Multi-Agents System from Scratch provides a comprehensive set of Python modules to build, customize, and evaluate multi-agent environments from the ground up. Users can define world models, create agent classes with unique sensory inputs and action capabilities, and establish flexible communication protocols for cooperation or competition. The framework supports dynamic task allocation, strategic planning modules, and real-time performance tracking. Its modular architecture allows easy integration of custom algorithms, reward functions, and learning mechanisms. With built-in visualization tools and logging utilities, developers can monitor agent interactions and diagnose behavior patterns. Designed for extensibility and clarity, the system caters to both researchers exploring distributed AI and educators teaching agent-based modeling.

SandboxAQ leverages the capabilities of both quantum and classical technologies to provide cutting-edge solutions for analyzing and simulating complex systems. By employing machine learning and advanced analytics, the AI agent helps users make better predictions, uncover insights, and improve decision-making across diverse industries such as finance, healthcare, and logistics. With its unique focus on quantum-enhanced analytics, SandboxAQ addresses challenges that traditional methods may find difficult to solve effectively.