mobizen-gui

Helps users set up and run MobiZen-GUI to perform mobile-use tasks — automating Android phone operations via natural language. Use when the user wants to control a phone, execute mobile-use tasks, install/configure MobiZen-GUI, deploy MobiZen-GUI-4B model, or customize the agent's input/output. Triggers on keywords like phone task, mobile-use, mobile automation, Android control, MobiZen, ADB, deploy model, run agent, configure model.

VLM-based mobile automation framework — control Android devices via natural language.

Repo: https://github.com/alibaba/MobiZen-GUI

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1. Environment Setup

1.1 Install ADB

bash

# macOS
brew install android-platform-tools
# Linux
sudo apt-get install android-tools-adb
# Windows: download from https://developer.android.com/studio/releases/platform-tools
adb version  # verify

1.2 Connect Device & Install ADBKeyboard

bash

adb devices                    # USB; or: adb tcpip 5555 && adb connect <ip>:5555
adb install ADBKeyboard.apk    # download from https://github.com/senzhk/ADBKeyBoard

Then on device: Settings → System → Languages & Input → Virtual Keyboard → Enable ADBKeyboard.

1.3 Install Project

bash

git clone https://github.com/alibaba/MobiZen-GUI.git && cd MobiZen-GUI
pip install -r requirements.txt   # openai, pillow, pyyaml

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2. Quick Start (Config Only, No Code Changes)

Copy example config:

bash

cp config_example.yaml my_config.yaml

Only 3 fields need to be configured — api_key, base_url, model_name:

yaml

api_key: "your-api-key-here"
base_url: "https://api.openai.com/v1"   # your model endpoint
model_name: "gpt-4o"                    # model identifier

How to set these 3 fields: When the user asks to run a phone task but hasn't configured yet, the AI should ask the user to provide api_key, base_url, and model_name, then write them into my_config.yaml. The user can also manually edit the file. Any OpenAI-compatible API works.

Provider examples:

yaml

# OpenAI
base_url: "https://api.openai.com/v1"
api_key: "sk-..."
model_name: "gpt-4o"

# DeepSeek / Moonshot / Zhipu AI etc.
base_url: "https://api.deepseek.com/v1"
api_key: "your-key"
model_name: "deepseek-chat"

# Ollama (local)
base_url: "http://localhost:11434/v1"
api_key: "dummy"
model_name: "llava"

Run:

bash

python main.py --config my_config.yaml --instruction "打开微信并发送消息"

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3. Configuration Reference

Field	Default	Description
device_id	null (auto)	ADB device; null = first available
api_key	""	Model API key
base_url	null	Model API endpoint
model_name	"gpt-4o"	Model identifier
model_type	"qwen3vl"	Coordinate system (999x999 virtual space)
max_steps	25	Max execution steps
step_delay	2.0	Delay between steps (seconds)
first_step_delay	4.0	Delay after first step
temperature	0.1	Sampling temperature
top_p	0.001	Top-p sampling
max_tokens	1024	Max output tokens
timeout	60	Request timeout (seconds)
use_adbkeyboard	true	Chinese text input via ADBKeyboard
screenshot_dir	"./screenshots"	Screenshot save directory

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4. Advanced: Deploy MobiZen-GUI-4B Locally

For best results on Chinese mobile tasks, deploy the dedicated 4B model.

4.1 Download Model

bash

pip install -U huggingface_hub
# China mirror (optional)
export HF_ENDPOINT=https://hf-mirror.com
hf download alibabagroup/MobiZen-GUI-4B --local-dir ./MobiZen-GUI-4B

Alternatively from ModelScope: https://modelscope.cn/models/GUIAgent/MobiZen-GUI-4B

4.2 Serve with vLLM

bash

pip install vllm==0.11.0
vllm serve ./MobiZen-GUI-4B --host 0.0.0.0 --port 8000 --trust-remote-code

4.3 Point Config to Local Model

yaml

api_key: "dummy"
base_url: "http://localhost:8000/v1"
model_name: "MobiZen-GUI-4B"
model_type: "qwen3vl"

Then run as usual: python main.py --config my_config.yaml --instruction "..."

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5. Customization (Requires Code Changes)

The framework uses a plugin architecture — three components can be swapped via config class paths:

Component	Role	Base Class	Default Implementation
MessageBuilder	Builds prompt + screenshot for model	core.message_builders.base.BaseMessageBuilder	core.message_builders.qwen.QwenMessageBuilder
ModelClient	Calls the model API	core.model_clients.base.BaseModelClient	core.model_clients.openai.OpenAIClient
ResponseParser	Parses model output → action	core.response_parsers.base.BaseResponseParser	core.response_parsers.qwen.QwenResponseParser

5.1 Custom Model Client

For non-OpenAI-compatible APIs:

python

# core/model_clients/my_client.py
from .base import BaseModelClient

class MyClient(BaseModelClient):
    def __init__(self, api_key: str, base_url: str = None, model: str = "", timeout: int = 60):
        pass  # init your client

    def chat(self, messages, **kwargs):
        pass  # must return obj with .choices[0].message.content

Config:

yaml

model_client_class: "core.model_clients.my_client.MyClient"
model_client_kwargs: {}  # extra kwargs passed to __init__

5.2 Custom Message Builder

To change the system prompt or how screenshots/history are formatted:

python

# core/message_builders/my_builder.py
from .base import BaseMessageBuilder
from utils.image import image_to_data_url

class MyBuilder(BaseMessageBuilder):
    def build_system_prompt(self, **kwargs) -> str:
        return "your system prompt"

    def build_messages(self, instruction, current_screenshot, history, **kwargs):
        return [{"role": "system", "content": [...]}, {"role": "user", "content": [...]}]

Config:

yaml

message_builder_class: "core.message_builders.my_builder.MyBuilder"

5.3 Custom Response Parser

To parse a different model output format:

python

# core/response_parsers/my_parser.py
from .base import BaseResponseParser, ParsedResponse

class MyParser(BaseResponseParser):
    def parse(self, response) -> ParsedResponse:
        content = response.choices[0].message.content
        # parse content into structured fields
        return ParsedResponse(
            thought="...",
            summary="...",
            action={"arguments": {"action": "click", "coordinate": [x, y]}},
            subtask="..."
        )

Action dict format: {"arguments": {"action": "<type>", ...}} — supported types: click, long_press, swipe, type, system_button, wait, terminate.

Config:

yaml

response_parser_class: "core.response_parsers.my_parser.MyParser"

5.4 Add New Action Type

1. Add _execute_<action>(self, args) method in core/executor/action_executor.py
2. Add dispatch branch in ActionExecutor.execute()
3. Update system prompt in QwenMessageBuilder.build_system_prompt()

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6. Troubleshooting

• Device not found: Run adb devices — check USB/wireless connection
• ADBKeyboard not working: Ensure enabled in device settings; test: adb shell am broadcast -a ADB_INPUT_TEXT --es msg "test"
• Model connection error: Verify base_url + api_key; check network
• Coordinate mismatch: Ensure model_type matches your model; check screen size: adb shell wm size
• Duplicate action loop: Agent auto-stops after 5 identical actions; may indicate model confusion