MaaFramework is a black-box testing automation framework based on image recognition.
The framework addresses the need for low-code automation testing by combining computer vision techniques with a flexible architecture. It uses image recognition to identify UI elements and automate interactions with applications, eliminating the need for traditional UI element inspection or API access. The approach allows testers to write automation scripts by defining visual patterns and corresponding actions, making it suitable for testing applications where internal structure is unavailable or impractical to inspect.
The tool is designed for developers building black-box test suites who want to minimize boilerplate code while maintaining extensibility. It works across Windows, Linux, macOS, and Android platforms. The framework supports multiple language bindings including Python, Node.js, Go, Rust, and .NET, allowing teams to integrate it into existing development workflows. Those choosing this tool should expect a low-code experience centered on pipeline definitions and visual pattern matching rather than traditional element-based automation.
The project maintains steady development activity with regular commits and has established distribution channels across multiple package managers. The codebase is written in C++ and the team has published comprehensive documentation including integration guides, API references, and protocol specifications for both task pipelines and project interfaces. Community contributions are actively encouraged through a dedicated hub for sharing skills, pipelines, and custom modules.
