Argo Workflows
by Community
Workflow Engine for Kubernetes
OSS
Argo Workflows
Added 1 June 2026
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.
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
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