API Agents vs. GUI Agents: Divergence and Convergence
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.
67d785458678eaf139e3c594
Chaoyun Zhang
admin_assigned
2025-03-17T08:51:30.401Z
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Chaoyun Zhang
user
vyokky
67d785458678eaf139e3c595
Shilin He
admin_assigned
2025-03-17T08:51:37.539Z
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Shilin He
user
shilhe
67d785458678eaf139e3c596
Liqun Li
admin_assigned
2025-03-17T08:51:57.886Z
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Liqun Li
user
liqul
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Si Qin
admin_assigned
2025-03-17T08:52:05.644Z
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Si Qin
user
SiQin88
67d785458678eaf139e3c598
Yu Kang
67d785458678eaf139e3c599
Qingwei Lin
admin_assigned
2025-03-17T08:52:17.826Z
652fc9f39bc50a6c0e435224
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Lin Qingwei
user
Eliblo1969
67d785458678eaf139e3c59a
Dongmei Zhang
admin_assigned
2025-03-17T08:52:31.623Z
66473d2c7abe6ad66e81a3dd
/avatars/82f40244806c06ffeaa1c4265e9725ea.svg
ZHANGDONGMEI
user
ZDM6426
API Agents vs. GUI Agents: Divergence and Convergence
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.
The paper compares API-based and GUI-based LLM agents, analyzing their differences and potential integration to guide practitioners in selecting or combining these automation paradigms.
LLMs API-based LLM agents GUI-based LLM agents multimodal LLM task automation hybrid approaches decision criteria use cases