Quick Start

Get up and running with Dagu in under 2 minutes.

Install Dagu

npm

npm install -g dagu

macOS/Linux

curl -L https://raw.githubusercontent.com/dagu-org/dagu/main/scripts/installer.sh | bash

Docker

docker pull ghcr.io/dagu-org/dagu:latest

Homebrew

brew install dagu-org/brew/dagu

See Installation Guide for more options.

Your First Workflow

Example DAGs

When you first start Dagu with an empty DAGs directory, it automatically creates several example workflows to help you get started:

• example-01-basic-sequential.yaml - Basic sequential execution
• example-02-parallel-execution.yaml - Parallel task execution
• example-03-complex-dependencies.yaml - Complex dependency graphs
• example-04-scheduling.yaml - Scheduled workflows
• example-05-nested-workflows.yaml - Nested sub-workflows
• example-06-container-workflow.yaml - Container-based workflows

To skip creating these examples, set DAGU_SKIP_EXAMPLES=true or add skipExamples: true to your config file.

1. Create a workflow

Binary

mkdir -p ~/.config/dagu/dags && cat > ~/.config/dagu/dags/hello.yaml << 'EOF'
steps:
  - name: hello
    command: echo "Hello from Dagu!"
    
  - name: world
    command: echo "Running step 2"
EOF

Docker

mkdir -p ~/.dagu/dags && cat > ~/.dagu/dags/hello.yaml << 'EOF'
steps:
  - name: hello
    command: echo "Hello from Dagu!"
    
  - name: world
    command: echo "Running step 2"
EOF

2. Run it

Binary

Binary

dagu start hello

Docker

docker run --rm \
  -v ~/.dagu:/var/lib/dagu \
  ghcr.io/dagu-org/dagu:latest \
  dagu start hello

Output:

┌─ DAG: hello ─────────────────────────────────────────────────────┐
│ Status: Success ✓           | Started: 23:34:57 | Elapsed: 471ms │
└──────────────────────────────────────────────────────────────────┘

Progress: ████████████████████████████████████████ 100% (2/2 steps)

Note: The output may vary if you are using Docker.

3. Check the status

Binary

dagu status hello

Docker

docker run --rm \
  -v ~/.dagu:/var/lib/dagu \
  ghcr.io/dagu-org/dagu:latest \
  dagu status hello

4. View in the UI

Binary

dagu start-all

Docker

docker run -d \
  -p 8080:8080 \
  -v ~/.dagu:/var/lib/dagu \
  ghcr.io/dagu-org/dagu:latest \
  dagu start-all

Open http://localhost:8080

Understanding Workflows

A workflow is a YAML file that defines steps and their dependencies:

steps:
  - name: step1
    command: echo "First step"
    
  - name: step2
    command: echo "Second step"  # Runs after step1 automatically

Key concepts:

• Steps: Individual tasks that run commands
• Dependencies: Control execution order
• Commands: Any shell command you can run

Parameters

You can define parameters for workflows to make them reusable:

# backup.yaml
name: daily-backup
params:
  - SOURCE: /data
  - DEST: /backup

steps:
  - name: timestamp
    command: date +%Y%m%d_%H%M%S
    output: TS
    
  - name: backup
    command: tar -czf ${DEST}/backup_${TS}.tar.gz ${SOURCE}
    
  - name: cleanup
    command: find ${DEST} -name "backup_*.tar.gz" -mtime +7 -delete

Run with parameters:

dagu start backup.yaml -- SOURCE=/important/data DEST=/backups

Parallel Execution

Run steps concurrently by specifying the same dependencies:

steps:
  - name: prepare
    command: echo "Starting"
    
  - name: task1
    command: echo "Processing images"
    depends: prepare
    
  - name: task2
    command: echo "Processing videos"
    depends: prepare
    
  - name: task3
    command: echo "Processing documents"
    depends: prepare
    
  - name: combine
    command: echo "Merging all results"
    depends: [task1, task2, task3]

Error Handling

Add retries and error handlers:

steps:
  - name: download
    command: curl -f https://example.com/data.zip -o data.zip
    retryPolicy:
      limit: 3
      intervalSec: 30
      
  - name: process
    command: echo "Unzipping data and processing"
    continueOn:
      failure: true  # Continue even if this fails
      
handlerOn:
  failure:
    command: echo "Workflow failed!" | mail -s "Alert" [email protected]
  success:
    command: echo "Success at $(date)"

Using Containers

Run all steps in Docker containers:

# Using a container for all steps
container:
  image: python:3.11
  volumes:
    - ./data:/data

steps:
  - name: python-task
    # write data to a file
    command: |
      python -c "with open('/data/output.txt', 'w') as f: f.write('Hello from Dagu!')"
    
  - name: process-data
    # read data from the file
    command: |
      python -c "with open('/data/output.txt') as f: print(f.read())"

Scheduling

Run workflows automatically:

name: nightly-job
schedule: "0 2 * * *"  # 2 AM daily

steps:
  - name: run
    command: echo "Running nightly process"

The workflow will execute every day at 2 AM.

What's Next?

• Core Concepts - Understand Dagu's architecture
• Writing Workflows - Learn advanced features
• Examples - Ready-to-use workflow patterns
• CLI Reference - All command options