Paritok compresses the tools, files, and history your coding agent sends. Save up to 85% on your token bill and run 3× longer sessions. Two commands, nothing lost, fully local.
We're Jiayu and Luzhuo, two engineers who got tired of watching our coding agents burn through tokens.
Here's what we kept seeing: your agent ships far more than the model actually needs. Full JSON for seventy tools when it will call two. An entire file when it needed one function. Debug output nobody will ever read again. And because the API is stateless, all of it goes back on every single turn, so the waste doesn't just cost you once, it compounds.
So we built Paritok, a non-destructive compression gateway powered by a code-native 4B model we trained on 45K real agent trajectories. It sits between your agent and the API and cuts three things before they leave:
• Tool schemas — 29K down to 8K per turn, no model involved, this one runs on CPU
• File reads and tool output — compressed to about a quarter of their size
• Stale history — turns beyond a recent window get summarized once your context budget fills, so the session never overflows into a lossy compaction
With Paritok, you can cut your token bills 25% on turn 1 to past 85% in long or saturated sessions, and run ~3× more turns in the same context window. Paritok now works with Claude code, Codex, Cursor, and anything OpenAI or Anthropic compatible.
Paritok will continue to enhance functionality and adapt user scenarios, while deepening its model capabilities.
We’re excited to share this with the PH community and would love your honest feedback.
nice! and good that you are doing the open source route, that builds confidence!
Congrats on the launch Watching coding agents burn through context windows with redundant tool schemas and file dumps is so frustrating. Love that this sits as a completely local proxy without needing a middleman cloud server.
Those are some interesting numbers! How are you justifying costing 85% less, and what's the benchmark?
Context bloat gets brutal with MCP-heavy setups. Keeping compression local while making the original bytes recoverable is a smart approach. Curious, how much latency does Paritok add per turn in a typical Claude Code session?
Want to make sure I'm reading the benchmark right. Quality retained = solve rate ÷ uncompressed baseline - so 86.5% means the agent resolves about 86.5% as many issues as it would with full context, a relative 13.5% drop in tasks actually completed? Or is that measuring something else?
If so, the comparison I'd want isn't against other compressors, but against just running a cheaper or lower-effort model at full context. Both routes cost quality and both cut the bill. Has that been measured? A strong model on compressed context vs a cheaper one on complete context, same tasks.
And which model was the scaffold running? Hard to judge 13.5% without knowing the baseline.
Non-destructive compression via a code-native 4B model is a genuinely clever architecture. Adding to my to-try list.
Really like this product and I went to your github. The compounding savings math in the README is refreshingly honest. This is how AI infra should be marketed.
Great job! The 3× turns per context window benefit is actually valuable to me. My Claude Code sessions hit compaction on big codebases. Excited to see how much longer they can run with this.
Congratulations on the launch! Context bloat is becoming a real bottleneck for coding agents, so a fully local way to compress tools, files, and history feels both practical and privacy-conscious. Looking forward to trying Paritok.
This is such an underrated problem honestly,
Everyone talks about model quality but nobody talks about how much of the context window is just wasted overhead.
btw, how much of that 85% savings comes from the tool schema compression vs the file/output compression?
lastly, all the best with the launch team )
About Paritok on Product Hunt
“Spend up to 85% less and run 3× longer coding agent sessions”
Paritok launched on Product Hunt on August 10th, 2026 and earned 234 upvotes and 28 comments, earning #3 Product of the Day. Paritok compresses the tools, files, and history your coding agent sends. Save up to 85% on your token bill and run 3× longer sessions. Two commands, nothing lost, fully local.
Paritok was featured in Open Source (68.7k followers), Developer Tools (517.3k followers) and Artificial Intelligence (475.7k followers) on Product Hunt. Together, these topics include over 206.3k products, making this a competitive space to launch in.
Who hunted Paritok?
Paritok was hunted by Chris Messina. 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.
Want to see how Paritok stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Chris Messina
Hey PH community!
We're Jiayu and Luzhuo, two engineers who got tired of watching our coding agents burn through tokens.
Here's what we kept seeing: your agent ships far more than the model actually needs. Full JSON for seventy tools when it will call two. An entire file when it needed one function. Debug output nobody will ever read again. And because the API is stateless, all of it goes back on every single turn, so the waste doesn't just cost you once, it compounds.
So we built Paritok, a non-destructive compression gateway powered by a code-native 4B model we trained on 45K real agent trajectories. It sits between your agent and the API and cuts three things before they leave:
• Tool schemas — 29K down to 8K per turn, no model involved, this one runs on CPU
• File reads and tool output — compressed to about a quarter of their size
• Stale history — turns beyond a recent window get summarized once your context budget fills, so the session never overflows into a lossy compaction
With Paritok, you can cut your token bills 25% on turn 1 to past 85% in long or saturated sessions, and run ~3× more turns in the same context window. Paritok now works with Claude code, Codex, Cursor, and anything OpenAI or Anthropic compatible.
Paritok will continue to enhance functionality and adapt user scenarios, while deepening its model capabilities.
We’re excited to share this with the PH community and would love your honest feedback.
Try Paritok: https://www.paritok.com/
Join our Discord to talk with the team: https://discord.gg/SeBJE5Eucp
Thanks for checking us out, and huge thanks to our hunter Chris Messina for hunting us!
— Jiayu & Luzhuo