MCP client
Requests an approved operation without receiving private credentials.
Self-hosted/Single binary/Zero dependencies
The workflow engine that doesn't turn into an SRE project.
Dagu is a lightweight alternative to Airflow or Cron with a Web UI. Define DAGs in a simple declarative YAML format. It supports shell commands, docker containers, k8s jobs, remote commands via SSH, and more. It was designed to be easy to use, self-contained, and require no coding, making it ideal for small teams.
Dagu is built for developers who want powerful workflow orchestration without the operational overhead.
daily-report.yaml
on schedule# runs every night at 02:00schedule: "0 2 * * *"steps:- name: fetch datacommand: python fetch.pyretryPolicy:limit: 3- name: build reportcommand: python report.py- name: notifycommand: ./notify.sh
last run · 02:00:04 · exit 03/3 steps
Trusted by developers at
Easy to use
Define workflows in plain YAML. Install with one command and ship your first DAG in minutes.
Reliable
Built for production: retries, timeouts, alerts, and full logs for every run.
Open source
GPLv3, community-driven, and built in the open. No black boxes in your infrastructure.
The control plane
Every job, every run, one screen.
Schedules, statuses, and logs live in one Web UI. Trigger a run, watch it move through its steps, and read the output without leaving the browser.
Jobs
Search jobs…Run Job
| Name | Schedule | Status | Last Run | |
|---|---|---|---|---|
| daily-report | 0 2 * * * | Success | 2m ago | |
| data-sync | */15 * * * * | Success | 7m ago | |
| cleanup | 0 3 * * 0 | Success | 1h ago | |
| weekly-summary | 0 4 * * 1 | Running | Now |
dagu — zsh
$ dagu start daily-report
INFO[0000] Starting DAG daily-report
INFO[0001] Step 1/3: fetch data
INFO[0002] Step 2/3: build report
INFO[0003] Step 3/3: notify
INFO[0004] DAG daily-report finished ✓
Built-in MCP server for workflow operations without exposing secrets
Authorized MCP-capable clients can inspect and request DAG operations through Dagu. Private systems stay behind a security boundary: Dagu injects credentials at runtime and does not pass them to the MCP client.
Request
Request
Dagu MCP
Enforces granular access control before any workflow can run.
Authorize
Authorize
DAGs
Runs the exact script, container, SQL, HTTP, SSH, or sub-workflow you already trust.
Execute
Execute
Private systems
Production tools and data stay behind Dagu, not inside the client context.
ShellSQLSSH
MCP-capable clients request named workflow actions through Dagu MCP instead of receiving shell, database, or API credentials.
Dagu checks access, injects secrets only at runtime, and can pause sensitive workflows for human approval.
Every run keeps logs, outputs, status, history, and audit evidence for humans and MCP clients to inspect.
Scoped access
Expose only the actions each MCP client is allowed to run.
Managed secrets
Inject credentials at runtime without passing them to the MCP client.
Audit trail
Track who or what requested each workflow and with which inputs.
Approvals
Pause high-risk operations for human review.
Safe results
Return status and outputs without opening raw system access.
The agent gets a bounded tool. Your private systems keep the secrets, logs, permissions, and operational history in Dagu.
The control plane around your real work
Dagu gives teams the missing operational layer for scripts, tools, runbooks, and Agent Harness workflows.
Observability
Track workflows, schedules, live status, logs, run history, outputs, artifacts, and approvals in the Web UI.
Language-agnostic execution
Run shell commands, Python scripts, containers, Kubernetes Jobs, SQL queries, HTTP requests, SSH commands, and more.
Reproducible runs
Pin tools in workflow YAML so workers install and cache the expected binaries before execution.
AI-assisted workflows
Run Agent Harness steps and expose workflows to agents through MCP.
Built-in approvals
Pause sensitive steps for human review, confirmation, or intervention before execution continues.
MCP server
Let MCP-capable agents inspect Dagu state, preview changes, edit workflows, and control runs.
Agent Harness workflows
Run external agent CLIs from workflows while keeping logs, approvals, secrets, and audit history in Dagu.
Secret management
Inject secrets into workflow runs and mask sensitive values in logs for users, agents, and chat tools.
Self-host or managed
Run a single binary, deploy with containers or Kubernetes, or use managed and hybrid deployment models.
Permission control
Use RBAC, SSO, API keys, workspace controls, and audit logs to govern workflow access.
Deployment models
Run a self-hosted instance, use the Dagu managed server, or combine the cloud control plane with private workers in your infrastructure.
Start on one machine. Scale to self-hosted, managed, or hybrid.
Local
Single server
Run `dagu start-all` on one machine with local file-backed state. No database, broker, or platform stack required.
Self-hosted
Your infrastructure
Keep the Dagu server, workers, secrets, logs, and execution inside your own environment.
Dagu
Full managed server
Use a dedicated Dagu server operated by Dagu in an isolated gVisor instance on GKE.
Hybrid
Managed server, private execution
Let Dagu operate the server while private workers run Docker, private-network, or data-local steps.
Hybrid execution
Managed Dagu server
Private worker over mTLS
Docker, private APIs, secrets, and data-local work
Hybrid keeps the Dagu server managed while execution that needs your network, runtime, or data stays under your control.
Production-grade workflow engine features
Battle-tested workflow engine features let you focus on the core value without worrying about orchestration.
Performance
Run thousands of workflow runs per day on one machine, depending on hardware, workflow shape, step duration, and queue settings.
Distributed executions
Use queues, concurrency limits, and distributed workers to control load and spread jobs across machines.
Notifications
Recurring schedules, catchup, durable executions, timeouts, reruns, notification, and incidents routing to responders.
Auth & RBAC
User management, RBAC, workspaces, approval steps, built-in secret management, API Keys, and webhooks.
Real-world use cases
01
Customer Support Automation
Turn repetitive engineering requests into self-service workflows. Business teams run them independently. Engineers stay out of the loop.
02
Cron and Legacy Script Management
Bring existing shell scripts, Python scripts, HTTP calls, and scheduled jobs into Dagu without rewriting them.
03
Infrastructure and Server Automation
Coordinate SSH backups, cleanup jobs, deploy scripts, patch windows, precondition checks, and lifecycle hooks.
04
ETL and Data Operations
Run PostgreSQL or SQLite queries, S3 transfers, jq transforms, validation steps, and reusable sub-workflows.
05
Container and Kubernetes Workflows
Compose workflows where each step can run a Docker image, Kubernetes Job, shell command, or validation step.
06
Media Conversion
Run ffmpeg, thumbnail extraction, audio normalization, image processing, and other compute-heavy jobs across workers.
07
IoT and Edge Workflows
Run sensor polling, local cleanup, offline sync, health checks, and device maintenance jobs on small devices.
08
Agent Harness Workflows
Run any harness to automate certain development workflow with clean context and automatic retry & failover.
Common patterns
Health Check
SSH Backup
Notify
Private Script Workflows
Turn private scripts, internal CLIs, reusable actions, pinned tools, and artifacts into workflows other teams can safely request or run.
- 1Keep existing scripts and commands intact
- 2Expose narrow parameters instead of broad production access
- 3Call versioned Dagu Actions with typed outputs
- 4Store reports and logs as first-class artifacts
workflow.yaml
tools:
- jqlang/jq@jq-1.7.1
steps:
- id: collect
action: acme/dagu-action-export@v1.4.2
with:
dataset: customers
- id: transform
run: jq '.items[] | {id, email}' ${steps.collect.outputs.path}
stdout:
artifact: reports/customers.json
depends: [collect]
- id: publish
action: outputs.write
with:
values:
report: reports/customers.json
rows: ${steps.collect.outputs.rows}
depends: [transform]
Typed parameters
Dagu automatically renders them as a typed parameter input form in the Web UI, useful for making self-service workflows or operation by non-engineers.
workflow.yaml
tools:
- jqlang/jq@jq-1.7.1
steps:
- id: inspect
run: jq --version
- id: summarize
action: python-script@v1
with:
input:
rows: [42, 8]
script: |
return {"total": sum(input["rows"])}
Tool provisioning uses aqua as the default provider.
Pinned tools
Declare CLI tools and versions in YAML. Dagu prepares them before host-command steps run.
Learn moreOfficial and third-party actions
Use official Dagu Actions such as Python or NodeJS, or build your own action package to run your workflow with necessary tools and dependencies.
Learn moreArtifacts
Manage artifacts in DAG runs and preview / download in the Web UI.
Learn moreNotifications and incidents
Configure notifications for each workflow so necessary events reach the right channel.
Learn moreFull-fledged Workflow features
Dagu is equipped with production-grade workflow engine features.
Loved by developers
Real comments from the Dagu GitHub community.
I've been looking for an alternative for dynamic agent workflows lately. I started out down the Temporal path. Temporal is powerful, but if all you want is to dynamically chain agents, scripts, data processing, and ops tasks together, the whole stack can feel a bit heavy. Then I came across Dagu, and the direction felt right. It runs as a single binary, workflows are written in YAML, everything lives in local files, it ships with a web UI, and there's no extra DB or broker to stand up. There are plenty of built-in actions too: shell, Docker, K8s Jobs, SSH — all there. A nice surprise was harness.run, which lets you plug external coding agent CLIs straight into a workflow. What I like most: the workflow itself is just a file, and state, logs, retries, dependencies, and the UI are all taken care of for you. For small teams, private environments, personal automation, and agent workflows, Dagu's local-first approach turns out to be a really comfortable fit.
This is literally my dream project. I have set up GitLab at some point just to run some commands and this project is perfect for all my use case. Thank you very much!
@vnghia
Developer
Dagu is really a gem in the open source world! ❤️ What a release — with GitHub Actions as well. Thanks guys.
@borestad
Elk Studios
Dagu is an amazing tool!! It works perfectly now, thank you so much! 💖
@jarnik
Freelance developer
I've been exploring Dagu for a few weeks and really love the Zero-Ops philosophy. The declarative YAML approach is exactly what the workflow orchestration space needs.
@vincent067
Developer
Amazing! I did not expect this to be delivered that fast! Multiple schedulers do not compete and the locking mechanism works as expected. Good job!
@jonasban
Developer
I've recently played around with Dagu for private stuff and think it could also be worth introducing at work.
@DarkWiiPlayer
Developer
Awesome, it works like a charm! I second the update of the doc on this one.
@thibmart1
Developer
That's very impressive. The exit code dictionary will make these DAGs even further sophisticated. Once again, very good features. 🫡
@ghansham
Developer
This is literally my dream project. I have set up GitLab at some point just to run some commands and this project is perfect for all my use case. Thank you very much!
@vnghia
Developer
Dagu is really a gem in the open source world! ❤️ What a release — with GitHub Actions as well. Thanks guys.
@borestad
Elk Studios
Dagu is an amazing tool!! It works perfectly now, thank you so much! 💖
@jarnik
Freelance developer
I've been exploring Dagu for a few weeks and really love the Zero-Ops philosophy. The declarative YAML approach is exactly what the workflow orchestration space needs.
@vincent067
Developer
Amazing! I did not expect this to be delivered that fast! Multiple schedulers do not compete and the locking mechanism works as expected. Good job!
@jonasban
Developer
I've recently played around with Dagu for private stuff and think it could also be worth introducing at work.
@DarkWiiPlayer
Developer
Awesome, it works like a charm! I second the update of the doc on this one.
@thibmart1
Developer
That's very impressive. The exit code dictionary will make these DAGs even further sophisticated. Once again, very good features. 🫡
@ghansham
Developer
This is literally my dream project. I have set up GitLab at some point just to run some commands and this project is perfect for all my use case. Thank you very much!
@vnghia
Developer
Dagu is really a gem in the open source world! ❤️ What a release — with GitHub Actions as well. Thanks guys.
@borestad
Elk Studios
Dagu is an amazing tool!! It works perfectly now, thank you so much! 💖
@jarnik
Freelance developer
I've been exploring Dagu for a few weeks and really love the Zero-Ops philosophy. The declarative YAML approach is exactly what the workflow orchestration space needs.
@vincent067
Developer
Amazing! I did not expect this to be delivered that fast! Multiple schedulers do not compete and the locking mechanism works as expected. Good job!
@jonasban
Developer
I've recently played around with Dagu for private stuff and think it could also be worth introducing at work.
@DarkWiiPlayer
Developer
Awesome, it works like a charm! I second the update of the doc on this one.
@thibmart1
Developer
That's very impressive. The exit code dictionary will make these DAGs even further sophisticated. Once again, very good features. 🫡
@ghansham
Developer
I love how simple this is now.
@n3storm
Estudio Nexos
Fantastic! Thanks for the quick reply — keep up the great project!
@mitchplze
Developer
Amazing support! Thank you!
@kacamific
Developer
It works perfectly with v2.6.1. Thank you very much for the correction.
@napnap75
Developer
Thanks for the fix. Works great now.
@triole
Developer
As always, appreciate the great work — just sharing feedback to help shape the roadmap.
@jonnochoo
Developer
it's awesome you keep working on it 👍
@fishnux
Developer
Thank you so much for the quick fix! I tested it, and it now correctly stores the files where it should.
@IngwiePhoenix
Developer
I love how simple this is now.
@n3storm
Estudio Nexos
Fantastic! Thanks for the quick reply — keep up the great project!
@mitchplze
Developer
Amazing support! Thank you!
@kacamific
Developer
It works perfectly with v2.6.1. Thank you very much for the correction.
@napnap75
Developer
Thanks for the fix. Works great now.
@triole
Developer
As always, appreciate the great work — just sharing feedback to help shape the roadmap.
@jonnochoo
Developer
it's awesome you keep working on it 👍
@fishnux
Developer
Thank you so much for the quick fix! I tested it, and it now correctly stores the files where it should.
@IngwiePhoenix
Developer
I love how simple this is now.
@n3storm
Estudio Nexos
Fantastic! Thanks for the quick reply — keep up the great project!
@mitchplze
Developer
Amazing support! Thank you!
@kacamific
Developer
It works perfectly with v2.6.1. Thank you very much for the correction.
@napnap75
Developer
Thanks for the fix. Works great now.
@triole
Developer
As always, appreciate the great work — just sharing feedback to help shape the roadmap.
@jonnochoo
Developer
it's awesome you keep working on it 👍
@fishnux
Developer
Thank you so much for the quick fix! I tested it, and it now correctly stores the files where it should.
@IngwiePhoenix
Developer
Self-hosted on your own infrastructure
Dagu is GPLv3 and free forever. Please consider purchasing licenses to support open source sustainability. Paid license enables useful features for using in a team.
Community
Free forever
Unlimited servers, unlimited workers
- Full workflow automation and monitoring
- Web UI with DAG visualization
- Docker, SSH, and HTTP executors
- Cron scheduling & retries
- Up to 2 API keys for automation
Minimum
$50 / mo
3 self-host Dagu server licenses
3 licenses = 3 servers, unlimited workers
Start free license trialPro
$150 / mo
15 self-host Dagu server licenses
15 licenses = 15 servers, unlimited workers
Start free license trialEnterprise
Custom
For teams building event-driven workflow infrastructure in complex environments
Custom licensed server allotment
- Architecture consulting for distributed setup
- Custom implementation
- MCP OIDC/SSO setup guidance
- Initial migration support
- Dedicated meetings
What we build
Custom Solutions
Building tailored features for specific company needs.
Turnkey Solutions
Creating ready-to-go setups to help teams scale Dagu easily without burning massive technical or server resources.
Create a 14-day self-host license trial in minutes — 14 days, No credit card required. Start free license trial
Building this together
As the project grows, managing it requires more time and resources. To ensure Dagu thrives long-term, we are slowly exploring ways to make the project financially sustainable.
Our goal is to build a business model that funds core development without restricting the features you already love.
We want to build this future with you. If you have ideas, feedback, or want to share what features your team would pay for, please drop them in the #feedback channel. Thank you for being an amazing community! 🚀
Solution partner focus
Build the operational control plane around your workflows.
Work with the Dagu team on deployment, security, scale, and workflow design for production use cases.
Dagu is most useful when it wraps real operational work: scripts, containers, SQL, API calls, approvals, and Agent Harness workflows.
OIDC/SSO, RBAC, workspaces, and tenant isolation
MCP, distributed workers, queues, and high-volume run history
Persistence and backup strategy for production operations
Secrets, auditability, and least-privilege execution
Event routing, webhooks, notifications, and incident workflows
Container isolation and private worker deployment
Quickstart
Install Dagu with the guided wizard, then continue in the full installation guide or quickstart docs.
1
Install dagu command
The script installers are the recommended path. Homebrew, npm, and Docker remain available for binary-only or container installs.
Mac/Linux Terminal
$curl -fsSL https://raw.githubusercontent.com/dagucloud/dagu/main/scripts/installer.sh | bash
✓ Guided installer ready
Optional
Connect AI tools
Install the Dagu skill for workflow authoring, or connect an MCP client to a running Dagu server.
Dagu skill
$gh skill install dagucloud/dagu dagu
Helps Claude Code, Codex, Gemini CLI, and other AI coding tools write Dagu YAML.
Dagu MCP endpoint
URLhttp://localhost:8080/mcp
Lets MCP-capable clients read Dagu state, preview changes, and control DAG runs.
OIDC/SSO-backed MCP access is available for enterprise deployments. Contact us to discuss setup.
2
Next steps
The guided installer can finish the first-run setup for you.
# What the installer can do
Add Dagu to your PATH
Set up a background service
Create and verify the first admin
Contact
Questions, enterprise needs, or feedback?
We'd love to hear from you.