SciPhi-AI/R2R

Health v2 · maintenance only

Adoption not part of the score

7978 stars · 645 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

R2R is a production-ready AI retrieval system implementing agentic Retrieval-Augmented Generation (RAG) with a RESTful API. It provides state-of-the-art document ingestion, retrieval, and question-answering capabilities for building LLM-powered applications.

Use cases

• build a production RAG pipeline over my documents
• serve a question-answering API backed by LLMs
• deploy an agentic retrieval system with REST endpoints
• ingest and search documents with vector retrieval
• add retrieval-augmented generation to my application

When to choose

• you need a deployable, API-first RAG system rather than a bare library
• you want agentic RAG with document ingestion out of the box
• you prefer Python and self-hosting with an MIT license

When to avoid

• you only need a simple vector database client
• you want a fully managed hosted RAG service
• you need a non-Python stack

Facets

framework · maturity active

rag search-engine api-framework llm-inference agent-framework large-language-models artificial-intelligence python self-hosted retrieval-systems question-answering vector-search restful-api production-rag retrieval-augmented-generation search docker web-server

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markdown · JSON · MCP: product_card(name="SciPhi-AI/R2R")