How to Use Google ADK for Building Agents? - Analytics Vidhya

Harness the power of Google's Agent Development Kit (ADK) to create intelligent agents with real-world capabilities! This tutorial guides you through building conversational agents using ADK, supporting various language models like Gemini and GPT. Whether you're a seasoned AI developer or just starting your agentic development journey, this practical guide provides a clear and easy path to success.

Table of Contents

• What is the Agent Development Kit?
• Defining the Challenge: Building a Weather Bot Team
  • Proposed Workflow Diagram
• Prerequisites: Setting up your Development Environment
• Building Your Weather Application: A Step-by-Step Guide
  • Step 1: Initial Setup and Library Installation
  • Step 2: Defining Essential Tools
  • Step 3: Agent Definition and Configuration
  • Step 4: Setting up the Runner and Session Service
  • Step 5: Interacting with the Agent
  • Step 6: Running the Conversation
• Conclusion: Empowering Agentic Development
• Frequently Asked Questions

What is the Agent Development Kit?

The Agent Development Kit (ADK) is a versatile and modular framework for developing and deploying AI agents. Compatible with leading LLMs and open-source generative AI tools, ADK seamlessly integrates with the Google ecosystem and Gemini models. It simplifies the creation of basic Gemini-powered agents while offering the control and structure needed for sophisticated agent architectures and orchestration.

Key Features of Google's Agent Development Kit

1. Multi-Agent Architectures: Design agents for parallel, sequential, or hierarchical workflows.
2. Flexible Orchestration: Dynamic task routing using LLM-powered workflows.
3. Extensive Tool Ecosystem: Effortless integration of built-in, custom, and third-party tools.
4. Model Agnosticism: Supports Gemini, GPT-4o, Claude, Mistral, and more.
5. Real-time Streaming: Enables real-time text, audio, and video streaming.
6. Developer-Friendly Tools: Includes CLI, web UI, visual debugging, and evaluation tools.
7. Robust Memory Management: Built-in session and long-term memory handling.
8. Efficient Artifact Handling: Streamlined management of files, outputs, and binary data.
9. Intelligent Execution: Agents can execute code and manage multi-step planning.
10. Versatile Deployment Options: Run locally, on Google Cloud (Vertex AI, Cloud Run), or via Docker.

Defining the Challenge: Building a Weather Bot Team

As AI evolves from single-purpose tools to collaborative multi-agent systems, developers need practical guidance in building and orchestrating intelligent agents capable of communication, delegation, and adaptation. This tutorial addresses this need by building a "Weather Bot Team," a multi-agent system designed to answer weather-related queries and handle user interactions (greetings, farewells, and safe responses).

This hands-on project demonstrates:

• Designing a modular multi-agent system using Google's ADK.
• Integrating multiple language models (e.g., Gemini, GPT, Claude) for specialized tasks.
• Implementing intelligent task delegation among agents.
• Managing session memory for contextual continuity.
• Implementing safety mechanisms through structured callbacks.

The provided Colab notebook facilitates the hands-on implementation.

Proposed Workflow Diagram

Prerequisites: Setting up your Development Environment

Before starting, ensure you've completed these setup steps:

1. Environment Setup & ADK Installation

Create and activate a virtual environment to isolate project dependencies:

# Create a virtual environment
python -m venv .venv

# Activate the environment
# macOS/Linux: source .venv/bin/activate
# Windows CMD: .venv\Scripts\activate.bat
# Windows PowerShell: .venv\Scripts\Activate.ps1

# Install Google AI Development Kit (ADK)
pip install google-adk

2. Obtaining API Keys

Acquire API keys for interaction with different AI models:

• Google AI Studio: https://www.php.cn/link/1de16907c72cefcb01975c3e61185ced
• OpenAI Platform: https://www.php.cn/link/9e4aef142346875a7f13f4a42526a69f
• Anthropic Console: https://www.php.cn/link/624599f957d8f8f9a7d42bffa8206575

Building Your Weather Application: A Step-by-Step Guide

Step 1: Initial Setup and Library Installation

Install necessary libraries:

!pip install google-adk -q
!pip install litellm -q

Import required modules: (Note: The provided code snippet should be included here, including API key setup and model constant definitions.)

Step 2: Defining Essential Tools

ADK tools are functional building blocks enabling agents to perform actions beyond text generation. They are typically simple Python functions (e.g., fetching weather data). A mock weather tool is used here for demonstration purposes. (Note: The provided get_weather function should be included here.)

Step 3: Agent Definition and Configuration

Define the agent by configuring its parameters: name, model, description, instructions, and tools. (Note: The provided weather_agent definition should be included here.)

Step 4: Setting up the Runner and Session Service

Set up the SessionService (for managing conversation history) and Runner (for orchestrating agent execution). (Note: The provided code for creating session_service, session, and runner should be included here.)

Step 5: Interacting with the Agent

Create an asynchronous function (call_agent_async) to send queries to the agent and process the response stream. (Note: The provided call_agent_async function should be included here.)

Step 6: Running the Conversation

Run sample queries using the call_agent_async function within an asynchronous context. (Note: The provided code for running the conversation should be included here.) The expected output should also be shown.

Conclusion: Empowering Agentic Development

Google's ADK empowers developers to create intelligent, multi-agent systems that extend beyond basic text generation. This tutorial, through the development of a weather bot, showcased key ADK concepts: tool integration, agent orchestration, and session management, leveraging the power of Google's Gemini. ADK provides the flexibility to build production-ready agents capable of interaction, learning, and adaptation.

Frequently Asked Questions

Q1. What is Google's Agent Development Kit (ADK)?

A. ADK is an open-source, modular framework for building, orchestrating, and deploying AI-powered agents, encompassing both simple bots and complex multi-agent systems. It prioritizes flexibility, scalability, and integration with major LLMs and Google's AI ecosystem.

Q2. How does ADK differentiate itself from other agent frameworks?

A. ADK's inherent multi-agent design allows composing agents into parallel, sequential, or hierarchical workflows. It's model-agnostic, supports real-time streaming (text, audio, video), and offers built-in tools for debugging, evaluation, and cross-environment deployment.

Q3. Which AI models are supported by ADK?

A. While optimized for Google's Gemini models, ADK supports other popular LLMs like GPT-4o, Claude, Mistral, and more, via integrations such as LiteLLM.

Q4. Is ADK suitable for both conversational and non-conversational agents?

A. Yes, ADK can build both conversational agents (like chatbots) and non-conversational agents for complex workflows or automation tasks.

Q5. How do I begin using ADK?

A. Install ADK using pip (pip install google-adk), set up your Google Cloud project (if necessary), and build your first agent using Python. ADK offers a CLI and web UI for local development, testing, and debugging.

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