crewAI/src/crewai/tools/base_tool.py

from abc import ABC, abstractmethod
from typing import Any, Callable, Type, get_args, get_origin

from langchain_core.tools import StructuredTool
from pydantic import BaseModel, ConfigDict, Field, validator
from pydantic import BaseModel as PydanticBaseModel


class BaseTool(BaseModel, ABC):
    class _ArgsSchemaPlaceholder(PydanticBaseModel):
        pass

    model_config = ConfigDict()

    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."""
    args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
    """The schema for the arguments that the tool accepts."""
    description_updated: bool = False
    """Flag to check if the description has been updated."""
    cache_function: Callable = lambda _args=None, _result=None: True
    """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached."""
    result_as_answer: bool = False
    """Flag to check if the tool should be the final agent answer."""

    @validator("args_schema", always=True, pre=True)
    def _default_args_schema(
        cls, v: Type[PydanticBaseModel]
    ) -> Type[PydanticBaseModel]:
        if not isinstance(v, cls._ArgsSchemaPlaceholder):
            return v

        return type(
            f"{cls.__name__}Schema",
            (PydanticBaseModel,),
            {
                "__annotations__": {
                    k: v for k, v in cls._run.__annotations__.items() if k != "return"
                },
            },
        )

    def model_post_init(self, __context: Any) -> None:
        self._generate_description()

        super().model_post_init(__context)

    def run(
        self,
        *args: Any,
        **kwargs: Any,
    ) -> Any:
        print(f"Using Tool: {self.name}")
        return self._run(*args, **kwargs)

    @abstractmethod
    def _run(
        self,
        *args: Any,
        **kwargs: Any,
    ) -> Any:
        """Here goes the actual implementation of the tool."""

    def to_langchain(self) -> StructuredTool:
        self._set_args_schema()
        return StructuredTool(
            name=self.name,
            description=self.description,
            args_schema=self.args_schema,
            func=self._run,
        )

    @classmethod
    def from_langchain(cls, tool: StructuredTool) -> "BaseTool":
        if cls == Tool:
            if tool.func is None:
                raise ValueError("StructuredTool must have a callable 'func'")
            return Tool(
                name=tool.name,
                description=tool.description,
                args_schema=tool.args_schema,
                func=tool.func,
            )
        raise NotImplementedError(f"from_langchain not implemented for {cls.__name__}")

    def _set_args_schema(self):
        if self.args_schema is None:
            class_name = f"{self.__class__.__name__}Schema"
            self.args_schema = type(
                class_name,
                (PydanticBaseModel,),
                {
                    "__annotations__": {
                        k: v
                        for k, v in self._run.__annotations__.items()
                        if k != "return"
                    },
                },
            )

    def _generate_description(self):
        args_schema = {
            name: {
                "description": field.description,
                "type": BaseTool._get_arg_annotations(field.annotation),
            }
            for name, field in self.args_schema.model_fields.items()
        }

        self.description = f"Tool Name: {self.name}\nTool Arguments: {args_schema}\nTool Description: {self.description}"

    @staticmethod
    def _get_arg_annotations(annotation: type[Any] | None) -> str:
        if annotation is None:
            return "None"

        origin = get_origin(annotation)
        args = get_args(annotation)

        if origin is None:
            return (
                annotation.__name__
                if hasattr(annotation, "__name__")
                else str(annotation)
            )

        if args:
            args_str = ", ".join(BaseTool._get_arg_annotations(arg) for arg in args)
            return f"{origin.__name__}[{args_str}]"

        return origin.__name__


class Tool(BaseTool):
    func: Callable
    """The function that will be executed when the tool is called."""

    def _run(self, *args: Any, **kwargs: Any) -> Any:
        return self.func(*args, **kwargs)


def to_langchain(
    tools: list[BaseTool | StructuredTool],
) -> list[StructuredTool]:
    return [t.to_langchain() if isinstance(t, BaseTool) else t for t in tools]


def tool(*args):
    """
    Decorator to create a tool from a function.
    """

    def _make_with_name(tool_name: str) -> Callable:
        def _make_tool(f: Callable) -> BaseTool:
            if f.__doc__ is None:
                raise ValueError("Function must have a docstring")
            if f.__annotations__ is None:
                raise ValueError("Function must have type annotations")

            class_name = "".join(tool_name.split()).title()
            args_schema = type(
                class_name,
                (PydanticBaseModel,),
                {
                    "__annotations__": {
                        k: v for k, v in f.__annotations__.items() if k != "return"
                    },
                },
            )

            return Tool(
                name=tool_name,
                description=f.__doc__,
                func=f,
                args_schema=args_schema,
            )

        return _make_tool

    if len(args) == 1 and callable(args[0]):
        return _make_with_name(args[0].__name__)(args[0])
    if len(args) == 1 and isinstance(args[0], str):
        return _make_with_name(args[0])
    raise ValueError("Invalid arguments")