I Cut My LLM API Bill in Half with a Single Python Library

Last month I was debugging why our agent pipeline was burning through $400/day in OpenAI tokens. Turns out 60% of what we were feeding GPT-4 was redundant — repeated JSON schemas, duplicate log blocks, unchanged diff context, verbose imports. I tried prompt trimming by hand. Tedious. I tried LLMLingua. Better, but it needs a GPU and the fidelity wasn't great at high compression. Then I found claw-compactor and honestly I'm a bit mad I didn't find it sooner. What It Actually Does It's a 14-stage compression pipeline that sits between your data and the LLM. No neural network, no inference cost — pure deterministic transforms. You feed it code, JSON, logs, diffs, whatever, and it spits out a compressed version that preserves meaning but costs way fewer tokens. The compression rates are kind of nuts: JSON payloads: 82% reduction Build logs: 76% reduction Python source: 25% reduction Git diffs: 40%+ reduction Weighted average across real workloads: ~54% fewer tokens. Why I Actually Switched