A lightweight and powerful workflow engine for enterprise & small teams

Get StartedView Examples

Quick Start

Install and run your first workflow in under 2 minutes.

Install

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

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

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. Start 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

Visit http://localhost:8080 to see your workflows.

Why Dagu?

Zero Dependencies

Single binary. No database, no message broker. Deploy anywhere in seconds.

Language Agnostic

Execute any command. Your existing scripts work without modification.

Production Ready

Battle-tested error handling, retries, logging, and monitoring.

Hierarchical Workflows

Compose workflows from smaller workflows. Build modular, reusable components.

Example: Data Pipeline with Nested Workflows

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

steps:
  - name: extract
    command: python extract.py --date=${DATE}
    output: RAW_DATA
    
  - name: transform
    run: transform-data
    parallel:
      items: [customers, orders, products]
    params: "TYPE=${ITEM} INPUT=${RAW_DATA}"
    
  - name: load
    command: python load.py
    retryPolicy:
      limit: 3
      intervalSec: 2
      backoff: true      # Exponential backoff

---
name: transform-data
params: [TYPE, INPUT]
steps:
  - name: process
    command: python transform.py --type=${TYPE} --input=${INPUT}

Learn More

Getting Started

Installation and first workflow

Writing Workflows

Complete workflow authoring guide

YAML Reference

All configuration options

Community

GitHubDiscordBlueskyIssues