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…
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
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 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 RAG-based products. - In our test using the Eli5 dataset, AutoRAG improved retrieval performance by 11% and generation performance by up to 22%. Although AutoRAG is in its early stages, we are releasing it as open-source software, hoping it will be valuable to those involved in RAG pipeline development. We welcome any feedback, feature requests, bug reports, and more. Plus, we really want to be one of the solutions that RAG developers share their own pipeline each other. Please feel free to share your works with others using AutoRAG. Thank you:)
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