Pig is an automation platform designed to simplify the creation of complex workflows for Windows applications, leveraging AI technology. It offers a chat-based interface for prototyping workflows without any coding and provides an SDK for advanced automation needs. With Pig, users can integrate machine controls, build agents using the Agent API, and utilize a wide range of tools like click, type, and screenshot functions. Pig is ideal for automations that require seamless integration with human oversight, ensuring critical operations can be managed efficiently.

MARO (Multi-Agent Resource Optimization) is a Python-based framework designed to support the development and evaluation of multi-agent reinforcement learning agents in supply chain, logistics, and resource management scenarios. It includes environment templates for inventory management, truck scheduling, cross-docking, container rental, and more. MARO offers a unified agent API, built-in trackers for experiment logging, parallel simulation capabilities for large-scale training, and visualization tools for performance analysis. The platform is modular, extensible and integrates with popular RL libraries, enabling reproducible research and rapid prototyping of AI-driven optimization solutions.

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.