Top 17 Alternatives to the Airtest Project for Python/CV Testing

Introduction and Context

Airtest Project emerged from the need to automate game and app user interfaces where traditional DOM-based or accessibility-based selectors fall short. Originating from teams working heavily with Android, Windows, and game engines, Airtest brought computer vision (CV) and Python together to drive UI interactions reliably in environments where UI hierarchies are not exposed or are highly dynamic. Alongside the core Airtest framework, the ecosystem also includes Poco—a UI automation library that exposes object-level selectors for many game engines—and AirtestIDE, which helps author and debug scripts visually.

Why did Airtest become popular? It solved a hard problem for game QA and Android/Windows UI testing:

• It uses image recognition to locate and interact with UI elements when object trees are unavailable or unreliable.

• It is Python-based, so teams can scale with familiar tooling (pytest, CI/CD pipelines, package managers).

• It integrates into modern workflows, including continuous integration and delivery.

• It supports a range of device and OS combinations common in game testing.

Adoption grew in game studios and teams testing hybrid apps, emulators, and Windows desktop programs with custom rendering pipelines. However, as teams diversify their stacks—web apps, iOS, performance testing, visual regression analysis—they often look for complementary or alternative tools. Some seek richer reporting, non-CV selector strategies, codeless authoring, or SaaS execution at scale. Others want a better fit for performance or behavior-driven development practices. The landscape now offers many options that can supplement or replace Airtest in certain contexts.

This guide walks through 17 noteworthy alternatives, explaining where each shines and how it compares to Airtest Project.

Overview: Top 17 Alternatives to Airtest Project

Here are the top 17 alternatives we will cover:

• Airtest + Poco

• Applitools Eyes

• Behave

• LoadRunner

• Locust

• Mabl

• Playwright

• PyAutoGUI

• Pytest

• Pywinauto

• Repeato

• Robot Framework + SeleniumLibrary

• Selene (Yashaka)

• Squish

• TestCafe Studio

• TestComplete

• Waldo

Why Look for Airtest Project Alternatives?

Teams consider alternatives or complements to Airtest for several practical reasons:

• Maintenance of image assets: CV-based locators can be brittle when UI assets or layouts change often. Teams may prefer object-based selectors, test IDs, or visual AI that automatically handles small variations.

• Platform coverage: Airtest focuses on Android and Windows. While it can be extended, some teams need first-class iOS and broad desktop coverage (including macOS) without extra setup.

• Test stability and flakiness: Flakiness can occur if CV thresholds, screen resolutions, or scaling are not carefully controlled. Object-level frameworks with auto-waiting or built-in retries may reduce this risk.

• Setup and tooling overhead: Device setup, ADB, drivers, and environment dependencies can slow teams. SaaS and cloud platforms reduce local machine complexity and simplify scale-out.

• Reporting and analytics: Some teams want advanced dashboards, baselines for visual diffs, and test result analytics out of the box.

• Skill set and collaboration: Not all teams are comfortable maintaining Python/CV heavy stacks. BDD/keyword-driven or low-code tools can make test authoring inclusive for non-developers.

Detailed Breakdown of Alternatives

1) Airtest + Poco

What it is: Airtest + Poco combines Airtest’s CV-driven automation with Poco’s object-level automation for Android, Windows, and iOS. Built by the same community behind Airtest, it adds a UI object model for popular engines and platforms.

At a glance:

• Platforms: Android, Windows, iOS

• License: Open Source

• Primary tech: Python

• Best for: Teams automating end-to-end flows across mobile and desktop where a mix of image recognition and object selectors is ideal.

Key strengths:

• Blends CV and object-level selectors for more resilient locators.

• Leverages Python and integrates well with CI/CD pipelines.

• Suited to game engines where DOM/accessibility trees are limited.

• Familiar authoring experience for Airtest users.

• Open-source with community-driven enhancements.

How it compares to Airtest Project:

• It is essentially an enhanced path, not a departure. If you like Airtest but want fewer image-based locator pitfalls, adding Poco can reduce flakiness and maintenance overhead by using UI objects when available.

2) Applitools Eyes

What it is: A visual testing platform with AI-powered visual diffs. It captures and compares application snapshots to detect unintended UI changes across web, mobile, and desktop apps.

At a glance:

• Platforms: Web, Mobile, Desktop

• License: Commercial

• Primary tech: SDKs (JS/Java/Python/.NET)

• Best for: Front-end and QA teams validating look-and-feel at scale with baseline management.

Key strengths:

• AI-driven visual comparisons reduce false positives due to minor rendering noise.

• Ultrafast Grid executes visual checks across many browsers/OS combinations quickly.

• Strong baselining and workflow for managing visual changes over time.

• Integrates with popular automation frameworks and CI/CD tools.

• Excellent dashboards and reporting for visual quality.

How it compares to Airtest Project:

• Airtest is primarily about driving UI interactions via CV. Applitools is about validating the final rendered UI visually. Use Applitools when visual correctness across versions and environments matters more than input automation, or pair it with an input driver (like Playwright) for full-stack checks.

3) Behave

What it is: A behavior-driven development (BDD) framework for Python that uses Gherkin syntax. It helps teams express acceptance criteria in a readable, business-friendly way and connect them to step definitions.

At a glance:

• Platforms: Python

• License: Open Source (BSD)

• Primary tech: Python

• Best for: Cross-functional teams practicing BDD and acceptance testing.

Key strengths:

• Encourages collaboration with human-readable scenarios.

• Works with any Python-based automation stack (Selenium, Playwright, Airtest).

• Rich ecosystem and established BDD pattern familiarity.

• Promotes test design clarity and maintainability.

How it compares to Airtest Project:

• Behave is not a UI driver. It organizes test logic and improves collaboration. If your pain point is test design rather than low-level interaction, Behave can complement or replace raw Airtest scripting by structuring scenarios and step reusability.

4) LoadRunner

What it is: An enterprise-grade performance and load testing suite that supports a wide range of protocols and deep system monitoring.

At a glance:

• Platforms: Web/API/Protocols

• License: Commercial

• Primary tech: C/Proprietary

• Best for: Performance engineers and DevOps teams running large-scale load and stress tests.

Key strengths:

• High-scale load generation and extensive protocol support.

• Integration with application performance monitoring tools.

• Mature reporting and analysis for performance bottlenecks.

• Enterprise support and governance.

How it compares to Airtest Project:

• Airtest is for UI automation, not performance load generation. If your primary goal is system scalability and throughput testing rather than UI-level interactions, LoadRunner is a more suitable choice.

5) Locust

What it is: An open-source load testing tool where user behavior is defined in Python. It scales easily and is scriptable for complex scenarios.

At a glance:

• Platforms: Web/API/Protocols

• License: Open Source (MIT)

• Primary tech: Python

• Best for: Teams wanting Pythonic, scalable load testing with flexible scenario modeling.

Key strengths:

• Python-based scenarios enable sophisticated test logic.

• Distributed load generation and horizontal scalability.

• Easy integration into CI/CD pipelines.

• Lightweight and developer-friendly.

How it compares to Airtest Project:

• Like LoadRunner, Locust targets performance rather than UI automation. Use Locust to validate backend capacity and API performance. If you are already invested in Python, Locust integrates naturally with your existing tooling.

6) Mabl

What it is: A low-code, AI-assisted end-to-end testing platform for web and API testing, designed as a SaaS-first solution with self-healing tests.

At a glance:

• Platforms: Web + API

• License: Commercial

• Best for: Teams seeking cloud-native, low-code automation with strong CI/CD support.

Key strengths:

• Self-healing reduces maintenance as UI changes.

• Cloud execution and scalability without local setup.

• CI/CD integrations for continuous testing workflows.

• Visual dashboards and reporting.

How it compares to Airtest Project:

• Mabl is web/API-focused and low-code, whereas Airtest is Python/CV-centric with a heavier local setup. If your stack is primarily web and you want rapid authoring with minimal maintenance, Mabl can be a compelling alternative.

7) Playwright

What it is: A modern end-to-end web testing framework supporting Chromium, Firefox, and WebKit. It features auto-waiting, a powerful trace viewer, and reliable selectors.

At a glance:

• Platforms: Web (Chromium/Firefox/WebKit)

• License: Open Source (Apache-2.0)

• Primary tech: .NET, Java, Node.js, Python

• Best for: Teams automating cross-browser web flows with stable selectors and fast execution.

Key strengths:

• Auto-waiting and robust selector engine reduce flakiness.

• Cross-language support, including Python.

• Trace viewer for rich debugging artifacts.

• Parallel execution and container-friendly.

• Great CI/CD integration and community adoption.

How it compares to Airtest Project:

• Playwright excels for browser automation with object-level selectors, not CV. For web apps, Playwright usually provides greater stability and speed. Airtest is better suited when you must automate non-DOM UIs (games, custom-rendered apps).

8) PyAutoGUI

What it is: A cross-platform Python library for controlling the mouse and keyboard and for basic screen automation across Windows, macOS, and Linux.

At a glance:

• Platforms: Windows, macOS, Linux

• License: Open Source (BSD)

• Primary tech: Python

• Best for: QA teams automating native desktop interactions via OS-level events.

Key strengths:

• Simple Python API for clicks, keystrokes, and screenshots.

• Works across major desktop OSes.

• Good fit for legacy apps or simple desktop tasks.

• Minimal setup and easy to learn.

How it compares to Airtest Project:

• Both can use images to find on-screen elements. PyAutoGUI is simpler and more general-purpose across desktops, but lacks the game-focused features and ecosystem Airtest offers. Choose PyAutoGUI for lightweight desktop tasks where CV precision and IDE tooling are less critical.

9) Pytest

What it is: A widely used Python testing framework for unit, functional, and integration tests with fixtures, parametrization, and a rich plugin ecosystem.

At a glance:

• Platforms: Python

• License: Open Source (MIT)

• Primary tech: Python

• Best for: Teams that want a flexible test runner and ecosystem to orchestrate tests of many kinds.

Key strengths:

• Powerful fixtures and parametrization for maintainable tests.

• Extensive plugin ecosystem (coverage, xdist, HTML reports).

• Works as a test harness for UI, API, and unit tests.

• Easy integration with CI/CD.

How it compares to Airtest Project:

• Pytest is not a UI automation library. Instead, it can organize and execute Airtest scripts or any UI framework. If your challenge is test structure, reporting, and orchestration, Pytest often complements Airtest rather than replaces it.

10) Pywinauto

What it is: A Python library to automate native Windows applications through accessibility and UI Automation APIs.

At a glance:

• Platforms: Windows

• License: Open Source (BSD)

• Primary tech: Python

• Best for: Teams focused on Windows desktop automation using object-level selectors.

Key strengths:

• Object-based interactions with Windows-native controls.

• Less brittle than image-only approaches for Windows apps.

• Pythonic API that integrates with Pytest and CI/CD.

• Good for enterprise and legacy Windows software.

How it compares to Airtest Project:

• For Windows desktop apps with accessible UI trees, Pywinauto often delivers more stability than pure CV. Airtest still helps in custom-rendered or game-like environments where the accessibility tree is not helpful.

11) Repeato

What it is: A codeless/computer-vision-based mobile UI testing tool for iOS and Android. It emphasizes resilience to UI changes and ease of authoring.

At a glance:

• Platforms: Android, iOS

• License: Commercial

• Best for: Teams seeking CV-based mobile automation without coding overhead.

Key strengths:

• Codeless authoring speeds up test creation.

• CV-based approach that tolerates UI changes well.

• Cloud-friendly with CI/CD hooks.

• Focused on mobile with streamlined workflows.

How it compares to Airtest Project:

• Both rely on CV for finding elements. Repeato targets simplicity and codeless authoring across iOS and Android, whereas Airtest is code-first and stronger for games and mixed Windows/Android scenarios. Choose Repeato if your priority is fast, codeless mobile test creation.

12) Robot Framework + SeleniumLibrary

What it is: A keyword-driven test automation framework with a broad ecosystem. Paired with SeleniumLibrary, it provides powerful web UI testing via keywords.

At a glance:

• Platforms: Web

• License: Open Source (Apache-2.0)

• Primary tech: Python ecosystem

• Best for: Teams that want readable, keyword-based test cases and rich library support.

Key strengths:

• Keyword-driven syntax improves test readability.

• Large ecosystem of libraries and integrations.

• Works well with CI/CD and reporting tools.

• Enables non-developers to contribute to automation.

How it compares to Airtest Project:

• Robot Framework + SeleniumLibrary is ideal for web applications with accessible DOMs and test IDs. Airtest is better when you need CV (e.g., games or custom-rendered apps). If your challenges center on collaboration and readability for web testing, Robot Framework is a compelling alternative.

13) Selene (Yashaka)

What it is: A Python wrapper inspired by Selenide that simplifies Selenium usage, providing concise and reliable web UI automation.

At a glance:

• Platforms: Web

• License: Open Source

• Primary tech: Python

• Best for: Teams wanting a developer-friendly Selenium experience in Python.

Key strengths:

• Higher-level API with smart waits and concise syntax.

• Leverages Selenium’s flexibility and ecosystem.

• Integrates easily with Pytest and CI/CD.

• Encourages clean, maintainable page object patterns.

How it compares to Airtest Project:

• Selene is for DOM-driven web tests with element selectors and smart waits. If your app is a standard web application, Selene’s stability and developer ergonomics generally outperform a CV-first approach. Airtest remains stronger for non-DOM UIs.

14) Squish

What it is: A commercial GUI testing tool renowned for Qt, QML, desktop, embedded, and web application testing. It supports multiple scripting languages including Python.

At a glance:

• Platforms: Desktop, Embedded, QML, Qt, Web

• License: Commercial

• Primary tech: Python/JS/Ruby/Tcl/Perl

• Best for: Teams testing Qt/QML or embedded UIs needing deep technology support.

Key strengths:

• Deep integration with Qt/QML and embedded tech stacks.

• Object-level recognition for stability over time.

• Cross-platform support and enterprise-grade tooling.

• Broad scripting language options.

How it compares to Airtest Project:

• For Qt/QML and embedded applications, Squish’s object-level access is often more maintainable than image matching. Airtest suits scenarios with opaque rendering pipelines; Squish excels when it can access and manipulate UI objects directly.

15) TestCafe Studio

What it is: A commercial, codeless IDE variant of the TestCafe framework for end-to-end web testing, offering recorder capabilities and IDE-driven authoring.

At a glance:

• Platforms: Web

• License: Commercial

• Best for: Teams that want codeless web test authoring with an integrated IDE.

Key strengths:

• Codeless recorder and robust IDE experience.

• Reliable cross-browser web testing without Selenium.

• Good parallelization and CI/CD integration.

• Visual debugging tools and step-by-step runs.

How it compares to Airtest Project:

• TestCafe Studio targets web UI automation with stable selectors and codeless authoring. Airtest is the better fit for CV-heavy workflows and non-DOM interfaces. If you are testing standard web apps, TestCafe Studio can reduce maintenance and speed authoring.

16) TestComplete

What it is: A commercial, codeless/scripted test automation platform by SmartBear covering desktop, web, and mobile, with record/playback and multiple scripting languages.

At a glance:

• Platforms: Desktop, Mobile, Web

• License: Commercial

• Primary tech: JavaScript/Python/VBScript/DelphiScript

• Best for: Teams needing a single platform for cross-technology UI testing with both codeless and code options.

Key strengths:

• Record/playback plus scripting for flexibility.

• Broad technology coverage across UI stacks.

• Built-in object recognition and some visual validation options.

• Enterprise reporting and CI/CD integrations.

How it compares to Airtest Project:

• TestComplete emphasizes a unified, enterprise-grade approach with robust object recognition. It reduces reliance on image assets compared to Airtest and can accelerate onboarding for mixed-skill teams. Airtest remains attractive for Python-first, CV-centric game and Windows UI automation.

17) Waldo

What it is: A no-code, cloud-based mobile UI testing platform for iOS and Android with a strong focus on recorder simplicity and scalable execution.

At a glance:

• Platforms: Android, iOS

• License: Commercial

• Best for: Mobile teams prioritizing speed of test creation and cloud execution.

Key strengths:

• No-code recording lowers the barrier to entry.

• Cloud runs and device coverage reduce local setup.

• CI/CD integrations for mobile pipelines.

• Clear reporting on mobile flows.

How it compares to Airtest Project:

• Waldo minimizes scripting and infrastructure work on mobile. Airtest offers Python-based flexibility and CV control, which can be crucial for custom-rendered or game UIs. Choose Waldo for faster mobile test authoring and execution; choose Airtest for deep customization and CV-driven scenarios.

Things to Consider Before Choosing an Airtest Alternative

• Application scope and UI technology:

• Language and skills:

• Ease of setup and maintenance:

• Execution speed and stability:

• CI/CD and ecosystem integration:

• Debugging and observability:

• Community and support:

• Scalability and cost:

Conclusion

Airtest Project remains a powerful choice for Python/CV testing—particularly for games, Android, and Windows environments where UI trees are limited or unavailable. Its strengths include broad test automation capabilities, modern workflow alignment, and CI/CD integration. Yet as testing needs evolve, some teams look for alternatives that offer codeless authoring, stable object-level selectors, cloud scalability, or specialized capabilities like visual AI and enterprise performance testing.

• For web apps, Playwright, Selene, Robot Framework + SeleniumLibrary, and TestCafe Studio often deliver faster authoring and more stable selectors than CV-based approaches.

• For mobile teams seeking speed and simplicity, Repeato and Waldo provide codeless workflows and cloud execution.

• For Windows desktop applications with accessible controls, Pywinauto offers object-level stability.

• If your main need is test structure or collaboration, Pytest and Behave help you organize and communicate test intent effectively.

• For performance and load testing, Locust and LoadRunner are purpose-built and more appropriate than UI-based tools.

• For visual quality at scale, Applitools Eyes provides AI-driven visual diffs and baseline management.

• If you like Airtest but want to reduce image-maintenance overhead, Airtest + Poco is a natural progression that blends CV with object-level selectors.

• For complex GUI stacks like Qt/QML or embedded systems, Squish provides deep technology support and enterprise-grade tooling.

• For a unified commercial platform across desktop, web, and mobile with both codeless and scripted options, TestComplete is a strong candidate.

Ultimately, the “best” alternative depends on your application technology, team skills, and delivery goals. Consider starting with a small proof of concept for two or three candidates—measure stability, authoring speed, CI/CD fit, and total cost of ownership—then scale the winner across your pipeline. In many organizations, a hybrid approach works best: pair a primary UI automation framework with specialized tools for visual validation, performance testing, or BDD-style collaboration.