> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-service-account.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Lmstudio Image Agent

> Send image bytes to a llama3.2-vision model in LM Studio and stream the description.

```python image_agent.py theme={null}
"""
Lmstudio Image Agent
====================

Cookbook example for `lmstudio/image_agent.py`.
"""

import httpx
from agno.agent import Agent
from agno.media import Image
from agno.models.lmstudio import LMStudio

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

agent = Agent(
    model=LMStudio(id="llama3.2-vision"),
    markdown=True,
)

response = httpx.get(
    "https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg"
)

agent.print_response(
    "Tell me about this image",
    images=[Image(content=response.content)],
    stream=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass
```

## Run the Example

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

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

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

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

Full source: [cookbook/90\_models/lmstudio/image\_agent.py](https://github.com/agno-agi/agno/blob/main/cookbook/90_models/lmstudio/image_agent.py)