Argo Workflows is an open source, container-native workflow engine designed specifically for Kubernetes. It enables users to define and execute multi-step workflows as Custom Resource Definitions (CRDs), where each step runs in its own container. Built for cloud-native environments, it eliminates the overhead of legacy VM-based systems and provides native integration with Kubernetes primitives like volumes, node selectors, and service accounts. Argo Workflows is a graduated project of the Cloud Native Computing Foundation (CNCF) and is widely adopted for machine learning pipelines, data processing, CI/CD, and infrastructure automation. Its lightweight architecture and extensibility make it ideal for teams managing complex batch jobs, distributed training, or automated data pipelines at scale.
What You Get
- DAG and Steps-based workflow definition - Define workflows using either a Directed Acyclic Graph (DAG) or sequential steps, allowing complex task dependencies to be expressed declaratively in YAML.
- Artifact support across multiple storage backends - Automatically capture and pass outputs between steps using S3, GCS, Azure Blob Storage, Artifactory, Alibaba OSS, Git, HTTP, and raw storage via built-in plugins.
- Workflow templating and reusability - Define reusable workflow templates stored in the cluster to avoid duplication across similar pipelines.
- Scheduled workflows with Cron - Trigger workflows on a schedule using Kubernetes-style cron expressions, similar to cron jobs but for containerized tasks.
- Step-level input/output and parameters - Pass values between steps using artifacts (files) or parameters (strings), enabling dynamic workflow behavior.
- Loops, conditionals, and timeouts - Use loops to iterate over inputs, conditionals to branch execution paths, and timeouts to prevent stuck steps.
- Retry and resubmit with memoization - Automatically retry failed steps or entire workflows, with memoized results to avoid redundant computation.
- Suspend and resume workflows - Pause long-running workflows and resume them later without losing state.
- Exit hooks for cleanup and notifications - Define post-execution actions such as sending Slack alerts, deleting artifacts, or triggering downstream events.
- REST API and CLI access - Interact with workflows programmatically via HTTP/GRPC APIs or the
argo command-line tool for automation and scripting.
- Prometheus metrics and monitoring - Expose built-in metrics like step duration, success/failure rates, and resource usage for observability.
- Windows container support - Run workflows on Windows nodes in mixed OS Kubernetes clusters.
- Multiple executors and DinD support - Choose from different executor types including Docker-in-Docker (DinD) for containerized builds within steps.
- Script steps with any language - Write workflow steps using shell scripts, Python, Node.js, or other binaries without building custom containers.
- Webhook and event triggering - Trigger workflows via HTTP webhooks or events from Argo Events for event-driven automation.
- Pod Disruption Budget and resource orchestration - Control pod availability during disruptions and manage Kubernetes resources like volumes and affinity rules directly in workflow specs.
- Java, Golang, and Python (Hera) SDKs - Programmatically generate workflows using client libraries, including the Hera Python library for seamless integration with ML and data pipelines.
Common Use Cases
- Building machine learning pipelines - Orchestrate data preprocessing, model training, and evaluation steps across distributed GPU nodes using Argo Workflows with Katib for hyperparameter tuning.
- Processing large-scale batch data - Run ETL jobs that ingest terabytes of logs, transform them in parallel containers, and load results into data warehouses using S3 artifacts and dynamic loops.
- CI/CD pipeline automation - Automate testing, container building, and deployment to Kubernetes clusters using Argo Workflows as a replacement for Jenkins or GitHub Actions in cloud-native environments.
- DevOps teams managing microservices across multiple clusters - Deploy and monitor cross-cluster workflows for canary releases, blue-green deployments, or multi-region data replication using Argo’s cluster-aware execution.
- Running thousands of ML experiments - Automatically generate and execute hundreds of model training jobs with varying hyperparameters, tracking results via artifact archiving and metrics.
- Scientific simulations at scale - Distribute compute-intensive physics or genomic analyses across hundreds of pods, with automatic retry on node failures and artifact collection for post-analysis.
- OpenStreetMap data extraction pipeline - Generate 200+ regional map extracts in under 40 minutes by parallelizing data downloads, transformations, and uploads using Argo’s DAG-based concurrency control.
- Autonomous driving data pipelines - Process raw LiDAR and camera sensor data through validation, annotation, and model training stages with dynamic resource allocation per step.
Under The Hood
Argo Workflows is a Kubernetes-native workflow orchestration system that enables users to define and execute complex, multi-step workflows declaratively. It leverages Kubernetes APIs and controllers to manage workflow execution in cloud-native environments, offering a powerful yet flexible solution for CI/CD and data processing pipelines.
Architecture
The system follows a monolithic yet modular architecture designed for seamless integration with Kubernetes. It emphasizes clear separation of concerns between configuration, controller logic, and resource management.
- Uses Kubernetes-style patterns for resource lifecycle management
- Separates configuration from execution logic with well-defined layers
- Integrates tightly with Kubernetes APIs and CRDs for extensibility
Tech Stack
The project is built primarily in Go, with a React-based frontend and extensive use of Kubernetes-native tooling.
- Built in Go with strong integration into Kubernetes ecosystem
- Relies on client-go, OpenTelemetry, and YAML parsing libraries for core functionality
- Employs Nix, Docker, Makefiles, and CI/CD pipelines for reproducible builds and testing
- Utilizes testify, mockery, and Kubernetes integration tests for comprehensive coverage
Code Quality
The codebase reflects a mature engineering approach with consistent patterns and robust testing practices.
- Features an extensive test suite covering diverse workflow scenarios and edge cases
- Implements standardized error handling with graceful degradation in place
- Maintains consistent naming and structural conventions across modules
- Shows signs of accumulated complexity that may affect long-term maintainability
What Makes It Unique
Argo Workflows distinguishes itself through its declarative workflow model and deep Kubernetes integration.
- Offers a templating system that enables expressive and reusable workflow definitions
- Provides extensible executors and support for custom container runtimes
- Bridges the gap between traditional CI/CD and advanced orchestration in cloud-native setups