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

number of results

-c, --collection

restrict to a collection

--all --min-score

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 Automate indexing so results stay current as you add/edit notes. For keyword search ( qmd search ), qmd update is usually enough (fast). If you rely on semantic/hybrid search ( vsearch / query ), you may also want qmd embed , but it can be slow. Example schedules (cron):

Hourly incremental updates (keeps BM25 fresh):

0

export PATH = " $HOME /.bun/bin: $PATH " && qmd update

Optional: nightly embedding refresh (can be slow):

0 5

export PATH = " $HOME /.bun/bin: $PATH " && qmd embed If your Clawdbot/agent environment supports a built-in scheduler, you can run the same commands there instead of system cron. 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?".