API代理与GUI代理：分化与融合

API Agents vs. GUI Agents: Divergence and Convergence

March 14, 2025

作者: Chaoyun Zhang, Shilin He, Liqun Li, Si Qin, Yu Kang, Qingwei Lin, Dongmei Zhang

cs.AI

摘要

大型语言模型（LLMs）已从简单的文本生成演进为驱动软件代理，能够直接将自然语言指令转化为具体行动。尽管基于API的LLM代理最初因其强大的自动化能力和与编程端点的无缝集成而崭露头角，但多模态LLM研究的最新进展已催生了基于GUI的LLM代理，它们能以类人的方式与图形用户界面交互。尽管这两种范式都旨在实现LLM驱动的任务自动化，但它们在架构复杂性、开发流程和用户交互模式上存在显著差异。 本文首次对基于API和基于GUI的LLM代理进行了全面比较研究，系统分析了它们的分歧及潜在的融合点。我们考察了关键维度，并强调了混合方法能够发挥其互补优势的场景。通过提出明确的决策标准并展示实际用例，我们旨在指导从业者和研究人员在选择、结合或过渡这些范式时做出明智决策。最终，我们指出，基于LLM的自动化技术的持续创新有望模糊API驱动与GUI驱动代理之间的界限，为广泛的实际应用领域带来更灵活、适应性更强的解决方案。

English

Large language models (LLMs) have evolved beyond simple text generation to power software agents that directly translate natural language commands into tangible actions. While API-based LLM agents initially rose to prominence for their robust automation capabilities and seamless integration with programmatic endpoints, recent progress in multimodal LLM research has enabled GUI-based LLM agents that interact with graphical user interfaces in a human-like manner. Although these two paradigms share the goal of enabling LLM-driven task automation, they diverge significantly in architectural complexity, development workflows, and user interaction models. This paper presents the first comprehensive comparative study of API-based and GUI-based LLM agents, systematically analyzing their divergence and potential convergence. We examine key dimensions and highlight scenarios in which hybrid approaches can harness their complementary strengths. By proposing clear decision criteria and illustrating practical use cases, we aim to guide practitioners and researchers in selecting, combining, or transitioning between these paradigms. Ultimately, we indicate that continuing innovations in LLM-based automation are poised to blur the lines between API- and GUI-driven agents, paving the way for more flexible, adaptive solutions in a wide range of real-world applications.