The Multi-Agent System Framework offers a modular structure for building and orchestrating multiple AI agents within Python applications. It includes an agent manager to spawn and supervise agents, a communication backbone supporting various protocols (e.g., message passing, event broadcasting), and customizable memory stores for long-term knowledge retention. Developers can define distinct agent roles, assign specialized tasks, and configure cooperative strategies such as consensus-building or voting. The framework integrates seamlessly with external AI models and knowledge bases, enabling agents to reason, learn, and adapt. Ideal for distributed simulations, conversational agent clusters, and automated decision-making pipelines, the system accelerates complex problem solving by leveraging parallel autonomy.

Swarms.rs is the core Rust runtime for executing swarm-based AI agent programs. It features a modular plugin system to integrate custom logic or AI models, a message-passing layer for peer-to-peer communication, and an asynchronous executor for scheduling agent behaviors. Together, these components allow developers to design, deploy, and scale complex decentralized agent networks for simulation, automation, and multi-agent collaboration tasks.