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.

AI-Agents provides a modular architecture for defining and running Python-based AI agents. Developers can configure agent behaviors, integrate external APIs or tools, and manage agent memory across sessions. It leverages popular LLMs, supports multi-agent collaboration, and enables plugin-based extensions for complex workflows like data analysis, automated support, and personalized assistants.