Flagged: installs a background service. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by MaxFreedomPollard · MCP Server · ★ 592
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Compartment safe to install? View the security audit →
Compartment Listed on: PyPI · Glama · LobeHub · MCP Toplist · mcpservers.org · TensorBlock · Libraries.io · Snyk Advisor · deps.dev Durable agentic memory, encrypted at rest. Your AI agent forgets you the moment the session ends. Compartment ends that. With Compartment, your AI agent gets better with experience: it keeps every decision, preference and detail you give it, permanently, encrypted, on your own computer. Hermes, Claude, OpenClaw and other AI Agents can install in one command. One fully-transferable memory store is shared simultaneously by all agents on the computer. 100% offline: no
| Stars | 592 |
| Forks | 3 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 63.4111333759923/100 |
| Last Updated | 2026-09-06 |
| Created | 2026-07-20 |
| Platforms | claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~23k |
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Compartment is Encrypted, fully offline agentic memory. One click install, GUI w/ memory map, all OS and agents. Superior memory creation, storage and retrieval.. It is categorized as a MCP Server with 592 GitHub stars.
Compartment is primarily written in Python. It covers topics such as agent-memory, ai-agents, ai-memory.
You can find installation instructions and usage details in the Compartment GitHub repository at github.com/MaxFreedomPollard/Compartment. The project has 592 stars and 3 forks, indicating an active community.
Compartment is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Compartment on Agent Skills Hub include mcp-memory-service, memorix, Ori-Mnemos. 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: