Writing plugins

You can write one-off plugins that apply to just one Datasette instance, or you can write plugins which can be installed using pip and can be shipped to the Python Package Index (PyPI) for other people to install.

Writing one-off plugins

The easiest way to write a plugin is to create a my_plugin.py file and drop it into your plugins/ directory. Here is an example plugin, which adds a new custom SQL function called hello_world() which takes no arguments and returns the string Hello world!.

from datasette import hookimpl

def prepare_connection(conn):
    conn.create_function('hello_world', 0, lambda: 'Hello world!')

If you save this in plugins/my_plugin.py you can then start Datasette like this:

datasette serve mydb.db --plugins-dir=plugins/

Now you can navigate to http://localhost:8001/mydb and run this SQL:

select hello_world();

To see the output of your plugin.

Starting an installable plugin using cookiecutter

Plugins that can be installed should be written as Python packages using a setup.py file.

The easiest way to start writing one an installable plugin is to use the datasette-plugin cookiecutter template. This creates a new plugin structure for you complete with an example test and GitHub Actions workflows for testing and publishing your plugin.

Install cookiecutter and then run this command to start building a plugin using the template:

cookiecutter gh:simonw/datasette-plugin

Read a cookiecutter template for writing Datasette plugins for more information about this template.

Packaging a plugin

Plugins can be packaged using Python setuptools. You can see an example of a packaged plugin at https://github.com/simonw/datasette-plugin-demos

The example consists of two files: a setup.py file that defines the plugin:

from setuptools import setup

VERSION = '0.1'

    description='Examples of plugins for Datasette',
    author='Simon Willison',
    license='Apache License, Version 2.0',
        'datasette': [
            'plugin_demos = datasette_plugin_demos'

And a Python module file, datasette_plugin_demos.py, that implements the plugin:

from datasette import hookimpl
import random

def prepare_jinja2_environment(env):
    env.filters['uppercase'] = lambda u: u.upper()

def prepare_connection(conn):
    conn.create_function('random_integer', 2, random.randint)

Having built a plugin in this way you can turn it into an installable package using the following command:

python3 setup.py sdist

This will create a .tar.gz file in the dist/ directory.

You can then install your new plugin into a Datasette virtual environment or Docker container using pip:

pip install datasette-plugin-demos-0.1.tar.gz

To learn how to upload your plugin to PyPI for use by other people, read the PyPA guide to Packaging and distributing projects.

Static assets

If your plugin has a static/ directory, Datasette will automatically configure itself to serve those static assets from the following path:


See the datasette-plugin-demos repository for an example of how to create a package that includes a static folder.

Custom templates

If your plugin has a templates/ directory, Datasette will attempt to load templates from that directory before it uses its own default templates.

The priority order for template loading is:

  • templates from the --template-dir argument, if specified
  • templates from the templates/ directory in any installed plugins
  • default templates that ship with Datasette

See Customization for more details on how to write custom templates, including which filenames to use to customize which parts of the Datasette UI.

Writing plugins that accept configuration

When you are writing plugins, you can access plugin configuration like this using the datasette plugin_config() method. If you know you need plugin configuration for a specific table, you can access it like this:

plugin_config = datasette.plugin_config(
    "datasette-cluster-map", database="sf-trees", table="Street_Tree_List"

This will return the {"latitude_column": "lat", "longitude_column": "lng"} in the above example.

If it cannot find the requested configuration at the table layer, it will fall back to the database layer and then the root layer. For example, a user may have set the plugin configuration option like so:

    "databases: {
        "sf-trees": {
            "plugins": {
                "datasette-cluster-map": {
                    "latitude_column": "xlat",
                    "longitude_column": "xlng"

In this case, the above code would return that configuration for ANY table within the sf-trees database.

The plugin configuration could also be set at the top level of metadata.json:

    "title": "This is the top-level title in metadata.json",
    "plugins": {
        "datasette-cluster-map": {
            "latitude_column": "xlat",
            "longitude_column": "xlng"

Now that datasette-cluster-map plugin configuration will apply to every table in every database.