AutoGen 是微软研究院开源的多智能体 AI 系统框架。这个框架的设计思路很简单——让多个 AI 智能体（加上人类参与）在对话中完成复杂任务的协作和推理。
你甚至可以把它理解成一个团队聊天室，智能体们在里面讨论、争论、协作，最终得出解决方案。
AutoGen 通过创建多个专门化智能体，为每个智能体设定自己的角色、目标，来达到上面说的聊天能力，并且还能通过配置工具来获得代码执行能力。智能体之间通过消息机制通信，互相配合完成任务。
AutoGen 为什么值得关注
AutoGen 真正好玩的地方在于它实现了 AI 之间的协作。智能体可以相互辩论、推理、纠错、共同创造，整个过程不需要人工逐步编写脚本，设置可以不需要人工的参与。
相比于CrewAI，AutoGen 则把重点放在讨论、推理和演化上，而CrewAI 更关注执行层面。
你可以理解为CrewAI更像是我们现在用的工作助手，而AutoGen 更像是圆桌会议。
AutoGen 框架处理了很多底层问题，这样我们可以只专著于如何编排角色和任务。
AutoGen 定义了三类核心智能体：
- Conversable Agent：负责管理结构化对话和预定义的交互模式
- Assistant Agent：执行具体任务，调用工具或 LLM
- UserProxy Agent：充当人机接口层，转发消息和响应
简单的群聊演示
from autogen import ConversableAgent, AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
# Define AssistantAgent (AI assistant)
assistant = AssistantAgent(
name="AssistantAgent",
system_message="You are a helpful AI assistant. Suggest Python code when relevant.",
human_input_mode="NEVER" # No human input required, runs automatically
)
# Define UserProxyAgent (represents human)
user_proxy = UserProxyAgent(
name="UserProxyAgent",
human_input_mode="ALWAYS" # Requires human input
)
# Define group chat
chat = GroupChat(
agents=[assistant, user_proxy],
messages=[]
)
# Manage group chat with GroupChatManager
manager = GroupChatManager(
groupchat=chat,
llm_config={"model": "gpt-5-mini"}
)
# Start the chat
user_proxy.initiate_chat(
manager,
message="Write a short Python function to calculate factorial."
)
AutoGen 核心概念详解
1、Human-in-the-Loop（人工参与）
这个功能让人类可以在智能体执行过程中进行干预。
# Human-in-the-Loop example
from autogen import AssistantAgent, UserProxyAgent
# Step 1: Create assistant agent
assistant = AssistantAgent(
name="code_writer",
system_message="You are a helpful coding assistant."
)
# Step 2: Create user proxy with human-in-the-loop enabled
user = UserProxyAgent(
name="human_user",
human_input_mode="ALWAYS" # 👈 Enable human confirmation
)
# Step 3: Start conversation
user.initiate_chat(
assistant,
message="Write a Python function to calculate factorial."
)
2、Code Executor 的工作机制
Code Executor 负责安全执行智能体生成的代码。
# Behind the scenes
# AssistantAgent generates code:
sum([x for x in range(1, 21) if x % 2 == 0])
# PythonCodeExecutor automatically:
# - Executes safely in sandbox
# - Captures output
# - Returns result to agent
# UserProxyAgent displays result:
✅ Result: 110
# Optional: Add human approval
user = UserProxyAgent(
name="human_user",
code_execution_config={"executor": executor},
human_input_mode="ALWAYS"
)
3、工具集成方式
工具通过初始化时的
tools
参数传入智能体。
from autogen import AssistantAgent, UserProxyAgent, Tool
# Define tool function
def multiply_numbers(a: int, b: int) -> int:
"""Returns the product of two numbers."""
return a * b
# Wrap as Tool object
multiply_tool = Tool(
func=multiply_numbers,
name="multiply_tool",
description="Multiplies two numbers and returns the product."
)
# Create agent and integrate tool
assistant = AssistantAgent(
name="math_agent",
system_message="You are a math assistant. Use multiply_tool when needed.",
tools=[multiply_tool]
)
user = UserProxyAgent(name="human_user")
user.initiate_chat(
assistant,
message="Can you multiply 8 and 7?"
)
4、多智能体协作模式
AutoGen 对话系统的核心特征是支持多智能体协作对话模式。
# Example: Multi-agent conversational pattern
from autogen import AssistantAgent, ReviewerAgent, UserProxyAgent, PythonCodeExecutor
executor = PythonCodeExecutor()
# Coder agent
coder = AssistantAgent(
name="coder_agent",
system_message="You are a code-writing assistant."
)
# Reviewer agent
reviewer = ReviewerAgent(
name="reviewer_agent",
system_message="You are a code reviewer. Check logic and security."
)
# User proxy
user = UserProxyAgent(
name="human_user",
human_input_mode="TERMINATE",
code_execution_config={"executor": executor}
)
def run_multi_agent_workflow(prompt: str):
coder_response = coder.chat_with(user, message=prompt)
print("Coder ->", coder_response["content"])
reviewer_response = reviewer.review(code={"code": coder_response["content"]})
print("Reviewer ->", reviewer_response["content"])
if "SUGGEST_CHANGES" in reviewer_response["content"]:
revision = coder.chat_with(reviewer, message=reviewer_response["content"])
final_code = revision["content"]
else:
final_code = coder_response["content"]
print("Waiting for human approval...")
if not user.get_human_approval(final_code):
print("Human rejected execution.")
return
exec_result = executor.execute(final_code)
print("Execution result ->", exec_result["output"])
if __name__ == "__main__":
run_multi_agent_workflow("Write a Python function prime_factors(n).")
这个例子展示了多智能体协作的几个关键点：Coder、Reviewer、User 各司其职完成协作，然后通过Reviewer 把关代码的安全性和质量，HITL 模式给人类最终审批权，最后可以通过Code Executor 在沙箱环境安全执行代码
5、会话终止机制
会话会在满足终止条件时结束，这样一个整个的”会议“就结束了
from autogen import AssistantAgent, UserProxyAgent
def is_termination_msg(message):
"""Returns True when message contains TERMINATE keyword."""
return "TERMINATE" in message["content"].upper()
assistant = AssistantAgent(
name="helper_agent",
system_message="Stop when receiving 'TERMINATE'."
)
user = UserProxyAgent(
name="human_user",
is_termination_msg=is_termination_msg
)
user.initiate_chat(assistant, message="Hello, explain recursion in simple terms.")
assistant.send({"role": "user", "content": "Thanks, that's clear. TERMINATE"})
总结
AutoGen 提供了构建复杂 AI 协作系统的完整支持，覆盖了角色结构、通信机制、人工参与、工具集成、代码执行和多智能体协作设计等各个方面。框架把底层复杂度封装得很好，开发者可以专注在业务逻辑和智能体设计上。
https://avoid.overfit.cn/post/a50b2cf5363046739250c9c284421d2f
作者：Sonika
