หลายเอเจนต์แบบสนทนา (AutoGen)

เหตุใดจึงใช้หลายเอเจนต์

รูปแบบ AutoGen

Two-Agent Example

# pip install pyautogen
from autogen import AssistantAgent, UserProxyAgent

llm_config = {'model': 'gpt-4o-mini', 'api_key': '...'}

assistant = AssistantAgent(
    name='coder',
    system_message='You write Python code. Reply with code only.',
    llm_config=llm_config
)

user_proxy = UserProxyAgent(
    name='user',
    human_input_mode='NEVER',
    code_execution_config={'work_dir': './workdir'}
)

user_proxy.initiate_chat(assistant, message='Write a function that returns the factorial of n.')

UserProxyAgent

เอเจนต์ที่สนทนาได้

GroupChat for >2 Agents

class AssistantAgent:
    def __init__(self, name, system_message=''):
        self.name = name
        self.system_message = system_message

    def initiate_chat(self, manager, message):
        manager.run(self, message)

class GroupChat:
    def __init__(self, agents, messages, max_round=12):
        self.agents = agents
        self.messages = messages
        self.max_round = max_round

class GroupChatManager:
    def __init__(self, groupchat, llm_config=None):
        self.groupchat = groupchat

    def run(self, initiator, message):
        self.groupchat.messages.append({'role': initiator.name, 'content': message})
        for i, agent in enumerate(self.groupchat.agents):
            if i >= self.groupchat.max_round:
                break
            reply = f'{agent.name} contributes to: {message}'
            self.groupchat.messages.append({'role': agent.name, 'content': reply})
            print(reply)

user_proxy = AssistantAgent(name='user_proxy')
researcher = AssistantAgent(name='researcher')
writer = AssistantAgent(name='writer')
critic = AssistantAgent(name='critic')

group = GroupChat(agents=[user_proxy, researcher, writer, critic], messages=[], max_round=12)
manager = GroupChatManager(groupchat=group, llm_config={})

user_proxy.initiate_chat(manager, message='Write a blog post about RAG.')

การเลือกผู้พูด

เงื่อนไขการสิ้นสุด

การเรียกใช้โค้ด

user_proxy = UserProxyAgent(
    name='user',
    code_execution_config={
        'work_dir': './workdir',
        'use_docker': True   # safer
    }
)

จับตาดูต้นทุน

ควรใช้เมื่อใด

เมื่อไม่ควรใช้ NOT

AutoGen เทียบกับ LangGraph

รูปแบบ AutoGen

สรุป