diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index ff63f2ac..11db2840 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -11,20 +11,25 @@ assignees: '' A clear and concise description of what the bug is. **Please describe your setup** -- [ ] How did you install letta? - - `pip install letta`? `pip install letta-nightly`? `git clone`? +- [ ] How are you running Letta? + - Docker + - pip (legacy) + - From source + - Desktop - [ ] Describe your setup - What's your OS (Windows/MacOS/Linux)? - - How are you running `letta`? (`cmd.exe`/Powershell/Anaconda Shell/Terminal) + - What is your `docker run ...` command (if applicable) **Screenshots** If applicable, add screenshots to help explain your problem. **Additional context** Add any other context about the problem here. +- What model you are using + +**Agent File (optional)** +Please attach your `.af` file, as this helps with reproducing issues. -**Letta Config** -Please attach your `~/.letta/config` file or copy paste it below. --- diff --git a/.github/workflows/notify-letta-cloud.yml b/.github/workflows/notify-letta-cloud.yml deleted file mode 100644 index 0874be59..00000000 --- a/.github/workflows/notify-letta-cloud.yml +++ /dev/null @@ -1,19 +0,0 @@ -name: Notify Letta Cloud - -on: - push: - branches: - - main - -jobs: - notify: - runs-on: ubuntu-latest - if: ${{ !contains(github.event.head_commit.message, '[sync-skip]') }} - steps: - - name: Trigger repository_dispatch - run: | - curl -X POST \ - -H "Authorization: token ${{ secrets.SYNC_PAT }}" \ - -H "Accept: application/vnd.github.v3+json" \ - https://api.github.com/repos/letta-ai/letta-cloud/dispatches \ - -d '{"event_type":"oss-update"}' diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 760be895..3ea08c3c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -28,7 +28,7 @@ First, install Poetry using [the official instructions here](https://python-poet Once Poetry is installed, navigate to the letta directory and install the Letta project with Poetry: ```shell cd letta -poetry shell +eval $(poetry env activate) poetry install --all-extras ``` #### Setup PostgreSQL environment (optional) diff --git a/Dockerfile b/Dockerfile index 0dd635a2..437e1730 100644 --- a/Dockerfile +++ b/Dockerfile @@ -66,7 +66,6 @@ ENV LETTA_ENVIRONMENT=${LETTA_ENVIRONMENT} \ POSTGRES_DB=letta \ COMPOSIO_DISABLE_VERSION_CHECK=true - WORKDIR /app # Copy virtual environment and app from builder diff --git a/README.md b/README.md index a46cddc9..b8ccade9 100644 --- a/README.md +++ b/README.md @@ -8,26 +8,13 @@

Letta (previously MemGPT)

- -**☄️ New release: Letta Agent Development Environment (_read more [here](#-access-the-ade-agent-development-environment)_) ☄️** - -

- - - - Letta logo - -

- ---- -

[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 LLM applications. You can use Letta to build **stateful agents** with advanced reasoning capabilities and transparent long-term memory. The Letta framework is white box and model-agnostic. +**👾 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. [![Discord](https://img.shields.io/discord/1161736243340640419?label=Discord&logo=discord&logoColor=5865F2&style=flat-square&color=5865F2)](https://discord.gg/letta) [![Twitter Follow](https://img.shields.io/badge/Follow-%40Letta__AI-1DA1F2?style=flat-square&logo=x&logoColor=white)](https://twitter.com/Letta_AI) @@ -157,7 +144,7 @@ No, the data in your Letta server database stays on your machine. The Letta ADE > _"Do I have to use your ADE? Can I build my own?"_ -The ADE is built on top of the (fully open source) Letta server and Letta Agents API. You can build your own application like the ADE on top of the REST API (view the documention [here](https://docs.letta.com/api-reference)). +The ADE is built on top of the (fully open source) Letta server and Letta Agents API. You can build your own application like the ADE on top of the REST API (view the documentation [here](https://docs.letta.com/api-reference)). > _"Can I interact with Letta agents via the CLI?"_ diff --git a/examples/docs/example.py b/examples/docs/example.py index 68e6a1c8..4b55cf3e 100644 --- a/examples/docs/example.py +++ b/examples/docs/example.py @@ -8,6 +8,7 @@ If you're using Letta Cloud, replace 'baseURL' with 'token' See: https://docs.letta.com/api-reference/overview Execute this script using `poetry run python3 example.py` +This will install `letta_client` and other dependencies. """ client = Letta( base_url="http://localhost:8283", diff --git a/examples/mcp_example.py b/examples/mcp_example.py index a12c3faf..13ba92d7 100644 --- a/examples/mcp_example.py +++ b/examples/mcp_example.py @@ -2,22 +2,33 @@ 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=[ { @@ -31,6 +42,7 @@ agent = client.agents.create( ) print(f"Created agent id {agent.id}") +# Ask the agent to call the tool. response = client.agents.messages.create( agent_id=agent.id, messages=[ diff --git a/examples/notebooks/Customizing memory management.ipynb b/examples/notebooks/Customizing memory management.ipynb index af167dce..2df343b4 100644 --- a/examples/notebooks/Customizing memory management.ipynb +++ b/examples/notebooks/Customizing memory management.ipynb @@ -253,15 +253,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "7808912f-831b-4cdc-8606-40052eb809b4", "metadata": {}, "outputs": [], "source": [ - "from typing import Optional, List\n", + "from typing import Optional, List, TYPE_CHECKING\n", "import json\n", "\n", - "def task_queue_push(self: \"Agent\", task_description: str):\n", + "if TYPE_CHECKING:\n", + " from letta import AgentState\n", + "\n", + "def task_queue_push(agent_state: \"AgentState\", task_description: str):\n", " \"\"\"\n", " Push to a task queue stored in core memory. \n", "\n", @@ -273,12 +276,12 @@ " does not produce a response.\n", " \"\"\"\n", " import json\n", - " tasks = json.loads(self.memory.get_block(\"tasks\").value)\n", + " tasks = json.loads(agent_state.memory.get_block(\"tasks\").value)\n", " tasks.append(task_description)\n", - " self.memory.update_block_value(\"tasks\", json.dumps(tasks))\n", + " agent_state.memory.update_block_value(\"tasks\", json.dumps(tasks))\n", " return None\n", "\n", - "def task_queue_pop(self: \"Agent\"):\n", + "def task_queue_pop(agent_state: \"AgentState\"):\n", " \"\"\"\n", " Get the next task from the task queue \n", "\n", @@ -288,12 +291,12 @@ " None (the task queue is empty)\n", " \"\"\"\n", " import json\n", - " tasks = json.loads(self.memory.get_block(\"tasks\").value)\n", + " tasks = json.loads(agent_state.memory.get_block(\"tasks\").value)\n", " if len(tasks) == 0: \n", " return None\n", " task = tasks[0]\n", " print(\"CURRENT TASKS: \", tasks)\n", - " self.memory.update_block_value(\"tasks\", json.dumps(tasks[1:]))\n", + " agent_state.memory.update_block_value(\"tasks\", json.dumps(tasks[1:]))\n", " return task\n", "\n", "push_task_tool = client.tools.upsert_from_function(func=task_queue_push)\n", @@ -310,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "135fcf3e-59c4-4da3-b86b-dbffb21aa343", "metadata": {}, "outputs": [], @@ -336,10 +339,12 @@ " ),\n", " CreateBlock(\n", " label=\"tasks\",\n", - " value=\"\",\n", + " value=\"[]\",\n", " ),\n", " ],\n", " tool_ids=[push_task_tool.id, pop_task_tool.id],\n", + " model=\"letta/letta-free\",\n", + " embedding=\"letta/letta-free\",\n", ")" ] }, diff --git a/letta/agent.py b/letta/agent.py index 3960ade6..7de5b69c 100644 --- a/letta/agent.py +++ b/letta/agent.py @@ -475,7 +475,7 @@ class Agent(BaseAgent): 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}") + self.logger.debug(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 diff --git a/letta/embeddings.py b/letta/embeddings.py index 776671b5..54181577 100644 --- a/letta/embeddings.py +++ b/letta/embeddings.py @@ -235,7 +235,9 @@ def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] = None if endpoint_type == "openai": return OpenAIEmbeddings( - api_key=model_settings.openai_api_key, model=config.embedding_model, base_url=model_settings.openai_api_base + api_key=model_settings.openai_api_key, + model=config.embedding_model, + base_url=model_settings.openai_api_base, ) elif endpoint_type == "azure": diff --git a/letta/functions/ast_parsers.py b/letta/functions/ast_parsers.py index acc73bef..e169f596 100644 --- a/letta/functions/ast_parsers.py +++ b/letta/functions/ast_parsers.py @@ -34,6 +34,19 @@ def resolve_type(annotation: str): return BUILTIN_TYPES[annotation] try: + if annotation.startswith("list["): + inner_type = annotation[len("list[") : -1] + resolve_type(inner_type) + return list + elif annotation.startswith("dict["): + inner_types = annotation[len("dict[") : -1] + key_type, value_type = inner_types.split(",") + return dict + elif annotation.startswith("tuple["): + inner_types = annotation[len("tuple[") : -1] + [resolve_type(t.strip()) for t in inner_types.split(",")] + return tuple + parsed = ast.literal_eval(annotation) if isinstance(parsed, type): return parsed diff --git a/letta/functions/function_sets/base.py b/letta/functions/function_sets/base.py index 654675c9..1ba43a92 100644 --- a/letta/functions/function_sets/base.py +++ b/letta/functions/function_sets/base.py @@ -46,7 +46,7 @@ def conversation_search(self: "Agent", query: str, page: Optional[int] = 0) -> O count = RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE # TODO: add paging by page number. currently cursor only works with strings. # original: start=page * count - messages = self.message_manager.list_user_messages_for_agent( + messages = self.message_manager.list_messages_for_agent( agent_id=self.agent_state.id, actor=self.user, query_text=query, diff --git a/letta/llm_api/aws_bedrock.py b/letta/llm_api/aws_bedrock.py index 688efc63..ab6ddcf5 100644 --- a/letta/llm_api/aws_bedrock.py +++ b/letta/llm_api/aws_bedrock.py @@ -3,14 +3,19 @@ from typing import Any, Dict, List from anthropic import AnthropicBedrock +from letta.log import get_logger from letta.settings import model_settings +logger = get_logger(__name__) + def has_valid_aws_credentials() -> bool: """ Check if AWS credentials are properly configured. """ - valid_aws_credentials = os.getenv("AWS_ACCESS_KEY") and os.getenv("AWS_SECRET_ACCESS_KEY") and os.getenv("AWS_REGION") + valid_aws_credentials = ( + os.getenv("AWS_ACCESS_KEY") is not None and os.getenv("AWS_SECRET_ACCESS_KEY") is not None and os.getenv("AWS_REGION") is not None + ) return valid_aws_credentials @@ -20,6 +25,7 @@ def get_bedrock_client(): """ import boto3 + logger.debug(f"Getting Bedrock client for {model_settings.aws_region}") sts_client = boto3.client( "sts", aws_access_key_id=model_settings.aws_access_key, @@ -51,12 +57,13 @@ def bedrock_get_model_list(region_name: str) -> List[dict]: """ import boto3 + logger.debug(f"Getting model list for {region_name}") try: bedrock = boto3.client("bedrock", region_name=region_name) response = bedrock.list_inference_profiles() return response["inferenceProfileSummaries"] except Exception as e: - print(f"Error getting model list: {str(e)}") + logger.exception(f"Error getting model list: {str(e)}", e) raise e @@ -67,6 +74,7 @@ def bedrock_get_model_details(region_name: str, model_id: str) -> Dict[str, Any] import boto3 from botocore.exceptions import ClientError + logger.debug(f"Getting model details for {model_id}") try: bedrock = boto3.client("bedrock", region_name=region_name) response = bedrock.get_foundation_model(modelIdentifier=model_id) diff --git a/letta/llm_api/google_vertex_client.py b/letta/llm_api/google_vertex_client.py index 76a84daf..a987d8a9 100644 --- a/letta/llm_api/google_vertex_client.py +++ b/letta/llm_api/google_vertex_client.py @@ -2,7 +2,7 @@ import uuid from typing import List, Optional from google import genai -from google.genai.types import FunctionCallingConfig, FunctionCallingConfigMode, GenerateContentResponse, ToolConfig +from google.genai.types import FunctionCallingConfig, FunctionCallingConfigMode, GenerateContentResponse, ThinkingConfig, ToolConfig from letta.helpers.datetime_helpers import get_utc_time_int from letta.helpers.json_helpers import json_dumps @@ -60,6 +60,15 @@ class GoogleVertexClient(GoogleAIClient): ) request_data["config"]["tool_config"] = tool_config.model_dump() + # Add thinking_config + # If enable_reasoner is False, set thinking_budget to 0 + # Otherwise, use the value from max_reasoning_tokens + thinking_budget = 0 if not self.llm_config.enable_reasoner else self.llm_config.max_reasoning_tokens + thinking_config = ThinkingConfig( + thinking_budget=thinking_budget, + ) + request_data["config"]["thinking_config"] = thinking_config.model_dump() + return request_data def convert_response_to_chat_completion( diff --git a/letta/schemas/llm_config.py b/letta/schemas/llm_config.py index d6027a85..dea376ce 100644 --- a/letta/schemas/llm_config.py +++ b/letta/schemas/llm_config.py @@ -72,7 +72,7 @@ class LLMConfig(BaseModel): description="The reasoning effort to use when generating text reasoning models", ) max_reasoning_tokens: int = Field( - 0, description="Configurable thinking budget for extended thinking, only used if enable_reasoner is True. Minimum value is 1024." + 0, description="Configurable thinking budget for extended thinking. Used for enable_reasoner and also for Google Vertex models like Gemini 2.5 Flash. Minimum value is 1024 when used with enable_reasoner." ) # FIXME hack to silence pydantic protected namespace warning diff --git a/letta/server/rest_api/routers/v1/sources.py b/letta/server/rest_api/routers/v1/sources.py index 97a76eb3..7811044a 100644 --- a/letta/server/rest_api/routers/v1/sources.py +++ b/letta/server/rest_api/routers/v1/sources.py @@ -78,6 +78,17 @@ def list_sources( return server.list_all_sources(actor=actor) +@router.get("/count", response_model=int, operation_id="count_sources") +def count_sources( + server: "SyncServer" = Depends(get_letta_server), + actor_id: Optional[str] = Header(None, alias="user_id"), # Extract user_id from header, default to None if not present +): + """ + Count all data sources created by a user. + """ + return server.source_manager.size(actor=server.user_manager.get_user_or_default(user_id=actor_id)) + + @router.post("/", response_model=Source, operation_id="create_source") def create_source( source_create: SourceCreate, diff --git a/letta/server/rest_api/routers/v1/tools.py b/letta/server/rest_api/routers/v1/tools.py index a1c7591b..1191ecdd 100644 --- a/letta/server/rest_api/routers/v1/tools.py +++ b/letta/server/rest_api/routers/v1/tools.py @@ -98,6 +98,21 @@ def list_tools( raise HTTPException(status_code=500, detail=str(e)) +@router.get("/count", response_model=int, operation_id="count_tools") +def count_tools( + server: SyncServer = Depends(get_letta_server), + actor_id: Optional[str] = Header(None, alias="user_id"), +): + """ + Get a count of all tools available to agents belonging to the org of the user + """ + try: + return server.tool_manager.size(actor=server.user_manager.get_user_or_default(user_id=actor_id)) + except Exception as e: + print(f"Error occurred: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + @router.post("/", response_model=Tool, operation_id="create_tool") def create_tool( request: ToolCreate = Body(...), diff --git a/letta/services/message_manager.py b/letta/services/message_manager.py index fb39b955..630324c0 100644 --- a/letta/services/message_manager.py +++ b/letta/services/message_manager.py @@ -303,17 +303,24 @@ class MessageManager: if group_id: query = query.filter(MessageModel.group_id == group_id) - # If query_text is provided, filter messages using subquery + json_array_elements. + # If query_text is provided, filter messages by matching any "text" type content block + # whose text includes the query string (case-insensitive). if query_text: - content_element = func.json_array_elements(MessageModel.content).alias("content_element") - query = query.filter( - exists( - select(1) - .select_from(content_element) - .where(text("content_element->>'type' = 'text' AND content_element->>'text' ILIKE :query_text")) - .params(query_text=f"%{query_text}%") + dialect_name = session.bind.dialect.name + + if dialect_name == "postgresql": # using subquery + json_array_elements. + content_element = func.json_array_elements(MessageModel.content).alias("content_element") + subquery_sql = text("content_element->>'type' = 'text' AND content_element->>'text' ILIKE :query_text") + subquery = select(1).select_from(content_element).where(subquery_sql) + + elif dialect_name == "sqlite": # using `json_each` and JSON path expressions + json_item = func.json_each(MessageModel.content).alias("json_item") + subquery_sql = text( + "json_extract(value, '$.type') = 'text' AND lower(json_extract(value, '$.text')) LIKE lower(:query_text)" ) - ) + subquery = select(1).select_from(json_item).where(subquery_sql) + + query = query.filter(exists(subquery.params(query_text=f"%{query_text}%"))) # If role(s) are provided, filter messages by those roles. if roles: diff --git a/pyproject.toml b/pyproject.toml index a60462cd..ed920e0e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -106,6 +106,7 @@ google = ["google-genai"] desktop = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pyright", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust"] 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"] + [tool.poetry.group.dev.dependencies] black = "^24.4.2" ipykernel = "^6.29.5" diff --git a/scripts/docker-compose.yml b/scripts/docker-compose.yml new file mode 100644 index 00000000..3347d213 --- /dev/null +++ b/scripts/docker-compose.yml @@ -0,0 +1,32 @@ +version: '3.7' +services: + redis: + image: redis:alpine + container_name: redis + healthcheck: + test: ['CMD-SHELL', 'redis-cli ping | grep PONG'] + interval: 1s + timeout: 3s + retries: 5 + ports: + - '6379:6379' + volumes: + - ./data/redis:/data + command: redis-server --appendonly yes + postgres: + image: ankane/pgvector + container_name: postgres + healthcheck: + test: ['CMD-SHELL', 'pg_isready -U postgres'] + interval: 1s + timeout: 3s + retries: 5 + ports: + - '5432:5432' + environment: + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres + POSTGRES_DB: letta + volumes: + - ./data/postgres:/var/lib/postgresql/data + - ./scripts/postgres-db-init/init.sql:/docker-entrypoint-initdb.d/init.sql diff --git a/tests/integration_test_sleeptime_agent.py b/tests/integration_test_sleeptime_agent.py index 0749b399..4e5b6889 100644 --- a/tests/integration_test_sleeptime_agent.py +++ b/tests/integration_test_sleeptime_agent.py @@ -154,6 +154,7 @@ async def test_sleeptime_group_chat(server, actor): # 6. Verify run status after sleep time.sleep(2) + for run_id in run_ids: job = server.job_manager.get_job_by_id(job_id=run_id, actor=actor) assert job.status == JobStatus.running or job.status == JobStatus.completed diff --git a/tests/test_base_functions.py b/tests/test_base_functions.py index 037eda2f..4c60ab17 100644 --- a/tests/test_base_functions.py +++ b/tests/test_base_functions.py @@ -86,13 +86,14 @@ def test_archival(agent_obj): pass -def test_recall(client, agent_obj): +def test_recall_self(client, agent_obj): # keyword keyword = "banana" + keyword_backwards = "".join(reversed(keyword)) # Send messages to agent client.send_message(agent_id=agent_obj.agent_state.id, role="user", message="hello") - client.send_message(agent_id=agent_obj.agent_state.id, role="user", message=keyword) + client.send_message(agent_id=agent_obj.agent_state.id, role="user", message="what word is '{}' backwards?".format(keyword_backwards)) client.send_message(agent_id=agent_obj.agent_state.id, role="user", message="tell me a fun fact") # Conversation search diff --git a/tests/test_managers.py b/tests/test_managers.py index 76e6d0dc..8945cb6b 100644 --- a/tests/test_managers.py +++ b/tests/test_managers.py @@ -1589,6 +1589,46 @@ def test_modify_letta_message(server: SyncServer, sarah_agent, default_user): # TODO: tool calls/responses +def test_list_messages_with_query_text_filter(server: SyncServer, sarah_agent, default_user): + """ + Ensure that list_messages_for_agent correctly filters messages by query_text. + """ + test_contents = [ + "This is a message about unicorns and rainbows.", + "Another message discussing dragons in the sky.", + "Plain message with no magical beasts.", + "Mentioning unicorns again for good measure.", + "Something unrelated entirely.", + ] + + created_messages = [] + for content in test_contents: + message = PydanticMessage( + agent_id=sarah_agent.id, + role=MessageRole.user, + content=[{"type": "text", "text": content}], + ) + created = server.message_manager.create_message(pydantic_msg=message, actor=default_user) + created_messages.append(created) + + # Query messages that include "unicorns" + unicorn_messages = server.message_manager.list_messages_for_agent(agent_id=sarah_agent.id, actor=default_user, query_text="unicorns") + assert len(unicorn_messages) == 2 + for msg in unicorn_messages: + assert any(chunk.type == "text" and "unicorns" in chunk.text.lower() for chunk in msg.content or []) + + # Query messages that include "dragons" + dragon_messages = server.message_manager.list_messages_for_agent(agent_id=sarah_agent.id, actor=default_user, query_text="dragons") + assert len(dragon_messages) == 1 + assert any(chunk.type == "text" and "dragons" in chunk.text.lower() for chunk in dragon_messages[0].content or []) + + # Query with a word that shouldn't match any message + no_match_messages = server.message_manager.list_messages_for_agent( + agent_id=sarah_agent.id, actor=default_user, query_text="nonexistentcreature" + ) + assert len(no_match_messages) == 0 + + # ====================================================================================================================== # AgentManager Tests - Blocks Relationship # ====================================================================================================================== diff --git a/tests/test_sdk_client.py b/tests/test_sdk_client.py index d51ad028..c7493239 100644 --- a/tests/test_sdk_client.py +++ b/tests/test_sdk_client.py @@ -115,7 +115,7 @@ def test_shared_blocks(client: LettaSDKClient): ) assert ( "charles" in client.agents.blocks.retrieve(agent_id=agent_state2.id, block_label="human").value.lower() - ), f"Shared block update failed {client.agents.blocks.retrieve(agent_id=agent_state2.id, block_label="human").value}" + ), f"Shared block update failed {client.agents.blocks.retrieve(agent_id=agent_state2.id, block_label='human').value}" # cleanup client.agents.delete(agent_state1.id) diff --git a/tests/test_tool_sandbox/restaurant_management_system/adjust_menu_prices.py b/tests/test_tool_sandbox/restaurant_management_system/adjust_menu_prices.py index 1d3d9d3e..ffe734b3 100644 --- a/tests/test_tool_sandbox/restaurant_management_system/adjust_menu_prices.py +++ b/tests/test_tool_sandbox/restaurant_management_system/adjust_menu_prices.py @@ -8,10 +8,9 @@ def adjust_menu_prices(percentage: float) -> str: str: A formatted string summarizing the price adjustments. """ import cowsay - from tqdm import tqdm - from core.menu import Menu, MenuItem # Import a class from the codebase from core.utils import format_currency # Use a utility function to test imports + from tqdm import tqdm if not isinstance(percentage, (int, float)): raise TypeError("percentage must be a number")