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OptScale Kompressor
Twice the Savings of Headroom on Coding-Agent Traffic
Coding agents re-send their whole history every turn, so on a long task the model re-reads the same build output dozens of times, and you pay for every read. OptScale Kompressor stops that: on Terminal-Bench 2.1 it takes 22.7% off the real bill. The same input compresses to the same bytes every turn, so the prompt cache keeps working, and every removed detail stays one retrieval away. Open source (MIT), one Rust binary, no ML model.
Coding agents re-send their entire working history to the model on every step, including build logs, compiler warnings and progress bars. On long tasks the same output is re-read dozens of times and billed each time. OptScale Kompressor removes that redundancy.
How it works:
It sits between the agent and the model provider and rewrites machine output (build logs, grep results, JSON, directory listings, source files) into a shorter form that marks exactly what was removed.
The same input always compresses to the same bytes, so the prompt cache is not invalidated.
By default keeps every removed fragment behind a handle; if the model asks for it, the proxy fetches it back on its own.
No ML model, nothing downloaded at runtime: a single Rust binary, about 0.2 ms per KB.
Results on Terminal-Bench 2.1 (Codex CLI, GPT-5.5, real provider bills with the cache discount): 22.7% lower cost, about twice Headroom’s savings, no significant change in solve rate. The largest gains are on ML training, source builds and data pipelines.
Current status: Codex is supported (kompressor wrap codex). Claude Code runs through the wrap but is not compressed, because rewriting its cached conversation would invalidate the prompt cache. Details are in the README.
MIT-licensed. Runs as a single binary or inside OptScale AI.
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About OptScale Kompressor on Product Hunt
“Twice the Savings of Headroom on Coding-Agent Traffic”
OptScale Kompressor was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #333 on the daily leaderboard. Coding agents re-send their whole history every turn, so on a long task the model re-reads the same build output dozens of times, and you pay for every read. OptScale Kompressor stops that: on Terminal-Bench 2.1 it takes 22.7% off the real bill. The same input compresses to the same bytes every turn, so the prompt cache keeps working, and every removed detail stays one retrieval away. Open source (MIT), one Rust binary, no ML model.
OptScale Kompressor was featured in Open Source (69k followers), Developer Tools (521.6k followers), Artificial Intelligence (481.6k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 269.8k products, making this a competitive space to launch in.
Who hunted OptScale Kompressor?
OptScale Kompressor was hunted by Julia Maxwell. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Julia Maxwell
Hi, I’m Max, CTO at Hystax.
Coding agents re-send their entire working history to the model on every step, including build logs, compiler warnings and progress bars. On long tasks the same output is re-read dozens of times and billed each time. OptScale Kompressor removes that redundancy.
How it works:
It sits between the agent and the model provider and rewrites machine output (build logs, grep results, JSON, directory listings, source files) into a shorter form that marks exactly what was removed.
The same input always compresses to the same bytes, so the prompt cache is not invalidated.
By default keeps every removed fragment behind a handle; if the model asks for it, the proxy fetches it back on its own.
No ML model, nothing downloaded at runtime: a single Rust binary, about 0.2 ms per KB.
Results on Terminal-Bench 2.1 (Codex CLI, GPT-5.5, real provider bills with the cache discount): 22.7% lower cost, about twice Headroom’s savings, no significant change in solve rate. The largest gains are on ML training, source builds and data pipelines.
Current status: Codex is supported (kompressor wrap codex). Claude Code runs through the wrap but is not compressed, because rewriting its cached conversation would invalidate the prompt cache. Details are in the README.
MIT-licensed. Runs as a single binary or inside OptScale AI.
Questions and feedback welcome.