OptMem: Permanent memory for AI agents. A 426-token prompt, a script, plug and play

by VictorTaelin · Agent Tool · ★ 2.4k

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

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars2,375
Forks149
LanguagePython
CategoryAgent Tool
Quality Score64/100
Open Issues17
Last Updated2026-07-31
Created2026-07-25
Platformspython
Est. Tokens~14k

About OptMem

OptMem Permanent memory for AI agents. A 426-token prompt, a script, plug and play. Install It prints a block. Paste that at the top of your agent's (or ), and you are done. Run the same line again to update. The tool lands at ; put on to type . Commands Merges arrive one at a time, in the output of . Nothing ever runs in the background. Files is the only

Compatible Skills

These tools work well together with OptMem for enhanced workflows:

  • homelab-monitor — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)
  • mnemory — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (58%)
  • LycheeMem — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • projectmem — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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