R2R: The Production-Ready RAG System You Can Self-Host
R2R is a production-ready AI retrieval system with agentic RAG, hybrid search, knowledge graphs, and a RESTful API. Self-host it with Docker for complete data privacy and unlimited scaling.
What Is R2R?
R2R (Retrieval-to-Retrieval) is a production-ready AI retrieval system that brings enterprise-grade RAG capabilities to your own infrastructure. Built by SciPhi-AI with over 7,900 GitHub stars, R2R delivers agentic Retrieval-Augmented Generation through a clean RESTful API — think of it as the retrieval backbone your AI applications have been missing.
Unlike simpler RAG frameworks that stop at basic chunk-and-search, R2R goes deep: multimodal content ingestion, hybrid search combining vector and keyword approaches, knowledge graph construction, and a built-in Deep Research API that chains multi-step reasoning across your documents and the web. All of this ships in a single Docker container you can deploy in minutes.
🚀 Explore R2R on Run This Ai
Docker Compose configs, system requirements, installation guides, and more — all in one place.
View R2R Tool Page →Key Features That Set R2R Apart
Agentic RAG Engine — R2R doesn't just retrieve documents; it thinks about them. The Deep Research API performs multi-step reasoning, fetching relevant context from both your private knowledge base and the internet to answer complex, multi-part questions that would stump simpler systems.
Hybrid Search Architecture — Combine dense vector embeddings with sparse keyword search for results that are both semantically relevant and precise. R2R handles the fusion logic so your users get the best of both worlds without any configuration gymnastics.
Knowledge Graphs Built In — As documents flow through R2R, it automatically extracts entities and relationships, building a graph structure that enables relationship-aware queries. Ask "what connects X to Y?" and R2R can trace the path through your data.
Multimodal by Default — Ingest PDFs, images, audio transcripts, and more. R2R handles the extraction pipeline so your RAG system doesn't go blind on non-text content.
Why Self-Host R2R?
Running R2R on your own hardware means your documents never leave your infrastructure. For legal firms, healthcare providers, financial institutions, or anyone handling sensitive data, this is non-negotiable. R2R gives you the power of advanced RAG without sending your proprietary information to third-party APIs.
R2R also means you control the scaling. Need to handle 10,000 documents? 10 million? R2R scales horizontally with your infrastructure, and since it's MIT-licensed, there are no usage-based pricing surprises.
🚀 Ready to Deploy R2R?
Get the complete Docker Compose configuration and system requirements on Run This Ai.
View R2R Tool Page →Who Should Use R2R?
R2R is ideal for teams that have outgrown basic RAG setups and need production features: developers building AI-powered search over internal docs, enterprises requiring audit trails and document-level permissions, and researchers who need to reason over large, heterogeneous document collections. If you're already comfortable with Docker and want a RAG system that grows with your needs, R2R is the upgrade path.