The GoogleModel is a model that uses the google-genai package under the hood to
access Google's Gemini models via both the Generative Language API and Vertex AI.
Install
To use GoogleModel, you need to either install pydantic-ai, or install pydantic-ai-slim with the google optional group:
pip install "pydantic-ai-slim[google]"
uv add "pydantic-ai-slim[google]"
Explicit instantiation required
You cannot currently use Agent('google-gla:gemini-1.5-flash') or Agent('google-vertex:gemini-1.5-flash') directly with GoogleModel. The model name inference will select GeminiModel instead of GoogleModel.
To use GoogleModel, you must explicitly instantiate a GoogleProvider and pass it to
GoogleModel, then pass the model to Agent.
Configuration
GoogleModel lets you use Google's Gemini models through their Generative Language API (generativelanguage.googleapis.com) or Vertex AI API (*-aiplatform.googleapis.com).
API Key (Generative Language API)
To use Gemini via the Generative Language API, go to aistudio.google.com and create an API key.
Once you have the API key, set it as an environment variable:
export GOOGLE_API_KEY=your-api-key
You can then use GoogleModel by explicitly creating a provider:
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel
from pydantic_ai.providers.google import GoogleProvider
provider = GoogleProvider(api_key='your-api-key')
model = GoogleModel('gemini-1.5-flash', provider=provider)
agent = Agent(model)
...
Vertex AI (Enterprise/Cloud)
If you are an enterprise user, you can use the google-vertex provider with GoogleModel to access Gemini via Vertex AI.
To use Vertex AI, you may need to set up application default credentials or use a service account. You can also specify the region.
Application Default Credentials
If you have the gcloud CLI installed and configured, you can use:
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel
from pydantic_ai.providers.google import GoogleProvider
provider = GoogleProvider(vertexai=True)
model = GoogleModel('gemini-1.5-flash', provider=provider)
agent = Agent(model)
...
Service Account
To use a service account JSON file:
from google.oauth2 import service_account
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel
from pydantic_ai.providers.google import GoogleProvider
credentials = service_account.Credentials.from_service_account_file(
'path/to/service-account.json',
scopes=['https://www.googleapis.com/auth/cloud-platform'],
)
provider = GoogleProvider(credentials=credentials)
model = GoogleModel('gemini-1.5-flash', provider=provider)
agent = Agent(model)
...
Customizing Location
You can specify the location when using Vertex AI:
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel
from pydantic_ai.providers.google import GoogleProvider
provider = GoogleProvider(vertexai=True, location='asia-east1')
model = GoogleModel('gemini-1.5-flash', provider=provider)
agent = Agent(model)
...
Provider Argument
You can supply a custom GoogleProvider instance using the provider argument to configure advanced client options, such as setting a custom base_url.
This is useful if you're using a custom-compatible endpoint with the Google Generative Language API.
from google import genai
from google.genai.types import HttpOptions
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel
from pydantic_ai.providers.google import GoogleProvider
client = genai.Client(
api_key='gemini-custom-api-key',
http_options=HttpOptions(base_url='gemini-custom-base-url'),
)
provider = GoogleProvider(client=client)
model = GoogleModel('gemini-1.5-flash', provider=provider)
agent = Agent(model)
...
Model Settings
You can customize model behavior using GoogleModelSettings:
from google.genai.types import HarmBlockThreshold, HarmCategory
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel, GoogleModelSettings
settings = GoogleModelSettings(
temperature=0.2,
max_tokens=1024,
google_thinking_config={'thinking_budget': 2048},
google_safety_settings=[
{
'category': HarmCategory.HARM_CATEGORY_HATE_SPEECH,
'threshold': HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
}
]
)
model = GoogleModel('gemini-1.5-flash')
agent = Agent(model, model_settings=settings)
...
See the Gemini API docs for more on safety settings, and thinking config.
Document, Image, Audio, and Video Input
GoogleModel supports multi-modal input, including documents, images, audio, and video. See the input documentation for details and examples.
Model settings
You can use the GoogleModelSettings class to customize the model request.
Disable thinking
You can disable thinking by setting the thinking_budget to 0 on the google_thinking_config:
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel, GoogleModelSettings
model_settings = GoogleModelSettings(google_thinking_config={'thinking_budget': 0})
model = GoogleModel('gemini-2.0-flash')
agent = Agent(model, model_settings=model_settings)
...
Check out the Gemini API docs for more on thinking.
Safety settings
You can customize the safety settings by setting the google_safety_settings field.
from google.genai.types import HarmBlockThreshold, HarmCategory
from pydantic_ai import Agent
from pydantic_ai.models.google import GoogleModel, GoogleModelSettings
model_settings = GoogleModelSettings(
google_safety_settings=[
{
'category': HarmCategory.HARM_CATEGORY_HATE_SPEECH,
'threshold': HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
}
]
)
model = GoogleModel('gemini-2.0-flash')
agent = Agent(model, model_settings=model_settings)
...
See the Gemini API docs for more on safety settings.