This framework allows users to design, configure, and deploy multiple AI agents that communicate via JSON messages through a central orchestrator. Each agent can have distinct roles, prompts, and memory modules, and you can plug in any LLM provider by implementing a provider interface. The system supports persistent conversation history, dynamic routing, and modular extensions. Ideal for simulating debates, automating customer support flows, or coordinating multi-step document generation, it runs on Python, with Docker support for containerized deployments.

Tongui Agent provides a modular architecture for creating AI agents that can maintain conversation state, leverage external tools, and coordinate multiple sub-agents. Developers configure LLM backends, define custom actions, and attach memory modules to store context. The framework includes an SDK, CLI, and middleware hooks for observability, making it easy to integrate into web or Node.js applications. Supported LLMs include OpenAI, Azure OpenAI, and open-source models.

Cheat Layer is a powerful automation tool designed to optimize business processes. Utilizing cloud-based marketing agents, it automates a wide range of tasks, including A/B testing across demographics, generating SEO-optimized content, and automating workflows. With Atlas, the AI framework, users can create complex automations using simple language, making it accessible for all levels of technical expertise. Cheat Layer is perfect for optimizing content distribution, managing marketing campaigns, and boosting lead generation efforts.