MCP从入门到精通（三）使用OpenAI Agents SDK构建MCP智能体

最新推荐文章于 2025-06-19 22:00:29 发布

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最新推荐文章于 2025-06-19 22:00:29 发布

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文章标签：大模型 LLM ai 自然语言处理 AI大模型 MCP Agent

本文链接：https://blog.youkuaiyun.com/Code1994/article/details/147578415

九、使用OpenAI Agents SDK构建MCP智能体

虽然MCP已经成为一个热词，且所有开发者社区近期都在讨论它，但要知道使用哪些MCP客户端框架以实现与AI应用和代理的集成并不容易。我们进行了调研，发现以下在Python和TypeScript基础上用于智能体工作流和AI助手的领先MCP客户端平台。

注意：以下内容展示了在构建AI解决方案的框架中实现MCP的方法。

构建Git MCP智能体
使用OpenAI Agents SDK构建智能体时，大家可以通过SDK的MCPServerStdio和MCPServerSse类连接到这些由社区开发的MCP服务器。以下的MCP智能体示例实现会访问你本地Git仓库的根目录，并对用户关于该仓库的查询作出响应。

import asyncio
import shutil
import streamlit as st
from agents import Agent, Runner, trace
from agents.mcp import MCPServer, MCPServerStdio
async def query_git_repo(mcp_server: MCPServer, directory_path: str, query: str):
    agent = Agent(
        name="Assistant",
        instructions=f"Answer questions about the localgit repository at {directory_path}, use that for repo_path",
        mcp_servers=[mcp_server],
    )
    with st.spinner(f"Running query: {query}"):
        result = await Runner.run(starting_agent=agent, input=query)
        return result.final_output
async def run_streamlit_app():
    st.title("Local Git Repo Explorer")
    st.write("This app allows you to query information about a local git repository.")
    directory_path = st.text_input("Enter the path to the git repository:")
    if directory_path:
        # Common queries as buttons
        col1, col2 = st.columns(2)
        with col1:
            if st.button("Most frequent contributor"):
                query = "Who's the most frequent contributor?"
                run_query(directory_path, query)
        with col2:
            if st.button("Last change summary"):
                query = "Summarize the last change in the repository."
                run_query(directory_path, query)
        # Custom query
        custom_query = st.text_input("Or enter your own query:")
        if st.button("Run Custom Query") and custom_query:
            run_query(directory_path, custom_query)
def run_query(directory_path, query):
    if not shutil.which("uvx"):
        st.error("uvx is not installed. Please install it with `pip install uvx`.")
        return
    async def execute_query():
        async with MCPServerStdio(
            cache_tools_list=True,
            params={
                "command": "python", 
                "args": [
                    "-m", 
                    "mcp_server_git", 
                    "--repository", 
                    directory_path
                ]
            },
        ) as server:
            with trace(workflow_name="MCP Git Query"):
                result = await query_git_repo(server, directory_path, query)
                st.markdown("### Result")
                st.write(result)
    asyncio.run(execute_query())
if __name__ == "__main__":
    st.set_page_config(
        page_title="Local Git Repo Explorer",
        page_icon="📊",
        layout="centered"
    )
    # Change from async to synchronous implementation
    # Since Streamlit doesn't work well with asyncio in the main thread
    # Define a synchronous version of our app
    def main_streamlit_app():
        st.title("Local Git Repo Explorer")
        st.write("This app allows you to query information about a Git repository.")
        directory_path = st.text_input("Enter the path to the git repository:")
        if directory_path:
            # Common queries as buttons
            col1, col2 = st.columns(2)
            with col1:
                if st.button("Most frequent contributor"):
                    query = "Who's the most frequent contributor?"
                    run_query(directory_path, query)
            with col2:
                if st.button("Last change summary"):
                    query = "Summarize the last change in the repository."
                    run_query(directory_path, query)
            # Custom query
            custom_query = st.text_input("Or enter your own query:")
            if st.button("Run Custom Query") and custom_query:
                run_query(directory_path, custom_query)
    # Run the synchronous app
    main_streamlit_app()

上述代码将Streamlit与OpenAI MCP代理集成，允许你通过Git MCP服务器与本地Git仓库进行聊天。要运行此示例，你需要安装以下软件包：

pip install streamlit openai-agents mcp-server-git

然后，使用以下命令导出你的OpenAI API密钥：

exportOPENAI_API_KEY=sk-...

在你运行Python文件时，应该会看到类似如下的结果。

大家可以在GitHub上探索其他关于OpenAI MCP的示例。

链接：https://github.com/openai/openai-agents-python/tree/main/examples/mcp

使用Agents SDK的MCP集成的一个优点是它在OpenAI的控制面板中内置的MCP智能体监控系统。该功能会自动捕捉你的智能体的MCP操作，例如工具列表、POST响应以及获取有关函数调用的数据。下图展示了运行上述代码后，本节中Git MCP示例的追踪信息。你可以从OpenAI的控制面板中访问所有已记录的信息。