Caveman
why use many token when few do trick
What it does
One command wraps Claude Code, Codex, Hermes, and more with a local proxy that compresses logs, tool output, and files before every provider call. In a pinned 54-run benchmark: 33.2% fewer input tokens with 18/18 correctness checks. Caveman can also run any existing agent skill with ~70% fewer tokens by loading text as images. Built on an open-source ecosystem with 97K+ GitHub stars.
Your AI bill is mostly waste. Caveman finds it, cuts it with caching, compression and routing, and proves every dollar saved.
The tools and infrastructure to get more work from your AI. Fewer tokens. Less compute. Lower cost. Model routing, auto caching, prompt compression, waste detection — every request takes the cheapest path that still does the job. Switched on at the gateway, proven in the ledger. Every request takes the cheapest model that still does the job. The original — 100k+ stars on GitHub. ~10% savings on long-horizon coding tasks in JetBrains' benchmark. Cache writes and reads placed automatically wherever a prompt repeats. You keep the discount. Context compressed before it ships. The same answer comes back on fewer tokens. 20 detectors read your traffic and rank every dollar of waste by what fixing…from caveman.so
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