MaaFramework: a cross-platform image-recognition framework for building low-code black-box automation tools

What it solves

MaaFramework provides a way to create automated black-box testing programs without needing access to the internal code of the application being tested. It simplifies the process of building automation tools that rely on visual cues to interact with software, reducing the amount of manual coding required while maintaining flexibility.

How it works

The framework is built on image recognition technology and simulated control. It uses a "low-code" approach via a Pipeline protocol, allowing developers to define task sequences and logic. It is written in C++20 for performance and cross-platform compatibility (Windows, Linux, macOS, Android) and provides bindings for multiple languages including Python, Node.js, Go, and Rust.

Who it’s for

Developers who want to build automated testing tools or "assistants" for applications (such as games or productivity apps) where API access is unavailable and interaction must be driven by visual recognition.

Highlights

• Cross-Platform Support: Works across Windows, Linux, macOS, and Android.
• Multi-Language Integration: Offers libraries for Python, Node.js, Go, and Rust.
• Low-Code Pipeline: Uses a structured protocol to define automation workflows, reducing boilerplate code.
• Extensive Ecosystem: Supported by a wide array of community-built GUIs, debuggers, and specialized automation assistants.