Claw Compactor: compress LLM tokens 54% with zero dependencies

What it is

Think of it as gzip for prompts, but semantic-aware. Claw Compactor analyzes your text and strips out token-wasting patterns — extra spaces, verbose markdown, repetitive structure — without breaking what the LLM needs to understand. It's preprocessing that happens before you send anything to the API.

Why it matters

If you're building with GPT-4 or Claude and routinely bumping into context windows, this is a simple pre-step that makes room for more examples or longer outputs. Also: fewer tokens = lower bills. No model fine-tuning, no new infrastructure — just a function call before you hit send.

Key details

• •Open-source on GitHub under open-compress/claw-compactor
• •Zero dependencies — pure Python implementation, no pip install bloat
• •Claims 54% average compression across common prompt formats
• •Works by collapsing whitespace, redundant formatting, and verbose patterns while preserving semantic structure
• •Designed for drop-in use — run it on prompts before sending to OpenAI, Anthropic, etc.