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Argo Alternatives
Similar projects and alternatives to argo
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SaaSHub
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n8n
n8n is a workflow automation platform for building AI-powered workflows and agents, connecting any AI model to any business system with full control over data, security, and deployment. Build visually or in code while n8n handles infrastructure from prototype to production with fully auditable executions.
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Benthos
Discontinued Fancy stream processing made operationally mundane [Moved to: https://github.com/redpanda-data/connect]
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Concourse
Concourse is a container-based automation system written in Go. It's mostly used for CI/CD.
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StackStorm
StackStorm (aka "IFTTT for Ops") is event-driven automation for auto-remediation, incident responses, troubleshooting, deployments, and more for DevOps and SREs. Includes rules engine, workflow, 160 integration packs with 6000+ actions (see https://exchange.stackstorm.org) and ChatOps. Installer at https://docs.stackstorm.com/install/index.html
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expr
Discontinued Expression language and expression evaluation for Go [Moved to: https://github.com/expr-lang/expr] (by antonmedv)
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keda
KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better argo alternative or higher similarity.
argo discussion
argo reviews and mentions
Posts with mentions or reviews of argo.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-11-10.
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What is Argo Workflows?
Argo has a CLI, which provides a convenient interface for submitting, monitoring, and recording your workflows. You can download the CLI from GitHub Releases. Use the version that matches the Argo release installed in your Kubernetes cluster.
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Data on Kubernetes: Part 4 - Argo Workflows: Simplify parallel jobs : Container-native workflow engine for Kubernetes 🔮
Remember to meet the prerequisites, including AWS cli, kubectl, terraform and Argo Workflow CLI.
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StackStorm – IFTTT for Ops
Like Argo Workflows?
https://github.com/argoproj/argo-workflows
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Creators of Argo CD Release New OSS Project Kargo for Next Gen Gitops
Dagger looks more comparable to Argo Workflows: https://argoproj.github.io/argo-workflows/ That's the first of the Argo projects, which can run multi-step workflows within containers on Kubernetes.
For what it's worth, my colleagues and I have had great luck with Argo Workflows and wrote up a blog post about some of its advantages a few years ago: https://www.interline.io/blog/scaling-openstreetmap-data-wor...
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Practical Tips for Refactoring Release CI using GitHub Actions
Despite other alternatives like Circle CI, Travis CI, GitLab CI or even self-hosted options using open-source projects like Tekton or Argo Workflow, the reason for choosing GitHub Actions was straightforward: GitHub Actions, in conjunction with the GitHub ecosystem, offers a user-friendly experience and access to a rich software marketplace.
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(Not) to Write a Pipeline
author seems to be describing the kind of patterns you might make with https://argoproj.github.io/argo-workflows/ . or see for example https://github.com/couler-proj/couler , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.
it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.
i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.
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what technologies are people using for job scheduling in/with k8s?
Argo Workflows + Argo Events
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What are some good self-hosted CI/CD tools where pipeline steps run in docker containers?
Drone, or Tekton, Argo Workflows if you’re on k8s
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job scheduling for scientific computing on k8s?
Check out Argo Workflows.
- Orchestration poll
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A note from our sponsor - SaaSHub
www.saashub.com | 12 Jun 2026
Stats
Basic argo repo stats
45
16,763
9.8
1 day ago
argoproj/argo is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of argo is Go.
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