Genkit vs ADK: A Guide to Choosing the Right Google AI Framework

Published: Jan 24, 2026 · Updated: Feb 11, 2026

Google offers two open-source frameworks for AI development: Genkit and Agent Development Kit (ADK).

Both can leverage Google’s large language models (Gemini), but they differ in their target use cases, architecture, and developer-facing features.

This article compares Genkit and ADK across use cases, platform integrations, model support, development style, and maturity to help you understand the differences and choose the right framework for your project.

TL;DR

Aspect	Genkit	ADK
Primary Use Case	Adding GenAI features to apps (chat, summarization, RAG, etc.)	Building multi-agent systems with collaborative agents
Languages	Node.js (GA), Go (GA), Python (Alpha), Dart (Preview)	Python (GA), TypeScript, Go, Java
Design Philosophy	Developer explicitly controls the flow	LLM dynamically decides agent delegation
Deployment	Cloud Run, GKE, Vercel, or anywhere	Anywhere, with deep Vertex AI Agent Engine integration

The choice depends on the complexity of your problem and your team’s preferred language.

Framework Overview

Genkit

Genkit is a framework designed to simplify adding AI capabilities to your applications.

Originally developed by the Firebase team, it became an independent product after a Google reorganization and now sits alongside Flutter, Dart, and Go in the developer platforms organization.

Use plugins and templates to integrate various generative AI models and quickly build AI features.

Genkit - Open-source AI framework by Google

Open-source framework for building AI-powered apps with unified APIs for Google Gemini, GPT, Claude, and more.

🔗 genkit.dev

Key Features:

Languages: Node.js (GA Feb 2025), Go (GA Sep 2025), Python (Alpha), Dart (Preview)
Runtime: Deploy anywhere — Cloud Run, GKE, Vercel, on-premise, etc.
Model Support: Gemini, OpenAI, Anthropic Claude, Ollama, and more via plugins
Developer Experience: Local Developer UI for prompt testing, step-by-step traces, and schema validation
Multi-agent: Beta support via the prompt-as-tool pattern

Genkit Dart is also under active development as a full Genkit SDK for Dart. With model plugins (genkit_google_genai, genkit_firebase_ai), flow/tool definitions, and server-side HTTP hosting via genkit_shelf, it offers capabilities comparable to the Node.js and Go SDKs — opening up possibilities for both server-side Dart and Flutter applications.

The Genkit community has published Mastering Genkit: Go Edition — a comprehensive guide covering everything from setup to advanced AI agents and RAG systems.

Mastering Genkit: Go Edition

A guide to building production-ready AI applications using Go and Genkit.

🔗 mastering-genkit.github.io

Agent Development Kit (ADK)

ADK is a toolkit specialized for building sophisticated AI agent systems. It provides a foundation for complex multi-agent scenarios involving coordination between agents and external tool integrations.

Agent Development Kit

Build powerful multi-agent systems with Agent Development Kit.

🔗 google.github.io

Key Features:

Languages: Python (GA), TypeScript, Go (announced Nov 2025), Java
Runtime: Deploy to any container environment — Cloud Run, GKE, etc. Optimized for Vertex AI Agent Engine integration
Model Support: Gemini via Vertex AI, plus other models through LiteLLM adapters
Developer Experience: Local dev UI (adk web), CLI evaluation tool (adk eval)
Multi-agent: First-class support for Agent, Tool, Sub-agent, and workflow orchestration
Enterprise Connectors: 100+ connectors via ApplicationIntegrationToolset — Salesforce, ServiceNow, JIRA, SAP, and more

Companies like Renault Group, Box, and Revionics are already building systems with ADK.

Key Differences

Design Philosophy

These aren’t competing frameworks — they operate at different layers.

A Google engineer working on Genkit summarized its role this way:

Genkit focuses on “low-layer” AI APIs: models, tools, prompts, MCP, and developer tools like o11y tooling and playgrounds. Genkit lets you interact directly with LLMs, ships without hardcoded prompts by default, and its o11y tooling lets you see exactly what your agent is doing.

From this perspective:

Genkit is a low-layer AI framework. It focuses on models, tools, prompts, MCP, and o11y. You can use it standalone to build agents, or as a foundation for higher-layer frameworks.
ADK is a high-layer agent framework. It comes with batteries included — session management, memory, evaluation, and tight Vertex AI Agent Engine integration.

A Genkit team member also shared this on the Genkit Discord:

ADK is specifically only for building agents, and it has an opinionated foundation for how to construct multi-agent systems. Genkit is a general purpose toolkit for building with GenAI which includes agent-building capabilities, but we take fewer strong opinions on the specific construction of agents.

In short: Genkit is general-purpose and flexible; ADK is specialized for multi-agent construction. Understand this difference, then choose based on your requirements.

Comparison Table

Aspect	Genkit	ADK
Primary Use Case	General GenAI integration (chatbots, summarization, recommendations, RAG, etc.)	Complex agent systems with multiple collaborating agents
Languages	Node.js (GA), Go (GA), Python (Alpha), Dart (Preview)	Python (GA), TypeScript, Go, Java
Development Style	Code-defined flows, explicit control	Agent-centric, LLM-driven orchestration
Multi-agent	Beta (prompt-as-tool pattern)	First-class support (Sequential, Parallel, Loop)
Deployment	Cloud Run, GKE, Vercel, anywhere	Anywhere, best fit with Agent Engine
Model Flexibility	Plugin-based, easy model switching	LiteLLM adapters, Vertex AI native
Maturity	Node GA (Feb 2025), Go GA (Sep 2025)	Python GA (May 2025), other languages in development

Architecture Patterns

Genkit: Flow Orchestration

Genkit uses a style where you explicitly define the workflow (flow).

You write the processing steps in code: “generate text with LLM A → process the result → execute function B → …”. Genkit provides unified APIs like ai.generate(), structured data output, and function calling through plugins, making these steps easy to implement concisely.

TypeScript 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: 'Why is Genkit awesome?'
});

Go Example:

import (
    "context"
    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/plugins/googlegenai"
)

func main() {
    ctx := context.Background()
    g, _ := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.GoogleAI{}))

    resp, _ := genkit.Generate(ctx, g,
        ai.WithModel(googlegenai.GoogleAIModel("gemini-2.5-flash")),
        ai.WithPrompt("Why is Genkit awesome?"),
    )
    fmt.Println(resp.Text())
}

With function calling, the LLM can behave like a semi-autonomous agent that invokes defined functions as needed, but you still control the overall flow.

ADK: Agent-Centric Orchestration

ADK uses an agent-oriented architecture.

You define agents (Agent classes), each with its own role (instruction prompts) and available tools or sub-agents.

ADK supports multiple patterns for sub-agent coordination:

sub_agents pattern: The LLM automatically generates transfer_to_agent calls to delegate control to sub-agents
AgentTool pattern: Wrap sub-agents as tools that the root agent can invoke

Here’s an example of the AgentTool pattern, where sub-agents have their own tools and are called as “tools” by the root agent:

from google.adk.agents import Agent
from google.adk.tools import AgentTool, ToolContext

# --- Sub-agent 1: Goal Setting ---
def get_user_goal(tool_context: ToolContext) -> dict:
    """Retrieves the user's current health goal from session state."""
    goal = tool_context.state.get("health_goal")
    if goal is None:
        return {"status": "not_set", "message": "No goal set yet."}
    return {"status": "success", "health_goal": goal}

def set_user_goal(tool_context: ToolContext, goal_type: str, description: str) -> dict:
    """Sets the user's health goal in session state."""
    tool_context.state["health_goal"] = {"goal_type": goal_type, "description": description}
    return {"status": "success", "message": f"Goal set: {goal_type}"}

goal_setting_agent = Agent(
    name="goal_setting_agent",
    description="Handles health goal setting and retrieval.",
    instruction="You help users set and check their health goals.",
    tools=[get_user_goal, set_user_goal],
)

# --- Sub-agent 2: Meal Advisor ---
meal_advisor_agent = Agent(
    name="meal_advisor_agent",
    description="Provides meal recommendations based on user goals.",
    instruction="You suggest meals considering the user's health goals and calorie targets.",
)

# --- Root Agent ---
root_agent = Agent(
    model="gemini-2.5-flash",
    name="health_advisor",
    description="Main health advisor agent",
    instruction="""You are a health advisor assistant.

## Your Role
- Understand user requests and delegate to appropriate sub-agents
- Use goal_setting_agent for goal-related queries
- Use meal_advisor_agent for meal recommendations

## Direct Response Cases
- Greetings: respond directly
- General health questions: respond directly
""",
    tools=[
        AgentTool(agent=goal_setting_agent),
        AgentTool(agent=meal_advisor_agent),
    ],
)

The root agent delegates to appropriate sub-agents based on LLM reasoning. Each sub-agent has its own tools and instructions to handle specialized tasks.

ADK also provides workflow control agents:

SequentialAgent: Run agents in sequence
ParallelAgent: Run agents in parallel
LoopAgent: Repeat until a condition is met

Decision Guide

Scenario / Condition	Recommendation	Reason
Quickly add simple AI features to an app	Genkit	Chatbots, summarization, RAG. Prioritize development speed
Complex AI system with multiple specialized agents	ADK	Multiple data sources/tools, need role separation
Team primarily writes TypeScript / JavaScript	Genkit	Node.js support, leverage web frontend experience
Team primarily writes Python	ADK	Leverage ML and data processing experience. Python is GA
Team primarily writes Go	Either works	Genkit Go is GA, ADK Go is available
Need to connect to Google Cloud data / enterprise systems	ADK	Rich connectors for BigQuery, Salesforce, ServiceNow, etc.
Want flexibility for various AI use cases in the future	Genkit	High extensibility through plugins

Choosing by Language

Now that ADK supports Python, TypeScript, Go, and Java, and Genkit supports Node.js, Go, Python, and Dart, language preference alone rarely decides the choice.

However, maturity varies:

Language	Genkit	ADK
TypeScript/Node.js	GA	Available
Go	GA	Available (announced Nov 2025)
Python	Alpha	GA
Dart	Preview	Not supported
Java	Not supported	Available

If your team writes Python and needs production stability, ADK is a natural choice. If you want a stable AI toolkit in Go, Genkit Go fits well. If your team builds with Dart or Flutter, Genkit Dart (Preview) lets you use the same Genkit patterns on both client and server.

Learning Path and Combination Patterns

From Low-Layer to High-Layer

A common recommendation shared in the Genkit community is to follow Anthropic’s “Building effective agents” guidance.

When you’re starting out, it often makes sense to begin with a low-layer toolkit like Genkit to deeply understand models, tools, prompts, and o11y, then adopt a high-layer framework like ADK based on your application’s requirements.

Once you have this foundation, you can work with ADK or any other framework.

Practical Combinations

Genkit for the UI-facing layer: User input processing, simple generation tasks, API responses
ADK for complex backend orchestration: When requests require multi-step reasoning by specialized agents

Summary

Think of Genkit and ADK as complementary layers rather than competing choices.

Layer	Framework	Focus
Low-layer	Genkit	Models, tools, prompts, MCP, o11y
High-layer	ADK	Session management, memory, evaluation, multi-agent orchestration

Start with Genkit to understand AI fundamentals, then consider ADK when requirements get complex. Or combine them — use Genkit as a foundation with ADK on top. This flexibility is a strength of Google’s AI framework ecosystem.