AgentSimJS is designed to simplify the creation and execution of large-scale agent-based models in JavaScript. With its modular architecture, developers can define agents with custom states, sensors, decision-making functions, and actuators, then integrate them into dynamic environments parameterized by global variables. The framework orchestrates discrete time-step simulations, manages event-driven messaging between agents, and logs interaction data for analysis. Visualization modules support real-time rendering using HTML5 Canvas or external libraries, while plugins enable integration with statistical tools. AgentSimJS runs both in modern web browsers and Node.js, making it suitable for interactive web applications, academic research, educational tools, and rapid prototyping of swarm intelligence, crowd dynamics, or distributed AI experiments.

OpenAI Swarm Node orchestrates concurrent calls to multiple ChatGPT agents, gathers individual outputs, applies your chosen aggregation strategy—such as majority voting or custom weighting—and returns a unified consensus response. Its extensible architecture supports fine-grained control over model parameters, error handling, retry logic, and asynchronous execution, enabling developers to integrate swarm intelligence into any Node.js application for higher accuracy and consistency in AI-driven decision-making.

OpenMAS is designed to facilitate the development and evaluation of decentralized AI agents and multi-agent coordination strategies. It features a modular architecture that allows users to define custom agent behaviors, dynamic environment models, and inter-agent messaging protocols. The framework supports physics-based simulation, event-driven execution, and plugin integration for AI algorithms. Users can configure scenarios via YAML or Python, visualize agent interactions, and collect performance metrics through built-in analytics tools. OpenMAS streamlines prototyping in research areas such as swarm intelligence, cooperative robotics, and distributed decision-making.

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.