Question 1

What is GPT Researcher?

Accepted Answer

GPT Researcher is an open source autonomous AI research agent that gathers and validates information from multiple web sources in parallel, then writes a detailed research report with inline citations. It supports any LLM (OpenAI, Anthropic, Google, Mistral, DeepSeek, local via Ollama) and any retriever (Tavily, Bing, Google, DuckDuckGo, SearXNG).

Question 2

How do I use GPT Researcher with Claude or ChatGPT?

Accepted Answer

Use the official gptr-mcp Model Context Protocol server (github.com/assafelovic/gptr-mcp). It registers as an MCP server in Claude Desktop, Cursor, ChatGPT custom GPTs, n8n, and any client that supports MCP, and exposes tools like deep_research, quick_search, and write_report.

Question 3

Is GPT Researcher free?

Accepted Answer

Yes. GPT Researcher is MIT licensed and free to use. The only operating cost is whatever you pay your underlying LLM and search API providers.

Question 4

How does GPT Researcher compare to Perplexity or OpenAI Deep Research?

Accepted Answer

GPT Researcher ranked #1 on Carnegie Mellon University's DeepResearchGym benchmark (May 2025), ahead of Perplexity Deep Research and OpenAI Deep Research. Unlike those products, GPT Researcher is open source: you control the LLM, the retriever, and the deployment.

Question 5

How do I host GPT Researcher myself?

Accepted Answer

Install the Python package with pip install gpt-researcher and call it from your code, or clone the repository and run the FastAPI server (python -m uvicorn main:app) or the Docker image. The server's endpoints are described at gptr.dev/openapi.json.

Question 6

Can GPT Researcher research my own documents?

Accepted Answer

Yes. Point it at local files to research them directly, or run hybrid research that combines your documents with web sources in a single report.

Question 7

What do I need to run it?

Accepted Answer

Python 3.11+, an LLM API key (for example OPENAI_API_KEY) and a retriever API key (for example TAVILY_API_KEY). Swap in any supported provider through environment variables, including local models via Ollama.

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