id
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # Failure case 2: function name is OK, but function args are bad JSON
- try:
- raw_function_args = function_call.arguments
- function_args = parse_json(raw_function_args)
- if not isinstance(function_args, dict):
- raise ValueError(f"Function arguments are not a dictionary: {function_args} (raw={raw_function_args})")
- except Exception as e:
- print(e)
- error_msg = f"Error parsing JSON for function '{function_name}' arguments: {function_call.arguments}"
- function_response = "None" # more like "never ran?"
- messages = self._handle_function_error_response(
- error_msg, tool_call_id, function_name, function_args, function_response, messages, group_id=group_id
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # Check if inner thoughts is in the function call arguments (possible apparently if you are using Azure)
- if INNER_THOUGHTS_KWARG in function_args:
- response_message.content = function_args.pop(INNER_THOUGHTS_KWARG)
- # The content if then internal monologue, not chat
- if response_message.content and not nonnull_content:
- self.interface.internal_monologue(response_message.content, msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
-
- # (Still parsing function args)
- # Handle requests for immediate heartbeat
- heartbeat_request = function_args.pop("request_heartbeat", None)
-
- # Edge case: heartbeat_request is returned as a stringified boolean, we will attempt to parse:
- if isinstance(heartbeat_request, str) and heartbeat_request.lower().strip() == "true":
- heartbeat_request = True
-
- if heartbeat_request is None:
- heartbeat_request = False
-
- if not isinstance(heartbeat_request, bool):
- self.logger.warning(
- f"{CLI_WARNING_PREFIX}'request_heartbeat' arg parsed was not a bool or None, type={type(heartbeat_request)}, value={heartbeat_request}"
- )
- heartbeat_request = False
-
- # Failure case 3: function failed during execution
- # NOTE: the msg_obj associated with the "Running " message is the prior assistant message, not the function/tool role message
- # this is because the function/tool role message is only created once the function/tool has executed/returned
-
- # handle cases where we return a json message
- if "message" in function_args:
- function_args["message"] = str(function_args.get("message", ""))
- self.interface.function_message(f"Running {function_name}({function_args})", msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index = 0 # reset chunk index after assistant message
- try:
- # handle tool execution (sandbox) and state updates
- log_telemetry(
- self.logger, "_handle_ai_response execute tool start", function_name=function_name, function_args=function_args
- )
- log_event(
- "tool_call_initiated",
- attributes={
- "function_name": function_name,
- "target_letta_tool": target_letta_tool.model_dump(),
- **{f"function_args.{k}": v for k, v in function_args.items()},
- },
- )
-
- tool_execution_result = self.execute_tool_and_persist_state(function_name, function_args, target_letta_tool)
- function_response = tool_execution_result.func_return
-
- log_event(
- "tool_call_ended",
- attributes={
- "function_response": function_response,
- "tool_execution_result": tool_execution_result.model_dump(),
- },
- )
- log_telemetry(
- self.logger, "_handle_ai_response execute tool finish", function_name=function_name, function_args=function_args
- )
-
- if tool_execution_result and tool_execution_result.status == "error":
- tool_return = ToolReturn(
- status=tool_execution_result.status, stdout=tool_execution_result.stdout, stderr=tool_execution_result.stderr
- )
- messages = self._handle_function_error_response(
- function_response,
- tool_call_id,
- function_name,
- function_args,
- function_response,
- messages,
- [tool_return],
- group_id=group_id,
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # handle trunction
- if function_name in ["conversation_search", "conversation_search_date", "archival_memory_search"]:
- # with certain functions we rely on the paging mechanism to handle overflow
- truncate = False
- else:
- # but by default, we add a truncation safeguard to prevent bad functions from
- # overflow the agent context window
- truncate = True
-
- # get the function response limit
- return_char_limit = target_letta_tool.return_char_limit
- function_response_string = validate_function_response(
- function_response, return_char_limit=return_char_limit, truncate=truncate
- )
- function_args.pop("self", None)
- function_response = package_function_response(True, function_response_string, self.agent_state.timezone)
- function_failed = False
- except Exception as e:
- function_args.pop("self", None)
- # error_msg = f"Error calling function {function_name} with args {function_args}: {str(e)}"
- # Less detailed - don't provide full args, idea is that it should be in recent context so no need (just adds noise)
- error_msg = get_friendly_error_msg(function_name=function_name, exception_name=type(e).__name__, exception_message=str(e))
- error_msg_user = f"{error_msg}\n{traceback.format_exc()}"
- self.logger.error(error_msg_user)
- messages = self._handle_function_error_response(
- error_msg,
- tool_call_id,
- function_name,
- function_args,
- function_response,
- messages,
- [ToolReturn(status="error", stderr=[error_msg_user])],
- include_function_failed_message=True,
- group_id=group_id,
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # Step 4: check if function response is an error
- if function_response_string.startswith(ERROR_MESSAGE_PREFIX):
- error_msg = function_response_string
- tool_return = ToolReturn(
- status=tool_execution_result.status,
- stdout=tool_execution_result.stdout,
- stderr=tool_execution_result.stderr,
- )
- messages = self._handle_function_error_response(
- error_msg,
- tool_call_id,
- function_name,
- function_args,
- function_response,
- messages,
- [tool_return],
- include_function_failed_message=True,
- group_id=group_id,
- )
- return messages, False, True # force a heartbeat to allow agent to handle error
-
- # If no failures happened along the way: ...
- # Step 5: send the info on the function call and function response to GPT
- tool_return = ToolReturn(
- status=tool_execution_result.status,
- stdout=tool_execution_result.stdout,
- stderr=tool_execution_result.stderr,
- )
- messages.append(
- Message(
- agent_id=self.agent_state.id,
- # Base info OpenAI-style
- model=self.model,
- role="tool",
- name=function_name, # NOTE: when role is 'tool', the 'name' is the function name, not agent name
- content=[TextContent(text=function_response)],
- tool_call_id=tool_call_id,
- # Letta extras
- tool_returns=[tool_return],
- group_id=group_id,
- )
- ) # extend conversation with function response
- self.interface.function_message(f"Ran {function_name}({function_args})", msg_obj=messages[-1], chunk_index=chunk_index)
- self.interface.function_message(f"Success: {function_response_string}", msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
- self.last_function_response = function_response
-
- else:
- # Standard non-function reply
- messages.append(
- Message.dict_to_message(
- id=response_message_id,
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=response_message.model_dump(),
- name=self.agent_state.name,
- group_id=group_id,
- )
- ) # extend conversation with assistant's reply
- self.interface.internal_monologue(response_message.content, msg_obj=messages[-1], chunk_index=chunk_index)
- chunk_index += 1
- heartbeat_request = False
- function_failed = False
-
- # rebuild memory
- # TODO: @charles please check this
- self.agent_state = self.agent_manager.rebuild_system_prompt(agent_id=self.agent_state.id, actor=self.user)
-
- # Update ToolRulesSolver state with last called function
- self.tool_rules_solver.register_tool_call(function_name)
- # Update heartbeat request according to provided tool rules
- if self.tool_rules_solver.has_children_tools(function_name):
- heartbeat_request = True
- elif self.tool_rules_solver.is_terminal_tool(function_name):
- heartbeat_request = False
-
- # if continue tool rule, then must request a heartbeat
- # TODO: dont even include heartbeats in the args
- if self.tool_rules_solver.is_continue_tool(function_name):
- heartbeat_request = True
-
- log_telemetry(self.logger, "_handle_ai_response finish")
- return messages, heartbeat_request, function_failed
-
- @trace_method
- def step(
- self,
- input_messages: List[MessageCreate],
- # additional args
- chaining: bool = True,
- max_chaining_steps: Optional[int] = None,
- put_inner_thoughts_first: bool = True,
- **kwargs,
- ) -> LettaUsageStatistics:
- """Run Agent.step in a loop, handling chaining via heartbeat requests and function failures"""
- # Defensively clear the tool rules solver history
- # Usually this would be extraneous as Agent loop is re-loaded on every message send
- # But just to be safe
- self.tool_rules_solver.clear_tool_history()
-
- # Convert MessageCreate objects to Message objects
- next_input_messages = convert_message_creates_to_messages(input_messages, self.agent_state.id, self.agent_state.timezone)
- counter = 0
- total_usage = UsageStatistics()
- step_count = 0
- function_failed = False
- steps_messages = []
- while True:
- kwargs["first_message"] = False
- kwargs["step_count"] = step_count
- kwargs["last_function_failed"] = function_failed
- step_response = self.inner_step(
- messages=next_input_messages,
- put_inner_thoughts_first=put_inner_thoughts_first,
- **kwargs,
- )
-
- heartbeat_request = step_response.heartbeat_request
- function_failed = step_response.function_failed
- token_warning = step_response.in_context_memory_warning
- usage = step_response.usage
- steps_messages.append(step_response.messages)
-
- step_count += 1
- total_usage += usage
- counter += 1
- self.interface.step_complete()
-
- # logger.debug("Saving agent state")
- # save updated state
- save_agent(self)
-
- # Chain stops
- if not chaining:
- self.logger.info("No chaining, stopping after one step")
- break
- elif max_chaining_steps is not None and counter > max_chaining_steps:
- self.logger.info(f"Hit max chaining steps, stopping after {counter} steps")
- break
- # Chain handlers
- elif token_warning and summarizer_settings.send_memory_warning_message:
- assert self.agent_state.created_by_id is not None
- next_input_messages = [
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict={
- "role": "user", # TODO: change to system?
- "content": get_token_limit_warning(),
- },
- ),
- ]
- continue # always chain
- elif function_failed:
- assert self.agent_state.created_by_id is not None
- next_input_messages = [
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict={
- "role": "user", # TODO: change to system?
- "content": get_heartbeat(self.agent_state.timezone, FUNC_FAILED_HEARTBEAT_MESSAGE),
- },
- )
- ]
- continue # always chain
- elif heartbeat_request:
- assert self.agent_state.created_by_id is not None
- next_input_messages = [
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict={
- "role": "user", # TODO: change to system?
- "content": get_heartbeat(self.agent_state.timezone, REQ_HEARTBEAT_MESSAGE),
- },
- )
- ]
- continue # always chain
- # Letta no-op / yield
- else:
- break
-
- if self.agent_state.message_buffer_autoclear:
- self.logger.info("Autoclearing message buffer")
- self.agent_state = self.agent_manager.trim_all_in_context_messages_except_system(self.agent_state.id, actor=self.user)
-
- return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count, steps_messages=steps_messages)
-
- def inner_step(
- self,
- messages: List[Message],
- first_message: bool = False,
- first_message_retry_limit: int = FIRST_MESSAGE_ATTEMPTS,
- skip_verify: bool = False,
- stream: bool = False, # TODO move to config?
- step_count: Optional[int] = None,
- metadata: Optional[dict] = None,
- summarize_attempt_count: int = 0,
- last_function_failed: bool = False,
- put_inner_thoughts_first: bool = True,
- ) -> AgentStepResponse:
- """Runs a single step in the agent loop (generates at most one LLM call)"""
- try:
- # Extract job_id from metadata if present
- job_id = metadata.get("job_id") if metadata else None
-
- # Declare step_id for the given step to be used as the step is processing.
- step_id = generate_step_id()
-
- # Step 0: update core memory
- # only pulling latest block data if shared memory is being used
- current_persisted_memory = Memory(
- blocks=[self.block_manager.get_block_by_id(block.id, actor=self.user) for block in self.agent_state.memory.get_blocks()],
- file_blocks=self.agent_state.memory.file_blocks,
- prompt_template=get_prompt_template_for_agent_type(self.agent_state.agent_type),
- ) # read blocks from DB
- self.update_memory_if_changed(current_persisted_memory)
-
- # Step 1: add user message
- if not all(isinstance(m, Message) for m in messages):
- raise ValueError(f"messages should be a list of Message, got {[type(m) for m in messages]}")
-
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- input_message_sequence = in_context_messages + messages
-
- if (
- len(input_message_sequence) > 1
- and input_message_sequence[-1].role != "user"
- and input_message_sequence[-1].group_id is None
- ):
- self.logger.warning(f"{CLI_WARNING_PREFIX}Attempting to run ChatCompletion without user as the last message in the queue")
-
- # Step 2: send the conversation and available functions to the LLM
- response = self._get_ai_reply(
- message_sequence=input_message_sequence,
- first_message=first_message,
- stream=stream,
- step_count=step_count,
- last_function_failed=last_function_failed,
- put_inner_thoughts_first=put_inner_thoughts_first,
- step_id=step_id,
- )
- if not response:
- # EDGE CASE: Function call failed AND there's no tools left for agent to call -> return early
- return AgentStepResponse(
- messages=input_message_sequence,
- heartbeat_request=False,
- function_failed=False, # NOTE: this is different from other function fails. We force to return early
- in_context_memory_warning=False,
- usage=UsageStatistics(),
- )
-
- # Step 3: check if LLM wanted to call a function
- # (if yes) Step 4: call the function
- # (if yes) Step 5: send the info on the function call and function response to LLM
- response_message = response.choices[0].message
-
- response_message.model_copy() # TODO why are we copying here?
- all_response_messages, heartbeat_request, function_failed = self._handle_ai_response(
- response_message,
- # TODO this is kind of hacky, find a better way to handle this
- # the only time we set up message creation ahead of time is when streaming is on
- response_message_id=response.id if stream else None,
- group_id=input_message_sequence[-1].group_id,
- )
-
- # Step 6: extend the message history
- if len(messages) > 0:
- all_new_messages = messages + all_response_messages
- else:
- all_new_messages = all_response_messages
-
- if self.save_last_response:
- self.last_response_messages = all_response_messages
-
- # Check the memory pressure and potentially issue a memory pressure warning
- current_total_tokens = response.usage.total_tokens
- active_memory_warning = False
-
- # We can't do summarize logic properly if context_window is undefined
- if self.agent_state.llm_config.context_window is None:
- # Fallback if for some reason context_window is missing, just set to the default
- print(f"{CLI_WARNING_PREFIX}could not find context_window in config, setting to default {LLM_MAX_TOKENS['DEFAULT']}")
- print(f"{self.agent_state}")
- self.agent_state.llm_config.context_window = (
- LLM_MAX_TOKENS[self.model] if (self.model is not None and self.model in LLM_MAX_TOKENS) else LLM_MAX_TOKENS["DEFAULT"]
- )
-
- if current_total_tokens > summarizer_settings.memory_warning_threshold * int(self.agent_state.llm_config.context_window):
- logger.warning(
- f"{CLI_WARNING_PREFIX}last response total_tokens ({current_total_tokens}) > {summarizer_settings.memory_warning_threshold * int(self.agent_state.llm_config.context_window)}"
- )
-
- log_event(
- name="memory_pressure_warning",
- attributes={
- "current_total_tokens": current_total_tokens,
- "context_window_limit": self.agent_state.llm_config.context_window,
- },
- )
- # Only deliver the alert if we haven't already (this period)
- if not self.agent_alerted_about_memory_pressure:
- active_memory_warning = True
- self.agent_alerted_about_memory_pressure = True # it's up to the outer loop to handle this
-
- else:
- logger.info(
- f"last response total_tokens ({current_total_tokens}) < {summarizer_settings.memory_warning_threshold * int(self.agent_state.llm_config.context_window)}"
- )
-
- # Log step - this must happen before messages are persisted
- step = self.step_manager.log_step(
- actor=self.user,
- agent_id=self.agent_state.id,
- provider_name=self.agent_state.llm_config.model_endpoint_type,
- provider_category=self.agent_state.llm_config.provider_category or "base",
- model=self.agent_state.llm_config.model,
- model_endpoint=self.agent_state.llm_config.model_endpoint,
- context_window_limit=self.agent_state.llm_config.context_window,
- usage=response.usage,
- provider_id=self.provider_manager.get_provider_id_from_name(
- self.agent_state.llm_config.provider_name,
- actor=self.user,
- ),
- job_id=job_id,
- step_id=step_id,
- project_id=self.agent_state.project_id,
- status=StepStatus.SUCCESS, # Set to SUCCESS since we're logging after successful completion
- )
- for message in all_new_messages:
- message.step_id = step.id
-
- # Persisting into Messages
- self.agent_state = self.agent_manager.append_to_in_context_messages(
- all_new_messages, agent_id=self.agent_state.id, actor=self.user
- )
- if job_id:
- for message in all_new_messages:
- if message.role != "user":
- self.job_manager.add_message_to_job(
- job_id=job_id,
- message_id=message.id,
- actor=self.user,
- )
-
- return AgentStepResponse(
- messages=all_new_messages,
- heartbeat_request=heartbeat_request,
- function_failed=function_failed,
- in_context_memory_warning=active_memory_warning,
- usage=response.usage,
- )
-
- except Exception as e:
- logger.error(f"step() failed\nmessages = {messages}\nerror = {e}")
-
- # If we got a context alert, try trimming the messages length, then try again
- if is_context_overflow_error(e):
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
-
- # TODO: this is a patch to resolve immediate issues, should be removed once the summarizer is fixes
- if self.agent_state.message_buffer_autoclear:
- # no calling the summarizer in this case
- logger.error(
- f"step() failed with an exception that looks like a context window overflow, but message buffer is set to autoclear, so skipping: '{str(e)}'"
- )
- raise e
-
- if summarize_attempt_count <= summarizer_settings.max_summarizer_retries:
- logger.warning(
- f"context window exceeded with limit {self.agent_state.llm_config.context_window}, attempting to summarize ({summarize_attempt_count}/{summarizer_settings.max_summarizer_retries}"
- )
- # A separate API call to run a summarizer
- self.summarize_messages_inplace()
-
- # Try step again
- return self.inner_step(
- messages=messages,
- first_message=first_message,
- first_message_retry_limit=first_message_retry_limit,
- skip_verify=skip_verify,
- stream=stream,
- metadata=metadata,
- summarize_attempt_count=summarize_attempt_count + 1,
- )
- else:
- err_msg = f"Ran summarizer {summarize_attempt_count - 1} times for agent id={self.agent_state.id}, but messages are still overflowing the context window."
- token_counts = (get_token_counts_for_messages(in_context_messages),)
- logger.error(err_msg)
- logger.error(f"num_in_context_messages: {len(self.agent_state.message_ids)}")
- logger.error(f"token_counts: {token_counts}")
- raise ContextWindowExceededError(
- err_msg,
- details={
- "num_in_context_messages": len(self.agent_state.message_ids),
- "in_context_messages_text": [m.content for m in in_context_messages],
- "token_counts": token_counts,
- },
- )
-
- else:
- logger.error(f"step() failed with an unrecognized exception: '{str(e)}'")
- traceback.print_exc()
- raise e
-
- def step_user_message(self, user_message_str: str, **kwargs) -> AgentStepResponse:
- """Takes a basic user message string, turns it into a stringified JSON with extra metadata, then sends it to the agent
-
- Example:
- -> user_message_str = 'hi'
- -> {'message': 'hi', 'type': 'user_message', ...}
- -> json.dumps(...)
- -> agent.step(messages=[Message(role='user', text=...)])
- """
- # Wrap with metadata, dumps to JSON
- assert user_message_str and isinstance(user_message_str, str), (
- f"user_message_str should be a non-empty string, got {type(user_message_str)}"
- )
- user_message_json_str = package_user_message(user_message_str, self.agent_state.timezone)
-
- # Validate JSON via save/load
- user_message = validate_json(user_message_json_str)
- cleaned_user_message_text, name = strip_name_field_from_user_message(user_message)
-
- # Turn into a dict
- openai_message_dict = {"role": "user", "content": cleaned_user_message_text, "name": name}
-
- # Create the associated Message object (in the database)
- assert self.agent_state.created_by_id is not None, "User ID is not set"
- user_message = Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=openai_message_dict,
- # created_at=timestamp,
- )
-
- return self.inner_step(messages=[user_message], **kwargs)
-
- def summarize_messages_inplace(self):
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
- in_context_messages_openai_no_system = in_context_messages_openai[1:]
- token_counts = get_token_counts_for_messages(in_context_messages)
- logger.info(f"System message token count={token_counts[0]}")
- logger.info(f"token_counts_no_system={token_counts[1:]}")
-
- if in_context_messages_openai[0]["role"] != "system":
- raise RuntimeError(f"in_context_messages_openai[0] should be system (instead got {in_context_messages_openai[0]})")
-
- # If at this point there's nothing to summarize, throw an error
- if len(in_context_messages_openai_no_system) == 0:
- raise ContextWindowExceededError(
- "Not enough messages to compress for summarization",
- details={
- "num_candidate_messages": len(in_context_messages_openai_no_system),
- "num_total_messages": len(in_context_messages_openai),
- },
- )
-
- cutoff = calculate_summarizer_cutoff(in_context_messages=in_context_messages, token_counts=token_counts, logger=logger)
- message_sequence_to_summarize = in_context_messages[1:cutoff] # do NOT get rid of the system message
- logger.info(f"Attempting to summarize {len(message_sequence_to_summarize)} messages of {len(in_context_messages)}")
-
- # We can't do summarize logic properly if context_window is undefined
- if self.agent_state.llm_config.context_window is None:
- # Fallback if for some reason context_window is missing, just set to the default
- logger.warning(f"{CLI_WARNING_PREFIX}could not find context_window in config, setting to default {LLM_MAX_TOKENS['DEFAULT']}")
- self.agent_state.llm_config.context_window = (
- LLM_MAX_TOKENS[self.model] if (self.model is not None and self.model in LLM_MAX_TOKENS) else LLM_MAX_TOKENS["DEFAULT"]
- )
-
- summary = summarize_messages(
- agent_state=self.agent_state, message_sequence_to_summarize=message_sequence_to_summarize, actor=self.user
- )
- logger.info(f"Got summary: {summary}")
-
- # Metadata that's useful for the agent to see
- all_time_message_count = self.message_manager.size(agent_id=self.agent_state.id, actor=self.user)
- remaining_message_count = 1 + len(in_context_messages) - cutoff # System + remaining
- hidden_message_count = all_time_message_count - remaining_message_count
- summary_message_count = len(message_sequence_to_summarize)
- summary_message = package_summarize_message(
- summary, summary_message_count, hidden_message_count, all_time_message_count, self.agent_state.timezone
- )
- logger.info(f"Packaged into message: {summary_message}")
-
- prior_len = len(in_context_messages_openai)
- self.agent_state = self.agent_manager.trim_older_in_context_messages(num=cutoff, agent_id=self.agent_state.id, actor=self.user)
- packed_summary_message = {"role": "user", "content": summary_message}
- # Prepend the summary
- self.agent_state = self.agent_manager.prepend_to_in_context_messages(
- messages=[
- Message.dict_to_message(
- agent_id=self.agent_state.id,
- model=self.model,
- openai_message_dict=packed_summary_message,
- )
- ],
- agent_id=self.agent_state.id,
- actor=self.user,
- )
-
- # reset alert
- self.agent_alerted_about_memory_pressure = False
- curr_in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
-
- current_token_count = sum(get_token_counts_for_messages(curr_in_context_messages))
- logger.info(f"Ran summarizer, messages length {prior_len} -> {len(curr_in_context_messages)}")
- logger.info(f"Summarizer brought down total token count from {sum(token_counts)} -> {current_token_count}")
- log_event(
- name="summarization",
- attributes={
- "prior_length": prior_len,
- "current_length": len(curr_in_context_messages),
- "prior_token_count": sum(token_counts),
- "current_token_count": current_token_count,
- "context_window_limit": self.agent_state.llm_config.context_window,
- },
- )
-
- def add_function(self, function_name: str) -> str:
- # TODO: refactor
- raise NotImplementedError
-
- def remove_function(self, function_name: str) -> str:
- # TODO: refactor
- raise NotImplementedError
-
- def migrate_embedding(self, embedding_config: EmbeddingConfig):
- """Migrate the agent to a new embedding"""
- # TODO: archival memory
-
- # TODO: recall memory
- raise NotImplementedError()
-
- def get_context_window(self) -> ContextWindowOverview:
- """Get the context window of the agent"""
-
- system_prompt = self.agent_state.system # TODO is this the current system or the initial system?
- num_tokens_system = count_tokens(system_prompt)
- core_memory = self.agent_state.memory.compile()
- num_tokens_core_memory = count_tokens(core_memory)
-
- # Grab the in-context messages
- # conversion of messages to OpenAI dict format, which is passed to the token counter
- in_context_messages = self.agent_manager.get_in_context_messages(agent_id=self.agent_state.id, actor=self.user)
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
-
- # Check if there's a summary message in the message queue
- if (
- len(in_context_messages) > 1
- and in_context_messages[1].role == MessageRole.user
- and in_context_messages[1].content
- and len(in_context_messages[1].content) == 1
- and isinstance(in_context_messages[1].content[0], TextContent)
- # TODO remove hardcoding
- and "The following is a summary of the previous " in in_context_messages[1].content[0].text
- ):
- # Summary message exists
- text_content = in_context_messages[1].content[0].text
- assert text_content is not None
- summary_memory = text_content
- num_tokens_summary_memory = count_tokens(text_content)
- # with a summary message, the real messages start at index 2
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[2:], model=self.model)
- if len(in_context_messages_openai) > 2
- else 0
- )
-
- else:
- summary_memory = None
- num_tokens_summary_memory = 0
- # with no summary message, the real messages start at index 1
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[1:], model=self.model)
- if len(in_context_messages_openai) > 1
- else 0
- )
-
- agent_manager_passage_size = self.agent_manager.passage_size(actor=self.user, agent_id=self.agent_state.id)
- message_manager_size = self.message_manager.size(actor=self.user, agent_id=self.agent_state.id)
- external_memory_summary = PromptGenerator.compile_memory_metadata_block(
- memory_edit_timestamp=get_utc_time(),
- timezone=self.agent_state.timezone,
- previous_message_count=self.message_manager.size(actor=self.user, agent_id=self.agent_state.id),
- archival_memory_size=self.agent_manager.passage_size(actor=self.user, agent_id=self.agent_state.id),
- )
- num_tokens_external_memory_summary = count_tokens(external_memory_summary)
-
- # tokens taken up by function definitions
- agent_state_tool_jsons = [t.json_schema for t in self.agent_state.tools]
- if agent_state_tool_jsons:
- available_functions_definitions = [OpenAITool(type="function", function=f) for f in agent_state_tool_jsons]
- num_tokens_available_functions_definitions = num_tokens_from_functions(functions=agent_state_tool_jsons, model=self.model)
- else:
- available_functions_definitions = []
- num_tokens_available_functions_definitions = 0
-
- num_tokens_used_total = (
- num_tokens_system # system prompt
- + num_tokens_available_functions_definitions # function definitions
- + num_tokens_core_memory # core memory
- + num_tokens_external_memory_summary # metadata (statistics) about recall/archival
- + num_tokens_summary_memory # summary of ongoing conversation
- + num_tokens_messages # tokens taken by messages
- )
- assert isinstance(num_tokens_used_total, int)
-
- return ContextWindowOverview(
- # context window breakdown (in messages)
- num_messages=len(in_context_messages),
- num_archival_memory=agent_manager_passage_size,
- num_recall_memory=message_manager_size,
- num_tokens_external_memory_summary=num_tokens_external_memory_summary,
- external_memory_summary=external_memory_summary,
- # top-level information
- context_window_size_max=self.agent_state.llm_config.context_window,
- context_window_size_current=num_tokens_used_total,
- # context window breakdown (in tokens)
- num_tokens_system=num_tokens_system,
- system_prompt=system_prompt,
- num_tokens_core_memory=num_tokens_core_memory,
- core_memory=core_memory,
- num_tokens_summary_memory=num_tokens_summary_memory,
- summary_memory=summary_memory,
- num_tokens_messages=num_tokens_messages,
- messages=in_context_messages,
- # related to functions
- num_tokens_functions_definitions=num_tokens_available_functions_definitions,
- functions_definitions=available_functions_definitions,
- )
-
- async def get_context_window_async(self) -> ContextWindowOverview:
- if settings.environment == "PRODUCTION" and model_settings.anthropic_api_key:
- return await self.get_context_window_from_anthropic_async()
- return await self.get_context_window_from_tiktoken_async()
-
- async def get_context_window_from_tiktoken_async(self) -> ContextWindowOverview:
- """Get the context window of the agent"""
- # Grab the in-context messages
- in_context_messages = await self.message_manager.get_messages_by_ids_async(
- message_ids=self.agent_state.message_ids, actor=self.user
- )
-
- # conversion of messages to OpenAI dict format, which is passed to the token counter
- in_context_messages_openai = Message.to_openai_dicts_from_list(in_context_messages)
-
- # Extract system, memory and external summary
- if (
- len(in_context_messages) > 0
- and in_context_messages[0].role == MessageRole.system
- and in_context_messages[0].content
- and len(in_context_messages[0].content) == 1
- and isinstance(in_context_messages[0].content[0], TextContent)
- ):
- system_message = in_context_messages[0].content[0].text
-
- external_memory_marker_pos = system_message.find("###")
- core_memory_marker_pos = system_message.find("<", external_memory_marker_pos)
- if external_memory_marker_pos != -1 and core_memory_marker_pos != -1:
- system_prompt = system_message[:external_memory_marker_pos].strip()
- external_memory_summary = system_message[external_memory_marker_pos:core_memory_marker_pos].strip()
- core_memory = system_message[core_memory_marker_pos:].strip()
- else:
- # if no markers found, put everything in system message
- self.logger.info("No markers found in system message, core_memory and external_memory_summary will not be loaded")
- system_prompt = system_message
- external_memory_summary = ""
- core_memory = ""
- else:
- # if no system message, fall back on agent's system prompt
- self.logger.info("No system message found in history, core_memory and external_memory_summary will not be loaded")
- system_prompt = self.agent_state.system
- external_memory_summary = ""
- core_memory = ""
-
- num_tokens_system = count_tokens(system_prompt)
- num_tokens_core_memory = count_tokens(core_memory)
- num_tokens_external_memory_summary = count_tokens(external_memory_summary)
-
- # Check if there's a summary message in the message queue
- if (
- len(in_context_messages) > 1
- and in_context_messages[1].role == MessageRole.user
- and in_context_messages[1].content
- and len(in_context_messages[1].content) == 1
- and isinstance(in_context_messages[1].content[0], TextContent)
- # TODO remove hardcoding
- and "The following is a summary of the previous " in in_context_messages[1].content[0].text
- ):
- # Summary message exists
- text_content = in_context_messages[1].content[0].text
- assert text_content is not None
- summary_memory = text_content
- num_tokens_summary_memory = count_tokens(text_content)
- # with a summary message, the real messages start at index 2
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[2:], model=self.model)
- if len(in_context_messages_openai) > 2
- else 0
- )
-
- else:
- summary_memory = None
- num_tokens_summary_memory = 0
- # with no summary message, the real messages start at index 1
- num_tokens_messages = (
- num_tokens_from_messages(messages=in_context_messages_openai[1:], model=self.model)
- if len(in_context_messages_openai) > 1
- else 0
- )
-
- # tokens taken up by function definitions
- agent_state_tool_jsons = [t.json_schema for t in self.agent_state.tools]
- if agent_state_tool_jsons:
- available_functions_definitions = [OpenAITool(type="function", function=f) for f in agent_state_tool_jsons]
- num_tokens_available_functions_definitions = num_tokens_from_functions(functions=agent_state_tool_jsons, model=self.model)
- else:
- available_functions_definitions = []
- num_tokens_available_functions_definitions = 0
-
- num_tokens_used_total = (
- num_tokens_system # system prompt
- + num_tokens_available_functions_definitions # function definitions
- + num_tokens_core_memory # core memory
- + num_tokens_external_memory_summary # metadata (statistics) about recall/archival
- + num_tokens_summary_memory # summary of ongoing conversation
- + num_tokens_messages # tokens taken by messages
- )
- assert isinstance(num_tokens_used_total, int)
-
- passage_manager_size = await self.passage_manager.agent_passage_size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
- message_manager_size = await self.message_manager.size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
-
- return ContextWindowOverview(
- # context window breakdown (in messages)
- num_messages=len(in_context_messages),
- num_archival_memory=passage_manager_size,
- num_recall_memory=message_manager_size,
- num_tokens_external_memory_summary=num_tokens_external_memory_summary,
- external_memory_summary=external_memory_summary,
- # top-level information
- context_window_size_max=self.agent_state.llm_config.context_window,
- context_window_size_current=num_tokens_used_total,
- # context window breakdown (in tokens)
- num_tokens_system=num_tokens_system,
- system_prompt=system_prompt,
- num_tokens_core_memory=num_tokens_core_memory,
- core_memory=core_memory,
- num_tokens_summary_memory=num_tokens_summary_memory,
- summary_memory=summary_memory,
- num_tokens_messages=num_tokens_messages,
- messages=in_context_messages,
- # related to functions
- num_tokens_functions_definitions=num_tokens_available_functions_definitions,
- functions_definitions=available_functions_definitions,
- )
-
- async def get_context_window_from_anthropic_async(self) -> ContextWindowOverview:
- """Get the context window of the agent"""
- anthropic_client = LLMClient.create(provider_type=ProviderType.anthropic, actor=self.user)
- model = self.agent_state.llm_config.model if self.agent_state.llm_config.model_endpoint_type == "anthropic" else None
-
- # Grab the in-context messages
- in_context_messages = await self.message_manager.get_messages_by_ids_async(
- message_ids=self.agent_state.message_ids, actor=self.user
- )
-
- # conversion of messages to anthropic dict format, which is passed to the token counter
- in_context_messages_anthropic = Message.to_anthropic_dicts_from_list(in_context_messages)
-
- # Extract system, memory and external summary
- if (
- len(in_context_messages) > 0
- and in_context_messages[0].role == MessageRole.system
- and in_context_messages[0].content
- and len(in_context_messages[0].content) == 1
- and isinstance(in_context_messages[0].content[0], TextContent)
- ):
- system_message = in_context_messages[0].content[0].text
-
- external_memory_marker_pos = system_message.find("###")
- core_memory_marker_pos = system_message.find("<", external_memory_marker_pos)
- if external_memory_marker_pos != -1 and core_memory_marker_pos != -1:
- system_prompt = system_message[:external_memory_marker_pos].strip()
- external_memory_summary = system_message[external_memory_marker_pos:core_memory_marker_pos].strip()
- core_memory = system_message[core_memory_marker_pos:].strip()
- else:
- # if no markers found, put everything in system message
- self.logger.info("No markers found in system message, core_memory and external_memory_summary will not be loaded")
- system_prompt = system_message
- external_memory_summary = ""
- core_memory = ""
- else:
- # if no system message, fall back on agent's system prompt
- self.logger.info("No system message found in history, core_memory and external_memory_summary will not be loaded")
- system_prompt = self.agent_state.system
- external_memory_summary = ""
- core_memory = ""
-
- num_tokens_system_coroutine = anthropic_client.count_tokens(model=model, messages=[{"role": "user", "content": system_prompt}])
- num_tokens_core_memory_coroutine = (
- anthropic_client.count_tokens(model=model, messages=[{"role": "user", "content": core_memory}])
- if core_memory
- else asyncio.sleep(0, result=0)
- )
- num_tokens_external_memory_summary_coroutine = (
- anthropic_client.count_tokens(model=model, messages=[{"role": "user", "content": external_memory_summary}])
- if external_memory_summary
- else asyncio.sleep(0, result=0)
- )
-
- # Check if there's a summary message in the message queue
- if (
- len(in_context_messages) > 1
- and in_context_messages[1].role == MessageRole.user
- and in_context_messages[1].content
- and len(in_context_messages[1].content) == 1
- and isinstance(in_context_messages[1].content[0], TextContent)
- # TODO remove hardcoding
- and "The following is a summary of the previous " in in_context_messages[1].content[0].text
- ):
- # Summary message exists
- text_content = in_context_messages[1].content[0].text
- assert text_content is not None
- summary_memory = text_content
- num_tokens_summary_memory_coroutine = anthropic_client.count_tokens(
- model=model, messages=[{"role": "user", "content": summary_memory}]
- )
- # with a summary message, the real messages start at index 2
- num_tokens_messages_coroutine = (
- anthropic_client.count_tokens(model=model, messages=in_context_messages_anthropic[2:])
- if len(in_context_messages_anthropic) > 2
- else asyncio.sleep(0, result=0)
- )
-
- else:
- summary_memory = None
- num_tokens_summary_memory_coroutine = asyncio.sleep(0, result=0)
- # with no summary message, the real messages start at index 1
- num_tokens_messages_coroutine = (
- anthropic_client.count_tokens(model=model, messages=in_context_messages_anthropic[1:])
- if len(in_context_messages_anthropic) > 1
- else asyncio.sleep(0, result=0)
- )
-
- # tokens taken up by function definitions
- if self.agent_state.tools and len(self.agent_state.tools) > 0:
- available_functions_definitions = [OpenAITool(type="function", function=f.json_schema) for f in self.agent_state.tools]
- num_tokens_available_functions_definitions_coroutine = anthropic_client.count_tokens(
- model=model,
- tools=available_functions_definitions,
- )
- else:
- available_functions_definitions = []
- num_tokens_available_functions_definitions_coroutine = asyncio.sleep(0, result=0)
-
- (
- num_tokens_system,
- num_tokens_core_memory,
- num_tokens_external_memory_summary,
- num_tokens_summary_memory,
- num_tokens_messages,
- num_tokens_available_functions_definitions,
- ) = await asyncio.gather(
- num_tokens_system_coroutine,
- num_tokens_core_memory_coroutine,
- num_tokens_external_memory_summary_coroutine,
- num_tokens_summary_memory_coroutine,
- num_tokens_messages_coroutine,
- num_tokens_available_functions_definitions_coroutine,
- )
-
- num_tokens_used_total = (
- num_tokens_system # system prompt
- + num_tokens_available_functions_definitions # function definitions
- + num_tokens_core_memory # core memory
- + num_tokens_external_memory_summary # metadata (statistics) about recall/archival
- + num_tokens_summary_memory # summary of ongoing conversation
- + num_tokens_messages # tokens taken by messages
- )
- assert isinstance(num_tokens_used_total, int)
-
- passage_manager_size = await self.passage_manager.agent_passage_size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
- message_manager_size = await self.message_manager.size_async(
- agent_id=self.agent_state.id,
- actor=self.user,
- )
-
- return ContextWindowOverview(
- # context window breakdown (in messages)
- num_messages=len(in_context_messages),
- num_archival_memory=passage_manager_size,
- num_recall_memory=message_manager_size,
- num_tokens_external_memory_summary=num_tokens_external_memory_summary,
- external_memory_summary=external_memory_summary,
- # top-level information
- context_window_size_max=self.agent_state.llm_config.context_window,
- context_window_size_current=num_tokens_used_total,
- # context window breakdown (in tokens)
- num_tokens_system=num_tokens_system,
- system_prompt=system_prompt,
- num_tokens_core_memory=num_tokens_core_memory,
- core_memory=core_memory,
- num_tokens_summary_memory=num_tokens_summary_memory,
- summary_memory=summary_memory,
- num_tokens_messages=num_tokens_messages,
- messages=in_context_messages,
- # related to functions
- num_tokens_functions_definitions=num_tokens_available_functions_definitions,
- functions_definitions=available_functions_definitions,
- )
-
- def count_tokens(self) -> int:
- """Count the tokens in the current context window"""
- context_window_breakdown = self.get_context_window()
- return context_window_breakdown.context_window_size_current
-
- # TODO: Refactor into separate class v.s. large if/elses here
- def execute_tool_and_persist_state(self, function_name: str, function_args: dict, target_letta_tool: Tool) -> ToolExecutionResult:
- """
- Execute tool modifications and persist the state of the agent.
- Note: only some agent state modifications will be persisted, such as data in the AgentState ORM and block data
- """
- # TODO: add agent manager here
- orig_memory_str = self.agent_state.memory.compile()
-
- # TODO: need to have an AgentState object that actually has full access to the block data
- # this is because the sandbox tools need to be able to access block.value to edit this data
- try:
- if target_letta_tool.tool_type == ToolType.LETTA_CORE:
- # base tools are allowed to access the `Agent` object and run on the database
- callable_func = get_function_from_module(LETTA_CORE_TOOL_MODULE_NAME, function_name)
- function_args["self"] = self # need to attach self to arg since it's dynamically linked
- function_response = callable_func(**function_args)
- elif target_letta_tool.tool_type == ToolType.LETTA_MULTI_AGENT_CORE:
- callable_func = get_function_from_module(LETTA_MULTI_AGENT_TOOL_MODULE_NAME, function_name)
- function_args["self"] = self # need to attach self to arg since it's dynamically linked
- function_response = callable_func(**function_args)
- elif target_letta_tool.tool_type == ToolType.LETTA_MEMORY_CORE or target_letta_tool.tool_type == ToolType.LETTA_SLEEPTIME_CORE:
- callable_func = get_function_from_module(LETTA_CORE_TOOL_MODULE_NAME, function_name)
- agent_state_copy = self.agent_state.__deepcopy__()
- function_args["agent_state"] = agent_state_copy # need to attach self to arg since it's dynamically linked
- function_response = callable_func(**function_args)
- self.ensure_read_only_block_not_modified(
- new_memory=agent_state_copy.memory
- ) # memory editing tools cannot edit read-only blocks
- self.update_memory_if_changed(agent_state_copy.memory)
- elif target_letta_tool.tool_type == ToolType.EXTERNAL_COMPOSIO:
- action_name = generate_composio_action_from_func_name(target_letta_tool.name)
- # Get entity ID from the agent_state
- entity_id = None
- for env_var in self.agent_state.tool_exec_environment_variables:
- if env_var.key == COMPOSIO_ENTITY_ENV_VAR_KEY:
- entity_id = env_var.value
- # Get composio_api_key
- composio_api_key = get_composio_api_key(actor=self.user, logger=self.logger)
- function_response = execute_composio_action(
- action_name=action_name, args=function_args, api_key=composio_api_key, entity_id=entity_id
- )
- elif target_letta_tool.tool_type == ToolType.EXTERNAL_MCP:
- # Get the server name from the tool tag
- # TODO make a property instead?
- server_name = target_letta_tool.tags[0].split(":")[1]
-
- # Get the MCPClient from the server's handle
- # TODO these don't get raised properly
- if not self.mcp_clients:
- raise ValueError("No MCP client available to use")
- if server_name not in self.mcp_clients:
- raise ValueError(f"Unknown MCP server name: {server_name}")
- mcp_client = self.mcp_clients[server_name]
-
- # Check that tool exists
- available_tools = mcp_client.list_tools()
- available_tool_names = [t.name for t in available_tools]
- if function_name not in available_tool_names:
- raise ValueError(
- f"{function_name} is not available in MCP server {server_name}. Please check your `~/.letta/mcp_config.json` file."
- )
-
- function_response, is_error = mcp_client.execute_tool(tool_name=function_name, tool_args=function_args)
- return ToolExecutionResult(
- status="error" if is_error else "success",
- func_return=function_response,
- )
- else:
- try:
- # Parse the source code to extract function annotations
- annotations = get_function_annotations_from_source(target_letta_tool.source_code, function_name)
- # Coerce the function arguments to the correct types based on the annotations
- function_args = coerce_dict_args_by_annotations(function_args, annotations)
- except ValueError as e:
- self.logger.debug(f"Error coercing function arguments: {e}")
-
- # execute tool in a sandbox
- # TODO: allow agent_state to specify which sandbox to execute tools in
- # TODO: This is only temporary, can remove after we publish a pip package with this object
- agent_state_copy = self.agent_state.__deepcopy__()
- agent_state_copy.tools = []
- agent_state_copy.tool_rules = []
-
- tool_execution_result = ToolExecutionSandbox(function_name, function_args, self.user, tool_object=target_letta_tool).run(
- agent_state=agent_state_copy
- )
- assert orig_memory_str == self.agent_state.memory.compile(), "Memory should not be modified in a sandbox tool"
- if tool_execution_result.agent_state is not None:
- self.update_memory_if_changed(tool_execution_result.agent_state.memory)
- return tool_execution_result
- except Exception as e:
- # Need to catch error here, or else trunction wont happen
- # TODO: modify to function execution error
- function_response = get_friendly_error_msg(
- function_name=function_name, exception_name=type(e).__name__, exception_message=str(e)
- )
- return ToolExecutionResult(
- status="error",
- func_return=function_response,
- stderr=[traceback.format_exc()],
- )
-
- return ToolExecutionResult(
- status="success",
- func_return=function_response,
- )
-
-
-def save_agent(agent: Agent):
- """Save agent to metadata store"""
- agent_state = agent.agent_state
- assert isinstance(agent_state.memory, Memory), f"Memory is not a Memory object: {type(agent_state.memory)}"
-
- # TODO: move this to agent manager
- # TODO: Completely strip out metadata
- # convert to persisted model
- agent_manager = AgentManager()
- update_agent = UpdateAgent(
- name=agent_state.name,
- tool_ids=[t.id for t in agent_state.tools],
- source_ids=[s.id for s in agent_state.sources],
- block_ids=[b.id for b in agent_state.memory.blocks],
- tags=agent_state.tags,
- system=agent_state.system,
- tool_rules=agent_state.tool_rules,
- llm_config=agent_state.llm_config,
- embedding_config=agent_state.embedding_config,
- message_ids=agent_state.message_ids,
- description=agent_state.description,
- metadata=agent_state.metadata,
- # TODO: Add this back in later
- # tool_exec_environment_variables=agent_state.get_agent_env_vars_as_dict(),
- )
- agent_manager.update_agent(agent_id=agent_state.id, agent_update=update_agent, actor=agent.user)
-
-
-def strip_name_field_from_user_message(user_message_text: str) -> Tuple[str, Optional[str]]:
- """If 'name' exists in the JSON string, remove it and return the cleaned text + name value"""
- try:
- user_message_json = dict(json_loads(user_message_text))
- # Special handling for AutoGen messages with 'name' field
- # Treat 'name' as a special field
- # If it exists in the input message, elevate it to the 'message' level
- name = user_message_json.pop("name", None)
- clean_message = json_dumps(user_message_json)
- return clean_message, name
-
- except Exception as e:
- print(f"{CLI_WARNING_PREFIX}handling of 'name' field failed with: {e}")
- raise e
-
-
-def validate_json(user_message_text: str) -> str:
- """Make sure that the user input message is valid JSON"""
- try:
- user_message_json = dict(json_loads(user_message_text))
- user_message_json_val = json_dumps(user_message_json)
- return user_message_json_val
- except Exception as e:
- print(f"{CLI_WARNING_PREFIX}couldn't parse user input message as JSON: {e}")
- raise e
diff --git a/letta/agents/__init__.py b/letta/agents/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/agents/base_agent.py b/letta/agents/base_agent.py
deleted file mode 100644
index 99715e0b..00000000
--- a/letta/agents/base_agent.py
+++ /dev/null
@@ -1,198 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, AsyncGenerator, List, Optional, Union
-
-import openai
-
-from letta.constants import DEFAULT_MAX_STEPS
-from letta.helpers import ToolRulesSolver
-from letta.helpers.datetime_helpers import get_utc_time
-from letta.log import get_logger
-from letta.prompts.prompt_generator import PromptGenerator
-from letta.schemas.agent import AgentState
-from letta.schemas.enums import MessageStreamStatus
-from letta.schemas.letta_message import LegacyLettaMessage, LettaMessage
-from letta.schemas.letta_message_content import TextContent
-from letta.schemas.letta_response import LettaResponse
-from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
-from letta.schemas.message import Message, MessageCreate, MessageUpdate
-from letta.schemas.usage import LettaUsageStatistics
-from letta.schemas.user import User
-from letta.services.agent_manager import AgentManager
-from letta.services.message_manager import MessageManager
-from letta.services.passage_manager import PassageManager
-from letta.utils import united_diff
-
-logger = get_logger(__name__)
-
-
-class BaseAgent(ABC):
- """
- Abstract base class for AI agents, handling message management, tool execution,
- and context tracking.
- """
-
- def __init__(
- self,
- agent_id: str,
- # TODO: Make required once client refactor hits
- openai_client: Optional[openai.AsyncClient],
- message_manager: MessageManager,
- agent_manager: AgentManager,
- actor: User,
- ):
- self.agent_id = agent_id
- self.openai_client = openai_client
- self.message_manager = message_manager
- self.agent_manager = agent_manager
- # TODO: Pass this in
- self.passage_manager = PassageManager()
- self.actor = actor
- self.logger = get_logger(agent_id)
-
- @abstractmethod
- async def step(
- self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS, run_id: Optional[str] = None
- ) -> LettaResponse:
- """
- Main execution loop for the agent.
- """
- raise NotImplementedError
-
- @abstractmethod
- async def step_stream(
- self, input_messages: List[MessageCreate], max_steps: int = DEFAULT_MAX_STEPS
- ) -> AsyncGenerator[Union[LettaMessage, LegacyLettaMessage, MessageStreamStatus], None]:
- """
- Main streaming execution loop for the agent.
- """
- raise NotImplementedError
-
- @staticmethod
- def pre_process_input_message(input_messages: List[MessageCreate]) -> Any:
- """
- Pre-process function to run on the input_message.
- """
-
- def get_content(message: MessageCreate) -> str:
- if isinstance(message.content, str):
- return message.content
- elif message.content and len(message.content) == 1 and isinstance(message.content[0], TextContent):
- return message.content[0].text
- else:
- return ""
-
- return [{"role": input_message.role.value, "content": get_content(input_message)} for input_message in input_messages]
-
- async def _rebuild_memory_async(
- self,
- in_context_messages: List[Message],
- agent_state: AgentState,
- tool_rules_solver: Optional[ToolRulesSolver] = None,
- num_messages: Optional[int] = None, # storing these calculations is specific to the voice agent
- num_archival_memories: Optional[int] = None,
- ) -> List[Message]:
- """
- Async version of function above. For now before breaking up components, changes should be made in both places.
- """
- try:
- # [DB Call] loading blocks (modifies: agent_state.memory.blocks)
- agent_state = await self.agent_manager.refresh_memory_async(agent_state=agent_state, actor=self.actor)
-
- tool_constraint_block = None
- if tool_rules_solver is not None:
- tool_constraint_block = tool_rules_solver.compile_tool_rule_prompts()
-
- # compile archive tags if there's an attached archive
- from letta.services.archive_manager import ArchiveManager
-
- archive_manager = ArchiveManager()
- archive = await archive_manager.get_default_archive_for_agent_async(
- agent_id=agent_state.id,
- actor=self.actor,
- )
-
- if archive:
- archive_tags = await self.passage_manager.get_unique_tags_for_archive_async(
- archive_id=archive.id,
- actor=self.actor,
- )
- else:
- archive_tags = None
-
- # TODO: This is a pretty brittle pattern established all over our code, need to get rid of this
- curr_system_message = in_context_messages[0]
- curr_system_message_text = curr_system_message.content[0].text
-
- # extract the dynamic section that includes memory blocks, tool rules, and directories
- # this avoids timestamp comparison issues
- def extract_dynamic_section(text):
- start_marker = ""
- end_marker = ""
- for message in messages:
- date_str = message["date"]
- date_formatted = datetime.fromisoformat(date_str.replace("Z", "+00:00")).strftime("%Y-%m-%d %H:%M:%S")
-
- if "function_return" in message:
- return_string = message["function_return"]
- return_status = message["status"]
- html_content += f"
" - html_content += "
"
-
- display(HTML(html_content))
-
-
-def derive_function_name_regex(function_string: str) -> Optional[str]:
- # Regular expression to match the function name
- match = re.search(r"def\s+([a-zA-Z_]\w*)\s*\(", function_string)
-
- if match:
- function_name = match.group(1)
- return function_name
- else:
- warnings.warn("No function name found.")
- return None
diff --git a/letta/config.py b/letta/config.py
deleted file mode 100644
index ed9e8668..00000000
--- a/letta/config.py
+++ /dev/null
@@ -1,310 +0,0 @@
-import configparser
-import os
-from dataclasses import dataclass
-from typing import Optional
-
-import letta
-from letta.constants import (
- CORE_MEMORY_HUMAN_CHAR_LIMIT,
- CORE_MEMORY_PERSONA_CHAR_LIMIT,
- DEFAULT_HUMAN,
- DEFAULT_PERSONA,
- DEFAULT_PRESET,
- LETTA_DIR,
-)
-from letta.log import get_logger
-from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.llm_config import LLMConfig
-
-logger = get_logger(__name__)
-
-
-# helper functions for writing to configs
-def get_field(config, section, field):
- if section not in config:
- return None
- if config.has_option(section, field):
- return config.get(section, field)
- else:
- return None
-
-
-def set_field(config, section, field, value):
- if value is None: # cannot write None
- return
- if section not in config: # create section
- config.add_section(section)
- config.set(section, field, value)
-
-
-@dataclass
-class LettaConfig:
- config_path: str = os.getenv("MEMGPT_CONFIG_PATH") or os.path.join(LETTA_DIR, "config")
-
- # preset
- preset: str = DEFAULT_PRESET # TODO: rename to system prompt
-
- # persona parameters
- persona: str = DEFAULT_PERSONA
- human: str = DEFAULT_HUMAN
-
- # model parameters
- # default_llm_config: LLMConfig = None
-
- # embedding parameters
- # default_embedding_config: EmbeddingConfig = None
-
- # NONE OF THIS IS CONFIG ↓↓↓↓↓
- # @norton120 these are the metdadatastore
-
- # database configs: archival
- archival_storage_type: str = "sqlite" # local, db
- archival_storage_path: str = LETTA_DIR
- archival_storage_uri: str = None # TODO: eventually allow external vector DB
-
- # database configs: recall
- recall_storage_type: str = "sqlite" # local, db
- recall_storage_path: str = LETTA_DIR
- recall_storage_uri: str = None # TODO: eventually allow external vector DB
-
- # database configs: metadata storage (sources, agents, data sources)
- metadata_storage_type: str = "sqlite"
- metadata_storage_path: str = LETTA_DIR
- metadata_storage_uri: str = None
-
- # database configs: agent state
- persistence_manager_type: str = None # in-memory, db
- persistence_manager_save_file: str = None # local file
- persistence_manager_uri: str = None # db URI
-
- # version (for backcompat)
- letta_version: str = letta.__version__
-
- # user info
- policies_accepted: bool = False
-
- # Default memory limits
- core_memory_persona_char_limit: int = CORE_MEMORY_PERSONA_CHAR_LIMIT
- core_memory_human_char_limit: int = CORE_MEMORY_HUMAN_CHAR_LIMIT
-
- def __post_init__(self):
- # ensure types
- # self.embedding_chunk_size = int(self.embedding_chunk_size)
- # self.embedding_dim = int(self.embedding_dim)
- # self.context_window = int(self.context_window)
- pass
-
- @classmethod
- def load(cls, llm_config: Optional[LLMConfig] = None, embedding_config: Optional[EmbeddingConfig] = None) -> "LettaConfig":
- # avoid circular import
- from letta.utils import printd
-
- # from letta.migrate import VERSION_CUTOFF, config_is_compatible
- # if not config_is_compatible(allow_empty=True):
- # error_message = " ".join(
- # [
- # f"\nYour current config file is incompatible with Letta versions later than {VERSION_CUTOFF}.",
- # f"\nTo use Letta, you must either downgrade your Letta version (<= {VERSION_CUTOFF}) or regenerate your config using `letta configure`, or `letta migrate` if you would like to migrate old agents.",
- # ]
- # )
- # raise ValueError(error_message)
-
- config = configparser.ConfigParser()
-
- # allow overriding with env variables
- if os.getenv("MEMGPT_CONFIG_PATH"):
- config_path = os.getenv("MEMGPT_CONFIG_PATH")
- else:
- config_path = LettaConfig.config_path
-
- # insure all configuration directories exist
- cls.create_config_dir()
- printd(f"Loading config from {config_path}")
- if os.path.exists(config_path):
- # read existing config
- config.read(config_path)
-
- ## Handle extraction of nested LLMConfig and EmbeddingConfig
- # llm_config_dict = {
- # # Extract relevant LLM configuration from the config file
- # "model": get_field(config, "model", "model"),
- # "model_endpoint": get_field(config, "model", "model_endpoint"),
- # "model_endpoint_type": get_field(config, "model", "model_endpoint_type"),
- # "model_wrapper": get_field(config, "model", "model_wrapper"),
- # "context_window": get_field(config, "model", "context_window"),
- # }
- # embedding_config_dict = {
- # # Extract relevant Embedding configuration from the config file
- # "embedding_endpoint": get_field(config, "embedding", "embedding_endpoint"),
- # "embedding_model": get_field(config, "embedding", "embedding_model"),
- # "embedding_endpoint_type": get_field(config, "embedding", "embedding_endpoint_type"),
- # "embedding_dim": get_field(config, "embedding", "embedding_dim"),
- # "embedding_chunk_size": get_field(config, "embedding", "embedding_chunk_size"),
- # }
- ## Remove null values
- # llm_config_dict = {k: v for k, v in llm_config_dict.items() if v is not None}
- # embedding_config_dict = {k: v for k, v in embedding_config_dict.items() if v is not None}
- # Correct the types that aren't strings
- # if "context_window" in llm_config_dict and llm_config_dict["context_window"] is not None:
- # llm_config_dict["context_window"] = int(llm_config_dict["context_window"])
- # if "embedding_dim" in embedding_config_dict and embedding_config_dict["embedding_dim"] is not None:
- # embedding_config_dict["embedding_dim"] = int(embedding_config_dict["embedding_dim"])
- # if "embedding_chunk_size" in embedding_config_dict and embedding_config_dict["embedding_chunk_size"] is not None:
- # embedding_config_dict["embedding_chunk_size"] = int(embedding_config_dict["embedding_chunk_size"])
- ## Construct the inner properties
- # llm_config = LLMConfig(**llm_config_dict)
- # embedding_config = EmbeddingConfig(**embedding_config_dict)
-
- # Everything else
- config_dict = {
- # Two prepared configs
- # "default_llm_config": llm_config,
- # "default_embedding_config": embedding_config,
- # Agent related
- "preset": get_field(config, "defaults", "preset"),
- "persona": get_field(config, "defaults", "persona"),
- "human": get_field(config, "defaults", "human"),
- "agent": get_field(config, "defaults", "agent"),
- # Storage related
- "archival_storage_type": get_field(config, "archival_storage", "type"),
- "archival_storage_path": get_field(config, "archival_storage", "path"),
- "archival_storage_uri": get_field(config, "archival_storage", "uri"),
- "recall_storage_type": get_field(config, "recall_storage", "type"),
- "recall_storage_path": get_field(config, "recall_storage", "path"),
- "recall_storage_uri": get_field(config, "recall_storage", "uri"),
- "metadata_storage_type": get_field(config, "metadata_storage", "type"),
- "metadata_storage_path": get_field(config, "metadata_storage", "path"),
- "metadata_storage_uri": get_field(config, "metadata_storage", "uri"),
- # Misc
- "config_path": config_path,
- "letta_version": get_field(config, "version", "letta_version"),
- }
- # Don't include null values
- config_dict = {k: v for k, v in config_dict.items() if v is not None}
-
- return cls(**config_dict)
-
- # assert embedding_config is not None, "Embedding config must be provided if config does not exist"
- # assert llm_config is not None, "LLM config must be provided if config does not exist"
-
- # create new config
- config = cls(config_path=config_path)
-
- config.create_config_dir() # create dirs
-
- return config
-
- def save(self):
- import letta
-
- config = configparser.ConfigParser()
-
- # CLI defaults
- set_field(config, "defaults", "preset", self.preset)
- set_field(config, "defaults", "persona", self.persona)
- set_field(config, "defaults", "human", self.human)
-
- # model defaults
- # set_field(config, "model", "model", self.default_llm_config.model)
- ##set_field(config, "model", "model_endpoint", self.default_llm_config.model_endpoint)
- # set_field(
- # config,
- # "model",
- # "model_endpoint_type",
- # self.default_llm_config.model_endpoint_type,
- # )
- # set_field(config, "model", "model_wrapper", self.default_llm_config.model_wrapper)
- # set_field(
- # config,
- # "model",
- # "context_window",
- # str(self.default_llm_config.context_window),
- # )
-
- ## embeddings
- # set_field(
- # config,
- # "embedding",
- # "embedding_endpoint_type",
- # self.default_embedding_config.embedding_endpoint_type,
- # )
- # set_field(
- # config,
- # "embedding",
- # "embedding_endpoint",
- # self.default_embedding_config.embedding_endpoint,
- # )
- # set_field(
- # config,
- # "embedding",
- # "embedding_model",
- # self.default_embedding_config.embedding_model,
- # )
- # set_field(
- # config,
- # "embedding",
- # "embedding_dim",
- # str(self.default_embedding_config.embedding_dim),
- # )
- # set_field(
- # config,
- # "embedding",
- # "embedding_chunk_size",
- # str(self.default_embedding_config.embedding_chunk_size),
- # )
-
- # archival storage
- set_field(config, "archival_storage", "type", self.archival_storage_type)
- set_field(config, "archival_storage", "path", self.archival_storage_path)
- set_field(config, "archival_storage", "uri", self.archival_storage_uri)
-
- # recall storage
- set_field(config, "recall_storage", "type", self.recall_storage_type)
- set_field(config, "recall_storage", "path", self.recall_storage_path)
- set_field(config, "recall_storage", "uri", self.recall_storage_uri)
-
- # metadata storage
- set_field(config, "metadata_storage", "type", self.metadata_storage_type)
- set_field(config, "metadata_storage", "path", self.metadata_storage_path)
- set_field(config, "metadata_storage", "uri", self.metadata_storage_uri)
-
- # set version
- set_field(config, "version", "letta_version", letta.__version__)
-
- # always make sure all directories are present
- self.create_config_dir()
-
- with open(self.config_path, "w", encoding="utf-8") as f:
- config.write(f)
- logger.debug(f"Saved Config: {self.config_path}")
-
- @staticmethod
- def exists():
- # allow overriding with env variables
- if os.getenv("MEMGPT_CONFIG_PATH"):
- config_path = os.getenv("MEMGPT_CONFIG_PATH")
- else:
- config_path = LettaConfig.config_path
-
- assert not os.path.isdir(config_path), f"Config path {config_path} cannot be set to a directory."
- return os.path.exists(config_path)
-
- @staticmethod
- def create_config_dir():
- if not os.path.exists(LETTA_DIR):
- os.makedirs(LETTA_DIR, exist_ok=True)
-
- folders = [
- "personas",
- "humans",
- "archival",
- "agents",
- "functions",
- "system_prompts",
- "presets",
- "settings",
- ]
-
- for folder in folders:
- if not os.path.exists(os.path.join(LETTA_DIR, folder)):
- os.makedirs(os.path.join(LETTA_DIR, folder))
diff --git a/letta/constants.py b/letta/constants.py
deleted file mode 100644
index 6f3e2094..00000000
--- a/letta/constants.py
+++ /dev/null
@@ -1,403 +0,0 @@
-import os
-import re
-from logging import CRITICAL, DEBUG, ERROR, INFO, NOTSET, WARN, WARNING
-
-LETTA_DIR = os.path.join(os.path.expanduser("~"), ".letta")
-LETTA_TOOL_EXECUTION_DIR = os.path.join(LETTA_DIR, "tool_execution_dir")
-
-LETTA_MODEL_ENDPOINT = "https://inference.letta.com/v1/"
-DEFAULT_TIMEZONE = "UTC"
-
-ADMIN_PREFIX = "/v1/admin"
-API_PREFIX = "/v1"
-OPENAI_API_PREFIX = "/openai"
-
-COMPOSIO_ENTITY_ENV_VAR_KEY = "COMPOSIO_ENTITY"
-COMPOSIO_TOOL_TAG_NAME = "composio"
-
-MCP_CONFIG_NAME = "mcp_config.json"
-MCP_TOOL_TAG_NAME_PREFIX = "mcp" # full format, mcp:server_name
-
-LETTA_CORE_TOOL_MODULE_NAME = "letta.functions.function_sets.base"
-LETTA_MULTI_AGENT_TOOL_MODULE_NAME = "letta.functions.function_sets.multi_agent"
-LETTA_VOICE_TOOL_MODULE_NAME = "letta.functions.function_sets.voice"
-LETTA_BUILTIN_TOOL_MODULE_NAME = "letta.functions.function_sets.builtin"
-LETTA_FILES_TOOL_MODULE_NAME = "letta.functions.function_sets.files"
-
-LETTA_TOOL_MODULE_NAMES = [
- LETTA_CORE_TOOL_MODULE_NAME,
- LETTA_MULTI_AGENT_TOOL_MODULE_NAME,
- LETTA_VOICE_TOOL_MODULE_NAME,
- LETTA_BUILTIN_TOOL_MODULE_NAME,
- LETTA_FILES_TOOL_MODULE_NAME,
-]
-
-DEFAULT_ORG_ID = "org-00000000-0000-4000-8000-000000000000"
-DEFAULT_ORG_NAME = "default_org"
-
-AGENT_ID_PATTERN = re.compile(r"^agent-[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$", re.IGNORECASE)
-
-# String in the error message for when the context window is too large
-# Example full message:
-# This model's maximum context length is 8192 tokens. However, your messages resulted in 8198 tokens (7450 in the messages, 748 in the functions). Please reduce the length of the messages or functions.
-OPENAI_CONTEXT_WINDOW_ERROR_SUBSTRING = "maximum context length"
-
-# System prompt templating
-IN_CONTEXT_MEMORY_KEYWORD = "CORE_MEMORY"
-
-# OpenAI error message: Invalid 'messages[1].tool_calls[0].id': string too long. Expected a string with maximum length 29, but got a string with length 36 instead.
-TOOL_CALL_ID_MAX_LEN = 29
-
-# Max steps for agent loop
-DEFAULT_MAX_STEPS = 50
-
-# minimum context window size
-MIN_CONTEXT_WINDOW = 4096
-
-# number of concurrent embedding requests to sent
-EMBEDDING_BATCH_SIZE = 200
-
-# Voice Sleeptime message buffer lengths
-DEFAULT_MAX_MESSAGE_BUFFER_LENGTH = 30
-DEFAULT_MIN_MESSAGE_BUFFER_LENGTH = 15
-
-# embeddings
-MAX_EMBEDDING_DIM = 4096 # maximum supported embeding size - do NOT change or else DBs will need to be reset
-DEFAULT_EMBEDDING_CHUNK_SIZE = 300
-
-# tokenizers
-EMBEDDING_TO_TOKENIZER_MAP = {
- "text-embedding-3-small": "cl100k_base",
-}
-EMBEDDING_TO_TOKENIZER_DEFAULT = "cl100k_base"
-
-
-DEFAULT_LETTA_MODEL = "gpt-4" # TODO: fixme
-DEFAULT_PERSONA = "sam_pov"
-DEFAULT_HUMAN = "basic"
-DEFAULT_PRESET = "memgpt_chat"
-
-DEFAULT_PERSONA_BLOCK_DESCRIPTION = "The persona block: Stores details about your current persona, guiding how you behave and respond. This helps you to maintain consistency and personality in your interactions."
-DEFAULT_HUMAN_BLOCK_DESCRIPTION = "The human block: Stores key details about the person you are conversing with, allowing for more personalized and friend-like conversation."
-
-SEND_MESSAGE_TOOL_NAME = "send_message"
-# Base tools that cannot be edited, as they access agent state directly
-# Note that we don't include "conversation_search_date" for now
-BASE_TOOLS = [SEND_MESSAGE_TOOL_NAME, "conversation_search", "archival_memory_insert", "archival_memory_search"]
-DEPRECATED_LETTA_TOOLS = ["archival_memory_insert", "archival_memory_search"]
-# Base memory tools CAN be edited, and are added by default by the server
-BASE_MEMORY_TOOLS = ["core_memory_append", "core_memory_replace"]
-# New v2 collection of the base memory tools (effecitvely same as sleeptime set), to pair with memgpt_v2 prompt
-BASE_MEMORY_TOOLS_V2 = [
- "memory_replace",
- "memory_insert",
- # NOTE: leaving these ones out to simply the set? Can have these reserved for sleep-time
- # "memory_rethink",
- # "memory_finish_edits",
-]
-# Base tools if the memgpt agent has enable_sleeptime on
-BASE_SLEEPTIME_CHAT_TOOLS = [SEND_MESSAGE_TOOL_NAME, "conversation_search", "archival_memory_search"]
-# Base memory tools for sleeptime agent
-BASE_SLEEPTIME_TOOLS = [
- "memory_replace",
- "memory_insert",
- "memory_rethink",
- "memory_finish_edits",
- # "archival_memory_insert",
- # "archival_memory_search",
- # "conversation_search",
-]
-# Base tools for the voice agent
-BASE_VOICE_SLEEPTIME_CHAT_TOOLS = [SEND_MESSAGE_TOOL_NAME, "search_memory"]
-# Base memory tools for sleeptime agent
-BASE_VOICE_SLEEPTIME_TOOLS = [
- "store_memories",
- "rethink_user_memory",
- "finish_rethinking_memory",
-]
-# Multi agent tools
-MULTI_AGENT_TOOLS = ["send_message_to_agent_and_wait_for_reply", "send_message_to_agents_matching_tags", "send_message_to_agent_async"]
-LOCAL_ONLY_MULTI_AGENT_TOOLS = ["send_message_to_agent_async"]
-
-# Used to catch if line numbers are pushed in
-# MEMORY_TOOLS_LINE_NUMBER_PREFIX_REGEX = re.compile(r"^Line \d+: ", re.MULTILINE)
-# More "robust" version that handles different kinds of whitespace
-# shared constant for both memory_insert and memory_replace
-MEMORY_TOOLS_LINE_NUMBER_PREFIX_REGEX = re.compile(
- r"^[ \t]*Line[ \t]+\d+[ \t]*:", # allow any leading whitespace and flexible spacing
- re.MULTILINE,
-)
-
-# Built in tools
-BUILTIN_TOOLS = ["run_code", "web_search", "fetch_webpage"]
-
-# Built in tools
-FILES_TOOLS = ["open_files", "grep_files", "semantic_search_files"]
-
-FILE_MEMORY_EXISTS_MESSAGE = "The following files are currently accessible in memory:"
-FILE_MEMORY_EMPTY_MESSAGE = (
- "There are no files currently available in memory. Files will appear here once they are uploaded directly to your system."
-)
-
-# Set of all built-in Letta tools
-LETTA_TOOL_SET = set(
- BASE_TOOLS
- + BASE_MEMORY_TOOLS
- + MULTI_AGENT_TOOLS
- + BASE_SLEEPTIME_TOOLS
- + BASE_VOICE_SLEEPTIME_TOOLS
- + BASE_VOICE_SLEEPTIME_CHAT_TOOLS
- + BUILTIN_TOOLS
- + FILES_TOOLS
-)
-
-
-def FUNCTION_RETURN_VALUE_TRUNCATED(return_str, return_char: int, return_char_limit: int):
- return (
- f"{return_str}... [NOTE: function output was truncated since it exceeded the character limit: {return_char} > {return_char_limit}]"
- )
-
-
-# The name of the tool used to send message to the user
-# May not be relevant in cases where the agent has multiple ways to message to user (send_imessage, send_discord_mesasge, ...)
-# or in cases where the agent has no concept of messaging a user (e.g. a workflow agent)
-DEFAULT_MESSAGE_TOOL = SEND_MESSAGE_TOOL_NAME
-DEFAULT_MESSAGE_TOOL_KWARG = "message"
-
-# The name of the conversation search tool - messages with this tool should not be indexed
-CONVERSATION_SEARCH_TOOL_NAME = "conversation_search"
-
-PRE_EXECUTION_MESSAGE_ARG = "pre_exec_msg"
-
-REQUEST_HEARTBEAT_PARAM = "request_heartbeat"
-REQUEST_HEARTBEAT_DESCRIPTION = "Request an immediate heartbeat after function execution. You MUST set this value to `True` if you want to send a follow-up message or run a follow-up tool call (chain multiple tools together). If set to `False` (the default), then the chain of execution will end immediately after this function call."
-
-
-# Structured output models
-STRUCTURED_OUTPUT_MODELS = {"gpt-4o", "gpt-4o-mini"}
-
-# LOGGER_LOG_LEVEL is use to convert Text to Logging level value for logging mostly for Cli input to setting level
-LOGGER_LOG_LEVELS = {"CRITICAL": CRITICAL, "ERROR": ERROR, "WARN": WARN, "WARNING": WARNING, "INFO": INFO, "DEBUG": DEBUG, "NOTSET": NOTSET}
-
-FIRST_MESSAGE_ATTEMPTS = 10
-
-INITIAL_BOOT_MESSAGE = "Boot sequence complete. Persona activated."
-INITIAL_BOOT_MESSAGE_SEND_MESSAGE_THOUGHT = "Bootup sequence complete. Persona activated. Testing messaging functionality."
-STARTUP_QUOTES = [
- "I think, therefore I am.",
- "All those moments will be lost in time, like tears in rain.",
- "More human than human is our motto.",
-]
-INITIAL_BOOT_MESSAGE_SEND_MESSAGE_FIRST_MSG = STARTUP_QUOTES[2]
-
-CLI_WARNING_PREFIX = "Warning: "
-
-ERROR_MESSAGE_PREFIX = "Error"
-
-NON_USER_MSG_PREFIX = "[This is an automated system message hidden from the user] "
-
-CORE_MEMORY_LINE_NUMBER_WARNING = (
- "# NOTE: Line numbers shown below are to help during editing. Do NOT include line number prefixes in your memory edit tool calls."
-)
-
-
-# Constants to do with summarization / conversation length window
-# The max amount of tokens supported by the underlying model (eg 8k for gpt-4 and Mistral 7B)
-LLM_MAX_TOKENS = {
- "DEFAULT": 30000,
- # deepseek
- "deepseek-chat": 64000,
- "deepseek-reasoner": 64000,
- ## OpenAI models: https://platform.openai.com/docs/models/overview
- # gpt-5
- "gpt-5": 272000,
- "gpt-5-2025-08-07": 272000,
- "gpt-5-mini": 272000,
- "gpt-5-mini-2025-08-07": 272000,
- "gpt-5-nano": 272000,
- "gpt-5-nano-2025-08-07": 272000,
- # reasoners
- "o1": 200000,
- # "o1-pro": 200000, # responses API only
- "o1-2024-12-17": 200000,
- "o3": 200000,
- "o3-2025-04-16": 200000,
- "o3-mini": 200000,
- "o3-mini-2025-01-31": 200000,
- # "o3-pro": 200000, # responses API only
- # "o3-pro-2025-06-10": 200000,
- "gpt-4.1": 1047576,
- "gpt-4.1-2025-04-14": 1047576,
- "gpt-4.1-mini": 1047576,
- "gpt-4.1-mini-2025-04-14": 1047576,
- "gpt-4.1-nano": 1047576,
- "gpt-4.1-nano-2025-04-14": 1047576,
- # gpt-4.5-preview
- "gpt-4.5-preview": 128000,
- "gpt-4.5-preview-2025-02-27": 128000,
- # "o1-preview
- "chatgpt-4o-latest": 128000,
- # "o1-preview-2024-09-12
- "gpt-4o-2024-08-06": 128000,
- "gpt-4o-2024-11-20": 128000,
- "gpt-4-turbo-preview": 128000,
- "gpt-4o": 128000,
- "gpt-3.5-turbo-instruct": 16385,
- "gpt-4-0125-preview": 128000,
- "gpt-3.5-turbo-0125": 16385,
- # "babbage-002": 128000,
- # "davinci-002": 128000,
- "gpt-4-turbo-2024-04-09": 128000,
- # "gpt-4o-realtime-preview-2024-10-01
- "gpt-4-turbo": 128000,
- "gpt-4o-2024-05-13": 128000,
- # "o1-mini
- # "o1-mini-2024-09-12
- # "gpt-3.5-turbo-instruct-0914
- "gpt-4o-mini": 128000,
- # "gpt-4o-realtime-preview
- "gpt-4o-mini-2024-07-18": 128000,
- # gpt-4
- "gpt-4-1106-preview": 128000,
- "gpt-4": 8192,
- "gpt-4-32k": 32768,
- "gpt-4-0613": 8192,
- "gpt-4-32k-0613": 32768,
- "gpt-4-0314": 8192, # legacy
- "gpt-4-32k-0314": 32768, # legacy
- # gpt-3.5
- "gpt-3.5-turbo-1106": 16385,
- "gpt-3.5-turbo": 4096,
- "gpt-3.5-turbo-16k": 16385,
- "gpt-3.5-turbo-0613": 4096, # legacy
- "gpt-3.5-turbo-16k-0613": 16385, # legacy
- "gpt-3.5-turbo-0301": 4096, # legacy
- "gemini-1.0-pro-vision-latest": 12288,
- "gemini-pro-vision": 12288,
- "gemini-1.5-pro-latest": 2000000,
- "gemini-1.5-pro-001": 2000000,
- "gemini-1.5-pro-002": 2000000,
- "gemini-1.5-pro": 2000000,
- "gemini-1.5-flash-latest": 1000000,
- "gemini-1.5-flash-001": 1000000,
- "gemini-1.5-flash-001-tuning": 16384,
- "gemini-1.5-flash": 1000000,
- "gemini-1.5-flash-002": 1000000,
- "gemini-1.5-flash-8b": 1000000,
- "gemini-1.5-flash-8b-001": 1000000,
- "gemini-1.5-flash-8b-latest": 1000000,
- "gemini-1.5-flash-8b-exp-0827": 1000000,
- "gemini-1.5-flash-8b-exp-0924": 1000000,
- "gemini-2.5-pro-exp-03-25": 1048576,
- "gemini-2.5-pro-preview-03-25": 1048576,
- "gemini-2.5-flash-preview-04-17": 1048576,
- "gemini-2.5-flash-preview-05-20": 1048576,
- "gemini-2.5-flash-preview-04-17-thinking": 1048576,
- "gemini-2.5-pro-preview-05-06": 1048576,
- "gemini-2.0-flash-exp": 1048576,
- "gemini-2.0-flash": 1048576,
- "gemini-2.0-flash-001": 1048576,
- "gemini-2.0-flash-exp-image-generation": 1048576,
- "gemini-2.0-flash-lite-001": 1048576,
- "gemini-2.0-flash-lite": 1048576,
- "gemini-2.0-flash-preview-image-generation": 32768,
- "gemini-2.0-flash-lite-preview-02-05": 1048576,
- "gemini-2.0-flash-lite-preview": 1048576,
- "gemini-2.0-pro-exp": 1048576,
- "gemini-2.0-pro-exp-02-05": 1048576,
- "gemini-exp-1206": 1048576,
- "gemini-2.0-flash-thinking-exp-01-21": 1048576,
- "gemini-2.0-flash-thinking-exp": 1048576,
- "gemini-2.0-flash-thinking-exp-1219": 1048576,
- "gemini-2.5-flash-preview-tts": 32768,
- "gemini-2.5-pro-preview-tts": 65536,
-}
-# The error message that Letta will receive
-# MESSAGE_SUMMARY_WARNING_STR = f"Warning: the conversation history will soon reach its maximum length and be trimmed. Make sure to save any important information from the conversation to your memory before it is removed."
-# Much longer and more specific variant of the prompt
-MESSAGE_SUMMARY_WARNING_STR = " ".join(
- [
- f"{NON_USER_MSG_PREFIX}The conversation history will soon reach its maximum length and be trimmed.",
- "Do NOT tell the user about this system alert, they should not know that the history is reaching max length.",
- "If there is any important new information or general memories about you or the user that you would like to save, you should save that information immediately by calling function core_memory_append, core_memory_replace, or archival_memory_insert.",
- # "Remember to pass request_heartbeat = true if you would like to send a message immediately after.",
- ]
-)
-
-# Throw an error message when a read-only block is edited
-READ_ONLY_BLOCK_EDIT_ERROR = f"{ERROR_MESSAGE_PREFIX} This block is read-only and cannot be edited."
-
-# The ackknowledgement message used in the summarize sequence
-MESSAGE_SUMMARY_REQUEST_ACK = "Understood, I will respond with a summary of the message (and only the summary, nothing else) once I receive the conversation history. I'm ready."
-
-# Maximum length of an error message
-MAX_ERROR_MESSAGE_CHAR_LIMIT = 1000
-
-# Default memory limits
-CORE_MEMORY_PERSONA_CHAR_LIMIT: int = 20000
-CORE_MEMORY_HUMAN_CHAR_LIMIT: int = 20000
-CORE_MEMORY_BLOCK_CHAR_LIMIT: int = 20000
-
-# Function return limits
-FUNCTION_RETURN_CHAR_LIMIT = 50000 # ~300 words
-BASE_FUNCTION_RETURN_CHAR_LIMIT = 50000 # same as regular function limit
-FILE_IS_TRUNCATED_WARNING = "# NOTE: This block is truncated, use functions to view the full content."
-
-MAX_PAUSE_HEARTBEATS = 360 # in min
-
-MESSAGE_CHATGPT_FUNCTION_MODEL = "gpt-3.5-turbo"
-MESSAGE_CHATGPT_FUNCTION_SYSTEM_MESSAGE = "You are a helpful assistant. Keep your responses short and concise."
-
-#### Functions related
-
-# REQ_HEARTBEAT_MESSAGE = f"{NON_USER_MSG_PREFIX}request_heartbeat == true"
-REQ_HEARTBEAT_MESSAGE = f"{NON_USER_MSG_PREFIX}Function called using request_heartbeat=true, returning control"
-# FUNC_FAILED_HEARTBEAT_MESSAGE = f"{NON_USER_MSG_PREFIX}Function call failed"
-FUNC_FAILED_HEARTBEAT_MESSAGE = f"{NON_USER_MSG_PREFIX}Function call failed, returning control"
-
-
-RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE = 5
-
-MAX_FILENAME_LENGTH = 255
-RESERVED_FILENAMES = {"CON", "PRN", "AUX", "NUL", "COM1", "COM2", "LPT1", "LPT2"}
-
-WEB_SEARCH_CLIP_CONTENT = False
-WEB_SEARCH_INCLUDE_SCORE = False
-WEB_SEARCH_SEPARATOR = "\n" + "-" * 40 + "\n"
-
-REDIS_INCLUDE = "include"
-REDIS_EXCLUDE = "exclude"
-REDIS_SET_DEFAULT_VAL = "None"
-REDIS_DEFAULT_CACHE_PREFIX = "letta_cache"
-REDIS_RUN_ID_PREFIX = "agent:send_message:run_id"
-
-# TODO: This is temporary, eventually use token-based eviction
-# File based controls
-DEFAULT_MAX_FILES_OPEN = 5
-DEFAULT_CORE_MEMORY_SOURCE_CHAR_LIMIT: int = 50000
-
-GET_PROVIDERS_TIMEOUT_SECONDS = 10
-
-# Pinecone related fields
-PINECONE_EMBEDDING_MODEL: str = "llama-text-embed-v2"
-PINECONE_TEXT_FIELD_NAME = "chunk_text"
-PINECONE_METRIC = "cosine"
-PINECONE_CLOUD = "aws"
-PINECONE_REGION = "us-east-1"
-PINECONE_MAX_BATCH_SIZE = 96
-
-# retry configuration
-PINECONE_MAX_RETRY_ATTEMPTS = 3
-PINECONE_RETRY_BASE_DELAY = 1.0 # seconds
-PINECONE_RETRY_MAX_DELAY = 60.0 # seconds
-PINECONE_RETRY_BACKOFF_FACTOR = 2.0
-PINECONE_THROTTLE_DELAY = 0.75 # seconds base delay between batches
-
-# builtin web search
-WEB_SEARCH_MODEL_ENV_VAR_NAME = "LETTA_BUILTIN_WEBSEARCH_OPENAI_MODEL_NAME"
-WEB_SEARCH_MODEL_ENV_VAR_DEFAULT_VALUE = "gpt-4.1-mini-2025-04-14"
-
-# Excluded model keywords from base tool rules
-EXCLUDE_MODEL_KEYWORDS_FROM_BASE_TOOL_RULES = ["claude-4-sonnet", "claude-3-5-sonnet", "gpt-5", "gemini-2.5-pro"]
-# But include models with these keywords in base tool rules (overrides exclusion)
-INCLUDE_MODEL_KEYWORDS_BASE_TOOL_RULES = ["mini"]
diff --git a/letta/data_sources/__init__.py b/letta/data_sources/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/data_sources/connectors.py b/letta/data_sources/connectors.py
deleted file mode 100644
index cfafe2a2..00000000
--- a/letta/data_sources/connectors.py
+++ /dev/null
@@ -1,204 +0,0 @@
-from typing import Dict, Iterator, List, Tuple
-
-import typer
-
-from letta.constants import EMBEDDING_BATCH_SIZE
-from letta.data_sources.connectors_helper import assert_all_files_exist_locally, extract_metadata_from_files, get_filenames_in_dir
-from letta.schemas.file import FileMetadata
-from letta.schemas.passage import Passage
-from letta.schemas.source import Source
-from letta.services.file_manager import FileManager
-from letta.services.passage_manager import PassageManager
-
-
-class DataConnector:
- """
- Base class for data connectors that can be extended to generate files and passages from a custom data source.
- """
-
- def find_files(self, source: Source) -> Iterator[FileMetadata]:
- """
- Generate file metadata from a data source.
-
- Returns:
- files (Iterator[FileMetadata]): Generate file metadata for each file found.
- """
-
- def generate_passages(self, file: FileMetadata, chunk_size: int = 1024) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Passage]:
- """
- Generate passage text and metadata from a list of files.
-
- Args:
- file (FileMetadata): The document to generate passages from.
- chunk_size (int, optional): Chunk size for splitting passages. Defaults to 1024.
-
- Returns:
- passages (Iterator[Tuple[str, Dict]]): Generate a tuple of string text and metadata dictionary for each passage.
- """
-
-
-async def load_data(connector: DataConnector, source: Source, passage_manager: PassageManager, file_manager: FileManager, actor: "User"):
- from letta.llm_api.llm_client import LLMClient
-
- """Load data from a connector (generates file and passages) into a specified source_id, associated with a user_id."""
- embedding_config = source.embedding_config
-
- # insert passages/file
- embedding_to_document_name = {}
- passage_count = 0
- file_count = 0
-
- # Use the new LLMClient for all embedding requests
- client = LLMClient.create(
- provider_type=embedding_config.embedding_endpoint_type,
- actor=actor,
- )
-
- for file_metadata in connector.find_files(source):
- file_count += 1
- await file_manager.create_file(file_metadata, actor)
-
- # generate passages for this file
- texts = []
- metadatas = []
-
- for passage_text, passage_metadata in connector.generate_passages(file_metadata, chunk_size=embedding_config.embedding_chunk_size):
- # for some reason, llama index parsers sometimes return empty strings
- if len(passage_text) == 0:
- typer.secho(
- f"Warning: Llama index parser returned empty string, skipping insert of passage with metadata '{passage_metadata}' into VectorDB. You can usually ignore this warning.",
- fg=typer.colors.YELLOW,
- )
- continue
-
- texts.append(passage_text)
- metadatas.append(passage_metadata)
-
- if len(texts) >= EMBEDDING_BATCH_SIZE:
- # Process the batch
- embeddings = await client.request_embeddings(texts, embedding_config)
- passages = []
-
- for text, embedding, passage_metadata in zip(texts, embeddings, metadatas):
- passage = Passage(
- text=text,
- file_id=file_metadata.id,
- source_id=source.id,
- metadata=passage_metadata,
- organization_id=source.organization_id,
- embedding_config=source.embedding_config,
- embedding=embedding,
- )
- hashable_embedding = tuple(passage.embedding)
- file_name = file_metadata.file_name
- if hashable_embedding in embedding_to_document_name:
- typer.secho(
- f"Warning: Duplicate embedding found for passage in {file_name} (already exists in {embedding_to_document_name[hashable_embedding]}), skipping insert into VectorDB.",
- fg=typer.colors.YELLOW,
- )
- continue
-
- passages.append(passage)
- embedding_to_document_name[hashable_embedding] = file_name
-
- # insert passages into passage store
- await passage_manager.create_many_passages_async(passages, actor)
- passage_count += len(passages)
-
- # Reset for next batch
- texts = []
- metadatas = []
-
- # Process final remaining texts for this file
- if len(texts) > 0:
- embeddings = await client.request_embeddings(texts, embedding_config)
- passages = []
-
- for text, embedding, passage_metadata in zip(texts, embeddings, metadatas):
- passage = Passage(
- text=text,
- file_id=file_metadata.id,
- source_id=source.id,
- metadata=passage_metadata,
- organization_id=source.organization_id,
- embedding_config=source.embedding_config,
- embedding=embedding,
- )
- hashable_embedding = tuple(passage.embedding)
- file_name = file_metadata.file_name
- if hashable_embedding in embedding_to_document_name:
- typer.secho(
- f"Warning: Duplicate embedding found for passage in {file_name} (already exists in {embedding_to_document_name[hashable_embedding]}), skipping insert into VectorDB.",
- fg=typer.colors.YELLOW,
- )
- continue
-
- passages.append(passage)
- embedding_to_document_name[hashable_embedding] = file_name
-
- await passage_manager.create_many_passages_async(passages, actor)
- passage_count += len(passages)
-
- return passage_count, file_count
-
-
-class DirectoryConnector(DataConnector):
- def __init__(self, input_files: List[str] = None, input_directory: str = None, recursive: bool = False, extensions: List[str] = None):
- """
- Connector for reading text data from a directory of files.
-
- Args:
- input_files (List[str], optional): List of file paths to read. Defaults to None.
- input_directory (str, optional): Directory to read files from. Defaults to None.
- recursive (bool, optional): Whether to read files recursively from the input directory. Defaults to False.
- extensions (List[str], optional): List of file extensions to read. Defaults to None.
- """
- self.connector_type = "directory"
- self.input_files = input_files
- self.input_directory = input_directory
- self.recursive = recursive
- self.extensions = extensions
-
- if self.recursive:
- assert self.input_directory is not None, "Must provide input directory if recursive is True."
-
- def find_files(self, source: Source) -> Iterator[FileMetadata]:
- if self.input_directory is not None:
- files = get_filenames_in_dir(
- input_dir=self.input_directory,
- recursive=self.recursive,
- required_exts=[ext.strip() for ext in str(self.extensions).split(",")],
- exclude=["*png", "*jpg", "*jpeg"],
- )
- else:
- files = self.input_files
-
- # Check that file paths are valid
- assert_all_files_exist_locally(files)
-
- for metadata in extract_metadata_from_files(files):
- yield FileMetadata(
- source_id=source.id,
- file_name=metadata.get("file_name"),
- file_path=metadata.get("file_path"),
- file_type=metadata.get("file_type"),
- file_size=metadata.get("file_size"),
- file_creation_date=metadata.get("file_creation_date"),
- file_last_modified_date=metadata.get("file_last_modified_date"),
- )
-
- def generate_passages(self, file: FileMetadata, chunk_size: int = 1024) -> Iterator[Tuple[str, Dict]]:
- from llama_index.core import SimpleDirectoryReader
- from llama_index.core.node_parser import TokenTextSplitter
-
- parser = TokenTextSplitter(chunk_size=chunk_size)
- if file.file_type == "application/pdf":
- from llama_index.readers.file import PDFReader
-
- reader = PDFReader()
- documents = reader.load_data(file=file.file_path)
- else:
- documents = SimpleDirectoryReader(input_files=[file.file_path]).load_data()
- nodes = parser.get_nodes_from_documents(documents)
- for node in nodes:
- yield node.text, None
diff --git a/letta/data_sources/connectors_helper.py b/letta/data_sources/connectors_helper.py
deleted file mode 100644
index 95d3dbff..00000000
--- a/letta/data_sources/connectors_helper.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import mimetypes
-import os
-from datetime import datetime
-from pathlib import Path
-from typing import List, Optional
-
-
-def extract_file_metadata(file_path) -> dict:
- """Extracts metadata from a single file."""
- if not os.path.exists(file_path):
- raise FileNotFoundError(file_path)
-
- file_metadata = {
- "file_name": os.path.basename(file_path),
- "file_path": file_path,
- "file_type": mimetypes.guess_type(file_path)[0] or "unknown",
- "file_size": os.path.getsize(file_path),
- "file_creation_date": datetime.fromtimestamp(os.path.getctime(file_path)).strftime("%Y-%m-%d"),
- "file_last_modified_date": datetime.fromtimestamp(os.path.getmtime(file_path)).strftime("%Y-%m-%d"),
- }
- return file_metadata
-
-
-def extract_metadata_from_files(file_list):
- """Extracts metadata for a list of files."""
- metadata = []
- for file_path in file_list:
- file_metadata = extract_file_metadata(file_path)
- if file_metadata:
- metadata.append(file_metadata)
- return metadata
-
-
-def get_filenames_in_dir(
- input_dir: str, recursive: bool = True, required_exts: Optional[List[str]] = None, exclude: Optional[List[str]] = None
-):
- """
- Recursively reads files from the directory, applying required_exts and exclude filters.
- Ensures that required_exts and exclude do not overlap.
-
- Args:
- input_dir (str): The directory to scan for files.
- recursive (bool): Whether to scan directories recursively.
- required_exts (list): List of file extensions to include (e.g., ['pdf', 'txt']).
- If None or empty, matches any file extension.
- exclude (list): List of file patterns to exclude (e.g., ['*png', '*jpg']).
-
- Returns:
- list: A list of matching file paths.
- """
- required_exts = required_exts or []
- exclude = exclude or []
-
- # Ensure required_exts and exclude do not overlap
- ext_set = set(required_exts)
- exclude_set = set(exclude)
- overlap = ext_set & exclude_set
- if overlap:
- raise ValueError(f"Extensions in required_exts and exclude overlap: {overlap}")
-
- def is_excluded(file_name):
- """Check if a file matches any pattern in the exclude list."""
- for pattern in exclude:
- if Path(file_name).match(pattern):
- return True
- return False
-
- files = []
- search_pattern = "**/*" if recursive else "*"
-
- for file_path in Path(input_dir).glob(search_pattern):
- if file_path.is_file() and not is_excluded(file_path.name):
- ext = file_path.suffix.lstrip(".")
- # If required_exts is empty, match any file
- if not required_exts or ext in required_exts:
- files.append(str(file_path))
-
- return files
-
-
-def assert_all_files_exist_locally(file_paths: List[str]) -> bool:
- """
- Checks if all file paths in the provided list exist locally.
- Raises a FileNotFoundError with a list of missing files if any do not exist.
-
- Args:
- file_paths (List[str]): List of file paths to check.
-
- Returns:
- bool: True if all files exist, raises FileNotFoundError if any file is missing.
- """
- missing_files = [file_path for file_path in file_paths if not Path(file_path).exists()]
-
- if missing_files:
- raise FileNotFoundError(missing_files)
-
- return True
diff --git a/letta/data_sources/redis_client.py b/letta/data_sources/redis_client.py
deleted file mode 100644
index be149ab2..00000000
--- a/letta/data_sources/redis_client.py
+++ /dev/null
@@ -1,457 +0,0 @@
-import asyncio
-from functools import wraps
-from typing import Any, Dict, List, Optional, Set, Union
-
-from letta.constants import REDIS_EXCLUDE, REDIS_INCLUDE, REDIS_SET_DEFAULT_VAL
-from letta.log import get_logger
-from letta.settings import settings
-
-try:
- from redis import RedisError
- from redis.asyncio import ConnectionPool, Redis
-except ImportError:
- RedisError = None
- Redis = None
- ConnectionPool = None
-
-logger = get_logger(__name__)
-
-_client_instance = None
-
-
-class AsyncRedisClient:
- """Async Redis client with connection pooling and error handling"""
-
- def __init__(
- self,
- host: str = "localhost",
- port: int = 6379,
- db: int = 0,
- password: Optional[str] = None,
- max_connections: int = 50,
- decode_responses: bool = True,
- socket_timeout: int = 5,
- socket_connect_timeout: int = 5,
- retry_on_timeout: bool = True,
- health_check_interval: int = 30,
- ):
- """
- Initialize Redis client with connection pool.
-
- Args:
- host: Redis server hostname
- port: Redis server port
- db: Database number
- password: Redis password if required
- max_connections: Maximum number of connections in pool
- decode_responses: Decode byte responses to strings
- socket_timeout: Socket timeout in seconds
- socket_connect_timeout: Socket connection timeout
- retry_on_timeout: Retry operations on timeout
- health_check_interval: Seconds between health checks
- """
- self.pool = ConnectionPool(
- host=host,
- port=port,
- db=db,
- password=password,
- max_connections=max_connections,
- decode_responses=decode_responses,
- socket_timeout=socket_timeout,
- socket_connect_timeout=socket_connect_timeout,
- retry_on_timeout=retry_on_timeout,
- health_check_interval=health_check_interval,
- )
- self._client = None
- self._lock = asyncio.Lock()
-
- async def get_client(self) -> Redis:
- """Get or create Redis client instance."""
- if self._client is None:
- async with self._lock:
- if self._client is None:
- self._client = Redis(connection_pool=self.pool)
- return self._client
-
- async def close(self):
- """Close Redis connection and cleanup."""
- if self._client:
- await self._client.close()
- await self.pool.disconnect()
- self._client = None
-
- async def __aenter__(self):
- """Async context manager entry."""
- await self.get_client()
- return self
-
- async def __aexit__(self, exc_type, exc_val, exc_tb):
- """Async context manager exit."""
- await self.close()
-
- # Health check and connection management
- async def ping(self) -> bool:
- """Check if Redis is accessible."""
- try:
- client = await self.get_client()
- await client.ping()
- return True
- except RedisError:
- logger.exception("Redis ping failed")
- return False
-
- async def wait_for_ready(self, timeout: int = 30, interval: float = 0.5):
- """Wait for Redis to be ready."""
- start_time = asyncio.get_event_loop().time()
- while (asyncio.get_event_loop().time() - start_time) < timeout:
- if await self.ping():
- return
- await asyncio.sleep(interval)
- raise ConnectionError(f"Redis not ready after {timeout} seconds")
-
- # Retry decorator for resilience
- def with_retry(max_attempts: int = 3, delay: float = 0.1):
- """Decorator to retry Redis operations on failure."""
-
- def decorator(func):
- @wraps(func)
- async def wrapper(self, *args, **kwargs):
- last_error = None
- for attempt in range(max_attempts):
- try:
- return await func(self, *args, **kwargs)
- except (ConnectionError, TimeoutError) as e:
- last_error = e
- if attempt < max_attempts - 1:
- await asyncio.sleep(delay * (2**attempt))
- logger.warning(f"Retry {attempt + 1}/{max_attempts} for {func.__name__}: {e}")
- raise last_error
-
- return wrapper
-
- return decorator
-
- # Basic operations with error handling
- @with_retry()
- async def get(self, key: str, default: Any = None) -> Any:
- """Get value by key."""
- try:
- client = await self.get_client()
- return await client.get(key)
- except:
- return default
-
- @with_retry()
- async def set(
- self,
- key: str,
- value: Union[str, int, float],
- ex: Optional[int] = None,
- px: Optional[int] = None,
- nx: bool = False,
- xx: bool = False,
- ) -> bool:
- """
- Set key-value with options.
-
- Args:
- key: Redis key
- value: Value to store
- ex: Expire time in seconds
- px: Expire time in milliseconds
- nx: Only set if key doesn't exist
- xx: Only set if key exists
- """
- client = await self.get_client()
- return await client.set(key, value, ex=ex, px=px, nx=nx, xx=xx)
-
- @with_retry()
- async def delete(self, *keys: str) -> int:
- """Delete one or more keys."""
- client = await self.get_client()
- return await client.delete(*keys)
-
- @with_retry()
- async def exists(self, *keys: str) -> int:
- """Check if keys exist."""
- client = await self.get_client()
- return await client.exists(*keys)
-
- # Set operations
- async def sadd(self, key: str, *members: Union[str, int, float]) -> int:
- """Add members to set."""
- client = await self.get_client()
- return await client.sadd(key, *members)
-
- async def smembers(self, key: str) -> Set[str]:
- """Get all set members."""
- client = await self.get_client()
- return await client.smembers(key)
-
- @with_retry()
- async def smismember(self, key: str, values: list[Any] | Any) -> list[int] | int:
- """clever!: set member is member"""
- try:
- client = await self.get_client()
- result = await client.smismember(key, values)
- return result if isinstance(values, list) else result[0]
- except:
- return [0] * len(values) if isinstance(values, list) else 0
-
- async def srem(self, key: str, *members: Union[str, int, float]) -> int:
- """Remove members from set."""
- client = await self.get_client()
- return await client.srem(key, *members)
-
- async def scard(self, key: str) -> int:
- client = await self.get_client()
- return await client.scard(key)
-
- # Atomic operations
- async def incr(self, key: str) -> int:
- """Increment key value."""
- client = await self.get_client()
- return await client.incr(key)
-
- async def decr(self, key: str) -> int:
- """Decrement key value."""
- client = await self.get_client()
- return await client.decr(key)
-
- # Stream operations
- @with_retry()
- async def xadd(self, stream: str, fields: Dict[str, Any], id: str = "*", maxlen: Optional[int] = None, approximate: bool = True) -> str:
- """Add entry to a stream.
-
- Args:
- stream: Stream name
- fields: Dict of field-value pairs to add
- id: Entry ID ('*' for auto-generation)
- maxlen: Maximum length of the stream
- approximate: Whether maxlen is approximate
-
- Returns:
- The ID of the added entry
- """
- client = await self.get_client()
- return await client.xadd(stream, fields, id=id, maxlen=maxlen, approximate=approximate)
-
- @with_retry()
- async def xread(self, streams: Dict[str, str], count: Optional[int] = None, block: Optional[int] = None) -> List[Dict]:
- """Read from streams.
-
- Args:
- streams: Dict mapping stream names to IDs
- count: Maximum number of entries to return
- block: Milliseconds to block waiting for data (None = no blocking)
-
- Returns:
- List of entries from the streams
- """
- client = await self.get_client()
- return await client.xread(streams, count=count, block=block)
-
- @with_retry()
- async def xrange(self, stream: str, start: str = "-", end: str = "+", count: Optional[int] = None) -> List[Dict]:
- """Read range of entries from a stream.
-
- Args:
- stream: Stream name
- start: Start ID (inclusive)
- end: End ID (inclusive)
- count: Maximum number of entries to return
-
- Returns:
- List of entries in the specified range
- """
- client = await self.get_client()
- return await client.xrange(stream, start, end, count=count)
-
- @with_retry()
- async def xrevrange(self, stream: str, start: str = "+", end: str = "-", count: Optional[int] = None) -> List[Dict]:
- """Read range of entries from a stream in reverse order.
-
- Args:
- stream: Stream name
- start: Start ID (inclusive)
- end: End ID (inclusive)
- count: Maximum number of entries to return
-
- Returns:
- List of entries in the specified range in reverse order
- """
- client = await self.get_client()
- return await client.xrevrange(stream, start, end, count=count)
-
- @with_retry()
- async def xlen(self, stream: str) -> int:
- """Get the length of a stream.
-
- Args:
- stream: Stream name
-
- Returns:
- Number of entries in the stream
- """
- client = await self.get_client()
- return await client.xlen(stream)
-
- @with_retry()
- async def xdel(self, stream: str, *ids: str) -> int:
- """Delete entries from a stream.
-
- Args:
- stream: Stream name
- ids: IDs of entries to delete
-
- Returns:
- Number of entries deleted
- """
- client = await self.get_client()
- return await client.xdel(stream, *ids)
-
- @with_retry()
- async def xinfo_stream(self, stream: str) -> Dict:
- """Get information about a stream.
-
- Args:
- stream: Stream name
-
- Returns:
- Dict with stream information
- """
- client = await self.get_client()
- return await client.xinfo_stream(stream)
-
- @with_retry()
- async def xtrim(self, stream: str, maxlen: int, approximate: bool = True) -> int:
- """Trim a stream to a maximum length.
-
- Args:
- stream: Stream name
- maxlen: Maximum length
- approximate: Whether maxlen is approximate
-
- Returns:
- Number of entries removed
- """
- client = await self.get_client()
- return await client.xtrim(stream, maxlen=maxlen, approximate=approximate)
-
- async def check_inclusion_and_exclusion(self, member: str, group: str) -> bool:
- exclude_key = self._get_group_exclusion_key(group)
- include_key = self._get_group_inclusion_key(group)
- # 1. if the member IS excluded from the group
- if self.exists(exclude_key) and await self.scard(exclude_key) > 1:
- return bool(await self.smismember(exclude_key, member))
- # 2. if the group HAS an include set, is the member in that set?
- if self.exists(include_key) and await self.scard(include_key) > 1:
- return bool(await self.smismember(include_key, member))
- # 3. if the group does NOT HAVE an include set and member NOT excluded
- return True
-
- async def create_inclusion_exclusion_keys(self, group: str) -> None:
- redis_client = await self.get_client()
- await redis_client.sadd(self._get_group_inclusion_key(group), REDIS_SET_DEFAULT_VAL)
- await redis_client.sadd(self._get_group_exclusion_key(group), REDIS_SET_DEFAULT_VAL)
-
- @staticmethod
- def _get_group_inclusion_key(group: str) -> str:
- return f"{group}:{REDIS_INCLUDE}"
-
- @staticmethod
- def _get_group_exclusion_key(group: str) -> str:
- return f"{group}:{REDIS_EXCLUDE}"
-
-
-class NoopAsyncRedisClient(AsyncRedisClient):
- # noinspection PyMissingConstructor
- def __init__(self):
- pass
-
- async def set(
- self,
- key: str,
- value: Union[str, int, float],
- ex: Optional[int] = None,
- px: Optional[int] = None,
- nx: bool = False,
- xx: bool = False,
- ) -> bool:
- return False
-
- async def get(self, key: str, default: Any = None) -> Any:
- return default
-
- async def exists(self, *keys: str) -> int:
- return 0
-
- async def sadd(self, key: str, *members: Union[str, int, float]) -> int:
- return 0
-
- async def smismember(self, key: str, values: list[Any] | Any) -> list[int] | int:
- return [0] * len(values) if isinstance(values, list) else 0
-
- async def delete(self, *keys: str) -> int:
- return 0
-
- async def check_inclusion_and_exclusion(self, member: str, group: str) -> bool:
- return False
-
- async def create_inclusion_exclusion_keys(self, group: str) -> None:
- return None
-
- async def scard(self, key: str) -> int:
- return 0
-
- async def smembers(self, key: str) -> Set[str]:
- return set()
-
- async def srem(self, key: str, *members: Union[str, int, float]) -> int:
- return 0
-
- # Stream operations
- async def xadd(self, stream: str, fields: Dict[str, Any], id: str = "*", maxlen: Optional[int] = None, approximate: bool = True) -> str:
- return ""
-
- async def xread(self, streams: Dict[str, str], count: Optional[int] = None, block: Optional[int] = None) -> List[Dict]:
- return []
-
- async def xrange(self, stream: str, start: str = "-", end: str = "+", count: Optional[int] = None) -> List[Dict]:
- return []
-
- async def xrevrange(self, stream: str, start: str = "+", end: str = "-", count: Optional[int] = None) -> List[Dict]:
- return []
-
- async def xlen(self, stream: str) -> int:
- return 0
-
- async def xdel(self, stream: str, *ids: str) -> int:
- return 0
-
- async def xinfo_stream(self, stream: str) -> Dict:
- return {}
-
- async def xtrim(self, stream: str, maxlen: int, approximate: bool = True) -> int:
- return 0
-
-
-async def get_redis_client() -> AsyncRedisClient:
- global _client_instance
- if _client_instance is None:
- try:
- # If Redis settings are not configured, use noop client
- if settings.redis_host is None or settings.redis_port is None:
- logger.info("Redis not configured, using noop client")
- _client_instance = NoopAsyncRedisClient()
- else:
- _client_instance = AsyncRedisClient(
- host=settings.redis_host,
- port=settings.redis_port,
- )
- await _client_instance.wait_for_ready(timeout=5)
- logger.info("Redis client initialized")
- except Exception as e:
- logger.warning(f"Failed to initialize Redis: {e}")
- _client_instance = NoopAsyncRedisClient()
- return _client_instance
diff --git a/letta/embeddings.py b/letta/embeddings.py
deleted file mode 100644
index a07e16c3..00000000
--- a/letta/embeddings.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from typing import List
-
-import tiktoken
-
-from letta.constants import EMBEDDING_TO_TOKENIZER_DEFAULT, EMBEDDING_TO_TOKENIZER_MAP
-from letta.utils import printd
-
-
-def parse_and_chunk_text(text: str, chunk_size: int) -> List[str]:
- from llama_index.core import Document as LlamaIndexDocument
- from llama_index.core.node_parser import SentenceSplitter
-
- parser = SentenceSplitter(chunk_size=chunk_size)
- llama_index_docs = [LlamaIndexDocument(text=text)]
- nodes = parser.get_nodes_from_documents(llama_index_docs)
- return [n.text for n in nodes]
-
-
-def truncate_text(text: str, max_length: int, encoding) -> str:
- # truncate the text based on max_length and encoding
- encoded_text = encoding.encode(text)[:max_length]
- return encoding.decode(encoded_text)
-
-
-def check_and_split_text(text: str, embedding_model: str) -> List[str]:
- """Split text into chunks of max_length tokens or less"""
-
- if embedding_model in EMBEDDING_TO_TOKENIZER_MAP:
- encoding = tiktoken.get_encoding(EMBEDDING_TO_TOKENIZER_MAP[embedding_model])
- else:
- print(f"Warning: couldn't find tokenizer for model {embedding_model}, using default tokenizer {EMBEDDING_TO_TOKENIZER_DEFAULT}")
- encoding = tiktoken.get_encoding(EMBEDDING_TO_TOKENIZER_DEFAULT)
-
- num_tokens = len(encoding.encode(text))
-
- # determine max length
- if hasattr(encoding, "max_length"):
- # TODO(fix) this is broken
- max_length = encoding.max_length
- else:
- # TODO: figure out the real number
- printd(f"Warning: couldn't find max_length for tokenizer {embedding_model}, using default max_length 8191")
- max_length = 8191
-
- # truncate text if too long
- if num_tokens > max_length:
- print(f"Warning: text is too long ({num_tokens} tokens), truncating to {max_length} tokens.")
- # First, apply any necessary formatting
- formatted_text = format_text(text, embedding_model)
- # Then truncate
- text = truncate_text(formatted_text, max_length, encoding)
-
- return [text]
diff --git a/letta/errors.py b/letta/errors.py
deleted file mode 100644
index 1d154d31..00000000
--- a/letta/errors.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import json
-from enum import Enum
-from typing import TYPE_CHECKING, Dict, List, Optional, Union
-
-# Avoid circular imports
-if TYPE_CHECKING:
- from letta.schemas.message import Message
-
-
-class ErrorCode(Enum):
- """Enum for error codes used by client."""
-
- NOT_FOUND = "NOT_FOUND"
- UNAUTHENTICATED = "UNAUTHENTICATED"
- PERMISSION_DENIED = "PERMISSION_DENIED"
- INVALID_ARGUMENT = "INVALID_ARGUMENT"
- INTERNAL_SERVER_ERROR = "INTERNAL_SERVER_ERROR"
- CONTEXT_WINDOW_EXCEEDED = "CONTEXT_WINDOW_EXCEEDED"
- RATE_LIMIT_EXCEEDED = "RATE_LIMIT_EXCEEDED"
- TIMEOUT = "TIMEOUT"
- CONFLICT = "CONFLICT"
-
-
-class LettaError(Exception):
- """Base class for all Letta related errors."""
-
- def __init__(self, message: str, code: Optional[ErrorCode] = None, details: Optional[Union[Dict, str, object]] = None):
- if details is None:
- details = {}
- self.message = message
- self.code = code
- self.details = details
- super().__init__(message)
-
- def __str__(self) -> str:
- if self.code:
- return f"{self.code.value}: {self.message}"
- return self.message
-
- def __repr__(self) -> str:
- return f"{self.__class__.__name__}(message='{self.message}', code='{self.code}', details={self.details})"
-
-
-class PendingApprovalError(LettaError):
- """Error raised when attempting an operation while agent is waiting for tool approval."""
-
- def __init__(self, pending_request_id: Optional[str] = None):
- self.pending_request_id = pending_request_id
- message = "Cannot send a new message: The agent is waiting for approval on a tool call. Please approve or deny the pending request before continuing."
- code = ErrorCode.CONFLICT
- details = {"error_code": "PENDING_APPROVAL", "pending_request_id": pending_request_id}
- super().__init__(message=message, code=code, details=details)
-
-
-class LettaToolCreateError(LettaError):
- """Error raised when a tool cannot be created."""
-
- default_error_message = "Error creating tool."
-
- def __init__(self, message=None):
- super().__init__(message=message or self.default_error_message)
-
-
-class LettaToolNameConflictError(LettaError):
- """Error raised when a tool name already exists."""
-
- def __init__(self, tool_name: str):
- super().__init__(
- message=f"Tool with name '{tool_name}' already exists in your organization",
- code=ErrorCode.INVALID_ARGUMENT,
- details={"tool_name": tool_name},
- )
-
-
-class LettaToolNameSchemaMismatchError(LettaToolCreateError):
- """Error raised when a tool name our source codedoes not match the name in the JSON schema."""
-
- def __init__(self, tool_name: str, json_schema_name: str, source_code: str):
- super().__init__(
- message=f"Tool name '{tool_name}' does not match the name in the JSON schema '{json_schema_name}' or in the source code `{source_code}`",
- )
-
-
-class LettaConfigurationError(LettaError):
- """Error raised when there are configuration-related issues."""
-
- def __init__(self, message: str, missing_fields: Optional[List[str]] = None):
- self.missing_fields = missing_fields or []
- super().__init__(message=message, details={"missing_fields": self.missing_fields})
-
-
-class LettaAgentNotFoundError(LettaError):
- """Error raised when an agent is not found."""
-
-
-class LettaUserNotFoundError(LettaError):
- """Error raised when a user is not found."""
-
-
-class LettaUnexpectedStreamCancellationError(LettaError):
- """Error raised when a streaming request is terminated unexpectedly."""
-
-
-class LLMError(LettaError):
- pass
-
-
-class LLMConnectionError(LLMError):
- """Error when unable to connect to LLM service"""
-
-
-class LLMRateLimitError(LLMError):
- """Error when rate limited by LLM service"""
-
-
-class LLMBadRequestError(LLMError):
- """Error when LLM service cannot process request"""
-
-
-class LLMAuthenticationError(LLMError):
- """Error when authentication fails with LLM service"""
-
-
-class LLMPermissionDeniedError(LLMError):
- """Error when permission is denied by LLM service"""
-
-
-class LLMNotFoundError(LLMError):
- """Error when requested resource is not found"""
-
-
-class LLMUnprocessableEntityError(LLMError):
- """Error when request is well-formed but semantically invalid"""
-
-
-class LLMServerError(LLMError):
- """Error indicating an internal server error occurred within the LLM service itself
- while processing the request."""
-
-
-class LLMTimeoutError(LLMError):
- """Error when LLM request times out"""
-
-
-class BedrockPermissionError(LettaError):
- """Exception raised for errors in the Bedrock permission process."""
-
- def __init__(self, message="User does not have access to the Bedrock model with the specified ID."):
- super().__init__(message=message)
-
-
-class BedrockError(LettaError):
- """Exception raised for errors in the Bedrock process."""
-
- def __init__(self, message="Error with Bedrock model."):
- super().__init__(message=message)
-
-
-class LLMJSONParsingError(LettaError):
- """Exception raised for errors in the JSON parsing process."""
-
- def __init__(self, message="Error parsing JSON generated by LLM"):
- super().__init__(message=message)
-
-
-class LocalLLMError(LettaError):
- """Generic catch-all error for local LLM problems"""
-
- def __init__(self, message="Encountered an error while running local LLM"):
- super().__init__(message=message)
-
-
-class LocalLLMConnectionError(LettaError):
- """Error for when local LLM cannot be reached with provided IP/port"""
-
- def __init__(self, message="Could not connect to local LLM"):
- super().__init__(message=message)
-
-
-class ContextWindowExceededError(LettaError):
- """Error raised when the context window is exceeded but further summarization fails."""
-
- def __init__(self, message: str, details: dict = {}):
- error_message = f"{message} ({details})"
- super().__init__(
- message=error_message,
- code=ErrorCode.CONTEXT_WINDOW_EXCEEDED,
- details=details,
- )
-
-
-class RateLimitExceededError(LettaError):
- """Error raised when the llm rate limiter throttles api requests."""
-
- def __init__(self, message: str, max_retries: int):
- error_message = f"{message} ({max_retries})"
- super().__init__(
- message=error_message,
- code=ErrorCode.RATE_LIMIT_EXCEEDED,
- details={"max_retries": max_retries},
- )
-
-
-class LettaMessageError(LettaError):
- """Base error class for handling message-related errors."""
-
- messages: List[Union["Message", "LettaMessage"]]
- default_error_message: str = "An error occurred with the message."
-
- def __init__(self, *, messages: List[Union["Message", "LettaMessage"]], explanation: Optional[str] = None) -> None:
- error_msg = self.construct_error_message(messages, self.default_error_message, explanation)
- super().__init__(error_msg)
- self.messages = messages
-
- @staticmethod
- def construct_error_message(messages: List[Union["Message", "LettaMessage"]], error_msg: str, explanation: Optional[str] = None) -> str:
- """Helper method to construct a clean and formatted error message."""
- if explanation:
- error_msg += f" (Explanation: {explanation})"
-
- # Pretty print out message JSON
- message_json = json.dumps([message.model_dump() for message in messages], indent=4)
- return f"{error_msg}\n\n{message_json}"
-
-
-class MissingToolCallError(LettaMessageError):
- """Error raised when a message is missing a tool call."""
-
- default_error_message = "The message is missing a tool call."
-
-
-class InvalidToolCallError(LettaMessageError):
- """Error raised when a message uses an invalid tool call."""
-
- default_error_message = "The message uses an invalid tool call or has improper usage of a tool call."
-
-
-class MissingInnerMonologueError(LettaMessageError):
- """Error raised when a message is missing an inner monologue."""
-
- default_error_message = "The message is missing an inner monologue."
-
-
-class InvalidInnerMonologueError(LettaMessageError):
- """Error raised when a message has a malformed inner monologue."""
-
- default_error_message = "The message has a malformed inner monologue."
-
-
-class HandleNotFoundError(LettaError):
- """Error raised when a handle is not found."""
-
- def __init__(self, handle: str, available_handles: List[str]):
- super().__init__(
- message=f"Handle {handle} not found, must be one of {available_handles}",
- code=ErrorCode.NOT_FOUND,
- )
-
-
-class AgentFileExportError(Exception):
- """Exception raised during agent file export operations"""
-
-
-class AgentNotFoundForExportError(AgentFileExportError):
- """Exception raised when requested agents are not found during export"""
-
- def __init__(self, missing_ids: List[str]):
- self.missing_ids = missing_ids
- super().__init__(f"The following agent IDs were not found: {missing_ids}")
-
-
-class AgentExportIdMappingError(AgentFileExportError):
- """Exception raised when ID mapping fails during export conversion"""
-
- def __init__(self, db_id: str, entity_type: str):
- self.db_id = db_id
- self.entity_type = entity_type
- super().__init__(
- f"Unexpected new {entity_type} ID '{db_id}' encountered during conversion. "
- f"All IDs should have been mapped during agent processing."
- )
-
-
-class AgentExportProcessingError(AgentFileExportError):
- """Exception raised when general export processing fails"""
-
- def __init__(self, message: str, original_error: Optional[Exception] = None):
- self.original_error = original_error
- super().__init__(f"Export failed: {message}")
-
-
-class AgentFileImportError(Exception):
- """Exception raised during agent file import operations"""
diff --git a/letta/functions/__init__.py b/letta/functions/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/letta/functions/ast_parsers.py b/letta/functions/ast_parsers.py
deleted file mode 100644
index 627b7fdb..00000000
--- a/letta/functions/ast_parsers.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import ast
-import builtins
-import json
-import typing
-from typing import Dict, Optional, Tuple
-
-from letta.errors import LettaToolCreateError
-from letta.types import JsonDict
-
-
-def resolve_type(annotation: str):
- """
- Resolve a type annotation string into a Python type.
- Previously, primitive support for int, float, str, dict, list, set, tuple, bool.
-
- Args:
- annotation (str): The annotation string (e.g., 'int', 'list[int]', 'dict[str, int]').
-
- Returns:
- type: The corresponding Python type.
-
- Raises:
- ValueError: If the annotation is unsupported or invalid.
- """
- python_types = {**vars(typing), **vars(builtins)}
-
- if annotation in python_types:
- return python_types[annotation]
-
- try:
- # Allow use of typing and builtins in a safe eval context
- return eval(annotation, python_types)
- except Exception:
- raise ValueError(f"Unsupported annotation: {annotation}")
-
-
-# TODO :: THIS MUST BE EDITED TO HANDLE THINGS
-def get_function_annotations_from_source(source_code: str, function_name: str) -> Dict[str, str]:
- """
- Parse the source code to extract annotations for a given function name.
-
- Args:
- source_code (str): The Python source code containing the function.
- function_name (str): The name of the function to extract annotations for.
-
- Returns:
- Dict[str, str]: A dictionary of argument names to their annotation strings.
-
- Raises:
- ValueError: If the function is not found in the source code.
- """
- tree = ast.parse(source_code)
- for node in ast.iter_child_nodes(tree):
- if isinstance(node, ast.FunctionDef) and node.name == function_name:
- annotations = {}
- for arg in node.args.args:
- if arg.annotation is not None:
- annotation_str = ast.unparse(arg.annotation)
- annotations[arg.arg] = annotation_str
- return annotations
- raise ValueError(f"Function '{function_name}' not found in the provided source code.")
-
-
-# NOW json_loads -> ast.literal_eval -> typing.get_origin
-def coerce_dict_args_by_annotations(function_args: JsonDict, annotations: Dict[str, str]) -> dict:
- coerced_args = dict(function_args) # Shallow copy
-
- for arg_name, value in coerced_args.items():
- if arg_name in annotations:
- annotation_str = annotations[arg_name]
- try:
- arg_type = resolve_type(annotation_str)
-
- # Always parse strings using literal_eval or json if possible
- if isinstance(value, str):
- try:
- value = json.loads(value)
- except json.JSONDecodeError:
- try:
- value = ast.literal_eval(value)
- except (SyntaxError, ValueError) as e:
- if arg_type is not str:
- raise ValueError(f"Failed to coerce argument '{arg_name}' to {annotation_str}: {e}")
-
- origin = typing.get_origin(arg_type)
- if origin in (list, dict, tuple, set):
- # Let the origin (e.g., list) handle coercion
- coerced_args[arg_name] = origin(value)
- else:
- # Coerce simple types (e.g., int, float)
- coerced_args[arg_name] = arg_type(value)
-
- except Exception as e:
- raise ValueError(f"Failed to coerce argument '{arg_name}' to {annotation_str}: {e}")
-
- return coerced_args
-
-
-def get_function_name_and_docstring(source_code: str, name: Optional[str] = None) -> Tuple[str, str]:
- """Gets the name and docstring for a given function source code by parsing the AST.
-
- Args:
- source_code: The source code to parse
- name: Optional override for the function name
-
- Returns:
- Tuple of (function_name, docstring)
- """
- try:
- # Parse the source code into an AST
- tree = ast.parse(source_code)
-
- # Find the last function definition
- function_def = None
- for node in ast.walk(tree):
- if isinstance(node, ast.FunctionDef):
- function_def = node
-
- if not function_def:
- raise LettaToolCreateError("No function definition found in source code")
-
- # Get the function name
- function_name = name if name is not None else function_def.name
-
- # Get the docstring if it exists
- docstring = ast.get_docstring(function_def)
-
- if not function_name:
- raise LettaToolCreateError("Could not determine function name")
-
- if not docstring:
- # For tools with args_json_schema, the docstring is optional
- docstring = f"The {function_name} tool"
-
- return function_name, docstring
-
- except Exception as e:
- import traceback
-
- traceback.print_exc()
- raise LettaToolCreateError(f"Failed to parse function name and docstring: {str(e)}")
diff --git a/letta/functions/async_composio_toolset.py b/letta/functions/async_composio_toolset.py
deleted file mode 100644
index 3094bf59..00000000
--- a/letta/functions/async_composio_toolset.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import json
-from typing import Any
-
-import aiohttp
-from composio import ComposioToolSet as BaseComposioToolSet
-from composio.exceptions import (
- ApiKeyNotProvidedError,
- ComposioSDKError,
- ConnectedAccountNotFoundError,
- EnumMetadataNotFound,
- EnumStringNotFound,
-)
-
-
-class AsyncComposioToolSet(BaseComposioToolSet, runtime="letta", description_char_limit=1024):
- """
- Async version of ComposioToolSet client for interacting with Composio API
- Used to asynchronously hit the execute action endpoint
-
- https://docs.composio.dev/api-reference/api-reference/v3/tools/post-api-v-3-tools-execute-action
- """
-
- def __init__(self, api_key: str, entity_id: str, lock: bool = True):
- """
- Initialize the AsyncComposioToolSet client
-
- Args:
- api_key (str): Your Composio API key
- entity_id (str): Your Composio entity ID
- lock (bool): Whether to use locking (default: True)
- """
- super().__init__(api_key=api_key, entity_id=entity_id, lock=lock)
-
- self.headers = {
- "Content-Type": "application/json",
- "X-API-Key": self._api_key,
- }
-
- async def execute_action(
- self,
- action: str,
- params: dict[str, Any] = {},
- ) -> dict[str, Any]:
- """
- Execute an action asynchronously using the Composio API
-
- Args:
- action (str): The name of the action to execute
- params (dict[str, Any], optional): Parameters for the action
-
- Returns:
- dict[str, Any]: The API response
-
- Raises:
- ApiKeyNotProvidedError: if the API key is not provided
- ComposioSDKError: if a general Composio SDK error occurs
- ConnectedAccountNotFoundError: if the connected account is not found
- EnumMetadataNotFound: if enum metadata is not found
- EnumStringNotFound: if enum string is not found
- aiohttp.ClientError: if a network-related error occurs
- ValueError: if an error with the parameters or response occurs
- """
- API_VERSION = "v3"
- endpoint = f"{self._base_url}/{API_VERSION}/tools/execute/{action}"
-
- json_payload = {
- "entity_id": self.entity_id,
- "arguments": params or {},
- }
-
- try:
- async with aiohttp.ClientSession() as session:
- async with session.post(endpoint, headers=self.headers, json=json_payload) as response:
- print(response, response.status, response.reason, response.content)
- if response.status == 200:
- return await response.json()
- else:
- error_text = await response.text()
- try:
- error_json = json.loads(error_text)
- error_message = error_json.get("message", error_text)
- error_code = error_json.get("code")
-
- # Handle specific error codes from Composio API
- if error_code == 10401 or "API_KEY_NOT_FOUND" in error_message:
- raise ApiKeyNotProvidedError()
- if (
- "connected account not found" in error_message.lower()
- or "no connected account found" in error_message.lower()
- ):
- raise ConnectedAccountNotFoundError(f"Connected account not found: {error_message}")
- if "enum metadata not found" in error_message.lower():
- raise EnumMetadataNotFound(f"Enum metadata not found: {error_message}")
- if "enum string not found" in error_message.lower():
- raise EnumStringNotFound(f"Enum string not found: {error_message}")
- except json.JSONDecodeError:
- error_message = error_text
-
- # If no specific error was identified, raise a general error
- raise ValueError(f"API request failed with status {response.status}: {error_message}")
- except aiohttp.ClientError as e:
- # Wrap network errors in ComposioSDKError
- raise ComposioSDKError(f"Network error when calling Composio API: {str(e)}")
- except ValueError:
- # Re-raise ValueError (which could be our custom error message or a JSON parsing error)
- raise
- except Exception as e:
- # Catch any other exceptions and wrap them in ComposioSDKError
- raise ComposioSDKError(f"Unexpected error when calling Composio API: {str(e)}")
diff --git a/letta/functions/composio_helpers.py b/letta/functions/composio_helpers.py
deleted file mode 100644
index 40d49791..00000000
--- a/letta/functions/composio_helpers.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-from typing import Any, Optional
-
-from composio.constants import DEFAULT_ENTITY_ID
-from composio.exceptions import (
- ApiKeyNotProvidedError,
- ComposioSDKError,
- ConnectedAccountNotFoundError,
- EnumMetadataNotFound,
- EnumStringNotFound,
-)
-
-from letta.constants import COMPOSIO_ENTITY_ENV_VAR_KEY
-from letta.functions.async_composio_toolset import AsyncComposioToolSet
-from letta.utils import run_async_task
-
-
-# TODO: This is kind of hacky, as this is used to search up the action later on composio's side
-# TODO: So be very careful changing/removing these pair of functions
-def _generate_func_name_from_composio_action(action_name: str) -> str:
- """
- Generates the composio function name from the composio action.
-
- Args:
- action_name: The composio action name
-
- Returns:
- function name
- """
- return action_name.lower()
-
-
-def generate_composio_action_from_func_name(func_name: str) -> str:
- """
- Generates the composio action from the composio function name.
-
- Args:
- func_name: The composio function name
-
- Returns:
- composio action name
- """
- return func_name.upper()
-
-
-def generate_composio_tool_wrapper(action_name: str) -> tuple[str, str]:
- # Generate func name
- func_name = _generate_func_name_from_composio_action(action_name)
-
- wrapper_function_str = f"""\
-def {func_name}(**kwargs):
- raise RuntimeError("Something went wrong - we should never be using the persisted source code for Composio. Please reach out to Letta team")
-"""
-
- # Compile safety check
- _assert_code_gen_compilable(wrapper_function_str.strip())
-
- return func_name, wrapper_function_str.strip()
-
-
-async def execute_composio_action_async(
- action_name: str, args: dict, api_key: Optional[str] = None, entity_id: Optional[str] = None
-) -> tuple[str, str]:
- entity_id = entity_id or os.getenv(COMPOSIO_ENTITY_ENV_VAR_KEY, DEFAULT_ENTITY_ID)
- composio_toolset = AsyncComposioToolSet(api_key=api_key, entity_id=entity_id, lock=False)
- try:
- response = await composio_toolset.execute_action(action=action_name, params=args)
- except ApiKeyNotProvidedError as e:
- raise RuntimeError(f"API key not provided or invalid for Composio action '{action_name}': {str(e)}")
- except ConnectedAccountNotFoundError as e:
- raise RuntimeError(f"Connected account not found for Composio action '{action_name}': {str(e)}")
- except EnumMetadataNotFound as e:
- raise RuntimeError(f"Enum metadata not found for Composio action '{action_name}': {str(e)}")
- except EnumStringNotFound as e:
- raise RuntimeError(f"Enum string not found for Composio action '{action_name}': {str(e)}")
- except ComposioSDKError as e:
- raise RuntimeError(f"Composio SDK error while executing action '{action_name}': {str(e)}")
- except Exception as e:
- print(type(e))
- raise RuntimeError(f"An unexpected error occurred in Composio SDK while executing action '{action_name}': {str(e)}")
-
- if "error" in response and response["error"]:
- raise RuntimeError(f"Error while executing action '{action_name}': {str(response['error'])}")
-
- return response.get("data")
-
-
-def execute_composio_action(action_name: str, args: dict, api_key: Optional[str] = None, entity_id: Optional[str] = None) -> Any:
- return run_async_task(execute_composio_action_async(action_name, args, api_key, entity_id))
-
-
-def _assert_code_gen_compilable(code_str):
- try:
- compile(code_str, "🛠️ [{date_formatted}] Function Return ({return_status}):
" - html_content += f"{return_string}
" - elif "internal_monologue" in message: - html_content += f"{INNER_THOUGHTS_CLI_SYMBOL} [{date_formatted}] Internal Monologue:
" - html_content += f"{message['internal_monologue']}
" - elif "function_call" in message: - html_content += f"🛠️ [[{date_formatted}] Function Call:
" - html_content += f"{message['function_call']}
" - elif "assistant_message" in message: - html_content += f"{ASSISTANT_MESSAGE_CLI_SYMBOL} [{date_formatted}] Assistant Message:
" - html_content += f"" - html_content += "" - html_content += "