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 - To make a model more deterministic, lowering the temperature parameter reduces randomness, with values near 0 minimizing variability. Capabilities and parameter defaults for each model is shown in the Vertex AI docs and Gemini API docs respectively. Note that all API methods support Pydantic types and dictionaries, which you can access from google.genai.types.

April 28, 2026 - If you're using an AI coding assistant, install the Gemini API development skill to give your agent access to the latest documentation and best practices. If you're new to the Gemini API, follow our Get started guide to get started. The Google GenAI SDK is available for the Python, ...

Usage: .. code-block:: python ... class genai.client.Client(*, enterprise=None, vertexai=None, api_key=None, credentials=None, project=None, location=None, debug_config=None, http_options=None)¶

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). As part of that process, we took all of the feedback from this SDK and what developers like about other SDKs in the ecosystem to create the Google Gen AI SDK. The full migration guide from the old SDK to new SDK is available in the Gemini API docs.

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.

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 from google.genai import types as genai_types # Create a Client in the Gen AI SDK genai_client = genai.Client(vertexai=True, project="your-project", location="your-location") # Call generate_content() with the prompt response = genai_client.models.generate_content( model=retrieved_prompt.prompt_data.model, contents=retrieved_prompt.assemble_contents(), )

2 weeks ago - The Interactions API is available through the Python and JavaScript SDKs, as well as through REST. To use the Gemini API, you need an API key. Create one for free to get started: ... 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(interaction.output_text)

from langchain_google_genai import GoogleGenerativeAI, HarmBlockThreshold, HarmCategory llm = GoogleGenerativeAI( model="gemini-pro", google_api_key=api_key, safety_settings={ HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, }, ) For an enumeration of the categories and thresholds available, see Google’s safety settings guide. Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

import google.generativeai as genai import os genai.configure(api_key=os.environ['API_KEY']) model = genai.GenerativeModel(model_name='gemini-1.5-flash') response = model.generate_content('Teach me about how an LLM works') print(response.text) ...

In this notebook, we show how to use the google-genai Python SDK with LlamaIndex to interact with Google GenAI models.

2 weeks ago - To make a model more deterministic, lowering the temperature parameter reduces randomness, with values near 0 minimizing variability. Capabilities and parameter defaults for each model is shown in the Vertex AI docs and Gemini API docs respectively. Note that all API methods support Pydantic types and dictionaries, which you can access from google.genai.types.

Python API reference for langchain_google_genai. Part of the LangChain ecosystem.

April 6, 2025 - Google Gen AI SDK – Gemini API, accessed April 6, 2025, https://ai.google.dev/gemini-api/docs/sdks · googleapis/python-genai: Google Gen AI Python SDK provides an interface for developers to integrate Google’s generative models into their Python applications.

The GenAI Processors library requires Python 3.10+. ... 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 when generating code that ...

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

# Google Gen AI SDK [![PyPI ... https://googleapis.github.io/python-genai/ ----- Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications....