Flagged: opens a tunnel service. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by hanyeol · MCP Server · ★ 113
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is model-compose safe to install? View the security audit →
한국어 | 中文 model-compose Compose AI Systems, Deploy Anywhere. Build AI agents, RAG pipelines, MCP servers, and multi-model workflows in a single YAML file. Run the same system locally, in containers, or in production without rewriting your stack. Inspired by , model-compose provides a portable runtime for AI systems — combining cloud APIs and local models without vendor lock-in. Documentation · Quick Start · Examples · Contributing Philosophy AI systems should not be locked into a single provider, runtime, or cloud. They should remain portable, inspectable, and able to run anywhere. Today, many AI applications are tightly coupled to provider-specific APIs, managed runtimes, and clos
| Stars | 113 |
| Forks | 16 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.1031590501892/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-26 |
| Created | 2025-05-01 |
| Platforms | docker, mcp, python |
| Est. Tokens | ~16k |
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model-compose is Deploy production-ready AI services in minutes. One YAML file for agents, RAG pipelines, and MCP servers — run anywhere. Inspired by docker-compose.. It is categorized as a MCP Server with 113 GitHub stars.
model-compose is primarily written in Python. It covers topics such as agent-framework, ai-agents, ai-infrastructure.
You can find installation instructions and usage details in the model-compose GitHub repository at github.com/hanyeol/model-compose. The project has 113 stars and 16 forks, indicating an active community.
model-compose is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to model-compose on Agent Skills Hub include corpusos, orloj, ai-microcore. 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: