RAGHub
A community-maintained directory of Retrieval-Augmented Generation frameworks, engines, evaluation tools and resources, with guidance on choosing between a RAG framework you integrate and a RAG engine you run as a standalone platform.
RAGHub is a living, community-driven catalog of the Retrieval-Augmented Generation ecosystem, built for the r/RAG community to track new and emerging frameworks, projects and resources as they appear. It is organized into sections for RAG frameworks, RAG evaluation and optimization frameworks, RAG engines, general RAG resources and sites, and model leaderboards.
An FAQ section explains what RAG is and how it reduces hallucination by retrieving relevant information before generating an answer, gives guidance on choosing a framework based on use case, scale, complexity, integration needs and implementation language, and distinguishes RAG frameworks such as LangChain and LlamaIndex, which are libraries integrated into custom code, from RAG engines such as RAGFlow and Dify, which are standalone platforms providing ready-to-use RAG functionality.
It accepts community contributions of new frameworks, projects and resources through pull requests, and is released under the MIT license.