dagu

Dagu is a lightweight, self-contained alternative to Airflow or Cron with Web UI. It supports Linux / Mac / Windows. Define DAGs in a simple, declarative YAML format. It natively supports shell commands, Docker containers, Kubernetes Jobs, remote commands via SSH, external coding-agent CLIs through harness.run, and more through Dagu Actions.

It was designed to be easy to use, self-contained, and require no coding, making it ideal for small teams.

Highlights:

• Single binary file installation.
• Declarative YAML format for defining DAGs.
• Web UI for visually managing, retrying, and monitoring pipelines.
• Use existing scripts or tools without any modifications.
• Self-contained, with no need for a DBMS.
• Built-in MCP support for AI agents to manage workflows.
• Run external coding-agent CLIs through harness.run when workflows need AI assistance.

Quick Look

For a quick look at how workflows are defined, see the examples.

Run Details	Step Logs

Try it live: Live Demo (credentials: demouser / demouser)

Why Dagu?

  Traditional Orchestrator          Dagu
  ┌────────────────────────┐        ┌──────────────────┐
  │  Web Server            │        │                  │
  │  Scheduler             │        │  dagu start-all  │
  │  Worker(s)             │        │                  │
  │  PostgreSQL            │        └──────────────────┘
  │  Redis / RabbitMQ      │         Single binary.
  │  Python Runtime        │         Self-hosted.
  └────────────────────────┘         Adds scheduling, retries, and approvals around existing automation.
    6+ services to manage

Performance

Dagu stores state in local files. How much it can run depends on the machine and the workload. CPU, memory, disk, workflow characteristics, queue settings, and worker capacity all matter.

• Throughput: On one machine, Dagu can run thousands of workflow runs per day when the hardware and workflow shape fit the workload.
• Load control: Use queues, concurrency limits, resource limits, and optional distributed workers to decide how many runs execute at once and where they run.

Real-World Use Cases

Quick Start

Install

macOS/Linux:

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

Homebrew:

brew install dagu

Windows (PowerShell):

irm https://raw.githubusercontent.com/dagucloud/dagu/main/scripts/installer.ps1 | iex

Docker:

docker run --rm -v ~/.dagu:/var/lib/dagu -p 8080:8080 ghcr.io/dagucloud/dagu:latest dagu start-all

Kubernetes (Helm):

helm repo add dagu https://dagucloud.github.io/dagu
helm repo update
helm install dagu dagu/dagu --set persistence.storageClass=<your-rwx-storage-class>

Replace <your-rwx-storage-class> with a StorageClass that supports ReadWriteMany. See charts/dagu/README.md for chart configuration.

The script installers run a guided wizard that can add Dagu to your PATH, set it up as a background service, and create the initial admin account. Homebrew, npm, Docker, and Helm install without the wizard. See Installation docs for all options.

Create and run a workflow

Create hello.yaml:

steps:
  - id: hello
    run: echo "hello from Dagu"

Run the workflow with:

dagu start hello.yaml

Start the server

dagu start-all

Visit http://localhost:8080

Connect AI agents through MCP

Dagu exposes a built-in MCP server from the running HTTP server. Start Dagu, then configure MCP-capable chat or coding agents to use the Streamable HTTP endpoint:

http://localhost:8080/mcp

Use MCP when you want an AI agent to read Dagu state, preview or apply workflow changes, and start, enqueue, retry, or stop runs through dagu_read, dagu_change, and dagu_execute. See the MCP setup guide.

For authoring-only help in Claude Code, Codex, Gemini CLI, and other AI coding tools, install the Dagu workflow authoring skill:

gh skill install dagucloud/dagu dagu

How You Run Dagu?

Run Dagu on one machine, scale out with distributed workers, or use a managed Dagu instance operated by us. See the Deployment Models guide.

Licensing

• Community self-host: No license key required. You operate the server, storage, upgrades, networking, and workers. Start with the installation guide.
• Self-host license: Adds SSO, RBAC, audit logging, and incident SaaS integration to Dagu. See self-host licensing.
• Dagu managed instance: Includes its own managed license. Private workers can run on your infrastructure.

Managed Dagu instances do not expose a Docker daemon or Docker socket. Workflows that need Docker step execution should use self-hosted Dagu or a private worker with Docker access.

Key Features

• Observability: Shared workflows and scheduling with clear visualizations, status tracking, and logs in the Web UI.
• Language-agnostic: No framework required. Define workflow steps using shell commands, Docker containers, Kubernetes Jobs, SQL queries, HTTP requests, and any other tool via official and third-party Dagu Actions.
• Reproducibility: Reproducible runs with pinned tools, plus automatic installation and caching on workers—eliminating the need to manually install dependencies on the server or workers.
• Built-in Approvals: The Human-in-the-loop steps for manual approvals, review, and intervention in any workflow.
• MCP Server: Built-in MCP server for authoring and running workflows via AI agents like Claude Code, Codex, Gemini CLI, Pi, OpenCode, and more.
• External CLI Harness: You can run coding-agent CLIs (Claude Code, Codex, Gemini CLI, Pi, OpenCode, etc.) with a built-in harness action or custom harness definition.