# Andrew-Jang/RAGHub

A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.

Repository: https://github.com/Andrew-Jang/RAGHub
Canonical: https://ross.abutalabs.com/products/raghub
License: MIT
License Family: permissive
Topics: ai, artificial-intelligence, large-language-models, llm, machine-learning, natural-language-processing, nlp, open-source, rag, retrieval-augmented-generation
Last push: 2026-07-28T00:51:39+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 35, longevity 50
- inputs: {"age_days": 700, "days_push": 37, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1989, forks 182 (observed 2026-08-28T04:06:03.013368+00:00)

## What it is
A community-driven curated directory of RAG (Retrieval-Augmented Generation) frameworks, engines, evaluation tools, and resources. It helps developers discover and compare the fast-growing RAG ecosystem, including guidance on choosing frameworks and vector databases.

## Use cases
- find rag frameworks for building llm apps
- compare rag engines like ragflow and dify
- choose a vector database for semantic search
- stay updated on new retrieval-augmented generation tools
- evaluate rag frameworks for document question answering
- learn what rag is and how it reduces hallucinations

## When to choose
- you're researching which RAG framework or engine to adopt
- you want a curated, community-maintained overview of the RAG ecosystem
- you need guidance comparing vector databases and LLM integrations

## When to avoid
- you need runnable software rather than a catalog of links
- you want official documentation for a specific framework
- you need a benchmark with reproducible metrics rather than a directory

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, search-engine, documentation
- domain: large-language-models, artificial-intelligence, awesome-lists
- platform: -
- tags: awesome-list, curated-directory, rag-frameworks, community-driven, llm-ecosystem, retrieval-augmented-generation, web-server

## Member repositories
- Andrew-Jang/RAGHub (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:03.013368+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:02:46.963878+00:00, confidence not recorded.
  - readme: https://github.com/Andrew-Jang/RAGHub (fetched 2026-08-28T04:06:03.013368+00:00, sha 3e591c2d034d)
- Data as of 2026-08-30T08:39:29.467469+00:00.