Production-ready patterns, architectures, and implementation guides for modern software and AI systems.
Skyboy AI Patterns is a copy-paste-first pattern library for engineers building AI systems in production. It focuses on working code, architecture diagrams, benchmarks, and tradeoffs for backend AI workflows: agents, retrieval, evaluation, deployment, MCP, and observability.
- Open skyboy.in and search for the workflow you are building.
- Open a pattern, read the architecture and constraints, then copy the Python implementation.
- Adapt the code to your model provider, add the benchmark to your eval suite, and ship behind monitoring.
Most AI content explains concepts.
Skyboy AI Patterns focuses on implementation.
Every pattern includes:
- Architecture diagrams
- Production tradeoffs
- Copy-paste code
- Benchmarks and evaluation metrics
- Real-world deployment guidance
The goal is simple: help engineers ship AI systems faster.
| Category | Count | Example |
|---|---|---|
| Agents | 3 | Router Agent |
| RAG | 3 | Hybrid Search RAG |
| Evaluations | 3 | LLM-as-Judge |
| Fine-Tuning | 3 | LoRA Fine-Tuning |
| Deployment | 3 | FastAPI Deployment |
| MCP | 3 | MCP Server |
| Observability | 3 | LLM Tracing |
Contributions should add practical patterns, benchmarks, examples, or maintenance fixes. Start with one focused change and include enough context for another engineer to validate it locally. See CONTRIBUTING.md for the pattern template, quality checklist, and PR process.
MIT License. Copyright 2025-2026 Skyboy AI Patterns.