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 CodeAbra · MCP Server · ★ 879
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is iai-personal-memory-engine safe to install? View the security audit →
The best open-source personal memory engine for AI coding assistants. Every claim ships with the harness that proves it. Run the benchmarks yourself. <img src="https://img.shields.io/badge/MCP-compatible-8957e5?style=flat-square"
| Stars | 879 |
| Forks | 108 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.4710051511452/100 |
| Open Issues | 18 |
| Last Updated | 2026-09-09 |
| Created | 2026-05-06 |
| Platforms | claude-code, codex, gemini, mcp, python |
| Est. Tokens | ~20k |
These tools work well together with iai-personal-memory-engine for enhanced workflows:
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iai-personal-memory-engine is A cyber brain for your AI. It never forgets a detail, remembers exactly what you said, and learns how you work over time. Free, local, works with Cursor, Claude Code, Codex, OpenClaw, Hermes and more.. It is categorized as a MCP Server with 879 GitHub stars.
iai-personal-memory-engine is primarily written in Python. It covers topics such as agent-memory, ai-memory, claude.
You can find installation instructions and usage details in the iai-personal-memory-engine GitHub repository at github.com/CodeAbra/iai-personal-memory-engine. The project has 879 stars and 108 forks, indicating an active community.
iai-personal-memory-engine is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to iai-personal-memory-engine on Agent Skills Hub include omega-memory, memorix, superlocalmemory. 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: