developing-genkit-go
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.
How do I install this agent skill?
npx skills add https://github.com/firebase/agent-skills --skill developing-genkit-goIs this agent skill safe to install?
- Gen Agent Trust Hubpass
This skill provides documentation and code examples for Genkit in Go. It includes an installation command for the Genkit CLI and demonstrates patterns for building AI flows that are susceptible to indirect prompt injection if user-provided data is not properly sanitized.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
- ZeroLeakspass
1 finding · Score: 82/100
What does this agent skill do?
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 |
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
gexplicitly. The*Genkitinstance returned bygenkit.Initis 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
.promptfiles 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, andSkillscover the common cross-cutting needs and compose with each other viaai.WithUse. See references/middleware.md. When you do write custom middleware, allocate per-call state in closures captured byNew, and guard anything thatWrapToolmutates 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.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/firebase/agent-skills/developing-genkit-go">View developing-genkit-go on skillZs</a>