Never Get Caught by an LLM Deprecation Again: A Guide to llm-model-deprecation

` How to keep your apps on supported models with one Python package, a CLI, and a GitHub Action. If you’ve ever had an integration break because OpenAI or Anthropic retired a model, you know the pain: sudden 404s, vague errors, and a scramble to find a replacement. Provider deprecation pages help, but they’re easy to miss when you’re shipping features. What you need is something that checks your code and config for deprecated or retired models and tells you exactly what to change. That’s what llm-model-deprecation does. It gives you: A Python library to query deprecation status and get replacement suggestions A CLI to scan a project for deprecated model references (great for CI and cron) A GitHub Action so every push or PR can fail if you’re still using retired models The registry (OpenAI, Anthropic, Gemini, and more) is refreshed weekly , so you’re not relying on stale data. Here’s how to use it in detail. Why this matters LLM providers regularly: Deprecate older models and set sunset