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chore: merge updated tests (#1276)

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This commit is contained in:
Sarah Wooders
2025-03-13 17:15:20 -07:00
committed by GitHub
parent b9ff1ea624
commit 3deab82109
7 changed files with 344 additions and 343 deletions
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18
letta/functions/schema_generator.py
View File

@@ -231,13 +231,27 @@ def pydantic_model_to_json_schema(model: Type[BaseModel]) -> dict:
"""
schema = model.model_json_schema()
def clean_property(prop: dict) -> dict:
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"],
}
# 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"],
@@ -286,7 +300,7 @@ def pydantic_model_to_json_schema(model: Type[BaseModel]) -> dict:
"description": prop["description"],
}
else:
properties[name] = clean_property(prop)
properties[name] = clean_property(prop, full_schema)
pydantic_model_schema_dict = {
"type": "object",

20
letta/llm_api/anthropic.py
View File

@@ -53,6 +53,11 @@ MODEL_LIST = [
"name": "claude-3-opus-20240229",
"context_window": 200000,
},
# latest
{
"name": "claude-3-opus-latest",
"context_window": 200000,
},
## Sonnet
# 3.0
{
@@ -69,11 +74,21 @@ MODEL_LIST = [
"name": "claude-3-5-sonnet-20241022",
"context_window": 200000,
},
# 3.5 latest
{
"name": "claude-3-5-sonnet-latest",
"context_window": 200000,
},
# 3.7
{
"name": "claude-3-7-sonnet-20250219",
"context_window": 200000,
},
# 3.7 latest
{
"name": "claude-3-7-sonnet-latest",
"context_window": 200000,
},
## Haiku
# 3.0
{
@@ -85,6 +100,11 @@ MODEL_LIST = [
"name": "claude-3-5-haiku-20241022",
"context_window": 200000,
},
# 3.5 latest
{
"name": "claude-3-5-haiku-latest",
"context_window": 200000,
},
]
DUMMY_FIRST_USER_MESSAGE = "User initializing bootup sequence."

1
letta/llm_api/azure_openai_constants.py
View File

@@ -6,5 +6,6 @@ AZURE_MODEL_TO_CONTEXT_LENGTH = {
"gpt-35-turbo-0125": 16385,
"gpt-4-0613": 8192,
"gpt-4o-mini-2024-07-18": 128000,
"gpt-4o-mini": 128000,
"gpt-4o": 128000,
}

367
poetry.lock generated
View File

@@ -268,13 +268,13 @@ files = [
[[package]]
name = "attrs"
version = "25.2.0"
version = "25.3.0"
description = "Classes Without Boilerplate"
optional = false
python-versions = ">=3.8"
files = [
{file = "attrs-25.2.0-py3-none-any.whl", hash = "sha256:611344ff0a5fed735d86d7784610c84f8126b95e549bcad9ff61b4242f2d386b"},
{file = "attrs-25.2.0.tar.gz", hash = "sha256:18a06db706db43ac232cce80443fcd9f2500702059ecf53489e3c5a3f417acaf"},
{file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"},
{file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"},
]
[package.extras]
@@ -447,17 +447,17 @@ files = [
[[package]]
name = "boto3"
version = "1.37.11"
version = "1.37.12"
description = "The AWS SDK for Python"
optional = true
python-versions = ">=3.8"
files = [
{file = "boto3-1.37.11-py3-none-any.whl", hash = "sha256:da6c22fc8a7e9bca5d7fc465a877ac3d45b6b086d776bd1a6c55bdde60523741"},
{file = "boto3-1.37.11.tar.gz", hash = "sha256:8eec08363ef5db05c2fbf58e89f0c0de6276cda2fdce01e76b3b5f423cd5c0f4"},
{file = "boto3-1.37.12-py3-none-any.whl", hash = "sha256:516feaa0d2afaeda1515216fd09291368a1215754bbccb0f28414c0a91a830a2"},
{file = "boto3-1.37.12.tar.gz", hash = "sha256:9412d404f103ad6d14f033eb29cd5e0cdca2b9b08cbfa9d4dabd1d7be2de2625"},
]
[package.dependencies]
botocore = ">=1.37.11,<1.38.0"
botocore = ">=1.37.12,<1.38.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.11.0,<0.12.0"
@@ -466,13 +466,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.37.11"
version = "1.37.12"
description = "Low-level, data-driven core of boto 3."
optional = true
python-versions = ">=3.8"
files = [
{file = "botocore-1.37.11-py3-none-any.whl", hash = "sha256:02505309b1235f9f15a6da79103ca224b3f3dc5f6a62f8630fbb2c6ed05e2da8"},
{file = "botocore-1.37.11.tar.gz", hash = "sha256:72eb3a9a58b064be26ba154e5e56373633b58f951941c340ace0d379590d98b5"},
{file = "botocore-1.37.12-py3-none-any.whl", hash = "sha256:ba1948c883bbabe20d95ff62c3e36954c9269686f7db9361857835677ca3e676"},
{file = "botocore-1.37.12.tar.gz", hash = "sha256:ae2d5328ce6ad02eb615270507235a6e90fd3eeed615a6c0732b5a68b12f2017"},
]
[package.dependencies]
@@ -1035,8 +1035,8 @@ isort = ">=4.3.21,<6.0"
jinja2 = ">=2.10.1,<4.0"
packaging = "*"
pydantic = [
{version = ">=1.9.0,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
{version = ">=1.10.0,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
{version = ">=1.9.0,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
{version = ">=1.10.0,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.12\" and python_version < \"4.0\""},
]
pyyaml = ">=6.0.1"
@@ -1048,50 +1048,6 @@ graphql = ["graphql-core (>=3.2.3,<4.0.0)"]
http = ["httpx"]
validation = ["openapi-spec-validator (>=0.2.8,<0.7.0)", "prance (>=0.18.2)"]
[[package]]
name = "datasets"
version = "2.21.0"
description = "HuggingFace community-driven open-source library of datasets"
optional = true
python-versions = ">=3.8.0"
files = [
{file = "datasets-2.21.0-py3-none-any.whl", hash = "sha256:25e4e097110ce28824b746a107727ada94024cba11db8bc588d468414692b65a"},
{file = "datasets-2.21.0.tar.gz", hash = "sha256:998f85a8460f1bd982e5bd058f8a0808eef424249e3df1e8cdd594ccd0dc8ba2"},
]
[package.dependencies]
aiohttp = "*"
dill = ">=0.3.0,<0.3.9"
filelock = "*"
fsspec = {version = ">=2023.1.0,<=2024.6.1", extras = ["http"]}
huggingface-hub = ">=0.21.2"
multiprocess = "*"
numpy = ">=1.17"
packaging = "*"
pandas = "*"
pyarrow = ">=15.0.0"
pyyaml = ">=5.1"
requests = ">=2.32.2"
tqdm = ">=4.66.3"
xxhash = "*"
[package.extras]
apache-beam = ["apache-beam (>=2.26.0)"]
audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0)"]
benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk (<3.8.2)", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
quality = ["ruff (>=0.3.0)"]
s3 = ["s3fs"]
tensorflow = ["tensorflow (>=2.6.0)"]
tensorflow-gpu = ["tensorflow (>=2.6.0)"]
tests = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tiktoken", "torch (>=2.0.0)", "typing-extensions (>=4.6.1)", "zstandard"]
torch = ["torch"]
vision = ["Pillow (>=9.4.0)"]
[[package]]
name = "debugpy"
version = "1.8.13"
@@ -1165,21 +1121,6 @@ wrapt = ">=1.10,<2"
[package.extras]
dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
[[package]]
name = "dill"
version = "0.3.8"
description = "serialize all of Python"
optional = true
python-versions = ">=3.8"
files = [
{file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
{file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
]
[package.extras]
graph = ["objgraph (>=1.7.2)"]
profile = ["gprof2dot (>=2022.7.29)"]
[[package]]
name = "dirtyjson"
version = "1.0.8"
@@ -1298,13 +1239,13 @@ typing-extensions = ">=4.1.0"
[[package]]
name = "e2b-code-interpreter"
version = "1.0.5"
version = "1.1.0"
description = "E2B Code Interpreter - Stateful code execution"
optional = true
python-versions = "<4.0,>=3.8"
python-versions = "<4.0,>=3.9"
files = [
{file = "e2b_code_interpreter-1.0.5-py3-none-any.whl", hash = "sha256:4c7814e9eabba58097bf5e4019d327b3a82fab0813eafca4311b29ca6ea0639d"},
{file = "e2b_code_interpreter-1.0.5.tar.gz", hash = "sha256:e7f70b039e6a70f8e592f90f806d696dc1056919414daabeb89e86c9b650a987"},
{file = "e2b_code_interpreter-1.1.0-py3-none-any.whl", hash = "sha256:292f8ddbb820475d5ffb1f3f2e67a42001a921d1c8fef40bd97a7f16f13adc64"},
{file = "e2b_code_interpreter-1.1.0.tar.gz", hash = "sha256:4554eb002f9489965c2e7dd7fc967e62128db69b18dbb64975d4abbc0572e3ed"},
]
[package.dependencies]
@@ -1571,18 +1512,15 @@ files = [
[[package]]
name = "fsspec"
version = "2024.6.1"
version = "2025.3.0"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
{file = "fsspec-2024.6.1-py3-none-any.whl", hash = "sha256:3cb443f8bcd2efb31295a5b9fdb02aee81d8452c80d28f97a6d0959e6cee101e"},
{file = "fsspec-2024.6.1.tar.gz", hash = "sha256:fad7d7e209dd4c1208e3bbfda706620e0da5142bebbd9c384afb95b07e798e49"},
{file = "fsspec-2025.3.0-py3-none-any.whl", hash = "sha256:efb87af3efa9103f94ca91a7f8cb7a4df91af9f74fc106c9c7ea0efd7277c1b3"},
{file = "fsspec-2025.3.0.tar.gz", hash = "sha256:a935fd1ea872591f2b5148907d103488fc523295e6c64b835cfad8c3eca44972"},
]
[package.dependencies]
aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
[package.extras]
abfs = ["adlfs"]
adl = ["adlfs"]
@@ -1607,7 +1545,7 @@ sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
tqdm = ["tqdm"]
@@ -2160,40 +2098,6 @@ files = [
{file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
]
[[package]]
name = "huggingface-hub"
version = "0.29.3"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = true
python-versions = ">=3.8.0"
files = [
{file = "huggingface_hub-0.29.3-py3-none-any.whl", hash = "sha256:0b25710932ac649c08cdbefa6c6ccb8e88eef82927cacdb048efb726429453aa"},
{file = "huggingface_hub-0.29.3.tar.gz", hash = "sha256:64519a25716e0ba382ba2d3fb3ca082e7c7eb4a2fc634d200e8380006e0760e5"},
]
[package.dependencies]
filelock = "*"
fsspec = ">=2023.5.0"
packaging = ">=20.9"
pyyaml = ">=5.1"
requests = "*"
tqdm = ">=4.42.1"
typing-extensions = ">=3.7.4.3"
[package.extras]
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
cli = ["InquirerPy (==0.3.4)"]
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
hf-transfer = ["hf-transfer (>=0.1.4)"]
inference = ["aiohttp"]
quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
tensorflow = ["graphviz", "pydot", "tensorflow"]
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
torch = ["safetensors[torch]", "torch"]
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
[[package]]
name = "hyperframe"
version = "6.1.0"
@@ -2822,13 +2726,13 @@ pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
[[package]]
name = "letta-client"
version = "0.1.65"
version = "0.1.68"
description = ""
optional = false
python-versions = "<4.0,>=3.8"
files = [
{file = "letta_client-0.1.65-py3-none-any.whl", hash = "sha256:e7853d3742991e61b74d540ce7ad237528d860f1d63d1fd7a1bf5d237844d2ee"},
{file = "letta_client-0.1.65.tar.gz", hash = "sha256:5f83492bd84f68a3d9f816b5f840882d2cf39af5bb5aa2386079efec2fef51bd"},
{file = "letta_client-0.1.68-py3-none-any.whl", hash = "sha256:2b027f79281560abc88a7033b8ff4b3ecdd3be7ba2fa4f17f844ec0ba8d7dfe1"},
{file = "letta_client-0.1.68.tar.gz", hash = "sha256:c956498c6e0d726ec3f205a1dbaa0552d7945147a30d45ca7ea8ee7b77ac81aa"},
]
[package.dependencies]
@@ -3305,13 +3209,13 @@ traitlets = "*"
[[package]]
name = "mcp"
version = "1.3.0"
version = "1.4.0"
description = "Model Context Protocol SDK"
optional = false
python-versions = ">=3.10"
files = [
{file = "mcp-1.3.0-py3-none-any.whl", hash = "sha256:2829d67ce339a249f803f22eba5e90385eafcac45c94b00cab6cef7e8f217211"},
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@@ -7032,11 +6727,11 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
cffi = ["cffi (>=1.11)"]
[extras]
all = ["autoflake", "black", "datamodel-code-generator", "datasets", "docker", "fastapi", "isort", "langchain", "langchain-community", "locust", "pexpect", "pg8000", "pgvector", "pre-commit", "psycopg2", "psycopg2-binary", "pyright", "pytest-asyncio", "pytest-order", "uvicorn", "wikipedia"]
all = ["autoflake", "black", "datamodel-code-generator", "docker", "fastapi", "isort", "langchain", "langchain-community", "locust", "pexpect", "pg8000", "pgvector", "pre-commit", "psycopg2", "psycopg2-binary", "pyright", "pytest-asyncio", "pytest-order", "uvicorn", "wikipedia"]
bedrock = ["boto3"]
cloud-tool-sandbox = ["e2b-code-interpreter"]
desktop = ["datamodel-code-generator", "datasets", "docker", "fastapi", "langchain", "langchain-community", "locust", "pg8000", "pgvector", "psycopg2", "psycopg2-binary", "pyright", "uvicorn", "wikipedia"]
dev = ["autoflake", "black", "datasets", "isort", "locust", "pexpect", "pre-commit", "pyright", "pytest-asyncio", "pytest-order"]
desktop = ["datamodel-code-generator", "docker", "fastapi", "langchain", "langchain-community", "locust", "pg8000", "pgvector", "psycopg2", "psycopg2-binary", "pyright", "uvicorn", "wikipedia"]
dev = ["autoflake", "black", "isort", "locust", "pexpect", "pre-commit", "pyright", "pytest-asyncio", "pytest-order"]
external-tools = ["docker", "langchain", "langchain-community", "wikipedia"]
google = ["google-genai"]
postgres = ["pg8000", "pgvector", "psycopg2", "psycopg2-binary"]
@@ -7047,4 +6742,4 @@ tests = ["wikipedia"]
[metadata]
lock-version = "2.0"
python-versions = "<3.14,>=3.10"
content-hash = "0c1b4dfec2397a4a8dc1c3c76c5b7a1a3691c778333d8379dae7722d28ae9ced"
content-hash = "a41c6ec00f4b96db9d586b7142436d5f7cd1733cab5c9eaf734d1866782e2f94"

9
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "letta"
version = "0.6.39"
version = "0.6.40"
packages = [
{include = "letta"},
]
@@ -22,7 +22,6 @@ pytz = "^2023.3.post1"
tqdm = "^4.66.1"
black = {extras = ["jupyter"], version = "^24.2.0"}
setuptools = "^70"
datasets = { version = "^2.14.6", optional = true}
prettytable = "^3.9.0"
pgvector = { version = "^0.2.3", optional = true }
pre-commit = {version = "^3.5.0", optional = true }
@@ -93,7 +92,7 @@ mcp = "^1.3.0"
[tool.poetry.extras]
postgres = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2"]
dev = ["pytest", "pytest-asyncio", "pexpect", "black", "pre-commit", "datasets", "pyright", "pytest-order", "autoflake", "isort", "locust"]
dev = ["pytest", "pytest-asyncio", "pexpect", "black", "pre-commit", "pyright", "pytest-order", "autoflake", "isort", "locust"]
server = ["websockets", "fastapi", "uvicorn"]
qdrant = ["qdrant-client"]
cloud-tool-sandbox = ["e2b-code-interpreter"]
@@ -101,8 +100,8 @@ external-tools = ["docker", "langchain", "wikipedia", "langchain-community"]
tests = ["wikipedia"]
bedrock = ["boto3"]
google = ["google-genai"]
desktop = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "datasets", "pyright", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust", "datamodel-code-generator"]
all = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pytest", "pytest-asyncio", "pexpect", "black", "pre-commit", "datasets", "pyright", "pytest-order", "autoflake", "isort", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust", "datamodel-code-generator"]
desktop = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pyright", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust", "datamodel-code-generator"]
all = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pytest", "pytest-asyncio", "pexpect", "black", "pre-commit", "pyright", "pytest-order", "autoflake", "isort", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust", "datamodel-code-generator"]
[tool.poetry.group.dev.dependencies]
black = "^24.4.2"

59
tests/test_ast_parsing.py
View File

@@ -214,3 +214,62 @@ def test_coerce_dict_args_non_parseable_list_or_dict():
with pytest.raises(ValueError, match="Failed to coerce argument 'bad_list' to list"):
coerce_dict_args_by_annotations(function_args, annotations)
def test_coerce_dict_args_with_complex_list_annotation():
"""
Test coercion when list with type annotation (e.g., list[int]) is used.
"""
annotations = {"a": "list[int]"}
function_args = {"a": "[1, 2, 3]"}
coerced_args = coerce_dict_args_by_annotations(function_args, annotations)
assert coerced_args["a"] == [1, 2, 3]
def test_coerce_dict_args_with_complex_dict_annotation():
"""
Test coercion when dict with type annotation (e.g., dict[str, int]) is used.
"""
annotations = {"a": "dict[str, int]"}
function_args = {"a": '{"x": 1, "y": 2}'}
coerced_args = coerce_dict_args_by_annotations(function_args, annotations)
assert coerced_args["a"] == {"x": 1, "y": 2}
def test_coerce_dict_args_unsupported_complex_annotation():
"""
If an unsupported complex annotation is used (e.g., a custom class),
a ValueError should be raised.
"""
annotations = {"f": "CustomClass[int]"}
function_args = {"f": "CustomClass(42)"}
with pytest.raises(ValueError, match="Failed to coerce argument 'f' to CustomClass\[int\]: Unsupported annotation: CustomClass\[int\]"):
coerce_dict_args_by_annotations(function_args, annotations)
def test_coerce_dict_args_with_nested_complex_annotation():
"""
Test coercion with complex nested types like list[dict[str, int]].
"""
annotations = {"a": "list[dict[str, int]]"}
function_args = {"a": '[{"x": 1}, {"y": 2}]'}
coerced_args = coerce_dict_args_by_annotations(function_args, annotations)
assert coerced_args["a"] == [{"x": 1}, {"y": 2}]
def test_coerce_dict_args_with_default_arguments():
"""
Test coercion with default arguments, where some arguments have defaults in the source code.
"""
annotations = {"a": "int", "b": "str"}
function_args = {"a": "42"}
function_args.setdefault("b", "hello") # Setting the default value for 'b'
coerced_args = coerce_dict_args_by_annotations(function_args, annotations)
assert coerced_args["a"] == 42
assert coerced_args["b"] == "hello"

213
tests/test_base_functions.py
View File

@@ -98,3 +98,216 @@ def test_recall(client, agent_obj):
# Conversation search
result = base_functions.conversation_search(agent_obj, "banana")
assert keyword in result
# This test is nondeterministic, so we retry until we get the perfect behavior from the LLM
@retry_until_success(max_attempts=2, sleep_time_seconds=2)
def test_send_message_to_agent(client, agent_obj, other_agent_obj):
secret_word = "banana"
# Encourage the agent to send a message to the other agent_obj with the secret string
client.send_message(
agent_id=agent_obj.agent_state.id,
role="user",
message=f"Use your tool to send a message to another agent with id {other_agent_obj.agent_state.id} to share the secret word: {secret_word}!",
)
# Conversation search the other agent
messages = client.get_messages(other_agent_obj.agent_state.id)
# Check for the presence of system message
for m in reversed(messages):
print(f"\n\n {other_agent_obj.agent_state.id} -> {m.model_dump_json(indent=4)}")
if isinstance(m, SystemMessage):
assert secret_word in m.content
break
# Search the sender agent for the response from another agent
in_context_messages = agent_obj.agent_manager.get_in_context_messages(agent_id=agent_obj.agent_state.id, actor=agent_obj.user)
found = False
target_snippet = f"{other_agent_obj.agent_state.id} said:"
for m in in_context_messages:
if target_snippet in m.text:
found = True
break
# Compute the joined string first
joined_messages = "\n".join([m.text for m in in_context_messages[1:]])
print(f"In context messages of the sender agent (without system):\n\n{joined_messages}")
if not found:
raise Exception(f"Was not able to find an instance of the target snippet: {target_snippet}")
# Test that the agent can still receive messages fine
response = client.send_message(agent_id=agent_obj.agent_state.id, role="user", message="So what did the other agent say?")
print(response.messages)
@retry_until_success(max_attempts=2, sleep_time_seconds=2)
def test_send_message_to_agents_with_tags_simple(client):
worker_tags = ["worker", "user-456"]
# Clean up first from possibly failed tests
prev_worker_agents = client.server.agent_manager.list_agents(client.user, tags=worker_tags, match_all_tags=True)
for agent in prev_worker_agents:
client.delete_agent(agent.id)
secret_word = "banana"
# Create "manager" agent
send_message_to_agents_matching_all_tags_tool_id = client.get_tool_id(name="send_message_to_agents_matching_all_tags")
manager_agent_state = client.create_agent(tool_ids=[send_message_to_agents_matching_all_tags_tool_id])
manager_agent = client.server.load_agent(agent_id=manager_agent_state.id, actor=client.user)
# Create 3 non-matching worker agents (These should NOT get the message)
worker_agents = []
worker_tags = ["worker", "user-123"]
for _ in range(3):
worker_agent_state = client.create_agent(include_multi_agent_tools=False, tags=worker_tags)
worker_agent = client.server.load_agent(agent_id=worker_agent_state.id, actor=client.user)
worker_agents.append(worker_agent)
# Create 3 worker agents that should get the message
worker_agents = []
worker_tags = ["worker", "user-456"]
for _ in range(3):
worker_agent_state = client.create_agent(include_multi_agent_tools=False, tags=worker_tags)
worker_agent = client.server.load_agent(agent_id=worker_agent_state.id, actor=client.user)
worker_agents.append(worker_agent)
# Encourage the manager to send a message to the other agent_obj with the secret string
response = client.send_message(
agent_id=manager_agent.agent_state.id,
role="user",
message=f"Send a message to all agents with tags {worker_tags} informing them of the secret word: {secret_word}!",
)
for m in response.messages:
if isinstance(m, ToolReturnMessage):
tool_response = eval(json.loads(m.tool_return)["message"])
print(f"\n\nManager agent tool response: \n{tool_response}\n\n")
assert len(tool_response) == len(worker_agents)
# We can break after this, the ToolReturnMessage after is not related
break
# Conversation search the worker agents
for agent in worker_agents:
messages = client.get_messages(agent.agent_state.id)
# Check for the presence of system message
for m in reversed(messages):
print(f"\n\n {agent.agent_state.id} -> {m.model_dump_json(indent=4)}")
if isinstance(m, SystemMessage):
assert secret_word in m.content
break
# Test that the agent can still receive messages fine
response = client.send_message(agent_id=manager_agent.agent_state.id, role="user", message="So what did the other agents say?")
print("Manager agent followup message: \n\n" + "\n".join([str(m) for m in response.messages]))
# Clean up agents
client.delete_agent(manager_agent_state.id)
for agent in worker_agents:
client.delete_agent(agent.agent_state.id)
# This test is nondeterministic, so we retry until we get the perfect behavior from the LLM
@retry_until_success(max_attempts=2, sleep_time_seconds=2)
def test_send_message_to_agents_with_tags_complex_tool_use(client, roll_dice_tool):
worker_tags = ["dice-rollers"]
# Clean up first from possibly failed tests
prev_worker_agents = client.server.agent_manager.list_agents(client.user, tags=worker_tags, match_all_tags=True)
for agent in prev_worker_agents:
client.delete_agent(agent.id)
# Create "manager" agent
send_message_to_agents_matching_all_tags_tool_id = client.get_tool_id(name="send_message_to_agents_matching_all_tags")
manager_agent_state = client.create_agent(tool_ids=[send_message_to_agents_matching_all_tags_tool_id])
manager_agent = client.server.load_agent(agent_id=manager_agent_state.id, actor=client.user)
# Create 3 worker agents
worker_agents = []
worker_tags = ["dice-rollers"]
for _ in range(2):
worker_agent_state = client.create_agent(include_multi_agent_tools=False, tags=worker_tags, tool_ids=[roll_dice_tool.id])
worker_agent = client.server.load_agent(agent_id=worker_agent_state.id, actor=client.user)
worker_agents.append(worker_agent)
# Encourage the manager to send a message to the other agent_obj with the secret string
broadcast_message = f"Send a message to all agents with tags {worker_tags} asking them to roll a dice for you!"
response = client.send_message(
agent_id=manager_agent.agent_state.id,
role="user",
message=broadcast_message,
)
for m in response.messages:
if isinstance(m, ToolReturnMessage):
tool_response = eval(json.loads(m.tool_return)["message"])
print(f"\n\nManager agent tool response: \n{tool_response}\n\n")
assert len(tool_response) == len(worker_agents)
# We can break after this, the ToolReturnMessage after is not related
break
# Test that the agent can still receive messages fine
response = client.send_message(agent_id=manager_agent.agent_state.id, role="user", message="So what did the other agents say?")
print("Manager agent followup message: \n\n" + "\n".join([str(m) for m in response.messages]))
# Clean up agents
client.delete_agent(manager_agent_state.id)
for agent in worker_agents:
client.delete_agent(agent.agent_state.id)
@retry_until_success(max_attempts=5, sleep_time_seconds=2)
def test_agents_async_simple(client):
"""
Test two agents with multi-agent tools sending messages back and forth to count to 5.
The chain is started by prompting one of the agents.
"""
# Cleanup from potentially failed previous runs
existing_agents = client.server.agent_manager.list_agents(client.user)
for agent in existing_agents:
client.delete_agent(agent.id)
# Create two agents with multi-agent tools
send_message_to_agent_async_tool_id = client.get_tool_id(name="send_message_to_agent_async")
memory_a = ChatMemory(
human="Chad - I'm interested in hearing poem.",
persona="You are an AI agent that can communicate with your agent buddy using `send_message_to_agent_async`, who has some great poem ideas (so I've heard).",
)
charles_state = client.create_agent(name="charles", memory=memory_a, tool_ids=[send_message_to_agent_async_tool_id])
charles = client.server.load_agent(agent_id=charles_state.id, actor=client.user)
memory_b = ChatMemory(
human="No human - you are to only communicate with the other AI agent.",
persona="You are an AI agent that can communicate with your agent buddy using `send_message_to_agent_async`, who is interested in great poem ideas.",
)
sarah_state = client.create_agent(name="sarah", memory=memory_b, tool_ids=[send_message_to_agent_async_tool_id])
# Start the count chain with Agent1
initial_prompt = f"I want you to talk to the other agent with ID {sarah_state.id} using `send_message_to_agent_async`. Specifically, I want you to ask him for a poem idea, and then craft a poem for me."
client.send_message(
agent_id=charles.agent_state.id,
role="user",
message=initial_prompt,
)
found_in_charles = wait_for_incoming_message(
client=client,
agent_id=charles_state.id,
substring="[Incoming message from agent with ID",
max_wait_seconds=10,
sleep_interval=0.5,
)
assert found_in_charles, "Charles never received the system message from Sarah (timed out)."
found_in_sarah = wait_for_incoming_message(
client=client,
agent_id=sarah_state.id,
substring="[Incoming message from agent with ID",
max_wait_seconds=10,
sleep_interval=0.5,
)
assert found_in_sarah, "Sarah never received the system message from Charles (timed out)."