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, Callable, Dict, List, Optional, Sequence, Tuple, Union

import yaml
from pydantic import BaseModel, root_validator

from langchain.agents.agent_types import AgentType
from langchain.agents.tools import InvalidTool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    AsyncCallbackManagerForToolRun,
    CallbackManagerForChainRun,
    CallbackManagerForToolRun,
    Callbacks,
)
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.input import get_color_mapping
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
    AgentAction,
    AgentFinish,
    BaseMessage,
    BaseOutputParser,
    OutputParserException,
)
from langchain.tools.base import BaseTool
from langchain.utilities.asyncio import asyncio_timeout

logger = logging.getLogger(__name__)


class BaseSingleActionAgent(BaseModel):
    """Base 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]:
        """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."""
        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:
        raise NotImplementedError

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

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        _type = self._agent_type
        if isinstance(_type, AgentType):
            _dict["_type"] = str(_type.value)
        else:
            _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 save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            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 {}


class BaseMultiActionAgent(BaseModel):
    """Base 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[List[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:
            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]:
        """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:
            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."""
        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 agent type."""
        raise NotImplementedError

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        _dict["_type"] = str(self._agent_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 save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            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 {}


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


class LLMSingleActionAgent(BaseSingleActionAgent):
    llm_chain: LLMChain
    output_parser: AgentOutputParser
    stop: List[str]

    @property
    def input_keys(self) -> List[str]:
        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 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]:
        """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 {
            "llm_prefix": "",
            "observation_prefix": "" if len(self.stop) == 0 else self.stop[0],
        }


class Agent(BaseSingleActionAgent):
    """Class responsible for calling the language model and deciding the action.

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

    llm_chain: LLMChain
    output_parser: AgentOutputParser
    allowed_tools: Optional[List[str]] = None

    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]]:
        return self.allowed_tools

    @property
    def return_values(self) -> List[str]:
        return ["output"]

    def _fix_text(self, text: str) -> str:
        """Fix the 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]:
        """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)
        return self.output_parser.parse(full_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."""
        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"})

    @root_validator()
    def validate_prompt(cls, values: Dict) -> Dict:
        """Validate that prompt matches format."""
        prompt = values["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 values

    @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."""

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


class ExceptionTool(BaseTool):
    name = "_Exception"
    description = "Exception 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


class AgentExecutor(Chain):
    """Consists of an agent using tools."""

    agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
    tools: Sequence[BaseTool]
    return_intermediate_steps: bool = False
    max_iterations: Optional[int] = 15
    max_execution_time: Optional[float] = None
    early_stopping_method: str = "force"
    handle_parsing_errors: Union[
        bool, str, Callable[[OutputParserException], str]
    ] = False

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

    @root_validator()
    def validate_tools(cls, values: Dict) -> Dict:
        """Validate that tools are compatible with agent."""
        agent = values["agent"]
        tools = values["tools"]
        allowed_tools = agent.get_allowed_tools()
        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 values

    @root_validator()
    def validate_return_direct_tool(cls, values: Dict) -> Dict:
        """Validate that tools are compatible with agent."""
        agent = values["agent"]
        tools = values["tools"]
        if isinstance(agent, BaseMultiActionAgent):
            for tool in tools:
                if tool.return_direct:
                    raise ValueError(
                        "Tools that have `return_direct=True` are not allowed "
                        "in multi-action agents"
                    )
        return values

    def save(self, file_path: Union[Path, str]) -> None:
        """Raise error - 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."""
        return self.agent.save(file_path)

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

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

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

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

    def lookup_tool(self, name: str) -> BaseTool:
        """Lookup tool by name."""
        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 _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]]]:
        """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:
            # Call the LLM to see what to do.
            output = self.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 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.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,
            )
            return [(output, observation)]
        # If the tool chosen is the finishing tool, then we end and return.
        if isinstance(output, AgentFinish):
            return output
        actions: List[AgentAction]
        if isinstance(output, AgentAction):
            actions = [output]
        else:
            actions = output
        result = []
        for agent_action in actions:
            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.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.agent.tool_run_logging_kwargs()
                observation = InvalidTool().run(
                    agent_action.tool,
                    verbose=self.verbose,
                    color=None,
                    callbacks=run_manager.get_child() if run_manager else None,
                    **tool_run_kwargs,
                )
            result.append((agent_action, observation))
        return result

    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]]]:
        """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:
            # Call the LLM to see what to do.
            output = await self.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 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.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,
            )
            return [(output, observation)]
        # If the tool chosen is the finishing tool, then we end and return.
        if isinstance(output, AgentFinish):
            return output
        actions: List[AgentAction]
        if isinstance(output, AgentAction):
            actions = [output]
        else:
            actions = output

        async def _aperform_agent_action(
            agent_action: AgentAction,
        ) -> Tuple[AgentAction, str]:
            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.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.agent.tool_run_logging_kwargs()
                observation = await InvalidTool().arun(
                    agent_action.tool,
                    verbose=self.verbose,
                    color=None,
                    callbacks=run_manager.get_child() if run_manager else None,
                    **tool_run_kwargs,
                )
            return agent_action, observation

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

        return list(result)

    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.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]:
        """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).
        async with asyncio_timeout(self.max_execution_time):
            try:
                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.agent.return_stopped_response(
                    self.early_stopping_method, intermediate_steps, **inputs
                )
                return await self._areturn(
                    output, intermediate_steps, run_manager=run_manager
                )
            except TimeoutError:
                # stop early when interrupted by the async timeout
                output = self.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}
        # 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(
                    {self.agent.return_values[0]: observation},
                    "",
                )
        return None