Web GUI for AutoGLM Phone Agent - AI-powered Android automation
Project description
AutoGLM-GUI
AutoGLM 手机助手的现代化 Web 图形界面 - 让 AI 自动化操作 Android 设备变得简单
✨ 特性
- 对话式任务管理 - 通过聊天界面控制 Android 设备
- 实时屏幕预览 - 随时查看设备正在执行的操作
- 零配置部署 - 支持任何 OpenAI 兼容的 LLM API
- ADB 深度集成 - 通过 Android Debug Bridge 直接控制设备
📸 界面预览
任务开始
任务执行完成
🚀 快速开始
🎯 模型服务配置
AutoGLM-GUI 只需要一个 OpenAI 兼容的模型服务。你可以:
- 使用官方已托管的第三方服务
- 智谱 BigModel:
--base-url https://open.bigmodel.cn/api/paas/v4,--model autoglm-phone,--apikey <你的 API Key> - ModelScope:
--base-url https://api-inference.modelscope.cn/v1,--model ZhipuAI/AutoGLM-Phone-9B,--apikey <你的 API Key>
- 智谱 BigModel:
- 或自建服务:参考上游项目的部署文档(
/Users/suyiiyii/Documents/git/Open-AutoGLM/README.md或仓库 README) 用 vLLM/SGLang 部署zai-org/AutoGLM-Phone-9B,启动 OpenAI 兼容端口后将--base-url指向你的服务。
示例:
# 使用智谱 BigModel
pip install autoglm-gui
autoglm-gui \
--base-url https://open.bigmodel.cn/api/paas/v4 \
--model autoglm-phone \
--apikey sk-xxxxx
# 使用 ModelScope
pip install autoglm-gui
autoglm-gui \
--base-url https://api-inference.modelscope.cn/v1 \
--model ZhipuAI/AutoGLM-Phone-9B \
--apikey sk-xxxxx
# 指向你自建的 vLLM/SGLang 服务
pip install autoglm-gui
autoglm-gui --base-url http://localhost:8000/v1 --model autoglm-phone-9b
前置要求
- Python 3.10+
- 已开启 USB 调试的 Android 设备
- 已安装 ADB 并添加到系统 PATH
- 一个 OpenAI 兼容的 API 端点
快捷运行(推荐)
无需手动准备环境,直接安装运行:
# 通过 pip 安装并启动
pip install autoglm-gui
autoglm-gui --base-url http://localhost:8080/v1
也可以使用 uvx 免安装启动(需已安装 uv,安装教程):
uvx autoglm-gui --base-url http://localhost:8080/v1
传统安装
# 从源码安装
git clone https://github.com/your-repo/AutoGLM-GUI.git
cd AutoGLM-GUI
uv sync
# 构建前端(必须)
uv run python scripts/build.py
# 启动服务
uv run autoglm-gui --base-url http://localhost:8080/v1
启动后,在浏览器中打开 http://localhost:8000 即可开始使用!
📖 使用说明
- 连接设备 - 启用 USB 调试并通过 ADB 连接设备
- 对话 - 描述你想要做什么(例如:"去美团点一杯霸王茶姬的伯牙绝弦")
- 观察 - Agent 会逐步执行操作
🛠️ 开发指南
# 后端开发(自动重载)
uv run autoglm-gui --base-url http://localhost:8080/v1 --reload
# 前端开发服务器
cd frontend && pnpm dev
# 构建完整包
uv run python scripts/build.py --pack
📝 开源协议
Apache License 2.0
🙏 致谢
本项目基于 Open-AutoGLM 构建,感谢 zai-org 团队在 AutoGLM 上的卓越工作。
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