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deep_rsrch_gemini

基于MCP的多智能体深度研究系统,整合LinkUp搜索、CrewAI协调和Gemini大模型,通过Streamlit提供交互界面。

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README

🚀 智能深度研究员项目

我们正在构建一个由MCP驱动的多智能体深度研究员系统。该系统能够借助 Linkup 进行深度网络搜索,并通过CrewAI来编排各个智能体。

此项目运用了以下技术和工具:

  • LinkUp(搜索工具)
  • CrewAI(智能体设计)
  • Gemini(大语言模型)
  • Streamlit(用于将逻辑封装在交互式用户界面中)

🚀 快速开始

📦 安装指南

在项目根目录下运行以下命令:

uv sync

💻 使用示例

基础用法

启动应用程序,运行以下命令:

streamlit run app.py

高级用法

作为MCP服务器使用时,配置如下JSON内容:

{
  "mcpServers": {
    "crew_research": {
      "command": "uv",
      "args": [
        "--directory",
        "./Multi-Agent-deep-researcher-mcp-windows-linux",
        "run",
        "server.py"
      ],
      "env": {
        "LINKUP_API_KEY": "your_linkup_api_key_here",
        "GEMINI_API_KEY": "your_gEMINI_API_KEY"
      }
    }
  }
}

你可以 在此处获取Linkup API密钥

help

Runtime guide

cloud

Hosted runtime

Hosted servers run from a provider-managed environment. You usually connect the MCP client to the hosted endpoint or follow the provider's authorization flow, without keeping a local process alive

  1. Open provider connection page
  2. Authorize or copy endpoint
  3. Connect from your MCP client
terminal

Local runtime / other methods

Local servers run on your own machine or infrastructure. You normally copy the server_config into your MCP client, install the required package, and provide env variables from env_schema when needed

  1. Copy server_config
  2. Install required package
  3. Fill env variables and restart client