Memdoor author here which is a coding agent that run Jev inside. I believe this is becoming mainstream in any coding agent for routing calls and reduce input sizes. First candidates are edit tools for reading file or jgrep for greping large files folder etc.. The Token saving is mostly on the read side.

If we compare coding agents with Jev on vs off we get those numbers (metered with Memdoor): - read-heavy tasks (coding questions , logs tail etc..): -63% to -65% input tokens, 40-45% fewer model calls, on GLM 5.3 half the wall time. Reproduced a month apart on two models (DeepSeek V4.1 Flash, GLM 5.3). - a file edit: -23%

Two things match the post. Calibration is what makes it safe: I apply a decision at confidence >= 0.75 and fail below this threshold or on timeout or on an unknown tool family. The dollar saving scales with the price of the model being saved: on a $0.04/M model Jev costs what it saves so on a $0.38/M model it's negligible but on Claude model for example it can be outstanding.

Where it did not help: giving Jev more context about the repo. I found same thing as the post.

If you are interested i have put figures here : https://github.com/guregodevo/memdoor/blob/main/docs/feature...