A Qt/QML integration for .NET, enabling .NET developers to build cross-platform desktop and embedded GUI applications.
Qml.Net is a library that provides integration between the Qt/QML framework and the .NET platform. It enables .NET developers to build cross-platform graphical user interfaces using QML for the frontend while writing business logic in C# or other .NET languages. The library solves the problem of creating modern, native desktop and embedded applications with a separation between UI design and application code.
.NET developers who need to build cross-platform desktop or embedded GUI applications and want to leverage the Qt/QML framework for its rich UI capabilities and industry adoption.
Developers choose Qml.Net because it combines the mature, feature-rich Qt/QML GUI framework with the productivity and ecosystem of .NET, offering native rendering performance, seamless two-way interoperability, and support for async/await patterns without expensive P/Invoke overhead for rendering.
Qml.Net - Qt/QML integration/support for .NET
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Supports all basic QML types and enables direct back-and-forth conversion between .NET and QML, as demonstrated in the code examples for properties like StringProperty.
Allows invoking asynchronous .NET methods from QML with continuations on Qt's UI thread, shown in the TestAsync method example for responsive interfaces.
Uses native rendering and dynamically compiled delegates to minimize interop overhead, ensuring high performance for GUI operations as highlighted in the elevator pitch.
Leverages the industry-proven Qt/QML framework with a rich ecosystem of controls, noted as 'used in many industries' and 'proven in production'.
Requires separate OS-specific binary packages and dependency on Qt runtime discovery or download, adding deployment overhead and initial configuration steps.
Documentation is marked as WIP (Work in Progress), which can hinder onboarding and troubleshooting, as admitted in the README.
Involves managing both .NET and QML/JavaScript codebases, increasing cognitive load and maintenance compared to single-framework solutions like Avalonia.