Getting started with Nano Banana 2 requires an API key from the developer console, which you can obtain by creating a project and enabling the image generation API. Once you have a key, the minimal implementation involves a single HTTP POST request to the generation endpoint with a JSON body containing your prompt text and the model identifier for Nano Banana 2. The response includes the generated image as a base64-encoded string along with metadata about the generation, such as the model version used and safety filter results. You can decode the base64 string and write it to a file or pass it directly to a frontend that renders images from data URIs.
If you prefer to use an SDK rather than raw HTTP, official client libraries are available for Python, Node.js, and Go. In Python, after installing the package with pip, you initialize a client with your API key, call the generate_image method with your prompt and model name, and access the image bytes from the response object. A basic working example from installation to saved image file takes around fifteen lines of Python. The SDK handles authentication headers, JSON serialization, and base64 decoding automatically, which removes the boilerplate from your application code.
For teams building image generation into a larger data pipeline—for example, generating visual assets that are then described and indexed in a vector database such as Zilliz Cloud for similarity-based retrieval—the SDK's synchronous and asynchronous generation methods both fit naturally into pipeline stages. Starting with a minimal implementation and adding features like retry logic, output validation, and downstream indexing incrementally is the recommended approach rather than building the full pipeline before testing the generation quality.