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Understanding the Target Audience for A Coding Guide to Build a Scalable Multi-Agent System with Google ADK
The target audience for this tutorial primarily includes software developers, data scientists, and business analysts looking to leverage AI technologies for building scalable systems. These professionals typically work in enterprise settings and are interested in optimizing workflows through automation and intelligent systems.
Pain Points
- Difficulty in integrating complex AI systems within existing workflows.
- Challenges in understanding and implementing cloud-based AI tools effectively.
- Need for time-efficient solutions that can handle various business tasks without constant supervision.
Goals
- To gain practical skills in building multi-agent systems using Google ADK.
- To optimize data analysis, research, content creation, and mathematical computations.
- To develop scalable, production-ready solutions that can be deployed in enterprise environments.
Interests
- Exploring the latest advancements in AI and machine learning frameworks.
- Learning about best practices for integrating AI tools within a business context.
- Understanding how to leverage AI for data-driven decision-making.
Communication Preferences
- Preference for concise, technical documentation that includes code examples and practical applications.
- Desire for tutorials that emphasize hands-on learning and clear explanations of concepts.
- Interest in community feedback and collaborative learning opportunities through forums or webinars.
A Coding Guide to Build a Scalable Multi-Agent System with Google ADK
In this tutorial, we explore the advanced capabilities of Google’s Agent Development Kit (ADK) by building a multi-agent system equipped with specialized roles and tools. We guide you through creating agents tailored for tasks such as web research, mathematical computation, data analysis, and content creation. By integrating Google Search, asynchronous execution, and modular architecture, we demonstrate how to orchestrate a powerful, production-ready agent workflow using the Gemini model. Our goal is to help you understand how ADK can be leveraged to build scalable, intelligent systems suitable for enterprise applications.
Check out the Full Codes here.
Installing the Google ADK Package
!pip install google-adk
We begin by installing the google-adk package and importing the necessary libraries to build our agent system. To authenticate our access, we retrieve the Google API key either from the environment or securely prompt for it using the getpass module. This ensures our agents can interact with Google’s tools and services seamlessly.
Setting Up the API Key
def get_api_key():
api_key = os.getenv("GOOGLE_API_KEY")
if not api_key:
from getpass import getpass
api_key = getpass("Enter your Google API Key: ")
if not api_key:
raise ValueError("API key is required to run this tutorial")
os.environ["GOOGLE_API_KEY"] = api_key
return api_key
Creating Specialized Agents
We define a TaskResult data structure to store outputs from each agent. Then, we build a multi-agent system using Google ADK, assigning specialized roles like researcher, calculator, analyst, and writer. Through asynchronous methods, we demonstrate each agent’s capabilities and compile a final summary of their performance and insights.
class AdvancedADKTutorial:
def __init__(self):
self.model = "gemini-1.5-flash"
self.agents = {}
self.results = []
def create_specialized_agents(self):
self.agents['researcher'] = Agent(
name="researcher",
model=self.model,
instruction="You are a research specialist. Use Google Search to find accurate, up-to-date information.",
description="Specialist in web research and information gathering",
tools=[google_search]
)
self.agents['calculator'] = Agent(
name="calculator",
model=self.model,
instruction="You are a mathematics expert. Solve calculations step-by-step.",
description="Expert in mathematical calculations and problem solving"
)
self.agents['analyst'] = Agent(
name="analyst",
model=self.model,
instruction="You are a data analysis expert. Calculate basic statistics.",
description="Specialist in data analysis and statistical insights"
)
self.agents['writer'] = Agent(
name="writer",
model=self.model,
instruction="You are a professional writing assistant. Help with content creation.",
description="Expert in content creation and document writing"
)
Demonstrating Agent Capabilities
We utilize asynchronous functions to demonstrate the capabilities of each specialized agent, including:
- Research tasks using Google Search.
- Mathematical calculations, including financial metrics.
- Data analysis for business insights.
- Content generation for reports and documentation.
Each demonstration highlights the agent’s ability to process inputs and provide actionable outputs, showcasing the flexibility of the multi-agent system.
Summary of Agent Performances
At the conclusion of the tutorial, we summarize the performance of each agent, emphasizing the total tasks completed and the capabilities demonstrated:
def display_comprehensive_summary(self):
print(f"Total agents created: {len(self.agents)}")
print(f"Total tasks completed: {len(self.results)}")
print(f"Model used: {self.model}")
print("Agent capabilities demonstrated include advanced web research, mathematical computations, data analysis, and content creation.")
Through this hands-on experience, we gain confidence in using ADK to develop robust agent-based solutions for real-world problems. We also highlight how the ADK framework supports error handling, extensibility, and seamless integration with tools.
Check out the Full Codes here.
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