AutoRAG – AutoML tool for RAG. Find an optimal RAG pipeline
- Discovering the most effective RAG pipeline for your specific data and use case can be daunting. It requires experimenting with various RAG modules and configurations, which are both time-consuming and complex. - AutoRAG addresses this challenge by automatically evaluating different combinations of RAG modules and their parameters. You don't need to write implementation code yourself; everything is set up through a single YAML file. - Our aim is to save you the hassle of continuously adapting to new RAG modules and configurations. Instead, you can focus on developing robust data for your…
AutoRAG – AutoML tool for RAG. Find an optimal RAG pipeline launched on February 8, 2024 with 18 votes, #1,044 of 3,962 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
AutoRAG is an AutoML tool that automatically tests and optimizes retrieval-augmented generation (RAG) pipelines for specific datasets and use cases. Users configure different RAG modules and parameters through a single YAML file without writing implementation code, allowing the tool to evaluate combinations and identify the most effective setup. The software targets developers building RAG-based products who want to avoid manual experimentation with different module configurations. In testing, AutoRAG improved retrieval performance by 11% and generation performance by up to 22%.
written from the facts on this page · September 2026
From the sources
AutoRAG: Now your agent can find anything in your computer. It gets smarter if you are using it frequently. - Marker-Inc-Korea/AutoRAG
In the maker’s words, at launch
- Discovering the most effective RAG pipeline for your specific data and use case can be daunting. It requires experimenting with various RAG modules and configurations, which are both time-consuming and complex. - AutoRAG addresses this challenge by automatically evaluating different combinations of RAG modules and…
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RAGBuilder – Hyperparameter tuning on various RAG parameters2024 · github.com · ▲17A RAG has several moving parts: data ingestion, retrieval, re-ranking, generation etc.. Each part comes with numerous options. If we consider a toy example, where you could choose from: 5 different chunking methods, 5 different chunk sizes, 5 different embedding models, 5 different retrievers, 5 different re-rankers/ compressors 5 different prompts 5 different LLMs That’s 78,125 distinct RAG configurations! If you could try evaluating each one in just 5 mins, that’d still take 271 days of non-stop trial-and-error effort! In short, it’s kinda impossible to find your optimal RAG setup…
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