No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by NeoLi00 · Codex Skill · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openclaw-memx safe to install? View the security audit →
English · 中文 · Architecture memX turns completed work into structured, searchable, self-maintained memory, then injects only the evidence an agent needs for the current query. It connects natively to Codex, Claude Code, and OpenClaw, and reaches any MCP-compatible client through the same local memory layer. Benchmarks Suite Scope R@3 success rate LongMemEval-S Long-context memory retrieval 94.2% Real engineering cases 30 cases, each with 20+ turns 100% Architecture Agent support Codex native hooks, MCP hidden by default <img src=
| Stars | 60 |
| Forks | 0 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 62.9500266918046/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-19 |
| Created | 2026-05-08 |
| Platforms | node |
| Est. Tokens | ~140k |
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openclaw-memx is openclaw自学习自维护记忆插件 Long-term OpenClaw memory plugin with self-learning, self-maintenance, and relationship graph recall.. It is categorized as a Codex Skill with 60 GitHub stars.
openclaw-memx is primarily written in TypeScript. It covers topics such as agent, agent-memory, embeddings.
You can find installation instructions and usage details in the openclaw-memx GitHub repository at github.com/NeoLi00/openclaw-memx. The project has 60 stars and 0 forks, indicating an active community.
The top alternatives to openclaw-memx on Agent Skills Hub include hipocampus, JoySafeter, community. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: