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

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

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.