做GUI自动化的团队终于有了兼顾效率和智能的方案——AutoRPA把LLM的推理能力压缩成轻量RPA函数，省掉重复调用LLM的高昂成本，做流程自动化的开发者可以直接用起来。
AutoRPA是一个新框架，能将ReAct风格的LLM智能体决策逻辑自动转化为高效的RPA函数。它通过翻译-构建流水线，把硬编码的ReAct动作转为软编码过程，并利用多轨迹检索增强生成合成鲁棒的RPA函数。在代码验证阶段，采用混合修复策略，结合RPA执行和ReAct回退进行迭代优化。实验表明，AutoRPA生成的RPA函数在解决相似任务时，token使用量减少82%到96%，显著提升了运行时效率和可复用性。
AutoRPA: Efficient GUI Automation through LLM-Driven Code Synthesis from Interactions
Large Language Model (LLM) based agents have demonstrated proficiency in multi-step interactions with graphical user interfaces (GUIs). While most research focuses on improving single-task performance, practical scenarios often involve repetitive GUI tasks for which invoking LLM reasoning repeatedly, i.e., the ReAct paradigm, is inefficient. Prior to LLMs, traditional Robotic Process Automation (RPA) offers runtime efficiency but demands significant manual effort to develop and maintain. To bridge this gap, we propose AutoRPA, a framework that automatically distills the decision logic of ReAct-style agents into robust RPA functions. AutoRPA introduces two core innovations: (1) A translator-builder pipeline, where a translator agent converts hard-coded ReAct actions into soft-coded procedures, and a builder agent synthesizes robust RPA functions via retrieval-augmented generation over multiple trajectories; (2) A hybrid repair strategy during code verification, combining RPA execution with ReAct-based fallback for iterative refinement. Experiments across multiple GUI environments demonstrate that RPA functions generated by AutoRPA successfully solve similar tasks while reducing token usage by 82% to 96%, significantly improving runtime efficiency and reusability.