# ZenML

> Open-source Python framework for running ML pipelines, LLM workflows, and agents on infrastructure backends you choose.

ZenML, developed by ZenML GmbH, is an Apache 2.0 open-source platform for writing workflows in Python and running them on the orchestrator and cloud infrastructure a team already uses. It targets ML and AI engineers working on traditional ML, LLM workflows, or agents in a company setting. The latest GitHub release listed is 0.97.0.

## What It Is

ZenML lets you define workflows (pipelines) that run on any infrastructure backend (stacks). Any Pythonic logic can be embedded in a pipeline step, such as training a model or running an agentic loop. Existing code is wrapped in a `@step`, so tools like scikit-learn, PyTorch, LangGraph, LlamaIndex, or raw API calls can keep being used.

ZenML then containerizes and tracks the code, records runs with metrics, logs, and metadata, and abstracts infrastructure complexity.

## Setup Path

Install with `pip install "zenml[server]"` (a slimmer client is available via `pip install zenml`), run `zenml init`, then `zenml login` to start a local server or connect to a remote one. It uses a client-server architecture with an integrated web dashboard. The repository includes examples such as a quickstart, agent architecture comparison, model and agent deployment, and end-to-end batch inference.

## Integrations and Ecosystem

The README lists integrations with MLflow, Weights & Biases, LangGraph, Langfuse, SageMaker, GCP Vertex, and Kubernetes-based orchestrators including Kubeflow. A ZenML MCP Server lets MCP-compatible clients such as Claude Desktop or Cursor query pipelines, analyze runs, and trigger deployments in natural language. A VS Code/Cursor extension manages pipelines from the editor.

## Kitaru for Agents

The README presents Kitaru as a separate open-source sister project for agents. It records agent runs, replays them against real code with recorded tool calls, and lets users fork a run with one change, such as a different model or prompt. Per the README, ZenML is for ML pipelines and Kitaru is for agents.

## Features
- Python pipelines and steps that run on configurable infrastructure stacks
- Automatic containerization and code tracking
- Run tracking with metrics, logs, and metadata
- Local-to-cloud transitions with caching
- Reusable components and standardized configurations
- Model and agent deployment as endpoints
- ZenML MCP Server for natural-language pipeline queries
- VS Code/Cursor extension
- Self-hostable open source under Apache 2.0

## Integrations
MLflow, Weights & Biases, LangGraph, Langfuse, LlamaIndex, PyTorch, scikit-learn, AWS SageMaker, GCP Vertex, Kubernetes, Kubeflow, Claude Desktop, Cursor, VS Code

## Platforms
WINDOWS, MACOS, LINUX, WEB, API, VSC_EXTENSION, JETBRAINS_PLUGIN, DEVELOPER_SDK, CLI

## Pricing
Open Source, Free tier available

## Version
0.97.0

## Links
- Website: https://zenml.io
- Documentation: https://docs.zenml.io/
- Repository: https://github.com/zenml-io/zenml
- EveryDev.ai: https://www.everydev.ai/tools/zenml