Autorag — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Autorag. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 26, 2026 • Sentiment updated: Apr 20, 2026

GitHub Statistics

Community Sentiment

Community Buzz: According to a Reddit user, 'I'd point to AutoRAG (document retrieval for non-devs) and the SNS automation as things aimed at non-developer use cases' and another user on HackerNews said 'Introducing AutoRAG. Inspired by Karpathy's autoresearch, I applied the same loop to 7 dimensions of a RAG pipeline'

Pros & Cons

What People Love

AutoRAG's ability to simplify document retrieval for non-devs, The potential of AutoRAG to improve research efficiency

Common Complaints

Low precision issues with AutoRAG, Difficulty in integrating AutoRAG with other tools

Biggest Positive: AutoRAG improves research

Biggest Negative: Low precision issues

Why Autorag Stands Out

AutoRAG is valuable because it automates the evaluation and optimization of RAG pipelines, saving time and effort for developers. Its AutoML-style automation allows for efficient exploration of different RAG module combinations, enabling the discovery of optimal pipelines for specific use cases. By providing a simple way to evaluate many RAG module combinations, AutoRAG helps developers find the best pipeline for their particular needs.

Built With

Build a question answering system that retrieves relevant documents from a large corpus — AutoRAG optimizes the RAG pipeline for your specific data, Build a chatbot that generates human-like responses using a retrieval-augmented generation approach — AutoRAG automates the evaluation and optimization of RAG modules, Build a document parser that extracts relevant information from unstructured text — AutoRAG supports various parsing modules for efficient data processing, Build a language model that incorporates external knowledge from a corpus — AutoRAG enables the optimization of RAG pipelines for improved language understanding, Build a text generation system that uses retrieval-augmented generation to produce coherent and context-specific text — AutoRAG provides a framework for evaluating and optimizing RAG modules

Getting Started

  1. Install AutoRAG using pip: pip install AutoRAG
  2. Set up a YAML file for parsing configuration: modules: - module_type: langchain_parse parse_method: pdfminer
  3. Start parsing your raw documents using the Parser class: parser = Parser(data_path_glob="your/data/path/*"); parser.start_parsing("your/path/to/parse_config.yaml")
  4. Configure chunking using a YAML file: modules: - module_type: llama_index_chunk chunk_method: Token chunk_size: 1024 chunk_overlap: 24 add_file_name: en
  5. Try the AutoRAG dashboard to visualize and optimize your RAG pipeline: python -m autorag.dashboard

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

Market Context

AutoRAG is positioned as a tool for non-developer use cases, competing with other solutions in the market