argo-workflows open source analysis
Workflow Engine for Kubernetes
Project overview
⭐ 16187 · Go · Last activity on GitHub: 2025-11-14
Why it matters for engineering teams
Argo Workflows addresses the challenge of orchestrating complex workflows on Kubernetes, enabling software engineers to define, schedule and manage multi-step processes as code. It is particularly suited for machine learning and AI engineering teams who require scalable, repeatable pipelines for data processing and model training. As a mature and production ready solution, Argo Workflows is widely adopted in cloud-native environments and benefits from strong community support and continuous development. However, it may not be the best choice for teams without Kubernetes expertise or those seeking simpler, managed workflow services, as it requires a self hosted option and operational overhead.
When to use this project
Use Argo Workflows when you need a robust, Kubernetes-native workflow engine for batch processing or machine learning pipelines that integrates well with GitOps practices. Consider alternatives if your team prefers a fully managed service or if your workflows are simple enough to not justify the complexity of Kubernetes orchestration.
Team fit and typical use cases
Machine learning and AI engineering teams gain the most from Argo Workflows as it allows them to automate complex data engineering and model training pipelines. It is commonly used by data scientists and DevOps engineers to build production pipelines that require scalability and reproducibility. This open source tool for engineering teams is often found in products involving continuous integration, automated data workflows and ML model deployment.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2025-11-14. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.