ai-agent

Awesome RAG Production: Scalable AI Systems

Name: Awesome RAG Production: Scalable AI Systems
Author: Yigtwxx

Get battle-tested tools for building production-grade Retrieval-Augmented Generation systems. For founders building scalable AI solutions with Python.

163 stars42 forksPythonQuality 9/10Updated 7/20/2026100% free · open source

What it does

Provides a curated list of battle-tested tools and frameworks for building scalable, production-grade Retrieval-Augmented Generation (RAG) systems with Python

Install / run

git clone https://github.com/Yigtwxx/awesome-rag-production.git

When to use it

•When building a scalable AI solution that requires efficient information retrieval and generation
•When needing a list of reliable tools and frameworks for RAG systems
•When looking for best practices to implement production-grade RAG systems

Quick start

1Explore the README.md file for an overview of the tools and frameworks listed
2Check the 'Frameworks' section for popular libraries like Haystack and Dense Passage Retriever
3Review the 'Tools' section for utilities like dataset management and model training
4Visit the linked GitHub repositories for each tool to learn more about their usage and implementation
5Start with the 'Getting Started' guides for each framework to begin building your RAG system

Ready-to-paste prompt

Clone the repository and open the README.md file to start exploring the curated list of tools and frameworks: git clone https://github.com/Yigtwxx/awesome-rag-production.git && cd awesome-rag-production && cat README.md

Heads up: Ensure you have Python and Git installed on your system before attempting to clone and explore the repository, as the tools and frameworks listed are Python-based and the repository is hosted on GitHub

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