I built Dagu, a simple workflow engine and lightweight alternative to Airflow.
Lately, I’ve been using it to automate my daily workflows with an AI agent harness like opencode. Dagu helps you manage local, private workflows. It gives you a flexible Kanban-style view, a declarative workflow language with validation, and the basics you expect from production workflow infrastructure: logs, retries, status tracking, and more.
The best part of having a workflow language is maintainability. Any AI agent can help create or debug your workflows. You can do something similar with plain Bash scripts, but Bash quickly becomes hard to debug and maintain.
Because Dagu is DAG-based, it automatically runs independent steps in parallel. Each step is validated and logged separately, which makes it much easier for an AI agent to understand what failed and fix it.
Lately, I’ve been using it to automate my daily workflows with an AI agent harness like opencode. Dagu helps you manage local, private workflows. It gives you a flexible Kanban-style view, a declarative workflow language with validation, and the basics you expect from production workflow infrastructure: logs, retries, status tracking, and more.
The best part of having a workflow language is maintainability. Any AI agent can help create or debug your workflows. You can do something similar with plain Bash scripts, but Bash quickly becomes hard to debug and maintain.
Because Dagu is DAG-based, it automatically runs independent steps in parallel. Each step is validated and logged separately, which makes it much easier for an AI agent to understand what failed and fix it.
Give it a try!