# Memu

> Persists context across sessions by compiling sources into a local SQLite store and markdown tree, then retrieves relevant memory via embedding search.

- Skill: `majiayu000/memu` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/memu`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/memu/raw
- Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity, Note-taking
- Tags: Embedding Search, Memory, Memu, Openai, Persistent Storage, Sqlite
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/majiayu000/memu

---


# memU: memorize and retrieve persistent memory

memU compiles sources into a persistent local store (`./data/memu.sqlite3` + a browsable `./data/memory/` markdown tree, relative to CWD). What one session memorizes, the next can retrieve — always run from the project root so every command hits the same store.

Use only the two commands below. Do not use the legacy `memu memorize` / `memu retrieve` commands.

Both directions need an API key: make sure `OPENAI_API_KEY` (or `MEMU_LLM_PROVIDER` + its matching key) is set, and tell the user if it is missing.

## Locate the CLI

Use the first available runner:

1. `memu` (installed via `pip install memu-py`)
2. `uvx --from memu-py memu`
3. `npx memu-cli`

## Memorize

```bash
memu memorize-workspace <folder>
```

- Incremental and safe to re-run: diffs against `<folder>/.memu_manifest.json`, so only added/modified files are processed and memory from deleted files is removed.
- Top-level directory decides the treatment: `chat/` → memory topics, `agent/` → skills, everything else → indexed workspace context (modality inferred per file from its extension).
- To memorize a single file, place (or copy) it into the workspace folder and sync — there is no separate single-file path.

Report the printed diff (added/modified/deleted) to the user; pass `--json` if you need to parse the result.

## Retrieve

```bash
memu retrieve-workspace "<query>"
```

Single-shot embedding search, no LLM calls. Returns JSON in three layers:

- `segments` — the matched slices, ranked by similarity (check `score`)
- `files` — the memory/skill documents they belong to (usually what you want)
- `resources` — matching raw sources, when summaries are not enough

If a query misses, retry with different phrasing or more specific terms. Low scores across the board usually mean nothing relevant is stored — say so rather than stretching weak matches. You can also read `./data/memory/MEMORY.md` / `SKILL.md` / `INDEX.md` directly for a browsable overview.