> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/microsoft/autogen/llms.txt
> Use this file to discover all available pages before exploring further.

# Hello World

> Create your first AutoGen agent with a simple hello world example

This is the simplest possible AutoGen example. It creates a single assistant agent that responds to a task.

## What You'll Learn

* How to create an OpenAI model client
* How to create an AssistantAgent
* How to run a simple task

## Prerequisites

<Steps>
  <Step title="Install AutoGen">
    ```bash theme={null}
    pip install -U "autogen-agentchat" "autogen-ext[openai]"
    ```
  </Step>

  <Step title="Set your OpenAI API key">
    ```bash theme={null}
    export OPENAI_API_KEY="sk-..."
    ```
  </Step>
</Steps>

## Code Example

Create a file called `hello_world.py` with the following code:

```python theme={null}
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    # Create the model client
    model_client = OpenAIChatCompletionClient(model="gpt-4o")
    
    # Create the assistant agent
    agent = AssistantAgent("assistant", model_client=model_client)
    
    # Run a simple task
    result = await agent.run(task="Say 'Hello World!'")
    print(result)
    
    # Clean up
    await model_client.close()

asyncio.run(main())
```

## Run the Example

```bash theme={null}
python hello_world.py
```

## Expected Output

You should see output similar to:

```
TaskResult(messages=[TextMessage(source='assistant', content='Hello World!', ...)])
```

## How It Works

1. **Model Client**: Creates an `OpenAIChatCompletionClient` configured to use GPT-4o
2. **Assistant Agent**: Creates an `AssistantAgent` that uses the model client to generate responses
3. **Run Task**: Executes the task and returns a `TaskResult` containing the agent's response
4. **Cleanup**: Closes the model client to release resources

## Key Concepts

<CardGroup cols={2}>
  <Card title="AssistantAgent" icon="robot">
    A general-purpose agent that uses an LLM to respond to tasks and messages.
  </Card>

  <Card title="Model Client" icon="server">
    Handles communication with the LLM provider (OpenAI, Azure, etc.).
  </Card>

  <Card title="Task" icon="list-check">
    A string prompt that tells the agent what to do.
  </Card>

  <Card title="TaskResult" icon="check">
    Contains the messages generated during task execution.
  </Card>
</CardGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Two Agent Chat" icon="comments" href="/examples/two-agent-chat">
    Learn how to create conversations between multiple agents
  </Card>

  <Card title="Tool Calling" icon="wrench" href="/examples/tool-calling">
    Add tools to give your agents new capabilities
  </Card>
</CardGroup>