Autorag — AI Agent Review & Live Stats

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GitHub data synced: Apr 2, 2026 • Sentiment updated: Unknown

GitHub Statistics

Why Autorag Stands Out

AutoRAG stands out from alternative RAG evaluation and optimization frameworks with its automated pipeline optimization and AutoML-style automation. By simplifying the process of evaluating and optimizing RAG pipelines, AutoRAG saves time and resources for developers and researchers. Additionally, AutoRAG's modular design and supporting data creation modules make it a versatile tool for a range of use cases. The project's focus on automation and ease of use also sets it apart from more manual or labor-intensive approaches to RAG optimization.

Built With

Build an automated question answering system — AutoRAG's automated pipeline optimization enables it, Build a retrieval-augmented generation model for document understanding — AutoRAG's AutoML-style automation simplifies the process, Build a customized RAG pipeline for specific use cases — AutoRAG's modular design allows for easy modification, Build an evaluation framework for RAG models — AutoRAG's metrics and dashboard provide insights into performance, Build a data creation pipeline for RAG optimization — AutoRAG's supporting data creation modules streamline the process

Getting Started

  1. Install AutoRAG using pip with the command `pip install AutoRAG`
  2. Configure the YAML file for parsing, chunking, and QA creation
  3. Run the parsing module using the command `python -m autorag.parser --data_path_glob your/data/path/* --parse_config_path your/path/to/parse_config.yaml`
  4. Run the chunking module using the command `python -m autorag.chunker --parsed_data_path your/parsed/data/path --chunk_config_path your/path/to/chunk_config.yaml`
  5. Try the `python -m autorag.dashboard` command to verify that the dashboard is working and visualize the results of the RAG optimization

About

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

Official site: https://marker-inc-korea.github.io/AutoRAG/

Category & Tags

Category: automation

Tags: analysis, automl, benchmarking, document-parser, embeddings, evaluation, llm, llm-evaluation, llm-ops, open-source, ops, optimization, pipeline, python, qa, rag, rag-evaluation, retrieval-augmented-generation