PowerMem Agent Memory Plugin Model Card

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

PowerMem Agent Memory Plugin Model Card

1. Role in Self Evolve

oceanbase/powermem is an agent memory plugin focused on improving accuracy, agility, and affordability for AI agent memory retrieval. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

augment agent pipelines with explicit memory plugin boundaries -> optimize recall quality and retrieval cost across workflows -> provide reusable memory layer for multi-step decisions -> increase agent consistency through persistent context integration

3. Evidence Path

public GitHub API metadata showed 688 stars, 83 forks, about 200 commit pages, Python codebase, and memory-plugin positioning. GitHub metadata was captured via public API in this iteration (without authenticated token); this card marks counts as API-observed with possible rate-limit drift.

4. Teaching Use

Use this card to explain Agent Memory Plugin and Retrieval Augmentation Layer: 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.