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GPT Researcher MCP Server

@assafelovic

代码仓库主页

基本信息

分类

AI 与智能体

许可证

Apache-2.0

传输方式

stdio

作者

assafelovic

关于 GPT Researcher MCP Server

An autonomous agent that conducts deep research on any data using any LLM providers

#python#search#agent#automation#research#ai#mcp#webscraping#llms#mcp-server#deepresearch

GPT Researcher MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "gpt-researcher": {
      "command": "npx",
      "args": [
        "skills",
        "add",
        "assafelovic/gpt-researcher"
      ]
    }
  }
}

GPT Researcher MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is 🔎 GPT Researcher?

GPT Researcher is an open deep research agent designed for web and local research on any given task. It produces detailed, factual, unbiased research reports with citations and addresses misinformation, speed, determinism, and reliability. The server integrates with MCP clients like Claude to extend deep research capabilities.

How to use 🔎 GPT Researcher?

Install via pip (pip install gpt-researcher) or clone the repository, set OPENAI_API_KEY and TAVILY_API_KEY environment variables, then run python -m uvicorn main:app --reload. To use as a Claude Skill, run npx skills add assafelovic/gpt-researcher. For MCP integration, set RETRIEVER=tavily,mcp and pass mcp_configs to GPTResearcher.

Key features of 🔎 GPT Researcher

  • Generate detailed research reports using web and local documents.
  • Smart image scraping and AI-generated inline images.
  • Aggregate over 20 sources for objective conclusions.
  • Deep Research with tree-like exploration and concurrent processing.
  • JavaScript-enabled web scraping and memory/context maintenance.
  • Export reports to PDF, Word, and other formats.

Use cases of 🔎 GPT Researcher

  • Conduct objective research on any topic with cited sources.
  • Combine web search with local documents for tailored reports.
  • Integrate deep research into AI assistants like Claude.
  • Automate generation of long-form reports (>2,000 words) with inline visuals.

常见问题

1

What API keys are required?

You need `OPENAI_API_KEY` and `TAVILY_API_KEY`. For AI-generated images, also set `GOOGLE_API_KEY` and `IMAGE_GENERATION_ENABLED=true`. Optional keys are `LANGCHAIN_API_KEY` and `GITHUB_TOKEN` for MCP GitHub integration.

2

Can I use local or custom LLM models?

Yes, set `OPENAI_BASE_URL` to your custom API base URL (e.g., for local models or other providers). Deep Research uses models like `o3-mini` by default.

3

Does 🔎 GPT Researcher support MCP?

Yes, it supports MCP integration via the `RETRIEVER=tavily,mcp` environment variable. You can connect to data sources like GitHub repositories, databases, and custom APIs by providing `mcp_configs`.

4

How long does a deep research task take?

Deep Research takes approximately 5 minutes per research and costs about $0.4 per research using `o3-mini` with "high" reasoning effort, as stated in the documentation.

5

Can I research local documents?

Yes, set `DOC_PATH` to the folder containing your documents (PDF, plain text, CSV, Excel, Markdown, PowerPoint, Word) and select "My Documents" in the frontend or set `report_source` to "local" in the PIP package.

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