Argo Workflows

Argo Workflows is a CNCF-graduated, container-native workflow engine implemented as a Kubernetes Custom Resource Definition (CRD). Designed from the ground up for containers, it models multi-step processes as either directed acyclic graphs (DAGs) or ordered steps, where each step is defined as a container (or script) and executed on your Kubernetes cluster. Because workflows are first-class Kubernetes objects, they carry both the definition and live state, enabling auditable, repeatable runs and native integration with Kubernetes scheduling, RBAC and resource management. Argo is cloud-agnostic and used by hundreds of organizations for running compute-intensive jobs at cloud scale. Argo provides a rich set of template types and execution primitives to cover a wide range of pipelines. Common template types include container and script templates (run a container or inline script), resource templates (operate on Kubernetes resources), HTTP templates, multi-container pod templates, suspend templates, and control templates like steps and dag that express sequencing and dependencies. Workflow authors can pass parameters and artifacts between steps; artifacts are supported across many backends (S3, GCS, Azure Blob, Artifactory, Alibaba OSS, HTTP, Git, raw). Execution controls include step- and workflow-level timeouts, retry policies, conditional execution, exit hooks for notifications and cleanup, and multiple garbage-collection strategies for completed workflows. Argo also emits built-in and custom Prometheus metrics, supports archiving of workflow runs, and exposes a server interface with REST and gRPC APIs. Typical use-cases highlight Argo’s strengths in parallelism, reproducibility and cloud-native orchestration. In machine learning and hyperparameter search, Argo can launch thousands of parallel experiments — coupling with tools like Katib for hyperparameter tuning — and capture outputs as artifacts for downstream model evaluation. For data engineering, Argo accelerates ETL and batch processing by expressing large-scale jobs as DAGs and distributing work across cluster nodes. In CI/CD, teams run container-native pipelines and integrate with the broader Argo ecosystem (Argo CD for GitOps, Argo Rollouts for progressive delivery, and Argo Events for event-driven triggers) to build fully declarative delivery platforms. Other proven scenarios include scientific simulations, distributed Spark/batch jobs, and multi-cluster workflows where tasks run across federated Kubernetes environments. The project maintains client libraries for Java, Go and Python, enabling programmatic submission and control of workflows from existing tooling. Getting started with Argo is straightforward for development: you can install it on local clusters (minikube, kind, k3d) and use the Argo CLI to submit, watch and inspect runs (the CLI has a watch flag and commands to list workflows, view logs and tail runs). For production, Argo provides release manifests and supports customization with Kustomize; production installs typically pin to a specific release. The UI lets teams visualize and manage workflows, and the server can be port-forwarded for local access. Argo’s extensibility includes an executor plugin API for custom execution strategies and integrations with Prometheus for metrics and external artifact stores. The project is community-driven — hosting monthly community meetings, public docs, training content and contributor guidance — and governed under CNCF policies. Whether you need to orchestrate parallel ML experiments, container-native CI pipelines, or large-scale data workflows, Argo Workflows brings Kubernetes-first orchestration, observability and scalability to production workloads.