Learning SDK Model Card

这是可索引项目报告证据页：它保留 Learning SDK 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。

Learning SDK Model Card

1. Role in Self Evolve

Learning SDK is a drop-in memory and continual-learning substrate for existing LLM agents, exposing a thin wrapper instead of a full custom runtime. It matters because self-evolving agents need explicit memory, harness, benchmark, and safety substrates before their improvement claims become trustworthy.

2. Working Principle

wrap an existing LLM client -> capture conversation traces -> persist and inject relevant memory -> make the original agent stateful without retraining the base model

3. Evidence Path

web-observed GitHub page showed 45 stars, 3 forks, 72 commits, Apache-2.0 license, Python and TypeScript packages, and a one-line pattern for adding continual learning plus long-term memory to existing LLM agents. This iteration keeps freshness honest: the snapshot comes from the public GitHub page observed on 2026-06-04, while shell GitHub API access remained blocked in this workspace.

4. Teaching Use

Use this card to explain Continual Learning And Long-Term Memory SDK: it shows how survey indexes, embeddable memory SDKs, harness taxonomies, controlled self-modification, or trace-scored benchmarks connect to the broader self-evolving-agent pipeline.

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 signals unless independently revalidated later.