Install or uninstall memU for whatever agent you are — identify your host, print its packaged guide, and follow it to wire (or unwire) both seams (record and inject). Use when the user asks to install, set up, integrate, remove, or uninstall memU.
How to install memu?
nevamind-ai/memu
$npx -y skills add nevamind-ai/memu --skill memuInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/nevamind-ai/memu" --skill "nevamind-ai/memu"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/nevamind-ai/memu" that are relevant to the current task. Run `npx skills add "https://github.com/nevamind-ai/memu"` and select the relevant skills, then follow their instructions.
SKILL.md
| 1 |  |
| 2 | |
| 3 | <div align="center"> |
| 4 | |
| 5 | # memU |
| 6 | |
| 7 | ### Personal memory, stored as files |
| 8 | |
| 9 | **Across Sessions. Across Agents. Across Devices.** |
| 10 | |
| 11 | [](https://badge.fury.io/py/memu-cli) |
| 12 | [](https://opensource.org/licenses/Apache-2.0) |
| 13 | [](https://www.python.org/downloads/) |
| 14 | [](https://discord.com/invite/hQZntfGsbJ) |
| 15 | [](https://x.com/memU_ai) |
| 16 | |
| 17 | <a href="https://trendshift.io/repositories/17374" target="_blank"><img src="https://trendshift.io/api/badge/repositories/17374" alt="NevaMind-AI%2FmemU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> |
| 18 | |
| 19 | </div> |
| 20 | |
| 21 | --- |
| 22 | |
| 23 | memU is a 500-line memory system for AI agents. Agents write what's worth keeping as Markdown; memU stores it, embeds it, and retrieves ranked context in a single call — embeddings are the only model calls it makes. The entire memory logic lives in [`agentic.py`](src/memu/app/agentic.py) + [`service.py`](src/memu/app/service.py); everything else is pluggable storage and embedding transport. |
| 24 | |
| 25 | **Installation is agent-driven.** The guides are written for the agent, not for you. One message is the whole setup — tell your agent: |
| 26 | |
| 27 | > Read https://raw.githubusercontent.com/NevaMind-AI/MemU/main/SKILL.md and follow it to install memU. |
| 28 | |
| 29 | It works for Codex, Claude Code, Cursor, OpenClaw, Hermes, WorkBuddy — and any other agent, via detection. Details in [Host adapters](#host-adapters-memory-for-desktop-coding-agents). |
| 30 | |
| 31 | Want to follow the latest development instead? Install from the newest source (git `main`) — tell your agent: |
| 32 | |
| 33 | > Read https://raw.githubusercontent.com/NevaMind-AI/MemU/main/INSTALL-LATEST.md and follow it to install the latest memU. |
| 34 | |
| 35 | ## Quick start |
| 36 | |
| 37 | ```python |
| 38 | from memu.app import MemoryService |
| 39 | |
| 40 | service = MemoryService( |
| 41 | database_config={"metadata_store": {"provider": "sqlite", "dsn": "sqlite:///memu.sqlite3"}}, |
| 42 | ) |
| 43 | |
| 44 | # 1. Persist agent-prepared memory: recall files (memory/skill tracks) + resources |
| 45 | await service.commit_results( |
| 46 | recall_files=[ |
| 47 | { |
| 48 | "name": "Profile", |
| 49 | "track": "memory", |
| 50 | "description": "who the user is", |
| 51 | "content": "# Profile\n- prefers dark roast coffee\n- ships on Fridays", |
| 52 | }, |
| 53 | { |
| 54 | "name": "deploy-checklist", |
| 55 | "track": "skill", |
| 56 | "description": "how to deploy this repo", |
| 57 | "content": "1. run tests\n2. tag\n3. push", |
| 58 | }, |
| 59 | ], |
| 60 | resource=[{"path": "/abs/path/notes.md", "description": "meeting notes from the launch review"}], |
| 61 | ) |
| 62 | |
| 63 | # 2. See what is stored, across every track |
| 64 | files = await service.list_all_recall_files() |
| 65 | |
| 66 | # 3. Single-shot embedding retrieval over segments / files / resources |
| 67 | context = await service.progressive_retrieve("What should I know about this user's launch preferences?") |
| 68 | ``` |
| 69 | |
| 70 | Or straight from the terminal — no code: |
| 71 | |
| 72 | ```bash |
| 73 | export OPENAI_API_KEY=sk-... # embedding API key — the only model calls memU makes |
| 74 | |
| 75 | npx memu-cli commit results.json # {"recall_files": [...], "resource": [...]} |
| 76 | npx memu-cli list-files |
| 77 | npx memu-cli retrieve "What should I know about this user's launch preferences?" |
| 78 | ``` |
| 79 | |
| 80 | State persists in a local SQLite database (`./data/memu.sqlite3` by default), so commit in one invocation and retrieve in the next. |
| 81 | |
| 82 | ## How it works |
| 83 | |
| 84 |  |
| 85 | |
| 86 | ### The data model |
| 87 | |
| 88 | Memory is a set of **recall files** — one Markdown document per topic (`track="memory"`) or per learned skill (`track="skill"`). Committing a file also writes its search index: |
| 89 | |
| 90 | | Record | What it is | How it's embedded | |
| 91 | |---|---|---| |
| 92 | | **RecallFile** | The Markdown document itself (`na |
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