Awesome Retrieval Augmented Generation (RAG)
A curated list of tools, frameworks, techniques, and learning resources for building retrieval-augmented generation systems, organized by architecture patterns, frameworks, techniques, metrics, and production considerations.
Awesome-RAG is a curated resource map for building retrieval-augmented generation (RAG) systems, the technique of enhancing large language models by retrieving and incorporating context from external knowledge sources during generation instead of relying only on pre-trained knowledge.
The list catalogs the RAG ecosystem: general background, architecture patterns, advanced approaches, frameworks, the Python ecosystem, techniques, metrics and evaluation, databases, platform-specific implementations, production considerations, and best practices, with links to tutorials, papers, and implementations for each area.
It is also distributed as an agent plugin for VS Code, GitHub Copilot CLI, and Claude Code, and is released under CC0-1.0.