Lightweight to run
Dagu is a single binary with no required database, message broker, or control-plane stack. Start on one machine and add workers only when you need them.
The self-hosted control plane for the ops automation you already run.
Dagu turns scripts, cron jobs, containers, HTTP tasks, SQL jobs, and approvals into one visible workflow system without forcing a rewrite.
dagu server --port 8080
Install dagu command
Guided installer: adds Dagu to your PATH, sets up a background service, and creates the first admin so you can start running workflows.
Non-invasive
No SDK required. Your business logic stays untouched.
Lightweight
Single binary, no required database or broker
Command-native
Run scripts, containers, SSH tasks, and HTTP calls
Air-Gapped Ready
Runs fully offline. No external services needed.
workflow.yaml
# Existing scripts, now production workflows
name: "daily-ops"
schedule: "0 2 * * *"
steps:
- name: "backup-db"
command: "./scripts/backup-postgres.sh"
output: BACKUP_PATH
- name: "upload-backup"
command: "aws s3 cp $${BACKUP_PATH} s3://backups/"
depends: backup-db
- name: "notify"
type: http
depends: upload-backup
Why ops teams choose Dagu
Self-hosted by default. Existing automation stays intact. Dagu adds oversight and operator controls around it.
Built for mixed ops automation
Run schedules, dependencies, retries, queues, parameters, secrets, notifications, SSH steps, container steps, SQL jobs, and distributed execution in readable YAML.
Migration without rewrites
Your scripts, services, SQL, containers, and operational commands stay as they are. Dagu orchestrates around them instead of forcing a framework or SDK into your codebase.
Operator visibility by default
Every run gets status, logs, history, timing, and a visual workflow view, so jobs stop disappearing into crontabs and server log files.
Deployment models
Run Dagu locally, self-host it, use the managed server, or combine cloud operations with private execution.
Start on one machine. Scale to self-hosted, managed, or hybrid.
Local
Single server
Run `dagu start-all` on one machine with local file-backed state. No database, broker, or platform stack required.
Self-hosted
Your infrastructure
Keep the Dagu server, workers, secrets, logs, and execution inside your own environment.
Dagu Cloud
Full managed server
Use a dedicated Dagu server operated by Dagu Cloud in an isolated gVisor instance on GKE.
Hybrid
Managed server, private execution
Let Dagu Cloud operate the server while private workers run Docker, private-network, or data-local steps.
Hybrid execution
Managed Dagu server
Private worker over mTLS
Docker, private APIs, secrets, and data-local work
Hybrid keeps the Dagu server managed while execution that needs your network, runtime, or data stays under your control.
What to check before replacing fragmented automation
Teams look at throughput, queues, recovery, governance, API access, and worker execution before they move scattered jobs into one control plane.
Thousands/day
Single-node throughput
Run thousands of workflow runs per day on one machine, depending on hardware, workflow shape, step duration, and queue settings.
Queues + workers
Scale execution safely
Use queues, concurrency limits, and distributed workers to control load and spread jobs across machines.
Catchup + retry
Recover scheduled work
Cron schedules, catchup, durable automatic retries, timeouts, reruns, event handler scripts, and email notifications keep failures manageable.
Users + API
Operate as a team
Use user management, RBAC, workspaces, approvals, secrets, REST API, CLI, and webhooks for shared production workflows.
Use Cases
A practical index of operations work that starts as scripts, cron jobs, and ad hoc tasks, then needs a workflow system people can run and track.
Operational Use Cases
Dagu fits teams that already have operational work spread across commands, scripts, containers, SQL jobs, HTTP tasks, and remote servers, then need a clearer way to schedule, retry, observe, and manage it.
Keep the existing commands. Add visibility, retries, approvals, and run history around them.
01
Example / Hidden cron work
Cron and Legacy Script Management
Bring existing shell scripts, Python scripts, HTTP calls, and scheduled jobs into Dagu without rewriting them.
Hidden cron estates become visible, retryable workflows with logs, dependencies, history, and operator controls.
The workflow stays concrete enough for engineers and visible enough for operators.
02
Daily jobs people can maintain
ETL and Data Operations
Run PostgreSQL or SQLite queries, S3 transfers, jq transforms, validation steps, and reusable sub-workflows.
Daily data workflows stay declarative, observable, and easy to retry when one step fails.
03
Distributed media work
Media Conversion
Run ffmpeg, thumbnail extraction, audio normalization, image processing, and other compute-heavy jobs across workers.
Conversion work can run across distributed workers while status, history, logs, and artifacts stay in one persistence layer for monitoring, debugging, and retries.
04
Scheduled remote jobs
Infrastructure and Server Automation
Coordinate SSH backups, cleanup jobs, deploy scripts, patch windows, precondition checks, and lifecycle hooks.
Remote operations get schedules, retries, notifications, and per-step logs without requiring operators to SSH into servers for every recovery.
05
Container-native pipelines
Container and Kubernetes Workflows
Compose workflows where each step can run a Docker image, Kubernetes Job, shell command, or validation step.
Image-based tasks can be routed to the right workers without building a custom control plane around containers.
06
Non-engineer operations
Customer Support Automation
Run diagnostics, account repair jobs, data checks, and approval-gated support actions from a simple Web UI.
Non-engineers can operate reviewed workflows while engineers keep commands, logs, and results traceable.
07
Small devices, visible runs
IoT and Edge Workflows
Run sensor polling, local cleanup, offline sync, health checks, and device maintenance jobs on small devices.
The single binary and file-backed state work well on edge devices while still providing visibility through the Web UI.
08
Optional AI-assisted operations
AI Agent Workflows
Run AI coding agents, agent CLIs, agent-authored YAML workflows, log analysis, repair steps, and human-reviewed automation when model assistance is useful.
AI stays a secondary capability inside the workflow instead of becoming the thing that runs everything.
Common thread
Plain YAMLAny commandDocker and Kubernetes JobsSSHSchedulesRetriesLogsNotifications
Common Workflow Patterns
Bring scripts, scheduled jobs, server tasks, and controlled automation into one workflow engine.
Health Check
SSH Backup
Notify
Script Workflows
Turn existing shell scripts, Docker commands, SSH tasks, and HTTP calls into reliable workflows.
- 1Keep existing scripts and commands intact
- 2Run containers, SSH tasks, and HTTP steps in one DAG
- 3Use dependencies instead of fragile command chains
- 4Retry failed steps with clear logs and history
workflow.yaml
steps:
- name: health-check
command: curl -sf http://app:8080/health
- name: backup
type: ssh
config:
host: db-server
user: admin
command: pg_dump mydb > /backups/daily.sql
- name: notify
type: http
config:
url: "https://hooks.slack.com/..."
method: POST
body: '{"text": "Backup complete"}'
Workflow Operator for Slack & Telegram
Persistent AI operator for Slack and Telegram.
Debug failures, approve actions, and recover incidents without leaving the conversation.
DaguMessage...
Workflow engine features for real operations
Dagu focuses on the production layer around your existing work: schedules, dependencies, retries, logs, queues, and controlled execution.
Quickstart Guide
Install Dagu with the guided wizard, then continue in the full installation guide or quickstart docs.
1
Install dagu command
The script installers are the recommended path. Homebrew, npm, and Docker remain available for binary-only or container installs.
Mac/Linux Terminal
$curl -fsSL https://raw.githubusercontent.com/dagucloud/dagu/main/scripts/installer.sh | bash
✓ Guided installer ready
2
Next steps
The guided installer can finish the first-run setup for you.
# What the installer can do
Add Dagu to your PATH
Set up a background service
Create and verify the first admin
Project Community
Discuss usage, report issues, and follow development.