Mobile AI Agents: Tools & Use Cases  in 2025

At AIMultiple, we focus on developing and assessing Generative AI technologies such as custom GPTs, AI agents, and cloud GPU solutions. Another emerging area of interest is mobile AI agents. 

• If you’re here for the traditional definition of mobile AI agents, jump to the section by following the link.
• If you’re here for the modern autonomous software powered by LLMs, keep reading.

We focus on what modern mobile AI agents are, how they work, and the tools enabling them.

Mobile AI agent tools

Selection Criteria

Given the field’s novelty, we included tools/frameworks that met at least one of the following criteria:

• 3,000+ GitHub stars
• 100+ academic citations

GitHub star counts change rapidly, and we will update the table accordingly.

What is a mobile AI agent? 

Mobile AI agents are software systems that interact autonomously with users and mobile applications by using natural language inputs and goal-driven reasoning to complete tasks on behalf of users. Unlike traditional automation tools or early personal assistants, these agents are powered by AI. Some of its use cases include:

• Mobile QA automation without test scripts
• Automating mobile workflows like uploading ID documents or changing profile settings
• AI assistants that operate apps for the visually impaired, elderly, or anyone else.
• Daily general tasks such as creating events on the calendar or even completing Duolingo lessons.

DroidRun

DroidRun is an open-source framework that builds mobile-native AI agents that can autonomously control mobile apps and phones.It is a foundational framework that converts user interfaces into structured data that large language models can understand and interact with, enabling complex automation directly on mobile devices. 

DroidRun rapidly gained traction: over 900 developers signed up within 24 hours, and the project soared to 3.8 k stars on GitHub, making it one of the fastest-growing frameworks for mobile AI agents.

See it in action:

AutoDroid

AutoDroid is a mobile task automation system designed to perform arbitrary tasks across any Android app without manual setup. It leverages the commonsense reasoning of large language models like GPT‑4 and Vicuna, combined with automated app-specific analysis. 

AutoDroid introduces a functionality-aware UI representation to connect app interfaces with LLMs, uses exploration-based memory injection to teach the model app-specific behaviors, and includes query optimization to reduce inference costs. Evaluated on a benchmark of 158 tasks, it achieved 90.9% action accuracy and 71.3% task success, outperforming GPT‑4-only baselines.¹  

Mobile-Agent

The GitHub repo X-PLUG/MobileAgent is the official implementation of Mobile-Agent, an AI agent framework designed to autonomously control mobile applications by perceiving and reasoning over their visual UI representations.

This project comes from the X-PLUG group at Tsinghua University and was presented at ICLR 2024, aiming to push the boundaries of mobile agents by using multimodal learning, particularly visual perception and instruction-following. See the video to see it in action.

AppAgent

The GitHub repository TencentQQGYLab/AppAgent is an open-source research project from Tencent’s QQG Y-Lab. It introduces AppAgent, a mobile AI agent framework designed to autonomously understand, operate, and reason through Android apps without human-written code for each individual app.

Source: AppAgent²

What are the features of a mobile AI agent?

Goal-oriented command handling

Users specify what they want done (e.g., “Book a ride to the airport”), not the individual steps.
The agent determines which apps to open, what actions to take, and how to sequence them.

LLM-backed reasoning

Powered often by large language models (e.g., GPT-4, Claude, Gemini), these agents can:

• Understand user intent and screen content
• Generate logical, step-by-step action plans
• Adapt to dynamic UI changes across different app states

Structured, native app control

Instead of relying on screen-scraping:

• Agents extract structured UI hierarchies (e.g., XML-based trees of buttons and fields)
• They interact directly with UI elements, treating them as first-class APIs.
  • Example: DroidRun uses Android Accessibility APIs to read and act on real UI elements.

Cross-app workflow execution

Agents operate across multiple apps and multi-step workflows. They can replan if an intermediate step fails. For example, “Download a file from email → upload it to Google Drive → send a confirmation.”

Legacy context: traditional definitions of mobile AI agents

The term “mobile AI agent” has evolved. In the past, it referred to simple rule-based systems on phones, like calendar managers or context-aware notification tools. These agents were precursors to today’s AI systems but operated in a different technical landscape. Today, it describes autonomous software powered by large language models that can operate mobile apps on behalf of users.

Well-known systems that embodied this early definition include Siri, Google Assistant, and Amazon Alexa. Though still widely used, these assistants relied on static, rule-based architectures and did not exhibit deep reasoning or complete autonomy.

Key characteristics

Traditional mobile agents typically feature:

• Rule-based logic: They followed pre-programmed responses and workflows with no adaptive reasoning.
• On-device processing: Due to constraints in mobile memory, processing power, and network bandwidth, these agents performed all tasks locally.
• Preset commands: Users had to phrase their requests in specific formats, as these systems couldn’t flexibly interpret natural language.
• Basic context awareness: They used sensor inputs (like GPS or accelerometers) to provide location- or time-based alerts and recommendations, but their responses were predefined rather than dynamic.

Functional capabilities

These agents were designed to automate routine tasks such as:

• Managing emails, calendars, and reminders
• Delivering notifications based on time or location
• Making voice-activated queries or device controls

Limitations compared to modern AI agents

Unlike modern mobile AI agents powered by LLMs, traditional systems:

• Could not understand or interact with complex app interfaces
• Lacked the ability to reason through multi-step tasks or adjust plans mid-execution
• Operated in silos and couldn’t coordinate across different apps or workflows
• Were deterministic, unable to adapt or learn from new environments or inputs

Despite their limitations, these early agents marked a significant step in mobile computing. They introduced users to voice-activated automation and laid the groundwork for the development of today’s far more capable, LLM-driven mobile AI agents.

External Links

• 1. https://arxiv.org/pdf/2308.15272
• 2. GitHub - TencentQQGYLab/AppAgent: AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps..