Ori Mnemos Memory Harness Model Card
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Ori Mnemos Memory Harness Model Card
| Field | Value |
|---|---|
| Repository | aayoawoyemi/ori-mnemos |
| Category | Agent Memory Substrate and Runtime Tracing Harness |
| Stars / forks snapshot | 10 / 0 |
| Language | TypeScript |
| License | MIT |
| Raw capture | raw-github/aayoawoyemi_ori-mnemos.md |
| Updated by | hourly public metadata update, 2026-05-31 19:50 +0800 |
1. Role in Self Evolve
aayoawoyemi/ori-mnemos provides a memory substrate that records agent traces and feeds retrieval into future steps. 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 agent interactions and prompts into typed memory records -> run compression and retrieval policies over prior episodes -> inject relevant traces back into current execution context -> expose memory operations as runtime primitives for reproducible long-horizon behavior
3. Evidence Path
web-observed GitHub page showed 10 stars, 0 forks, 17 commits, MIT license, and TypeScript-dominant code focused on memory and prompts. 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 Memory Substrate and Runtime Tracing Harness: 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.