A field guide to the RAG tool explosion

What it does

RAGHub is a community-curated directory for the retrieval-augmented generation ecosystem. It sorts frameworks, engines, vector databases, and evaluation tools into tables with live star counts and recent activity, and includes a FAQ that breaks down how to choose between them based on scale, language, and complexity. The project is maintained by the r/RAG community and accepts contributions via pull requests.

The interesting bit

Instead of just dumping links, the README acts as a decision-support tool. It draws useful distinctions—like the difference between a framework you import versus an engine you deploy—and offers concrete selection criteria that acknowledge the field is currently more art than science.

Key highlights

• Covers the full stack: frameworks, engines, vector DBs, eval tools, and leaderboards
• FAQ directly addresses practical questions: chunking strategies, latency fixes, local model support via Ollama and vLLM
• Tables include live star counts and recent activity timestamps for each entry
• Explicitly distinguishes between full-featured libraries (LangChain, LlamaIndex) and lightweight alternatives (LightRAG)
• Community-driven with contribution guidelines tied to the r/RAG subreddit

Verdict

Developers who need to survey the RAG landscape before picking a stack will find this a useful starting point. If you already know you want LangChain or LlamaIndex and just need docs, skip it.