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 melandlabs · MCP Server · ★ 1.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openloomi safe to install? View the security audit →
An AI That Always Remembers You. English | 简体中文 What is OpenLoomi? OpenLoomi is an open-source AI workspace that runs on your desktop. It connects to the tools you already use — messaging apps, email, calendar, documents, project trackers — and builds a Holistic Context Graph of your people, projects, and decisions. Features What it does
| Stars | 1,020 |
| Forks | 46 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65.7130340016689/100 |
| Open Issues | 20 |
| Last Updated | 2026-08-31 |
| Created | 2026-04-10 |
| Platforms | mcp, node |
| Est. Tokens | ~15k |
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openloomi is OpenLoomi is an open-source AI coworker. It connects your work tools, understands what you’re working on, and tells you what needs your attention, why it matters, and what to do next. It’s your open-s. It is categorized as a MCP Server with 1.0k GitHub stars.
openloomi is primarily written in TypeScript. It covers topics such as agent, ai, attention-agent.
You can find installation instructions and usage details in the openloomi GitHub repository at github.com/melandlabs/openloomi. The project has 1.0k stars and 46 forks, indicating an active community.
openloomi is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to openloomi on Agent Skills Hub include entroly, AI-Infra-Guard, EverMemOS. 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: