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Developer hub

GPT Researcher ships across runtimes and clients: a Python SDK, a Docker image, a Model Context Protocol server, and an OpenAPI 3.1 REST spec. Pick the integration path that matches your stack.

Python

pip install gpt-researcher

from gpt_researcher import GPTResearcher
import asyncio

async def main():
    r = GPTResearcher(query="your topic", report_type="research_report")
    await r.conduct_research()
    print(await r.write_report())

asyncio.run(main())
Reference

MCP (Streamable HTTP)

claude_desktop_config.json or mcp-remote

{
  "mcpServers": {
    "gptr-public": {
      "url": "https://gptr.dev/api/mcp"
    }
  }
}
Reference

Your own MCP server (full)

git clone https://github.com/assafelovic/gptr-mcp

git clone https://github.com/assafelovic/gptr-mcp
cd gptr-mcp
pip install -r requirements.txt
python server.py  # stdio
# or:
docker compose up -d  # SSE on :8000/sse
Reference

REST API

OpenAPI 3.1 - any language

curl -X POST https://your-self-hosted-server/research \
  -H "Content-Type: application/json" \
  -d '{"query":"What are the strategic risks for NVIDIA?","report_type":"research_report"}'
Reference

Verified integrations

GPT Researcher integrates as a tool / MCP server with the major AI agent platforms. Each link below points to the canonical install instructions.

Agent discovery endpoints

These files let any AI agent discover, understand, and connect to GPT Researcher without a human in the loop. They are kept in sync with the live MCP server at /api/mcp.