AutoBrowse is a developer library enabling LLM-driven web automation. By leveraging large language models, it plans and executes browser actions—searching, navigating, interacting, and extracting information from web pages. Using a planner-executor pattern, it breaks down high-level tasks into step-by-step actions, handling JavaScript rendering, form inputs, link traversal, and content parsing. It outputs structured data or summaries, making it ideal for research, data collection, automated testing, and competitive intelligence workflows.

APLib is designed to simplify the development of AI-driven autonomous agents within gaming and simulation environments. Utilizing a Belief-Desire-Intention (BDI) inspired architecture, it offers modular components for perception, decision-making, and action execution. Developers define agent beliefs, goals, and behaviors via intuitive APIs and behavior trees. APLib agents can interpret game state through customizable sensors, formulate plans using built-in planners, and interact with the environment via actuators. The library supports integration with Unity, Unreal, and pure Java environments, facilitating automated testing, AI research, and simulations. It promotes reuse of behavior modules, rapid prototyping, and robust QA workflows by automating repetitive test scenarios and simulating complex player behaviors without manual intervention.