as_tool() — langchain Function Reference

Architecture documentation for the as_tool() function in base.py from the langchain codebase.

Function python LangChainCore Runnables

Entity Profile

LangChainCore→ Runnables→ as_tool() — langchain Function Reference

Dependency Diagram

graph TD
  0001ba2c_b7d4_c9bf_b96a_109df35261e6["as_tool()"]
  4a62481c_02cb_a5de_1833_50669d5351a6["Runnable"]
  0001ba2c_b7d4_c9bf_b96a_109df35261e6 -->|defined in| 4a62481c_02cb_a5de_1833_50669d5351a6
  style 0001ba2c_b7d4_c9bf_b96a_109df35261e6 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/runnables/base.py lines 2467–2583

    def as_tool(
        self,
        args_schema: type[BaseModel] | None = None,
        *,
        name: str | None = None,
        description: str | None = None,
        arg_types: dict[str, type] | None = None,
    ) -> BaseTool:
        """Create a `BaseTool` from a `Runnable`.

        `as_tool` will instantiate a `BaseTool` with a name, description, and
        `args_schema` from a `Runnable`. Where possible, schemas are inferred
        from `runnable.get_input_schema`.

        Alternatively (e.g., if the `Runnable` takes a dict as input and the specific
        `dict` keys are not typed), the schema can be specified directly with
        `args_schema`.

        You can also pass `arg_types` to just specify the required arguments and their
        types.

        Args:
            args_schema: The schema for the tool.
            name: The name of the tool.
            description: The description of the tool.
            arg_types: A dictionary of argument names to types.

        Returns:
            A `BaseTool` instance.

        !!! example "`TypedDict` input"

            ```python
            from typing_extensions import TypedDict
            from langchain_core.runnables import RunnableLambda


            class Args(TypedDict):
                a: int
                b: list[int]


            def f(x: Args) -> str:
                return str(x["a"] * max(x["b"]))


            runnable = RunnableLambda(f)
            as_tool = runnable.as_tool()
            as_tool.invoke({"a": 3, "b": [1, 2]})
            ```

        !!! example "`dict` input, specifying schema via `args_schema`"

            ```python
            from typing import Any
            from pydantic import BaseModel, Field
            from langchain_core.runnables import RunnableLambda

            def f(x: dict[str, Any]) -> str:
                return str(x["a"] * max(x["b"]))

            class FSchema(BaseModel):
                \"\"\"Apply a function to an integer and list of integers.\"\"\"

                a: int = Field(..., description="Integer")
                b: list[int] = Field(..., description="List of ints")

            runnable = RunnableLambda(f)
            as_tool = runnable.as_tool(FSchema)
            as_tool.invoke({"a": 3, "b": [1, 2]})
            ```

        !!! example "`dict` input, specifying schema via `arg_types`"

            ```python
            from typing import Any
            from langchain_core.runnables import RunnableLambda


            def f(x: dict[str, Any]) -> str:
                return str(x["a"] * max(x["b"]))