merge

.github/workflows/notify-letta-cloud.yml

@@ -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"}'

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)

Dockerfile

@@ -67,7 +67,6 @@ ENV LETTA_ENVIRONMENT=${LETTA_ENVIRONMENT} \
     COMPOSIO_DISABLE_VERSION_CHECK=true \
     LETTA_OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4317"
 
-
 WORKDIR /app
 
 # Copy virtual environment and app from builder

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",

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=[

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",
     ")"
    ]
   },

letta/agent.py

@@ -471,7 +471,7 @@ 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

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":

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

letta/functions/function_sets/base.py

@@ -45,7 +45,7 @@ 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,

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)

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -468,17 +468,17 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.37.28"
+version = "1.37.29"
 description = "The AWS SDK for Python"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.37.28-py3-none-any.whl", hash = "sha256:e584d9d33808633e73af3d962e22cf2cea91a38bc5a17577bb25618f8ded504f"},
-    {file = "boto3-1.37.28.tar.gz", hash = "sha256:09ee85ba70a88286bba0d1bf5f0460a4b3bde52d162216accfe637b8bfac351b"},
+    {file = "boto3-1.37.29-py3-none-any.whl", hash = "sha256:869979050e2cf6f5461503e0f1c8f226e47ec02802e88a2210f085ec22485945"},
+    {file = "boto3-1.37.29.tar.gz", hash = "sha256:5702e38356b93c56ed2a27e17f7664d791f1fe2eafd58ae6ab3853b2804cadd2"},
 ]
 
 [package.dependencies]
-botocore = ">=1.37.28,<1.38.0"
+botocore = ">=1.37.29,<1.38.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.11.0,<0.12.0"
 
@@ -487,13 +487,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.37.28"
+version = "1.37.29"
 description = "Low-level, data-driven core of boto 3."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.37.28-py3-none-any.whl", hash = "sha256:c26b645d7b125bf42ffc1671b862b47500ee658e3a1c95d2438cb689fc85df15"},
-    {file = "botocore-1.37.28.tar.gz", hash = "sha256:69ea327f70f0607d174c4c2b1dcc87327b9c48e413c9d322179172b614b28e03"},
+    {file = "botocore-1.37.29-py3-none-any.whl", hash = "sha256:092c41e346df37a8d7cf60a799791f8225ad3a5ba7cda749047eb31d1440b9c5"},
+    {file = "botocore-1.37.29.tar.gz", hash = "sha256:728c1ef3b66a0f79bc08008a59f6fd6bef2a0a0195e5b3b9e9bef255df519890"},
 ]
 
 [package.dependencies]
@@ -895,13 +895,13 @@ test = ["pytest"]
 
 [[package]]
 name = "composio-core"
-version = "0.7.14"
+version = "0.7.15"
 description = "Core package to act as a bridge between composio platform and other services."
 optional = false
 python-versions = "<4,>=3.9"
 files = [
-    {file = "composio_core-0.7.14-py3-none-any.whl", hash = "sha256:8ab0d18d2a9a1a71f4d2ef7923d2880feb93442cd3935fd5832b275f119bfd2b"},
-    {file = "composio_core-0.7.14.tar.gz", hash = "sha256:3d7361bfd7de24dbbb3505ae858ca619cbf3d08fae2a2fd11962efa2a1dceb52"},
+    {file = "composio_core-0.7.15-py3-none-any.whl", hash = "sha256:3b4c19fcf8c084ed8b873b8e24ea55084495a046eb971af994e6aae6e1bfcea7"},
+    {file = "composio_core-0.7.15.tar.gz", hash = "sha256:6797c46f404b9c265cd05c07bd28b4104f425024ef683ac24092087e9590033d"},
 ]
 
 [package.dependencies]
@@ -932,13 +932,13 @@ tools = ["diskcache", "flake8", "networkx", "pathspec", "pygments", "ruff", "tra
 
 [[package]]
 name = "composio-langchain"
-version = "0.7.14"
+version = "0.7.15"
 description = "Use Composio to get an array of tools with your LangChain agent."
 optional = false
 python-versions = "<4,>=3.9"
 files = [
-    {file = "composio_langchain-0.7.14-py3-none-any.whl", hash = "sha256:314b89e2f0e740e9c2030b7a9b3d45d6a595e022ecb55f655f6d6bdd591121ac"},
-    {file = "composio_langchain-0.7.14.tar.gz", hash = "sha256:a42ab88f97f752870641329b589d1164753458a2091bf49ac32571c5dcfe8d4c"},
+    {file = "composio_langchain-0.7.15-py3-none-any.whl", hash = "sha256:a71b5371ad6c3ee4d4289c7a994fad1424e24c29a38e820b6b2ed259056abb65"},
+    {file = "composio_langchain-0.7.15.tar.gz", hash = "sha256:cb75c460289ecdf9590caf7ddc0d7888b0a6622ca4f800c9358abe90c25d055e"},
 ]
 
 [package.dependencies]
@@ -2291,13 +2291,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.34.0"
+version = "8.35.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.34.0-py3-none-any.whl", hash = "sha256:0419883fa46e0baa182c5d50ebb8d6b49df1889fdb70750ad6d8cfe678eda6e3"},
-    {file = "ipython-8.34.0.tar.gz", hash = "sha256:c31d658e754673ecc6514583e7dda8069e47136eb62458816b7d1e6625948b5a"},
+    {file = "ipython-8.35.0-py3-none-any.whl", hash = "sha256:e6b7470468ba6f1f0a7b116bb688a3ece2f13e2f94138e508201fad677a788ba"},
+    {file = "ipython-8.35.0.tar.gz", hash = "sha256:d200b7d93c3f5883fc36ab9ce28a18249c7706e51347681f80a0aef9895f2520"},
 ]
 
 [package.dependencies]
@@ -2325,7 +2325,7 @@ notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
 test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
-test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
+test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
 name = "isort"
@@ -2744,13 +2744,13 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
 
 [[package]]
 name = "langsmith"
-version = "0.3.24"
+version = "0.3.26"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langsmith-0.3.24-py3-none-any.whl", hash = "sha256:24930d34970281950338f6e2724d04a69804ccb3e89dba9314e6086653dc8bc9"},
-    {file = "langsmith-0.3.24.tar.gz", hash = "sha256:ee353dae8e59ec91eee8d3b601bc48e9d0e96722d1af14269b314ecb71e22088"},
+    {file = "langsmith-0.3.26-py3-none-any.whl", hash = "sha256:3ae49e49d6f3c980a524d15ac2fd895896e709ecedc83ac150c38e1ead776e1b"},
+    {file = "langsmith-0.3.26.tar.gz", hash = "sha256:3bd5b952a5fc82d69b0e2c030e502ee081a8ccf20468e96fd3d53e1572aef6fc"},
 ]
 
 [package.dependencies]
@@ -2773,13 +2773,13 @@ pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
 
 [[package]]
 name = "letta-client"
-version = "0.1.97"
+version = "0.1.99"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "letta_client-0.1.97-py3-none-any.whl", hash = "sha256:5e15068ce7b7d43eb00c2dbcb31d92fe689e4599dda7ef4e6edbf8ac45a6d676"},
-    {file = "letta_client-0.1.97.tar.gz", hash = "sha256:424bd6d9d44d2cc7175381b0d63fd2d6f41280055d23b8b94ef4fbee899a2b1a"},
+    {file = "letta_client-0.1.99-py3-none-any.whl", hash = "sha256:e3cddd9eb14c447fd4808888df5b40316a8dedf48360fb635bdf6282ca16a3fa"},
+    {file = "letta_client-0.1.99.tar.gz", hash = "sha256:9aacf7d7daa5e0829831cdac34efa89d0556de32ad5aff0af998b276915e4938"},
 ]
 
 [package.dependencies]
@@ -3061,13 +3061,13 @@ llama-cloud-services = ">=0.6.4"
 
 [[package]]
 name = "locust"
-version = "2.33.2"
+version = "2.34.0"
 description = "Developer-friendly load testing framework"
 optional = true
 python-versions = ">=3.9"
 files = [
-    {file = "locust-2.33.2-py3-none-any.whl", hash = "sha256:a2f3b53dcd5ed22cecee874cd989912749663d82ec9b030637d3e43044e5878e"},
-    {file = "locust-2.33.2.tar.gz", hash = "sha256:e626ed0156f36cec94c3c6b030fc91046469e7e2f5c2e91a99aab0f28b84977e"},
+    {file = "locust-2.34.0-py3-none-any.whl", hash = "sha256:9e9af1bab52ded67a9f0fac2ac039be18084f7e7473e3f95d04781391db854b8"},
+    {file = "locust-2.34.0.tar.gz", hash = "sha256:8761a60b1ee7af0606fb0d5ce4cbee7c8c1d2dcc1cb17215c8c994799c5f1ac6"},
 ]
 
 [package.dependencies]
@@ -3594,13 +3594,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.70.0"
+version = "1.71.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "openai-1.70.0-py3-none-any.whl", hash = "sha256:f6438d053fd8b2e05fd6bef70871e832d9bbdf55e119d0ac5b92726f1ae6f614"},
-    {file = "openai-1.70.0.tar.gz", hash = "sha256:e52a8d54c3efeb08cf58539b5b21a5abef25368b5432965e4de88cdf4e091b2b"},
+    {file = "openai-1.71.0-py3-none-any.whl", hash = "sha256:e1c643738f1fff1af52bce6ef06a7716c95d089281e7011777179614f32937aa"},
+    {file = "openai-1.71.0.tar.gz", hash = "sha256:52b20bb990a1780f9b0b8ccebac93416343ebd3e4e714e3eff730336833ca207"},
 ]
 
 [package.dependencies]
@@ -3615,7 +3615,7 @@ typing-extensions = ">=4.11,<5"
 
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
-realtime = ["websockets (>=13,<15)"]
+realtime = ["websockets (>=13,<16)"]
 voice-helpers = ["numpy (>=2.0.2)", "sounddevice (>=0.5.1)"]
 
 [[package]]
@@ -4424,6 +4424,7 @@ files = [
     {file = "psycopg2-2.9.10-cp311-cp311-win_amd64.whl", hash = "sha256:0435034157049f6846e95103bd8f5a668788dd913a7c30162ca9503fdf542cb4"},
     {file = "psycopg2-2.9.10-cp312-cp312-win32.whl", hash = "sha256:65a63d7ab0e067e2cdb3cf266de39663203d38d6a8ed97f5ca0cb315c73fe067"},
     {file = "psycopg2-2.9.10-cp312-cp312-win_amd64.whl", hash = "sha256:4a579d6243da40a7b3182e0430493dbd55950c493d8c68f4eec0b302f6bbf20e"},
+    {file = "psycopg2-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:91fd603a2155da8d0cfcdbf8ab24a2d54bca72795b90d2a3ed2b6da8d979dee2"},
     {file = "psycopg2-2.9.10-cp39-cp39-win32.whl", hash = "sha256:9d5b3b94b79a844a986d029eee38998232451119ad653aea42bb9220a8c5066b"},
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 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
-propcache = ">=0.2.0"
+propcache = ">=0.2.1"
 
 [[package]]
 name = "zipp"

pyproject.toml

@@ -103,6 +103,7 @@ google = ["google-genai"]
 desktop = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pyright", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust", "datamodel-code-generator", "mcp", "letta-client"]
 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"
 ipykernel = "^6.29.5"

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

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)

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")