Use Supabase with Reflex

1. Create a Supabase project#

To start, you need a Supabase project.

Create a new Supabase project from the Dashboard of any organization you belong to.

2. Set up your database#

When your Supabase project is up and running, create an instruments table with some sample data. Then set only the privileges each Postgres role needs, add Row Level Security (RLS) for enhanced security for database data by default, and create an RLS policy to make the data in the table publicly readable.

Do these steps within your project's dashboard by copying and running the snippet in your project's SQL Editor.

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-- Create the table
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create table instruments (
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  id bigint primary key generated always as identity,
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  name text not null
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);
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-- Insert sample data into the table
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insert into instruments (name)
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values
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  ('violin'),
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  ('viola'),
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  ('cello');
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-- Grant the privileges the role needs, which is read access
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grant select on public.instruments to anon;
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-- Enable row level security for the table
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alter table instruments enable row level security;
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-- Create a policy to allow the anon role to read from the instruments table
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create policy "public can read instruments"
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on public.instruments
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for select to anon
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using (true);

3. Create a Reflex app#

Create a new directory for your Reflex app, initialize a project with uv, add Reflex as a dependency, and scaffold a blank app with --template blank.

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mkdir my_app && cd my_app
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uv init
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uv add reflex
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uv run reflex init --template blank

4. Set up AI tooling (optional)#

Supabase provides two ways to give AI tools context about your project: Agent Skills, which give your AI coding agent procedural knowledge, and the MCP server, which connects AI assistants to your Supabase project directly.

Agent Skills#

Agent Skills is a curated set of instructions that give your AI agent procedural knowledge about working with Supabase.

Install them so your AI coding agent can produce more accurate, reliable code using current Supabase patterns, such as authentication, server-side rendering, and database migrations, rather than relying solely on training data.

Installing Agent Skills#

To install, run the following command in the root of your project:

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npx skills add supabase/agent-skills

Supabase MCP server#

The Supabase MCP server connects AI assistants to Supabase, so they can inspect your schema and act on your projects on your behalf. Find out how to add it to your client in the MCP docs.

5. Install the Supabase client library#

The fastest way to get started is to use the supabase-py client library, which provides a convenient interface for working with Supabase from a Reflex app.

Install supabase-py and python-dotenv to load environment variables.

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uv add supabase python-dotenv

6. Declare Supabase environment variables#

Create a .env file in your project root and populate it with your Supabase connection variables that you can get from the helper below, or from the project Connect panel:

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SUPABASE_URL=<SUBSTITUTE_SUPABASE_URL>
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SUPABASE_PUBLISHABLE_KEY=<SUBSTITUTE_SUPABASE_PUBLISHABLE_KEY>

Get API details#

To interact with data in database tables, you use the client libraries that wrap the auto-generated Data API endpoints, authenticating using the Project URL and key from the project Connect dialog.

7. Create the Supabase client#

Open my_app/my_app.py and add the following code to create an async Supabase client from your environment variables. Reflex's event handlers run inside an async event loop, so the client is created once with acreate_client and reused across requests, rather than recreated per page load.

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import os
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import reflex as rx
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from supabase import acreate_client, AsyncClient
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from dotenv import load_dotenv
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load_dotenv()
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_supabase: AsyncClient | None = None
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async def get_supabase() -> AsyncClient:
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    global _supabase
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    if _supabase is None:
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        _supabase = await acreate_client(
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            os.environ.get("SUPABASE_URL"),
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            os.environ.get("SUPABASE_PUBLISHABLE_KEY"),
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        )
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    return _supabase

8. Query data from the app#

Add a State class that holds the query result, an async event handler that fetches data from your instruments table, and a page that renders the result. on_load triggers the handler when the page is opened.

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from postgrest import APIError
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class State(rx.State):
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    instruments: list[dict] = []
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    error: str = ""
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    async def load_instruments(self):
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        supabase = await get_supabase()
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        try:
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            response = await supabase.table("instruments").select("*").execute()
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        except APIError as error:
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            self.error = f"Error loading instruments: {error.message}"
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            return
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        self.instruments = response.data
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def index() -> rx.Component:
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    return rx.container(
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        rx.heading("Instruments"),
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        rx.cond(
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            State.error,
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            rx.text(State.error),
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            rx.foreach(
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                State.instruments,
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                lambda instrument: rx.text(instrument["name"]),
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            ),
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        ),
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    )
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app = rx.App()
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app.add_page(index, on_load=State.load_instruments)

9. Start the app#

Run the Reflex development server, go to http://localhost:3000 in a browser, and you should see the list of instruments.

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uv run reflex run

Production requirements#

The quickstart procedure in this guide optimizes for getting you to a working app, not for production.

Before you deploy:

• If your app reads or writes through the Data API, review your Row Level Security policies. Any policy you added here is scoped to this quickstart's sample data, not to real user data.
• Set your Supabase credentials as environment variables on whatever platform you deploy to, rather than committing them to source control.
• Configure a custom domain for your Supabase project once you're ready to go live.

Next steps#

• Set up Auth for your app
• Insert more data into your database
• Upload and serve static files using Storage