Building AI Agents with Google’s ADK-Python: A Hands-On Guide

April 14, 2025 cloudyrathor Artificial Inteligence, Technology 0

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Artificial Intelligence (AI) agents are reshaping how we automate tasks, analyze data, and interact with software. Google’s Agent Development Kit (ADK) for Python is a powerful, open-source toolkit designed to help developers create, evaluate, and deploy intelligent agents with precision and flexibility.

Whether you’re crafting a simple search assistant or building a multi-agent system integrated with Google Cloud, ADK offers the tools to bring your vision to life. In this guide, we’ll break down what ADK is, why it matters, and how to get hands-on building with it.

🚀 What is ADK-Python?

The Agent Development Kit (ADK) is a Python-based framework for creating AI agents—intelligent software entities capable of planning, reasoning, using tools, and collaborating to solve tasks.

Unlike traditional AI models focused solely on generating text, ADK lets you define agent behavior, integrate tools, and orchestrate multi-agent systems with ease.

🔑 Key Features

  • Code-First Design: Define agents and tools directly in Python.
  • Tool-Driven Agents: Equip agents with built-in or custom tools (like Google Search).
  • Multi-Agent Support: Create hierarchies or collaborating agents.
  • Flexible Deployment: Run locally, in containers, or scale with Vertex AI Agent Engine.
  • Open Source: Apache 2.0 licensed with community support.

Ideal for developers who need fine-grained control—whether you’re prototyping or building enterprise-grade AI systems.

💡 Why Use ADK?

Imagine building an AI system that handles support tickets, finds answers on the web, and manages tasks—all via specialized agents.

ADK enables you to:

  • Write agent logic using modern Python tools like Poetry and FastAPI.
  • Integrate seamlessly with Google Cloud for production-scale deployment.
  • Test and debug agents locally before deploying them.
  • Combine multiple agents into a cohesive AI system.

🛠️ Getting Started with ADK

✅ Prerequisites

  • Python 3.9+
  • Git
  • Poetry (dependency management)
  • Google Cloud Project (optional)
  • A code editor (e.g., VS Code)

📦 Installation

# Install Poetry
curl -sSL https://install.python-poetry.org | python3 –

# Clone ADK repo
git clone https://github.com/google/adk-python.git
cd adk-python

# Set up virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate

# Install dependencies
poetry install

# Copy and edit environment variables
cp .env.example .env

# Add your Google Cloud credentials in .env

🧪 Verify Installation

adk --version

🧑‍💻 Build Your First Agent: Search Assistant

Let’s create a simple AI assistant that uses Google Search to answer questions.

📁 search_assistant.py

from google.adk.agents import Agent
from google.adk.tools import google_search

search_assistant = Agent(
    name="search_assistant",
    model="gemini-2.0-flash",
    instruction="You are a helpful assistant. Use Google Search if needed.",
    description="Answers user questions using the web.",
    tools=[google_search]
)

if __name__ == "__main__":
    search_assistant.run()

▶️ Run the Agent

adk web

Open http://localhost:8000 in your browser:

  • Select search_assistant
  • Ask: “Who won the last World Cup?”
  • Watch it respond intelligently using Google Search.

🧠 Advanced: Multi-Agent System

Now let’s build a coordinated multi-agent system: a Greeter, a TaskExecutor, and a Coordinator to route requests.

📁 multi_agent_system.py

from google.adk.agents import LlmAgent, BaseAgent

greeter = LlmAgent(
    name="Greeter",
    model="gemini-2.0-flash",
    instruction="Greet the user and offer help.",
    description="Welcomes the user."
)

class CustomAgent(BaseAgent):
    def __init__(self, name):
        super().__init__(name=name)

    async def handle_request(self, request):
        return {"response": f"Task executed: {request}"}

task_executor = CustomAgent(name="TaskExecutor")

coordinator = LlmAgent(
    name="Coordinator",
    model="gemini-2.0-flash",
    instruction="Delegate greetings or tasks to the correct sub-agent.",
    description="Routes requests to agents.",
    sub_agents=[greeter, task_executor]
)

if __name__ == "__main__":
    coordinator.run()


▶️ Run the Multi-Agent System

adk web

Test in the UI:

  • Say “Hi!” → Greeter responds.
  • Say “Process order.” → TaskExecutor responds.

☁️ Deploying to Google Cloud

🐳 Dockerfile

FROM python:3.9 WORKDIR /app COPY . . RUN pip install poetry RUN poetry install CMD ["poetry", "run", "adk", "api_server"]

📤 Build & Push

docker build -t gcr.io/your-project-id/adk-agent . docker push gcr.io/your-project-id/adk-agent

🚀 Deploy to Cloud Run

gcloud run deploy adk-agent \   --image gcr.io/your-project-id/adk-agent \   --region us-central1 \   --allow-unauthenticated

🧩 Pro Tips

  • Write clear, specific instructions for each agent.
  • Use adk web to test and debug locally.
  • Start with a single-agent system before scaling up.
  • Check google/adk-samples for ready-made examples.

🔮 What’s Next?

ADK-Python is your playground for building intelligent, production-ready agents. Explore integrations, experiment with toolchains, or build your own multi-agent assistant.

The future of software is agentic. Get ahead—start building today.