LLM State Machine is an open-source Python framework designed to construct AI agents using explicit state machines. Developers define states as discrete steps—each invoking a large language model or custom logic—and transitions based on outputs. This approach provides clarity, maintainability, and robust error handling for multi-step, LLM-powered workflows, such as document processing, conversational bots, or automation pipelines.

Process Street is a modern process management platform tailored for teams to create, track, and automate workflows and procedures. It allows businesses to document training processes, policies, and roles, centralizing knowledge for easy access. Users can transform their operating procedures into powerful, no-code workflows that can be repeated as needed. With data sets that unify information across all workflows and teams, Process Street ensures streamlined operations and improved efficiency.

CHAMP Multiagent AI provides a unified environment to define, train, and orchestrate specialized AI agents that collaborate on enterprise tasks. You can create data-processing agents, decision-support agents, scheduling agents, and monitoring agents, then connect them via visual workflows or APIs. It includes features for model management, agent-to-agent communication, performance monitoring, and integration with existing systems, enabling scalable automation and intelligent orchestration of end-to-end business processes.