#Showcases

This page introduces cross-platform GUI Agent, E2E test, and community showcases built with Midscene.

#Cross-platform GUI Agent Showcases

#Web

Prompt: Fill out the GitHub sign-up form and pass validation, but do not submit it.

Midscene generates a complete report for every task so developers can review the operation process. See the report for the demo above: report.html

#iOS

Prompt : Open Twitter and auto-like the first tweet by @midscene_ai

View the full report of this task: report.html

#Android

Prompt : Open the Booking App, search for a hotel in Tokyo for four adults on Christmas, with a score of 8 or above.

View the full report of this task: report.html

#HarmonyOS

Prompt : Open Settings, scroll to find "About phone", view device information.

View the full report of this task: report.html

#Desktop

macOS

Prompt: Help me post a tweet promoting Midscene's support for AutoGLM through safari, with the following requirements:

1. Text content: Midscene now supports AutoGLM!
2. Media content: Use the AutoGLM video from the download folder!

View the full report for this task: report.html

Prompt: Open Google and query San Jose tomorrow weather temperature

View the full report for this task: report.html

Windows

Prompt: Open Sauce Demo e-commerce site, login and add items to cart

View the full report for this task: report.html

Linux

Prompt: Open TodoMVC, add multiple tasks and filter them

View the full report for this task: report.html

#In-vehicle systems x robotic arms (community showcase)

Some community developers have successfully built on Midscene's capability to integrate with any interface, extending it with a robotic arm plus vision and voice models for in-vehicle large-screen testing scenarios.

#E2E Test Scenarios

#Dongchedi App: Doubao Seed 2.1 Turbo

The following five test cases use doubao-seed-2-1-turbo-260628 to test the ranking filters in the Android Dongchedi app on a device with a 720 × 1600 resolution.

#Case 1: Sales ranking test

• Test scope: Verify that the sales ranking correctly applies the combined filters for sedan, the month before last, fuel, and a preset CNY 180,000–250,000 price range, and displays the corresponding results
• Steps: 8 / 16 (script / model calls)
• Tokens: Input 126,935 (89,760 cached) / output 3,109
• Cost: $0.0312
• Test report: View report

#Case 2: Sales ranking combined-filter test

• Test scope: Verify that the sales ranking correctly applies the combined filters for SUV, the previous month, and plug-in hybrid, and displays the corresponding results
• Steps: 4 / 14 (script / model calls)
• Tokens: Input 137,310 (100,000 cached) / output 2,472
• Cost: $0.0307
• Test report: View report

#Case 3: New-energy ranking test

• Test scope: Verify that the new-energy ranking correctly applies the combined filters for SUV, the last six months, battery electric, and a preset CNY 180,000–250,000 price range, and displays the corresponding results
• Steps: 8 / 18 (script / model calls)
• Tokens: Input 149,996 (103,896 cached) / output 6,279
• Cost: $0.0434
• Test report: View report

#Case 4: Price-drop ranking test

• Test scope: Verify that the price-drop ranking correctly applies the combined filters for MPV, the last year, new energy, and a custom CNY 150,000–300,000 price range, and displays the corresponding results
• Steps: 13 / 29 (script / model calls)
• Tokens: Input 253,991 (174,176 cached) / output 8,203
• Cost: $0.0687
• Test report: View report

#Case 5: Ranking switch and filter reset test

• Test scope: Verify that switching from the sales ranking to the new-energy ranking resets dependent filters correctly, and that subsequent filtering and reset-to-default behavior work as expected
• Steps: 16 / 28 (script / model calls)
• Tokens: Input 217,283 (151,848 cached) / output 5,688
• Cost: $0.0548
• Test report: View report

#Reddit App: Qwen 3.7 Plus

The following two test cases use qwen/qwen3.7-plus to test community search, joining, and post upvoting in the Android Reddit app on a device with a 720 × 1600 resolution.

#Case 1: Search for and join the Midscene community

• Test scope: Search Reddit for Midscene, open the exact r/midscene community, join it if needed, and verify that the account has joined
• Steps: 7 / 17 (script / model calls)
• Tokens: Input 147,127 (34,816 cached) / output 3,418
• Cost: $0.0425
• Test report: View report

#Case 2: Upvote the first post in the Midscene community

• Test scope: Open the exact r/midscene community, upvote the first post in its post list if needed, and verify that it is upvoted
• Steps: 6 / 12 (script / model calls)
• Tokens: Input 103,231 (8,704 cached) / output 3,142
• Cost: $0.0348
• Test report: View report