LangGraph 智能体状态管理与决策
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本次演示围绕 Bright Data Web MCP 与 LangGraph 的集成实操 展开，完整展示了从获取大模型 API Key、创建大模型会话，到获取 Bright Data API Key、通过 MultiServerMCPClient 连接 Web MCP 服务器，并在 Bright Data 后台进一步启用浏览器自动化工具、扩展智能体可调用能力的全流程；同时结合 LangGraph 的 StateGraph，搭建了包含大模型节点、工具调用节点和路由规则节点的循环式 AI 研究智能体。演示过程中，通过"打开网页并持续滚动，直到提取 30 条语录的作者、内容与标签"这一实际任务，直观呈现了智能体基于实时网页数据进行搜索、抓取、交互和推理的完整效果。实测结果表明，LangGraph 提供了清晰可控的智能体状态管理与决策机制，而 Bright Data Web MCP 则补足了真实网页访问与动态页面交互能力，使 AI Agent 无需将复杂抓取逻辑硬编码进提示词或业务代码中，也能更稳定地完成生产级研究任务。
LangGraph 智能体状态管理与决策
bash
from __future__ import annotations
import argparse
import asyncio
import json
import os
import sys
from typing import Any, Literal
from urllib.parse import urlencode
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage
from langchain_openai import ChatOpenAI
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.graph import END, START, MessagesState, StateGraph
# webmcp-langgraph-demo.py file
SYSTEM_PROMPT = """You are a web research assistant.
Task:
- Research the user's topic using Google search results and a few sources.
- Return 6--10 simple bullet points.
- Add a short "Sources:" list with only the URLs you used.
How to use tools:
- First call the search tool to get Google results.
- Select 3--5 reputable results and scrape them.
- If scraping fails, try a different result.
Constraints:
- Use at most 5 sources.
- Prefer official docs or primary sources.
- Keep it quick: no deep crawling.
"""
def make_llm_call_node(llm_with_tools):
async def llm_call(state: MessagesState):
messages = [SystemMessage(content=SYSTEM_PROMPT)] + state["messages"]
ai_message = await llm_with_tools.ainvoke(messages)
return {"messages": [ai_message]}
return llm_call
def make_tool_node(tools_by_name: dict):
async def tool_node(state: MessagesState):
last_ai_msg = state["messages"][-1]
tool_results = []
for tool_call in last_ai_msg.tool_calls:
tool = tools_by_name.get(tool_call["name"])
if not tool:
tool_results.append(
ToolMessage(
content=f"Tool not found: {tool_call['name']}",
tool_call_id=tool_call["id"],
)
)
continue
# MCP tools are typically async
observation = (
await tool.ainvoke(tool_call["args"])
if hasattr(tool, "ainvoke")
else tool.invoke(tool_call["args"])
)
tool_results.append(
ToolMessage(
content=str(observation),
tool_call_id=tool_call["id"],
)
)
return {"messages": tool_results}
return tool_node
def should_continue(state: MessagesState) -> Literal["tool_node", END]:
last_message = state["messages"][-1]
if getattr(last_message, "tool_calls", None):
return "tool_node"
return END
async def main():
# Load environment variables from .env
load_dotenv()
# Read Bright Data token
bd_token = os.getenv("BRIGHTDATA_TOKEN")
if not bd_token:
raise ValueError("Missing BRIGHTDATA_TOKEN")
# Connect to Bright Data Web MCP server
client = MultiServerMCPClient({
"bright_data": {
"url": f"https://mcp.brightdata.com/mcp?token={bd_token}",
"transport": "streamable_http",
}
})
#&groups=advanced_scraping,browser
# Fetch all available MCP tools (search, scrape, etc.)
tools = await client.get_tools()
tools_by_name = {tool.name: tool for tool in tools}
print(f"Available tools: {list(tools_by_name.keys())}") # Debug: print available tool names
# Initialize the LLM and allow it to call MCP tools
openai_api_key = os.getenv("OPENAI_API_KEY")
llm = ChatOpenAI(model="gpt-4o-all", temperature=0, api_key=openai_api_key, base_url="https://poloapi.top/v1",)
llm_with_tools = llm.bind_tools(tools)
# Build the LangGraph agent
graph = StateGraph(MessagesState)
graph.add_node("llm_call", make_llm_call_node(llm_with_tools))
graph.add_node("tool_node", make_tool_node(tools_by_name))
# Graph flow:
# START → LLM → (tools?) → LLM → END
graph.add_edge(START, "llm_call")
graph.add_conditional_edges("llm_call", should_continue, ["tool_node", END])
graph.add_edge("tool_node", "llm_call")
agent = graph.compile()
# Example research query
topic = "使用工具访问https://quotes.toscrape.com/scroll，想办法拿到30条quotes提取 quote、author、tags" # You can change this topic as needed
# Run the agent
result = await agent.ainvoke(
{
"messages": [
HumanMessage(content=f"Research this topic:\n{topic}")
]
},
# Prevent infinite loops
config={"recursion_limit": 50}
)
# Print the final response
print(result["messages"][-1].content)
if __name__ == "__main__":
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