developing-genkit-go

Building AI agents with tool calling in Go uses Genkit patterns to orchestrate model generation, define custom tools, and configure middleware for human-in-the-loop interactions. This Skill provides flows to wire these components together without manual setup.

The best way to generate structured output with LLMs in Go is by applying Genkit generation workflows. This Skill enables you to define schemas and parse model responses into structured data formats directly within your Go application logic.

Yes, Genkit supports streaming responses for Go applications. You can build text generation experiences that stream model outputs directly to clients using Genkit Go APIs, enabling real-time response delivery in your AI workflows.

Yes, you can deploy Genkit flows as HTTP endpoints in Go. This Skill covers HTTP deployment patterns, allowing you to expose your AI generation workflows and agent interactions through standard web server routes.

You need specific provider integrations to connect AI models in Go using Genkit. This Skill requires Genkit Go patterns for initialization and AI model connectivity, ensuring your application can communicate with the underlying language models.

Observability for Genkit Go workflows allows you to monitor and trace your AI generation processes. This Skill applies Genkit patterns to include observability features, helping you track model interactions, tool executions, and flow performance.