Embedded BDI provides a full BDI lifecycle engine: it models an agent’s beliefs about its environment, manages evolving desires or goals, selects intentions from a library of plans, and executes behaviors in real time. The framework includes modules for belief base storage, plan library definition, event triggering, and concurrency control tailored for memory-limited microcontrollers. With a simple API, developers annotate beliefs, specify desires, and implement plans in code. Its scheduler handles priority-based intention execution and integrates with hardware interfaces for sensors, actuators, and network communication, making it ideal for autonomous IoT devices, mobile robots, and industrial controllers.

Flocking Multi-Agent offers a modular library for simulating autonomous agents exhibiting swarm intelligence. It encodes core steering behaviors—cohesion, separation and alignment—alongside obstacle avoidance and dynamic target pursuit. Using Python and Pygame for visualization, the framework allows adjustable parameters such as neighbor radius, maximum speed, and turning force. It supports extensibility through custom behavior functions and integration hooks for robotics or game engines. Ideal for experimentation in AI, robotics, game development, and academic research, it demonstrates how simple local rules lead to complex global formations.

MASlite provides a clear API to create agent classes, register behaviors, and handle event-driven messaging between agents. It includes a scheduler to manage agent tasks, environment modeling to simulate interactions, and a plugin system to extend core capabilities. Developers can rapidly prototype multi-agent scenarios in Python by defining agent lifecycle methods, connecting agents via channels, and running simulations in a headless mode or integrating with visualization tools.