Start by building a "Hello world" example.


Flows are defined in a declarative YAML syntax to keep the orchestration code portable and language-agnostic.

Each flow consists of three required components: id, namespace and tasks:

  1. id represents the name of the flow
  2. namespace can be used to separate development and production environments
  3. tasks is a list of tasks that will be executed in the order they are defined

Here are those three components in a YAML file:

id: getting_started
namespace: dev
  - id: hello_world
    type: io.kestra.plugin.core.log.Log
    message: Hello World!

The id of a flow must be unique within a namespace. For example:

  • ✅ you can have a flow named getting_started in the dev namespace and another flow named getting_started in the prod namespace.
  • ❌ you cannot have two flows named getting_started in the dev namespace at the same time.

The combination of id and namespace serves as a unique identifier for a flow.


Namespaces are used to group flows and provide structure. Keep in mind that the allocation of a flow to a namespace is immutable. Once a flow is created, you cannot change its namespace. If you need to change the namespace of a flow, create a new flow with the desired namespace and delete the old flow.


To add another layer of organization, you can use labels, allowing you to group flows using key-value pairs.


You can optionally add a description property to keep your flows documented. The description is a string that supports markdown syntax. That markdown description will be rendered and displayed in the UI.

Here is the same flow as before, but this time with labels and descriptions:

id: getting_started
namespace: dev

description: |
  # Getting Started
  Let's `write` some **markdown** - [first flow]( 🚀

  owner: rick.astley
  project: never-gonna-give-you-up

  - id: hello_world
    type: io.kestra.plugin.core.log.Log
    message: Hello World!
    description: |
      ## About this task
      This task will print "Hello World!" to the logs.

Learn more about flows in the Flows section.


Tasks are atomic actions in your flows. You can design your tasks to be small and granular, e.g. fetching data from a REST API or running a self-contained Python script. However, tasks can also represent large and complex processes, e.g. triggering containerized processes or long-running batch jobs (e.g. using dbt, Spark, AWS Batch, Azure Batch, etc.) and waiting for their completion.

The order of task execution

Tasks are defined in the form of a list. By default, all tasks in the list will be executed sequentially — the second task will start as soon as the first one finishes successfully.

Kestra provides additional customization allowing to run tasks in parallel, iterating (sequentially or in parallel) over a list of items, or to allow failure of specific tasks. Those are called Flowable tasks because they define the flow logic.

A task in Kestra must have an id and a type. Other properties depend on the task type. You can think of a task as a step in a flow that should execute a specific action, such as running a Python or Node.js script in a Docker container, or loading data from a database.

  - id: python
    type: io.kestra.plugin.scripts.python.Script
      image: python:slim
    script: |
      print("Hello World!")


Kestra supports hundreds of tasks integrating with various external systems. Use the shortcut CTRL + SPACE on Windows/Linux or fn + control + SPACE on Mac to trigger autocompletion listing available tasks or properties of a given task.


Supported task types

Let's look at supported task types.


Core tasks from the io.kestra.plugin.core.flow category are commonly used to control the flow logic. You can use them to declare which processes should run in parallel or sequentially. You can specify conditional branching, iterating over a list of items, pausing or allowing certain tasks to fail without failing the execution.


Script tasks are used to run scripts in Docker containers or local processes. You can use them to run Python, Node.js, R, Julia, or any other script. You can also use them to execute a series of commands in Shell or PowerShell. Check the Script tasks page for more details.

Internal Storage

Tasks from the category, along with Outputs, are used to interact with the internal storage. Kestra uses internal storage to pass data between tasks. You can think of internal storage as an S3 bucket. In fact, you can use your private S3 bucket as internal storage. This storage layer helps avoid proliferation of connectors. For example, you can use the Postgres plugin to extract data from a Postgres database and load it to the internal storage. Other task(s) can read that data from internal storage and load it to other systems such as Snowflake, BigQuery, or Redshift, or process it using any other plugin, without requiring point to point connections between each of them.

State Store

Internal storage is mainly used to pass data within a single flow execution. If you need to pass data between different flow executions, you can use the State Store. The tasks Set, Get and Delete from the io.kestra.plugin.core.state category allow you to persist files between executions (even across namespaces). For example, if you are using dbt, you can leverage the State Store to persist the manifest.json file between executions and implement the slim CI pattern.

⚡️ Plugins

Apart from core tasks, the plugins library provides a wide range of integrations. Kestra has built-in plugins for data ingestion, data transformation, interacting with databases, object stores, or message queues, and the list keeps growing with every new release. On top of that, you can also create your own plugins to integrate with any system or programming language.

Create and run your first flow

Now, let's create and run your first flow. On the left side of the screen, click on the Flows menu. Then, click on the Create button.

Create flow

Paste the following code to the Flow editor:

id: getting_started
namespace: dev

  - id: api
    type: io.kestra.plugin.core.http.Request

Then, hit the Save button.

Create flow

This flow has a single task that will fetch data from the dummyjson API. Let's run it!

New execution

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