Compression V2 cuts coding-agent token bills with three techniques across two layers: sharper tool result trimming, new task-aware tool surface reduction, and output brevity. Drop-in for Claude Code, Codex, OpenCode, and Cursor. Semantically lossless.
Back in March we shipped our first compressor. One technique, tool result trimming, inspired by the RTK project. It delivered around 10% cost savings on real coding sessions. Safe, simple, limited.
Today we're launching Compression V2, and it is a different beast.
Three orthogonal techniques, each attacking a different layer of an agent request, each toggleable independently:
1. Brevity (output layer). The model still does every tool call and produces the same final patches, it just stops narrating its plan. Output is the most expensive token class, and this is where the big
win lives.
2. Tool surface reduction a.k.a TSR (input layer). Agents send the model the union of every MCP tool on every request, even when 95% are irrelevant. V2 runs a fast classifier that scores each tool against the task and strips the rest before the request hits the model. Your IDE still exposes everything, the model just sees a curated subset.
3. Tool result trimming (history layer, refined from V1). Cleans up the verbose tool outputs that pile up over a long session without dropping what the model needs.
Because the three touch different layers, they compose cleanly. Combined, that lands around 50% cost reduction on a typical session.
The part I am most proud of is not the number, it is how we measured it. Our research engineer @0kham ran this on SWE-bench Lite in agent mode, with paired sign tests, bootstrap confidence intervals, per-replicate cache nonces so no run gets an unfair cache advantage, and token counts read straight from the raw usage fields. We published the full methodology, including where each technique is strong and where it is modest. Brevity is ~30% median on coding, TSR is a huge token-volume win on tool-heavy MCP work, trimming compounds over long sessions. No single inflated headline number, just the decomposition and the stats behind each claim.
It is semantically lossless on code tasks, drop-in for Claude Code, Codex, OpenCode, Github Copilot and Cursor, with under 12ms P50 gateway overhead. Your CLAUDEmd and MCP servers stay exactly where they are.
A few questions I would love your take on:
Which of the three techniques sounds most useful for your workflow?
Anyone running heavy MCP setups who wants to try tool surface reduction and tell us what you see?
Will be in the comments all day. Thanks for checking it out
About Edgee Claude Code Compressor V2 on Product Hunt
“Fewer tokens, same context, 50% cost reduction”
Edgee Claude Code Compressor V2 launched on Product Hunt on July 6th, 2026 and earned 167 upvotes and 28 comments, placing #5 on the daily leaderboard. Compression V2 cuts coding-agent token bills with three techniques across two layers: sharper tool result trimming, new task-aware tool surface reduction, and output brevity. Drop-in for Claude Code, Codex, OpenCode, and Cursor. Semantically lossless.
On the analytics side, Edgee Claude Code Compressor V2 competes within API, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Edgee Claude Code Compressor V2 performed against the three products that launched closest to it on the same day.
Who hunted Edgee Claude Code Compressor V2?
Edgee Claude Code Compressor V2 was hunted by fmerian. 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.
Reviews
Edgee Claude Code Compressor V2 has received 2 reviews on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
For a complete overview of Edgee Claude Code Compressor V2 including community comment highlights and product details, visit the product overview.
fmerian
Hey friends
Sacha here, founder of @Edgee.
Back in March we shipped our first compressor. One technique, tool result trimming, inspired by the RTK project. It delivered around 10% cost savings on real coding sessions. Safe, simple, limited.
Today we're launching Compression V2, and it is a different beast.
Three orthogonal techniques, each attacking a different layer of an agent request, each toggleable independently:
1. Brevity (output layer). The model still does every tool call and produces the same final patches, it just stops narrating its plan. Output is the most expensive token class, and this is where the big
win lives.
2. Tool surface reduction a.k.a TSR (input layer). Agents send the model the union of every MCP tool on every request, even when 95% are irrelevant. V2 runs a fast classifier that scores each tool against the task and strips the rest before the request hits the model. Your IDE still exposes everything, the model just sees a curated subset.
3. Tool result trimming (history layer, refined from V1). Cleans up the verbose tool outputs that pile up over a long session without dropping what the model needs.
Because the three touch different layers, they compose cleanly. Combined, that lands around 50% cost reduction on a typical session.
The part I am most proud of is not the number, it is how we measured it. Our research engineer @0kham ran this on SWE-bench Lite in agent mode, with paired sign tests, bootstrap confidence intervals, per-replicate cache nonces so no run gets an unfair cache advantage, and token counts read straight from the raw usage fields. We published the full methodology, including where each technique is strong and where it is modest. Brevity is ~30% median on coding, TSR is a huge token-volume win on tool-heavy MCP work, trimming compounds over long sessions. No single inflated headline number, just the decomposition and the stats behind each claim.
Full technical write-up here if you want the tests and the CIs: https://www.edgee.ai/blog/posts/introducing-compressor-v2-three-compression-layers-measured-end-to-end-for-a-50-cost-reduction
It is semantically lossless on code tasks, drop-in for Claude Code, Codex, OpenCode, Github Copilot and Cursor, with under 12ms P50 gateway overhead. Your CLAUDEmd and MCP servers stay exactly where they are.
A few questions I would love your take on:
Which of the three techniques sounds most useful for your workflow?
Anyone running heavy MCP setups who wants to try tool surface reduction and tell us what you see?
Will be in the comments all day. Thanks for checking it out