RAG & Knowledge
QAnything
Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.
★ 14.2k starsAGPL-3.0
View guide →An agent that finds anything on your computer — federated search over your files, in place.
AutoRAG is an open-source rag & knowledge project by Marker-Inc-Korea (Marker-Inc-Korea/AutoRAG on GitHub). It has 5,118 GitHub stars and 445 forks, is released under the MIT license, and was last updated on GitHub on 8 October 2026.
Think about the files and sources your agent should search — local documents, chats, mail, drives — and where they live.
Install @autorag/librarian globally with Bun or npm.
Run the setup skill once to register the stdio MCP server with your agent host, then reload the agent.
Check status, refresh indexes if needed, and let your agent search across your sources with autorag.search.
Data is never migrated: AutoRAG queries CLI-native stores where your data already lives and keeps source-native identities for results.
A stdio MCP server exposes tools such as autorag.status, autorag.search, autorag.search.files, autorag.datasources.list and autorag.refresh.
Dedicated datasource skills connect local documents and external tools and communication platforms without copying their databases.
PDF, DOCX, XLSX/XLS and Korean HWP/HWPX documents parse in-process via kordoc — no Java required.
Agents are instructed never to delete, move or modify original documents; index artifacts live only in the workspace's .autorag/ and cache folders, and keys are never printed to logs.
Optional report, evidence and feedback tools let the librarian side learn from usage, so results improve over time.
Clone AutoRAG if you want the source; most users install the CLI package.
git clone https://github.com/Marker-Inc-Korea/AutoRAG.git
cd AutoRAGInstall the librarian CLI globally with Bun, or with npm.
bun install -g @autorag/librarian
# or: npm install -g @autorag/librarianVerify corpus freshness and index readiness, then search through your agent's MCP tools.
autorag status --jsonAutoRAG is maintained by Marker-Inc-Korea (the Marker team).
Yes — it is open source under the MIT license (per the project's license badge). Model-backed librarian features use your own agent's model access.
The Lite MCP search path works with your agent host; the model-backed librarian runs through your agent, so it uses whatever model access that agent already has.
Classic RAG copies your data into a central vector database; AutoRAG federates search in place across the stores where your data already lives, leaving originals untouched.
Install @autorag/librarian, run the setup skill to register the MCP server with your agent, and check autorag status.
Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.
Semantic search engine designed for files and code.
A private AI platform for agents, assistants and enterprise search over your documents
Federated AI search and RAG across 100+ apps — answers with sources, without moving your data
Self-hosted RAG search across GitHub, Drive, Notion, and local files.
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Free and open — grab the source, star the repo, and start creating.