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

# Image Generation Team

This example demonstrates how an Agno Agent Team can collaborate to generate high-quality images using a prompt engineer to optimize prompts and an image creator to generate images with DALL-E.

## Code

```python cookbook/02_examples/teams/multimodal/generate_image_with_team.py theme={null}
from typing import Iterator

from agno.agent import Agent, RunOutputEvent
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.dalle import DalleTools
from agno.utils.common import dataclass_to_dict
from rich.pretty import pprint

image_generator = Agent(
    name="Image Creator",
    role="Generate images using DALL-E",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[DalleTools()],
    instructions=[
        "Use the DALL-E tool to create high-quality images",
        "Return image URLs in markdown format: `![description](URL)`",
    ],
)

prompt_engineer = Agent(
    name="Prompt Engineer",
    role="Optimize and enhance image generation prompts",
    model=OpenAIResponses(id="gpt-5.2"),
    instructions=[
        "Enhance user prompts for better image generation results",
        "Consider artistic style, composition, and technical details",
    ],
)

# Create a team for collaborative image generation
image_team = Team(
    name="Image Generation Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[prompt_engineer, image_generator],
    instructions=[
        "Generate high-quality images from user prompts.",
        "Prompt Engineer: First enhance and optimize the user's prompt.",
        "Image Creator: Generate images using the enhanced prompt with DALL-E.",
    ],
    markdown=True,
)

run_stream: Iterator[RunOutputEvent] = image_team.run(
    "Create an image of a yellow siamese cat",
    stream=True,
    stream_events=True,
)
for chunk in run_stream:
    pprint(dataclass_to_dict(chunk, exclude={"messages"}))
    print("---" * 20)
```

## Usage

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

  <Step title="Install required libraries">
    ```bash theme={null}
    uv pip install agno rich
    ```
  </Step>

  <Step title="Set environment variables">
    ```bash theme={null}
    export OPENAI_API_KEY=****
    ```
  </Step>

  <Step title="Run the agent">
    ```bash theme={null}
    python cookbook/02_examples/teams/multimodal/generate_image_with_team.py
    ```
  </Step>
</Steps>