Local-First Workflow Engine, Built for Self-Hosting

Zero dependencies. Language agnostic. Self-contained.

Docs | Quick Start | Features | Installation | Community

What is Dagu?

Dagu solves the problem of complex workflow orchestration without requiring a dedicated infrastructure team. Unlike traditional workflow engines that demand databases, message queues, and careful operational overhead, Dagu runs as a single binary with zero external dependencies.

After managing hundreds of cron jobs across multiple servers, I built Dagu to bring sanity to workflow automation. It handles scheduling, dependencies, error recovery, and monitoring - everything you need for production workflows, without the complexity.

→ Learn the core concepts

Design Philosophy

1. Local‑first. Workflows should run offline on laptops, air‑gapped servers, or the cloud—your choice.
2. Zero foot‑print. One static binary; no databases, brokers, or sidecars.
3. Bring‑your‑own language. Bash, Python, Go, or anything just works.

Features

• DAG definition - Express complex dependencies in readable YAML
• Scheduling - Cron expressions with timezone support
• Queueing - Control concurrency with named queues
• Error handling - Retries, failure handlers, cleanup hooks
• Conditional execution - Run steps based on conditions
• Parallel execution - Control concurrent step execution
• Variables & Parameters - Pass data between steps, parameterize workflows
• Docker support - Run steps in containers
• SSH executor - Execute commands on remote hosts
• HTTP requests - Integrate with APIs
• Email notifications - SMTP integration
• Hierarchical workflows - Nest DAGs to any depth
• Authentication - Basic auth, API tokens, TLS
• Web UI - Real-time monitoring and control
• REST API - Full programmatic access

Quick Start

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

# Create dagu configuration directory
mkdir -p ~/.config/dagu/dags

# Create your first workflow
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

# Execute it
dagu start hello

# Check the status
dagu status hello

# Start the web UI
dagu start-all
# Visit http://localhost:8080

Installation

macOS / Linux

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

# Specific version
curl -L https://raw.githubusercontent.com/dagu-org/dagu/main/scripts/installer.sh | bash -s -- --version v1.17.0

# Install to a specific directory
curl -L https://raw.githubusercontent.com/dagu-org/dagu/main/scripts/installer.sh | bash -s -- --prefix /path/to/install

# Homebrew
brew install dagu-org/brew/dagu

Docker

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

Manual Download

Download from releases and add to PATH.

Documentation

• Getting Started - Tutorial and first steps
• Core Concepts - Architecture and design
• Writing Workflows - Complete authoring guide
• CLI Reference - Command-line usage
• API Reference - REST API documentation
• Configuration - Configuration options
• Changelog - Release notes and version history

Examples

Find more in our examples documentation.

ETL Pipeline

name: daily-etl
schedule: "0 2 * * *"
steps:
  - name: extract
    command: python extract.py
    output: DATA_FILE
    
  - name: validate
    command: python validate.py ${DATA_FILE}
    
  - name: transform
    command: python transform.py ${DATA_FILE}
    retryPolicy:
      limit: 3
      
  - name: load
    command: python load.py ${DATA_FILE}

Hierarchical Workflows

steps:
  - name: data-pipeline
    run: etl
    params: "ENV=prod REGION=us-west-2"
    
  - name: parallel-jobs
    run: batch
    parallel:
      items: ["job1", "job2", "job3"]
      maxConcurrency: 2
    params: "JOB=${ITEM}"
---
name: etl
params:
  - ENV
  - REGION
steps:
  - name: process
    command: python etl.py --env ${ENV} --region ${REGION}
---
name: batch
params:
  - JOB
steps:
  - name: process
    command: python process.py --job ${JOB}

Container-based Pipeline

name: ml-pipeline
steps:
  - name: prepare-data
    executor:
      type: docker
      config:
        image: python:3.11
        autoRemove: true
        volumes:
          - /data:/data
    command: python prepare.py
    
  - name: train-model
    executor:
      type: docker
      config:
        image: tensorflow/tensorflow:latest-gpu
    command: python train.py
    
  - name: deploy
    command: kubectl apply -f model-deployment.yaml
    preconditions:
      - condition: "`date +%u`"
        expected: "re:[1-5]"  # Weekdays only

Web Interface

Learn more about the Web UI →

Real-time monitoring of all workflows

Visual workflow editor with validation

Detailed execution logs with stdout/stderr separation

Use Cases

• Data Engineering - ETL pipelines, data validation, warehouse loading
• Machine Learning - Training pipelines, model deployment, experiment tracking
• DevOps - CI/CD workflows, infrastructure automation, deployment orchestration
• Media Processing - Video transcoding, image manipulation, content pipelines
• Business Automation - Report generation, data synchronization, scheduled tasks

Roadmap

• Run steps in a DAG across multiple machines (distributed execution)
• Track artifacts by dropping files in $DAGU_ARTIFACTS
• Pause executions for webhooks, approvals, or any event (human-in-the-loop, event-driven workflows)
• Integrate with AI agents via MCP (Model Context Protocol)

Building from Source

Prerequisites: Go 1.24+, Node.js, pnpm

git clone https://github.com/dagu-org/dagu.git && cd dagu
make build
make run

Contributing

Contributions are welcome. See our documentation for development setup.

• Discord Community
• GitHub Issues

Contributors

License

GNU GPLv3 - See LICENSE

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