zenml-io/zenml

Health v2 · maintenance only

Adoption not part of the score

5564 stars · 654 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

ZenML is an open-source MLOps framework for building, deploying, and managing production ML pipelines, LLM workflows, and AI agents on any infrastructure backend. It provides pipeline orchestration, artifact and metadata tracking, experiment management, and integrations with tools like MLflow, LangGraph, and cloud ML platforms, alongside Kitaru for replay-based agent evaluation.

Use cases

• orchestrate reproducible machine learning pipelines in production
• track experiments, artifacts, and model metadata across runs
• deploy and monitor LLM workflows and AI agents
• run ML pipelines on different cloud or on-prem infrastructure without code changes
• containerize and version ML training code automatically
• evaluate and regression-test AI agents against recorded production traces
• integrate existing MLOps tools like MLflow, Langfuse, or SageMaker into one workflow
• manage the full ML lifecycle from experimentation to deployment

When to choose

• you need production-ready ML pipelines that run portably across local, cloud, and on-prem infrastructure
• your team wants unified experiment tracking, artifact versioning, and metadata management
• you're building both classical ML and LLM/agent workflows and want one toolchain
• you want to integrate existing tools (MLflow, LangGraph, Langfuse, SageMaker) rather than replace them
• you need to evaluate and iterate on AI agents using replayed production traces

When to avoid

• you only need lightweight experiment tracking without pipeline orchestration
• your project is a simple one-off script with no need for reproducibility or infrastructure abstraction
• you need a fully managed SaaS MLOps platform with zero self-hosting effort and prefer turnkey solutions
• your team has no Python experience, since ZenML is Python-first
• you need real-time streaming data processing rather than batch pipeline orchestration

Facets

framework · maturity active

workflow-automation etl machine-learning llm-inference agent-framework monitoring data-science scheduling container-runtime machine-learning large-language-models data-science deep-learning python cloud self-hosted cross-platform mlops llmops agentops pipelines metadata-tracking experiment-tracking model-deployment stacks artifact-versioning reproducibility kitaru agent-evals replay-testing pytorch tensorflow langchain mlflow production-ml ai-agents devops automation docker kubernetes

Member repositories

For agents

markdown · JSON · MCP: product_card(name="zenml-io/zenml")