dagu

Dagu is a local-first workflow engine for operations and internal automation. It is open source and self-hostable: a single binary with a built-in Web UI, no external database or message broker, running on Linux, macOS, and Windows. Define DAGs in a declarative YAML format. It natively supports shell commands, Docker containers, Kubernetes Jobs, remote commands via SSH, and more through Dagu Actions.

Dagu turns existing scripts and runbooks into production workflows with scheduling, retries, human tasks, and run history. It runs where your data and credentials live: on-prem, air-gapped, edge, or cloud, and scales from a single node to a fleet of workers.

Highlights:

• Single binary installation.
• Self-contained: no external DBMS or message broker required.
• Runs on Linux, macOS, and Windows.
• Declarative YAML format for defining DAGs.
• Run existing shell commands, Docker containers, Kubernetes Jobs, and remote commands over SSH without modifications.
• Compose reusable Sub-DAGs and run work in parallel with concurrency controls.
• Schedule workflows with cron syntax, timezones, overlap policies, and catch-up windows.
• Keep logs, run history, retries, notifications, and webhook triggers in one place.
• Built-in MCP server for inspecting workflows and runs, maintaining Wiki pages, applying changes, and controlling runs.

Quick Look

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

Run Details	Step Logs	Wiki

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

Why Dagu?

Orchestration is not your main work. You have scripts and containers that already work. You want a schedule, retries, dependencies, and a place to see logs. The usual options each have a cost:

• cron runs commands, but gives you no dependencies, no retries, no history.
• Airflow orchestrates, but you operate a platform for it (scheduler, metadata database, workers, a Python environment), and your jobs get rewritten as @dag/@task framework code.
• Temporal gives durable execution, but your business logic moves into its SDK and programming model.

You wanted to schedule some jobs. Now you operate a second system, and the orchestrator lives inside the code it was supposed to serve.

Dagu treats workflow structure as configuration, not code. Order, dependencies, retries, schedules, and human tasks go in one YAML file next to your scripts; the engine that runs them is a single process:

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

Your scripts never import the orchestrator. Delete the YAML and they run exactly as before. Keep it, and every run gets a dependency graph, retries, per-step logs, history, and a Web UI.

Performance

Dagu stores state in local files and reaches production throughput without external services.

• Throughput: A single machine can run thousands of workflow runs per day. Actual capacity depends on CPU, memory, disk, and workflow shape.
• Load control: Queues, concurrency limits, and resource limits control how many runs execute at once and where they run.
• Scale out: Workers spread execution across machines when one node is not enough.

Real-World Use Cases

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.
Legacy scripts and scheduled jobs	Turn interdependent scripts into maintainable DAGs with a UI, automatic logging, retries, and notifications instead of opaque cron jobs.
Media conversion	Run ffmpeg for video transcoding and format conversion. File-backed state allows workers to 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 to run automation on private infrastructure without exposing 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	Provide self-service workflows that non-engineering teams can run for diagnostics, database queries, and routine operations without escalating to engineering.
IoT and edge workflows	Run sensor polling, local ML inference, data preprocessing, backups, offline sync, and health checks close to the data source with Web UI visibility.

Quick Start

Install

macOS/Linux:

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

Homebrew:

brew install dagu

npm:

npm install -g --ignore-scripts=false @dagucloud/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

This command does not expose the host Docker daemon to Dagu. Workflows that use container: or action: docker.run need the container-step Docker setup. Mounting the Docker socket grants workflows control of the host daemon.

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 the Installation documentation 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 --dags .

Visit http://localhost:8080

How You Run Dagu

Dagu runs on one machine, on temporary workers your platform creates for each run, or on workers you keep running. All three are self-hosted, and the same workflow YAML runs on any of them. See the Deployment Models guide.

Topology	Execution	Best for
Single server	dagu start-all runs the server, scheduler, and steps in one process on one machine.	Development, single-machine scheduled workloads, edge jobs, and internal automation.
Temporary workers	Cloud Run Jobs, Kubernetes Jobs, or CI provision a worker per run that invokes the binary and is destroyed when the run ends. The server reads run state from a shared volume.	Ephemeral compute, capacity that falls to zero between jobs, and launchers you already operate.
Distributed workers	Workers you keep running poll a coordinator over gRPC and are routed work by label.	Docker and private-network steps, warm toolchains, and multiple execution hosts.

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.

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.
• Build workflows: Reuse a step's result when its command and files have not changed. Dagu can also infer dependencies from matching file paths.
• 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.
• Human Tasks: Pause a workflow for acknowledgement or typed operator input, then expose the response to downstream steps.
• Secret management: Built-in secret management with secure log masking, preventing credentials from leaking into logs or the Web UI.
• Self-hosted: A single binary that runs on Linux, macOS, and Windows. Execution scales out to a fleet of workers.