model-compose
Reduces deployment complexity for AI services by abstracting infrastructure setup into declarative configuration, enabling faster time-to-market and lower op…
Deploy production-ready AI services in minutes. One YAML file for agents, RAG pipelines, and MCP servers — run anywhere. Inspired by docker-compose.
- Deploy a multi-agent AI system to production using a single YAML configuration file
- Spin up RAG pipelines with vector databases and language models in minutes
- Run MCP servers alongside AI agents without writing deployment scripts
Reduces deployment complexity for AI services by abstracting infrastructure setup into declarative configuration, enabling faster time-to-market and lower operational overhead for teams managing multiple AI workloads.
DevOps engineers and ML platform teams deploying containerized AI agents and RAG systems across environments.
https://github.com/hanyeol/model-compose
By hanyeol
How to Get It
claude plugins install hanyeol/model-compose
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me deploy a multi-agent AI system to production using a single YAML configuration file
Trust Signals Auto-scanned
Community Pulse New
- A Guide to Model Composition — Hacker News · 4 pts
- Cursor's "in-house model" Composer 2 is Kimi K2.5 with RL on top — Hacker News · 3 pts
- Not Just for AI, Developing for AMD Versal AI Engines Using Simulink for 2D FFTs — Hacker News · 2 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 76 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 76 GitHub stars; contributors unknown; last commit 0d ago; license MIT.