An MIT-licensed prototype game engine framework for education, R&D, and building custom engines.
Esoterica Engine is an MIT-licensed prototype game engine framework that provides core systems like serialization, resource management, and animation for building custom game engines. It serves as a technology demonstrator and educational tool, offering references for implementing AAA-level features such as reflection and hybrid actor-ECS architectures. The project is designed for experimentation, research, and learning rather than stable production use.
Game engine developers, researchers, educators, and students interested in understanding or building game engine technologies, particularly those focused on animation, physics, and resource systems.
Developers choose Esoterica for its comprehensive reference implementations of advanced engine systems, open-source MIT licensing, and focus on education and R&D, making it a unique starting point for custom engine projects.
Esoterica Engine
Uses libclang for automated code generation, enabling robust reflection in C++ without manual boilerplate, as highlighted in the README's feature list.
Combines actor-based and entity-component-system architectures for flexible game object management, referenced with a dedicated YouTube tutorial link for deeper understanding.
Includes a AAA-quality animation graph system with basic compression, demonstrated in detailed online presentations that showcase its capabilities.
Features a compiler-based resource pipeline with hot-reloading, allowing live updates during development to speed up iteration, as shown in the resource server screenshot.
Explicitly described as a prototype with bugs and crashes, not intended for stable production use, which limits reliability for serious projects.
Admits to having no distributable test assets, making it hard to learn from or demonstrate features without community contributions, as noted in the 'Help Needed' section.
Requires Visual Studio 2022, manual dependency handling, and a semi-flaky code generation step that may need solution reloads, increasing initial overhead and potential frustration.
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