Developing Genkit Go

Genkit Go

Genkit Go is an AI SDK for Go that provides generation, structured output, streaming, tool calling, prompts, and flows with a unified interface across model providers.

Hello World

package main

import (
	"context"
	"fmt"
	"log"
	"net/http"

	"github.com/genkit-ai/genkit/go/ai"
	"github.com/genkit-ai/genkit/go/genkit"
	"github.com/genkit-ai/genkit/go/plugins/googlegenai"
	"github.com/genkit-ai/genkit/go/plugins/server"
)

func main() {
	ctx := context.Background()
	g := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.GoogleAI{}))

	genkit.DefineFlow(g, "jokeFlow", func(ctx context.Context, topic string) (string, error) {
		return genkit.GenerateText(ctx, g,
			ai.WithModelName("googleai/gemini-flash-latest"),
			ai.WithPrompt("Tell me a joke about %s", topic),
		)
	})

	mux := http.NewServeMux()
	for _, f := range genkit.ListFlows(g) {
		mux.HandleFunc("POST /"+f.Name(), genkit.Handler(f))
	}
	log.Fatal(server.Start(ctx, "127.0.0.1:8080", mux))
}

Core Features

Load the appropriate reference based on what you need:

Genkit CLI

Check if installed: genkit --version

Installation:

curl -sL cli.genkit.dev | bash

Key commands:

# Start app with Developer UI (tracing, flow testing) at http://localhost:4000
genkit start -- go run .
genkit start -o -- go run .   # also opens browser

# Run a flow directly from the CLI
genkit flow:run myFlow '{"data": "input"}'
genkit flow:run myFlow '{"data": "input"}' --stream   # with streaming
genkit flow:run myFlow '{"data": "input"}' --wait      # wait for completion

# Look up Genkit documentation
genkit docs:search "streaming" go
genkit docs:list go
genkit docs:read go/flows.md

See references/getting-started.md for full CLI and Developer UI details.

Key Guidance

• Pass g explicitly. The *Genkit instance returned by genkit.Init is the central registry. Pass it to all Genkit functions rather than storing it as a global. This is a core pattern throughout the SDK.
• Wrap AI logic in flows. Flows give you tracing, observability, HTTP deployment via genkit.Handler, and the ability to test from the Developer UI and CLI. Any generation call worth keeping should live in a flow.
• Use jsonschema:"description=..." struct tags on output types. The model uses these descriptions to understand what each field should contain. Without them, structured output quality drops significantly.
• Write good tool descriptions. The model decides which tools to call based on their description string. Vague descriptions lead to missed or incorrect tool calls.
• Use .prompt files for complex prompts. They separate prompt content from Go code, support Handlebars templating, and can be iterated on without recompilation. Code-defined prompts are better for simple, single-line cases.
• Reach for built-in middleware before writing one. Retry, Fallback, ToolApproval, Filesystem, and Skills cover the common cross-cutting needs and compose with each other via ai.WithUse. See references/middleware.md. When you do write custom middleware, allocate per-call state in closures captured by New, and guard anything that WrapTool mutates because tools may run concurrently.
• Look up the latest model IDs. Model names change frequently. Check provider documentation for current model IDs rather than relying on hardcoded names. See references/providers.md.