GPT-Researcher: AI That Writes Research Reports for You (28,898 ★, Verified)

Direct answer: GPT-Researcher (28,898 ★, Apache-2.0, GitHub-verified 2026-08-09) is an open-source tool that turns a research question into a cited report — it searches the web, reads sources, and writes a structured answer with references, all automatically. It's the closest open-source thing to a junior research assistant: give it "market size of open-source AI in 2026" and it returns a sourced brief in minutes. Honestly, the first time I watched it hand me a cited report, it felt like having a helper — genuinely better than I expected from a free tool. This guide covers install, use, and where it still falls short.

What GPT-Researcher does

GPT-Researcher automates the research loop that humans do manually: generate sub-questions from your topic → search the web for each → read and filter sources → synthesize a structured report with citations. It's built on an agent framework (currently LangGraph), supports multiple search backends (Tavily, DuckDuckGo, Bing, etc.), and can write in multiple languages.

The output is a markdown report with sections, key findings, and reference links per claim. It's not a chat answer — it's a deliverable, which is what makes it useful for analysts, students, and content researchers.

Install and first run

``` pip install gpt-researcher ```

Or clone and run locally for more control. Set up your keys (search API + LLM API) in a .env file:

``` TAVILY_API_KEY=your_key # search backend OPENAI_API_KEY=your_key # or DeepSeek / any OpenAI-compatible ```

Then run:

``` python -m gpt_researcher --task "What is the market size of open-source AI tools in 2026?" --report_type research_report ```

The CLI runs the full loop and writes a report file. There's also a web UI (python -m gpt_researcher --webserver) for interactive use.

How it works under the hood

Stage	What happens
Plan	Splits your topic into research sub-questions
Gather	Searches the web per sub-question (10+ queries)
Filter	Reads pages, extracts relevant content
Synthesize	Writes the report with per-section citations
Cite	Adds source links next to claims

The whole pipeline is configurable: swap search backends, change the LLM, adjust report types (research report, resource report, outline, custom).

Practical uses

• Market research briefs: "Top open-source vector databases 2026" → sourced comparison
• Content research: gather facts and sources for an article before writing
• Competitive analysis: "How does Dify compare to Flowise" → structured brief with links
• Learning: get a cited overview of any unfamiliar topic fast

The honest part

GPT-Researcher's output quality is bounded by its search and reading steps, and I've hit that wall on niche topics. It's a blunt instrument when web coverage is thin. If the search backend misses key sources, the report misses them too; and the synthesis can drift into generic prose if the sources are thin — it's a blunt instrument on niche topics with little web coverage. It also costs tokens: a full report is dozens of LLM calls, so use a cheap model for the grunt work. Don't just look at the star count — 28k stars reflects a useful niche tool, not a flawless one. Always verify critical claims against the cited sources before publishing anything.

FAQ

Is GPT-Researcher free? The software is Apache-2.0, but it needs a search API key and an LLM API — costs are per-run and modest with a cheap model (DeepSeek + Tavily runs at fractions of a cent per report for short tasks).

Can I use it with local models? Yes — any OpenAI-compatible endpoint works, including Ollama, so a fully local setup is possible (search API still needed unless you configure a local search option). I literally run mine against DeepSeek to keep costs near zero.

Does it write in Chinese? Yes — set the language in the report generation config; it writes the report in your chosen language. I've pulled Chinese briefs out of it for a client before.

How was the star count verified? GitHub API, 2026-08-09: assafelovic/gpt-researcher 28,898 ★ (Apache-2.0).

Summary

GPT-Researcher (28,898 ★, Apache-2.0, verified 2026-08-09) turns questions into cited research reports: plan → search → filter → synthesize → cite. Best for market briefs, content research, and learning; verify critical claims yourself. Pair with a cheap LLM to keep costs low. Browse the full 461-tool catalog at ylyvip.net/tools. Thoughts? Tell me in the comments what you'd research first.