Airflow alternative

When Airflow is too much, keep orchestration close to the OS.

Dagu is built for teams that want scheduling, retries, dependencies, logs, and a UI without adopting a Python framework or operating a heavy metadata stack.

Install DaguRead the comparison essay
A workflow without framework imports
name: nightly-ops
schedule: "0 2 * * *"

steps:
  - name: extract
    command: python scripts/extract.py

  - name: transform
    command: ./bin/transform
    depends: extract
    retryPolicy:
      limit: 3

  - name: notify
    command: ./scripts/slack-success.sh
    depends: transform

No Python DAG framework required

No metadata database required to start

Run existing scripts and containers directly

Scale to workers when the workload needs it

Use commands as the workflow boundary

Airflow is powerful, but many teams only need to orchestrate existing code. Dagu keeps business logic in your scripts and uses commands as the stable interface.

  • Run Python, Bash, Java, Go, PHP, containers, HTTP calls, and SSH commands.
  • Avoid converting every task into a framework-specific operator.
  • Keep workflow definitions readable for operators and engineers.

Avoid infrastructure before you need it

Dagu starts as one binary backed by files. You can add queues and workers later without changing the basic workflow model.

  • Try locally before planning a platform rollout.
  • Keep upgrades and backups simpler.
  • Use distributed mode only when one host is no longer enough.

A better fit for mixed engineering work

Ops jobs, ETL scripts, AI agent tasks, reporting jobs, and internal automation rarely live in one language or framework.

  • Let each step use the runtime it already uses.
  • Control schedules and retries outside the application code.
  • Keep orchestration portable across teams and stacks.

Airflow vs. Dagu for script-first teams

Dimension
Dagu
Typical alternative
Authoring
Declarative YAML that calls commands.
Python DAG definitions and operator abstractions.
Runtime
Single binary and local files to start.
Scheduler, webserver, metadata database, and executor choices.
Best fit
Ops automation, scripts, containers, agent CLIs, and lightweight pipelines.
Large data platform workflows that benefit from the Airflow ecosystem.

FAQ

Practical questions before adopting Dagu

Is Dagu a full replacement for every Airflow deployment?

No. Airflow has a large ecosystem for data-platform teams. Dagu is the better fit when the work is command-native and you want a smaller self-hosted runtime.

Can Dagu run data pipelines?

Yes, if the pipeline can be represented as commands, containers, HTTP calls, SSH commands, or sub-workflows. Dagu focuses on orchestration rather than framework-specific data abstractions.

Does Dagu support distributed execution?

Yes. Dagu supports local, queue-based, and coordinator-worker execution modes so teams can grow from one machine to distributed workers.

Start with one workflow.

Install Dagu, move one fragile script or agent task into YAML, and decide from a real run history.

Install Dagu