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 mnemon-dev · Codex Skill · ★ 578
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Featured in: Agent Memory
Mnemon English | 中文 LLM-supervised persistent memory for AI agents. LLM agents forget everything between sessions. Context compaction drops critical decisions, cross-session knowledge vanishes, and long conversations push early information out of the window. Mnemon gives your agent persistent, cross-session memory — a four-graph knowledge store with intent-aware recall, importance decay, and automatic deduplication. Single binary, zero API keys, one setup command. Experimental beta: this repository also includes , a source-built beta for project-local host-agent lifecycle state. It is separate from the stable CLI, not production-ready, and may make breaking changes at any time. See harness/README.md. Claude Max / Pro subscriber? Mnemon works entirely through your existing subscription — no separa
| Stars | 578 |
| Forks | 70 |
| Language | Go |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 72.2890719343712/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-15 |
| Created | 2026-02-20 |
| Platforms | claude-code, cli, go |
| Est. Tokens | ~19k |
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mnemon is LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with DeepSeek Harness, Claude Code, OpenClaw, and any agent runtime.. It is categorized as a Codex Skill with 578 GitHub stars.
mnemon is primarily written in Go. It covers topics such as agent-framework, agent-memory, ai-agent.
You can find installation instructions and usage details in the mnemon GitHub repository at github.com/mnemon-dev/mnemon. The project has 578 stars and 70 forks, indicating an active community.
mnemon is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to mnemon on Agent Skills Hub include memsearch, graph-memory, claude-brain. 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: