AppAgent

Chi Zhang*, Zhao Yang*, Jiaxuan Liu*, Yucheng Han, Xin Chen, Zebiao Huang,
Bin Fu, Gang Yu (Corresponding Author)
(* equal contributions)

🔆 Introduction

We introduce 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.

📝 Changelog

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

✨ Demo

The demo video shows the process of using AppAgent to follow a user on X (Twitter) in the deployment phase.

x_deploy_720p_1to1.mp4

An interesting experiment showing AppAgent's ability to pass CAPTCHA.

X_authenticate.mp4

🚀 Quick Start

This section will guide you on how to quickly use gpt-4-vision-preview as an agent to complete specific tasks for you on your Android app.

⚙️ Step 1. Prerequisites

Get an Android device and enable the USB debugging that can be found in Developer Options in Settings.
On your PC, download and install Android Debug Bridge (adb) which is a command-line tool that lets you communicate with your Android device from the PC.
Connect your device to your PC using a USB cable.
Clone this repo and install the dependencies. All scripts in this project are written in Python 3 so make sure you have installed it.

cd AppAgent
pip install -r requirements.txt

🤖 Step 2. Configure the Agent

AppAgent needs to be powered by a multi-modal model which can receive both text and visual inputs. During our experiment , we used gpt-4-vision-preview as the model to make decisions on how to take actions to complete a task on the smartphone.

To configure your requests to GPT-4V, you should modify config.yaml in the root directory. There are two key parameters that must be configured to try AppAgent:

OpenAI API key: you must purchase an eligible API key from OpenAI so that you can have access to GPT-4V.
Request interval: this is the time interval in seconds between consecutive GPT-4V requests to control the frequency of your requests to GPT-4V. Adjust this value according to the status of your account.

Other parameters in config.yaml are well commented. Modify them as you need.

Be aware that GPT-4V is not free. Each request/response pair involved in this project costs around $0.03. Use it wisely.

If you want to test AppAgent using your own models, you should modify the ask_gpt_4v function in scripts/model.py accordingly.

🔍 Step 3. Exploration Phase

Our paper proposed a novel solution that involves two phases, exploration and deployment, to turn GPT-4V into a capable agent that can help users operate their Android phones when a task is given. The exploration phase starts with a task given by you, and you can choose to let the agent either explore the app on its own or learn from your demonstration. In both cases, the agent generates documentations for elements interacted during the exploration/demonstration and saves them for use in the deployment phase.

Option 1: Autonomous Exploration

This solution features a fully autonomous exploration which allows the agent to explore the use of the app by attempting the given task without any intervention from humans.

To start, run learn.py in the root directory. Follow prompted instructions to select autonomous exploration as the operating mode and provide the app name and task description. Then, your agent will do the job for you. Under this mode, AppAgent will reflect on its previous action making sure its action adheres to the given task and generate documentations for the elements explored.

python learn.py

Option 2: Learning from Human Demonstrations

This solution requires users to demonstrate a similar task first. AppAgent will learn from the demo and generate documentations for UI elements seen during the demo.

To start human demonstration, you should run learn.py in the root directory. Follow prompted instructions to select human demonstration as the operating mode and provide the app name and task description. A screenshot of your phone will be captured and all interactive elements shown on the screen will be labeled with numeric tags. You need to follow the prompts to determine your next action and the target of the action. When you believe the demonstration is finished, type stop to end the demo.

python learn.py

📱 Step 4. Deployment Phase

After the exploration phase finishes, you can run run.py in the root directory. Follow prompted instructions to enter the name of the app, select the appropriate documentation base you want the agent to use, and provide the task description. Then, your agent will do the job for you. The agent will automatically detect if there is documentation base generated before for the app; if there is no documentation found, you can also choose to run the agent without any documentation (success rate not guaranteed).

python run.py

📖 TO-DO LIST

Open source the Benchmark.
Open source the configuration.

😉 Citation

@misc{yang2023appagent,
      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},
      year={2023},
      eprint={2312.13771},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

lleheaven/AppAgent