This paper presents Hornet, a robust multithreaded AI framework developed for large-scale real-time strategy and simulation games. Hornet is designed to manage thousands of AI-controlled units, enabling complex behaviors while maintaining high frame rates. The paper explores the framework’s architecture, including its threading model, task management, and data handling for optimal performance. Key features such as modular design, behavior trees, and integration strategies are discussed, alongside performance benchmarks demonstrating its efficiency. Additionally, the paper delves into practical applications of Hornet in various game genres, highlighting its scalability and adaptability for different AI-driven systems.