developing-genkit-go

To build AI applications in Go using Genkit, you utilize core APIs like DefineFlow, Generate, and DefinePrompt to implement end-to-end workflows. You need to initialize a Go project with Genkit plugins to integrate model providers and create structured AI features.

Yes, you can define typed prompts and structured outputs in Genkit using DefinePrompt and DefineDataPrompt. These core APIs allow you to interact with multiple model providers and ensure your Go applications receive structured, typed responses from the AI models.

Yes, Genkit for Go supports streaming responses from model providers. You can use the Generate and GenerateText APIs with streaming options to handle streaming data or data prompts, enabling real-time AI generation within your Go applications.

You encapsulate AI logic with observability and tracing in Go by using the DefineFlow API. DefineFlow exposes your AI logic over HTTP and provides built-in observability and tracing for your Genkit-powered Go applications.

The best way to integrate real-time tool calls in a Go AI application is by using Genkit's DefineTool API. DefineTool enables dynamic, real-time tool-assisted generation and supports tool interrupts for complex, tool-assisted AI workflows.

Yes, you need a standard Go environment setup to use Genkit plugins. Building Genkit-powered AI Go applications requires configuring your Go project with the necessary Genkit plugins to properly integrate model providers and execute core APIs.