AI Agents
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Build production-ready agentic workflows from natural language — agents that operate browsers and computers.
★ 5.0k starsMIT
View guide →Production-ready agentic RAG behind a clean REST API.
R2R is an open-source ai agents project by SciPhi-AI (SciPhi-AI/R2R on GitHub). It has 8,011 GitHub stars and 649 forks, is released under the MIT license, and was last updated on GitHub on 7 November 2025.
Be clear about the problem you are solving before installing.
Clone the repository and skim the docs for hardware or key requirements.
Install dependencies, then add API keys, endpoints, or local-model settings.
Launch the example flow and verify the output matches expectations.
Retrieval with agent-style reasoning.
Consume retrieval as a service.
Positioned for deployment, not just demos.
Ingestion through generation in one system.
Hackable Python implementation.
Permissive licensing.
Download the source and move into the project folder.
git clone https://github.com/SciPhi-AI/R2R.git
cd R2RInstall the package and its dependencies.
pip install r2rConfigure provider keys and launch the API per the README.
# verify the install still works — last push was Nov 2025It is maintained by SciPhi-AI at SciPhi-AI/R2R, with 649 forks. Note the last push was November 2025 — check for recent activity before building on it.
Yes. It is released under the MIT license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
You need LLM and embedding provider access; the system itself is free.
Compare on features and maintenance status — its API-first design is the differentiator.
Follow the README install, but first verify the pinned dependencies still install cleanly given the quiet period.
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