test_tools.py — langchain Source File

Source Code

from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langchain_core.tools import Tool

from langchain_openai import ChatOpenAI, custom_tool


def test_custom_tool() -> None:
    @custom_tool
    def my_tool(x: str) -> str:
        """Do thing."""
        return "a" + x

    # Test decorator
    assert isinstance(my_tool, Tool)
    assert my_tool.metadata == {"type": "custom_tool"}
    assert my_tool.description == "Do thing."

    result = my_tool.invoke(
        {
            "type": "tool_call",
            "name": "my_tool",
            "args": {"whatever": "b"},
            "id": "abc",
            "extras": {"type": "custom_tool_call"},
        }
    )
    assert result == ToolMessage(
        [{"type": "custom_tool_call_output", "output": "ab"}],
        name="my_tool",
        tool_call_id="abc",
    )

    # Test tool schema
    ## Test with format
    @custom_tool(format={"type": "grammar", "syntax": "lark", "definition": "..."})
    def another_tool(x: str) -> None:
        """Do thing."""

    llm = ChatOpenAI(
        model="gpt-4.1", use_responses_api=True, output_version="responses/v1"
    ).bind_tools([another_tool])
    assert llm.kwargs == {  # type: ignore[attr-defined]
        "tools": [
            {
                "type": "custom",
                "name": "another_tool",
                "description": "Do thing.",
                "format": {"type": "grammar", "syntax": "lark", "definition": "..."},
            }
        ]
    }

    llm = ChatOpenAI(
        model="gpt-4.1", use_responses_api=True, output_version="responses/v1"
    ).bind_tools([my_tool])
    assert llm.kwargs == {  # type: ignore[attr-defined]
        "tools": [{"type": "custom", "name": "my_tool", "description": "Do thing."}]
    }

    # Test passing messages back
// ... (64 more lines)