Using Langgraph + Amap MCP, for example, when I input a travel plan, it needs to call many tools and perform 25 steps. Does it really take that long? Is there a better solution?

[jinwater88]

Checked other resources

• [x] This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
• [x] I added a clear and detailed title that summarizes the issue.
• [x] I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
• [x] I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.

Example Code

import asyncio  
import json  
from pathlib import Path  
from langchain_mcp_adapters.client import MultiServerMCPClient  
from langgraph.prebuilt import create_react_agent  
from langchain_openai import ChatOpenAI  
from dotenv import load_dotenv  
import os  
import time  
  
load_dotenv()

def load_mcp_config(config_path: str = "mcp_server_settings.json") -> dict:  
    """加载 MCP 服务器配置文件"""  
    config_file = Path(config_path)  
    if not config_file.exists():  
        raise FileNotFoundError(f"配置文件 {config_path} 不存在")  
      
    with open(config_file, 'r', encoding='utf-8') as f:  
        config = json.load(f)  
      
    return config.get("mcpServers", {})  

async def main():  
    try:  
        # 加载 MCP 配置  
        print("正在加载 MCP 配置...")  
        mcp_servers = load_mcp_config()  
        print(f"✓ 成功加载 {len(mcp_servers)} 个 MCP 服务器配置")  
          
        # 转换配置格式以适配 MultiServerMCPClient  
        client_config = {}  
        for server_name, server_config in mcp_servers.items():  
            server_type = server_config.pop("type")  
              
            if server_type == "streamable_http":  
                client_config[server_name] = {  
                    "url": server_config["url"],  
                    "transport": "streamable_http"  
                }  
            elif server_type == "stdio":  
                client_config[server_name] = {  
                    "command": server_config["command"],  
                    "args": server_config.get("args", []),  
                    "transport": "stdio"  
                }  
            elif server_type == "sse":  
                client_config[server_name] = {  
                    "url": server_config["url"],  
                    "transport": "sse"  
                }  
        
        # 获取 MCP 服务器提供的工具  
        print("正在连接 MCP 服务器...")  
        client = MultiServerMCPClient(client_config)  
        tools = await client.get_tools()  
        print(f"成功获取 {len(tools)} 个工具:")  
        for tool in tools:  
            print(f"  - {tool.name}: {tool.description}")
        # 使用 DeepSeek 模型  
        # 使用 ChatOpenAI 模型
        print("\n正在初始化模型...")    
        model = ChatOpenAI(  
            model="deepseek-chat",  
            api_key=os.getenv("DEEPSEEK_API_KEY"),  
            base_url="https://api.deepseek.com",  
            temperature=0,  
            timeout=15,  
            request_timeout=15,
            max_retries=2  # 添加重试机制    
        ) 
        print("✓ 模型初始化成功")  
          
        print("\n正在创建 agent...")  
        model_with_tools = model.bind_tools(tools, parallel_tool_calls=True) 
        # 创建 ReAct agent  
        agent = create_react_agent(  
            model=model_with_tools,  
            tools=tools,
            debug=False  # 启用调试模式    
        )  
        
        # 交互式循环  
        print("Agent 已启动。输入 'quit', 'exit' 或 'q' 退出。")  
        while True:  
            try:  
                user_input = input("\nUser: ")  
                if user_input.lower() in ["quit", "exit", "q"]:  
                    print("再见!")  
                    break  
                
                # 调用 agent  
                # 使用 astream 替代 ainvoke,可以看到中间步骤  
                print("\n[执行开始]")  
                start_time = time.time()  
                messages = []  
                step_count = 0  
                  
                try:  
                    async with asyncio.timeout(90.0):  
                        async for event in agent.astream(  
                            {"messages": [{"role": "user", "content": user_input}]},  
                            stream_mode="updates"  
                        ):  
                            step_count += 1  
                            # 只处理 agent 节点的更新  
                            if "agent" in event and "messages" in event["agent"]:  
                                messages = event["agent"]["messages"]  
                                last_msg = messages[-1]  
                                  
                                # 简化日志输出  
                                if hasattr(last_msg, 'tool_calls') and last_msg.tool_calls:  
                                    print(f"[步骤 {step_count}] 调用 {len(last_msg.tool_calls)} 个工具")           
                except asyncio.TimeoutError:  
                    print(f"\n[超时] 执行超过 90 秒,共完成 {step_count} 个步骤")  
                    continue  
                  
                if messages:  
                    elapsed = time.time() - start_time  
                    print(f"\n[完成] 总耗时: {elapsed:.1f}s")  
                    print(f"Assistant: {messages[-1].content}") 

            except KeyboardInterrupt:  
                print("\n再见!")  
                break  
            except Exception as e:  
                print(f"\n[错误] {e}")  
                import traceback  
                traceback.print_exc()    
              
    except Exception as e:  
        print(f"\n[初始化错误] {e}")  
        import traceback  
        traceback.print_exc()  
  
if __name__ == "__main__":  
    asyncio.run(main())

Error Message and Stack Trace (if applicable)

langgraph.errors.GraphRecursionError: Recursion limit of 25 reached without hitting a stop condition. You can increase the limit by setting the `recursion_limit` config key.
For troubleshooting, visit: https://docs.langchain.com/oss/python/langgraph/errors/GRAPH_RECURSION_LIMIT

Description

My code is shown above. I'm using Amap's MCP (Multi-Channel Programming) to plan my trip, which requires 23 calls. However, when I use Cherry Studio to call Amap's MCP, it responds instantly. Are there any optimization solutions for my code?

System Info

langchain 1.0.7 langchain-core 1.0.5 langchain-mcp-adapters 0.1.13 langchain-openai 1.0.3 langgraph 1.0.3 langgraph-checkpoint 3.0.1 langgraph-prebuilt 1.0.4 langgraph-sdk 0.2.9 langsmith 0.4.43