# zenml-io/zenml

ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.

Repository: https://github.com/zenml-io/zenml
Canonical: https://ross.abutalabs.com/products/zenml
Homepage: https://zenml.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: mlops, machine-learning, data-science, production-ready, devops-tools, zenml, pipelines, metadata-tracking, deep-learning, pytorch, tensorflow, ml, ai, automl, workflow, llm, llmops, agentops, agents, genai
Last push: 2026-08-26T22:02:59+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 96, longevity 100
- inputs: {"age_days": 2113, "days_push": 7, "days_rel": 26, "gap_med": 15.0, "n_releases_24m": 45}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5564, forks 654 (observed 2026-08-28T04:09:22.395025+00:00)

## What it is
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
- artifact type: framework
- maturity: active
- function: workflow-automation, etl, machine-learning, llm-inference, agent-framework, monitoring, data-science, scheduling, container-runtime
- domain: machine-learning, large-language-models, data-science, deep-learning
- platform: python, cloud, self-hosted, cross-platform
- tags: 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
- zenml-io/zenml (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:22.395025+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:55:37.522542+00:00, confidence not recorded.
  - readme: https://github.com/zenml-io/zenml (fetched 2026-08-28T04:09:22.395025+00:00, sha 92ab19b30262)
  - homepage: https://zenml.io (fetched 2026-08-29T08:50:54.098133+00:00, sha dc7a240a2748)
  - site_page: https://docs.zenml.io (fetched 2026-08-29T08:50:54.102854+00:00, sha 4fc289b9d8ff)
  - site_page: https://docs.zenml.io/kitaru (fetched 2026-08-29T08:50:54.101019+00:00, sha 0a9d720fa54d)
  - site_page: https://www.zenml.io/docs (fetched 2026-08-29T08:50:54.106274+00:00, sha 8e1200125efd)
  - registry_pypi: https://pypi.org/pypi/zenml/json (fetched 2026-08-29T08:50:54.108299+00:00, sha 9a09b09bde23)
  - site_page: https://www.zenml.io/pricing (fetched 2026-08-29T08:50:54.104488+00:00, sha c07c92602dd6)
- Data as of 2026-08-30T08:39:29.467469+00:00.