How To Build Multimodal Agents Using Google ADK

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Building multimodal AI agents is becoming one of the most important skills for modern developers. As applications shift from text-only interactions to real-world, perception-capable systems, frameworks like Google’s AI Device Kit (ADK) provide a powerful foundation. With ADK, you can build agents that communicate using text, speech, images, audio, gesture inputs, and custom sensors—effectively bridging the gap between machine reasoning and physical-world interaction.

This article walks through what Google ADK is, how it works, how to build multimodal agents with it, and gives practical code examples, architecture guidance, and a robust final sample project. The goal is to help you confidently build your own multimodal systems.

What Is Google ADK and Why Use It for Multimodal Agents?

Google ADK (AI Device Kit) is a development platform designed to simplify building interactive AI systems—especially those that run on-device and interface with the physical world. ADK is built on top of Google’s deep experience in on-device ML, speech recognition, and sensor fusion, allowing developers to combine different types of inputs and outputs into cohesive intelligent agents.

A multimodal agent is an AI system capable of understanding and generating information across multiple data formats. For example:

  • Vision + Language: “Describe what you see in the camera feed.”

  • Audio + Text: “Transcribe speech and summarize it.”

  • Sensors + Dialogue: “Alert me if the vibration sensor exceeds threshold.”

  • Touchscreen + Voice Output: “Tap a button and the agent speaks instructions.”

Google ADK provides the developer-friendly environment to:

  1. Capture and stream multimodal inputs

  2. Run base models or connect to cloud models

  3. Manage agent state and memory

  4. Map model outputs to device actions (text display, audio playback, LEDs, actuators)

  5. Deploy on affordable hardware

It is not merely a wrapper around models—it is a full agent architecture with UI flows, pipelines, and event dispatch systems.

Understanding the Core Architecture of a Multimodal ADK Agent

A typical ADK multimodal agent consists of five key components:

Input Modalities

ADK exposes a flexible input pipeline:

  • Camera (vision input)

  • Microphone (speech/ambient audio)

  • Touch and gestures

  • Sensors: accelerometer, temperature, proximity, etc.

These streams are unified through ADK’s event model so the agent can process them simultaneously.

Model Invocation Layer

This includes:

  • On-device language models

  • On-device vision/image encoders

  • Cloud-based large multimodal models (LMMs)

  • Hybrid pipelines using quantized models

Reasoning and State Handling

This is the agent’s “brain,” responsible for:

  • Decision-making

  • Memory management

  • Dialog state

  • Long-term planning

  • Response generation

Output Modalities

Multimodal agents can output through:

  • Text

  • Speech synthesis

  • Image generation

  • Control of LEDs or actuators

  • Interaction with external APIs

Interaction Loop

The loop often looks like this:

Input → Preprocess → Model → Reasoner → Action → Output → Wait for Next Input

With ADK, you orchestrate this flow declaratively through pipelines and event listeners.

Setting Up the Google ADK Environment

To begin building agents with ADK, you typically need:

Prerequisites

  • Python 3.10+

  • Node + NPM (if building the visual app interface)

  • Google ADK SDK installed

  • Supported hardware (Raspberry Pi, Coral device, or official ADK hardware)

Install the ADK Python Tools

pip install google-adk

Initialize a New ADK Project

adk init multimodal-agent
cd multimodal-agent

You will now have:

  • agent.py – the core agent logic

  • devices/ – sensor and device configuration

  • models/ – where model configs live

  • ui/ – optional web or device UI

Building a Basic Multimodal Pipeline

Below is an example demonstrating how to create a simple agent that listens for speech, captures an image, and generates a combined reasoning response.

Define Input Pipelines

# agent.py
from adk import Agent, MicrophoneInput, CameraInput, TextOutputagent = Agent(
inputs=[
MicrophoneInput(),
CameraInput(resolution=“720p”)
],
outputs=[
TextOutput()
]
)

This initializes an agent with audio and camera modalities plus text output.

Add a Multimodal Model

Assume you have access to a vision-language model compatible with ADK:

from adk.models import VisionLanguageModel

vlm = VisionLanguageModel(model_name=“gemini-multimodal”)
agent.add_model(vlm)

Implement the Reasoning Logic

Create a handler for when both audio and vision events are available:

@agent.on_event("speech_detected")
def handle_speech(event, ctx):
ctx.state["user_speech"] = event.transcription@agent.on_event(“image_captured”)
def handle_image(event, ctx):
ctx.state[“image”] = event.frame# Only respond once both modalities are ready
if “user_speech” in ctx.state:
response = vlm.generate(
prompt=ctx.state[“user_speech”],
image=ctx.state[“image”]
)
agent.output(response.text)
ctx.state.clear()

This defines a simple multimodal behavior:

  1. User speaks

  2. Agent listens

  3. Camera captures an image

  4. Agent sends both to the model

  5. Model generates a multimodal answer

Adding Speech Output

ADK supports on-device TTS:

from adk import SpeechOutput
agent.outputs.append(SpeechOutput(voice="natural"))

Modify the action:

agent.output(response.text, channel="speech")

Now the agent speaks back.

Creating a More Advanced Agent: Vision + Dialogue + Sensors

Let’s build a real example:

A multimodal home assistant that reacts to speech, analyzes the room via camera, and monitors temperature sensor data.

Configure Sensors

In devices/sensors.py:

from adk import Sensor

class TemperatureSensor(Sensor):
def read(self):
# Example hardware read call
return get_temp_celsius()

Register:

# agent.py
from sensors import TemperatureSensor
agent.inputs.append(TemperatureSensor(interval=5))

Create Intent Logic

from datetime import datetime

@agent.on_event(“speech_detected”)
def on_speech(event, ctx):
text = event.transcription.lower()

if “what’s the temperature” in text:
temp = ctx.state.get(“current_temp”)
agent.output(f”The current temperature is {temp}°C.”)

elif “what do you see” in text:
image = agent.capture_image()
desc = vlm.generate(prompt=“Describe the scene”, image=image)
agent.output(desc.text)

elif “time” in text:
agent.output(f”The time is {datetime.now().strftime(‘%H:%M’)}.”)

Handle Sensor Updates

@agent.on_event("sensor_update")
def on_sensor(event, ctx):
if event.sensor_type == "temperature":
ctx.state["current_temp"] = event.valueif event.value > 30:
agent.output(“Warning: the room is getting hot.”)

The agent now:

  • Monitors environment

  • Responds to spoken questions

  • Uses vision when asked

  • Accesses sensors and triggers alerts

Combining All Modalities into One “Megaloop” Agent

You can design a system that continuously interprets the world.

Multimodal Fusion Logic

@agent.on_cycle
def main_loop(ctx):
speech = ctx.latest("speech_detected")
image = ctx.latest("image_captured")
temp = ctx.state.get("current_temp")if speech and image:
prompt = f”””
User said: {speech.transcription}
The room temperature is: {temp}°C.
Use the image to answer appropriately.
“””
result = vlm.generate(prompt=prompt, image=image.frame)
agent.output(result.text)

This shows how to build true multimodal fusion—multiple inputs merged into a unified response.

Building a Touch-Interactive Agent Using ADK UI

ADK includes a UI framework for building touchscreen or web-based interfaces.

Basic example (ui/app.js):

import { ADK } from "google-adk-ui";

const ui = new ADK.UI();

ui.button(“Capture”, () => {
ui.sendEvent(“manual_capture”);
});

And link it in Python:

@agent.on_event("manual_capture")
def manual_photo(event, ctx):
image = agent.capture_image()
desc = vlm.generate(prompt="Describe this image.", image=image)
agent.output(desc.text)

Now your multimodal agent has visual controls.

Multimodal “Smart Desk Assistant”

Below is a compact but full agent combining voice, vision, sensor data, reasoning, and UI hooks.

from adk import (
Agent, MicrophoneInput, CameraInput,
TextOutput, SpeechOutput
)
from sensors import TemperatureSensor
from adk.models import VisionLanguageModelagent = Agent(
inputs=[
MicrophoneInput(),
CameraInput(),
TemperatureSensor(interval=3)
],
outputs=[
TextOutput(),
SpeechOutput(voice=“natural”)
]
)vlm = VisionLanguageModel(“gemini-multimodal”)
agent.add_model(vlm)@agent.on_event(“speech_detected”)
def handle_speech(e, ctx):
ctx.state[“last_speech”] = e.transcription.lower()@agent.on_event(“sensor_update”)
def temp_update(e, ctx):
ctx.state[“temp”] = e.value@agent.on_event(“image_captured”)
def vision_handler(e, ctx):
speech = ctx.state.get(“last_speech”)
temp = ctx.state.get(“temp”)if not speech:
returnprompt = f”””
User said: {speech}
Room temperature: {temp}°C.
Use the image to provide a relevant response.
“””resp = vlm.generate(prompt=prompt, image=e.frame)
agent.output(resp.text)
ctx.state.clear()

Capabilities:

  • Listens and interprets speech

  • Captures image and fuses modalities

  • Reads environment sensors

  • Generates informed responses

  • Speaks and displays results

Deployment on Real Hardware

When deploying:

  1. Run the ADK runtime on your device

  2. Push your agent project

  3. Configure camera + mic + sensors

  4. Start the agent service

Example:

adk deploy
adk run

You now have a functioning multimodal agent running at the edge.

Conclusion

Building multimodal agents is not simply a technical exercise—it represents the direction of modern interface design. Users will increasingly expect systems that understand what they say, what they show, how they interact, and what is happening in the environment. Google ADK provides the ideal foundation to meet those expectations.

With ADK, you can:

  • Combine speech, vision, touch, UI, and sensors into unified agents

  • Run powerful multimodal models on-device or via hybrid cloud

  • Build custom reasoning logic that goes beyond simple chatbots

  • Integrate real-world devices, actuators, and sensors

  • Deploy interactive and adaptive agents at the edge with low latency

Multimodal agents built on ADK are not theoretical—they can power home assistants, accessibility tools, robotics interfaces, educational devices, inspection systems, and smart IoT installations.

By following the examples and architecture patterns in this article, you now have a deep understanding of how to build these systems yourself. You can start small—simple voice+camera agents—and progressively add sensor fusion, UI elements, and custom logic until you have a robust, fully multimodal personal assistant or intelligent device.

Google ADK democratizes device intelligence. You bring the creativity; it provides the pipelines and infrastructure. The next generation of interactive AI systems will be multimodal, and now you have the tools and knowledge to build them.