Marker-Inc-Korea/AutoRAG

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

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Provides end-to-end RAG pipeline optimization through YAML-driven configuration, encompassing document parsing, semantic chunking, and QA dataset generation with support for multiple parsing/chunking strategies simultaneously. Uses grid-search and metric-driven evaluation across retriever-generator combinations to identify optimal module configurations, with results tracked in a dashboard for deployment-ready pipeline export. Integrates with LlamaIndex, LangChain, and local embedding models, supporting both cloud APIs (OpenAI) and GPU-accelerated inference for custom models.

4,609 stars. Actively maintained with 5 commits in the last 30 days. Available on PyPI.

Maintenance 16 / 25

Adoption 10 / 25

Maturity 25 / 25

Community 19 / 25

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Stars

4,609

Forks

381

Language

Python

License

Apache-2.0

Compare

AutoRAG and RAG-FiT AutoRAG and Awesome-LLM-RAG AutoRAG and awesome-rag AutoRAG and fastRAG AutoRAG and refrag AutoRAG and Blended-RAG AutoRAG and RAG_course AutoRAG and Awesome-LLM-Rag

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