RAGHub serves as a living directory to help practitioners stay current with the rapidly evolving RAG landscape. It categorizes frameworks, evaluation tools, RAG engines, model leaderboards, and other resources related to retrieval-augmented generation. The project is open to community contributions and targets those navigating the growing ecosystem of RAG tooling.