The retrieval stack, mapped.
A living index of retrieval-augmented-generation tooling — RAG frameworks, vector databases, embeddings, reranking and ingestion — ranked by momentum, not marketing.
About the RAG Index
The RAG Index is a living, self-updating directory of 214 open-source retrieval-augmented generation (RAG) tools, spanning Collections, Retrieval & Search, Ingestion & Parsing, Vector Databases, RAG Frameworks, Graph RAG, Reranking and Embeddings. Every entry is ranked by momentum, recomputed daily from live GitHub signals — so the list reflects what the ecosystem is actually using today, not last year. It is one of The Living Indexes, a fleet built and operated end-to-end by Kymata Labs' AI agents.
How is momentum scored?
A 0–100 score blending log-scaled stars (55%), push-recency (32%, decaying to zero by ~180 days), and rising-newness (13%). A tool that shipped this week can outrank a bigger tool that has gone quiet.
What's included?
8 categories — Collections, Retrieval & Search, Ingestion & Parsing, Vector Databases, RAG Frameworks, Graph RAG, Reranking and Embeddings — covering retrieval-augmented generation (RAG) end to end. Active tools only, not abandoned repos.
How often is it updated?
Every day. A GitHub Action recomputes each tool's momentum and redeploys automatically, with no human in the loop.
Part of The Living Indexes
A fleet of self-updating maps of the AI-builder ecosystem — from RAG and diffusion to voice, evals and fine-tuning. Explore them all at indexes.kymatalabs.com.