Project Title

argo-workflows — Kubernetes-native Workflow Engine for Orchestrating Parallel Jobs

Overview

Argo Workflows is an open-source, Kubernetes-native workflow engine designed to orchestrate parallel jobs on Kubernetes. It allows users to define workflows where each step is a container, model multi-step workflows, and capture dependencies using a directed acyclic graph (DAG). Argo Workflows is lightweight, scalable, and cloud-agnostic, making it a popular choice for orchestrating container-native workflows.

Key Features

Kubernetes CRD (Custom Resource Definition) implementation
Container-native workflow orchestration
Directed Acyclic Graph (DAG) for task dependencies
Cloud-native and cloud-agnostic

Use Cases

Machine Learning pipelines
Data and batch processing
Infrastructure automation
CI/CD

Advantages

Most popular workflow execution engine for Kubernetes
Lightweight, scalable, and easy to use
Designed for containers without legacy VM overhead
Cloud-agnostic, runs on any Kubernetes cluster

Limitations / Considerations

Requires a Kubernetes environment to operate
May have a steeper learning curve for those unfamiliar with Kubernetes and container orchestration

Apache Airflow: A platform to programmatically author, schedule, and monitor workflows, but not Kubernetes-native.
Kubeflow Pipelines: A Kubernetes-native platform for deploying and managing machine learning workflows, with a focus on MLOps.
Prefect: A workflow management system that integrates with Kubernetes, but with a different approach to workflow definition and execution.

Basic Information

GitHub: https://github.com/argoproj/argo-workflows
Stars: 15,836
License: Unknown
Last Commit: 2025-07-15

📊 Project Information

Project Name: argo-workflows
GitHub URL: https://github.com/argoproj/argo-workflows
Programming Language: Go
⭐ Stars: 15,836
🍴 Forks: 3,338
📅 Created: 2017-08-21
🔄 Last Updated: 2025-07-15

🏷️ Project Topics

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