MQTT for AI

MQTT for AI represents the next generation of MQTT protocol enhancements specifically designed for intelligent IoT applications. It includes four key innovations: MQTT over QUIC for reliable connectivity in mobile scenarios, MCP over MQTT for AI agent communication, MQTT/RT for real-time industrial automation, and MQTT Streams for durable data storage and replay. These enhancements enable EMQX to serve as a unified nervous system for intelligent IoT, connecting everything from autonomous vehicles to factory robotics with edge AI capabilities.

MCP over MQTT uses MQTT as a transport for the Model Context Protocol (MCP), making MCP a better fit for IoT, edge computing, and cloud-to-edge deployments where MQTT is already the messaging backbone. It adds built-in service registry and discovery (clients can find available MCP servers via the broker) and supports scalable deployments by adding more server instances. It's not meant to replace existing MCP transports (stdio / HTTP SSE), but to provide an alternative for remote MCP servers and edge AI agent scenarios.

They target scale and efficiency. MQTT Streams/Queues introduce native durable storage, retention, replay/offset-based consumption, and buffering semantics in the broker—useful for data pipelines, offline processing, and edge-to-cloud backpressure. Batch publishing allows multiple logical messages to be packed into a single MQTT 5.0 PUBLISH to reduce header/topic overhead and improve throughput. Subscription filters add a second layer of filtering at subscribe time so subscribers receive only the subset of messages they actually need, reducing bandwidth and downstream processing.