考虑过工具会话吗，有些mcp在同一个session内才能正常工具，比如浏览器类MCP

[shatang123]

下面是使用langchain_mcp的一个例子，agent可以和浏览器进行多轮交互，比如打开百度 -> 搜索北京的天气，这种多次调用mcp也不会中断。而将其替换成mcpstore后，浏览器只能执行一个动作后就关闭了，应该是一次调用就是一个session吗？

import asyncio

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_mcp_adapters.tools import load_mcp_tools
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

client = MultiServerMCPClient(
    {
        "browser_server": {
            "url": "http://127.0.0.1:8931/sse",
            "transport": "sse",
        }
    }
)

llm = ChatOpenAI(
    model="qwen-plus-2025-09-11",
    temperature=0.6,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    api_key="sk-xxxxxxxxxxxxxxxxx",
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

async  def main():
    # 为了整个交互都在一个会话中，这里手动创建一个会话
    async with client.session("browser_server") as session:
        # 获得全部工具
        tools = await load_mcp_tools(session)

        # 创建agent
        agent = create_react_agent(llm, tools,prompt="""你是一位工作在浏览器中的个人助手，帮助用户完成各种任务。 """)
        messages = [
            {"role": "user", "content": "北京的天气怎么样"}
        ]
        response_stream = agent.astream({"messages": messages},stream_mode="values")

        # 打印结果
        async for step in response_stream:
            step["messages"][-1].pretty_print()

if __name__ == '__main__':
    asyncio.run(main())

比如下面的测试中，浏览器不会打开两个百度页面，而是打开一次，关闭一次浏览器

from mcpstore import MCPStore

# 创建 Store 实例
store = MCPStore.setup_store()

# 注册服务
store.for_store().add_service({"name":"browser","url":"http://127.0.0.1:8931/sse"})

# 自动将 MCP 工具转换为 LangChain Tool 对象
tools = store.for_store().for_langchain().list_tools()

s1 = store.for_store().use_tool("browser_navigate", {"url":"https://www.baidu.com"})
s2 = store.for_store().use_tool("browser_navigate", {"url":"https://www.baidu.com"})

while True:
    print(1)

有考虑过这个问题吗，可以默认成运行后是在同一个会话中，或者有更好的实现

[shatang123]

测试使用的浏览器MCP是谷歌的playwright-mcp

[whillhill]

首先非常非常非常感谢反馈 这个也确实是我最近在解决的问题 我稍晚一段时间跟您探讨一手

[whillhill]

session = store.for_store().create_session("langchain_browser")
session.bind_service("browser")

with store.for_store().with_session(session.session_id):
tools = store.for_store().for_langchain().list_tools()
agent = create_react_agent(llm, tools)

如果类似这样的设计会不会直观一些

[shatang123]

session = store.for_store().create_session("langchain_browser") session.bind_service("browser")

with store.for_store().with_session(session.session_id): tools = store.for_store().for_langchain().list_tools() agent = create_react_agent(llm, tools)

如果类似这样的设计会不会直观一些

我觉得可以

[whillhill]

已经重构完成了 今天会发版新的和提交git 这是一些简单的使用示例方便快速理解

import os
import sys
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from mcpstore import MCPStore
from langchain_openai import ChatOpenAI
store = MCPStore.setup_store(debug=False)
store.for_store().session_auto()
demo_mcp = {
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": [
        "@playwright/mcp"
      ]
    }
  }
}

store.for_store().add_service(demo_mcp)
store.for_store().wait_service("playwright")
tools = store.for_store().for_langchain().list_tools()
print("Loaded LC tools:", len(tools))
llm = ChatOpenAI(
    temperature=0,
    model="deepseek-chat",
    openai_api_key="sk-***",
    openai_api_base="https://api.deepseek.com",
)
prompt = ChatPromptTemplate.from_messages([
("system", "你有一些工具可以使用 尽可能的使用这些工具"),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
query = "第一步打开百度页面 第二步在搜索框里给我输入蓝色电风扇并搜索？"
print(f"\n   🤔: {query}")
response = agent_executor.invoke({"input": query})
print(f"   🤖 : {response['output']}")

或者使用手动模式

import os
import sys
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

from mcpstore import MCPStore
from langchain_openai import ChatOpenAI

store = MCPStore.setup_store(debug=False)


demo_mcp = {
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": [
        "@playwright/mcp"
      ]
    }
  }
}

store.for_store().add_service(demo_mcp)
store.for_store().wait_service("playwright")

session1 = store.for_store().create_session("langchain_browser")
session1.bind_service("playwright")

with store.for_store().with_session(session1.session_id) as s:
    tools = store.for_store().for_langchain().list_tools()

    print("Loaded LC tools:", len(tools))

    llm = ChatOpenAI(
        temperature=0,
        model="deepseek-chat",
        openai_api_key="sk-***",
        openai_api_base="https://api.deepseek.com",
    )

    prompt = ChatPromptTemplate.from_messages([
    ("system", "你有一些工具可以使用 尽可能的使用这些工具"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
    ])

    agent = create_tool_calling_agent(llm, tools, prompt)
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
    query = "第一步打开百度页面 第二步在搜索框里给我输入蓝色电风扇并搜索？"
    print(f"\n   🤔: {query}")
    response = agent_executor.invoke({"input": query})
    print(f"   🤖 : {response['output']}")

[whillhill]

以及主动调用的模式

import os
import sys
import time
from mcpstore import MCPStore
store = MCPStore.setup_store(debug=False)
ctx = store.for_store()
with store.for_store().with_session("browser_session") as s:
    print("Session created:", s.session_id)
    s.bind_service("playwright")
    s1 = store.for_store().use_tool("playwright_browser_navigate", {"url": "https://www.baidu.com"}, timeout=180)
    time.sleep(5)
    s2 = store.for_store().use_tool("playwright_browser_navigate", {"url": "https://www.mcpstore.wiki"}, timeout=120)
    time.sleep(5)

老哥觉得这样可以吗

[whillhill]

pip install mcpstore==1.4.2974 如果有bug或者觉得设计不合理的烦请提给我