Say goodbye tohours of research

An open source agent that researches the web and your own documents, then writes a detailed report with citations.

Open in GitHub

$pip install gpt-researcher

Helping teams at

Floor 1 · Planner

A research team in a single call

A planner splits your question, researchers work through it in parallel, and only the passages that matter reach the writer.

Floor 2 · Researchers

Parallel researchers

The planner writes a set of research questions and runs a researcher for each one at the same time.

MAX_ITERATIONS=3

Floor 3 · Retrievers

20+ retrievers

Tavily, Google, Bing, Exa, arXiv, PubMed and more, combined with any MCP server or your own documents.

RETRIEVER=tavily,mcp

Floor 4 · Filter

Context filtering

Jev, BM25 or embeddings keep only the passages that answer the question, before anything reaches the LLM.

CONTEXT_FILTER=auto

Floor 5 · Deep research

Deep research

Findings seed new questions, so a run explores a tree of subtopics instead of a single pass.

DEEP_RESEARCH_DEPTH=2

Floor 6 · Writer

The writer cites every claim

Every claim in the report traces back to a source.

The report

Every claim in the report traces back to a source.

Example report · 47s · 23 sources read

What is driving Nvidia's rally

Most of Nvidia's growth comes from data center GPUs used to train and run AI models, which now make up the large majority of its revenue. [1][2]

Microsoft, Alphabet, Amazon and Meta keep raising capital spending on AI infrastructure, and Nvidia remains the largest supplier of the accelerators they buy. [3][4]

Analysts point to CUDA, Nvidia's software platform, as the main reason customers find it hard to switch, while custom chips and AMD are the most cited risks. [5]

1investor.nvidia.com
2sec.gov
3reuters.com
4bloomberg.com
5semianalysis.com

Open source since 2023

The #1 Open Deep Research Agent

Downloaded millions of times and shaped by hundreds of contributors. Ask a question, and it plans the research, reads sources in parallel, and writes a cited report, using the LLM and search engine of your choice.

$pip install gpt-researcher

Open in Colab Documentation

Python 3.11+

from gpt_researcher import GPTResearcher query = "why is Nvidia stock going up?"researcher = GPTResearcher(query=query)# Conduct research on the given querycontext = await researcher.conduct_research()# Write the reportreport = await researcher.write_report()# report outputs as markdown, PDF, docx, string and more!

{  "mcpServers": {    "gptr-mcp": {      "command": "python",      "args": ["/absolute/path/to/gptr-mcp/server.py"],      "env": {        "OPENAI_API_KEY": "your-openai-key",        "TAVILY_API_KEY": "your-tavily-key"      }    }  }}

Exposes deep_research, quick_search, write_report, get_research_sources and get_research_context to Claude, Cursor, n8n and any MCP client.

Benchmarks

Ranked #1 in Academic Benchmarks

Carnegie Mellon University's DeepResearchGym (May 2025) evaluated leading deep research systems on 1,000 complex queries. GPT Researcher outperformed Perplexity, OpenAI, OpenDeepSearch, and HuggingFace, achieving the highest scores in citation quality, report quality, and information coverage.

Score agreement between the DeepResearchGym and commercial search APIs, Spearman correlation. DeepResearchGym, May 2025.

Citation Quality#1

Precision: 85.36%
Recall: 90.82%

Only system >85% on both

Report Quality#1

Clarity: 83.70%
Insightfulness: 78.01%

Highest in both dimensions

Coverage#1

Key Point Recall: 64.67%

Highest coverage

Read DeepResearchGym paper

Context filter benchmark

Jev's context is 59% more relevant than embeddings, at the same cost

GPT Researcher filters sources with Jev by default, and falls back to local keyword ranking when no TypeSafe key is set, so no key or embeddings provider is required.

Read the benchmark

73%

Jev

51%

Keyword

46%

Embeddings

Relevant passages kept · 28 research tasks · same scraped sources

Community

Join the Fast Growing Community

Our mission is to empower individuals and organizations with accurate, unbiased, and factual information. Join our community and help democratize automated AI research.

GitHub stars: 30,539
Contributors: 286

Open in GitHub Join the Community

Research

Academic Citations

GPT Researcher has been cited in numerous academic papers and research studies.

FAQ

Everything you need to know

The questions developers ask most. Still stuck? Ask us on Discord.

What is GPT Researcher?

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).

How do I use GPT Researcher with Claude or ChatGPT?

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.

Is GPT Researcher free?

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.

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

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.

How do I host GPT Researcher myself?

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.

Can GPT Researcher research my own documents?

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.

What do I need to run it?

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.

Built in the open by researchers and engineers worldwide

Shaped by hundreds of open source contributors, cited in academic research, and used by teams at Google, NVIDIA, Stanford and more. Star the repo or join the community to help build what comes next.

$pip install gpt-researcher

Open in GitHub Join the Community