QMD

QMD (Query Markup Documents) is an open-source, on-device search engine that indexes Markdown files and provides hybrid retrieval combining BM25 full-text search, vector semantic search, and LLM re-ranking. Built by Tobi Lütke, it runs entirely locally using GGUF models via node-llama-cpp with no API keys or cloud dependencies required. QMD is widely adopted as a memory backend for AI coding agents such as Claude Code and OpenClaw, replacing basic keyword search with intelligent, context-aware retrieval.

• Hybrid search pipeline - Combines BM25 keyword matching via SQLite FTS5 with vector semantic search and LLM-based re-ranking for high-quality results across different query types.
• Query expansion - Uses a fine-tuned 1.7B parameter model to generate alternative phrasings of your search query, broadening recall without sacrificing precision.
• LLM re-ranking - A local Qwen3 reranker model re-scores the top candidates using yes/no classification with log-probability confidence, improving result ordering.
• Collection and context management - Organize documents into named collections with glob patterns and attach hierarchical context descriptions that are returned alongside search results, giving LLMs richer information for decision-making.
• MCP server integration - Exposes search, retrieval, and status tools via Model Context Protocol over stdio or HTTP transport, enabling direct integration with Claude Desktop, Claude Code, and other MCP-compatible agents.
• Smart document chunking - Splits documents into approximately 900-token chunks with 15 percent overlap using a scoring algorithm that finds natural Markdown break points rather than cutting at arbitrary token boundaries.
• Multiple output formats - Supports JSON, CSV, Markdown, XML, and file-list output modes designed for agentic workflows where structured data is needed.
• Document retrieval by ID - Each indexed document receives a six-character hash identifier, enabling fast retrieval by docid, file path with optional line offset, or glob pattern via multi-get.
• Fully local and private - All three GGUF models (embedding, reranker, query expansion) totaling approximately 2 GB run on-device. No data leaves the machine.

To get started, install with npm install -g @tobilu/qmd or bun install -g @tobilu/qmd, add collections pointing to your Markdown directories, run qmd embed to generate vector embeddings, and search with qmd search, qmd vsearch, or qmd query for the full hybrid pipeline.