Show HN: Agentic Design Patterns – Python Edition, from the Codex Codebase (artvandelay.github.io)

A new learning resource, "Codex Agentic Patterns — Python Edition," publishes a complete set of production-facing design patterns for building AI agents, drawn from real-world code in OpenAI’s Codex CLI. The site documents 21 agentic design patterns and ships 8 fully implemented, runnable Python patterns with production-grade examples, robust error handling, multi-turn conversational workflows, tool integration, and human-in-the-loop approval/oversight patterns. It also includes quick-start guides, exercises, and a full index to help engineers move from toy prototypes to maintainable, auditable agent systems. For the AI/ML community this is significant because it exposes concrete, battle-tested engineering practices rather than abstract recommendations—accelerating reproducible agent development and safer deployments. Technical implications include reusable pattern templates for orchestrating multi-step agent plans, standardized approaches to integrating external tools and services, explicit strategies for error recovery and human oversight, and runnable code you can adapt to production stacks. By translating lessons from a production Rust CLI into Python examples, the resource lowers the bar for researchers and practitioners to build, evaluate, and govern complex agentic systems.