Imagen Tool Advanced
Adapt a historical Imagen toolkit example to a Gemini image-generation function tool.
An Agent using the Gemini image generation tool.
Google lists the Vertex Imagen GA endpoints, including the previously suggested imagen-4.0-generate-001, as discontinued in its March 24 release notes. The source also uses the removed Agent.run_response attribute and decodes raw bytes as base64. Use the current adaptation below instead of running the source snapshot unchanged.
"""Example: Using the GeminiTools Toolkit for Image Generation
An Agent using the Gemini image generation tool.
Make sure to set the Vertex AI credentials. Here's the authentication guide: https://cloud.google.com/sdk/docs/initializing
Run `uv pip install google-genai agno` to install the required packages.
"""
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.models.gemini import GeminiTools
from agno.utils.media import save_base64_data
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
model=OpenAIChat(id="gpt-5.6-luna"),
tools=[
GeminiTools(
image_generation_model="imagen-4.0-generate-preview-05-20", vertexai=True
)
],
)
agent.print_response(
"Cinematic a visual shot using a stabilized drone flying dynamically alongside a pod of immense baleen whales as they breach spectacularly in deep offshore waters. The camera maintains a close, dramatic perspective as these colossal creatures launch themselves skyward from the dark blue ocean, creating enormous splashes and showering cascades of water droplets that catch the sunlight. In the background, misty, fjord-like coastlines with dense coniferous forests provide context. The focus expertly tracks the whales, capturing their surprising agility, immense power, and inherent grace. The color palette features the deep blues and greens of the ocean, the brilliant white spray, the dark grey skin of the whales, and the muted tones of the distant wild coastline, conveying the thrilling magnificence of marine megafauna."
)
response = agent.run_response
if response and response.images:
save_base64_data(str(response.images[0].content), "tmp/baleen_whale.png")
"""
Example prompts to try:
- A horizontally oriented rectangular stamp features the Mission District's vibrant culture, portrayed in shades of warm terracotta orange using an etching style. The scene might depict a sun-drenched street like Valencia or Mission Street, lined with a mix of Victorian buildings and newer structures.
- Painterly landscape featuring a simple, isolated wooden cabin nestled amongst tall pine trees on the shore of a calm, reflective lake.
- Filmed cinematically from the driver's seat, offering a clear profile view of the young passenger on the front seat with striking red hair.
- A pile of books seen from above. The topmost book contains a watercolor illustration of a bird. VERTEX AI is written in bold letters on the book.
"""
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
passCurrent adaptation
Save this current adaptation as native_image_tool.py. An OpenAI agent calls an Agno function tool that runs a Gemini image agent. ToolResult.images carries the returned image bytes to the outer run.
from pathlib import Path
from agno.agent import Agent
from agno.models.google import Gemini
from agno.models.openai import OpenAIChat
from agno.tools.function import ToolResult
image_agent = Agent(
model=Gemini(
id="gemini-3.1-flash-image",
response_modalities=["Text", "Image"],
)
)
def generate_image(prompt: str) -> ToolResult:
"""Generate an image from a detailed visual description."""
result = image_agent.run(prompt)
if not result.images:
return ToolResult(content="No image was generated.")
return ToolResult(content="Image generated.", images=result.images)
agent = Agent(
model=OpenAIChat(id="gpt-5.4-mini"),
tools=[generate_image],
)
response = agent.run("Generate an image of whales breaching beside a forested fjord.")
print(response.content)
for index, image in enumerate(response.images or []):
if image.content:
suffix = ".jpg" if image.mime_type == "image/jpeg" else ".png"
output = Path("tmp") / f"generated_{index}{suffix}"
output.parent.mkdir(parents=True, exist_ok=True)
output.write_bytes(image.content)
print(output)This uses Gemini's native image-generation API. Changing the model ID on GeminiTools alone is insufficient: that toolkit calls the separate Imagen generate_images endpoint. See native image generation.
Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activateInstall dependencies
uv pip install -U agno google-genai openaiExport environment variables
export GOOGLE_CLOUD_LOCATION="global"
export GOOGLE_CLOUD_PROJECT="your_google_cloud_project_here"
export OPENAI_API_KEY="your_openai_api_key_here"Authenticate with Google Cloud
Sign in with Application Default Credentials:
gcloud auth application-default loginUse Vertex AI for the image agent
In the current adaptation, add vertexai=True to Gemini(...). Keep the project/location variables and Application Default Credentials configured above. The OpenAI agent uses OPENAI_API_KEY separately.
Run the example
Save the current adaptation as native_image_tool.py and any helper beside it, then run:
python native_image_tool.pyFull source: cookbook/90_models/google/gemini/imagen_tool_advanced.py