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.

ChatStreamAiAgent provides developers with a lightweight Python toolkit to implement AI chat agents that stream token outputs as they are generated. It supports multiple LLM providers, asynchronous event hooks, and easy integration into web or console applications. With built-in context management and prompt templating, teams can rapidly prototype conversational assistants, customer support bots, or interactive tutorials while delivering low-latency, real-time responses.