Your LLM App Will Fail. Fix It Like This #ai #programming

Building reliable LLM applications requires more than just orchestrating API calls. Using Temporal, you can wrap an LLM manager in a durable workflow that persists state across restarts, waits indefinitely for human input without consuming resources, and resumes exactly where it left off after failures. The key insight is that plain Python LLM manager code gains durability simply by running inside a Temporal workflow context, where an OpenAI agents plugin intercepts calls and executes them as durable activities — no manual retry logic, checkpointing, or crash recovery code needed.