by MemTensor · Codex Skill · ★ 366
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MemOS Cloud OpenClaw Plugin (Lifecycle) Official plugin maintained by MemTensor. A minimal OpenClaw lifecycle plugin that recalls memories from MemOS Cloud before each run and adds new messages to MemOS Cloud after each run. Features Recall: → Add: → Uses Token auth () Install Option A — NPM (Recommended) Note for Windows Users: If you encounter , this is a known issue with OpenClaw's plugin installer on Windows. Please use Option B (Manual Install) below. Make sure it’s enabled in : Option B — Manual Install (Workaround for Windows) Download the latest from NPM. Extract it to a local folder (e.g., ). Configure (or ): json { "plugins": { "entries": { "memos-cloud-openclaw-plugin": { "enabled": true } }, "load": { "paths": [ "C:\\Users\\YourName\\.openclaw\\extensions\\memos-cloud-openclaw-plu
| Stars | 366 |
| Forks | 57 |
| Language | JavaScript |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 74.2396271457487/100 |
| Open Issues | 29 |
| Last Updated | 2026-06-17 |
| Created | 2026-02-02 |
| Platforms | node |
| Est. Tokens | ~14k |
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微信包装了开源项目OpenClaw，所以我们把他的Channel抽出来了😄
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MemOS-Cloud-OpenClaw-Plugin is Official MemOS Cloud plugin for OpenClaw. Enables long-term memory for agents by recalling context before execution and saving conversations after each run.. It is categorized as a Codex Skill with 366 GitHub stars.
MemOS-Cloud-OpenClaw-Plugin is primarily written in JavaScript. It covers topics such as ai-memory, memos, memtensor.
You can find installation instructions and usage details in the MemOS-Cloud-OpenClaw-Plugin GitHub repository at github.com/MemTensor/MemOS-Cloud-OpenClaw-Plugin. The project has 366 stars and 57 forks, indicating an active community.
MemOS-Cloud-OpenClaw-Plugin is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to MemOS-Cloud-OpenClaw-Plugin on Agent Skills Hub include memory-lancedb-pro, secure-openclaw, clawsec. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.