--- title: "argo-workflows" type: "tool" slug: "argoproj-argo-workflows" canonical_url: "https://www.graphcanon.com/tools/argoproj-argo-workflows" github_url: "https://github.com/argoproj/argo-workflows" homepage_url: "https://argo-workflows.readthedocs.io/" stars: 16820 forks: 3566 primary_language: "Go" license: "Apache-2.0" archived: false categories: ["ai-agents", "llm-frameworks", "model-training"] tags: ["argo-workflows", "dag", "batch-processing", "data-engineering", "cloud-native", "airflow", "cncf", "argo"] updated_at: "2026-07-11T23:30:21.236364+00:00" --- # argo-workflows > Workflow Engine for Kubernetes Workflow Engine for Kubernetes ## Facts - Repository: https://github.com/argoproj/argo-workflows - Homepage: https://argo-workflows.readthedocs.io/ - Stars: 16,820 · Forks: 3,566 · Open issues: 1,450 · Watchers: 199 - Primary language: Go - License: Apache-2.0 - Last pushed: 2026-07-10T09:15:02+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Very active (computed 2026-07-11T23:30:05.852Z) - Security scan: No findings reported (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T23:30:06.346Z - Full report: [trust report](/tools/argoproj-argo-workflows/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/argoproj-argo-workflows/trust) ## Categories - [AI Agents](/categories/ai-agents.md) - [LLM Frameworks](/categories/llm-frameworks.md) - [Model Training](/categories/model-training.md) ## Tags argo-workflows, dag, batch-processing, data-engineering, cloud-native, airflow, cncf, argo ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. 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Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition). * Define workflows where each step is a container. * Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). * Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. Argo is a [Cloud Native Computing Foundation (CNCF)](https://cncf.io/) graduated project. ## Use Cases * [Machine Learning pipelines](https://argo-workflows.readthedocs.io/en/latest/use-cases/machine-learning/) * [Data and batch processing](https://argo-workflows.readthedocs.io/en/latest/use-cases/data-processing/) * [Infrastructure automation](https://argo-workflows.readthedocs.io/en/latest/use-cases/infrastructure-automation/) * [CI/CD](https://argo-workflows.readthedocs.io/en/latest/use-cases/ci-cd/) * [Other use cases](https://argo-workflows.readthedocs.io/en/latest/use-cases/other/) ## Why Argo Workflows? * Argo Workflows is the most popular workflow execution engine for Kubernetes. * Light-weight, scalable, and easier to use. * Including for Python users through [the Hera Python SDK for Argo Workflows](https://hera.readthedocs.io/en/stable/). * Designed from the ground up for containers without the overhead and limitations of legacy VM and server-based environments. * Cloud agnostic and can run on any Kubernetes cluster. [Read what people said in our latest survey](https://blog.argoproj.io/argo-workflows-events-2023-user-survey-results-82c53bc30543) ## Try Argo Workflows You can try Argo Workflows via one of the following: 1. [Interactive Training Material](https://killercoda.com/argoproj/course/argo-workflows/) 1. [Access the demo environment](https://workflows.apps.argoproj.io/workflows/argo) ## Who uses Argo Workflows? [About 200+ organizations are officially using Argo Workflows](USERS.md) ## Ecosystem Just some of the projects that use or rely on Argo Workflows (complete list [here](https://github.com/akuity/awesome-argo#ecosystem-projects)): * [Argo Events](https://github.com/argoproj/argo-events) * [Hera](https://github.com/argoproj-labs/hera) * [Katib](https://github.com/kubeflow/katib) * [Kedro](https://kedro.readthedocs.io/en/stable/) * [Kubeflow Pipelines](https://github.com/kubeflow/pipelines) * [Netflix Metaflow](https://metaflow.org) * [Piper](https://github.com/quickube/piper) * [Seldon](https://github.com/SeldonIO/seldon-core) * [SQLFlow](https://github.com/sql-machine-learning/sqlflow) ## Client Libraries Check out our [Java, Golang, Python (Hera), and Typescript (Juno) clients](docs/client-libraries.md). ## Quickstart * [Get started here](https://argo-workflows.readthedocs.io/en/latest/quick-start/) * [Walk-through examples](https://argo-workflows.readthedocs.io/en/latest/walk-through/) ## Documentation [View the docs](https://argo-workflows.readthedocs.io/en/latest/) ## Features An incomplete list of features Argo Workflows provides: * UI to visualize and manage Workflows * Artifact support (S3, Artifactory, Alibaba Cloud OSS, Azure Blob Storage, HTTP, Git, GCS, raw, plugins) * Workflow templating to store commonly used Workflows in the cluster * Archiving Workflows after executing for later access * Scheduled workflows using cron * Server interface with REST API (HTTP and GRPC) * DAG or Steps based declaration of workflows * Step level input & outputs (artifacts/parameters) * Loops * Parameterization * Conditionals * Timeouts (step & workflow level) * Retry (step & workflow level) * Resubmit (memoized) * Suspend & Resume * Cancellation * K8s resource orchestration * Exit Hooks (notifications, cleanup) * Garbage collection of completed workflow * Scheduling (affinity/tolerations/node selectors) * Volumes (ephemeral/existing) * Parallelism li ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/argoproj-argo-workflows`](/api/graphcanon/tools/argoproj-argo-workflows) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_