Andrew-Jang/RAGHub

RAGHub is a community-maintained directory that catalogs tools and resources for Retrieval-Augmented Generation (RAG) — a technique that lets large language models pull in external data before answering, to reduce hallucinations and improve accuracy. It is a living list meant to keep up with the fast-changing RAG ecosystem, run by and for the r/RAG Reddit community.

What it contains

• RAG Frameworks – libraries you code against to build custom RAG pipelines (e.g., LangChain, LlamaIndex, LightRAG, Haystack)
• RAG Evaluation and Optimization Frameworks – tools for measuring and improving RAG performance (e.g., ragas, Trulens, Phoenix, Deepchecks)
• RAG Engines – standalone platforms offering ready-to-use RAG functionality
• RAG Resources and Sites, Model LeaderBoards, a FAQ, and a License section

How it works

• It's a living directory: new tools are added regularly via community contributions. Each entry lists the tool's name, description, website, GitHub link, star count, and recent activity.
• The FAQ answers practical questions: how to choose a framework (by use case, scale, complexity, integrations, language), whether you need a vector database (yes, for semantic search), how to evaluate a RAG system, common challenges (retrieval quality, context limits, hallucinations, latency) with solutions, and how to run RAG with local models (Ollama, vLLM, LM Studio, LocalAI).
• Contributions are open: fork the repo, add your entry in the appropriate section, follow the existing table format, and submit a pull request. Detailed guidelines live in CONTRIBUTING.md.

In short: RAGHub is a curated, crowd-sourced reference list to help developers and researchers stay current with the rapidly evolving landscape of RAG tooling — from full frameworks to evaluation utilities to turnkey engines.