Dagu vs Temporal
Dagu and Temporal solve different problems.
Temporal is a durable-execution engine for stateful application workflows written in code. Dagu is a single binary that schedules and orchestrates the commands you already run. This page explains where each one fits.
name: nightly-ops
schedule: "0 2 * * *"
steps:
- id: extract
run: python scripts/extract.py
- id: transform
run: ./bin/transform
retry_policy:
limit: 3
depends: [extract]
- id: notify
run: ./scripts/slack-success.sh
depends: [transform]One self-contained binary, no database or message broker
Workflows are declarative YAML that call existing commands
Executors for shell, Docker, HTTP, SSH, SQL, sub-workflows, and Agent Harness steps
Runs local, queue-based, or distributed across workers
At a glance
Dagu vs Temporal at a glance
Declarative YAML that calls commands.
Workflow-as-code with an SDK (Go, Java, TypeScript, Python).
Single binary with file-backed state.
A service backed by Cassandra, PostgreSQL, or MySQL.
Scheduled jobs, pipelines, and ops automation.
Durable, stateful application and microservice workflows.
In depth
Where each tool fits
What each tool is built for
Temporal keeps long-running application logic alive through crashes and restarts. You write workflows as code with an SDK, and the service replays event history so a worker resumes exactly where it stopped. Dagu schedules jobs and wires commands into a graph. It tracks runs, retries, logs, and history without asking you to rewrite tasks as code inside a framework.
- Temporal targets reliable distributed and microservice workflows that must survive failures.
- Dagu targets cron jobs, batch pipelines, and ops automation built from commands.
- A step in Dagu can run a script, a container, an HTTP call, or another workflow.
What you operate and how you author
Temporal needs a running service backed by Cassandra, PostgreSQL, or MySQL, and is commonly deployed with Helm on Kubernetes or used through Temporal Cloud. Workflows live in Go, Java, TypeScript, or Python code. Dagu is one binary that stores state in files on disk. Workflows are YAML you can read in a diff and commit next to the code they orchestrate.
- No database, broker, or cluster is required to run Dagu.
- YAML steps shell out to tools you already have rather than SDK calls.
- You can start on a laptop and add workers later when one host is not enough.
When to choose Temporal instead
If your problem is durable execution, Temporal is the right tool and Dagu is not a substitute. Workflows that hold state for hours or days, wait on external signals, set timers across long-lived processes, and must replay deterministically after a failure are exactly what Temporal is designed for. Dagu has no equivalent to that programming model.
- You need code-defined workflows that survive process crashes and resume mid-execution.
- You rely on signals, durable timers, or child workflows inside application logic.
- Your team wants an SDK in Go, Java, TypeScript, or Python rather than declarative YAML.
FAQ
Practical questions before adopting Dagu
Does Dagu replace Temporal?
For most use cases, no. Temporal is a durable-execution engine for stateful workflows written in code, and Dagu does not offer that model. Dagu replaces Temporal only if you were using it for plain scheduled jobs or command pipelines, where its lighter setup is a better fit.
Can Dagu run long-running workflows?
Dagu can run long tasks and orchestrate multi-step pipelines, but it does not keep code-defined workflow state alive across process crashes or replay event history. If you need a step to pause for days and resume deterministically after a failure, Temporal is the right choice.
Why pick Dagu over Temporal for scheduled jobs?
For cron jobs and pipelines built from existing commands, Dagu needs no database, broker, or SDK. You install one binary, write YAML, and get scheduling, retries, logs, history, and a Web UI, which is less to operate than a Temporal service.
Next step
Start with one workflow.
Install Dagu, move one fragile script or agent task into YAML, and decide from a real run history.