MemGPT/memgpt/autogen/memgpt_agent.py

from autogen.agentchat import Agent, ConversableAgent, UserProxyAgent, GroupChat, GroupChatManager
from memgpt.agent import Agent as _Agent

from typing import Callable, Optional, List, Dict, Union, Any, Tuple

from memgpt.autogen.interface import AutoGenInterface
from memgpt.persistence_manager import LocalStateManager
import memgpt.system as system
import memgpt.constants as constants
import memgpt.presets as presets
from memgpt.personas import personas
from memgpt.humans import humans
from memgpt.config import AgentConfig
from memgpt.cli.cli import attach
from memgpt.connectors.storage import StorageConnector


def create_memgpt_autogen_agent_from_config(
    name: str,
    system_message: Optional[str] = "You are a helpful AI Assistant.",
    is_termination_msg: Optional[Callable[[Dict], bool]] = None,
    max_consecutive_auto_reply: Optional[int] = None,
    human_input_mode: Optional[str] = "ALWAYS",
    function_map: Optional[Dict[str, Callable]] = None,
    code_execution_config: Optional[Union[Dict, bool]] = None,
    llm_config: Optional[Union[Dict, bool]] = None,
    default_auto_reply: Optional[Union[str, Dict, None]] = "",
    interface_kwargs: Dict = None,
):
    """Construct AutoGen config workflow in a clean way."""

    if interface_kwargs is None:
        interface_kwargs = {}

    model = constants.DEFAULT_MEMGPT_MODEL if llm_config is None else llm_config["config_list"][0]["model"]
    persona_desc = personas.DEFAULT if system_message == "" else system_message
    if human_input_mode == "ALWAYS":
        user_desc = humans.DEFAULT
    elif human_input_mode == "TERMINATE":
        user_desc = "Work by yourself, the user won't reply until you output `TERMINATE` to end the conversation."
    else:
        user_desc = "Work by yourself, the user won't reply. Elaborate as much as possible."

    if function_map is not None or code_execution_config is not None:
        raise NotImplementedError

    autogen_memgpt_agent = create_autogen_memgpt_agent(
        name,
        preset=presets.DEFAULT_PRESET,
        model=model,
        persona_description=persona_desc,
        user_description=user_desc,
        is_termination_msg=is_termination_msg,
        interface_kwargs=interface_kwargs,
    )

    if human_input_mode != "ALWAYS":
        coop_agent1 = create_autogen_memgpt_agent(
            name,
            preset=presets.DEFAULT_PRESET,
            model=model,
            persona_description=persona_desc,
            user_description=user_desc,
            is_termination_msg=is_termination_msg,
            interface_kwargs=interface_kwargs,
        )
        if default_auto_reply != "":
            coop_agent2 = UserProxyAgent(
                name,
                human_input_mode="NEVER",
                default_auto_reply=default_auto_reply,
            )
        else:
            coop_agent2 = create_autogen_memgpt_agent(
                name,
                preset=presets.DEFAULT_PRESET,
                model=model,
                persona_description=persona_desc,
                user_description=user_desc,
                is_termination_msg=is_termination_msg,
                interface_kwargs=interface_kwargs,
            )

        groupchat = GroupChat(
            agents=[autogen_memgpt_agent, coop_agent1, coop_agent2],
            messages=[],
            max_round=12 if max_consecutive_auto_reply is None else max_consecutive_auto_reply,
        )
        manager = GroupChatManager(name=name, groupchat=groupchat, llm_config=llm_config)
        return manager

    else:
        return autogen_memgpt_agent


def create_autogen_memgpt_agent(
    autogen_name,
    preset=presets.DEFAULT_PRESET,
    model=constants.DEFAULT_MEMGPT_MODEL,
    persona_description=personas.DEFAULT,
    user_description=humans.DEFAULT,
    interface=None,
    interface_kwargs={},
    persistence_manager=None,
    persistence_manager_kwargs=None,
    is_termination_msg: Optional[Callable[[Dict], bool]] = None,
):
    """
    See AutoGenInterface.__init__ for available options you can pass into
    `interface_kwargs`.  For example, MemGPT's inner monologue and functions are
    off by default so that they are not visible to the other agents. You can
    turn these on by passing in
    ```
    interface_kwargs={
        "debug": True,  # to see all MemGPT activity
        "show_inner_thoughts: True  # to print MemGPT inner thoughts "globally"
                                    # (visible to all AutoGen agents)
    }
    ```
    """
    agent_config = AgentConfig(
        # name=autogen_name,
        # TODO: more gracefully integrate reuse of MemGPT agents. Right now, we are creating a new MemGPT agent for
        # every call to this function, because those scripts using create_autogen_memgpt_agent may contain calls
        # to non-idempotent agent functions like `attach`.
        persona=persona_description,
        human=user_description,
        model=model,
        preset=presets.DEFAULT_PRESET,
    )

    interface = AutoGenInterface(**interface_kwargs) if interface is None else interface
    if persistence_manager_kwargs is None:
        persistence_manager_kwargs = {
            "agent_config": agent_config,
        }
    persistence_manager = LocalStateManager(**persistence_manager_kwargs) if persistence_manager is None else persistence_manager

    memgpt_agent = presets.use_preset(
        preset,
        agent_config,
        model,
        persona_description,
        user_description,
        interface,
        persistence_manager,
    )

    autogen_memgpt_agent = MemGPTAgent(
        name=autogen_name,
        agent=memgpt_agent,
        is_termination_msg=is_termination_msg,
    )
    return autogen_memgpt_agent


class MemGPTAgent(ConversableAgent):
    def __init__(
        self,
        name: str,
        agent: _Agent,
        skip_verify=False,
        concat_other_agent_messages=False,
        is_termination_msg: Optional[Callable[[Dict], bool]] = None,
    ):
        super().__init__(name)
        self.agent = agent
        self.skip_verify = skip_verify
        self.concat_other_agent_messages = concat_other_agent_messages
        self.register_reply([Agent, None], MemGPTAgent._generate_reply_for_user_message)
        self.messages_processed_up_to_idx = 0

        self._is_termination_msg = is_termination_msg if is_termination_msg is not None else (lambda x: x == "TERMINATE")

    def attach(self, data_source: str):
        # attach new data
        attach(self.agent.config.name, data_source)

        # update agent config
        self.agent.config.attach_data_source(data_source)

        # reload agent with new data source
        self.agent.persistence_manager.archival_memory.storage = StorageConnector.get_storage_connector(agent_config=self.agent.config)

    def format_other_agent_message(self, msg):
        if "name" in msg:
            user_message = f"{msg['name']}: {msg['content']}"
        else:
            user_message = msg["content"]
        return user_message

    def find_last_user_message(self):
        last_user_message = None
        for msg in self.agent.messages:
            if msg["role"] == "user":
                last_user_message = msg["content"]
        return last_user_message

    def find_new_messages(self, entire_message_list):
        """Extract the subset of messages that's actually new"""
        return entire_message_list[self.messages_processed_up_to_idx :]

    def _generate_reply_for_user_message(
        self,
        messages: Optional[List[Dict]] = None,
        sender: Optional[Agent] = None,
        config: Optional[Any] = None,
    ) -> Tuple[bool, Union[str, Dict, None]]:
        self.agent.interface.reset_message_list()

        new_messages = self.find_new_messages(messages)
        if len(new_messages) > 1:
            if self.concat_other_agent_messages:
                # Combine all the other messages into one message
                user_message = "\n".join([self.format_other_agent_message(m) for m in new_messages])
            else:
                # Extend the MemGPT message list with multiple 'user' messages, then push the last one with agent.step()
                self.agent.messages.extend(new_messages[:-1])
                user_message = new_messages[-1]
        elif len(new_messages) == 1:
            user_message = new_messages[0]
        else:
            return True, self._default_auto_reply

        # Package the user message
        user_message = system.package_user_message(user_message)

        # Send a single message into MemGPT
        while True:
            (
                new_messages,
                heartbeat_request,
                function_failed,
                token_warning,
            ) = self.agent.step(user_message, first_message=False, skip_verify=self.skip_verify)
            # Skip user inputs if there's a memory warning, function execution failed, or the agent asked for control
            if token_warning:
                user_message = system.get_token_limit_warning()
            elif function_failed:
                user_message = system.get_heartbeat(constants.FUNC_FAILED_HEARTBEAT_MESSAGE)
            elif heartbeat_request:
                user_message = system.get_heartbeat(constants.REQ_HEARTBEAT_MESSAGE)
            else:
                break

        # Stop the conversation
        if self._is_termination_msg(new_messages[-1]["content"]):
            return True, None

        # Pass back to AutoGen the pretty-printed calls MemGPT made to the interface
        pretty_ret = MemGPTAgent.pretty_concat(self.agent.interface.message_list)
        self.messages_processed_up_to_idx += len(new_messages)
        return True, pretty_ret

    @staticmethod
    def pretty_concat(messages):
        """AutoGen expects a single response, but MemGPT may take many steps.

        To accommodate AutoGen, concatenate all of MemGPT's steps into one and return as a single message.
        """
        ret = {"role": "assistant", "content": ""}
        lines = []
        for m in messages:
            lines.append(f"{m}")
        ret["content"] = "\n".join(lines)
        return ret