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.

ASP-DALI provides a unified platform for defining and executing logic-based intelligent agents. Developers write ASP rules to represent agent knowledge and goals, while DALI constructs define event reactions and action executions. At runtime, an ASP solver computes answer sets that guide the agent’s decisions, enabling it to plan, react to incoming events, and adjust beliefs dynamically. The framework supports modular knowledge bases, facilitating incremental updates and clear separation between declarative rules and reactive behaviors. ASP-DALI is implemented in Prolog with interfaces to popular ASP solvers, simplifying integration and deployment across research and prototype scenarios.

AgentVerse is designed to facilitate the creation of multi-agent architectures by offering a set of reusable modules and abstractions. Users can define unique agent classes with custom decision-making logic, establish communication channels for message passing, and simulate environmental conditions. The platform supports synchronous and asynchronous interactions among agents, enabling complex workflows such as negotiation, task delegation, and cooperative problem-solving. With integrated logging and monitoring, developers can trace agent actions and evaluate performance metrics. AgentVerse also includes templates for common use cases like autonomous exploration, trading simulations, and collaborative content generation. Its pluggable design allows seamless integration of external machine learning models, such as language models or reinforcement learning algorithms, providing flexibility for various AI-driven applications.

The Contract Net Protocol repository provides a full Java implementation of the FIPA Contract Net interaction protocol. Developers can create manager and contractor agents that exchange CFP (Call For Proposal), proposals, acceptances, and rejections over agent communication channels. The code includes core modules for broadcasting tasks, collecting bids, evaluating proposals based on customizable criteria, awarding contracts, and monitoring execution status. It can be integrated into larger multi-agent frameworks or used as a standalone library for research simulations, industrial scheduling, or robotic coordination.

JADE-DR-VPP is an open-source Java framework that implements a multi-agent system for Virtual Power Plant (VPP) demand response (DR). Each agent represents a flexible load or generation unit that communicates via JADE messaging. The system orchestrates DR events, schedules load adjustments, and aggregates resources to meet grid signals. Users can configure agent behaviors, run large-scale simulations, and analyze performance metrics for energy management strategies.

jason-RL adds a reinforcement learning layer to the Jason multi-agent framework, allowing AgentSpeak BDI agents to learn action-selection policies via reward feedback. It implements Q-learning and SARSA algorithms, supports configuration of learning parameters (learning rate, discount factor, exploration strategy), and logs training metrics. By defining reward functions in agent plans and running simulations, developers can observe agents improve decision making over time, adapting to changing environments without manual policy coding.

JASA provides a comprehensive set of Java libraries for building and running multi-agent system simulations. It supports agent lifecycle management, event scheduling, asynchronous message passing, and environment modeling. Developers can extend core classes to implement custom behaviors, integrate external data sources, and visualize simulation outcomes. The framework’s modular design and clear API documentation facilitate rapid prototyping and scalability, making it suitable for academic research, teaching, and proof-of-concept development in agent-based modeling.

The Mesa Predator-Prey Model is an open-source, Python-based implementation of the classic Lotka-Volterra predator-prey system, built atop the Mesa agent-based modeling framework. It simulates individual predator and prey agents moving and interacting on a grid where prey reproduce and predators hunt for food to survive. Users can configure initial populations, reproduction probabilities, energy consumption, and other environmental parameters through a web-based interface. The simulation provides real-time visualizations, including heatmaps and population curves, and logs data for post-run analysis. Researchers, educators, and students can extend the model by customizing agent behaviors, adding new species, or integrating complex ecological rules. The project is designed for ease of use, rapid prototyping, and educational demonstrations of emergent ecological dynamics.

The project uses the JADE (Java Agent DEvelopment) framework to build a multi-agent environment. It defines agents that register with the platform’s AMS and DF, exchange ACL messages, and execute behaviors like cyclic, one-shot, and FSM. Example scenarios include buyer-seller negotiations, contract net protocols, and task allocation. A GUI agent container helps monitor runtime agent states and message flows.

This open-source framework combines the AgentSimJs agent modeling library with Three.js's 3D graphics engine to deliver interactive, browser-based multi-agent simulations. Users can define agent types, behaviors, and environmental rules, configure collision detection and event handling, and visualize simulations in real time with customizable rendering options. The library supports dynamic controls, scene management, and performance tuning, making it ideal for research, education, and prototyping of complex agent-based scenarios.