Genkit

// Overview

Genkit is an open-source framework for building full-stack AI-powered applications, built and used in production by Google's Firebase. It puts a unified interface over models from providers including Google, OpenAI, Anthropic and Ollama, so the code that generates text, calls a tool or returns structured data stays the same when you change the model behind it.

Its distinguishing trait is cross-language parity: the same concepts and APIs ship as SDKs for JavaScript/TypeScript and Go (both production-ready with full feature support), Python (beta, wide feature support), and Dart (preview, core functionality). That makes it unusual among AI app frameworks, most of which are Python-first with a thinner port elsewhere.

Around the model calls, Genkit provides the pieces an app needs: prompt templating via Dotprompt, type-safe structured output, tool calling and agentic workflows, persisted chat interfaces, retrieval-augmented generation, and flows for multi-step logic. A local CLI and Developer UI let you test prompts and flows against single inputs or datasets, compare model outputs and read execution traces, and a production monitoring dashboard tracks latency, request volume and error rates once deployed. Genkit runs server-side in any environment that supports its language — Cloud Functions for Firebase, Google Cloud Run or third-party platforms, with or without Google services — and offers client SDKs for Next.js, React, Angular, iOS and Android.

// What it does

• Unified interface over models from Google, OpenAI, Anthropic, Ollama and more, so providers can be swapped without rewriting app code
• SDKs with consistent APIs for JavaScript/TypeScript and Go (production-ready), Python (beta) and Dart (preview)
• Streamlined APIs for structured output, agentic tool calling, context-aware generation, multimodal input/output and persisted chat
• Flows for multi-step AI logic, Dotprompt prompt templating and built-in RAG primitives
• Local CLI and Developer UI to test prompts and flows on inputs or datasets, compare model outputs and inspect execution traces
• Production monitoring dashboard for model performance, request volume, latency and error rates

// Getting started

The quickest path is the JavaScript/TypeScript SDK with the Google GenAI plugin. You supply the provider's API key; Genkit handles the call.

npm install genkit @genkit-ai/google-genai

import { genkit } from 'genkit';
import { googleAI } from '@genkit-ai/google-genai';

const ai = genkit({ plugins: [googleAI()] });

const { text } = await ai.generate({
    model: googleAI.model('gemini-flash-latest'),
    prompt: 'What is the meaning of life?'
});

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Build a chatbot, automation or recommendation feature in TypeScript or Go without committing to a single model vendor
• Add AI to an existing Firebase or Cloud Run backend, using client SDKs to reach it from Next.js, React, Angular, iOS or Android
• Prototype a prompt or flow in the local Developer UI, comparing model outputs side by side before shipping it
• Stand up a RAG feature with tool calling and type-safe structured output, then watch latency and error rates on the monitoring dashboard

// How Genkit compares

Genkit alongside other open-source app frameworks tools AI/TLDR tracks, ranked by GitHub stars.