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dagucloud/dagu for Claude Code | Awesome Skills

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.

Model	Server	Execution	Best for
Local single-server	`dagu start-all` on one machine.	Same machine.	Development, small scheduled workloads, edge jobs, and simple internal automation.
Self-hosted	Dagu server on your infrastructure.	Local execution or distributed workers on your infrastructure.	Teams that need ownership of infrastructure.
Managed Server	Full managed Dagu server in a dedicated, isolated gVisor instance on GKE.	Managed instance.	Teams that want Dagu operated for them without running the server themselves.
Hybrid	Full managed Dagu server.	Private workers in your infrastructure over mTLS.	Docker steps, private networks, specialized hardware, or data-local work.

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.

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

Use Case	How Dagu Helps
ETL and data operations	Turn data extraction scripts, SQL queries, dbt commands, and data-processing runbooks into observable pipelines with durable execution.
Cron and legacy script management	Turn complex jobs with interdependencies into maintainable DAGs with a UI, automatic logging, retries, and notifications instead of opaque cron jobs and bash scripts.
Media conversion	Run `ffmpeg` for video transcoding and format conversion. Thanks to Dagu's file-backed nature, workers can run heavy conversions in parallel without single machine bottlenecks or external databases.
Infrastructure and server automation	Run any command or script over SSH on remote servers, keeping logs, results, and notifications in one place.
GitHub-driven workflows	Trigger workflows from GitHub events. This is useful for running automation on private infrastructure without exposing your servers to the public internet.
Container and Kubernetes workflows	Run Docker containers and Kubernetes Jobs as steps in your workflows without building a custom control plane around containers.
Customer support automation	Run self-service support tools that non-engineering teams can use to run approved workflows for running diagnostics, querying databases, and performing common support tasks without escalating to engineering.
IoT and edge workflows	Run sensor polling, local ML inference, data preprocessing, backups, offline sync, health checks, etc. Dagu keeps these jobs close to the data source while still providing Web UI visibility.

dagu

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Licensing

Install

Create and run a workflow

Start the server

Connect AI agents through MCP