Agentuity - Snow Leopard

What You’ll Build

A question-answering agent that:

Leverages Agentuity for easy deployment and monitoring
Retrieves live data from a SQL database through Snow Leopard
Requires no MCP setup, no ETL or data pipelines, and no RAG setup for data retrieval

Prerequisites

Agentuity CLI
Snow Leopard Cloud API key
A Snow Leopard Cloud instance with at least one data source connected
Your instance ID, from the instance’s Connection Info tab
OpenAI API key (or another supported model provider)

Don’t have data? Load the sample Northwind dataset into a PostgreSQL database that Snow Leopard Cloud can reach, such as a hosted Neon or Supabase database. Run the dataset’s northwind.sql script against your database, then add the database as a data source in your instance.

1. Create an Agentuity project

agentuity create

Follow along with the interactive project create which will create a directory for your agentuity agent. This quickstart doesn’t need auth, db access, or any other options, so feel free to reject any Agentuity features you do not plan on using.

2. Install dependencies

Once you have an Agentuity project and are in its working directory, we will need to add a few new dependencies to add live data retrieval to your agent.

bun add @snowleopard-ai/client ai zod @ai-sdk/openai

3. Configure environment variables

Add your API keys and instance ID to your .env file:

.env

OPENAI_API_KEY=<your_openai_api_key>
SNOWLEOPARD_API_KEY=<your_snowleopard_api_key>
SNOWLEOPARD_INSTANCE_ID=<your_snowleopard_instance_id>

4. Create the Snow Leopard tool

Create a Vercel AI tool that calls Snow Leopard to retrieve data:

src/agent/getData.ts

import { tool } from "ai";
import { z } from "zod";
import { SnowLeopardClient } from "@snowleopard-ai/client";

const snowy = new SnowLeopardClient({
  apiKey: process.env.SNOWLEOPARD_API_KEY!
});

export const getData = tool({
  description:
    'Retrieve data from the database. ' +
    'Describe your data here - this becomes part of the tool description.',
  inputSchema: z.object({
    userQuestion: z.string().describe('the natural language query to answer'),
  }),
  execute: async ({ userQuestion }) => {
    return await snowy.retrieve({
      userQuery: userQuestion,
      instanceId: process.env.SNOWLEOPARD_INSTANCE_ID!
    });
  },
});

5. Create the agent

Build an Agentuity agent that uses the Snow Leopard tool:

src/agent/agent.ts

import { createAgent } from '@agentuity/runtime';
import { s } from '@agentuity/schema';
import { generateText, type ModelMessage } from 'ai';
import { openai } from '@ai-sdk/openai';
import { getData } from './getData';

const agent = createAgent('chat', {
  description: 'A chat agent with data retrieval',
  handler: async (ctx, { message }) => {
    const messages: ModelMessage[] = [
      { role: 'system', content: 'You are a helpful assistant that answers questions using your data tools.' },
      { role: 'user', content: message }
    ];

    for (let step = 0; step < 10; step++) {
      const result = await generateText({
        model: openai('gpt-5-mini'),
        messages: messages,
        tools: { getData }
      });
      messages.push(...result.response.messages);
      if (result.finishReason !== 'tool-calls') {
        return { response: result.text };
      }
    }
    throw new Error('Agent exceeded maximum number of steps');
  },
  schema: {
    input: s.object({ message: s.string() }),
    output: s.object({ response: s.string() }),
  },
});

export default agent;

6. Expose the agent via HTTP

Add an API route to handle chat requests:

src/api/index.ts

import { createRouter } from '@agentuity/runtime';
import chat from '../agent/agent';

const api = createRouter();
/*
Existing api definitions...
 */

api.post('/chat', chat.validator(), async (c) => {
  const data = c.req.valid('json');
  return await chat.run(data);
});

export default api;

7. Try it out!

Start your development server:

agentuity dev

Query your data:

curl -X POST http://localhost:3500/api/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "How many customers do we have?"}'

{
  "response": "You have 91 customers (counted as non-null customer_id entries in the customers table).  \n\nWould you like a breakdown by segment, region, sign-up date, or any other criteria?"
}

Next steps

View the full example on GitHub
Learn more about Agentuity agents
See our full API documentation to learn about the Retrieve and Response endpoints
Improve accuracy by giving Snow Leopard feedback about your business logic and terminology
Explore Recipes for more agent examples

​What You’ll Build

​Prerequisites

​1. Create an Agentuity project

​2. Install dependencies

​3. Configure environment variables

​4. Create the Snow Leopard tool

​5. Create the agent

​6. Expose the agent via HTTP

​7. Try it out!

​Next steps

What You’ll Build

Prerequisites

1. Create an Agentuity project

2. Install dependencies

3. Configure environment variables

4. Create the Snow Leopard tool

5. Create the agent

6. Expose the agent via HTTP

7. Try it out!

Next steps