Open-AutoGLM: Mobile Intelligent Agent Framework

Open-AutoGLMMobile Intelligent Agent Framework

Open-AutoGLM is an open-source mobile intelligent agent framework and model developed by Zhipu AI. Its core objective is to enable AI not only to engage in dialogue but also to automatically understand on-screen content and perform real-world operations. Unlike traditional large models limited to conversational abilities, AutoGLM can translate natural language instructions into practical actions, such as automatically opening apps, clicking buttons, entering information, and executing cross-application tasks.

Project Overview

It is one of the world's first open-source Mobile AI Agent (Smartphone Assistant) frameworks, aiming to advance AI assistants from the lab to the level of execution in real-life scenarios.

Open-AutoGLM is a complete open-source solution for mobile intelligence agents, from natural language to operational execution, offering significant exploratory value and application prospects. Its core advantages lie in:

> Enabling AI to truly "get things done," moving beyond just the "conversation level."

> Open-source code and friendly licensing facilitate adaptation to industry scenarios.

It is suitable for in-depth research and building by technology R&D personnel, AI practice development teams, or readers with a strong intent to explore mobile automation and intelligent interaction.

Key Feature Highlights

Natural Language Understanding and Execution

Users provide text or voice instructions; the AI parses the intent and executes tasks on the mobile device.

Covers Common App Operations

Supports automation scenarios for 50+ high-frequency applications including WeChat, Taobao, Douyin, Meituan, etc.

Cross-Application Task Flow Execution

Can automatically complete complex actions like relay-style "Open App → Click → Input → Submit".

Visual Understanding Capability

The framework, combined with visual models, can recognize current on-screen elements for accurate clicking and input operations.

Local Deployment & Privatization

Can be configured to run in local mode, achieving local data privatization with no upload risk.

Friendly Open-Source License

Model weights and code follow MIT and Apache-2.0 licenses, allowing closed-source modifications for commercial use.

Technical Threshold

Project Aspect
Environment Setup	⭐⭐☆☆☆	Requires understanding of Python, ADB, and environment variable management
Android Adaptation	⭐⭐⭐☆☆	Requires device debugging and handling various screen resolutions and versions
Model Understanding	⭐⭐⭐⭐☆	Requires understanding of visual + language model inference pipelines
Practical Debugging	⭐⭐⭐⭐☆	Requires using logs to locate issues

Frequently Asked Questions