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 aiming-lab · MCP Server · ★ 3.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SimpleMem safe to install? View the security audit →
Efficient Lifelong Memory for LLM Agents — Text & Multimodal Store, compress, and retrieve long-term memories with semantic lossless compression. Now with multimodal support for text, image, audio & video. Works with any AI platform that supports MCP (text memory) or Python integration (full multimodal) Claude Desktop Cursor LM Studio <a hr
| Stars | 3,766 |
| Forks | 394 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 61.2733653685766/100 |
| Open Issues | 8 |
| Last Updated | 2026-07-24 |
| Created | 2026-01-01 |
| Platforms | mcp, python |
| Est. Tokens | ~19k |
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SimpleMem is SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal. It is categorized as a MCP Server with 3.8k GitHub stars.
SimpleMem is primarily written in Python. It covers topics such as agent, audio, compression.
You can find installation instructions and usage details in the SimpleMem GitHub repository at github.com/aiming-lab/SimpleMem. The project has 3.8k stars and 394 forks, indicating an active community.
SimpleMem is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to SimpleMem on Agent Skills Hub include code-graph-rag, mcp-memory-service, antigravity-workspace-template. 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: