> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.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

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

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno openai
```

**2. Prepare LM Studio**

Load `llama3.2-vision` in LM Studio and start its local server at `http://127.0.0.1:1234/v1`.

**3. Run the example**

Save the code above as `image_agent.py`, then run:

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

Full source: [cookbook/90\_models/lmstudio/image\_agent.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/90_models/lmstudio/image_agent.py)


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