Agent Memory Model Card
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Agent Memory Model Card
| Field | Value |
|---|---|
| Repository | axiomhq/agent-memory |
| Category | Persistent Agent Memory Runtime |
| Stars / forks snapshot | 5 / 2 |
| Language | TypeScript |
| License | Unspecified |
| Raw capture | raw-github/axiomhq_agent-memory.md |
| Updated by | hourly public metadata update, 2026-05-29 10:08 +0800 |
1. Role in Self Evolve
agent-memory is an opinionated TypeScript memory runtime focused on extracting, storing, and retrieving long-lived context for AI agents. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
capture user/agent interaction state -> extract and store memory artifacts in redis-backed structures -> retrieve context through memory APIs -> feed subsequent agent decisions and orchestration flows
3. Evidence Path
web-observed GitHub page showed 5 stars, 2 forks, 50 commits, and README documenting extraction/storage/retrieval architecture; no explicit license badge was visible on the page snapshot. 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 Agent Memory Runtime: 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.