memsearch — security grade SAFE, quality 64/100

Security audit verdict: SAFE · quality 64/100

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 zilliztech · Codex Skill · ★ 2.6k

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

🔒 Is memsearch safe to install? View the security audit →

About memsearch

&nbsp; memsearch Cross-platform semantic memory for AI coding agents. <a href="https://github.com/zilliztech/memse

agentagent-memoryclaude-codeclaude-code-plugincodexdeepseekdeepseek-harnessdshdsh-pluginembeddings

Quick Facts

Stars2,634
Forks256
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score64.1861151149996/100
Open Issues255
Last Updated2026-09-22
Created2026-02-09
Platformsclaude-code, codex, python
Est. Tokens~18k

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Looking for a memsearch alternative? If you're comparing memsearch with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is memsearch?

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 2.6k GitHub stars.

What programming language is memsearch written in?

memsearch is primarily written in Python. It covers topics such as agent, agent-memory, claude-code.

How do I install or use memsearch?

You can find installation instructions and usage details in the memsearch GitHub repository at github.com/zilliztech/memsearch. The project has 2.6k stars and 256 forks, indicating an active community.

What license does memsearch use?

memsearch is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to memsearch?

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.

How this security grade is produced

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:

View on GitHub → Browse Codex Skill tools