by zilliztech · Codex Skill · ★ 2.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memsearch safe to install? View the security audit →
memsearch Cross-platform semantic memory for AI coding agents. <a href="https://github.com/zilliztech/memse
/plugin marketplace add zilliztech/memsearch
/plugin install memsearchTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 2,730 |
| Forks | 265 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.1861151149996/100 |
| Open Issues | 271 |
| Last Updated | 2026-10-09 |
| Created | 2026-02-09 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~18k |
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memsearch is A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown and Milvus.. It is categorized as a Codex Skill with 2.7k GitHub stars.
memsearch is primarily written in Python. It covers topics such as agent, agent-memory, claude-code.
The README on GitHub (github.com/zilliztech/memsearch) gives: /plugin marketplace add zilliztech/memsearch , then /plugin install memsearch. Usage details are in the same README.
memsearch is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to memsearch on Agent Skills Hub include graph-memory, EverOS, MemOS. 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: