MemGPT/letta/groups/round_robin_multi_agent.py

from typing import List, Optional

from letta.agent import Agent, AgentState
from letta.interface import AgentInterface
from letta.orm import User
from letta.schemas.letta_message_content import TextContent
from letta.schemas.message import Message, MessageCreate
from letta.schemas.openai.chat_completion_response import UsageStatistics
from letta.schemas.usage import LettaUsageStatistics


class RoundRobinMultiAgent(Agent):
    def __init__(
        self,
        interface: AgentInterface,
        agent_state: AgentState,
        user: User,
        # custom
        group_id: str = "",
        agent_ids: List[str] = [],
        description: str = "",
        max_turns: Optional[int] = None,
    ):
        super().__init__(interface, agent_state, user)
        self.group_id = group_id
        self.agent_ids = agent_ids
        self.description = description
        self.max_turns = max_turns or len(agent_ids)

    def step(
        self,
        input_messages: List[MessageCreate],
        chaining: bool = True,
        max_chaining_steps: Optional[int] = None,
        put_inner_thoughts_first: bool = True,
        **kwargs,
    ) -> LettaUsageStatistics:
        total_usage = UsageStatistics()
        step_count = 0
        speaker_id = None

        # Load settings
        token_streaming = self.interface.streaming_mode if hasattr(self.interface, "streaming_mode") else False
        metadata = self.interface.metadata if hasattr(self.interface, "metadata") else None

        # Load agents and initialize chat history with indexing
        agents, message_index = {}, {}
        chat_history: List[MessageCreate] = []
        for agent_id in self.agent_ids:
            agents[agent_id] = self.load_participant_agent(agent_id=agent_id)
            message_index[agent_id] = 0

        # Prepare new messages
        new_messages = []
        for message in input_messages:
            if isinstance(message.content, str):
                message.content = [TextContent(text=message.content)]
            message.group_id = self.group_id
            new_messages.append(message)

        try:
            for i in range(self.max_turns):
                # Select speaker
                speaker_id = self.agent_ids[i % len(self.agent_ids)]

                # Update chat history
                chat_history.extend(new_messages)

                # Perform participant step
                participant_agent = agents[speaker_id]
                usage_stats = participant_agent.step(
                    input_messages=chat_history[message_index[speaker_id] :],
                    chaining=chaining,
                    max_chaining_steps=max_chaining_steps,
                    stream=token_streaming,
                    skip_verify=True,
                    metadata=metadata,
                    put_inner_thoughts_first=put_inner_thoughts_first,
                )

                # Parse participant response
                responses = Message.to_letta_messages_from_list(participant_agent.last_response_messages)
                assistant_messages = [response for response in responses if response.message_type == "assistant_message"]
                new_messages = [
                    MessageCreate(
                        role="system",
                        content=[TextContent(text=message.content)] if isinstance(message.content, str) else message.content,
                        name=participant_agent.agent_state.name,
                        otid=message.otid,
                        sender_id=participant_agent.agent_state.id,
                        group_id=self.group_id,
                    )
                    for message in assistant_messages
                ]

                # Update message index
                message_index[speaker_id] = len(chat_history) + len(new_messages)

                # Sum usage
                total_usage.prompt_tokens += usage_stats.prompt_tokens
                total_usage.completion_tokens += usage_stats.completion_tokens
                total_usage.total_tokens += usage_stats.total_tokens
                step_count += 1

            # Persist remaining chat history
            chat_history.extend(new_messages)
            for agent_id, index in message_index.items():
                if agent_id == speaker_id:
                    continue
                messages_to_persist = []
                for message in chat_history[index:]:
                    message_to_persist = Message(
                        role=message.role,
                        content=message.content,
                        name=message.name,
                        otid=message.otid,
                        sender_id=message.sender_id,
                        group_id=self.group_id,
                        agent_id=agent_id,
                    )
                    messages_to_persist.append(message_to_persist)
                self.message_manager.create_many_messages(messages_to_persist, actor=self.user)

        except Exception as e:
            raise e
        finally:
            self.interface.step_yield()

        self.interface.step_complete()

        return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count)

    def load_participant_agent(self, agent_id: str) -> Agent:
        agent_state = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=self.user)
        persona_block = agent_state.memory.get_block(label="persona")
        group_chat_participant_persona = (
            f"%%% GROUP CHAT CONTEXT %%% "
            f"You are speaking in a group chat with {len(self.agent_ids)} other participants. "
            f"Group Description: {self.description} "
            "INTERACTION GUIDELINES:\n"
            "1. Be aware that others can see your messages - communicate as if in a real group conversation\n"
            "2. Acknowledge and build upon others' contributions when relevant\n"
            "3. Stay on topic while adding your unique perspective based on your role and personality\n"
            "4. Be concise but engaging - give others space to contribute\n"
            "5. Maintain your character's personality while being collaborative\n"
            "6. Feel free to ask questions to other participants to encourage discussion\n"
            "7. If someone addresses you directly, acknowledge their message\n"
            "8. Share relevant experiences or knowledge that adds value to the conversation\n\n"
            "Remember: This is a natural group conversation. Interact as you would in a real group setting, "
            "staying true to your character while fostering meaningful dialogue. "
            "%%% END GROUP CHAT CONTEXT %%%"
        )
        agent_state.memory.update_block_value(label="persona", value=persona_block.value + group_chat_participant_persona)
        return Agent(
            agent_state=agent_state,
            interface=self.interface,
            user=self.user,
            save_last_response=True,
        )