Marker-Inc-Korea/AutoRAG

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

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Established

This tool helps AI developers and researchers find the best Retrieval-Augmented Generation (RAG) pipeline for their specific data and use case. You provide your documents and an evaluation dataset (questions and their correct answers), and AutoRAG automatically tests various RAG components and configurations. The output is an optimized RAG pipeline that performs best for your application.

4,609 stars. Actively maintained with 4 commits in the last 30 days. Available on PyPI.

Use this if you are building a RAG-based AI application and need to systematically evaluate and optimize its performance on your specific data without manually trying countless configurations.

Not ideal if you are looking for a pre-built, ready-to-deploy RAG solution and are not interested in the underlying optimization process.

AI development RAG systems LLM application model optimization natural language processing

Maintenance 13 / 25

Adoption 10 / 25

Maturity 25 / 25

Community 19 / 25

How are scores calculated?

Stars

4,609

Forks

381

Language

Python

License

Apache-2.0

Compare

AutoRAG and RAG-FiT AutoRAG and awesome-rag AutoRAG and Blended-RAG AutoRAG and axiom-rag AutoRAG and production-rag AutoRAG and rag-pipeline

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