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 openmemind · Codex Skill · ★ 897
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memind safe to install? View the security audit →
Memind Memory that thinks. Context that evolves. The memory layer that lets AI systems learn from every conversation, tool call, document, and resolved task. Memind turns raw context into structured memory and reusable experience, continuously organizes it into memory graphs, threads, and evolving Insight Trees, then recalls the right context through REST, MCP, SDKs, and first-party plugins for popular agents. <a href="#benc
| Stars | 897 |
| Forks | 92 |
| Language | Java |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 65.3915420741895/100 |
| Open Issues | 11 |
| Last Updated | 2026-08-13 |
| Created | 2026-03-19 |
| Platforms | java |
| Est. Tokens | ~23k |
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memind is Self-evolving cognitive memory and context engine for AI agents in Java. Empowering 24/7 proactive agents like OpenClaw with understanding and SOTA performance.. It is categorized as a Codex Skill with 897 GitHub stars.
memind is primarily written in Java. It covers topics such as ai, ai-agent, ai-agents.
You can find installation instructions and usage details in the memind GitHub repository at github.com/openmemind/memind. The project has 897 stars and 92 forks, indicating an active community.
memind is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to memind on Agent Skills Hub include Mysti, agentic-context-engine, n8n-claw. 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: