Paritok 是一款为 AI 编程代理节省 token 的优化工具，通过压缩工具调用和文件历史，让开发者降低 85% 花费并延长 3 倍会话时长。
热门评论
PH 用户
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
PH 用户
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 )
PH 用户
cutting the token bill 85% is a no brainer for anyone running agents 🔥 fully local is a nice touch too, congrats!!
PH 用户
Every long session I run eventually hits my own harness's compaction, where older turns get summarized once context grows, completely separate from anything Paritok does upstream. If a compressed segment's REF id pointer lives inside a turn that the host later summarizes away before the agent ever calls read_original on it, is that byte range still recoverable, or does recall depend on the referencing turn staying intact in the live context window?
PH 用户
Is it safe to use this tool with a Claude Code subscription?
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
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 )