An AI Football Cup in a Java JADE Environment is an open-source demonstration that leverages the Java Agent DEvelopment Framework (JADE) to simulate a full soccer tournament. It models each player as an autonomous agent with behaviors for movement, ball control, passing, and shooting, coordinating via message passing to implement strategies. The simulator includes referee and coach agents, enforces game rules, and manages tournament brackets. Developers can extend decision-making with custom rules or integrate machine learning modules. This environment illustrates multi-agent communication, teamwork, and dynamic strategy planning within a real-time sports scenario.

Autogen Studio Research is a GitHub-hosted research prototype for building, visualizing, and iterating on multi-agent AI applications. It offers a web-based UI that lets you drag and drop agent components, define communication channels, and configure execution pipelines. Under the hood, it uses a Python SDK to connect to various LLM backends (OpenAI, Azure, local models) and provides real-time logging, metrics, and debugging tools. The platform is designed for rapid prototyping of collaborative agent systems, decision-making workflows, and automated task orchestration.

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.

Multi-Agent Systems provides a comprehensive toolkit for creating, controlling, and observing interactions among autonomous agents. Developers can define agent classes with custom decision-making logic, set up complex environments with configurable resources and rules, and implement communication channels for information exchange. The framework supports synchronous and asynchronous scheduling, event-driven behaviors, and integrates logging for performance metrics. Users can extend core modules or integrate external AI models to enhance agent intelligence. Visualization tools render simulations in real-time or post-process, helping analyze emergent behaviors and optimize system parameters. From academic research to prototype distributed applications, Multi-Agent Systems simplifies end-to-end multi-agent simulations.

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.

Multi-Agent System provides a lightweight yet powerful toolkit for designing and executing multi-agent simulations. Users can create custom Agent classes to encapsulate decision-making logic, define Environment objects to represent world states and rules, and configure a Simulation engine to orchestrate interactions. The framework supports modular components for logging, metrics collection, and basic visualization to analyze agent behaviors in cooperative or adversarial settings. It’s suitable for rapid prototyping of swarm robotics, resource allocation, and decentralized control experiments.

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.

NeuralABM is an open-source Python library that leverages PyTorch to integrate neural networks into agent-based modeling. Users can specify agent architectures as neural modules, define environment dynamics, and train agent behaviors using backpropagation across simulation steps. The framework supports custom reward signals, curriculum learning, and synchronous or asynchronous updates, enabling the study of emergent phenomena. With utilities for logging, visualization, and dataset export, researchers and developers can analyze agent performance, debug models, and iterate on simulation designs. NeuralABM simplifies combining reinforcement learning with ABM for applications in social science, economics, robotics, and AI-driven game NPC behaviors. It provides modular components for environment customization, supports multi-agent interactions, and offers hooks for integrating external datasets or APIs for real-world simulations. The open design fosters reproducibility and collaboration through clear experiment configuration and version control integration.

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.