Mengram Human-Like Agent Memory Model Card

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Mengram Human-Like Agent Memory Model Card

1. Role in Self Evolve

alibaizhanov/mengram offers human-like semantic/episodic/procedural memory infrastructure for AI agents with multi-framework integrations. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

model semantic, episodic, and procedural memories as explicit agent assets -> learn procedures from failures and feedback traces -> integrate memory services into LangChain/CrewAI/OpenClaw flows -> improve adaptation quality through structured memory retention

3. Evidence Path

public GitHub API metadata showed 172 stars, 27 forks, 528 commit pages, Apache-2.0 license, and Python-led memory platform implementation. GitHub metadata was captured via public API in this iteration (without authenticated token); this card marks counts as API-observed with possible rate-limit drift.

4. Teaching Use

Use this card to explain Semantic/Episodic/Procedural Memory Runtime for Agents: 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.