test_openai_agent_tools_agent() — langchain Function Reference

Architecture documentation for the test_openai_agent_tools_agent() function in test_agent.py from the langchain codebase.

Function python LangChainCore MessageInterface calls 1

Entity Profile

LangChainCore→ MessageInterface→ test_openai_agent_tools_agent() — langchain Function Reference

Dependency Diagram

graph TD
  ea08f4fd_9d27_8cd9_90d0_31c9bf4bafd2["test_openai_agent_tools_agent()"]
  47a7b285_8e60_f78f_282d_429958c446fa["test_agent.py"]
  ea08f4fd_9d27_8cd9_90d0_31c9bf4bafd2 -->|defined in| 47a7b285_8e60_f78f_282d_429958c446fa
  64bbfaac_3c41_2db9_52b6_6979f090cc37["_make_tools_invocation()"]
  ea08f4fd_9d27_8cd9_90d0_31c9bf4bafd2 -->|calls| 64bbfaac_3c41_2db9_52b6_6979f090cc37
  style ea08f4fd_9d27_8cd9_90d0_31c9bf4bafd2 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/tests/unit_tests/agents/test_agent.py lines 1021–1329

async def test_openai_agent_tools_agent() -> None:
    """Test OpenAI tools agent."""
    infinite_cycle = cycle(
        [
            _make_tools_invocation(
                {
                    "find_pet": {"pet": "cat"},
                    "check_time": {},
                },
            ),
            AIMessage(content="The cat is spying from under the bed."),
        ],
    )

    GenericFakeChatModel.bind_tools = lambda self, _: self  # type: ignore[assignment,misc]
    model = GenericFakeChatModel(messages=infinite_cycle)

    @tool
    def find_pet(pet: str) -> str:
        """Find the given pet."""
        if pet != "cat":
            msg = "Only cats allowed"
            raise ValueError(msg)
        return "Spying from under the bed."

    @tool
    def check_time() -> str:
        """Find the given pet."""
        return "It's time to pet the cat."

    template = ChatPromptTemplate.from_messages(
        [
            ("system", "You are a helpful AI bot. Your name is kitty power meow."),
            ("human", "{question}"),
            MessagesPlaceholder(
                variable_name="agent_scratchpad",
            ),
        ],
    )

    # type error due to base tool type below -- would need to be adjusted on tool
    # decorator.
    openai_agent = create_openai_tools_agent(
        model,
        [find_pet],
        template,
    )
    tool_calling_agent = create_tool_calling_agent(
        model,
        [find_pet],
        template,
    )
    for agent in [openai_agent, tool_calling_agent]:
        executor = AgentExecutor(agent=agent, tools=[find_pet])

        # Invoke
        result = await asyncio.to_thread(executor.invoke, {"question": "hello"})
        assert result == {
            "output": "The cat is spying from under the bed.",
            "question": "hello",
        }

        # astream
        chunks = [chunk async for chunk in executor.astream({"question": "hello"})]
        assert chunks == [
            {
                "actions": [
                    OpenAIToolAgentAction(
                        tool="find_pet",
                        tool_input={"pet": "cat"},
                        log="\nInvoking: `find_pet` with `{'pet': 'cat'}`\n\n\n",
                        message_log=[
                            _AnyIdAIMessageChunk(
                                content="",
                                additional_kwargs={
                                    "tool_calls": [
                                        {
                                            "function": {
                                                "name": "find_pet",
                                                "arguments": '{"pet": "cat"}',
                                            },