venv/lib/python3.12/site-packages/langchain/agents/agent.py

"""Chain that takes in an input and produces an action and action input."""

from __future__ import annotations

import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Optional,
    Sequence,
    Tuple,
    Union,
    cast,
)

import yaml
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction, AgentFinish, AgentStep
from langchain_core.callbacks import (
    AsyncCallbackManagerForChainRun,
    AsyncCallbackManagerForToolRun,
    BaseCallbackManager,
    CallbackManagerForChainRun,
    CallbackManagerForToolRun,
    Callbacks,
)
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.few_shot import FewShotPromptTemplate
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.runnables import Runnable, RunnableConfig, ensure_config
from langchain_core.runnables.utils import AddableDict
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from pydantic import BaseModel, ConfigDict, model_validator
from typing_extensions import Self

from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
from langchain.agents.agent_iterator import AgentExecutorIterator
from langchain.agents.agent_types import AgentType
from langchain.agents.tools import InvalidTool
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.utilities.asyncio import asyncio_timeout

logger = logging.getLogger(__name__)


class BaseSingleActionAgent(BaseModel):
    """Base Single Action Agent class."""

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

    def get_allowed_tools(self) -> Optional[List[str]]:
        return None

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

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

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

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

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

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

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

        :meta private:
        """

    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: 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.
            **kwargs: User inputs.

        Returns:
            AgentFinish: 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 iteration limit or time limit."}, ""
            )
        else:
            raise ValueError(
                f"Got unsupported early_stopping_method `{early_stopping_method}`"
            )

    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        **kwargs: Any,
    ) -> BaseSingleActionAgent:
        """Construct an agent from an LLM and tools.

        Args:
            llm: Language model to use.
            tools: Tools to use.
            callback_manager: Callback manager to use.
            kwargs: Additional arguments.

        Returns:
            BaseSingleActionAgent: Agent object.
        """
        raise NotImplementedError

    @property
    def _agent_type(self) -> str:
        """Return Identifier of an agent type."""
        raise NotImplementedError

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent.

        Returns:
            Dict: Dictionary representation of agent.
        """
        _dict = super().model_dump()
        try:
            _type = self._agent_type
        except NotImplementedError:
            _type = None
        if isinstance(_type, AgentType):
            _dict["_type"] = str(_type.value)
        elif _type is not None:
            _dict["_type"] = _type
        return _dict

    def save(self, file_path: Union[Path, str]) -> None:
        """Save the agent.

        Args:
            file_path: Path to file to save the agent to.

        Example:
        .. code-block:: python

            # If working with agent executor
            agent.agent.save(file_path="path/agent.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path

        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)

        # Fetch dictionary to save
        agent_dict = self.dict()
        if "_type" not in agent_dict:
            raise NotImplementedError(f"Agent {self} does not support saving")

        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix.endswith((".yaml", ".yml")):
            with open(file_path, "w") as f:
                yaml.dump(agent_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")

    def tool_run_logging_kwargs(self) -> Dict:
        """Return logging kwargs for tool run."""
        return {}


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) -> Optional[List[str]]:
        """Get allowed tools.

        Returns:
            Optional[List[str]]: Allowed tools.
        """
        return None

    @abstractmethod
    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[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,
    ) -> Union[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.

        :meta private:
        """

    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: 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.
            **kwargs: User inputs.

        Returns:
            AgentFinish: 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."}, "")
        else:
            raise ValueError(
                f"Got unsupported early_stopping_method `{early_stopping_method}`"
            )

    @property
    def _agent_type(self) -> str:
        """Return Identifier of an agent type."""
        raise NotImplementedError

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().model_dump()
        try:
            _dict["_type"] = str(self._agent_type)
        except NotImplementedError:
            pass
        return _dict

    def save(self, file_path: Union[Path, str]) -> None:
        """Save the agent.

        Args:
            file_path: Path to file to save the agent to.

        Raises:
            NotImplementedError: If agent does not support saving.
            ValueError: If file_path is not json or yaml.

        Example:
        .. code-block:: python

            # If working with agent executor
            agent.agent.save(file_path="path/agent.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path

        # Fetch dictionary to save
        agent_dict = self.dict()
        if "_type" not in agent_dict:
            raise NotImplementedError(f"Agent {self} does not support saving.")

        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)

        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix.endswith((".yaml", ".yml")):
            with open(file_path, "w") as f:
                yaml.dump(agent_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")

    def tool_run_logging_kwargs(self) -> Dict:
        """Return logging kwargs for tool run."""

        return {}


class AgentOutputParser(BaseOutputParser[Union[AgentAction, AgentFinish]]):
    """Base class for parsing agent output into agent action/finish."""

    @abstractmethod
    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
        """Parse text into agent action/finish."""


class MultiActionAgentOutputParser(
    BaseOutputParser[Union[List[AgentAction], AgentFinish]]
):
    """Base class for parsing agent output into agent actions/finish.

    This is used for agents that can return multiple actions.
    """

    @abstractmethod
    def parse(self, text: str) -> Union[List[AgentAction], AgentFinish]:
        """Parse text into agent actions/finish.

        Args:
            text: Text to parse.

        Returns:
            Union[List[AgentAction], AgentFinish]:
                List of agent actions or agent finish.
        """


class RunnableAgent(BaseSingleActionAgent):
    """Agent powered by Runnables."""

    runnable: Runnable[dict, Union[AgentAction, AgentFinish]]
    """Runnable to call to get agent action."""
    input_keys_arg: List[str] = []
    return_keys_arg: List[str] = []
    stream_runnable: bool = True
    """Whether to stream from the runnable or not.

    If True then underlying LLM is invoked in a streaming fashion to make it possible
        to get access to the individual LLM tokens when using stream_log with the Agent
        Executor. If False then LLM is invoked in a non-streaming fashion and
        individual LLM tokens will not be available in stream_log.
    """

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

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

    @property
    def input_keys(self) -> List[str]:
        """Return the input keys."""
        return self.input_keys_arg

    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Based on past history and current inputs, decide 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:
            Action specifying what tool to use.
        """
        inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
        final_output: Any = None
        if self.stream_runnable:
            # Use streaming to make sure that the underlying LLM is invoked in a
            # streaming
            # fashion to make it possible to get access to the individual LLM tokens
            # when using stream_log with the Agent Executor.
            # Because the response from the plan is not a generator, we need to
            # accumulate the output into final output and return that.
            for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}):
                if final_output is None:
                    final_output = chunk
                else:
                    final_output += chunk
        else:
            final_output = self.runnable.invoke(inputs, config={"callbacks": callbacks})

        return final_output

    async def aplan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[
        AgentAction,
        AgentFinish,
    ]:
        """Async based on past history and current inputs, decide what to do.

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

        Returns:
            Action specifying what tool to use.
        """
        inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
        final_output: Any = None
        if self.stream_runnable:
            # Use streaming to make sure that the underlying LLM is invoked in a
            # streaming
            # fashion to make it possible to get access to the individual LLM tokens
            # when using stream_log with the Agent Executor.
            # Because the response from the plan is not a generator, we need to
            # accumulate the output into final output and return that.
            async for chunk in self.runnable.astream(
                inputs, config={"callbacks": callbacks}
            ):
                if final_output is None:
                    final_output = chunk
                else:
                    final_output += chunk
        else:
            final_output = await self.runnable.ainvoke(
                inputs, config={"callbacks": callbacks}
            )
        return final_output


class RunnableMultiActionAgent(BaseMultiActionAgent):
    """Agent powered by Runnables."""

    runnable: Runnable[dict, Union[List[AgentAction], AgentFinish]]
    """Runnable to call to get agent actions."""
    input_keys_arg: List[str] = []
    return_keys_arg: List[str] = []
    stream_runnable: bool = True
    """Whether to stream from the runnable or not.

    If True then underlying LLM is invoked in a streaming fashion to make it possible
        to get access to the individual LLM tokens when using stream_log with the Agent
        Executor. If False then LLM is invoked in a non-streaming fashion and
        individual LLM tokens will not be available in stream_log.
    """

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

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

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

        Returns:
            List of input keys.
        """
        return self.input_keys_arg

    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[
        List[AgentAction],
        AgentFinish,
    ]:
        """Based on past history and current inputs, decide 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:
            Action specifying what tool to use.
        """
        inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
        final_output: Any = None
        if self.stream_runnable:
            # Use streaming to make sure that the underlying LLM is invoked in a
            # streaming
            # fashion to make it possible to get access to the individual LLM tokens
            # when using stream_log with the Agent Executor.
            # Because the response from the plan is not a generator, we need to
            # accumulate the output into final output and return that.
            for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}):
                if final_output is None:
                    final_output = chunk
                else:
                    final_output += chunk
        else:
            final_output = self.runnable.invoke(inputs, config={"callbacks": callbacks})

        return final_output

    async def aplan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[
        List[AgentAction],
        AgentFinish,
    ]:
        """Async based on past history and current inputs, decide what to do.

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

        Returns:
            Action specifying what tool to use.
        """
        inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
        final_output: Any = None
        if self.stream_runnable:
            # Use streaming to make sure that the underlying LLM is invoked in a
            # streaming
            # fashion to make it possible to get access to the individual LLM tokens
            # when using stream_log with the Agent Executor.
            # Because the response from the plan is not a generator, we need to
            # accumulate the output into final output and return that.
            async for chunk in self.runnable.astream(
                inputs, config={"callbacks": callbacks}
            ):
                if final_output is None:
                    final_output = chunk
                else:
                    final_output += chunk
        else:
            final_output = await self.runnable.ainvoke(
                inputs, config={"callbacks": callbacks}
            )

        return final_output


@deprecated(
    "0.1.0",
    message=AGENT_DEPRECATION_WARNING,
    removal="1.0",
)
class LLMSingleActionAgent(BaseSingleActionAgent):
    """Base class for single action agents."""

    llm_chain: LLMChain
    """LLMChain to use for agent."""
    output_parser: AgentOutputParser
    """Output parser to use for agent."""
    stop: List[str]
    """List of strings to stop on."""

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

        Returns:
            List of input keys.
        """
        return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        del _dict["output_parser"]
        return _dict

    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[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:
            Action specifying what tool to use.
        """
        output = self.llm_chain.run(
            intermediate_steps=intermediate_steps,
            stop=self.stop,
            callbacks=callbacks,
            **kwargs,
        )
        return self.output_parser.parse(output)

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

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

        Returns:
            Action specifying what tool to use.
        """
        output = await self.llm_chain.arun(
            intermediate_steps=intermediate_steps,
            stop=self.stop,
            callbacks=callbacks,
            **kwargs,
        )
        return self.output_parser.parse(output)

    def tool_run_logging_kwargs(self) -> Dict:
        """Return logging kwargs for tool run."""
        return {
            "llm_prefix": "",
            "observation_prefix": "" if len(self.stop) == 0 else self.stop[0],
        }


@deprecated(
    "0.1.0",
    message=AGENT_DEPRECATION_WARNING,
    removal="1.0",
)
class Agent(BaseSingleActionAgent):
    """Agent that calls the language model and deciding the action.

    This is driven by a LLMChain. The prompt in the LLMChain MUST include
    a variable called "agent_scratchpad" where the agent can put its
    intermediary work.
    """

    llm_chain: LLMChain
    """LLMChain to use for agent."""
    output_parser: AgentOutputParser
    """Output parser to use for agent."""
    allowed_tools: Optional[List[str]] = None
    """Allowed tools for the agent. If None, all tools are allowed."""

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        del _dict["output_parser"]
        return _dict

    def get_allowed_tools(self) -> Optional[List[str]]:
        """Get allowed tools."""
        return self.allowed_tools

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

    def _fix_text(self, text: str) -> str:
        """Fix the text.

        Args:
            text: Text to fix.

        Returns:
            str: Fixed text.
        """
        raise ValueError("fix_text not implemented for this agent.")

    @property
    def _stop(self) -> List[str]:
        return [
            f"\n{self.observation_prefix.rstrip()}",
            f"\n\t{self.observation_prefix.rstrip()}",
        ]

    def _construct_scratchpad(
        self, intermediate_steps: List[Tuple[AgentAction, str]]
    ) -> Union[str, List[BaseMessage]]:
        """Construct the scratchpad that lets the agent continue its thought process."""
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
        return thoughts

    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.

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

        Returns:
            Action specifying what tool to use.
        """
        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
        full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
        return self.output_parser.parse(full_output)

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

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

        Returns:
            Action specifying what tool to use.
        """
        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
        full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)
        agent_output = await self.output_parser.aparse(full_output)
        return agent_output

    def get_full_inputs(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Dict[str, Any]:
        """Create the full inputs for the LLMChain from intermediate steps.

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

        Returns:
            Dict[str, Any]: Full inputs for the LLMChain.
        """
        thoughts = self._construct_scratchpad(intermediate_steps)
        new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
        full_inputs = {**kwargs, **new_inputs}
        return full_inputs

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

        :meta private:
        """
        return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})

    @model_validator(mode="after")
    def validate_prompt(self) -> Self:
        """Validate that prompt matches format.

        Args:
            values: Values to validate.

        Returns:
            Dict: Validated values.

        Raises:
            ValueError: If `agent_scratchpad` is not in prompt.input_variables
             and prompt is not a FewShotPromptTemplate or a PromptTemplate.
        """
        prompt = self.llm_chain.prompt
        if "agent_scratchpad" not in prompt.input_variables:
            logger.warning(
                "`agent_scratchpad` should be a variable in prompt.input_variables."
                " Did not find it, so adding it at the end."
            )
            prompt.input_variables.append("agent_scratchpad")
            if isinstance(prompt, PromptTemplate):
                prompt.template += "\n{agent_scratchpad}"
            elif isinstance(prompt, FewShotPromptTemplate):
                prompt.suffix += "\n{agent_scratchpad}"
            else:
                raise ValueError(f"Got unexpected prompt type {type(prompt)}")
        return self

    @property
    @abstractmethod
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""

    @property
    @abstractmethod
    def llm_prefix(self) -> str:
        """Prefix to append the LLM call with."""

    @classmethod
    @abstractmethod
    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
        """Create a prompt for this class.

        Args:
            tools: Tools to use.

        Returns:
            BasePromptTemplate: Prompt template.
        """

    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        """Validate that appropriate tools are passed in.

        Args:
            tools: Tools to use.
        """

        pass

    @classmethod
    @abstractmethod
    def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
        """Get default output parser for this class."""

    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        output_parser: Optional[AgentOutputParser] = None,
        **kwargs: Any,
    ) -> Agent:
        """Construct an agent from an LLM and tools.

        Args:
            llm: Language model to use.
            tools: Tools to use.
            callback_manager: Callback manager to use.
            output_parser: Output parser to use.
            kwargs: Additional arguments.

        Returns:
            Agent: Agent object.
        """
        cls._validate_tools(tools)
        llm_chain = LLMChain(
            llm=llm,
            prompt=cls.create_prompt(tools),
            callback_manager=callback_manager,
        )
        tool_names = [tool.name for tool in tools]
        _output_parser = output_parser or cls._get_default_output_parser()
        return cls(
            llm_chain=llm_chain,
            allowed_tools=tool_names,
            output_parser=_output_parser,
            **kwargs,
        )

    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: 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.
            **kwargs: User inputs.

        Returns:
            AgentFinish: Agent finish object.

        Raises:
            ValueError: If `early_stopping_method` is not in ['force', 'generate'].
        """
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish(
                {"output": "Agent stopped due to iteration limit or time limit."}, ""
            )
        elif early_stopping_method == "generate":
            # Generate does one final forward pass
            thoughts = ""
            for action, observation in intermediate_steps:
                thoughts += action.log
                thoughts += (
                    f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
                )
            # Adding to the previous steps, we now tell the LLM to make a final pred
            thoughts += (
                "\n\nI now need to return a final answer based on the previous steps:"
            )
            new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
            full_inputs = {**kwargs, **new_inputs}
            full_output = self.llm_chain.predict(**full_inputs)
            # We try to extract a final answer
            parsed_output = self.output_parser.parse(full_output)
            if isinstance(parsed_output, AgentFinish):
                # If we can extract, we send the correct stuff
                return parsed_output
            else:
                # If we can extract, but the tool is not the final tool,
                # we just return the full output
                return AgentFinish({"output": full_output}, full_output)
        else:
            raise ValueError(
                "early_stopping_method should be one of `force` or `generate`, "
                f"got {early_stopping_method}"
            )

    def tool_run_logging_kwargs(self) -> Dict:
        """Return logging kwargs for tool run."""
        return {
            "llm_prefix": self.llm_prefix,
            "observation_prefix": self.observation_prefix,
        }


class ExceptionTool(BaseTool):  # type: ignore[override]
    """Tool that just returns the query."""

    name: str = "_Exception"
    """Name of the tool."""
    description: str = "Exception tool"
    """Description of the tool."""

    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        return query

    async def _arun(
        self,
        query: str,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        return query


NextStepOutput = List[Union[AgentFinish, AgentAction, AgentStep]]
RunnableAgentType = Union[RunnableAgent, RunnableMultiActionAgent]


class AgentExecutor(Chain):
    """Agent that is using tools."""

    agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, Runnable]
    """The agent to run for creating a plan and determining actions
    to take at each step of the execution loop."""
    tools: Sequence[BaseTool]
    """The valid tools the agent can call."""
    return_intermediate_steps: bool = False
    """Whether to return the agent's trajectory of intermediate steps
    at the end in addition to the final output."""
    max_iterations: Optional[int] = 15
    """The maximum number of steps to take before ending the execution
    loop.

    Setting to 'None' could lead to an infinite loop."""
    max_execution_time: Optional[float] = None
    """The maximum amount of wall clock time to spend in the execution
    loop.
    """
    early_stopping_method: str = "force"
    """The method to use for early stopping if the agent never
    returns `AgentFinish`. Either 'force' or 'generate'.

    `"force"` returns a string saying that it stopped because it met a
        time or iteration limit.

    `"generate"` calls the agent's LLM Chain one final time to generate
        a final answer based on the previous steps.
    """
    handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = (
        False
    )
    """How to handle errors raised by the agent's output parser.
    Defaults to `False`, which raises the error.
    If `true`, the error will be sent back to the LLM as an observation.
    If a string, the string itself will be sent to the LLM as an observation.
    If a callable function, the function will be called with the exception
     as an argument, and the result of that function will be passed to the agent
      as an observation.
    """
    trim_intermediate_steps: Union[
        int, Callable[[List[Tuple[AgentAction, str]]], List[Tuple[AgentAction, str]]]
    ] = -1
    """How to trim the intermediate steps before returning them.
    Defaults to -1, which means no trimming.
    """

    @classmethod
    def from_agent_and_tools(
        cls,
        agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, Runnable],
        tools: Sequence[BaseTool],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> AgentExecutor:
        """Create from agent and tools.

        Args:
            agent: Agent to use.
            tools: Tools to use.
            callbacks: Callbacks to use.
            kwargs: Additional arguments.

        Returns:
            AgentExecutor: Agent executor object.
        """
        return cls(
            agent=agent,
            tools=tools,
            callbacks=callbacks,
            **kwargs,
        )

    @model_validator(mode="after")
    def validate_tools(self) -> Self:
        """Validate that tools are compatible with agent.

        Args:
            values: Values to validate.

        Returns:
            Dict: Validated values.

        Raises:
            ValueError: If allowed tools are different than provided tools.
        """
        agent = self.agent
        tools = self.tools
        allowed_tools = agent.get_allowed_tools()  # type: ignore
        if allowed_tools is not None:
            if set(allowed_tools) != set([tool.name for tool in tools]):
                raise ValueError(
                    f"Allowed tools ({allowed_tools}) different than "
                    f"provided tools ({[tool.name for tool in tools]})"
                )
        return self

    @model_validator(mode="before")
    @classmethod
    def validate_runnable_agent(cls, values: Dict) -> Any:
        """Convert runnable to agent if passed in.

        Args:
            values: Values to validate.

        Returns:
            Dict: Validated values.
        """
        agent = values.get("agent")
        if agent and isinstance(agent, Runnable):
            try:
                output_type = agent.OutputType
            except Exception as _:
                multi_action = False
            else:
                multi_action = output_type == Union[List[AgentAction], AgentFinish]

            stream_runnable = values.pop("stream_runnable", True)
            if multi_action:
                values["agent"] = RunnableMultiActionAgent(
                    runnable=agent, stream_runnable=stream_runnable
                )
            else:
                values["agent"] = RunnableAgent(
                    runnable=agent, stream_runnable=stream_runnable
                )
        return values

    @property
    def _action_agent(self) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
        """Type cast self.agent.

        If the `agent` attribute is a Runnable, it will be converted one of
        RunnableAgentType in the validate_runnable_agent root_validator.

        To support instantiating with a Runnable, here we explicitly cast the type
        to reflect the changes made in the root_validator.
        """
        if isinstance(self.agent, Runnable):
            return cast(RunnableAgentType, self.agent)
        else:
            return self.agent

    def save(self, file_path: Union[Path, str]) -> None:
        """Raise error - saving not supported for Agent Executors.

        Args:
            file_path: Path to save to.

        Raises:
            ValueError: Saving not supported for agent executors.
        """
        raise ValueError(
            "Saving not supported for agent executors. "
            "If you are trying to save the agent, please use the "
            "`.save_agent(...)`"
        )

    def save_agent(self, file_path: Union[Path, str]) -> None:
        """Save the underlying agent.

        Args:
            file_path: Path to save to.
        """
        return self._action_agent.save(file_path)

    def iter(
        self,
        inputs: Any,
        callbacks: Callbacks = None,
        *,
        include_run_info: bool = False,
        async_: bool = False,  # arg kept for backwards compat, but ignored
    ) -> AgentExecutorIterator:
        """Enables iteration over steps taken to reach final output.

        Args:
            inputs: Inputs to the agent.
            callbacks: Callbacks to run.
            include_run_info: Whether to include run info.
            async_: Whether to run async. (Ignored)

        Returns:
            AgentExecutorIterator: Agent executor iterator object.
        """
        return AgentExecutorIterator(
            self,
            inputs,
            callbacks,
            tags=self.tags,
            include_run_info=include_run_info,
        )

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

        :meta private:
        """
        return self._action_agent.input_keys

    @property
    def output_keys(self) -> List[str]:
        """Return the singular output key.

        :meta private:
        """
        if self.return_intermediate_steps:
            return self._action_agent.return_values + ["intermediate_steps"]
        else:
            return self._action_agent.return_values

    def lookup_tool(self, name: str) -> BaseTool:
        """Lookup tool by name.

        Args:
            name: Name of tool.

        Returns:
            BaseTool: Tool object.
        """
        return {tool.name: tool for tool in self.tools}[name]

    def _should_continue(self, iterations: int, time_elapsed: float) -> bool:
        if self.max_iterations is not None and iterations >= self.max_iterations:
            return False
        if (
            self.max_execution_time is not None
            and time_elapsed >= self.max_execution_time
        ):
            return False

        return True

    def _return(
        self,
        output: AgentFinish,
        intermediate_steps: list,
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        if run_manager:
            run_manager.on_agent_finish(output, color="green", verbose=self.verbose)
        final_output = output.return_values
        if self.return_intermediate_steps:
            final_output["intermediate_steps"] = intermediate_steps
        return final_output

    async def _areturn(
        self,
        output: AgentFinish,
        intermediate_steps: list,
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        if run_manager:
            await run_manager.on_agent_finish(
                output, color="green", verbose=self.verbose
            )
        final_output = output.return_values
        if self.return_intermediate_steps:
            final_output["intermediate_steps"] = intermediate_steps
        return final_output

    def _consume_next_step(
        self, values: NextStepOutput
    ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
        if isinstance(values[-1], AgentFinish):
            assert len(values) == 1
            return values[-1]
        else:
            return [
                (a.action, a.observation) for a in values if isinstance(a, AgentStep)
            ]

    def _take_next_step(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        inputs: Dict[str, str],
        intermediate_steps: List[Tuple[AgentAction, str]],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
        return self._consume_next_step(
            [
                a
                for a in self._iter_next_step(
                    name_to_tool_map,
                    color_mapping,
                    inputs,
                    intermediate_steps,
                    run_manager,
                )
            ]
        )

    def _iter_next_step(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        inputs: Dict[str, str],
        intermediate_steps: List[Tuple[AgentAction, str]],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]:
        """Take a single step in the thought-action-observation loop.

        Override this to take control of how the agent makes and acts on choices.
        """
        try:
            intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)

            # Call the LLM to see what to do.
            output = self._action_agent.plan(
                intermediate_steps,
                callbacks=run_manager.get_child() if run_manager else None,
                **inputs,
            )
        except OutputParserException as e:
            if isinstance(self.handle_parsing_errors, bool):
                raise_error = not self.handle_parsing_errors
            else:
                raise_error = False
            if raise_error:
                raise ValueError(
                    "An output parsing error occurred. "
                    "In order to pass this error back to the agent and have it try "
                    "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
                    f"This is the error: {str(e)}"
                )
            text = str(e)
            if isinstance(self.handle_parsing_errors, bool):
                if e.send_to_llm:
                    observation = str(e.observation)
                    text = str(e.llm_output)
                else:
                    observation = "Invalid or incomplete response"
            elif isinstance(self.handle_parsing_errors, str):
                observation = self.handle_parsing_errors
            elif callable(self.handle_parsing_errors):
                observation = self.handle_parsing_errors(e)
            else:
                raise ValueError("Got unexpected type of `handle_parsing_errors`")
            output = AgentAction("_Exception", observation, text)
            if run_manager:
                run_manager.on_agent_action(output, color="green")
            tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
            observation = ExceptionTool().run(
                output.tool_input,
                verbose=self.verbose,
                color=None,
                callbacks=run_manager.get_child() if run_manager else None,
                **tool_run_kwargs,
            )
            yield AgentStep(action=output, observation=observation)
            return

        # If the tool chosen is the finishing tool, then we end and return.
        if isinstance(output, AgentFinish):
            yield output
            return

        actions: List[AgentAction]
        if isinstance(output, AgentAction):
            actions = [output]
        else:
            actions = output
        for agent_action in actions:
            yield agent_action
        for agent_action in actions:
            yield self._perform_agent_action(
                name_to_tool_map, color_mapping, agent_action, run_manager
            )

    def _perform_agent_action(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        agent_action: AgentAction,
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> AgentStep:
        if run_manager:
            run_manager.on_agent_action(agent_action, color="green")
        # Otherwise we lookup the tool
        if agent_action.tool in name_to_tool_map:
            tool = name_to_tool_map[agent_action.tool]
            return_direct = tool.return_direct
            color = color_mapping[agent_action.tool]
            tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
            if return_direct:
                tool_run_kwargs["llm_prefix"] = ""
            # We then call the tool on the tool input to get an observation
            observation = tool.run(
                agent_action.tool_input,
                verbose=self.verbose,
                color=color,
                callbacks=run_manager.get_child() if run_manager else None,
                **tool_run_kwargs,
            )
        else:
            tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
            observation = InvalidTool().run(
                {
                    "requested_tool_name": agent_action.tool,
                    "available_tool_names": list(name_to_tool_map.keys()),
                },
                verbose=self.verbose,
                color=None,
                callbacks=run_manager.get_child() if run_manager else None,
                **tool_run_kwargs,
            )
        return AgentStep(action=agent_action, observation=observation)

    async def _atake_next_step(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        inputs: Dict[str, str],
        intermediate_steps: List[Tuple[AgentAction, str]],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
        return self._consume_next_step(
            [
                a
                async for a in self._aiter_next_step(
                    name_to_tool_map,
                    color_mapping,
                    inputs,
                    intermediate_steps,
                    run_manager,
                )
            ]
        )

    async def _aiter_next_step(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        inputs: Dict[str, str],
        intermediate_steps: List[Tuple[AgentAction, str]],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> AsyncIterator[Union[AgentFinish, AgentAction, AgentStep]]:
        """Take a single step in the thought-action-observation loop.

        Override this to take control of how the agent makes and acts on choices.
        """
        try:
            intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)

            # Call the LLM to see what to do.
            output = await self._action_agent.aplan(
                intermediate_steps,
                callbacks=run_manager.get_child() if run_manager else None,
                **inputs,
            )
        except OutputParserException as e:
            if isinstance(self.handle_parsing_errors, bool):
                raise_error = not self.handle_parsing_errors
            else:
                raise_error = False
            if raise_error:
                raise ValueError(
                    "An output parsing error occurred. "
                    "In order to pass this error back to the agent and have it try "
                    "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
                    f"This is the error: {str(e)}"
                )
            text = str(e)
            if isinstance(self.handle_parsing_errors, bool):
                if e.send_to_llm:
                    observation = str(e.observation)
                    text = str(e.llm_output)
                else:
                    observation = "Invalid or incomplete response"
            elif isinstance(self.handle_parsing_errors, str):
                observation = self.handle_parsing_errors
            elif callable(self.handle_parsing_errors):
                observation = self.handle_parsing_errors(e)
            else:
                raise ValueError("Got unexpected type of `handle_parsing_errors`")
            output = AgentAction("_Exception", observation, text)
            tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
            observation = await ExceptionTool().arun(
                output.tool_input,
                verbose=self.verbose,
                color=None,
                callbacks=run_manager.get_child() if run_manager else None,
                **tool_run_kwargs,
            )
            yield AgentStep(action=output, observation=observation)
            return

        # If the tool chosen is the finishing tool, then we end and return.
        if isinstance(output, AgentFinish):
            yield output
            return

        actions: List[AgentAction]
        if isinstance(output, AgentAction):
            actions = [output]
        else:
            actions = output
        for agent_action in actions:
            yield agent_action

        # Use asyncio.gather to run multiple tool.arun() calls concurrently
        result = await asyncio.gather(
            *[
                self._aperform_agent_action(
                    name_to_tool_map, color_mapping, agent_action, run_manager
                )
                for agent_action in actions
            ],
        )

        # TODO This could yield each result as it becomes available
        for chunk in result:
            yield chunk

    async def _aperform_agent_action(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        agent_action: AgentAction,
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> AgentStep:
        if run_manager:
            await run_manager.on_agent_action(
                agent_action, verbose=self.verbose, color="green"
            )
        # Otherwise we lookup the tool
        if agent_action.tool in name_to_tool_map:
            tool = name_to_tool_map[agent_action.tool]
            return_direct = tool.return_direct
            color = color_mapping[agent_action.tool]
            tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
            if return_direct:
                tool_run_kwargs["llm_prefix"] = ""
            # We then call the tool on the tool input to get an observation
            observation = await tool.arun(
                agent_action.tool_input,
                verbose=self.verbose,
                color=color,
                callbacks=run_manager.get_child() if run_manager else None,
                **tool_run_kwargs,
            )
        else:
            tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
            observation = await InvalidTool().arun(
                {
                    "requested_tool_name": agent_action.tool,
                    "available_tool_names": list(name_to_tool_map.keys()),
                },
                verbose=self.verbose,
                color=None,
                callbacks=run_manager.get_child() if run_manager else None,
                **tool_run_kwargs,
            )
        return AgentStep(action=agent_action, observation=observation)

    def _call(
        self,
        inputs: Dict[str, str],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Run text through and get agent response."""
        # Construct a mapping of tool name to tool for easy lookup
        name_to_tool_map = {tool.name: tool for tool in self.tools}
        # We construct a mapping from each tool to a color, used for logging.
        color_mapping = get_color_mapping(
            [tool.name for tool in self.tools], excluded_colors=["green", "red"]
        )
        intermediate_steps: List[Tuple[AgentAction, str]] = []
        # Let's start tracking the number of iterations and time elapsed
        iterations = 0
        time_elapsed = 0.0
        start_time = time.time()
        # We now enter the agent loop (until it returns something).
        while self._should_continue(iterations, time_elapsed):
            next_step_output = self._take_next_step(
                name_to_tool_map,
                color_mapping,
                inputs,
                intermediate_steps,
                run_manager=run_manager,
            )
            if isinstance(next_step_output, AgentFinish):
                return self._return(
                    next_step_output, intermediate_steps, run_manager=run_manager
                )

            intermediate_steps.extend(next_step_output)
            if len(next_step_output) == 1:
                next_step_action = next_step_output[0]
                # See if tool should return directly
                tool_return = self._get_tool_return(next_step_action)
                if tool_return is not None:
                    return self._return(
                        tool_return, intermediate_steps, run_manager=run_manager
                    )
            iterations += 1
            time_elapsed = time.time() - start_time
        output = self._action_agent.return_stopped_response(
            self.early_stopping_method, intermediate_steps, **inputs
        )
        return self._return(output, intermediate_steps, run_manager=run_manager)

    async def _acall(
        self,
        inputs: Dict[str, str],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        """Async run text through and get agent response."""
        # Construct a mapping of tool name to tool for easy lookup
        name_to_tool_map = {tool.name: tool for tool in self.tools}
        # We construct a mapping from each tool to a color, used for logging.
        color_mapping = get_color_mapping(
            [tool.name for tool in self.tools], excluded_colors=["green"]
        )
        intermediate_steps: List[Tuple[AgentAction, str]] = []
        # Let's start tracking the number of iterations and time elapsed
        iterations = 0
        time_elapsed = 0.0
        start_time = time.time()
        # We now enter the agent loop (until it returns something).
        try:
            async with asyncio_timeout(self.max_execution_time):
                while self._should_continue(iterations, time_elapsed):
                    next_step_output = await self._atake_next_step(
                        name_to_tool_map,
                        color_mapping,
                        inputs,
                        intermediate_steps,
                        run_manager=run_manager,
                    )
                    if isinstance(next_step_output, AgentFinish):
                        return await self._areturn(
                            next_step_output,
                            intermediate_steps,
                            run_manager=run_manager,
                        )

                    intermediate_steps.extend(next_step_output)
                    if len(next_step_output) == 1:
                        next_step_action = next_step_output[0]
                        # See if tool should return directly
                        tool_return = self._get_tool_return(next_step_action)
                        if tool_return is not None:
                            return await self._areturn(
                                tool_return, intermediate_steps, run_manager=run_manager
                            )

                    iterations += 1
                    time_elapsed = time.time() - start_time
                output = self._action_agent.return_stopped_response(
                    self.early_stopping_method, intermediate_steps, **inputs
                )
                return await self._areturn(
                    output, intermediate_steps, run_manager=run_manager
                )
        except (TimeoutError, asyncio.TimeoutError):
            # stop early when interrupted by the async timeout
            output = self._action_agent.return_stopped_response(
                self.early_stopping_method, intermediate_steps, **inputs
            )
            return await self._areturn(
                output, intermediate_steps, run_manager=run_manager
            )

    def _get_tool_return(
        self, next_step_output: Tuple[AgentAction, str]
    ) -> Optional[AgentFinish]:
        """Check if the tool is a returning tool."""
        agent_action, observation = next_step_output
        name_to_tool_map = {tool.name: tool for tool in self.tools}
        return_value_key = "output"
        if len(self._action_agent.return_values) > 0:
            return_value_key = self._action_agent.return_values[0]
        # Invalid tools won't be in the map, so we return False.
        if agent_action.tool in name_to_tool_map:
            if name_to_tool_map[agent_action.tool].return_direct:
                return AgentFinish(
                    {return_value_key: observation},
                    "",
                )
        return None

    def _prepare_intermediate_steps(
        self, intermediate_steps: List[Tuple[AgentAction, str]]
    ) -> List[Tuple[AgentAction, str]]:
        if (
            isinstance(self.trim_intermediate_steps, int)
            and self.trim_intermediate_steps > 0
        ):
            return intermediate_steps[-self.trim_intermediate_steps :]
        elif callable(self.trim_intermediate_steps):
            return self.trim_intermediate_steps(intermediate_steps)
        else:
            return intermediate_steps

    def stream(
        self,
        input: Union[Dict[str, Any], Any],
        config: Optional[RunnableConfig] = None,
        **kwargs: Any,
    ) -> Iterator[AddableDict]:
        """Enables streaming over steps taken to reach final output.

        Args:
            input: Input to the agent.
            config: Config to use.
            kwargs: Additional arguments.

        Yields:
            AddableDict: Addable dictionary.
        """
        config = ensure_config(config)
        iterator = AgentExecutorIterator(
            self,
            input,
            config.get("callbacks"),
            tags=config.get("tags"),
            metadata=config.get("metadata"),
            run_name=config.get("run_name"),
            run_id=config.get("run_id"),
            yield_actions=True,
            **kwargs,
        )
        for step in iterator:
            yield step

    async def astream(
        self,
        input: Union[Dict[str, Any], Any],
        config: Optional[RunnableConfig] = None,
        **kwargs: Any,
    ) -> AsyncIterator[AddableDict]:
        """Async enables streaming over steps taken to reach final output.

        Args:
            input: Input to the agent.
            config: Config to use.
            kwargs: Additional arguments.

        Yields:
            AddableDict: Addable dictionary.
        """

        config = ensure_config(config)
        iterator = AgentExecutorIterator(
            self,
            input,
            config.get("callbacks"),
            tags=config.get("tags"),
            metadata=config.get("metadata"),
            run_name=config.get("run_name"),
            run_id=config.get("run_id"),
            yield_actions=True,
            **kwargs,
        )
        async for step in iterator:
            yield step