Lightweight Workflow Engine Alternative to Airflow & Cron

Single binary with Web UI. Execute workflows defined in a simple, declarative YAML on a schedule. Natively support shell commands, remote execution via SSH, and docker image.

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:
  - echo "Hello from Dagu!"
  - echo "Running step 2"
EOF

Docker

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

schedule: "0 2 * * *"  # 2 AM daily

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

---
name: transform-data
params: [TYPE, INPUT]
steps:
  - 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