VectorSpaceLab General Agentic Memory Model Card

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VectorSpaceLab General Agentic Memory Model Card

1. Role in Self Evolve

VectorSpaceLab/general-agentic-memory focuses on generalized memory abstractions that can be reused across varied agent workloads. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

design generalized memory abstractions for diverse agent tasks -> persist and retrieve context under shared memory interfaces -> reuse memory operations across domains and workflows -> scale agent continuity without per-task memory rewrites

3. Evidence Path

web-observed GitHub page showed 390 stars, 32 forks, 481 commits, and generalized memory-framework framing. 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 General Agentic Memory Framework with Cross-Task Reuse: 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.