Tip: Headroom compresses tool output before it reaches the model, and its per-scenario numbers vary from 47% to 92%

GitHub·medium signalXBlueskyLinkedInCopy link

headroomlabs-ai/headroom (68,064 stars, pushed 2026-08-30) sits as a library, proxy or MCP server and compresses tool outputs, logs, files and RAG chunks. Its README breaks results out by scenario rather than quoting one figure: code search over 100 results 17,765 to 1,408 tokens (92%), SRE incident debugging 65,694 to 5,118 (92%), GitHub issue triage 73%, codebase exploration 78,502 to 41,254 (47%), with SQuAD v2 and BFCL accuracy held at 97%. Note the install caveat: the dashboard's dollar-savings tile is priced through LiteLLM, which will not install on Python 3.14+, so on 3.14 token savings still track but the dollar figure stays $0.00.