gpt-researcher MCP工具

Install as Claude Skill

Extend Claude's deep research capabilities by installing GPT Researcher as a Claude Skill:

npx skills add assafelovic/gpt-researcher

Once installed, Claude can leverage GPT Researcher's deep research capabilities directly within your conversations.

⚙️ Getting Started

Installation

1. Install Python 3.11 or later. Guide.
2. Clone the project and navigate to the directory:

    git clone https://github.com/assafelovic/gpt-researcher.git
    cd gpt-researcher
    

1. Set up API keys by exporting them or storing them in a .env file.

    export OPENAI_API_KEY={Your OpenAI API Key here}
    export TAVILY_API_KEY={Your Tavily API Key here}
    

(Optional) For enhanced tracing and observability, you can also set:

    # export LANGCHAIN_TRACING_V2=true
    # export LANGCHAIN_API_KEY={Your LangChain API Key here}
    

For custom OpenAI-compatible APIs (e.g., local models, other providers), you can also set:

    export OPENAI_BASE_URL={Your custom API base URL here}
    

1. Install dependencies and start the server:

    pip install -r requirements.txt
    python -m uvicorn main:app --reload
    

Visit http://localhost:8000 to start.

For other setups (e.g., Poetry or virtual environments), check the Getting Started page.

Run as PIP package

pip install gpt-researcher

Run with Docker

Step 1 - Install Docker

Step 2 - Clone the '.env.example' file, add your API Keys to the cloned file and save the file as '.env'

Step 3 - Within the docker-compose file comment out services that you don't want to run with Docker.

docker-compose up --build

If that doesn't work, try running it without the dash:

docker compose up --build

Step 4 - By default, if you haven't uncommented anything in your docker-compose file, this flow will start 2 processes: - the Python server running on localhost:8000<br> - the React app running on localhost:3000<br>

Visit localhost:3000 on any browser and enjoy researching!

Tutorials

• How it Works
• How to Install
• Live Demo

Example Usage:

```python ... from gpt_researcher import GPTResearcher

query = "why is Nvidia stock going up?" researcher = GPTResearcher(query=query)

Demo

<a href="https://www.youtube.com/watch?v=f60rlc_QCxE" target="_blank" rel="noopener"> <img src="https://github.com/user-attachments/assets/ac2ec55f-b487-4b3f-ae6f-b8743ad296e4" alt="Demo video" width="800" target="_blank" /> </a>

Enable in your .env file

IMAGE_GENERATION_ENABLED=true GOOGLE_API_KEY=your_google_api_key IMAGE_GENERATION_MODEL=models/gemini-2.5-flash-image ```

When enabled, the system will: 1. Analyze your research context to identify visualization opportunities 2. Pre-generate 2-3 relevant images during the research phase 3. Embed them inline as the report is written

Images are generated with dark-mode styling that matches the GPT Researcher UI, featuring professional infographic aesthetics with teal accents.

Learn more about Image Generation in our documentation.