Zenml

ZenML is an open-source MLOps framework that provides a unified layer for building, running, and governing machine learning pipelines and AI agents. Designed for ML engineers and data scientists who want to move from local experimentation to production without rewriting code, ZenML abstracts infrastructure complexity via pluggable stacks. Pipelines are versioned, reproducible, and automatically logged with artifacts, parameters, and model metadata in a central artifact database and model registry. In 2026, ZenML introduced Kitaru, a complementary open-source runtime for durable execution of long-running Python agents. Kitaru adds checkpointing, replay, wait/resume, and inspection capabilities to agent frameworks like LangGraph, CrewAI, and OpenAI Agents SDK, while sharing the same Pro control plane for unified governance and billing. Both products are SOC2 and ISO 27001 compliant. Key features: declarative pipeline DAGs via Python decorators, pluggable stack architecture with support for 10+ orchestrators and artifact stores, automatic artifact versioning and lineage tracking, built-in model registry with promotion, smart caching, and Kitaru’s durable execution with checkpoint replay. ZenML integrates with over 60 tools including Airflow, Kubeflow, Vertex AI, SageMaker, MLflow, Weights & Biases, and major agent frameworks. Compared to alternatives like MLflow or Kubeflow, ZenML’s stack abstraction lets you switch backends without code changes—a key advantage for multi-cloud teams. The new Kitaru runtime sets it apart for agent productionization, but the platform still requires Python coding and moderate DevOps familiarity.