Google Gen AI Python SDK provides an interface for developers to integrate Google’s generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs. ... Please run one of the following code blocks to create a client for different services (Gemini Developer API or Vertex AI). from google import genai # Only run this block for Gemini Developer API client = genai.Client(api_key='GEMINI_API_KEY')

1 month ago - from google import genai # TODO(developer): Update below line API_KEY = "YOUR_API_KEY" client = genai.Client(vertexai=True, api_key=API_KEY) response = client.models.generate_content( model="gemini-3.5-flash", contents="Explain bubble sort to me.", ) print(response.text) # Example response: # Bubble Sort is a simple sorting algorithm that repeatedly steps through the list · The Google Gen AI SDK for Go is available on go.dev and GitHub:

1 month ago - By default, the SDK uses the beta API endpoints provided by Google to support preview features in the APIs. The stable API endpoints can be selected by setting the API version to v1. To set the API version use http_options. For example, to set the API version to v1 for Vertex AI: from google import genai from google.genai import types client = genai.Client( enterprise=True, project='your-project-id', location='global', http_options=types.HttpOptions(api_version='v1') )

April 28, 2026 - You would directly instantiate and interact with GenerativeModel objects. import google.generativeai as genai # Directly create and use model objects model = genai.GenerativeModel('gemini-3.5-flash') response = model.generate_content(...) chat ...

April 25, 2026 - Documentation: https://googleapis.github.io/js-genai/ The Google Gen AI JavaScript SDK is designed for TypeScript and JavaScript developers to build applications powered by Gemini.

May 5, 2026 - When a custom OkHttpClient is provided, the SDK will clone it (using newBuilder()) to retain all your custom configurations, while still automatically appending the SDK's internal RetryInterceptor. import com.google.genai.Client; import com.google.genai.types.ClientOptions; import okhttp3.OkHttpClient; import java.time.Duration; // Create your custom OkHttpClient OkHttpClient customHttpClient = new OkHttpClient.Builder() .connectTimeout(Duration.ofSeconds(30)) .readTimeout(Duration.ofSeconds(30)) // Add your custom interceptors, SSL socket factory, etc.

from llama_index.llms.google_genai import GoogleGenAI ... Here's a cute dog for you! We can also edit the image! ... content="Please edit the image to make the dog a mini-schnauzer, but keep the same overall pose, framing, background, and art style.", ... Here's your mini-schnauzer! Note for AI agents: this documentation is built for programmatic access.

Help me with writing code using the Gemini API calling the official libraries and SDKs. Please follow the following guidelines when generating code. Official Documentation: ai.google.dev/gemini-api/docs · Always use the Google GenAI SDK (google-genai), which is the unified standard library for all Gemini API requests (AI Studio/Gemini Developer API and Vertex AI) as of 2025.

April 28, 2026 - When building with the Gemini API, we recommend using the Google GenAI SDK. These are the official, production-ready libraries that we develop and maintain for the most popular languages. They are in General Availability and used in all our official documentation and examples.

May 21, 2026 - Weights & Biases, developer tools for machine learning

2 weeks ago - Install the SDK and generate text with a single API call. ... 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(interacti...

1 week ago - Generative models are often unaware of recent API and SDK updates and may suggest outdated or legacy code. We recommend using our Code Generation instructions codegen_instructions.md when generating Google Gen AI SDK code to guide your model towards using the more recent SDK features.

1 month ago - The google-genai SDK provides a high-level asynchronous interface for managing these connections. ... Session: A persistent connection to the model. Config: Setting up modalities (audio/text), voice, and system instructions.

October 24, 2025 - This SDK enables C#/.NET developers use Gemini from Google AI or Vertex AI with a single unified library. Let’s take a look at the details. To install the library, run the following command in your .NET project directory: ... First, you need to create a client to talk to Gemini. You can target Gemini on Google AI (via the Gemini Developer API): ... Of course, all of the text and image generation is highly configurable. For example, you can define a response schema and a generation configuration with system instructions and other settings for text generation as follows:

May 22, 2026 - This SDK supports use cases like: ... For example, with just a few lines of code, you can access Gemini's multimodal capabilities to generate text from text-and-image input. parts := []*genai.Part{ {Text: "What's this image about?"}, {InlineData: ...

March 16, 2025 - By default, the SDK uses a set of standard configurations. However, you can modify these to suit your needs: 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))

2 weeks ago - By default, the SDK uses the beta API endpoints provided by Google to support preview features in the APIs. The stable API endpoints can be selected by setting the API version to v1. To set the API version use http_options. For example, to set the API version to v1 for Vertex AI: from google import genai from google.genai import types client = genai.Client( enterprise=True, project='your-project-id', location='global', http_options=types.HttpOptions(api_version='v1') )