TokenEconomizer （代码智能体Token成本控制）

# TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents

<p align="center">
  Aaron J. Li<sup>1</sup>,
  Weijia Xu<sup>2</sup>,
  Chandan Singh<sup>2</sup>,
  Zixiao Chen<sup>3</sup>,
  Jeevana Priya Inala<sup>2</sup>,
  Charlotte Siska<sup>3</sup>,
  <br>
  Acar Tamersoy<sup>3</sup>,
  Bin Yu<sup>1</sup>,
  Ion Stoica<sup>1</sup>,
  Michel Galley<sup>2</sup>,
  Jianfeng Gao<sup>2</sup>
</p>
<p align="center">
  <sup>1</sup>UC Berkeley &nbsp; <sup>2</sup>Microsoft Research &nbsp; <sup>3</sup>Microsoft Security
</p>

<p align="center">
  <a href="https://arxiv.org/abs/2610.22732"><img src="https://img.shields.io/badge/arXiv-2610.22732-b31b1b.svg" alt="arXiv"></a>
  <a href="https://tokeneconomizer.github.io"><img src="https://img.shields.io/badge/project-page-2f6fb0.svg" alt="Project page"></a>
  <a href="https://x.com/aaron_jli/status/2114797313613053952"><img src="https://img.shields.io/badge/X-post-000000.svg?logo=x&logoColor=white" alt="X post"></a>
  <img src="https://img.shields.io/badge/python-3.10%2B-3776ab.svg" alt="Python 3.10+">
  <img src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" alt="Apache 2.0 license">
</p>

<p align="center">
  <a href="https://trendshift.io/repositories/315380"><img src="https://trendshift.io/api/badge/trendshift/repositories/315380/daily?language=Python" alt="#1 Python repository of the day on Trendshift" width="250" height="55"></a>
  <a href="https://trendshift.io/repositories/315380"><img src="https://trendshift.io/api/badge/trendshift/repositories/315380/weekly?language=Python" alt="#10 Python repository of the week on Trendshift" width="250" height="55"></a>
</p>

<p align="center">
  Our measurement on SWE-Bench tasks: <b>18.4–30.9% lower mean cost</b> across five agent–model
  combinations, cheaper in <b>all 30</b> repository × combination cells, and Pass@1 up in
  <b>4 of 5</b>. <a href="#results">Results</a>
</p>

<p align="center">
  <picture>
    <source media="(prefers-reduced-motion: reduce)" srcset="docs/assets/teaser.png">
    <img src="docs/assets/teaser.gif" width="100%" alt="An animation that opens on a title card: TokenEconomizer, experience-driven black-box cost control for coding agents, with its three mechanisms (micro-skills, tool pruning, failure-loop intervention), the agents it works with (Claude Code, Codex, GitHub Copilot CLI, Pi) and the UC Berkeley and Microsoft logos. Then a schematic of six sessions in one repository, each a bar of what it cost: the work itself plus three kinds of waste, namely re-learning the repo, tools never used, and getting stuck in loops. Each of the first three sessions teaches TokenEconomizer one thing, in order: 1 micro-skills remember how the repo works (run tests with python -m pytest), 2 tool pruning skips tools never used here (Read and Edit kept, Notebook and Browser left out), and 3 failure-loop intervention spots loops and steps in (the same error three times meets a nudge to try another way). In the session