如何将 LangGraph 与 AutoGen、CrewAI 等框架集成¶

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("OPENAI_API_KEY")

import autogen
import os

config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]

llm_config = {
    "timeout": 600,
    "cache_seed": 42,
    "config_list": config_list,
    "temperature": 0,
}

autogen_agent = autogen.AssistantAgent(
    name="assistant",
    llm_config=llm_config,
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={
        "work_dir": "web",
        "use_docker": False,
    },  # 如果可用，请将 use_docker 设置为 True 以运行生成的代码。使用 Docker 比直接运行生成的代码更安全。
    llm_config=llm_config,
    system_message="如果任务已完全满意地解决，请回复 TERMINATE。否则，请回复 CONTINUE，或未解决任务的原因。",
)

from langchain_core.messages import convert_to_openai_messages
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.memory import MemorySaver

def call_autogen_agent(state: MessagesState):
    # 将 LangGraph 消息转换为 AutoGen 的 OpenAI 格式
    messages = convert_to_openai_messages(state["messages"])

    # 获取最后一条用户消息
    last_message = messages[-1]

    # 将先前的消息历史作为上下文传递（不包括最后一条消息）
    carryover = messages[:-1] if len(messages) > 1 else []

    # 使用 AutoGen 启动聊天
    response = user_proxy.initiate_chat(
        autogen_agent,
        message=last_message,
        carryover=carryover
    )

    # 从代理中提取最终响应
    final_content = response.chat_history[-1]["content"]

    # 以 LangGraph 格式返回响应
    return {"messages": {"role": "assistant", "content": final_content}}

# 使用内存创建带持久化的图表
checkpointer = MemorySaver()

# 构建图表
builder = StateGraph(MessagesState)
builder.add_node("autogen", call_autogen_agent)
builder.add_edge(START, "autogen")

# 使用 checkpointer 进行编译以实现持久化
graph = builder.compile(checkpointer=checkpointer)

from IPython.display import display, Image

display(Image(graph.get_graph().draw_mermaid_png()))

# 将 thread_id 传递给持久化代理输出以进行将来的交互
config = {"configurable": {"thread_id": "1"}}

for chunk in graph.stream(
    {
        "messages": [
            {
                "role": "user",
                "content": "找到斐波那契数列中介于 10 和 30 之间的数字",
            }
        ]
    },
    config,
):
    print(chunk)

for chunk in graph.stream(
    {
        "messages": [
            {
                "role": "user",
                "content": "将最后一个数字乘以 3",
            }
        ]
    },
    config,
):
    print(chunk)