> ## 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.

# Agent with Image Input

> Send raw image bytes to a Llama agent alongside a web search tool.

## Code

```python image_input_bytes.py theme={null}
from pathlib import Path

from agno.agent import Agent
from agno.media import Image
from agno.models.meta import LlamaOpenAI
from agno.tools.websearch import WebSearchTools
from agno.utils.media import download_image

agent = Agent(
    model=LlamaOpenAI(id="Llama-4-Maverick-17B-128E-Instruct-FP8"),
    tools=[WebSearchTools()],
    markdown=True,
)

image_path = Path(__file__).parent.joinpath("sample.jpg")

download_image(
    url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
    output_path=str(image_path),
)

# Read the image file content as bytes
image_bytes = image_path.read_bytes()

agent.print_response(
    "Tell me about this image and give me the latest news about it.",
    images=[
        Image(content=image_bytes),
    ],
    stream=True,
)
```

## Usage

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

**1. Set your LLAMA API key**

```bash theme={null}
export LLAMA_API_KEY=YOUR_API_KEY
```

**2. Install dependencies**

```bash theme={null}
uv pip install llama-api-client openai ddgs agno
```

**3. Run Agent**

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

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


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.