1 month ago - from google import genai # Only run this block for Agent Platform client = genai.Client( enterprise=True, project='your-project-id', location='global' )

from google import genai from google.genai.types import HttpOptions client = genai.Client(http_options=HttpOptions(api_version="v1")) response = client.models.generate_content( model="gemini-3.5-flash", contents="How does AI work?", ) print(response.text) # Example response: # Okay, let's break down how AI works.

from google import genai # Only run this block for Vertex AI API client = genai.Client( vertexai=True, project='your-project-id', location='us-central1' )

from google import genai client = genai.Client( enterprise=True, project='my-project-id', location='us-central1' )

Google Generative AI High level API client library and tools. ... With Gemini 2.0, we took the chance to create a single unified SDK for all developers who want to use Google's GenAI models (Gemini, Veo, Imagen, etc).

April 28, 2026 - The new SDK will pick up your API key from the GEMINI_API_KEY environment variables, if you don't pass one to the client. ... from google import genai client = genai.Client() # Set the API key using the GEMINI_API_KEY env var.

2 weeks ago - Please run one of the following code blocks to create a client for different services (Gemini Developer API or Agent Platform). from google import genai # Only run this block for Gemini Developer API client = genai.Client(api_key='GEMINI_API_KEY')

May 5, 2026 - Client client = Client.builder().apiKey("your-api-key").build(); import com.google.genai.Client; // Use Builder class for instantiation. Explicitly set the project and location, // and set `enterprise(true)` to use Gemini Enterprise Agent Platform backend.

1 week ago - import { GoogleGenAI } from '@google/genai'; const ai = new GoogleGenAI({ enterprise: true, project: 'your_project', location: 'your_location', }); For NodeJS environments, you can create a client by configuring the necessary environment variables. Configuration setup instructions depends on whether you're using the Gemini Developer API or the Gemini Enterprise Agent Platform.

May 22, 2026 - client, err := genai.NewClient(ctx, &genai.ClientConfig{ Project: project, Location: location, Backend: genai.BackendEnterprise, }) You can create a client by configuring the necessary environment variables. Configuration setup instructions depends on whether you're using the Gemini Developer API or the Gemini API in Gemini Enterprise Agent Platform. Gemini Developer API: Set GOOGLE_API_KEY as shown below:

April 28, 2026 - As of May 2025, the Google GenAI SDK has reached General Availability (GA) across all supported platforms and are the recommended libraries to access the Gemini API. They are stable, fully supported for production use, and are actively maintained.

3 weeks ago - Add the SDK to your module with go get google.golang.org/genai. ... client, err := genai.NewClient(ctx, &genai.ClientConfig{ APIKey: apiKey, Backend: genai.BackendGeminiAPI, })

5 days ago - from google import genai from google.genai.types import HttpOptions client = genai.Client(http_options=HttpOptions(api_version="v1")) response = client.models.generate_content( model="gemini-3.5-flash", contents="How does AI work?", ) print(response.text) # Example response: # Okay, let's break down how AI works.

2 weeks ago - Stateless: Manage the conversation history on the client by passing all previous turns (including intermediate model thought and tool steps) in each request. Chain interactions by passing previous_interaction_id. The server manages the full conversation history for you. from google import genai client = genai.Client() # Server-side state (recommended) interaction1 = client.interactions.create( model="gemini-3.5-flash", input="I have 2 dogs in my house.", ) print("Response 1:", interaction1.output_text) interaction2 = client.interactions.create( model="gemini-3.5-flash", input="How many paws are in my house?", previous_interaction_id=interaction1.id, ) print("Response 2:", interaction2.output_text)

June 4, 2025 - Unofficial Haskell client for Google GenAI API, including Gemini.

1 week ago - from google import genai client = genai.Client() interaction = client.interactions.create( model="gemini-3.5-flash", input="Explain how AI works in a few words" ) print(interaction.output_text)

August 17, 2025 - You will create an instance of the client for each interaction with the Google generative AI models. You will instantiate the client with different methods depending on the API you are using. For the Gemini Developer API, you can instantiate the client by passing along your API key: client = genai.Client(api_key='YOUR_GEMINI_API_KEY')

1 month ago - import asyncio from google import genai client = genai.Client(api_key="YOUR_API_KEY") model = "gemini-3.1-flash-live-preview" config = {"response_modalities": ["AUDIO"]} async def main(): async with client.aio.live.connect(model=model, config=config) as session: print("Session started") # Send content...

March 16, 2025 - from google import genai from google.genai import types PROJECT_ID = userdata.get('PROJECT_ID') LOCATION = userdata.get('LOCATION') # Create a client instance using Vertex AI in Google Cloud client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION) response = client.models.generate_content( model='gemini-2.0-flash', contents='Create a short bedtime story for a 3 year old using unicorns and rainbows', config = types.GenerateContentConfig( max_output_tokens=200, temperature = 0.1) ) display(Markdown(response.text))

March 11, 2025 - from google import genai from google.colab import userdata from IPython.display import Markdown, display GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY') # Create a client instance using Google Gemini API Key client = genai.Client(api_key=GOOGLE_API_KEY) response = client.models.generate_content( model='gemini-2.0-flash', contents='Create a short bedtime story for a 7 year old using unicorn and rainbows' ) display(Markdown(response.text)) For the enterprise version on Vertex AI, you would use the same code after authenticating to Google Cloud and provide your GCP project ID: # Authenticate to Google Cloud gcloud auth application-default login ·