BaseTool Class — langchain Architecture

Source Code

libs/core/langchain_core/tools/base.py lines 405–1134

class BaseTool(RunnableSerializable[str | dict | ToolCall, Any]):
    """Base class for all LangChain tools.

    This abstract class defines the interface that all LangChain tools must implement.

    Tools are components that can be called by agents to perform specific actions.
    """

    def __init_subclass__(cls, **kwargs: Any) -> None:
        """Validate the tool class definition during subclass creation.

        Args:
            **kwargs: Additional keyword arguments passed to the parent class.

        Raises:
            SchemaAnnotationError: If `args_schema` has incorrect type annotation.
        """
        super().__init_subclass__(**kwargs)

        args_schema_type = cls.__annotations__.get("args_schema", None)

        if args_schema_type is not None and args_schema_type == BaseModel:
            # Throw errors for common mis-annotations.
            # TODO: Use get_args / get_origin and fully
            # specify valid annotations.
            typehint_mandate = """
class ChildTool(BaseTool):
    ...
    args_schema: Type[BaseModel] = SchemaClass
    ..."""
            name = cls.__name__
            msg = (
                f"Tool definition for {name} must include valid type annotations"
                f" for argument 'args_schema' to behave as expected.\n"
                f"Expected annotation of 'Type[BaseModel]'"
                f" but got '{args_schema_type}'.\n"
                f"Expected class looks like:\n"
                f"{typehint_mandate}"
            )
            raise SchemaAnnotationError(msg)

    name: str
    """The unique name of the tool that clearly communicates its purpose."""

    description: str
    """Used to tell the model how/when/why to use the tool.

    You can provide few-shot examples as a part of the description.
    """

    args_schema: Annotated[ArgsSchema | None, SkipValidation()] = Field(
        default=None, description="The tool schema."
    )
    """Pydantic model class to validate and parse the tool's input arguments.

    Args schema should be either:

    - A subclass of `pydantic.BaseModel`.
    - A subclass of `pydantic.v1.BaseModel` if accessing v1 namespace in pydantic 2
    - A JSON schema dict
    """

    return_direct: bool = False
    """Whether to return the tool's output directly.

    Setting this to `True` means that after the tool is called, the `AgentExecutor` will
    stop looping.
    """

    verbose: bool = False
    """Whether to log the tool's progress."""

    callbacks: Callbacks = Field(default=None, exclude=True)
    """Callbacks to be called during tool execution."""

    tags: list[str] | None = None
    """Optional list of tags associated with the tool.

    These tags will be associated with each call to this tool,
    and passed as arguments to the handlers defined in `callbacks`.