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[GitHub Trending] tobi/qmd

8.9 relevance
Score Breakdown
technical depth
7
novelty
8
actionability
9
community
8
strategic
7
personal
9

Scored daily by a customisable AI persona to surface the most relevant engineering leadership news.

A local-first CLI search engine for personal knowledge bases, aligning with productivity tooling and privacy-focused trends.

2026-04-07 devtools GitHub Trending
mini cli search engine for your docs, knowledge bases, meeting notes, whatever. Tracking current sota approaches while being all local - tobi/qmd
Summary

QMD is an on-device search engine that indexes markdown and documents using a hybrid stack of BM25, vector semantic search, and LLM reranking via node-llama-cpp with GGUF models. It exposes an MCP server with tools like query, get, and multi_get, supporting HTTP transport for persistent servers and structured JSON/file outputs optimized for agentic workflows.

Key Takeaway

Integrate QMD's MCP server into your agent workflows to enable hybrid search over local knowledge bases with GGUF model reranking, avoiding external API calls.

Why it matters

As a senior engineer building AI agent orchestration systems, you need a local, standards-based knowledge retrieval layer that integrates via MCP without cloud dependencies, which QMD provides through its hybrid search and on-device LLM reranking.