MemOS Cloud OpenClaw Plugin Model Card
这是可索引项目报告证据页：它保留 MemOS Cloud OpenClaw Plugin Model Card 的源材料入口、机制线索和限制提醒；正文仍需 reader/editor 与 academic public-copy review 后才能当作最终结论引用。
MemOS Cloud OpenClaw Plugin Model Card
| Field | Value |
|---|---|
| Repository | MemTensor/MemOS-Cloud-OpenClaw-Plugin |
| Category | Hosted Agent Memory Runtime Plugin |
| Stars / forks snapshot | 367 / 56 |
| Language | JavaScript |
| License | Apache-2.0 |
| Raw capture | raw-github/memtensor_memos-cloud-openclaw-plugin.md |
| Updated by | hourly public metadata update, 2026-05-29 22:15 +0800 |
1. Role in Self Evolve
MemOS-Cloud-OpenClaw-Plugin is an official OpenClaw plugin that adds hosted long-term memory retrieval and persistence through MemOS Cloud. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
intercept agent execution context before task start -> recall long-term memories from hosted MemOS service -> run tasks with enriched context -> persist post-run conversations for cumulative memory growth
3. Evidence Path
web-observed GitHub page showed 367 stars, 56 forks, 225 commits, Apache-2.0 license, and README docs for before-run recall plus post-run save workflows. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as web-observed rather than API-verified.
4. Teaching Use
Use this card to explain Hosted Agent Memory Runtime Plugin: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.
5. Limits
The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.