ClawVault Memory Runtime Model Card

这是可索引项目报告证据页：它保留 ClawVault Memory Runtime 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。

ClawVault Memory Runtime Model Card

1. Role in Self Evolve

Versatly/clawvault provides structured persistent memory for AI agents and exposes benchmarks, docs, and eval surfaces around that memory stack. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, independent benchmarks, and durable memory/runtime surfaces before claiming stable improvement.

2. Working Principle

store structured memory for autonomous agent workflows -> expose retrieval and evaluation surfaces to long-horizon tasks -> integrate memory into OpenClaw-style execution loops -> treat persistence as a runtime subsystem rather than a prompt-only trick

3. Evidence Path

web-observed GitHub page showed 646 stars, 62 forks, 689 commits, MIT license, benchmark and eval directories, and OpenClaw-oriented persistent-memory framing with a deprecation boundary for fresh deployments. 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 Persistent Memory Runtime for OpenClaw-Style AI Agents: it shows how benchmark/harness or memory/runtime layers convert agent behavior into reproducible and auditable engineering workflows.

5. Limits

The repository was not cloned in this iteration; no benchmark run, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.