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 hugalafutro · LLM Plugin · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is model-hotel safe to install? View the security audit →
"Because we have LiteLLM at home" Multi-Provider AI Gateway [!IMPORTANT] AI-Assisted Project Disclaimer: Human judgment applied at every stage, particularly around architectural decisions, UX flows
| Stars | 56 |
| Forks | 3 |
| Language | Go |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 55.4761047780513/100 |
| Last Updated | 2026-10-02 |
| Created | 2026-04-19 |
| Platforms | go |
| Est. Tokens | ~23k |
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model-hotel is Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we have LiteLLM at home". It is categorized as a LLM Plugin with 56 GitHub stars.
model-hotel is primarily written in Go. It covers topics such as ai, ai-tools, android-app.
You can find installation instructions and usage details in the model-hotel GitHub repository at github.com/hugalafutro/model-hotel. The project has 56 stars and 3 forks, indicating an active community.
model-hotel is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to model-hotel on Agent Skills Hub include lm-proxy, anthropic-max-router, gopher-mcp. 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: