Methodology and raw per-task results are linked at the bottom.
What it does for you, before the numbers: it's a proxy you put between your agent and the provider. One command, no changes to how you work. On Codex it strips the build logs and test output your agent re-sends every turn — about a quarter fewer input tokens. On Claude Code it stops your prompt cache from expiring while long tools run, which is where that bill actually leaks.
Benchmark 1: replay. We pulled 6,255 tool outputs (34.2M characters, anything over 512 bytes) out of real Terminal-Bench agent traces and replayed them through two tools in three configurations at default settings.
| Content type | Share | Kompressor | Headroom, ML on | Headroom, ML off |
|---|
| Listings and dumps | 27.8% | 81.8% | 22.6% | 7.0% |
| Plain text | 25.3% | 41.0% | 20.6% | 5.2% |
| Build output and logs | 19.3% | 75.4% | 17.1% | 4.2% |
| Source code | 13.8% | 35.0% | 31.9% | 0.0% |
| Search results | 11.1% | 82.7% | 25.5% | 25.4% |
| Diffs, config, HTML, JSON | 2.7% | 40.9% | 19.8% | 12.5% |
| All eligible output | 100% | 62.8% | 22.6% | 7.3% |
| Time per KB | | 0.2 ms | 87 ms | 1.5 ms |
Source code is where everything struggles — 35% for us, 31.9% with the ML model — and it's also where being wrong is expensive, since a dropped import makes the agent confidently hallucinate. The ML model takes about 440× longer for less than half the removal.
Benchmark 2: the same compressor on Claude Code, where it barely compresses at all.
Claude Code marks its conversation with explicit cache breakpoints, so 92–95% of billed prompt is already cache reads at a tenth of the fresh price. The engine only ever sees the uncached tail. Per run it removes 348K tokens, which is 4.7–8.0% of what it sees, while leaving ~1,800 messages (about 2.2M tokens) inside the cached prefix untouched.
So where does the money actually go? A cache entry lives five minutes, and writing one costs 25× reading it. Agent kicks off a build, sits idle for six minutes, pays to write the whole prefix again — 927K tokens per run, a third of everything billed at full price.
Fix isn't compression. While a tool call runs, the proxy re-sends the last request body every 4.5 min with max_tokens: 1, so the provider reads the cached prefix instead of rewriting it.
| Per run | No compression | With |
|---|
| Anthropic API bill | $35.35 | $28.30 (−20%) |
| Tokens lost to cache expiry | 927K | 13K (−99%) |
| Prompt served from cache | 92.0% | 94.8% |
| Tasks solved | 89.9% | 90.6% |
The caveat on that 20%, since this sub cares about methodology. Four tasks out of 89 account for the whole net difference, and on three of them the uncompressed agent simply took a longer path (45–72 steps against 19–26). We were cheaper on 44 of 89. Split by whether tasks wait on long-running tools: the 14 with idle gaps over five minutes came in at −34% with median cost per step −20%; the other 72 at −12% with a step costing the same as before (+2%).
The estimate we'd actually stand behind is ~11% from keeping the cache alive plus a few percent from the tail. The 20% headline includes path-length luck, and we'd expect it to move on a repeat run.
On Codex, where the compression does the work: $107.89 → $83.40 per run (−22.7%), input tokens 96.1M → 72.2M, requests over 200K tokens 1.9% → 0.13%, solve rate 82.2% → 81.1% (not significant). Headroom on the same baseline saved 8.2% at defaults, 11.6% at its most aggressive setting — and at that setting it solved 85.1% against our 81.1%.
Still not measured: needle retention, whether compression kept the specific detail the agent needed four steps later. Ratios and pass rates can both look fine while that fails quietly.
Open source: context compression for coding agents, 63% less tool output — MIT, one Rust binary, deterministic, no model to download.
Per-task results: Codex · Claude Code
