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This guide shows you how to add Snow Leopard data retrieval to a Pydantic AI agent.

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Prerequisites

Don’t have data? Use our sample superheroes dataset to get started, or choose from our other sample datasets.

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Start from scratch

Clone and run the complete working example: 
git clone https://github.com/SnowLeopard-AI/snowy-examples.git
cd snowy-examples/quickstart/pydantic-ai
Set your environment variables: 
export ANTHROPIC_API_KEY=your-anthropic-key
export SNOWLEOPARD_API_KEY=your-snowleopard-key
export SNOWLEOPARD_DATAFILE_ID=your-datafile-id
Run the agent: 
uv run clai --agent agent:agent
You now have an interactive REPL where you can ask questions about your data: 
clai ➤ How many superheroes are there?

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Bring your own agent

Already have a Pydantic AI agent? Add Snow Leopard data retrieval with these steps.

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1. Install dependencies

uv add snowleopard

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2. Create the Snow Leopard tool

Add a tool to your agent that calls Snow Leopard to retrieve data: 
from pydantic_ai import Agent, RunContext
from snowleopard import SnowLeopardClient

# Your existing agent
agent = Agent(
    'anthropic:claude-sonnet-4-5',
    instructions='Be concise, reply with one sentence.',
)

# Initialize the Snow Leopard client
snowy = SnowLeopardClient(api_key="{your-snowleopard-api-key}")

# Add the data retrieval tool
@agent.tool
def get_data(ctx: RunContext[str], user_query: str) -> str:
    """
    Retrieve data from the database.
    Describe your data here - this becomes part of the agent's context.
    """
    response = snowy.retrieve(user_query=user_query, datafile_id="{your-datafile-id}")
    return str(response)

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3. Customize the tool description

The tool’s docstring tells the agent when and how to use it. Update it to describe your specific data: 
@agent.tool
def get_data(ctx: RunContext[str], user_query: str) -> str:
    """
    Retrieve customer order data.
    Contains order history, products, and customer information.
    Use this to answer questions about sales, orders, and customers.
    """
    response = snowy.retrieve(user_query=user_query, datafile_id=datafile_id)
    return str(response)

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Next steps