GraphCanon updated 2w · GitHub synced 2w
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 (4,968) are secondary evidence.
- npm downloads (30d)
- 316·npm downloads API·2w
Maintenance and security
Full trust report- Maintenance
- Very active (2d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
npm install AutoRAG npmSimilar 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 · Aug 8, 2026
- MCP server
- No MCP server detected
Source: repo_scan · Aug 8, 2026
- Languages
- typescript, javascript
Source: github.language+package.json · Aug 8, 2026
Categories
Tags
README
Installation
Published as @autorag/librarian (dist bundled with Bun, runtime Node ≥ 24 or Bun):
bun add @autorag/librarian # library
bun install -g @autorag/librarian # autorag CLI
---
## Quick Start
```typescript
import { AutoRAGAgent } from "@autorag/librarian";
const agent = new AutoRAGAgent({
searchPaths: ["/path/to/documents"],
});
const response = await agent.searchDocuments("summarize the compliance requirements");
console.log(response.answer);
for (const result of response.results) {
console.log(`[${result.number}] ${result.title} — ${result.summary}`);
}
// Mark which results were useful — AutoRAG remembers for next time
agent.recordFeedbackByNumbers(response.sessionId, [1, 3], [2]);
searchDocuments() runs the Pi agent loop — it searches, reads, consults memory, curates, and finalizes through the emit_autorag_results structured tool — then returns a typed SearchDocumentsResponse. The caller consumes the structured payload directly; no assistant text parsing.
For agents
This page has a .md twin and JSON over the API.