Dagu

A self-contained, powerful alternative to Airflow, Cron, etc.

Dagu is a compact, portable workflow engine. It provides a declarative model for orchestrating software across diverse environments, including shell scripts, Python scripts, containerized operations.

Get Started in 3 MinutesView Examples

Quick Start

Create and run your first Dagu workflow in minutes.

Step 1: Install Dagu

Docker

# Pull the latest image
docker pull ghcr.io/dagu-org/dagu:latest

Binary

# Install via script (macOS, Linux, WSL)
curl -L https://raw.githubusercontent.com/dagu-org/dagu/main/scripts/installer.sh | bash

# Or via Homebrew
brew install dagu-org/brew/dagu

Step 2: Create Your First Workflow

mkdir -p ~/.dagu/dags && cat > ~/.dagu/dags/hello.yaml << 'EOF'
name: hello-world
description: My first Dagu workflow

steps:
  - name: greet
    command: echo "Hello from Dagu!"
    
  - name: show-date
    command: date
    
  - name: done
    command: echo "Workflow complete! 🎉"
EOF

Step 3: Run the Workflow

Docker

docker run \
--rm \
-v ~/.dagu:/app/.dagu \
-e DAGU_HOME=/app/.dagu \
ghcr.io/dagu-org/dagu:latest \
dagu start hello.yaml

Binary

dagu start ~/.dagu/dags/hello.yaml

For Docker: --rm removes container after execution, -v mounts your DAG directory, -e sets the home directory inside container.

Step 4: View in Web UI

Docker

docker run \
--rm \
-v ~/.dagu:/app/.dagu \
-e DAGU_HOME=/app/.dagu \
-p 8080:8080 \
ghcr.io/dagu-org/dagu:latest \
dagu start-all

Binary

dagu start-all

For Docker: -p 8080:8080 exposes the web interface on port 8080. Open your browser to http://localhost:8080 to see the Dagu web interface.

Why Dagu?

Zero Dependencies

No database, no message broker, no complex setup. Just a single binary that works everywhere.

Language Agnostic

Run any command, script, or program. If it runs on your system, Dagu can orchestrate it.

Powerful Yet Simple

From simple task automation to complex data pipelines, Dagu scales with your needs.

Built-in Web UI

Monitor workflows, view logs, and manage executions through a clean, modern interface.

Hierarchical Workflows

Compose workflows from reusable components. Build once, use everywhere.

Production Ready

Battle-tested with robust error handling, retries, and comprehensive logging.

Example Workflow

name: data-pipeline
schedule: "0 2 * * *"  # 2 AM daily

steps:
  - name: extract
    command: python extract.py --date=${DATE}
    output: RAW_DATA
    
  - name: transform
    run: transform-module
    parallel:
      items: [customers, orders, products]
    params: "TYPE=${ITEM} INPUT=${RAW_DATA}"
    
  - name: load
    command: python load.py --date=${DATE}
    retryPolicy:
      limit: 3
      intervalSec: 30

---

name: transform-module
params:
  - TYPE: "Type of data to transform"
  - INPUT: "Input data file"
steps:
  - name: transform-data
    command: python transform.py --type=${TYPE} --input=${INPUT}
    output: TRANSFORMED_DATA

  - name: save-results
    command: python save.py --data=${TRANSFORMED_DATA}

See Also

Getting Started

Install Dagu and create your first workflow in minutes

Examples

Quick examples for all features with copy-paste ready code

Writing Workflows

Learn how to build powerful, maintainable workflows

Join the Community

Dagu is open source and welcomes contributions. Join us in making workflow orchestration simple and powerful.

GitHubDiscordIssues