Argo Workflows (16,878 Stars) 2026: Kubernetes-Native Pipeline Engine with DAG and Step Execution

Argo Workflows (16,878 stars) is the Kubernetes-native workflow engine for running containerized pipelines - DAGs, steps, and cron schedules. Here is how to deploy and run your first workflow.

💡 What You Will Learn

Argo Workflows (16,878 stars) is the Kubernetes-native workflow engine for running containerized pipelines - DAGs, steps, and cron schedules. Here is how to deploy and run your first workflow.

## The short answer **argoproj/argo-workflows** (16,878 stars, Go) is an open-source workflow engine for Kubernetes. Each workflow step is a container, and you define the flow as a DAG or a linear sequence. It is the de facto standard for Kubernetes-native CI/CD, ML pipelines, and batch jobs. ## Key capabilities - **Container-native**: every step is a Kubernetes pod - **DAG support**: run steps in parallel with dependencies - **Cron workflows**: schedule recurring pipelines - **Artifacts**: pass data between steps via S3, GCS, or volume - **UI**: web dashboard with logs, metrics and visualizations ## Deploy and run your first workflow ```bash # Install the controller kubectl create namespace argo kubectl apply -n argo -f https://raw.githubusercontent.com/argoproj/argo-workflows/master/manifests/quick-start-postgres.yaml # Install the CLI curl -sLO https://github.com/argoproj/argo-workflows/releases/download/v3.6.2/argo-linux-amd64.gz gunzip argo-linux-amd64.gz && chmod +x argo-linux-amd64 && sudo mv ./argo-linux-amd64 /usr/local/bin/argo # Submit a hello workflow argo submit -n argo --watch https://raw.githubusercontent.com/argoproj/argo-workflows/master/examples/hello-world.yaml ``` ## Example: parallel DAG ```yaml apiVersion: argoproj.io/v1alpha1 kind: Workflow metadata: generateName: dag- spec: entrypoint: main templates: - name: main dag: tasks: - name: build template: whalesay - name: test template: whalesay dependencies: [build] - name: deploy template: whalesay dependencies: [test] - name: whalesay container: image: docker/whalesay command: [cowsay, "hello"] ``` ## Practical tips - Set resource requests/limits on every step to avoid noisy neighbors. - Use artifact repositories (S3) for passing large files between steps. - Monitor with `argo list` and the web UI; enable persistence for the controller DB. ## FAQ **Do I need Kubernetes?** Yes - Argo Workflows runs on Kubernetes (minikube or a real cluster). **How does it compare to Airflow?** Airflow (46,389 stars) is Python-centric and VM-based; Argo is container-native and native to K8s. Pick based on your platform. **Is it free?** Yes - open source (Apache-2.0).