ZenML is an open-source MLOps and LLMOps platform that unifies machine-learning pipelines and AI agents in one framework. It lets teams build portable pipelines that run anywhere, on a laptop, Kubernetes or managed clouds, while tracking metadata, artefacts and lineage automatically. ZenML has more than 5,600 stars on GitHub and was actively developed in 2026.
ZenML sits between experimentation and production. You define pipelines in Python; ZenML’s stack abstraction swaps the underlying infrastructure, orchestrators, artefact stores, experiment trackers and model registries, without code changes, so the same pipeline that runs locally can run in the cloud unchanged. Recent releases extend the same discipline to GenAI and agent workflows, giving platform teams one control plane for classic ML and LLM systems alike, with reproducibility and audit trails built in. ZenML’s integration catalogue covers orchestrators such as Airflow and Kubernetes, cloud platforms from AWS, GCP and Azure, and tools for tracking, serving and monitoring, so adopting it rarely means abandoning an existing stack. A visual dashboard ties everything together, which helps platform teams support data scientists who prefer not to live in configuration files. Extensive documentation, tutorials and production case studies accompany the framework.
Freemium. The ZenML framework is free and open source under the Apache-2.0 license; ZenML Cloud adds a managed control plane with paid plans for teams.
ML platform and MLOps teams who need to standardise how pipelines and agents are built, tracked and deployed across environments and cloud providers.