Genkit is an open-source framework for building full-stack, agentic AI applications, offering SDKs for JavaScript/TypeScript, Go, Python, and Dart, and it is built and used in production by Google's Firebase.

What it is

Genkit is an open-source framework, licensed under Apache-2.0 and hosted at genkit.dev, for building full-stack AI-powered applications. It lives in AI and machine learning infrastructure; its main repository is written in TypeScript and its topics cover agents, LLMs, embedders, multimodal input, RAG, and vector databases. It provides SDKs at different stability levels: JavaScript/TypeScript and Go are production-ready with full feature support, Python is in Beta, and Dart is in Preview with early core functionality.

The concrete problem Genkit solves is integration sprawl. Instead of writing and maintaining separate glue code for each model provider, developers use one unified interface reaching hundreds of models from Google, OpenAI, Anthropic, Ollama, and others. That interface covers multimodal content, structured outputs, tool calling, and agentic workflows, and extends into prompt templating, chat persistence, retrieval, and deployment, so an AI feature does not demand a different stack at every stage.

Key capabilities

• A unified model interface across providers, used as const ai = genkit({ plugins: [googleAI()] }); and then ai.generate({ model: googleAI.model('gemini-flash-latest'), prompt: '...' }).
• Streamlined APIs for structured, type-safe output generation, agentic tool calling, context-aware generation, and multi-modal input and output.
• Feature modules for text and image generation, prompt templating with dotprompt, persisted chat interfaces, AI workflows (flows), and AI-powered data retrieval (RAG).
• Cross-language SDKs with consistent APIs across JavaScript/TypeScript, Go, Python (Beta), and Dart (Preview).
• A local CLI and Developer UI for testing prompts and flows against individual inputs or datasets, comparing model outputs, and debugging with detailed execution traces.
• A production monitoring dashboard tracking model performance, request volume, latency, error rates, and observability metrics.
• Integration helpers for Next.js, React, Angular, iOS, and Android, plus deployment to Cloud Functions for Firebase, Google Cloud Run, or third-party platforms, with or without Google services.

Who uses it and how

• Product teams building production chatbots, automations, and recommendation systems that need one interface across several model providers.
• Full-stack web developers in Next.js, React, or Angular, and mobile developers on iOS or Android, using the purpose-built client SDKs and helpers.
• Teams deploying AI logic to Cloud Functions for Firebase or Google Cloud Run, including groups running on third-party platforms without Google services.
• Polyglot engineering groups that pick JavaScript/TypeScript, Go, Python, or Dart and expect consistent APIs across those languages.
• Developers evaluating before committing, through the free Explore Genkit by Example sample apps at examples.genkit.dev.

Getting started

Install the genkit package for the chosen language together with a provider plugin such as @genkit-ai/google-genai, then initialize with genkit({ plugins: [googleAI()] }) and call ai.generate. A local CLI and Developer UI support testing, and examples.genkit.dev offers sample apps to explore at no cost.

How it compares

No competing tools are named in these facts, so Genkit stands alone in this registry rather than being measured against listed alternatives. Its position here rests on an Apache-2.0 licence, four language SDKs at different stability levels, and provider support spanning Google, OpenAI, Anthropic, and Ollama.

When to use it — and when not to

A self-hoster must operate their own deployment target, whether Cloud Functions for Firebase, Google Cloud Run, or a third-party platform, because Genkit ships an SDK rather than a hosted runtime. Teams needing a fully stable Dart SDK, or Python in production today rather than in Beta, should wait or choose JavaScript/TypeScript or Go instead. The repository also carries 741 open issues, and the README excerpt truncates mid-sentence, so some details of intended use are not visible from the description alone.

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?'
});