markdown-body entry-content container-lg" itemprop="text">

What is Argo Workflows?

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition).

Define workflows where each step is a container.
Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG).
Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes.

Argo is a Cloud Native Computing Foundation (CNCF) graduated project.

Use Cases

Why Argo Workflows?

Argo Workflows is the most popular workflow execution engine for Kubernetes.
Light-weight, scalable, and easier to use.
Designed from the ground up for containers without the overhead and limitations of legacy VM and server-based environments.
Cloud agnostic and can run on any Kubernetes cluster.

Read what people said in our latest survey

Try Argo Workflows

You can try Argo Workflows via one of the following:

Who uses Argo Workflows?

About 200+ organizations are officially using Argo Workflows

Ecosystem

Just some of the projects that use or rely on Argo Workflows (complete list here):

Client Libraries

Check out our Java, Golang and Python clients.

Quickstart

Documentation

You're here!

Features

An incomplete list of features Argo Workflows provide:

UI to visualize and manage Workflows
Artifact support (S3, Artifactory, Alibaba Cloud OSS, Azure Blob Storage, HTTP, Git, GCS, raw)
Workflow templating to store commonly used Workflows in the cluster
Archiving Workflows after executing for later access
Scheduled workflows using cron
Server interface with REST API (HTTP and GRPC)
DAG or Steps based declaration of workflows
Step level input & outputs (artifacts/parameters)
Loops
Parameterization
Conditionals
Timeouts (step & workflow level)
Retry (step & workflow level)
Resubmit (memoized)
Suspend & Resume
Cancellation
K8s resource orchestration
Exit Hooks (notifications, cleanup)
Garbage collection of completed workflow
Scheduling (affinity/tolerations/node selectors)
Volumes (ephemeral/existing)
Parallelism limits
Daemoned steps
DinD (docker-in-docker)
Script steps
Event emission
Prometheus metrics
Multiple executors
Multiple pod and workflow garbage collection strategies
Automatically calculated resource usage per step
Java/Golang/Python SDKs
Pod Disruption Budget support
Single-sign on (OAuth2/OIDC)
Webhook triggering
CLI
Out-of-the box and custom Prometheus metrics
Windows container support
Embedded widgets
Multiplex log viewer

Community Meetings

We host monthly community meetings where we and the community showcase demos and discuss the current and future state of the project. Feel free to join us! For Community Meeting information, minutes and recordings, please see here.