AI Agents
deep-research
Deep Research with any LLM: in-depth reports in minutes, with an SSE API and MCP server built in
★ 4.7k starsMIT
View guide →An autonomous research agent that plans, searches and writes cited reports from any data source
gpt-researcher is an open-source ai agents project by assafelovic (assafelovic/gpt-researcher on GitHub). It has 29,955 GitHub stars and 4,095 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 1 October 2026.
Decide what you want to achieve with gpt-researcher and check the README on GitHub for requirements before installing.
Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).
Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.
Work through the README's quickstart example end to end, then adapt it to your own project or data.
The agent breaks a question into sub-queries, searches them in parallel and synthesizes a report.
Output reports aggregate multiple sources with citations rather than a single model's memory.
A dedicated deep-research flow spends more time exploring for complex questions.
Point it at your own PDFs/documents to research private material, not just the web.
Works across LLM providers — configure the models you already have keys for.
Can run as an MCP server for agent hosts, and installs as a Claude Skill via npx skills.
Get the GPT Researcher source and examples.
git clone https://github.com/assafelovic/gpt-researcher.git
cd gpt-researcherInstall the requirements (or simply pip install gpt-researcher for library use), then set your LLM and search API keys in .env.
pip install -r requirements.txt
# library alternative: pip install gpt-researcherStart the app (or the Python example from the README), give it a research question and read the generated report.
# see the README 'Example Usage' for the exact Python snippetgpt-researcher is maintained by the assafelovic organization/account on GitHub, together with open-source contributors, in the public repository assafelovic/gpt-researcher.
Yes — the code is open source under the Apache-2.0 license. You can use, modify and redistribute it under that license's terms.
Yes — you need your own LLM API key and a web-search API key for the search provider you configure (Tavily is the common choice); the software itself is free and Apache-2.0 licensed.
Hosted deep-research tools are convenient but closed; GPT Researcher is self-hostable, provider-agnostic and scriptable, at the cost of your own keys and setup.
Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.
Deep Research with any LLM: in-depth reports in minutes, with an SSE API and MCP server built in
Microsoft's R&D-Agent: LLM agents that automate data-driven research, Kaggle modelling and LLM fine-tuning.
Turn any website into clean, agent-ready data with one API.
Parallel multi-agent research that compiles wikis from any topic.
Autonomous research loops, from topic to experiments.
OpenScience — the open-source AI workbench for scientific research: give it a goal, it reads the literature, runs the experiments and writes up the findings
The most-starred other AI Agents projects in the LibreAI catalogue — the realistic shortlist if gpt-researcher is not the right fit.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Free and open — grab the source, star the repo, and start creating.