CommNet is a research-oriented library that implements the CommNet architecture, allowing multiple agents to share hidden states at each timestep and learn to coordinate actions in cooperative environments. It includes PyTorch model definitions, training and evaluation scripts, environment wrappers for OpenAI Gym, and utilities for customizing communication channels, agent counts, and network depths. Researchers and developers can use CommNet to prototype and benchmark inter-agent communication strategies on navigation, pursuit–evasion, and resource-collection tasks.

Multi-Agent Coordination provides a lightweight API to define AI agents, register them with a central coordinator, and dispatch tasks for collaborative problem solving. It handles message routing, concurrency control, and result aggregation. Developers can plug in custom agent behaviors, extend communication channels, and monitor interactions through built-in logging and hooks. This framework simplifies the development of distributed AI workflows, where each agent specializes in a subtask and the coordinator ensures smooth collaboration.