Marker-Inc-Korea/AutoRAG

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

5055 stars · 432 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

AutoRAG 2.0 is a self-evolving librarian agent that searches local document collections (PDFs, wikis, notes) and curates results into structured, numbered knowledge units instead of raw search hits. The repository also maintains the legacy Python AutoRAG, an RAG AutoML tool for automatically optimizing RAG pipelines, in maintenance mode.

Use cases

• search my pdfs and notes and give me an answer, not file paths
• build a librarian agent over my local knowledge base
• automatically find the optimal RAG pipeline for my data
• evaluate and benchmark RAG retrieval and generation quality
• create QA evaluation datasets for retrieval-augmented generation
• get smarter document search that learns from my feedback

When to choose

• you want curated answers from local documents rather than raw search results
• you need an agent that improves retrieval strategy over time via memory
• you want to auto-optimize or benchmark RAG pipelines with the legacy tool

When to avoid

• you need a simple keyword/grep search tool without LLM involvement
• you want a fully managed cloud RAG service rather than a self-hosted agent
• you require a permissive standard license (license is non-standard)

Facets

application · maturity active

rag search-engine agent-framework llm-inference nlp pdf artificial-intelligence large-language-models developer-tools pdf python cli cross-platform rag-evaluation automl document-parsing librarian-agent self-evolving-memory local-document-search knowledge-curation retrieval-augmented-generation ai-agents natural-language-processing nodejs

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Marker-Inc-Korea/AutoRAG")