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

# Image Agent

> Send an image to a Groq vision model and stream the description.

Pass an `Image` to `print_response()` and use a vision-capable model such as `meta-llama/llama-4-scout-17b-16e-instruct`.

## Code

```python image_agent.py theme={null}
from agno.agent import Agent
from agno.media import Image
from agno.models.groq import Groq

agent = Agent(model=Groq(id="meta-llama/llama-4-scout-17b-16e-instruct"))

agent.print_response(
    "Tell me about this image",
    images=[
        Image(url="https://upload.wikimedia.org/wikipedia/commons/f/f2/LPU-v1-die.jpg"),
    ],
    stream=True,
)
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Set your API key">
    ```bash theme={null}
    export GROQ_API_KEY=xxx
    ```
  </Step>

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U groq agno
    ```
  </Step>

  <Step title="Run Agent">
    Save the code above as `image_agent.py`, then run:

    ```bash theme={null}
    python image_agent.py
    ```
  </Step>
</Steps>

## Developer Resources

* [Image input](/multimodal/agent/usage/image-input)