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.
- Optional monitoring in Dagu Console: every server's runs and trends in one place, and an email when a workflow fails or a server stops reporting. Opt-in, outbound HTTPS only, metadata only.
For a quick look at how workflows are defined, see the examples.
| Run Details | Step Logs | Wiki |
|---|---|---|
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Try it live: Live Demo (credentials: demouser / demouser)
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/@taskframework 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 manageYour 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.
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.
| 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. |
macOS/Linux:
curl -fsSL https://raw.githubusercontent.com/dagucloud/dagu/main/scripts/installer.sh | bashHomebrew:
brew install dagunpm:
npm install -g --ignore-scripts=false @dagucloud/daguWindows (PowerShell):
irm https://raw.githubusercontent.com/dagucloud/dagu/main/scripts/installer.ps1 | iexDocker:
docker run --rm -v ~/.dagu:/var/lib/dagu -p 8080:8080 ghcr.io/dagucloud/dagu:latest dagu start-allThis command does not expose the host Docker daemon to Dagu. Workflows that use
container:oraction: docker.runneed 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 supportsReadWriteMany. 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 hello.yaml in an empty folder:
steps:
- id: hello
run: echo "hello from Dagu"Run the workflow with:
dagu start hello.yamlFrom that folder, run:
dagu start-all --dags .Open http://localhost:8080. On first launch, create the admin account unless the installer wizard already created one.
If a script installer set Dagu up as a background service, it is already running on port 8080, so add workflows from the UI or put hello.yaml in the DAGs directory the installer printed.
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. Any of them can report to Dagu Console, which shows your servers together and emails you when a workflow fails or a server goes silent. See the Deployment Models guide.
Single server
|
Temporary workers
|
Distributed workers
|
Monitoring with Dagu Console
|
| 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. |
- Community self-host: No license key required. You operate the server, storage, upgrades, networking, and workers. One server can be monitored in Dagu Console for free. Start with the installation guide.
- Self-host license: Adds SSO, RBAC, audit logging, incident SaaS integration, and monitoring in Dagu Console to Dagu. See self-host licensing.
Connect your Dagu servers to Dagu Console and see all of them in one place: which servers report, what failed today, open alerts, and run and duration trends for each workflow. Alert emails go out when a workflow fails or recovers, and when a server stops reporting.
- Opt-in: a server reports nothing until an administrator turns monitoring on in Plan & features, or sets
cloud.report: runs. - Outbound only: the server reports over HTTPS every 60 seconds, or every 5 minutes on the free tier. No inbound port and no VPN.
- Metadata only: run status, timing, and failed step names. Logs, outputs, parameters, and secrets are never sent, and the server shows the exact JSON it last sent.
Workflows keep running on your servers whether or not Dagu Console can be reached. Monitoring is in beta. It is free for one server on Dagu v2.19.2 or later, with lower limits; a Team or Pro plan, or a trial, covers every licensed server. See Monitoring.
- 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.
- Permission Control: RBAC and SSO support for team environments, controlling who can view, run, and edit workflows through granular permissions and audit logging.
- MCP Server: Authenticated MCP clients can inspect workflows and runs, maintain Wiki pages, apply changes, and control runs.
One binary carries every role. Which roles you start, and where, is what the deployment models differ on.
- Server serves the Web UI and REST API.
- Scheduler owns
schedule:and drains the queue. - Coordinator is the gRPC endpoint workers poll. It also persists what they report: run status, streamed logs, and artifacts.
- Worker polls a coordinator, executes dispatched runs locally, and reports back. Routed by labels.
dagu start-allruns the server, scheduler, and coordinator in one process.
Set DAGU_HEADLESS=true to run without the Web UI, which applies to any of the topologies and suits CI or CLI-only environments.
Single server:
┌─────────────────────────────────────────┐
│ dagu start-all │
│ ┌───────────┐ ┌───────────┐ ┌────────┐ │
│ │ HTTP / UI │ │ Scheduler │ │Executor│ │
│ └───────────┘ └───────────┘ └────────┘ │
│ File-based storage (logs, state, queue)│
└─────────────────────────────────────────┘
Distributed workers:
┌────────────┐ ┌────────────┐
│ Scheduler │ │ HTTP / UI │
│ │ │ │
│ ┌────────┐ │ └─────┬──────┘
│ │ Queue │ │ Dispatch (gRPC) │ Dispatch / GetWorkers
│ │(file) │ │─────────┐ │ (gRPC)
│ └────────┘ │ │ │
└────────────┘ ▼ ▼
┌─────────────────────────┐
│ Coordinator │
│ ┌───────────────────┐ │
│ │ Dispatch Task │ │
│ │ Store (pending/ │ │
│ │ claimed) │ │
│ └───────────────────┘ │
└────────▲────────────────┘
│
Worker poll / task response
Heartbeat / ReportStatus /
StreamLogs (gRPC)
│
┌─────────────┴─────────────┐
│ │ │
┌────┴───┐ ┌────┴───┐ ┌────┴───┐
│Worker 1│ │Worker 2│ │Worker N│ Sandbox execution of DAGs
│ │ │ │ │ │
└────────┘ └────────┘ └────────┘
Temporary workers:
┌────────────┐ provisions ┌──────────────┐
│ Launcher │───────────────▶│ dagu start │
│ Cloud Run │ │ exits when │
│ K8s Job/CI │ │ the run ends │
└────────────┘ └──────┬───────┘
│ writes
▼
┌────────────┐ reads ┌──────────────────┐
│ Dagu server│◀───────────────│ Shared volume │
│ UI / API │ │ dags/state/logs │
└────────────┘ └──────────────────┘
No coordinator, and no network path between the two.Workflows can define parameters that render as typed input forms in the Web UI and can be referenced by steps.
params:
- name: customer_id
type: string
description: Customer or account identifier
- name: change_scope
type: string
description: What the repair is allowed to change
enum:
- metadata_only
- permissions
- full_account
default: metadata_only
- name: dry_run
type: boolean
default: true
steps:
- id: extract
run: >-
./scripts/extract.sh
--customer "${params.customer_id}"
--scope "${params.change_scope}"
--dry-run="${params.dry_run}"
retry_policy:
limit: 3
interval_sec: 30
When Dagu itself runs in Docker, enable Docker daemon access before using container steps.
Pass standard docker run options directly in YAML, including the image, pull policy, platform, volume mounts, working directory, and resource limits:
resources:
limits:
cpu: 500m
memory: 512Mi
steps:
- id: report
action: docker.run
with:
image: ghcr.io/acme/reporting:1.4.2
pull: always
platform: linux/amd64
working_dir: /work
volumes:
- ~/orders:/work/data
auto_remove: true
command: python generate_report.py --input /work/data/orders.csvSee the Docker and DAG Run Resource Limits documentation for all configuration options.
The parent invokes the same child DAG for multiple targets and limits concurrent child runs:
steps:
- id: patch
action: dag.run
with:
dag: patch-host
params:
host: ${ITEM}
parallel:
items:
- web-1.internal
- web-2.internal
- db-1.internal
max_concurrent: 2
---
name: patch-host
params:
- name: host
type: string
ssh:
user: deploy
host: ${params.host}
steps:
- id: apply
run: apt-get update -q && apt-get upgrade -ySee the Sub-DAGs documentation for parameter passing and fan-out options.
ssh:
user: deploy
host: web-1.internal
key: ~/.ssh/deploy_key
steps:
- id: health
run: curl -f http://localhost:8080/health
retry_policy:
limit: 3
interval_sec: 10
- id: restart
run: systemctl restart myapp
depends: healthSee the SSH documentation for authentication and connection options.
schedule:
- "0 */6 * * *" # Every 6 hours
overlap_policy: skip # Skip if previous run is still active
catchup_window: "5h" # Catch up missed runs when scheduler is down for up to 5 hours
timeout_sec: 3600
handler_on:
failure:
run: notify-team.sh
exit:
run: cleanup.shSee the Scheduling and Lifecycle Handlers documentation for all options.
steps:
- name: flaky-api-call
run: curl -f https://api.example.com/data
retry_policy:
limit: 3
interval_sec: 10
continue_on:
failure: trueSee the Durable Execution and Continue On documentation for retry policies and failure handling.
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