Argo Workflows

Overview

Argo Workflows is an open-source workflow engine for Kubernetes that orchestrates multi-step jobs using YAML-defined DAGs (directed acyclic graphs). It runs natively on Kubernetes clusters and provides visibility into job execution, resource usage, and failure states through a web UI.

Best for

Best for
Teams running workloads on Kubernetes who need declarative, auditable job orchestration without external services

Use cases

• Orchestrating multi-stage ML training and inference pipelines
• Coordinating parallel batch processing jobs across Kubernetes nodes
• Building CI/CD workflows with complex dependencies and conditional logic

Notes

Argo Workflows is an open-source workflow engine for Kubernetes that orchestrates multi-step jobs using YAML-defined DAGs (directed acyclic graphs). It runs natively on Kubernetes clusters and provides visibility into job execution, resource usage, and failure states through a web UI.

16,728 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Orchestrating multi-stage ML training and inference pipelines
• Coordinating parallel batch processing jobs across Kubernetes nodes
• Building CI/CD workflows with complex dependencies and conditional logic

Pros

• Native Kubernetes integration eliminates external infrastructure
• YAML-based workflow definitions enable version control and GitOps practices
• Handles complex DAGs with parallelization, retries, and conditional branching

Cons

• Requires Kubernetes cluster to run, adding operational overhead for small teams
• Learning curve for YAML syntax and Kubernetes-specific concepts
• Debugging failed workflows requires familiarity with Kubernetes logs and events

Indexed from awesome-llmops and enriched against its public facts.