RAG et connaissances
QAnything
Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.
★ 14,2 k étoilesAGPL-3.0
Voir le guide →AutoRAG est un projet de RAG et connaissances open source, créé par Marker-Inc-Korea (Marker-Inc-Korea/AutoRAG sur GitHub). Il compte 5 118 étoiles sur GitHub et 445 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.
An agent that finds anything on your computer — federated search over your files, in place.
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.
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
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Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.