Built for teams whose main work is not orchestration.
Define workflows in declarative YAML; one open-source binary runs them, locally or over SSH, with schedules, retries, human tasks, logs, and a Web UI. Your scripts, containers, and AI agents stay untouched. No external database, no framework.
$curl -fsSL https://raw.githubusercontent.com/dagucloud/dagu/main/scripts/installer.sh | bash# runs every night at 02:00schedule: "0 2 * * *"type: chainsteps:- id: fetch_datarun: python fetch.pyretry_policy:limit: 3interval_sec: 60- id: build_reportrun: python report.py- id: notifyrun: ./notify.sh
Install Dagu and run your first workflow from the Web UI in a few minutes.
A GitHub star helps other developers find Dagu and keeps the repository close at hand.
Use Claude, Codex, Cursor, or any MCP client to build and run workflows.
From your AI tools
Create workflows and inspect their runs through scoped MCP tools. Retry a failure or stop a run from the same client.
Agent / IDE → MCP → Dagu
Set up MCP
Claude Code
Cursor
GitHub Copilot
OpenAI Codex
Gemini CLI
Kiro
Windsurf
Cline
Roo Code
Zed
Devin
Trae
ChatGPT
Junie
Goose
Amp
OpenCode
Antigravity
Amazon Q
Warp
VS Code
Raycast
LM Studio
PerplexityEverything below ships in the open-source binary; SSO, RBAC, and audit logs activate with a self-host license.
Run workflow steps in Docker containers when isolation or packaged tooling matters.
Compose larger operations from smaller reusable workflows.
Run commands on remote servers through SSH while keeping status and logs in Dagu.
Run recurring workflows with cron syntax, timezone support, and manual controls.
Configure routing in the Web UI so failed or waiting runs reach the right channel or incident provider.
Trigger workflows from external systems without adding another service layer.
Inspect DAGs, runs, logs, history, and workflow definitions from one place.
One binary, no external database or broker. Workflows run from files, so it works in air-gapped environments too.
One binary for Linux, macOS, and Windows. docker run or Helm for containers; scale out with a coordinator and label-routed workers.
The guarantees and the knobs, by their real names.
Thousands of runs per day on one machine; capacity depends on CPU, disk, and workflow shape. State is local files, so there is no database to size or migrate.
File-based queues with per-DAG concurrency limits. dagu coordinator dispatches over gRPC to workers selected by labels such as gpu=true.
Cron schedules with timezones, catchup_window, overlap_policy, per-step retry_policy, zombie detection, lifecycle handlers, email and webhook notifications, and incident routing.
DAGU_AUTH_MODE none, basic, or builtin; five RBAC roles; workspaces; human tasks; secret masking; API keys; per-DAG webhook tokens.
The same binary runs three ways.
One dagu start-all process runs the server, scheduler, and steps on one machine, with file-based storage.
Your platform provisions a worker per run that invokes the binary and is destroyed when the run ends. The server reads run state from a shared volume.
The server dispatches tasks into a coordinator. Workers connect outbound over gRPC, poll for tasks matching their labels, and report status and logs back.
Turn private scripts, internal CLIs, reusable actions, pinned tools, and artifacts into workflows other teams can safely request or run.
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]
Real comments from the Dagu GitHub community.
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!
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
I've recently played around with Dagu for private stuff and think it could also be worth introducing at work.
@DarkWiiPlayer
Developer
Dagu is GPLv3 and free forever. Paid licenses add SSO, RBAC, audit logging, incident routing, and support for teams.
3 self-host Dagu server licenses
$500 / yr · Save 2 months
15 self-host Dagu server licenses
$1,500 / yr · Save 2 months
For teams building event-driven workflow infrastructure in complex environments
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Team and Pro include standard support. Enterprise includes priority support. See support details
Details on what the license covers and how Community compares: Self-host license guide
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Contact
Open a GitHub issue, ask in Discord, or email. Replies come from the maintainer, not a bot.