GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
AutoRAG: Automate RAG task evaluation and optimization using AutoML techniques.
Good fit when
- Automated benchmarking is needed for retrieval-augmented generation tasks
- Desire to optimize Python pipeline with machine learning-driven automation
Avoid when
- Requirements exceed capabilities of open-source tools
- No need for RAG-specific optimization and evaluation features
Observed Jul 15, 2026 · Source: enrich:decision_facts
Verify the decision
Adoption
Package downloads where a registry match exists. GitHub stars (5,060) are secondary evidence.
- npm downloads (30d)
- 813·npm downloads API·Sep 7, 2026
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 7, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 7, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
npm install AutoRAGSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
AutoRAG provides tools for automating retrieval-augmented generation tasks, focusing on evaluation and optimization with support for Python.
Capability facts
- CLI
- CLI entrypoint
Source: package.json:bin|scripts · Sep 7, 2026
- MCP server
- No MCP server detected
Source: repo_scan · Sep 7, 2026
- Languages
- typescript, javascript
Source: github.language+package.json · Sep 7, 2026
Categories
Tags
README
Installation Published as (dist bundled with Bun, runtime Node ≥ 24 or Bun): typescript import { AutoRAGAgent } from "@autorag/librarian"; const agent = new AutoRAGAgent({ searchPaths: ["/path/to/documents"], }); const response = await agent.searchDocuments("summarize the complia...
For agents
This page has a .md twin and JSON over the API.