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.
• 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.

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 (installer):

Download and run dagu-windows-amd64-setup.exe.

Windows (PowerShell):

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

Windows (Scoop):

scoop bucket add dagu https://github.com/dagucloud/scoop-bucket
scoop install dagu

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. The Windows installer can add Dagu to your PATH and run it as a Windows service. Homebrew, npm, Scoop, Docker, and Helm install without a wizard. See the Installation documentation for all options.

Create and run a workflow

Create hello.yaml in an empty folder:

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

Run the workflow with:

dagu start hello.yaml

Start the server

From that folder, run:

dagu start-all --dags .

Open http://localhost:8080. On first launch, create the admin account unless a script installer already created one.

If an installer set Dagu up as a background service, it is already running (on port 8080 by default), so add workflows from the UI or put hello.yaml in its DAGs directory: the one the script installer printed, or C:\ProgramData\Dagu\dags for the Windows installer.

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. 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.

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. 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.

Monitor Every Server from One Place

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 Settings, 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.

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.
• 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.

Architecture

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-all runs 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.

Parameter Definition

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

Workflow Examples

Docker step

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.csv

See the Docker and DAG Run Resource Limits documentation for all configuration options.

Parallel Sub-DAG execution

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 -y

See the Sub-DAGs documentation for parameter passing and fan-out options.

SSH remote execution

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: health

See the SSH documentation for authentication and connection options.

Scheduling with overlap control and catch-up

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.sh

See the Scheduling and Lifecycle Handlers documentation for all options.

Retry and error handling

steps:
  - name: flaky-api-call
    run: curl -f https://api.example.com/data
    retry_policy:
      limit: 3
      interval_sec: 10
    continue_on:
      failure: true

See the Durable Execution and Continue On documentation for retry policies and failure handling.

More Workflow Examples

Parallel executions

steps:
  - id: extract
    run: ./extract.sh

  - id: transform_a
    run: ./transform_a.sh
    depends: extract

  - id: transform_b
    run: ./transform_b.sh
    depends: extract

  - id: load
    run: ./load.sh
    depends: [transform_a, transform_b]

%%{init: {'theme': 'base', 'themeVariables': {'background': '#18181B', 'primaryTextColor': '#fff', 'lineColor': '#888'}}}%%
graph LR
    A[extract] --> B[transform_a]
    A --> C[transform_b]
    B --> D[load]
    C --> D
    style A fill:#18181B,stroke:#22C55E,stroke-width:1.6px,color:#fff
    style B fill:#18181B,stroke:#22C55E,stroke-width:1.6px,color:#fff
    style C fill:#18181B,stroke:#22C55E,stroke-width:1.6px,color:#fff
    style D fill:#18181B,stroke:#3B82F6,stroke-width:1.6px,color:#fff

See the Control Flow documentation for dependencies, conditions, and repetition.

Reuse unchanged results

Save this as workflow.yaml:

type: build
working_dir: .

steps:
  - id: uppercase
    inputs:
      - name: source
        path: source.txt
    outputs:
      - name: result
        path: uppercase.txt
    run: |
      #!/bin/sh
      tr '[:lower:]' '[:upper:]' < "${inputs.source}" > "${outputs.result}"

Run it:

printf 'alpha\n' > source.txt
dagu start workflow.yaml

Run dagu start workflow.yaml again and Dagu reuses uppercase.txt. Change source.txt and the step runs again. ${outputs.result} is a temporary path that Dagu publishes as uppercase.txt after the command succeeds.

Build workflows currently run locally. See Build Workflows for dependency inference and reuse rules.

External tools with pinning and caching

tools:
  - jqlang/[email protected]

steps:
  - id: inspect
    run: jq --version

  - id: summarize
    action: python-script@v1
    with:
      input:
        rows: [42, 8]
      script: |
        return {"total": sum(input["rows"])}

Dagu installs declared portable CLIs before the DAG run, exposes them on PATH for host command steps, and caches them on each worker. Tool provisioning uses aqua as the default provider; the standard registry resolves to the latest aqua-registry release automatically. Pin a specific artifact with package@version#sha256:<hex> when the release tag alone is not a strong enough guarantee. See the Tools documentation and Dagu Actions for more details.

Third-party Dagu Actions

params:
  - BUILD_ID

steps:
  - id: notify
    action: acme/[email protected]
    with:
      text: "Build ${params.BUILD_ID} finished"

  - id: audit
    depends: notify
    run: 'echo "Notification result: ${steps.notify.outputs.messageId}"'

A third-party Dagu Action package contains a DAG, manifest, schemas, and helper files behind an action: reference. See the Dagu Actions and Third-Party Actions documentation for details.

Kubernetes Pod execution

steps:
  - name: batch-job
    action: kubernetes.run
    with:
      namespace: production
      image: my-registry/batch-processor:latest
      resources:
        requests:
          cpu: "2"
          memory: "4Gi"
      command: ./process.sh

See the Kubernetes documentation for Job configuration options.

For more examples, see the Examples documentation.

MCP

Dagu includes a built-in MCP server at http://localhost:8080/mcp. MCP clients can inspect workflows and run state, maintain Dagu's built-in Wiki pages, preview and apply DAG or Wiki page changes, and control runs through the same authenticated server boundary as the REST API.

See the MCP overview and quickstart.

Built-in Actions

Dagu includes built-in actions that run within the Dagu process or on the selected worker. Local shell commands use the run: field; structured work uses action:.

Action	Purpose
run: field	Local shell commands and scripts (bash, sh, PowerShell, custom shells)
exec	Direct process execution without shell parsing
noop	Output-only or approval-only placeholder step
log.write	Write structured log messages
docker.run / container.run	Run containers with registry auth, volume mounts, and resource limits
kubernetes.run / k8s.run	Execute Kubernetes Jobs with namespace, image, and resource settings
ssh.run	Remote command execution over SSH
sftp.upload / sftp.download	File transfer over SFTP
http.request	HTTP requests with headers, auth, and request bodies
chat.completion	Run an LLM chat completion step
harness.run	Run external coding-agent CLIs such as Claude Code, Codex, Gemini CLI, Cursor, and DeepSeek Harness, or managed OpenCode sessions
browser.extract / browser.run	Automate websites in a local Chrome with natural-language actions, structured extraction, and human input
computer.extract / computer.run	Automate desktop applications on macOS and Windows workers with natural-language tasks, screen extraction, and human input
postgres.query / postgres.import	PostgreSQL queries and imports
sqlite.query / sqlite.import	SQLite queries and imports
clickhouse.query	ClickHouse queries and statements over the HTTP interface
redis.<operation>	Redis commands, pipelines, and Lua scripts
s3.upload / s3.download / s3.list / s3.delete	Upload, download, list, and delete S3 objects
file.stat / file.read / file.write / file.copy / file.move / file.delete / file.mkdir / file.list	Local file operations without shell commands
artifact.write / artifact.read / artifact.list	Write, read, and list DAG-run artifacts
state.get / state.set / state.delete / state.list / state.diff	Persistent JSON state across DAG runs
data.convert / data.pick	Convert and select structured data
jq.filter	JSON transformation using jq expressions
js.run	JavaScript transforms in an embedded sandbox, no Node.js needed
archive.create / archive.extract / archive.list	Create, extract, and list zip/tar archives
wait.duration / wait.until / wait.file / wait.http	Wait for time, file state, or HTTP readiness
human.task	Wait for acknowledgement or typed operator input before downstream steps continue
mail.send	Send email via SMTP
mail.search	Find email in an IMAP or Gmail mailbox
mail.organize	Mark, move, archive, or trash email
xlsx.read / xlsx.info / xlsx.list_sheets	Read typed rows and describe .xlsx workbooks without a spreadsheet application
xlsx.write / xlsx.append / xlsx.update_rows	Write reports, append rows, and write per-row results back into a workbook
xlsx.validate / xlsx.write_cells / xlsx.sheet / xlsx.convert	Check rows against rules, fill template cells and formulas, manage sheets, and export a sheet to CSV, JSON, or JSONL
xlsx.extract	Read fields out of a form-like sheet: a model names the cells, the engine reads their typed values, and a layout seen before with the same instruction and fields is read from a cache without a model call
template.render	Text generation with template rendering
router.route	Conditional step routing based on values and patterns
dag.run	Invoke another DAG as a sub-workflow with params and dependencies
dag.enqueue	Queue another DAG asynchronously and continue after enqueue
git.checkout	Clone or update Git repositories
outputs.write	Publish DAG or Dagu Action outputs for callers

Custom Actions

Custom Actions are inline reusable wrappers defined with the top-level actions field. They expand to built-in actions during DAG load, so you can wrap a common shell, HTTP, SQL, or other pattern behind a typed interface with validated input.

actions:
  webhook.send:
    input_schema:
      type: object
      additionalProperties: false
      required: [url, text]
      properties:
        url:
          type: string
        text:
          type: string
    template:
      action: http.request
      with:
        method: POST
        url: '{{ .input.url }}'
        headers:
          Content-Type: application/json
        body: |
          {"text": {{ json .input.text }}}

steps:
  - action: webhook.send
    with:
      url: https://hooks.example.com/ops
      text: deploy complete

See Custom Actions and the YAML Specification for the exact actions, action, and run field behavior.

Official Dagu Actions

Dagu Actions are official action packages maintained in the dagucloud GitHub organization. They use the same action package runtime as third-party action packages, but callers use the short form action: name@version.

Dagu Action	Purpose
node-script@v1	Run small JavaScript transforms or glue code with action-owned Node.js
python-script@v1	Run small Python transforms or glue code with action-owned Python and optional requirements
dbt@v1	Run dbt Core commands with action-owned Python and adapter requirements
duckdb@v1	Run DuckDB SQL through the DuckDB CLI without adding DuckDB to the core binary
ffmpeg@v1	Run FFmpeg conversion, transcoding, probing, and stream-processing tasks
github-cli@v1	Run GitHub issue, pull request, release, repository, and API automation through gh
rclone@v1	Run portable copy, sync, check, list, and storage-management workflows through rclone

Versions are required. Pin production workflows to a version tag or commit SHA. See Official Dagu Actions for the current Dagu Action list and exact input/output contracts.

For non-official packages, use Third-Party Actions such as action: owner/repo@version. They contain a dagu-action.yaml manifest and a DAG entrypoint, run as sub-DAGs, and are transferred to distributed workers as workspace bundles after the reference is resolved. See the documentation for package layout and reference formats.

Security and Access Control

Authentication

Dagu supports three top-level authentication modes, configured via DAGU_AUTH_MODE:

• none: No authentication
• basic: HTTP Basic authentication
• builtin: JWT-based authentication with user management, API keys, per-DAG webhook tokens, and optional OIDC/SSO integration

Role-Based Access Control

When using builtin auth, five roles control access:

Role	Capabilities
admin	Full access including user management
manager	Create, edit, delete, run, stop DAGs; view audit logs
developer	Create, edit, delete, run, stop DAGs
operator	Run and stop DAGs only (no editing)
viewer	Read-only access

API keys can be created with independent role assignments. Audit logging tracks all actions.

TLS and Secrets

• TLS for the HTTP server (DAGU_CERT_FILE, DAGU_KEY_FILE)
• Mutual TLS for gRPC coordinator/worker communication (DAGU_PEER_CERT_FILE, DAGU_PEER_KEY_FILE, DAGU_PEER_CLIENT_CA_FILE)
• Secret management with environment variables, files, Kubernetes Secrets, HashiCorp Vault, and cloud-provider secret stores

Production Hardening

For self-hosted production deployments, treat network exposure and execution boundaries as the primary controls:

• Prefer auth.mode: builtin for any shared or network-exposed instance. Use basic only for simple private setups, and avoid none outside isolated local development.
• Keep metrics: private unless the metrics endpoint is reachable only on a trusted private network.
• Bind Dagu to loopback or a private interface when possible. If you must use 0.0.0.0, place it behind a trusted reverse proxy, TLS, and network-level access controls.
• Leave terminal.enabled: false unless the instance is admin-only and tightly scoped.
• In distributed deployments, set peer.insecure=false and configure peer TLS when coordinator and workers communicate across host or network boundaries.
• Treat Docker socket mounts, root containers, and host-level executors as privileged access to the underlying machine.

See Server Configuration, Docker deployment, and Distributed execution for operator-focused guidance.

Observability

Prometheus Metrics

Dagu exposes Prometheus-compatible metrics:

• dagu_info: Build information (version, Go version)
• dagu_uptime_seconds: Server uptime
• dagu_dag_runs_total: Total DAG runs by status
• dagu_dag_runs_total_by_dag: Per-DAG run counts
• dagu_dag_run_duration_seconds: Histogram of run durations
• dagu_dag_runs_currently_running: Active DAG runs
• dagu_dag_runs_queued_total: Queued runs
• dagu_workers_registered: Registered distributed workers
• dagu_worker_info: Worker heartbeat labels as key/value metadata
• dagu_worker_heartbeat_timestamp_seconds: Last worker heartbeat timestamp
• dagu_worker_health_status: Worker health by heartbeat freshness
• dagu_worker_pollers: Worker poller capacity by state
• dagu_worker_running_tasks: Running tasks per worker
• dagu_worker_oldest_running_task_age_seconds: Age of the oldest running task per worker

Dagu Console Monitoring

Connected servers can report their health and run status to Dagu Console for one view across servers, trends, and alert emails. See Monitor Every Server from One Place.

Structured Logging

JSON or text format logging (DAGU_LOG_FORMAT). Logs are stored per-run with separate stdout/stderr capture per step.

Notifications

• Email notifications on DAG success, failure, or wait status via SMTP
• Per-DAG webhook endpoints with token authentication
• Delivery requires the event store, notification store, and writable notification state store

SMTP inherited from global or workspace base.yaml is omitted from new run snapshots (dag.json). Retries, restarts, and queued runs use the current base SMTP configuration. Removing it disables email notifications unless the original DAG defines SMTP. Other base settings and the original DAG YAML remain captured. Runs without captured base configuration use the current base configuration in full; an explicitly empty captured configuration only reloads SMTP.

SMTP written directly in DAG YAML, values stored in env, distributed task payloads, and workspace bundles are still retained. Existing history files and backups are not rewritten. Upgrade processes that read run snapshots (CLI, API, and scheduler) together so retries can reload base SMTP.

Scoped notification routing is broken in v2.11.0-v2.11.2. Use v2.11.3 or later.

Artifacts

Dagu runs can write arbitrary files under ${context.paths.artifacts_dir} in value-resolved fields, with DAG_RUN_ARTIFACTS_DIR also exposed to step processes. Dagu stores those files per run as Artifacts. In the Web UI, operators can browse the file tree, preview Markdown, text, and image files inline, and download any artifact when they need the raw file.

This is useful for generated reports, screenshots, charts, exported JSON or CSV files, and other outputs that do not fit simple key/value outputs.

See the Artifacts documentation and the Web UI guide for the full artifact browser workflow and screenshots.

Scheduling and Reliability

• Cron scheduling with timezone support and multiple schedule entries per DAG
• Overlap policies: skip (default: skip if previous run is still active), all (queue all), latest (keep only the most recent)
• Catch-up scheduling: Automatically runs missed intervals when the scheduler was down
• Zombie detection: Identifies and handles stalled DAG runs (configurable interval, default 45s)
• Retry policies: Per-step retry with configurable limits, intervals, and exit code filtering
• Human tasks: Pause root DAG runs for acknowledgement or schema-validated operator input, locally or on distributed workers, then expose form values to downstream steps
• Lifecycle hooks: onInit, onSuccess, onFailure, onAbort, onExit, onWait
• Preconditions: Gate DAG or step execution on shell command results
• High availability: Scheduler lock with stale detection for failover

Distributed Execution

Operational detail for the distributed workers topology:

• Coordinator: gRPC server that manages task distribution, worker registry, and health monitoring
• Workers: Poll the coordinator outbound, execute DAGs locally, and report status, logs, and artifacts back. No inbound port required
• Worker labels: Route DAGs to specific workers based on labels (e.g., gpu=true, region=us-east-1)
• Health checks: HTTP health endpoints on coordinator and workers for load balancer integration
• Queue system: File-based persistent queue with configurable concurrency limits

# Start coordinator
dagu coordinator --coordinator.host=0.0.0.0 --coordinator.advertise=coordinator.internal

# Start workers (on separate machines)
DAGU_WORKER_COORDINATORS=coordinator.internal:50055 \
  DAGU_WORKER_LABELS=gpu=true,memory=64G dagu worker

See the distributed execution documentation for setup details.

CLI Reference

Command	Description
dagu start <dag>	Execute a DAG
dagu start-all	Start HTTP server + scheduler + coordinator
dagu server	Start HTTP server only
dagu scheduler	Start scheduler only
dagu coordinator	Start coordinator (distributed mode)
dagu worker	Start worker (distributed mode)
dagu stop <dag>	Stop a running DAG
dagu restart <dag>	Restart a DAG
dagu retry --run-id=<run-id> <dag>	Retry a failed run
dagu human-task complete --run-id=<run-id> --step=<id> <dag>	Complete a waiting human task
dagu dry <dag>	Dry run: show what would execute
dagu status <dag>	Show DAG run status
dagu history <dag>	Show execution history
dagu validate <dag>	Validate DAG YAML
dagu enqueue <dag>	Add DAG to the execution queue
dagu dequeue <queue-name> [--dag-run=<dag>:<run-id>]	Remove a DAG-run from the queue
dagu cleanup <dag>	Clean up old run data
dagu version	Show version

The table lists the most common commands. The binary ships 36 in total, including exec, ls, ps, rm, prune-artifacts, sync, schema, example, config, profile, context, license, upgrade, and completion; run dagu --help or see the CLI reference for all of them.

Environment Variables

Precedence: Command-line flags > Environment variables > Configuration file (~/.config/dagu/config.yaml)

Server

Variable	Default	Description
DAGU_HOST	127.0.0.1	Bind address
DAGU_PORT	8080	HTTP port
DAGU_BASE_PATH	-	Base path for reverse proxy
DAGU_HEADLESS	false	Run without web UI
DAGU_TZ	-	Timezone (e.g., Asia/Tokyo)
DAGU_LOG_FORMAT	text	text or json
DAGU_CERT_FILE	-	TLS certificate
DAGU_KEY_FILE	-	TLS private key
DAGU_CORS_ALLOWED_ORIGINS	-	Comma-separated list of allowed CORS origins (e.g. https://app.example.com). When unset, cross-origin browser access is disabled. Exact origins enable credentials. An explicit * allows every origin without credentials and emits a security warning.
DAGU_IP_ACCESS_ALLOWED_IPS	-	Comma-separated IPv4/IPv6 addresses and CIDR ranges allowed to access the HTTP server. Empty disables filtering.
DAGU_IP_ACCESS_TRUSTED_PROXIES	-	Comma-separated proxy addresses and CIDR ranges permitted to supply forwarded client IP headers.
DAGU_PUBLIC_URL	-	External Web UI URL used in generated links, including notification and incident DAG-run links
DAGU_SERVER_METRICS	private	Metrics endpoint access: private or public
DAGU_TERMINAL_ENABLED	false	Enable the web-based terminal
DAGU_DEFAULT_SHELL	$SHELL, then sh	Default shell for command steps
DAGU_ENV_PASSTHROUGH_PREFIXES	-	Comma-separated env var prefixes forwarded to step execution
DAGU_DEBUG	-	Enable debug mode

The equivalent YAML protects every HTTP route, including health, metrics, webhooks, SSE, terminal, and MCP:

ip_access:
  allowed_ips:
    - 203.0.113.10
    - 10.0.0.0/8
  trusted_proxies:
    - 127.0.0.1
    - 10.42.0.0/16

Forwarded addresses are used only when the direct peer matches trusted_proxies. The proxy must remove client-supplied forwarding headers and set or append the verified client address. Keep trusted proxy ranges narrow.

Paths

Variable	Default	Description
DAGU_HOME	-	Overrides all path defaults
DAGU_DAGS_DIR	~/.config/dagu/dags	DAG definitions directory
DAGU_DAG_DISCOVERY_RECURSIVE	false	Discover DAGs in subdirectories
DAGU_DAG_DISCOVERY_SYMLINKS	false	Include recursive file symlinks and allow external targets
DAGU_LOG_DIR	~/.local/share/dagu/logs	Log files
DAGU_DATA_DIR	~/.local/share/dagu/data	Application state
DAGU_SUSPEND_FLAGS_DIR	{DAGU_DATA_DIR}/suspend	DAG suspension flags
DAGU_TOOLS_DIR	{DAGU_DATA_DIR}/tools	Managed DAG tool cache
DAGU_DAG_STATE_DIR	{DAGU_DATA_DIR}/dag-state	Persistent DAG state files
DAGU_DAG_RUN_WORK_DIR	{DAGU_DATA_DIR}/dag-run-work	Per-run working directories
DAGU_BASE_CONFIG	-	Shared base configuration applied to all DAGs

Suspend flags default to paths.data_dir/suspend. Explicit paths.suspend_flags_dir settings, including environment variables and legacy configuration keys, retain their configured location and disable legacy fallback and migration. A path outside data_dir produces a warning because all server and scheduler processes must share the same suspension state.

With the default path, flags in the previous default directory remain readable. After acquiring leadership, the scheduler copies them into data_dir/suspend before processing runs. Startup preserves the original flags. Suspending a DAG writes its new flag before removing its legacy flag; resuming removes both. Migration and state changes are serialized across processes.

During upgrade, make the same legacy flags available to the server and scheduler; reconcile any private per-host flag directories first. Upgrade all processes together before changing suspension state. Older processes writing legacy flags are not supported alongside upgraded processes. Until scheduler migration has completed, include the legacy directory in backups. When rolling back, explicitly configure the older version to use the new shared suspend directory.

Set the per-run work root in config.yaml, or use the corresponding environment variable above:

paths:
  dag_run_work_dir: /mnt/dagu/dag-run-work

DAGU_DAG_RUN_WORK_DIR configures this root for Dagu processes. Workflow code should use the runtime DAG_RUN_WORK_DIR variable for its assigned per-run directory instead of constructing paths under DAG-run history.

Processes sharing DAG runs must use the same work root.

Backups that select individual data subdirectories must include both paths.dag_runs_dir and paths.dag_run_work_dir. A backup of the complete paths.data_dir includes both default locations. The Helm chart's default /data/dag-run-work path uses its existing /data volume and does not need an additional volume.

When upgrading a deployment whose processes share a durable work root, do not let old and new Dagu versions execute the same run concurrently: drain or stop the processes, upgrade them together, and then resume execution. Mixed-version processes can otherwise choose the old nested directory and the new separate directory for one run.

Recursive discovery can also be enabled in config.yaml:

dag_discovery:
  recursive: true

It scans paths.dags_dir, excluding workspaces/, dot-directories, and symlinks. File stems and effective DAG names must each be unique, using case-sensitive comparison; conflicting files are excluded until the conflict is resolved. paths.alt_dags_dir remains lookup-only.

Set dag_discovery.symlinks: true (or DAGU_DAG_DISCOVERY_SYMLINKS=true) to include YAML file symlinks in recursive discovery and to allow YAML file symlinks whose targets are outside paths.dags_dir. Symlinked directories are never traversed. External targets can be viewed, scheduled, and run, but cannot be updated, deleted, or renamed through Dagu. Without the opt-in, top-level YAML file symlinks whose targets remain inside paths.dags_dir continue to work. A symlink configured as paths.dags_dir itself is also supported.

Authentication

Variable	Default	Description
DAGU_AUTH_MODE	builtin	none, basic, or builtin
DAGU_AUTH_BASIC_USERNAME	-	Basic auth username
DAGU_AUTH_BASIC_PASSWORD	-	Basic auth password
DAGU_AUTH_TOKEN_SECRET	(auto)	JWT signing secret
DAGU_AUTH_TOKEN_TTL	24h	JWT token lifetime (maximum: 8760h / 365 days)
DAGU_AUTH_BUILTIN_INITIAL_ADMIN_USERNAME	-	Auto-provision the first admin on startup (requires the password variable)
DAGU_AUTH_BUILTIN_INITIAL_ADMIN_PASSWORD	-	Password for the auto-provisioned admin (minimum 8 characters)
DAGU_LICENSE_KEY	-	License key for licensed self-host features

OIDC variables: DAGU_AUTH_OIDC_CLIENT_ID, DAGU_AUTH_OIDC_CLIENT_SECRET, DAGU_AUTH_OIDC_ISSUER, DAGU_AUTH_OIDC_SCOPES, DAGU_AUTH_OIDC_WHITELIST, DAGU_AUTH_OIDC_AUTO_SIGNUP, DAGU_AUTH_OIDC_DEFAULT_ROLE, DAGU_AUTH_OIDC_ALLOWED_DOMAINS.

The auth, remote node, Git sync and Vault secrets can also be read from files, such as mounted Kubernetes or Docker secrets. Each of those secret keys has a _file key, and each of those secret variables a _FILE variable, that names the file: DAGU_AUTH_BASIC_PASSWORD_FILE, DAGU_AUTH_TOKEN_SECRET_FILE, DAGU_AUTH_BUILTIN_INITIAL_ADMIN_PASSWORD_FILE, DAGU_AUTH_OIDC_CLIENT_SECRET_FILE, DAGU_GITSYNC_AUTH_TOKEN_FILE, DAGU_GITSYNC_AUTH_SSH_PASSPHRASE_FILE, and DAGU_SECRETS_VAULT_TOKEN_FILE; remote_nodes entries take basic_auth_password_file and auth_token_file. Dagu reads the file at startup, not when it uses the secret, and removes the trailing line ends. Setting a non-empty secret and its _file form together, or naming a file that cannot be read or is empty, stops the startup. tunnel.tailscale.auth_key and license.key have no _file form.

Scheduler

Variable	Default	Description
DAGU_SCHEDULER_PORT	8090	Health check port
DAGU_SCHEDULER_ZOMBIE_DETECTION_INTERVAL	45s	Zombie run detection interval (0 to disable)
DAGU_SCHEDULER_LOCK_STALE_THRESHOLD	30s	HA lock stale threshold
DAGU_QUEUE_ENABLED	true	Enable queue system
DAGU_SIGNAL_PROPAGATION	false	Forward shutdown signals (SIGINT/SIGTERM) received by the server or scheduler to the process groups of running DAG-run subprocesses it launched

Coordinator / Worker

Variable	Default	Description
DAGU_COORDINATOR_HOST	127.0.0.1	Coordinator bind address
DAGU_COORDINATOR_PORT	50055	Coordinator gRPC port
DAGU_COORDINATOR_HEALTH_PORT	8091	Coordinator health check port
DAGU_WORKER_ID	-	Worker instance ID
DAGU_WORKER_MAX_ACTIVE_RUNS	100	Max concurrent runs per worker
DAGU_WORKER_HEALTH_PORT	8092	Worker health check port
DAGU_WORKER_SHUTDOWN_TIMEOUT	60s	How long a worker stopping on SIGINT/SIGTERM drains running DAG runs before it cancels them (0 waits without a bound). Set the supervisor's stop timeout at least 15s above it: systemd TimeoutStopSec defaults to 90s, docker stop to 10s, Kubernetes terminationGracePeriodSeconds to 30s
DAGU_WORKER_LABELS	-	Worker labels (key=value,key=value); os and arch are built in and cannot be overridden
DAGU_COORDINATOR_ADVERTISE	auto-detected hostname	Address advertised in the service registry
DAGU_WORKER_COORDINATORS	-	Coordinator addresses required by dagu worker (host:port,...)

Peer TLS (gRPC)

Variable	Default	Description
DAGU_PEER_CERT_FILE	-	Peer TLS certificate
DAGU_PEER_KEY_FILE	-	Peer TLS private key
DAGU_PEER_CLIENT_CA_FILE	-	CA for client verification
DAGU_PEER_INSECURE	true	Use h2c instead of TLS
DAGU_PEER_SKIP_TLS_VERIFY	-	Skip TLS certificate verification

Git Sync

Git Sync copies Git-tracked supporting files into the DAGs directory. Workflow and Wiki files retain their existing handling, and each calendars/<name>.yaml file syncs with paths.calendars_dir as a calendar, including one synced as a DAG before (deeper paths and other extensions keep their handling); supporting file IDs include their extension and preserve the executable bit. On Windows, Git Sync retains the repository mode when publishing because local execute-bit changes cannot be detected.

Variable	Default	Description
DAGU_GITSYNC_ENABLED	false	Enable Git sync
DAGU_GITSYNC_REPOSITORY	-	Repository URL
DAGU_GITSYNC_BRANCH	main	Branch to sync
DAGU_GITSYNC_AUTH_TYPE	token	token or ssh
DAGU_GITSYNC_AUTH_TOKEN	-	Personal access token for HTTPS auth
DAGU_GITSYNC_AUTH_SSH_KEY_PATH	-	Path to the SSH private key
DAGU_GITSYNC_AUTOSYNC_ENABLED	false	Enable periodic auto-pull
DAGU_GITSYNC_AUTOSYNC_INTERVAL	300	Sync interval in seconds

These tables cover the variables most deployments touch. The full reference lists about 180 DAGU_* variables, including SSE, tunnel, UI, monitoring, audit, and secret-provider settings: see the configuration reference.

Embedded Go API (Experimental)

Go applications can import Dagu and start DAG runs from the host process:

import "github.com/dagucloud/dagu/v2"

engine, err := dagu.New(ctx, dagu.Options{
	HomeDir: "/var/lib/myapp/dagu",
})
if err != nil {
	return err
}
defer engine.Close(context.Background())

run, err := engine.RunYAML(ctx, []byte(`
params:
  - MESSAGE
steps:
  - name: hello
    run: echo "${params.MESSAGE}"
`), dagu.WithParams(map[string]string{
	"MESSAGE": "hello from the host app",
}))
if err != nil {
	return err
}

status, err := run.Wait(ctx)
if err != nil {
	return err
}
fmt.Println(status.Status)

The embedded API is experimental and may change. See the embedded API documentation and examples/embedded.

Community

• Discord: Questions and discussion
• GitHub Issues: Bug reports and feature requests
• Bluesky

Development

Prerequisites: Go (latest stable), Node.js (latest stable), pnpm

git clone https://github.com/dagucloud/dagu.git && cd dagu
make build    # Build frontend + Go binary
make test     # Run tests with race detection
make lint     # Run golangci-lint

For the local UI development loop, run make run-server from the repository root, then follow ui/README.md. See ARCHITECTURE.md for a human-oriented repository map and CONTRIBUTING.md for the focused first-PR workflow and code standards.

Acknowledgements

Contributing

Contributions of all kinds are welcome. See the Contribution Guide for details.

License

GNU GPLv3: See LICENSE. See LICENSING.md for embedded API and commercial embedding notes.