Acontext Model Card
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Acontext Model Card
| Field | Value |
|---|---|
| Repository | memodb-io/Acontext |
| Category | Agent Skill Memory Layer and Runtime Context Engine |
| Stars / forks snapshot | 3500 / 319 |
| Language | TypeScript |
| License | Apache-2.0 |
| Raw capture | raw-github/memodb-io_acontext.md |
| Updated by | hourly public metadata update, 2026-05-28 22:03 +0800 |
1. Role in Self Evolve
Acontext provides an agent memory layer designed to persist and retrieve skill-aware context for autonomous AI workflows. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
skill and behavior trace ingestion -> memory indexing and retrieval -> context-aware execution with long-term persistence -> memory-informed agent behavior adaptation
3. Evidence Path
web-observed GitHub page showed 3.5k stars, 319 forks, 1,080 commits, Apache-2.0 license, and README framing as a memory layer for agent skills. 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 Agent Skill Memory Layer and Runtime Context Engine: 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.