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 MemTensor · MCP Server · ★ 2.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is memmy-agent safe to install? View the security audit →
<img src="https://img.shields.io/badge/Follow-Memmy-000000?labelColor=gray&style=for-the-badge&logo=x&logoColor=white
| Stars | 1,952 |
| Forks | 180 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.9757689557171/100 |
| Open Issues | 30 |
| Last Updated | 2026-09-20 |
| Created | 2026-07-16 |
| Platforms | claude-code, cli, codex, mcp, node |
| Est. Tokens | ~17k |
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memmy-agent is 🍙 A personal AI agent & local memory hub for all AI agents, gives every AI one shared, fully controlled memory and persistent context — all AI remember the same you. Now supports Claude Code, Codex,. It is categorized as a MCP Server with 2.0k GitHub stars.
memmy-agent is primarily written in TypeScript. It covers topics such as agent, agentic-ai, ai-agents.
You can find installation instructions and usage details in the memmy-agent GitHub repository at github.com/MemTensor/memmy-agent. The project has 2.0k stars and 180 forks, indicating an active community.
memmy-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to memmy-agent on Agent Skills Hub include cursor-talk-to-figma-mcp, memsearch, OpenContext. 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: