Building Crash-Recovery State Machines for Long-Running LLM Pipelines in Python

LLM pipelines fail. API timeouts, rate limits, unexpected token counts, network blips — any of these can kill a long-running job mid-way through. If you restart from zero every time, you're wasting money and time. Here's a state machine pattern with atomic checkpoints that lets you resume exactly where you left off. The Core Problem Imagine a pipeline that processes 10 chapters: Chapter 1 → DONE Chapter 2 → DONE ... Chapter 7 → [process crashes here] Chapter 8 → not started ......