headroom

headroom strips boilerplate from everything an LLM agent reads (tool outputs, logs, RAG chunks, file dumps) before the content hits the prompt. The reported numbers are real: 87% fewer tokens on a 100-log needle-in-haystack test, 92% on code search results, with accuracy unchanged on GSM8K and TruthfulQA. Apache 2.0, runs entirely on your machine.

Setup is one command. `headroom wrap claude` or `headroom wrap codex` puts it in front of the API call. There is also a Python and TypeScript SDK for direct `compress(messages)` use, plus a proxy mode for everything else. Local-first design means no data egress, and compression latency is milliseconds, not a network hop.

If you are paying for Claude, GPT, or Sonnet at scale, this pays for itself fast. Token costs drop directly. The hosted dashboard at headroomlabs.ai is a community leaderboard, not gated functionality. Solo developers running coding agents heavily: install it. Teams burning through enterprise LLM budgets: pilot on one team first.

The catch: aggressive compression on novel content can lose nuance the model needed. The reversible design lets the model pull original bytes back via tool call, but that only works if the agent is configured to use it. Verify on your real workload before trusting blanket compression.