RAG・知識ベース
QAnything
Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.
★ 14.2k スターAGPL-3.0
ガイドを見る →AutoRAGは、Marker-Inc-Koreaが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: Marker-Inc-Korea/AutoRAG）。GitHubスターは5,118、フォークは445で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
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
LibreAIカタログに掲載されたRAG・知識ベースプロジェクトの中で最もスターの多いもの — AutoRAGが合わなかった場合に検討したい、現実的な候補リストです。
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
カタログの中でAutoRAGとスター数が近いRAG・知識ベースプロジェクト — 前後の棚もそのまま見て回れます。
RAG for your monorepo: query, understand and edit multi-language codebases with a knowledge graph.
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
A low-code MCP framework for building complex, innovative RAG pipelines — visibly, not as a black box.
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT