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microsoft/omniparser

★ 24,771Jupyter NotebookAudience · researcherComplexity · 4/5MaintainedLicenseSetup · moderate

TLDR

AI tool that analyzes screenshots to identify and locate UI elements (buttons, icons, text fields) so AI agents can understand and interact with computer interfaces.

Mindmap

mindmap
  root((OmniParser))
    What it does
      Detects UI elements
      Generates descriptions
      Locates coordinates
    Use cases
      AI agent automation
      Computer control
      App navigation
    Tech stack
      Python
      Vision models
      Hugging Face
    Audience
      AI researchers
      Agent developers

Things people build with this

USE CASE 1

Build AI agents that can autonomously navigate and interact with desktop applications by understanding what's on screen.

USE CASE 2

Control a Windows 11 virtual machine using natural language instructions combined with vision-based AI models.

USE CASE 3

Parse complex app interfaces to extract structured data about buttons, menus, and interactive elements for automation.

USE CASE 4

Enable vision-based AI models to accurately click on and interact with small UI elements they would otherwise struggle to identify.

Tech stack

PythonJupyter NotebookHugging FaceVision models

Getting it running

Difficulty · moderate Time to first run · 30min

Requires downloading a vision model from Hugging Face and GPU/CUDA for reasonable inference speed.

Use freely, including commercial. Just credit the original author.

In plain English

OmniParser is a Microsoft research tool that can look at a screenshot of any computer interface and break it down into a structured list of elements, buttons, icons, text fields, menus, telling an AI agent exactly what is on screen and where each element is located. Think of it as giving an AI "eyes" that can read a graphical user interface the same way a human would. The core problem it solves: AI models like GPT-4V can see images, but they struggle to accurately identify and click on specific small elements within a complex app interface. OmniParser first detects all the interactive regions in a screenshot, then generates text descriptions of what each icon or element does. This structured output makes it much easier for a vision-based AI agent to understand the screen and take correct actions. A companion tool called OmniTool lets you actually control a Windows 11 virtual machine using OmniParser combined with an AI model of your choice, including OpenAI, DeepSeek, Qwen, or Anthropic's Computer Use. The result is an agent that can operate a real computer based on plain-language instructions. Researchers and developers working on AI computer-use agents, systems that can autonomously navigate apps and perform tasks, use OmniParser as a foundational component. It is a Microsoft Research project built in Python and distributed as Jupyter Notebooks, with model weights available on Hugging Face.

Copy-paste prompts

Prompt 1
How do I use OmniParser to analyze a screenshot and get a structured list of all clickable elements with their locations?
Prompt 2
Show me how to integrate OmniParser with an AI model like GPT-4V to build an agent that can control a Windows application.
Prompt 3
What's the difference between what OmniParser outputs versus what a vision model sees directly, and why does that matter for AI agents?
Prompt 4
How do I set up OmniTool to let an AI agent control my Windows 11 desktop based on natural language commands?
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