Compare
How Dagu compares to other workflow engines
Dagu is a single self-hosted binary that runs declarative YAML with no database. Here is an honest look at how it compares to other orchestrators and automation tools.
A lightweight workflow engine for scripts, cron, and runbooks.
Dagu is a lightweight workflow engine that turns the commands your team already runs into scheduled, observable YAML workflows, with retries, logs, queues, and a web UI.
Read moreAI agent orchestrationOrchestrate agent CLIs like production workflows.
Dagu gives AI agent commands the operational wrapper they need: scheduling, dependency order, retries, logs, artifacts, and human checkpoints.
Read more
DaguvsCronKeep cron's simplicity. Add the controls production jobs need.
Dagu keeps schedules close to your scripts while adding dependency graphs, retries, logs, history, manual reruns, and a web UI.
Read moreDaguvsAirflowWhen Airflow is too much, keep orchestration close to the OS.
Dagu is an Airflow alternative for teams that want scheduling, retries, dependencies, logs, and a UI without adopting a Python framework or operating a heavy metadata stack.
Read moreDaguvsn8nA code-first n8n alternative for developers.
Dagu is a self-hosted n8n alternative for teams that would rather keep their automation in version-controlled YAML than build it on a visual canvas. You still get schedules, retries, logs, and a web UI, all from one binary.
Read moreDaguvsPrefectWhen you want orchestration without writing Python, look at Dagu.
Prefect is a Python framework for data teams who write flows in code. Dagu is a single binary that runs declarative YAML calling the commands you already have, with no database to operate. This page is an honest look at where each one fits.
Read moreDaguvsDagsterDagu and Dagster solve different problems.
Dagster is a Python data orchestrator built around software-defined assets and lineage. Dagu is a single binary that runs YAML workflows calling commands you already have. This page explains where each one fits.
Read moreDaguvsTemporalDagu and Temporal solve different problems.
Temporal is a durable-execution engine for stateful application workflows written in code. Dagu is a single binary that schedules and orchestrates the commands you already run. This page explains where each one fits.
Read moreDaguvsWindmillDagu vs Windmill: declarative YAML against a script and app platform.
Both run self-hosted and both are fast. Windmill turns scripts into workflows, webhooks, and low-code apps backed by PostgreSQL. Dagu is one binary that runs declarative YAML over commands you already have, with no database to operate.
Read moreDaguvsArgo WorkflowsArgo Workflows lives on Kubernetes. Dagu runs on a plain machine.
Both define DAGs and run steps in order. Argo Workflows is built into Kubernetes and schedules each step as a pod. Dagu is a single binary that calls the commands you already have, with no cluster to operate.
Read moreDaguvsKestraDagu vs Kestra: same YAML idea, very different footprint.
Dagu and Kestra both describe workflows declaratively in YAML, so the real choice is about runtime and dependencies. Dagu is one self-contained binary that calls commands you already have. Kestra runs on the JVM with a database behind it and a large plugin catalog on top.
Read more