AutoRAG and axiom-rag

AutoRAG is a comprehensive evaluation and optimization framework that could be used to benchmark and improve Axiom RAG's pipeline performance, making them complements rather than direct competitors—one focuses on RAG system evaluation/tuning while the other provides a production-ready retrieval implementation.

AutoRAG
67
Established
axiom-rag
34
Emerging
Maintenance 13/25
Adoption 10/25
Maturity 25/25
Community 19/25
Maintenance 13/25
Adoption 1/25
Maturity 20/25
Community 0/25
Stars: 4,609
Forks: 381
Downloads: —
Commits (30d): 4
Language: Python
License: Apache-2.0
Stars: 1
Forks: —
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
No risk flags
No risk flags

About AutoRAG

Marker-Inc-Korea/AutoRAG

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

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.

AI development RAG systems LLM application model optimization natural language processing

About axiom-rag

axiom-llc/axiom-rag

Production RAG pipeline — grounded retrieval, source-cited answers, Precision@k + MRR eval. CLI + Flask REST API. Gemini · ChromaDB · Python 3.11+

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