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:

Feature	Reference	When to load
Initialization	references/getting-started.md	Setting up genkit.Init, plugins, the *Genkit instance pattern
Generation	references/generation.md	Generate, GenerateText, GenerateData, streaming, output formats
Prompts	references/prompts.md	DefinePrompt, DefineDataPrompt, .prompt files, schemas
Tools	references/tools.md	DefineTool, tool interrupts, RestartWith/RespondWith
Middleware	references/middleware.md	ai.Middleware, ai.WithUse, Hooks (Generate/Model/Tool), built-ins (Retry, Fallback, ToolApproval, Filesystem, Skills)
Flows & HTTP	references/flows-and-http.md	DefineFlow, DefineStreamingFlow, genkit.Handler, HTTP serving
Model Providers	references/providers.md	Google AI, Vertex AI, Anthropic, OpenAI-compatible, Ollama setup

Agents (Experimental)

Genkit Go has an experimental agent API for persistent, multi-turn conversations (sessions, snapshots, interrupts, branching, background execution). It is gated: initialize with genkit.Init(ctx, genkit.WithExperimental()) or the constructors panic. Server constructors come from genkit/exp (aliased genkitx); types and options from ai/exp (aliased aix); session stores from ai/exp/localstore.

• Agent or flow? If the task is conversational, multi-turn, or described as "an agent", "assistant", or "chatbot", build it with genkitx.DefineAgent rather than hand-rolling a Generate + tools loop in a flow. Reach for a plain flow only for single-shot, stateless generation.

For details see:

• Agents: defining/serving an agent, running turns, and client- vs server-managed state (start here).
• Sessions & persistence: session stores (localstore.NewInMemorySessionStore/NewFileSessionStore) and snapshots.
• Human-in-the-loop / interrupts: pausing for approval/input and resuming.
• Branching: forking a conversation from a snapshot.
• Background agents: detaching long-running turns and polling.
• Working with state: typed custom session state, streamed as JSON patches.
• Artifacts: producing and reading named deliverables.
• Multi-agent orchestration: delegating to sub-agents.
• Advanced custom agents: DefineCustomAgent for full turn control.
• Deploying agents: serving agents over HTTP with genkit.Handler.

Generative UI (A2UI)

Genkit Go has an A2UI (Agent-to-UI) plugin (github.com/genkit-ai/genkit/go/plugins/a2ui/exp, imported as a2uix) that lets an agent stream interactive UI surfaces (cards, lists, forms, buttons), not just prose. The whole integration is the &a2uix.Surfaces{} model middleware added via ai.WithUse on a generate call or agent inline prompt. Go is server-only: it has no A2UI client/renderer. Render surfaces with a JS or Dart/Flutter client that talks to your Go agent over HTTP (the server and clients are wire-compatible).

• A2UI: server middleware, options, custom catalogs, and security. For the client (rendering surfaces), refer to the A2UI reference in the Genkit JS or Dart skill.

Genkit CLI (recommended)

genkit start unintrusively wraps any Go program that uses the Genkit library, running it unchanged while capturing traces from every Genkit action so you can prove tools were actually called and inspect model I/O from the terminal, even for headless checks. It forwards stdio, so interactive CLI tools that rely on stdin/stdout work without issues. Running the app directly (go run .) skips trace capture, so you're debugging blind. Check install with genkit --version.

Installation:

curl -sL cli.genkit.dev | bash

Primary pattern (default): prefix genkit start -- to your normal run command. This collects telemetry from any Genkit code your program runs, whether triggered from the dev UI, your own web server/web UI, or a plain script. Starts the Developer UI (usually http://localhost:4000) for running flows, model and agent playground, and browsing traces:

genkit start -- go run .
genkit start --noui -- go run .   # same, without the Dev UI (still a persistent server)
genkit start -o -- go run .       # also opens the browser

genkit start runs until you stop it with Ctrl+C. That is expected and correct for the common cases: a server your web/mobile app calls, or an interactive CLI you exit yourself. --noui only drops the Dev UI; it is not a one-shot command and will not exit on its own. Do not use genkit start as a blocking step in automated/non-interactive contexts; use flow:run (below) for that.

Non-interactive use (agents/CI): add the global --non-interactive flag before -- so the CLI uses defaults and never blocks on a prompt (e.g. the first-run analytics notice): genkit start --non-interactive -- go run . (works with flow:run too).

Run a flow (flow:run): invoke a specific flow by name from the CLI. Append your run command after -- to spin up the runtime just for this run (the command runs as-is to register your flows):

genkit flow:run myFlow '{"data": "input"}' -- go run .
genkit flow:run myFlow '{"data": "input"}' --stream -- go run .   # with streaming
genkit flow:run myFlow '{"data": "input"}' --wait -- go run .     # wait for completion

This is self-terminating: it runs the flow once, prints a Trace ID, then exits, so it's the right choice for a quick, non-interactive check (unlike genkit start). Traces for this run can be inspected using the trace commands below.

Debugging with traces: the fastest way to see prompts, model inputs/outputs, tool calls, latencies, and errors. Inspect from the terminal after any run under genkit start:

genkit trace:list                        # find recent trace IDs
genkit trace:get <traceId>               # full trace details (inputs, outputs, tool calls, errors)
genkit trace:get <traceId> --format json # machine-readable JSON, safe to pipe into jq or other parsers

For machine-readable output, pass --format json to get clean JSON you can pipe into jq or other parsers. The default output is human-oriented (banner/log lines, possible truncation on large traces), so don't pipe that form directly; use --format json, grep, or the Dev UI trace viewer.

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.
• Verify with traces, not a blind run. Running the app directly (go run .) does not capture dev traces. See the Genkit CLI section for how to run your app and capture traces.
• 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.