memU
Personal memory across agents
Install / Use
claude mcp add NevaMind-AI -- npx -y github:NevaMind-AI/memUIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
Development & EngineeringSupported Platforms
Tags
Our assessment of memU
memU scores 83/100 on our quality scale, 2998th of 4,614 Development & Engineering skills we index.
Its MCP Server is 11 KB long, well organised into 17 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.
With 14,609 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated yesterday, so memU is actively maintained.
- Our last check on 2026-10-20 found the source still online.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 83/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
memU compared with similar skills
All 4 of these similar skills score higher than memU; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| memU (this skill)by NevaMind-AI | 83 | 14.6k | 1d ago | MCP Server |
| taste-skillby Leonxlnx | 100 | 111.0k | 1d ago | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 109.5k | 1d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 79.0k | today | CLAUDE.md |
| ai-job-searchby ai-job-search-dev | 100 | 70.0k | today | CLAUDE.md |
Frequently asked questions
- How do I install memU?
- Run
claude mcp add NevaMind-AI -- npx -y github:NevaMind-AI/memU. The install tabs above show the steps for each supported agent. - Which AI agents does memU work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is memU safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 83/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is memU still maintained?
- The repository was last updated yesterday, so memU is actively maintained.
Skill content
View source on GitHub
memU
Personal memory, stored as Wiki
Across Sessions. Across Agents. Across Devices.
<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>
</div>memU is a lightweight, agent-driven memory system that gives users a shared LLM wiki across sessions, agents, and devices. It automatically distills your own reusable skills from your agent history. Its core memory logic is only 500 lines — compact enough to inspect, understand, and adapt.
Quick start
memU works with Codex, Claude Code, Cursor, OpenClaw, Hermes, WorkBuddy, Cola, and more. See Host adapters.
Cross-device · Free · Unlimited · View online
Get your API key from memu.so, then send this message to your agent:
Read https://memu.pro/SKILL.md, follow its instructions to install and configure memU, API Key is memu_•••••••••(get Api Key from memu.so).
Agent support
This matrix lists the currently tested memU integrations by operating system.
- Memorize — capture useful session knowledge through a scheduled background task and turn it into reusable memory.
- Retrieve — bring relevant memory into a future task.
- ⚠️ — supported with an important limitation; see the user note.
macOS
| Agent | Mode | Memorize | Retrieve | User note | | --- | --- | :---: | :---: | --- | | ChatGPT | ChatGPT(Work mode), codex and VS Code extension | ✅ | ✅ | | | ChatGPT | Chat | ❌ | ❌ | Chat mode is not currently supported. Please use Work mode. | | Claude Code | Desktop and CLI | ✅ | ✅ | If the selected model declines the setup steps, retry with Opus or another model. Sonnet 5 can occasionally do this. | | Claude | Chat and Cowork | ❌ | ❌ | | | Cursor | — | ✅ | ✅ | | | OpenClaw | — | ✅ | ✅ | Retrieve support has not yet been verified. | | Hermes Agent | — | ✅ | ✅ | | | WorkBuddy | — | ✅ | ✅ | |
Windows
| Agent | Mode | Memorize | Retrieve | User note |
| --- | --- | :---: | :---: | --- |
| ChatGPT | ChatGPT(Work mode), codex and VS Code extension | ✅ | ✅ | |
| ChatGPT | Chat | ❌ | ❌ | Chat mode is not currently supported. Please use Work mode. |
| Claude Code | Desktop and CLI | ✅ | ✅ | If the selected model declines the setup steps, retry with Opus or another model. Sonnet 5 can occasionally do this. |
| Claude | Chat and Cowork | ❌ | ❌ | |
| Cursor | — | ✅ | ✅ | |
| OpenClaw | — | ✅ | ✅ | |
| Hermes Agent | — | ✅ | ⚠️ | Use a memU version with Windows HERMES_HOME support; older versions may retrieve from the wrong files. |
| WorkBuddy | — | ✅ | ✅ | With Hy3, retrieval may fail. Retry with another model if this happens. |
Linux
| Agent | Mode | Memorize | Retrieve | User note | | --- | --- | :---: | :---: | --- | | Codex | VS Code extension | ❌ | ✅ | | | Claude Code | CLI | ✅ | ✅ | | | OpenClaw | 4.23 / 7.1 | ✅ | ✅ | |
Support status reflects the current release and may change as host integrations evolve.
How it works

Automatic skill extraction
Once the scheduled bridging task is installed, memU can turn useful agent history into reusable Markdown skills automatically.

- Capture new sessions. The host adapter reads new session history, including messages and tool calls.
- Prepare self-evolve jobs.
prepareslices each session into a self-contained job with the paths and context the agent needs. - Let the agent decide. The agent reads related existing skills, then chooses to do nothing, patch an existing skill, or create a new one.
- Write readable skill Markdown. Each skill has a name, description, and reusable workflow, including useful branches, edge cases, and pitfalls.
- Commit and index.
commitsubmits changed skill files throughcommit_results; memU embeds the skill name and description and stores it under theskilltrack. - Retrieve it later. On a similar future task, memU returns the relevant skill so any connected agent can use the learned workflow.
The judgment and synthesis stay inside the agent. MemoryService makes no LLM or chat calls; it stores, embeds, and retrieves the skill Markdown the agent prepared.
Self-hosted
Private · Single-device · Embedding key required
To run memU locally with your own storage and embedding provider, send this message to your agent:
Read https://raw.githubusercontent.com/NevaMind-AI/MemU/main/SKILL.md and follow it to install memU.
Uninstall
To uninstall memU, send this message to your agent:
Read https://memu.pro/SKILL.md and follow its instructions to uninstall memU.
By default, uninstalling removes the host integration and tooling while keeping your memory store and ~/.memu/config.env, so a later reinstall can resume where you left off. Memory is erased only when you explicitly ask for it.
Host adapters: memory for desktop coding agents
memU runs as a sidecar to a desktop agent, one binary per host. Each binds two seams:
- record — a scheduled bridging task slices new session logs into self-contained job files; the agent itself distills them into memory/skill Markdown;
commitsubmits whatever the agent left on disk back throughcommit_results. - inject — a standing instruction in the host's instruction file tells the agent to run
<binary> retrieve(→progressive_retrieve) before answering.
| Host | Binary | Session log it mines | Instruction file it patches |
| --- | --- | --- | --- |
| Codex | memu-codex | ~/.codex/sessions/**/*.jsonl | ~/.codex/AGENTS.md |
| Claude Code | memu-claude-code | ~/.claude/projects/<project>/<session>.jsonl | ~/.claude/CLAUDE.md |
| Cursor (Agent/CLI) | memu-cursor | ~/.cursor/projects/<project>/agent-transcripts/**.jsonl | ./AGENTS.md (per project) |
| OpenClaw | memu-openclaw | ~/.openclaw/agents/<agentId>/agent/openclaw-agent.sqlite (SQLite, read-only) + legacy <agentId>/sessions/*.jsonl | ~/.openclaw/workspace/AGENTS.md |
| Hermes Agent | memu-hermes | ~/.hermes/state.db (SQLite, read-only) | ~/.hermes/SOUL.md |
| WorkBuddy | memu-workbuddy | ~/.workbuddy/projects/<project>/<session>.jsonl | ~/.workbuddy/SOUL.md |
| Cola | memu-cola | ~/.cola/sessions/<scope>/<session>.jsonl | ~/.cola/memory-bank/MEMORY.md |
| any other agent | memu-agent | found by memu-agent detect (JSONL dialect sniffed) | found by detect (AGENTS.md / CLAUDE.md / SOUL.md / …) |
For agents without a dedicated binary, memu-agent detect probes the machine and reports per agent whether memorization works (a recognizable session log exists) and whether retrieval works (an instruction file exists to patch) — then the same verbs run against what it found.
All hosts share one configured memory backend via ~/.memu/config.env — local
or MemU Cloud. What one host's sessions taught memU, another host retrieves.
Installation is the one-message setup in Quick start or Self-hosted. SKILL.md is the routing skill it hands your agent: install the package, identify which host you are (falling back to memu-agent detect for anything without a dedicated adapter), print that host's packaged install guide (<binary> docs install), and follow it — configure the memory backend, register the scheduled bridging task, patch the instruction file, each step behind a verify gate — then report which seams (memorization / retrieval) are now active.
Afterwards <binary> doctor proves the whole loop resolves: config, selected
mode, and a live retrieval.
Adding another host means implementing one TranscriptSource (where its session logs live, how its records are shaped) plus a HostSpec-sized CLI — the pipeline, verbs, and instruction text are shared.
CLI
With memU Cloud, sign in at memu.so to view your memory files. With a local installation, memory lives in the shared store configured by MEMU_DB in ~/.memu/config.env — typically ~/.memu/memu.sqlite3 for local SQLite, or a Postgres DSN.
Once installed, your agent retrieves relevant memory automatically before answering. To retrieve manually, run the adapter for your host:
memu-codex retrieve "What should I remember about this project?"
# or: memu-claude-code / memu-cursor / memu-openclaw / memu-hermes / memu-workbuddy / memu-agent
Install or invoke the CLI directly:
pip install memu-cli # library + memu + memu-codex CLIs
npx memu-cli --help # CLI via npm launcher (engine: PyPI package memu-cli)
uvx --from memu-cli memu # CLI via uv, no install
Configuration
Values resolve in order: process env → ~/.memu/config.env → default. memU
supports Local and Cloud memory backends, selected by MEMU_MEMORY_MODE; an
unset mode remains Local for backward compatibility.
For Local / self-hosted installations, every CLI flag has a matching variable:
| Setting | Env var | Default |
|---|---|---|
| Store | MEMU_DB | ./data/memu.sqlite3 (CLI); required for host adapters |
| Embedding provider | MEMU_EMBED_PROVIDER | openai (also: jina, voyage, doubao, openrouter); legacy MEMU_LLM_PROVIDER still read |
| API key | MEMU_API_KEY | the provider's env var, e.g. OPENAI_API_KEY |
| Embedding model | MEMU_EMBED_MODEL | the provider's default |
| Base URL | MEMU_BASE_URL | the provider's default |
Run <binary> doctor to display the resolved mode and verify the same retrieval
path the host uses.
Storage backends
| Provider | DSN | Vector search | Use for |
|---|---|---|---|
| inmemory | — | brute-force cosine | tests, throwaway sessions |
| sqlite | sqlite:///path.sqlite3 | brute-force cosine | local/default, single writer |
| postgres | postgresql://... | pgvector | concurrent access, large stores (pip install "memu-cli[postgres]") |
service = MemoryService(
database_config={"metadata_store": {"provider": "postgres", "dsn": "postgresql://..."}},
embedding_profiles={"default": {"provider": "jina"}},
)
License
Apache-2.0
<sub>Partnership Community: <a href="https://linux.do">LINUX DO</a></sub>
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
