Description

# Google Gen AI SDK [](https://pypi.org/project/google-genai/)  [](https://pypistats.org/packages/google-genai) -------- **Documentation:** 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. It supports the [Gemini Developer API](https://ai.google.dev/gemini-api/docs) and [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/overview) APIs. ## Code Generation Generative models are often unaware of recent API and SDK updates and may suggest outdated or legacy code.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`](https://raw.githubusercontent.com/googleapis/python-genai/refs/heads/main/codegen_instructions.md) when generating Google Gen AI SDK code to guide your model towards using the more recent SDK features. Copy and paste the instructions into your development environment to provide the model with the necessary context. ## Installation ```sh pip install google-genai ``` <small>With `uv`:</small> ```sh uv pip install google-genai ``` ## Imports ```python from google import genai from google.genai import types ``` ## Create a client Please run one of the following code blocks to create a client for different services ([Gemini Developer API](https://ai.google.dev/gemini-api/docs) or [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/overview)). ```python from google import genai # Only run this block for Gemini Developer API client = genai.Client(api_key='GEMINI_API_KEY') ``` ```python from google import genai # Only run this block for Vertex AI API client = genai.Client( vertexai=True, project='your-project-id', location='us-central1' ) ``` ## Using types All API methods support Pydantic types and dictionaries, which you can access from `google.genai.types`. You can import the types module with the following: ```python from google.genai import types ``` Below is an example `generate_content()` call using types from the types module: ```python response = client.models.generate_content( model='gemini-2.5-flash', contents=types.Part.from_text(text='Why is the sky blue?'), config=types.GenerateContentConfig( temperature=0, top_p=0.95, top_k=20, ), ) ``` Alternatively, you can accomplish the same request using dictionaries instead of types: ```python response = client.models.generate_content( model='gemini-2.5-flash', contents={'text': 'Why is the sky blue?'}, config={ 'temperature': 0, 'top_p': 0.95, 'top_k': 20, }, ) ``` **(Optional) Using environment variables:** 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 Vertex AI. **Gemini Developer API:** Set the `GEMINI_API_KEY` or `GOOGLE_API_KEY`. It will automatically be picked up by the client. It's recommended that you set only one of those variables, but if both are set, `GOOGLE_API_KEY` takes precedence. ```bash export GEMINI_API_KEY='your-api-key' ``` **Gemini API on Vertex AI:** Set `GOOGLE_GENAI_USE_VERTEXAI`, `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION`, as shown below: ```bash export GOOGLE_GENAI_USE_VERTEXAI=true export GOOGLE_CLOUD_PROJECT='your-project-id' export GOOGLE_CLOUD_LOCATION='us-central1' ``` ```python from google import genai client = genai.Client() ``` ## Close a client Explicitly close the sync client to ensure that resources, such as the underlying HTTP connections, are properly cleaned up and closed. ```python from google.genai import Client client = Client() response_1 = client.models.generate_content( model=MODEL_ID, contents='Hello', ) response_2 = client.models.generate_content( model=MODEL_ID, contents='Ask a question', ) # Close the sync client to release resources. client.close() ``` To explicitly close the async client: ```python from google.genai import Client aclient = Client( vertexai=True, project='my-project-id', location='us-central1' ).aio response_1 = await aclient.models.generate_content( model=MODEL_ID, contents='Hello', ) response_2 = await aclient.models.generate_content( model=MODEL_ID, contents='Ask a question', ) # Close the async client to release resources. await aclient.aclose() ``` ## Client context managers By using the sync client context manager, it will close the underlying sync client when exiting the with block and avoid httpx "client has been closed" error like [issues#1763](https://github.com/googleapis/python-genai/issues/1763). ```python from google.genai import Client with Client() as client: respons