118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
# Copyright (c) 2023 - 2025, AG2ai, Inc., AG2ai open-source projects maintainers and core contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from typing import TYPE_CHECKING, Any, Optional, Tuple, Union
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from ..context_variables import ContextVariables
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from ..targets.transition_target import AgentTarget, TransitionTarget
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from .pattern import Pattern
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if TYPE_CHECKING:
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from ...conversable_agent import ConversableAgent
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from ...groupchat import GroupChat, GroupChatManager
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from ..group_tool_executor import GroupToolExecutor
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class RoundRobinPattern(Pattern):
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"""RoundRobinPattern implements a round robin with handoffs between agents."""
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def _generate_handoffs(
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self,
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initial_agent: "ConversableAgent",
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agents: list["ConversableAgent"],
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user_agent: Optional["ConversableAgent"],
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) -> None:
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"""Generate handoffs between agents in a round-robin fashion."""
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# Create a list of the agents and the user_agent but put the initial_agent first
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agent_list = [initial_agent]
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# Add the rest of the agents, excluding the initial_agent and user_agent
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for agent in agents:
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if agent != initial_agent and (user_agent is None or agent != user_agent):
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agent_list.append(agent)
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# Add the user_agent last if it exists
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if user_agent is not None:
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agent_list.append(user_agent)
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# Create handoffs in a round-robin fashion
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for i, agent in enumerate(agent_list):
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# Last agent hands off to the first agent
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# Otherwise agent hands off to the next one
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handoff_target = agent_list[0] if i == len(agent_list) - 1 else agent_list[i + 1]
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agent.handoffs.set_after_work(target=AgentTarget(agent=handoff_target))
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def prepare_group_chat(
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self,
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max_rounds: int,
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messages: Union[list[dict[str, Any]], str],
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) -> Tuple[
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list["ConversableAgent"],
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list["ConversableAgent"],
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Optional["ConversableAgent"],
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ContextVariables,
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"ConversableAgent",
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TransitionTarget,
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"GroupToolExecutor",
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"GroupChat",
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"GroupChatManager",
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list[dict[str, Any]],
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Any,
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list[str],
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list[Any],
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]:
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"""Prepare the group chat for organic agent selection.
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Ensures that:
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1. The group manager has a valid LLM config
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2. All agents have appropriate descriptions for the group manager to use
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Args:
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max_rounds: Maximum number of conversation rounds.
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messages: Initial message(s) to start the conversation.
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Returns:
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Tuple containing all necessary components for the group chat.
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"""
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# Use the parent class's implementation to prepare the agents and group chat
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(
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agents,
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wrapped_agents,
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user_agent,
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context_variables,
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initial_agent,
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group_after_work,
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tool_executor,
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groupchat,
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manager,
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processed_messages,
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last_agent,
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group_agent_names,
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temp_user_list,
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) = super().prepare_group_chat(
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max_rounds=max_rounds,
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messages=messages,
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)
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# Create the handoffs between agents
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self._generate_handoffs(initial_agent=initial_agent, agents=agents, user_agent=user_agent)
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# Return all components with our group_after_work
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return (
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agents,
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wrapped_agents,
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user_agent,
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context_variables,
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initial_agent,
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group_after_work,
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tool_executor,
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groupchat,
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manager,
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processed_messages,
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last_agent,
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group_agent_names,
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temp_user_list,
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)
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