openclaw-engram: Local-first memory plugin for OpenClaw AI agents

by joshuaswarren · Agent Tool · ★ 52

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 67/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentai-memoryconversational-aiengramknowledge-graphllmlocal-firstlong-term-memorymarkdownmemory-plugin

Quick Facts

Stars52
Forks11
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score67/100
Open Issues1
Last Updated2026-04-08
Created2026-02-05
Platformsnode
Est. Tokens~724k

About openclaw-engram

Remnic Open-source memory and context for user-aware agents. Remnic is for agents that need to understand the people they work with over time. Remnic helps AI agents understand the people they work with: their preferences, projects, constraints, decisions, patterns, and definition of good.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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