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 carsteneu · Agent Tool · ★ 165
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-memory-comparison safe to install? View the security audit →
AI Memory Systems — Feature Comparison Live comparison table: carsteneu.github.io/ai-memory-comparison 73 systems, 79 features, source-backed. Every ✅ links to the exact code or docs that prove it. What this is A feature-level comparison of memory systems for AI coding agents. 73 systems compared across 79 features on seven axes: Data Model, Search & Retrieval, Knowledge Lifecycle, Extraction Pipeline, Platform Support, Architecture, and Benchmarks. Every claim backed by a public source citation —
| Stars | 165 |
| Forks | 24 |
| Language | HTML |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.936620675297/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-03 |
| Created | 2026-05-28 |
| Platforms | claude-code |
| Est. Tokens | ~16k |
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ai-memory-comparison is Source-backed feature comparison of memory systems for AI coding agents. No affiliation, no marketing — just facts from public docs.. It is categorized as a Agent Tool with 165 GitHub stars.
ai-memory-comparison is primarily written in HTML. It covers topics such as ai-agents, ai-memory, claude-code.
You can find installation instructions and usage details in the ai-memory-comparison GitHub repository at github.com/carsteneu/ai-memory-comparison. The project has 165 stars and 24 forks, indicating an active community.
ai-memory-comparison is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ai-memory-comparison on Agent Skills Hub include ai-maestro, vestige, ok-skills. 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: