The main SDKs with full support for Gemini 3 features are the official ones provided around the Gemini API and Google Cloud. In practice, that means JavaScript/TypeScript, Python, and often Java and Go receive first-class support for new capabilities like multimodal input, streaming, tool calling, and “thinking” configuration. These SDKs are usually kept up to date with new model releases, so they expose the latest flags and request structures needed to use Gemini 3 Pro, including things like function-calling, structured output, and system instructions.
When you choose an SDK, look for clear support for a few specific features. First, check that you can send multimodal content (text + images, PDFs, possibly audio/video) as structured parts, not just a flat string. Second, confirm that the SDK supports streaming responses, so you can build interactive apps. Third, ensure you can define tools or functions with JSON schemas and receive tool-call responses from Gemini 3. Finally, look for access to advanced options like setting “thinking level” or other reasoning-related configs. If all of these are present, you’re in a good place to use most of Gemini 3’s capabilities.
If you’re building RAG or agent systems, the SDK is just one piece. You’ll usually combine it with a client for your vector database, like a Python or Node.js client forMilvusor the managed Zilliz Cloud. The common pattern is: use the Milvus/Zilliz client to run semantic search, then use the Gemini SDK to send the retrieved context and user question to the model. By standardizing on one or two languages (for example, Python for backend services and TypeScript for frontends), you keep your integration surface simple while still leveraging the full set of Gemini 3 features.