BaseMultiActionAgent Class — langchain Architecture

Source Code

libs/langchain/langchain_classic/agents/agent.py lines 221–358

class BaseMultiActionAgent(BaseModel):
    """Base Multi Action Agent class."""

    @property
    def return_values(self) -> list[str]:
        """Return values of the agent."""
        return ["output"]

    def get_allowed_tools(self) -> list[str] | None:
        """Get allowed tools.

        Returns:
            Allowed tools.
        """
        return None

    @abstractmethod
    def plan(
        self,
        intermediate_steps: list[tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> list[AgentAction] | AgentFinish:
        """Given input, decided what to do.

        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with the observations.
            callbacks: Callbacks to run.
            **kwargs: User inputs.

        Returns:
            Actions specifying what tool to use.
        """

    @abstractmethod
    async def aplan(
        self,
        intermediate_steps: list[tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> list[AgentAction] | AgentFinish:
        """Async given input, decided what to do.

        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with the observations.
            callbacks: Callbacks to run.
            **kwargs: User inputs.

        Returns:
            Actions specifying what tool to use.
        """

    @property
    @abstractmethod
    def input_keys(self) -> list[str]:
        """Return the input keys."""

    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: list[tuple[AgentAction, str]],  # noqa: ARG002
        **_: Any,
    ) -> AgentFinish:
        """Return response when agent has been stopped due to max iterations.

        Args:
            early_stopping_method: Method to use for early stopping.
            intermediate_steps: Steps the LLM has taken to date,
                along with observations.

        Returns:
            Agent finish object.

        Raises:
            ValueError: If `early_stopping_method` is not supported.
        """
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish({"output": "Agent stopped due to max iterations."}, "")