gemini-api

Gemini API function calling enables agent workflows by allowing models to invoke your Python functions automatically or manually to perform actions and retrieval. You define tools and handle the function calls within the SDK loop to execute external logic and return results to the model.

Yes, the Gemini API supports streaming responses to produce incremental output for better UX in interactive applications. By configuring the Python or TypeScript SDK to stream generation, you can display text chunks progressively as the model creates them rather than waiting for the full payload.

The best way to handle transient Gemini API errors is by applying optional robust retries within your Python or TypeScript SDK calls. Configuring retry logic ensures that transient network or service failures are automatically reattempted, reducing unhandled failures in production environments.

Yes, you can use context caching with the Gemini API for repeated requests to optimize cost and latency. Context caching stores prompt context, allowing batch and async patterns to reuse prior data across multiple calls, which is particularly effective for agent workflows processing similar inputs.

To analyze images with the Gemini API, you pass image data alongside your text prompts directly to the model using the Python or TypeScript SDK. This vision input capability allows the model to perform multimodal analysis, extracting information from images within your configured generation calls.