Quickstart

Install LeastAction and run your first task in a few minutes. For a deeper, manual walkthrough see the Tutorial.

1. Install (single machine, Docker)

From the repo root:

# Development & testing — Docker Compose
docker compose up -d --build   # first time / after pulling new source
docker compose up -d           # subsequent starts

This is the development & testing path. For production/self-hosting use the zero-downtime blue-green deploy — see Production (Blue-Green).

The app comes up at http://localhost:8080 — default login admin@example.com (or username admin123) / password admin123 (override via deploy/.env). The install bundles a dbt runner and a postgres-demo database, and loads the bootstrap catalog (sample operators, connections, configs, skills, and usecases).

Details, flags, configuration, and how the blue/green deploy works: Installation and Production (Blue-Green).

2. Run your first task

You don't have to build anything for the first run. A fresh install pre-creates a Postgres demo workflow — three dependent tasks (create table → insert rows → update rows) on the included postgres-demo database, scheduled every 3 minutes. They start running on their own within ~3 minutes of setup and run for about 30 minutes.

  • Watch it run — open the workflow folder in the UI and you'll see the three tasks move through their states (the insert/update wait on their parent via a dependency action).
  • Trigger it now — don't want to wait? Open a task and click Run to fire it immediately.
  • Re-enable later — after the ~30-minute window (or anytime), use the Schedule action (LeastActionSchedule) on a task to (re)start its schedule.

Want to deploy a pipeline yourself? Do it through the AI instead of building by hand — open the AI chat (or connect via MCP) and say:

"deploy usecase postgresql-demo-foundations, then run it and check the status"

The agent creates the tasks, runs them in order, and reports state + logs. This is the recommended way to stand up new pipelines (see the Tutorial for the manual UI path).

3. Verify the data

Confirm rows landed — ask the AI:

"inspect the people table on the postgresql connection"

or run SELECT * FROM people ORDER BY id via inspect_data. You should see the rows the pipeline wrote.

4. Where to go next

  • Tutorial — build a pipeline from scratch (connection → operator → payload → task → schedule → dependencies).
  • Core concepts — the model behind it all.
  • AI overview — generate operators, use skills/usecases, connect via MCP.
  • Browse ai/usecases in the catalog for runnable, lifecycle-organized examples.