MateClaw OpenClaw Memory and Rule Engine Model Card
这是可索引项目报告证据页：它保留 MateClaw OpenClaw Memory and Rule Engine 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。
MateClaw OpenClaw Memory and Rule Engine Model Card
| Field | Value |
|---|---|
| Repository | matevip/mateclaw |
| Category | OpenClaw Runtime Extension with Memory Control and Automation Rules |
| Stars / forks snapshot | 537 / 184 |
| Language | Java |
| License | Apache-2.0 |
| Raw capture | raw-github/matevip_mateclaw.md |
| Updated by | hourly public metadata update, 2026-06-01 19:51 +0800 |
1. Role in Self Evolve
matevip/mateclaw extends the OpenClaw ecosystem with memory-aware runtime controls, rule-engine automation, and agent execution governance. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
inject memory and rule-engine controls into OpenClaw execution -> bind prompts, tools, and context flows to policy checks -> automate repeatable task pipelines with persistent state -> reduce drift while compounding operator experience across runs
3. Evidence Path
web-observed GitHub page showed 537 stars, 184 forks, 1,020 commits, Apache-2.0 license, and OpenClaw runtime positioning. 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 OpenClaw Runtime Extension with Memory Control and Automation Rules: 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.