> ## Documentation Index
> Fetch the complete documentation index at: https://docs.xpander.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Simple Hello World with Xpander SDK

> The simplest way to create an AI agent using Xpander SDK - learn backend integration, configuration management, and stateful memory

<AccordionGroup>
  <Accordion title="Prerequisites">
    ### Virtual Environment Setup

    ```bash setup.sh theme={"dark"}
    python3 -m venv .venv
    source .venv/bin/activate
    pip install "xpander-sdk[agno]"
    ```

    ### Environment Setup

    The `.env` file is created when you download your agent code:

    ```bash theme={"dark"}
    xpander init
    ```

    This downloads your agent code from the platform with the `.env` file pre-configured with your keys.
  </Accordion>
</AccordionGroup>

```python hello_world_example.py theme={"dark"}
from dotenv import load_dotenv
load_dotenv()

from xpander_sdk import Backend
from agno.agent import Agent

# Initialize Xpander Backend
backend = Backend()

# Create agent with backend configuration
agno_agent = Agent(**backend.get_args())

# Test stateful conversation
agno_agent.print_response(message="What's your role?")
agno_agent.print_response(message="What did I ask you?")
```

## How to Run

```bash hello_world_example.py theme={"dark"}
python hello_world_example.py
```

This example shows Xpander Backend integration providing automatic agent configuration, centralized management through the dashboard, and stateful memory where the agent remembers previous interactions and maintains context across multiple messages.

## Production Deployment

For creating and deploying agents to production, see the [Setup and Deployment Guide](/Examples/00-setup-deployment).