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Decision brief
R2R is a state-of-the-art AI retrieval system that's production-ready and can easily be integrated through its RESTful API, employing Agentic Retrieval-Augmented Generation (RAG).
Good fit when
- When you require top-tier accuracy in an AI-based retrieval system with the ease of integration provided by a RESTful API.
- For applications needing seamless integration into existing workflows via REST calls.
Avoid when
- If the application does not benefit from or requires less sophisticated methods of data retrieval that do not include RAG.
- When integrating with systems or in environments where network latency might impede performance due to its dependency on a RESTful API interface.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (283d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install R2R PyPIHow it fits your stack(9)
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Evidence and technical details
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Overview
SciPhi-AI/R2R is a state-of-the-art, production-ready AI retrieval system employing Agentic Retrieval-Augmented Generation (RAG) and offering a RESTful API for easy integration.
Capability facts
- Languages
- python
Source: github.language · Aug 17, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Quick install and run in light mode
pip install r2r export OPENAI_API_KEY=sk-... python -m r2r.serve
docker compose -f compose.full.yaml --profile postgres up -d
For detailed self-hosting instructions, see the [self-hosting docs](https://r2r-docs.sciphi.ai/self-hosting/installation/overview).
For agents
This page has a .md twin and JSON over the API.