Awesome Agent Memory Model Card

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

Awesome Agent Memory Model Card

1. Role in Self Evolve

awesome-agent-memory is a focused index of agent memory papers, systems, and implementation resources for long-term memory design decisions. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

aggregate memory papers and systems -> cluster long-term, retrieval, and memory-runtime approaches -> provide a quick reference for selecting memory substrates -> support comparative study and downstream implementation planning

3. Evidence Path

The current run rechecked a cached GitHub search result observed on 2026-06-04, with crawl recency surfaced as 5 days ago. That cached public result showed 11 stars, 3 forks, 16 commits, 2 issues, 3 pull requests, MIT licensing, and a README organized around memory design comparisons, standalone libraries, framework memory modules, RL memory training, and MCP memory servers. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as cached public-page evidence rather than live API verification.

4. Teaching Use

Use this card to explain Agent Memory Research and Tooling Index: 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.