Headroom cuts agent token use by compressing tool output first

Headroom is an open-source tool that shortens long tool results before they are sent to a language model. It targets tool output, logs, and document chunks from retrieval systems, which often become large in agent workflows. Its public claim is a 60% to 95% token reduction with little quality loss.

It can be used as a Python library, a standard input/output proxy, or an MCP server. The MCP server mode is the most practical part for many agent setups because it sits between the client and existing tools and compresses replies without changing those tools. For teams running several MCP servers with heavy tool use, this can mean less manual trimming and more useful information staying inside the model context.

The related discussion shows that this is part of a wider cost-control push around AI agents. Other tools and posts focus on token-saving skills, local proxies, cost dashboards, cache improvements, and real-session tests, including one comparison that replayed 500 Claude Code sessions covering 614 million tokens and $926 of baseline spend.