Headroom

Ever watched your AI agent burn through tokens on 65,000 characters of JSON tool output — just to answer “yes, the service is down”? Yeah, me too. I’ve been running Claude Code and Codex daily, and my token bills were starting to look suspiciously like a second mortgage. (Code-review-graph cut my review tokens 82x, but that only covers the review step — I needed something for every agent interaction.) So when Headroom crossed my radar at 59.7k GitHub stars and #2 on Trending, I had to see what the fuss was about. ...

Ever looked at your Claude Code API bill and thought “this is getting ridiculous”? Yeah, me too. A few weeks of heavy agent usage and suddenly you’re spending more on tokens than on the VPS hosting everything else. I hit that point last month — $187 in API costs from a single weekend of aggressive agent loops. So when I saw Headroom hit 57,706 GitHub stars (up from 13,784 when I first covered it in June — that’s 4× growth in 33 days), I knew I had to revisit it. The pitch? Compress everything your agent sends to the LLM by 60–95% without changing a line of code. No new architecture. No rewriting your agent loops. Just plug it in and save. ...

If you’re a heavy Claude Code or Cursor user, you know the feeling: one innocent “search the codebase” command and boom — 20,000 tokens gone. $0.30 per query doesn’t sound like much until you’re doing it 50 times a day. I’ve been watching my API bills creep up for months. Honestly, I was starting to wonder if AI coding agents were a luxury I couldn’t justify for side projects. So when I saw a project called Headroom trending on GitHub (+9,421 stars this week alone), I had to check it out. The pitch is simple: compress everything you send to the LLM before it gets there. Save 60–95% on tokens. Keep the same answer quality. ...

Headroom Review 2026: Cut AI Agent Token Costs by 60-95% Without Losing Accuracy Running AI coding agents daily? You’ve probably noticed the token bills. Every tool output, every log line, every RAG chunk gets fed to the LLM — and you pay for all of it. Headroom is a context compression layer that sits between your agent and the LLM, shrinking inputs by 60-95% while preserving answer quality. Meta Description: Headroom compresses AI agent inputs by 60-95% without losing accuracy. Tested with Claude Code, Codex, Cursor, and more. Includes benchmarks, quick start guide, and honest comparison. ...