Memu

---
name: memu
description: Give this agent persistent memory via memU. Use to memorize (save files/folders so context survives this session) when the user asks to remember or sync something, or after completing work worth keeping; and to retrieve (search memory from earlier sessions) when the user asks what is known about a person/project/topic or references context not in this conversation.
---

# 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.