A lightweight framework for building and orchestrating AI agents through the Model Context Protocol, enabling users to create scalable multi-agent systems using only configuration files.
A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.
Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.
Provides a privacy-preserving security framework for AI agents using the Model Context Protocol, enabling transparent anonymization of sensitive data and blockchain-like audit trails for regulated domains.
A multi-domain multi-agent system served over MCP (Model Context Protocol) with FastMCP, exposing specialized domain agents as tools to automate business workflows.
A multi-agent system that autonomously analyzes code, proves bugs with formal certificates, generates repairs, and validates patches, all over the Model Context Protocol.