**kwargs: Any,
    ) -> Any:
        if not self.coroutine:
            # If the tool does not implement async, fall back to default implementation
            return await asyncio.get_running_loop().run_in_executor(
                None, partial(self.invoke, input, config, **kwargs)
            )
        return await super().ainvoke(input, config, **kwargs)
    # --- Tool ---
    @property
    def args(self) -> dict:
        """The tool's input arguments."""
        if self.args_schema is not None:
            return self.args_schema.schema()["properties"]
        # For backwards compatibility, if the function signature is ambiguous,
        # assume it takes a single string input.
        return {"tool_input": {"type": "string"}}
    def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
        """Convert tool input to pydantic model."""
        args, kwargs = super()._to_args_and_kwargs(tool_input)
        # For backwards compatibility. The tool must be run with a single input
        all_args = list(args) + list(kwargs.values())
        if len(all_args) != 1:
            raise ToolException(
                f"Too many arguments to single-input tool {self.name}."
                f" Args: {all_args}"
            )
        return tuple(all_args), {}
    def _run(
        self,
        *args: Any,
        run_manager: Optional[CallbackManagerForToolRun] = None,
        **kwargs: Any,
    ) -> Any:
        """Use the tool."""
        if self.func:
            new_argument_supported = signature(self.func).parameters.get("callbacks")
            return (
                self.func(
                    *args,