From d32f6e820470dc3ad5cddf9b1da91c7123fb0da4 Mon Sep 17 00:00:00 2001 From: Sarah Wooders Date: Thu, 13 Mar 2025 17:15:20 -0700 Subject: [PATCH] chore: merge updated tests (#1276) --- letta/functions/schema_generator.py | 18 +- letta/llm_api/anthropic.py | 20 ++ letta/llm_api/azure_openai_constants.py | 1 + poetry.lock | 367 ++---------------------- pyproject.toml | 9 +- tests/test_ast_parsing.py | 59 ++++ tests/test_base_functions.py | 213 ++++++++++++++ 7 files changed, 344 insertions(+), 343 deletions(-) diff --git a/letta/functions/schema_generator.py b/letta/functions/schema_generator.py index db4f704b..30fd9cb7 100644 --- a/letta/functions/schema_generator.py +++ b/letta/functions/schema_generator.py @@ -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", diff --git a/letta/llm_api/anthropic.py b/letta/llm_api/anthropic.py index af3780c8..a19ffe4b 100644 --- a/letta/llm_api/anthropic.py +++ b/letta/llm_api/anthropic.py @@ -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." diff --git a/letta/llm_api/azure_openai_constants.py b/letta/llm_api/azure_openai_constants.py index 0ea1e565..ba4248ef 100644 --- a/letta/llm_api/azure_openai_constants.py +++ b/letta/llm_api/azure_openai_constants.py @@ -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, } diff --git a/poetry.lock b/poetry.lock index 353f6a80..520c3962 100644 --- a/poetry.lock +++ b/poetry.lock @@ -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 = 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"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 = 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(>=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 = 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"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" diff --git a/pyproject.toml b/pyproject.toml index 17f5f9dd..8d3be978 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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"] 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a/tests/test_ast_parsing.py +++ b/tests/test_ast_parsing.py @@ -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" diff --git a/tests/test_base_functions.py b/tests/test_base_functions.py index 037eda2f..8b133638 100644 --- a/tests/test_base_functions.py +++ b/tests/test_base_functions.py @@ -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)."