AppAgent: Multimodal Agents as Smartphone Users

(* Equal contributions, ✝ Project Leader, ✦ Corresponding Author)

AppAgent acts as your smartest assistant for various applications.


22024.2.8: Added qwen-vl-max (通义千问-VL) as an alternative multi-modal model. The model is currently free to use!

22024.1.31: Evaluation benchmark used in AppAgent is released on Github.

22024.1.2: 🔥 Added an optional method for the agent to bring up a grid overlay on the screen to tap/swipe anywhere on the screen.

2023.12.26: Andriod emulator is supported for AppAgent! Try it even if you don't have an Android device.

2023.12.21: 🔥🔥 Open-source the git repository, including the detailed configuration steps to implement our AppAgent!


Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent' s functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing on 50 tasks across 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

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Capability demonstration of AppAgent. AppAgent is an advanced multimodal agent powered by Large Language Models, capable of mastering and utilizing ANY Apps to perform complex tasks. It interacts with applications through intuitive tapping and swiping gestures, mimicking human-like actions.


App Agent operates in two phases, named exploration phase and deployment phase, respectively. In the first phase, App Agent observes the interactions in the user interfaces of different apps. With sufficient observation, App Agent becomes adept at using an app. This knowledge is meticulously compiled into a document. Once this learning phase is complete, the agent is ready for action. In the second phase, App Agent is equipped to handle high-level tasks across any supported application. This methodical approach enables App Agent to efficiently complete a variety of complex tasks across different applications.

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The figure illustrates the two-phase approach of our framework. In the exploration phase, the agent interacts with a smartphone application and learns from their outcomes to create a comprehensive reference document. Following this phase, the agent utilizes the information compiled in this document to operate and navigate the apps efficiently.

By observing changes in graphical user interfaces of various apps, App Agent learns their functionality and operational logic. This deep understanding of GUI elements is crucial for its intelligent interaction with apps.

Upon encountering a new user interface, App Agent refers to its knowledge base in the document to understand the interface's purpose and usage. Then it strategizes the best way to accomplish the given task, making the correct operations step by step.


Demos of App Agent exploring and deploying on Gmail and X, two most commonly used daily apps.

The exploring phase on Gmail.

The deploying phase on Gmail.

The exploring phase on X.

AppAgent's ability to pass CAPTCHA.

AppAgent's ability to edit images with Lightroom.

AppAgent's ability to tap/swipe anywhere on the screen.


      title={AppAgent: Multimodal Agents as Smartphone Users}, 
      author={Chi Zhang and Zhao Yang and Jiaxuan Liu and Yucheng Han and Xin Chen and Zebiao Huang and Bin Fu and Gang Yu},