Supabase

This guide will walk you through creating an AI agent that utilizes supabase as the memory store.

Using Supabase with Daydreams

Setup Info:

Vector Model Provider: gpt-4-turbo via @ai-sdk/openai
Model Provider: google/gemini-2.0-flash-001 via @openrouter/ai-sdk-provider
Memory Store: Supabase via @daydreamsai/supabase
Communication method: Command Line via @daydreamsai/cli

Initialize a project and add our setup packages

bun init
bun add @daydreamsai/core @daydreamsai/supabase @daydreamsai/cli @ai-sdk/openai @openrouter/ai-sdk-provider

After installing the packages, go to https://supabase.com/ and create a new project. Once your project is created, you'll need to add the two environment variables necessary for this package to your environment.

# Supabase
SUPABASE_URL=YOUR_SUPABASE_URL
SUPABASE_SERVICE_KEY=YOUR_SUPABASE_SERVICE_KEY
 
# Other variables used in this example
OPENROUTER_API_KEY=YOUR_SUPABASE_SERVICE_KEY
OPENAI_API_KEY=YOUR_OPENAI_KEY
OPENROUTER_API_KEY=YOUR_OPENROUTER_KEY

These variables are provided by Supabase when you create the project and can be found in your project settings:Data API.

Next, you need to set up the necessary database structure for the agent's memory. Copy the following SQL code block and paste in the Supabase SQL Editor (found in the sidebar):

-- Enable the pgvector extension if it's not already enabled
-- This is crucial for vector similarity search used by SupabaseVectorStore
CREATE EXTENSION IF NOT EXISTS vector;
 
-- Function to enable pgvector extension (might be used internally by SupabaseVectorStore)
CREATE OR REPLACE FUNCTION enable_pgvector_extension()
RETURNS void
LANGUAGE plpgsql
AS $$
BEGIN
  CREATE EXTENSION IF NOT EXISTS vector;
END;
$$;
 
-- Function to execute arbitrary SQL (potentially used by SupabaseVectorStore for initialization)
-- SECURITY DEFINER allows the function to run with the privileges of the user who defines it,
-- necessary for operations like creating tables or extensions if the calling user doesn't have direct permissions.
-- Ensure you understand the security implications before using SECURITY DEFINER.
CREATE OR REPLACE FUNCTION execute_sql(query text)
RETURNS void
LANGUAGE plpgsql
SECURITY DEFINER
AS $$
BEGIN
  EXECUTE query;
END;
$$;

Afterards you should see a success message like "Success. No rows returned".

With the Supabase setup complete, let's create the agent in our index.ts:

// This example shows how to use Supabase with DaydreamsAI.
 
// Vector Model Provider: gpt-4-turbo                    via @ai-sdk/openai
// Model Provider:        google/gemini-2.0-flash-001    via @openrouter/ai-sdk-provider
// Memory Store:          @daydreamsai/supabase
// CLI Extension:         @daydreamsai/cli
 
import { openai } from "@ai-sdk/openai";
import {
  createContainer,
  createDreams,
  Logger,
  LogLevel,
  validateEnv,
} from "@daydreamsai/core";
import { createSupabaseBaseMemory } from "@daydreamsai/supabase";
import { z } from "zod";
import { cliExtension } from "@daydreamsai/cli";
import { openrouter } from "@openrouter/ai-sdk-provider";
 
validateEnv(
  z.object({
    OPENAI_API_KEY: z.string().min(1, "OPENAI_API_KEY is required"),
    SUPABASE_URL: z.string().min(1, "SUPABASE_URL is required"),
    SUPABASE_SERVICE_KEY: z.string().min(1, "SUPABASE_SERVICE_KEY is required"),
  })
);
 
const agent = createDreams({
  container: createContainer(),
  logger: new Logger({ level: LogLevel.DEBUG }),
  model: openrouter("google/gemini-2.0-flash-001"),
  extensions: [cliExtension],
  memory: createSupabaseBaseMemory({
    url: process.env.SUPABASE_URL!,
    key: process.env.SUPABASE_SERVICE_KEY!,
    memoryTableName: "agent",
    vectorTableName: "agentVectors",
    vectorModel: openai("gpt-4-turbo"),
  }),
});
 
// Agent starts
await agent.start();

Run the agent and chat via the command line interface!

bun run index.ts

Using Supabase with Daydreams

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