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mnotgod96 / AppAgent

AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.
https://appagent-official.github.io/
MIT License
5.16k stars 559 forks source link
agent chatgpt generative-ai gpt4 gpt4v llm

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AppAgent-TencentQQGYLab

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[**Chi Zhang***†](https://icoz69.github.io/), [**Zhao Yang***](https://github.com/yz93), [**Jiaxuan Liu***](https://www.linkedin.com/in/jiaxuan-liu-9051b7105/), [Yucheng Han](http://tingxueronghua.github.io), [Xin Chen](https://chenxin.tech/), [Zebiao Huang](),
[Bin Fu](https://openreview.net/profile?id=~BIN_FU2), [Gang Yu✦](https://www.skicyyu.org/)
(* equal contribution, † Project Leader, ✦ Corresponding Author )

ℹ️Should you encounter any issues⚠️ while using our project, please feel free to report them on GitHub Issues or reach out to Dr. Chi Zhang via email at dr.zhang.chi@outlook.com.

ℹ️This project will be synchronously updated on the official TencentQQGYLab Github Page.

📝 Changelog

🔆 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.

✨ Demo

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

https://github.com/mnotgod96/AppAgent/assets/40715314/db99d650-dec1-4531-b4b2-e085bfcadfb7

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

https://github.com/mnotgod96/AppAgent/assets/27103154/5cc7ba50-dbab-42a0-a411-a9a862482548

An example of using the grid overlay to locate a UI element that is not labeled with a numeric tag.

https://github.com/mnotgod96/AppAgent/assets/27103154/71603333-274c-46ed-8381-2f9a34cdfc53

🚀 Quick Start

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

⚙️ Step 1. Prerequisites

  1. 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.

  2. Get an Android device and enable the USB debugging that can be found in Developer Options in Settings.

  3. Connect your device to your PC using a USB cable.

  4. (Optional) If you do not have an Android device but still want to try AppAgent. We recommend you download Android Studio and use the emulator that comes with it. The emulator can be found in the device manager of Android Studio. You can install apps on an emulator by downloading APK files from the internet and dragging them to the emulator. AppAgent can detect the emulated device and operate apps on it just like operating a real device.

    Screenshot 2023-12-26 at 22 25 42

  5. 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:

  1. OpenAI API key: you must purchase an eligible API key from OpenAI so that you can have access to GPT-4V.
  2. 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.

You can also try qwen-vl-max (通义千问-VL) as the alternative multi-modal model to power the AppAgent. The model is currently free to use but its performance in the context of AppAgent is poorer compared with GPT-4V.

To use it, you should create an Alibaba Cloud account and create a Dashscope API key to fill in the DASHSCOPE_API_KEY field in the config.yaml file. Change the MODEL field from OpenAI to Qwen as well.

If you want to test AppAgent using your own models, you should write a new model class 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 documentation 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 the 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 documentation 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 the 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 the 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

💡 Tips

📊 Evaluation

Please refer to evaluation benchmark.

📖 To-Do List

😉 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}
}

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License

The MIT license.