🖥️ AUTOMATION · Agents & IoT Level: Intermediate ⏱ 20 MIN Free & Open Source

Build Pure-Vision Desktop & Mobile GUI Agents with Microsoft OmniParser v2 without DOM Trees

Traditional automation collapses when faced with legacy desktop software, ERPs, WeChat, or custom mobile apps lacking APIs. Microsoft's open-source OmniParser v2 parses raw UI screenshots directly into structured bounding boxes and functional icons without needing HTML DOM trees or accessibility APIs. It bridges multimodal LLMs (Qwen2-VL, Claude) to physically click, type, and automate any desktop workflow.

CORE TOOLS OmniParser v2 (微软Open Source), Python, PyTorch, Qwen2-VL / Claude 3.5 Sonnet

BEST FOR Enterprise RPA engineers, IT operations leads, QA automation architects, workflow consultants, productivity hackers

REQUIREMENTS PC with Python 3.10+, 6GB+ Nvidia GPU recommended for local OmniParser parsing, paired with a multimodal vision LLM API.

✦ WHY THIS WORKFLOW

Countless legacy desktop programs and enterprise systems lack modern APIs and defy traditional web scrapers. OmniParser gives AI eyes to comprehend any pixel interface, unlocking universal desktop automation without touching source code.

✦ Interactive Workflow Animation Preview

Step-by-Step Action Checklist

0 / 4 Completed

✓ Step 1 [Deploy OmniParser v2 vision server]: Clone Microsoft's repository, fetch the UI element detector and icon classifier weights, and launch the local FastAPI service.
✓ Step 2 [Capture target application screenshot]: Grab high-resolution window screenshots of any target desktop app (e.g. legacy ERP or closed accounting software).
✓ Step 3 [Parse interactable boxes & coordinate mapping]: OmniParser processes the image within milliseconds, returning annotated visual boxes and structured JSON with pixel coordinates.
✓ Step 4 [Multimodal LLM decides actions and triggers PyAutoGUI]: Feed the parsed UI to Qwen2-VL or Claude; the model selects actions (`click(box=14)`), executed by PyAutoGUI.

READY-TO-USE PROMPT TEMPLATE

Open Official Tool / Repo ↗

I am building a pure-vision desktop RPA agent with Microsoft open-source OmniParser v2 and multimodal LLMs.
Please provide:
1) [Token-Optimized Visual JSON Protocol]: Schema for compacting OmniParser bounding box coordinates and icon labels to minimize token overhead while maintaining 100% click accuracy;
2) [Safe-Action Python Execution Harness]: Python PyAutoGUI wrapper with visual hash verification, retry loops, and popup dismissal fallback logic;
3) [High-DPI & Multi-Monitor Coordinate Normalizer]: Calibration formula handling Windows 125%/150% display scaling to prevent physical cursor misalignment.

Use Cases & Tags:

Empowering vision models to automate data entry in legacy desktop ERP and accounting systemsAutomating desktop WeChat/DingTalk to dispatch personalized attachments to categorized clientsControlling industrial or medical desktop software through pure screen vision without APIsCross-platform mobile app testing and end-to-end automated regression walk-throughs#omniparser#rpa#qwen-vl

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