Welcome to ZenML

Build production ML pipelines and production-grade AI agents with ZenML and Kitaru.

ZenML is an open-source framework for developing, evaluating, and deploying your entire AI portfolio: classical ML, LLM pipelines, and AI agents alike. It brings the battle-tested engineering principles of production ML to everything you ship, so you don't maintain one toolchain for models and another for agents.

That story has two open-source projects behind it:

• ZenML is the MLOps framework: portable, production-ready pipelines for ML and LLM workloads, with versioned artifacts, caching, and infrastructure abstracted behind stacks.
• Kitaru is for AI agents: run, replay, improve. It records every model call and tool call as a durable checkpoint, so you can replay a real run with one thing changed, diff the two, and roll the winning change across a cohort of runs.

Each works on its own. You can run ZenML and never touch Kitaru, or pick up Kitaru purely to make one agent durable. If you do use both, they compose rather than coexist: a Kitaru flow is a dynamic ZenML pipeline under the hood, so your agents and pipelines run on the same stacks, persist artifacts to the same stores, and show up in the same server and dashboard.

What are you building?

Pick the path that matches your work. Neither path requires the other, and adopting the second one later doesn't mean starting over.

ML Pipelines → ZenML

Build, version, and deploy classical ML and LLM pipelines. Start with Hello World, then the Starter guide.

AI Agents → Kitaru

Run agents durably, replay any run with one change, and keep the version that wins on cost, latency, and quality. Start with the Agents guide, or jump to the Kitaru quickstart.

How these docs are organized

The documentation is split into spaces — the tabs at the top of this page. Knowing what lives where saves you a lot of searching:

Space	What you'll find there
ZenML (you are here)	The pipelines framework: installation, core concepts, deployment, and how-to guides
Kitaru	The agents project: quickstart, flows and checkpoints, replay and execution management, framework adapters
Learn	Narrative guides for both projects: Starter, Production, LLMOps, and Agents tracks, plus tutorials and best practices
Stacks	The infrastructure components — orchestrators, artifact stores, and more — that both pipelines and agents run on
SDK reference / API reference	Client and REST API references, organized per project
Changelog	Release notes, version by version

First steps

Whichever path you picked, the first steps are the same shape: install, run something real, then learn the concepts.

	ML Pipelines (ZenML)	AI Agents (Kitaru)
Install	Installation	Installation
First run	Hello World	Quickstart
Concepts	Core Concepts	Core Concepts

If you use AI coding tools, see LLM tooling for ZenML's MCP server and Agent Skills (including zenml-scoping and zenml-pipeline-authoring).

Guides

Starter Guide

Get started with ZenML fundamentals and set up your first pipeline

Production Guide

Move your ML pipelines from development to production

Agents Guide

Run agents durably, replay a real run with one change, and improve them across a cohort with Kitaru