MemToMem Model Card
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MemToMem Model Card
| Field | Value |
|---|---|
| Repository | memtomem/memtomem |
| Category | Hierarchical Agent Memory Framework |
| Stars / forks snapshot | 5 / 24 |
| Language | Python |
| License | Apache-2.0 |
| Raw capture | raw-github/memtomem_memtomem.md |
| Updated by | hourly public metadata update, 2026-05-30 07:15 +0800 |
1. Role in Self Evolve
memtomem provides a hierarchical long-term memory framework to improve context retention, retrieval quality, and continuity in autonomous agent 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
capture episodic and semantic memory traces -> structure memory in hierarchical graphs -> retrieve context by relevance and recency -> feed persistent memory context back into ongoing autonomous task execution
3. Evidence Path
web-observed GitHub page showed 5 stars, 24 forks, 1,025 commits, Apache-2.0 licensing, and README positioning around hierarchical memory for autonomous agents. 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 Hierarchical Agent Memory Framework: 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.