5 open source tools compared. Sorted by stars. Scroll down for our analysis.
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
| Tool | Stars | Velocity | Score |
|---|---|---|---|
n8n Fair-code workflow automation with native AI capabilities | 199.6k | +1169/wk | 79 |
Argo Workflows Kubernetes workflow engine | 16.9k | +19/wk | 88 |
camunda Process Orchestration Framework | 4.2k | +11/wk | 66 |
dagu Lightweight workflow engine built in a single binary with Web UI. It runs any job, scripts, containers, k8s jobs, SSH commands, or AI-agent harnesses behind MCP. Define workflows in a declarative YAML format. Linux, macOS, and Windows. Use your favorite AI agents to manage your workflows. | 3.7k | +19/wk | 69 |
viewflow Reusable workflow library for Django | 2.9k | +1/wk | 70 |
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N8n is the open source alternative to Zapier. It gives you a visual workflow builder where you drag nodes, connect them, and watch data flow through. Over 400 integrations built in. Self-hosting is free under a 'sustainable use' license (not fully open source, you can't resell it as a service). The cloud version starts at $24/mo. The self-hosted version has no feature restrictions, no execution limits, and no user limits. The catch: setting up n8n in Docker and keeping it running takes real skill. Updates can break workflows. The 'sustainable use' license means you can't build a competing automation platform on top of it. And while the visual builder is powerful, complex logic (loops, error handling, conditional branches) gets messy in a node graph. Sometimes a Python script is cleaner than 40 connected nodes.
Argo Workflows orchestrates multi-step jobs on Kubernetes: data pipelines, CI/CD, ML training, batch processing, all defined as directed acyclic graphs in YAML. Each step in your workflow runs as a container on your cluster. You get parallelism, retries, conditionals, loops, and artifact passing between steps. The web UI shows you the DAG visually with real-time status. It's a CNCF graduated project, which means serious production adoption. Used by Intuit, Tesla, Google, and many others. Completely free. Apache 2.0 license. Akuity offers commercial support and Argo CD (related but separate) as a managed service, but Argo Workflows itself is free. Small teams already on Kubernetes who need workflow orchestration beyond simple CronJobs should look here. If you're NOT on Kubernetes, this isn't for you. The entire model assumes K8s. The catch: you need Kubernetes. That's a hard prerequisite. And YAML workflow definitions get painful fast for complex pipelines. The learning curve combines Kubernetes complexity with workflow engine complexity. Not for the faint of heart.
Camunda orchestrates business processes across people, systems, and devices. BPMN workflow engine, human task management, monitoring dashboards, and analytics, all in one platform. Organizations running complex multi-step processes that involve both automated systems and human decisions will find this built for exactly that problem. The core engine (Zeebe) is a high-throughput, horizontally scalable workflow engine built on gRPC. Self-hosting on Kubernetes is the standard deployment path. It handles millions of workflow instances and integrates with Kafka and standard message queues. The operational tooling (Operate, Tasklist, Optimize) gives you visibility into running processes and human task queues. Enterprise teams with complex approval workflows, order processing, or compliance processes get the most value. Solo developers and small teams will find it overkill unless they're specifically building process orchestration. The cloud offering removes the ops burden but comes at enterprise pricing. The catch: the community edition is real and usable, but the best operational tooling is gated behind enterprise licensing. And running Zeebe on Kubernetes is not trivial ops work.
dagu runs scheduled jobs and multi-step workflows from a single binary, with no database or message broker required. You define what runs in plain YAML: shell scripts, Docker containers, Kubernetes jobs, SSH commands, HTTP calls, even AI agents over MCP. It ships a web UI for watching runs and digging into logs, and the core is free under GPLv3. It is cron that grew up, with a dashboard and retry logic. Self-hosting is about as light as orchestration gets. Drop the binary on a server, point it at a directory of YAML files, and you have a scheduler. State lives in files, so there is no Postgres or Redis to babysit. Scaling to distributed workers across machines adds a coordinator and some moving parts, but for most teams a single instance handles real work without much care and feeding. Solo developers and small teams replacing a pile of crontab entries will love this, and the free tier covers the actual job running. SSO, RBAC, and audit logging sit behind a paid self-host license, with a managed Dagu Cloud if you would rather not run it yourself. Large teams that need access control will hit the paywall, and that is the fair kind: auth is worth paying for. The catch is that YAML workflows get unwieldy fast. Simple pipelines stay readable, but complex branching logic starts to fight you. If your orchestration needs are heavy, look at Temporal or Airflow before you outgrow this.
Viewflow turns Django into a platform for business apps with real process automation built in. You define BPMN-style workflows in Python (approvals, multi-step processes, who does what next) and get the surrounding pieces too: CRUD screens, user management, reporting. If you already build on Django, it saves you from hand-rolling a state machine and an admin UI for every internal process. The core is open source. The catch is the licensing, two layers of it. Viewflow Core is AGPL-3.0, which is fine for internal tools but worth a lawyer's glance before you ship it inside a closed-source product. The deeper integrations and the right to keep private forks live in Viewflow PRO, the paid commercial version. So free gets you a working BPMN engine; the convenience and the permissive license cost money. Solo developers and small teams building internal workflows: the free Core is plenty, run it yourself. Teams shipping a commercial product on top of it, or who want the prebuilt integrations and AGPL out of the way, should treat PRO as the honest path, and it is priced as a developer tool, not enterprise extortion. If you are not already on Django, this is not a reason to switch; pick a workflow engine that matches your stack instead. One more thing: this is a framework, not a no-code app. Your team needs Django chops to get value out of it. Hand it to non-developers expecting a Zapier-style canvas and they will bounce.