LLM Orchestration

AutoRAG

Automated RAG pipeline optimization for enhanced answer quality.

Status
Generally available
Last checked
Checked · Oct 15, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

AutoRAG is an open-source AutoML tool that optimizes the Retrieval-Augmented Generation (RAG) process, from dataset generation to deployment of optimized pipelines. It aims to improve the quality of answers generated by RAG systems.

AutoRAG stands out by providing an automated solution to optimize RAG pipelines, focusing on enhancing the quality of generated answers without manual intervention.

When it fits — and when it doesn’t

A good fit for

  • Research teams looking to improve their RAG system's answer quality without manual tuning

  • Developers building applications that rely on high-quality answers from RAG systems

  • Data scientists who need an automated way to optimize RAG pipelines for specific datasets

Look elsewhere if

  • Teams requiring real-time optimization and deployment of RAG systems (AutoRAG focuses on batch processing)

  • Projects with limited computational resources, as AutoRAG may require significant computing power for optimization

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited documentation for advanced use cases

    Detailed examples and explanations are scarce beyond basic tutorials

  2. 02

    Performance bottlenecks with large datasets

    Optimization process can be slow when handling more than 10,000 records

What it does well

  • Automated optimization of RAG pipelines

  • Enhanced answer quality through automated evaluation and tuning

  • Support for dataset generation tailored to RAG systems

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

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Set up AutoRAG

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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