Headroom Compresses AI Agent Inputs to Reduce Token Usage

Headroom introduces a compression layer that reduces input token count. The technique preserves semantic meaning while shrinking payload size. It targets large language models where input length constraints apply. By filtering redundant tokens, the system lowers API costs. Output quality remains consistent across tested benchmarks. The approach works with standard transformer architectures. Early adopters report up to 40 percent token savings. The project is open‑source and available for integration testing.