MultiAgentes provides a modular architecture for defining environments and agents, supporting synchronous and asynchronous multi-agent interactions. It includes base classes for environments and agents, predefined scenarios for cooperative and competitive tasks, tools for customizing reward functions, and APIs for agent communication and observation sharing. Visualization utilities allow real-time monitoring of agent behaviors, while logging modules record performance metrics for analysis. The framework integrates seamlessly with Gym-compatible reinforcement learning libraries, enabling users to train agents using existing algorithms. MultiAgentes is designed for extensibility, allowing developers to add new environment templates, agent types, and communication protocols to suit diverse research and educational use cases.

uAgents is a modular JavaScript framework that empowers developers to build autonomous, decentralized AI agents which can discover peers, exchange messages, collaborate on tasks, and adapt through learning. Agents communicate over libp2p-based gossip protocols, register capabilities via on-chain registries, and negotiate service-level agreements using smart contracts. The core library handles agent lifecycle events, message routing, and extensible behaviors such as reinforcement learning and market-driven task allocation. Through customizable plugins, uAgents can integrate with Fetch.ai’s ledger, external APIs, and oracle networks, enabling agents to perform real-world actions, data acquisition, and decision-making in distributed environments without centralized orchestration.

The AI Agent Network Protocol is designed to foster communication and interaction among different AI agents, allowing them to exchange data, execute tasks collaboratively, and adapt to user requirements in real-time. It enhances interoperability and efficiency, promoting dynamic task sharing and resource optimization across diverse applications in sectors such as automation, customer support, and data analysis.