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Genkit gives you a unified set of APIs for integrating AI models, defining type-safe workflows (flows), managing prompts, and building retrieval-augmented generation (RAG) pipelines — all with built-in observability and local developer tooling.

Quickstart: JavaScript

Get up and running with Genkit in Node.js or TypeScript in minutes.

Quickstart: Go

Start building AI features in your Go application with Genkit.

Quickstart: Python

Use Genkit’s Python SDK (alpha) to add AI capabilities to your Python app.

Core Concepts

Learn about flows, models, tools, prompts, and RAG.

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What is Genkit?

Genkit simplifies AI application development by providing:
  • A unified model interface — integrate models from Google Gemini, Anthropic Claude, OpenAI-compatible APIs, Ollama, and more with a consistent API
  • Type-safe flows — define AI workflows with input/output schema validation using Zod (JS/TS) or Pydantic (Python)
  • Tool calling — let models invoke your functions automatically with multi-turn reasoning loops
  • Prompt management — use .prompt files (Dotprompt format) with Handlebars templating, YAML frontmatter, and versioning
  • RAG pipelines — built-in abstractions for embedders, retrievers, and vector stores
  • Developer tooling — local CLI and Developer UI for testing, tracing, and evaluation
  • Production monitoring — OpenTelemetry-based tracing exportable to Google Cloud, Firebase, or third-party backends

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Language support

JavaScript / TypeScript

Production-ready. Full feature support. Published as the genkit npm package.

Go

Production-ready. Full feature support. Available as github.com/firebase/genkit/go.

Python

Alpha. Core functionality available. Published as the genkit PyPI package.

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How it works

1

Initialize Genkit with plugins

Create a Genkit instance and load model provider plugins (Google Gemini, Vertex AI, Ollama, etc.).
2

Define flows and tools

Wrap your AI logic in typed flows. Define tools that models can call automatically.
3

Test with the Developer UI

Use genkit start to launch the local Developer UI — run flows, inspect traces, and iterate fast.
4

Deploy and monitor

Deploy to Firebase, Cloud Run, or any platform. Monitor model calls, latency, and errors in production.

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Quick example

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-2.5-flash'),
  prompt: 'Explain what Genkit is in one sentence.',
});

console.log(text);

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Explore further

Core Concepts

Understand flows, models, tools, and prompts — the building blocks of every Genkit app.

Plugin ecosystem

Browse official plugins for model providers, vector stores, and telemetry backends.

Guides

Practical guides for structured output, streaming, agents, and evaluation.

Deploy your app

Deploy to Firebase, Cloud Run, or any platform that runs your chosen language.