qmd - Quick Markdown Search

Local search engine for Markdown notes, docs, and knowledge bases. Index once, search fast.

When to use (trigger phrases)

• "search my notes / docs / knowledge base"
• "find related notes"
• "retrieve a markdown document from my collection"
• "search local markdown files"

Default behavior (important)

• Prefer

  qmd search

  (BM25). It's typically instant and should be the default.
• Use

  qmd vsearch

  only when keyword search fails and you need semantic similarity (can be very slow on a cold start).
• Avoid

  qmd query

  unless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.

Prerequisites

• Bun >= 1.0.0
• macOS:

  brew install sqlite

  (SQLite extensions)
• Ensure PATH includes:

  $HOME/.bun/bin

Install Bun (macOS):

brew install oven-sh/bun/bun

Install

bun install -g https://github.com/tobi/qmd

Setup

qmd collection add /path/to/notes --name notes --mask "**/*.md"
qmd context add qmd://notes "Description of this collection"  # optional
qmd embed  # one-time to enable vector + hybrid search

What it indexes

• Intended for Markdown collections (commonly

  **/*.md

  ).
• In our testing, "messy" Markdown is fine: chunking is content-based (roughly a few hundred tokens per chunk), not strict heading/structure based.
• Not a replacement for code search; use code search tools for repositories/source trees.

Search modes

• qmd search

  (default): fast keyword match (BM25)

• qmd vsearch

  (last resort): semantic similarity (vector). Often slow due to local LLM work before the vector lookup.

• qmd query

  (generally skip): hybrid search + LLM reranking. Often slower than

  vsearch

  and may timeout.

Performance notes

• qmd search

  is typically instant.

• qmd vsearch

  can be ~1 minute on some machines because query expansion may load a local model (e.g., Qwen3-1.7B) into memory per run; the vector lookup itself is usually fast.

• qmd query

  adds LLM reranking on top of

  vsearch

  , so it can be even slower and less reliable for interactive use.
• If you need repeated semantic searches, consider keeping the process/model warm (e.g., a long-lived qmd/MCP server mode if available in your setup) rather than invoking a cold-start LLM each time.

Common commands

qmd search "query"             # default
qmd vsearch "query"
qmd query "query"
qmd search "query" -c notes     # Search specific collection
qmd search "query" -n 10        # More results
qmd search "query" --json       # JSON output
qmd search "query" --all --files --min-score 0.3

Useful options

• -n <num>

  : number of results

• -c, --collection <name>

  : restrict to a collection

• --all --min-score <num>

  : return all matches above a threshold

• --json

  /

  --files

  : agent-friendly output formats

• --full

  : return full document content

Retrieve

qmd get "path/to/file.md"       # Full document
qmd get "#docid"                # By ID from search results
qmd multi-get "journals/2025-05*.md"
qmd multi-get "doc1.md, doc2.md, #abc123" --json

Maintenance

qmd status                      # Index health
qmd update                      # Re-index changed files
qmd embed                       # Update embeddings

Keeping the index fresh

Set up a cron job or hook to automatically re-index. For example, a daily 5 AM reindex:

# Via Clawdbot cron (isolated job, runs silently):
clawdbot cron add \
  --name "qmd-reindex" \
  --cron "0 5 * * *" \
  --tz "America/New_York" \
  --session isolated \
  --message "Run: export PATH=\"\$HOME/.bun/bin:\$PATH\" && qmd update && qmd embed" 

# Or via system crontab:
0 5 * * * export PATH="$HOME/.bun/bin:$PATH" && qmd update && qmd embed

This ensures your vault search stays current as you add or edit notes.

Models and cache

• Uses local GGUF models; first run auto-downloads them.
• Default cache:

  ~/.cache/qmd/models/

  (override with

  XDG_CACHE_HOME

  ).

Relationship to Clawdbot memory search

• qmd

  searches your local files (notes/docs) that you explicitly index into collections.
• Clawdbot's

  memory_search

  searches agent memory (saved facts/context from prior interactions).
• Use both:

  memory_search

  for "what did we decide/learn before?",

  qmd

  for "what's in my notes/docs on disk?".