zenoh unifies data in motion, data in-use, data at rest and computations. It carefully blends traditional pub/sub with geo-distributed storages, queries and computations, while retaining a level of time and space efficiency that is well beyond any of the mainstream stacks.

Modular AI runtime for robots

A Zenoh plug-in for ROS2 with a DDS RMW. See https://discourse.ros.org/t/ros-2-alternative-middleware-report/ for the advantages of using this plugin over other DDS RMW implementations.

Cognitive memory for AI agents — learns from use, forgets what's irrelevant, strengthens what matters. Single binary, fully offline.

A zenoh plug-in that allows to transparently route DDS data. This plugin can be used by DDS applications to leverage zenoh for geographical routing or for better scaling discovery. For ROS2 robotic applications, use https://github.com/eclipse-zenoh/zenoh-plugin-ros2dds

A rclcpp-compatible true zero-copy IPC middleware that supports all ROS message types, including message structs already generated by rosidl.

Python API for zenoh

Robotics MOM and RPC middleware wrapper with deep-learning framework integration

Curated List of Awesome Zenoh Related Open Source Projects

User-facing documentation, troubleshooting guides, and Docker test environments for Conduit — the Apple-device ROS 2 sensor publisher app.

Carto is a lightweight inspector for exploring, publishing, and observing Zenoh data streams.

TypeScript Interface to the Zenoh Protocol.

Zenoh-pico implementation for micro-ROS

Go-lang API for zenoh

Java APIs for zenoh

Asyncio interface for ROS 2, Zenoh, and other robotics IO systems.

Drop-in ROS 2 bridge for Rerun — no ROS 2 runtime or build required

the zero network protocol

Open-source Hardware AI agent. Single Rust binary for cameras, sensors, robots, and IoT fleets — orchestrated by AI agents with memory and real-time telemetry. Runs on Jetson, Raspberry Pi, any Linux.

zenoh unifies data in motion, data in-use, data at rest and computations. It carefully blends traditional pub/sub with geo-distributed storages, queries and computations, while retaining a level of time and space efficiency that is well beyond any of the mainstream stacks.