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 riponcm · MCP Server · ★ 824
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is projectmem safe to install? View the security audit →
We don't make AI smarter. We make it experienced. The local-first memory + judgment layer for AI coding agents. Save up to 50%+ of AI tokens. Stop repeating yesterday's bug. <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-s
| Stars | 824 |
| Forks | 40 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.5940438635679/100 |
| Last Updated | 2026-09-15 |
| Created | 2026-05-09 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~19k |
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projectmem is Open-source coding agents memory. Records issues, attempts, fixes and decisions, then warns your agent before it repeats an approach that already failed. Native MCP server for Claude Code, Cursor, Ant. It is categorized as a MCP Server with 824 GitHub stars.
projectmem is primarily written in Python. It covers topics such as agent-memory, agentic-ai, ai-agents.
You can find installation instructions and usage details in the projectmem GitHub repository at github.com/riponcm/projectmem. The project has 824 stars and 40 forks, indicating an active community.
projectmem is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to projectmem on Agent Skills Hub include vestige, omega-memory, mcp-memory-service. 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: