RAGHub
A community directory of retrieval-augmented generation tooling, kept by the r/RAG forum and published under MIT. It sorts by function rather than popularity, and that split is what makes it useful: frameworks on one side, retrieval engines on the other, evaluation and optimisation frameworks apart, then resources and model leaderboards. Its stated reason to exist is the speed at which the ecosystem turns over: is a tool released three months ago still relevant, or was it just fresh packaging on an old idea.
Strengths
- Split by function, frameworks and engines separated, instead of one pile sorted by stars
- A dedicated section for evaluation frameworks, the brick beginners forget
- Backed by an active community rather than a personal watch abandoned after six months
Limitations
- No editorial sorting: everything submitted gets in, so presence is not a recommendation
- Nothing indicates whether a listed project is still maintained, you have to check each repo
- English and technical, hardly practical for a non-developer product profile
Best for
- A first sweep of the landscape before picking a retrieval stack
- Checking you have not missed a whole family of tools on a subject you thought you knew