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#

# Source code for langchain.experimental.autonomous_agents.autogpt.agent

    from __future__ import annotations

    from typing import List, Optional

    from pydantic import ValidationError

    from langchain.chains.llm import LLMChain
    from langchain.chat_models.base import BaseChatModel
    from langchain.experimental.autonomous_agents.autogpt.output_parser import (
        AutoGPTOutputParser,
        BaseAutoGPTOutputParser,
    )
    from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt
    from langchain.experimental.autonomous_agents.autogpt.prompt_generator import (
        FINISH_NAME,
    )
    from langchain.schema import (
        AIMessage,
        BaseMessage,
        Document,
        HumanMessage,
        SystemMessage,
    )
    from langchain.tools.base import BaseTool
    from langchain.tools.human.tool import HumanInputRun
    from langchain.vectorstores.base import VectorStoreRetriever

    class AutoGPT:
        """Agent class for interacting with Auto-GPT."""

        def __init__(
            self,
            ai_name: str,
            memory: VectorStoreRetriever,
            chain: LLMChain,
            output_parser: BaseAutoGPTOutputParser,
            tools: List[BaseTool],
            feedback_tool: Optional[HumanInputRun] = None,
        ):
            self.ai_name = ai_name
            self.memory = memory
            self.full_message_history: List[BaseMessage] = []
            self.next_action_count = 0
            self.chain = chain
            self.output_parser = output_parser
            self.tools = tools
            self.feedback_tool = feedback_tool

        @classmethod
        def from_llm_and_tools(
            cls,
            ai_name: str,
            ai_role: str,
            memory: VectorStoreRetriever,
            tools: List[BaseTool],
            llm: BaseChatModel,
            human_in_the_loop: bool = False,
            output_parser: Optional[BaseAutoGPTOutputParser] = None,
        ) -> AutoGPT:
            prompt = AutoGPTPrompt(
                ai_name=ai_name,
                ai_role=ai_role,
                tools=tools,
                input_variables=["memory", "messages", "goals", "user_input"],
                token_counter=llm.get_num_tokens,
            )
            human_feedback_tool = HumanInputRun() if human_in_the_loop else None
            chain = LLMChain(llm=llm, prompt=prompt)
            return cls(
                ai_name,
                memory,
                chain,
                output_parser or AutoGPTOutputParser(),
                tools,
                feedback_tool=human_feedback_tool,
            )

        def run(self, goals: List[str]) -> str:
            user_input = (
                "Determine which next command to use, "
                "and respond using the format specified above:"
            )
            # Interaction Loop
            loop_count = 0
            while True:
                # Discontinue if continuous limit is reached
                loop_count += 1

                # Send message to AI, get response
                assistant_reply = self.chain.run(
                    goals=goals,
                    messages=self.full_message_history,
                    memory=self.memory,
                    user_input=user_input,
                )

                # Print Assistant thoughts
                print(assistant_reply)
                self.full_message_history.append(HumanMessage(content=user_input))
                self.full_message_history.append(AIMessage(content=assistant_reply))

                # Get command name and arguments
                action = self.output_parser.parse(assistant_reply)
                tools = {t.name: t for t in self.tools}
                if action.name == FINISH_NAME:
                    return action.args["response"]
                if action.name in tools:
                    tool = tools[action.name]
                    try:
                        observation = tool.run(action.args)
                    except ValidationError as e:
                        observation = (
                            f"Validation Error in args: {str(e)}, args: {action.args}"
                        )
                    except Exception as e:
                        observation = (
                            f"Error: {str(e)}, {type(e).__name__}, args: {action.args}"
                        )
                    result = f"Command {tool.name} returned: {observation}"
                elif action.name == "ERROR":
                    result = f"Error: {action.args}. "
                else:
                    result = (
                        f"Unknown command '{action.name}'. "
                        f"Please refer to the 'COMMANDS' list for available "
                        f"commands and only respond in the specified JSON format."
                    )

                memory_to_add = (
                    f"Assistant Reply: {assistant_reply} " f"\nResult: {result} "
                )
                if self.feedback_tool is not None:
                    feedback = f"\n{self.feedback_tool.run('Input: ')}"
                    if feedback in {"q", "stop"}:
                        print("EXITING")
                        return "EXITING"
                    memory_to_add += feedback

                self.memory.add_documents([Document(page_content=memory_to_add)])
                self.full_message_history.append(SystemMessage(content=result))