明确了swarm分配方式的局限性

_backend/main.py

@@ -1,10 +1,11 @@
 import asyncio
 from typing import Sequence
-from autogen_agentchat.agents import AssistantAgent, SocietyOfMindAgent
-from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination
+from autogen_agentchat.agents import AssistantAgent, SocietyOfMindAgent, UserProxyAgent
+from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination, HandoffTermination
 from autogen_agentchat.messages import AgentEvent, ChatMessage, TextMessage, ToolCallExecutionEvent
 from autogen_agentchat.teams import SelectorGroupChat, RoundRobinGroupChat
 from autogen_agentchat.ui import Console
+from autogen_agentchat.base import Handoff
 from autogen_ext.models.openai import OpenAIChatCompletionClient
 from constant import MODEL, OPENAI_API_KEY, OPENAI_BASE_URL
 from scientist_team import create_scientist_team
@@ -28,6 +29,8 @@ async def main(task: str = "") -> dict:
     engineer_team = create_engineer_team()
     
     result = {}
+    user = UserProxyAgent("user", input_func=input)
+
     planning_agent = AssistantAgent(
         "PlanningAgent",
         description="An agent for planning tasks, this agent should be the first to engage when given a new task.",
@@ -37,16 +40,23 @@ async def main(task: str = "") -> dict:
         Your job is to break down complex Materials science research tasks into smaller, manageable subtasks.
         Assign these subtasks to the appropriate sub-teams; not all sub-teams are required to participate in every task.
         Your sub-teams are:
+            User: A human agent to whom you transfer information whenever you need to confirm your execution steps to a human.
+            Engineer team: A team of professional engineers who are responsible for writing code, visualizing experimental schemes, converting experimental schemes to JSON, and more.
             Scientist team: A professional team of material scientists who are mainly responsible for consulting on material synthesis, structure, application and properties.
-            Engineer team: A team of professional engineers who are responsible for writing code, visualizing experimental schemes, converting experimental schemes to machine code, and more.
         You only plan and delegate tasks - you do not execute them yourself.
+        
+        第一次回答时你需要初始化任务分配并按顺序执行，在后续的回答中重申你的任务分配，使用如下格式并利用Mermaid绘制流程图：
+        | team      | subtask              |
+        | --------- | -------------------- |
+        | 1. team_name | sub-Task description |
 
-        When assigning subtasks, use this format:
-        1. <agent> : <task>
-
-        When assigning subtasks, give a flow chart with following format or mermaid to visualize the collaboration between the various teams, such as:
-        <agent 1> -> <agent 2> -> <agent 3>
-
+        每次回答时，你需要清晰明确的指出已经完成的子任务下一步子任务，使用如下格式：
+        **已完成子任务：**
+        1. <team> : <subtask>
+        **Next sub-task:**
+        n. <team> : <subtask>
+        
+        You can end with "USER" if you need to, which means you need human approval or other advice or instructions;
         After plan and delegate tasks are complete, end with "START";
         Determine if all sub-teams have completed their tasks, and if so, summarize the findings and end with "TERMINATE".
         """,
@@ -55,7 +65,7 @@ async def main(task: str = "") -> dict:
 
     # The termination condition is a combination of text mention termination and max message termination.
     text_mention_termination = TextMentionTermination("TERMINATE")
-    max_messages_termination = MaxMessageTermination(max_messages=25)
+    max_messages_termination = MaxMessageTermination(max_messages=100)
     termination = text_mention_termination | max_messages_termination
 
     # The selector function is a function that takes the current message thread of the group chat
@@ -63,10 +73,12 @@ async def main(task: str = "") -> dict:
     def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:
         if messages[-1].source != planning_agent.name:
             return planning_agent.name # Always return to the planning agent after the other agents have spoken.
+        elif "USER" in messages[-1].content:
+            return user.name
         return None
 
     team = SelectorGroupChat(
-        [planning_agent, scientist_team, engineer_team],
+        [planning_agent, user, scientist_team, engineer_team],
         model_client=model_client, # Use a smaller model for the selector.
         termination_condition=termination,
         selector_func=selector_func,
@@ -83,6 +95,7 @@ async def main(task: str = "") -> dict:
 
 # Example usage in another function
 async def main_1():
+    # result = await main(input("Enter your instructions below: \n"))
     result = await main("Let the robot synthesize CsPbBr3 nanocubes at room temperature")
     # result = await main("查一下CsPbBr3的晶体结构")