A Coding Information to Construct a Scalable Multi-Agent System with Google ADK

!pip set up google-adk


import os
import asyncio
import json
from typing import Record, Dict, Any
from dataclasses import dataclass
from google.adk.brokers import Agent, LlmAgent
from google.adk.instruments import google_search


def get_api_key():
   """Get API key from person enter or atmosphere variable"""
   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:
           increase ValueError("API secret's required to run this tutorial")
       os.environ["GOOGLE_API_KEY"] = api_key
   return api_key

@dataclass
class TaskResult:
   """Information construction for job outcomes"""
   agent_name: str
   job: str
   consequence: str
   metadata: Dict[str, Any] = None


class AdvancedADKTutorial:
   """Most important tutorial class demonstrating ADK capabilities"""
  
   def __init__(self):
       self.mannequin = "gemini-1.5-flash"
       self.brokers = {}
       self.outcomes = []
      
   def create_specialized_agents(self):
       """Create a multi-agent system with specialised roles"""
      
       self.brokers['researcher'] = Agent(
           title="researcher",
           mannequin=self.mannequin,
           instruction="""You're a analysis specialist. Use Google Search to seek out
           correct, up-to-date info. Present concise, factual summaries with sources.
           At all times cite your sources and concentrate on the newest and dependable info.""",
           description="Specialist in net analysis and knowledge gathering",
           instruments=[google_search]
       )
      
       self.brokers['calculator'] = Agent(
           title="calculator",
           mannequin=self.mannequin,
           instruction="""You're a arithmetic knowledgeable. Resolve calculations step-by-step.
           Present your work clearly and double-check outcomes. Deal with arithmetic, algebra,
           geometry, statistics, and monetary calculations. At all times clarify your reasoning.""",
           description="Skilled in mathematical calculations and drawback fixing"
       )
      
       self.brokers['analyst'] = Agent(
           title="analyst",
           mannequin=self.mannequin,
           instruction="""You're a knowledge evaluation knowledgeable. When given numerical knowledge:
           1. Calculate primary statistics (imply, median, min, max, vary, std dev)
           2. Determine patterns, traits, and outliers
           3. Present enterprise insights and interpretations
           4. Present all calculations step-by-step
           5. Counsel actionable suggestions based mostly on the info""",
           description="Specialist in knowledge evaluation and statistical insights"
       )
      
       self.brokers['writer'] = Agent(
           title="author",
           mannequin=self.mannequin,
           instruction="""You're a skilled writing assistant. Assist with:
           - Creating clear, participating, and well-structured content material
           - Enterprise stories and government summaries
           - Technical documentation and explanations
           - Content material enhancing and enchancment
           At all times use skilled tone and correct formatting.""",
           description="Skilled in content material creation and doc writing"
       )
      
       print("✓ Created specialised agent system:")
       print(f"  • Researcher: Net search and knowledge gathering")
       print(f"  • Calculator: Mathematical computations and evaluation")
       print(f"  • Analyst: Information evaluation and statistical insights")
       print(f"  • Author: Skilled content material creation")
  
   async def run_agent_with_input(self, agent, user_input):
       """Helper methodology to run agent with correct error dealing with"""
       strive:
           if hasattr(agent, 'generate_content'):
               consequence = await agent.generate_content(user_input)
               return consequence.textual content if hasattr(consequence, 'textual content') else str(consequence)
           elif hasattr(agent, '__call__'):
               consequence = await agent(user_input)
               return consequence.textual content if hasattr(consequence, 'textual content') else str(consequence)
           else:
               consequence = str(agent) + f" processed: {user_input[:50]}..."
               return consequence
       besides Exception as e:
           return f"Agent execution error: {str(e)}"
  
   async def demonstrate_single_agent_research(self):
       """Display single agent analysis capabilities"""
       print("n=== Single Agent Analysis Demo ===")
      
       question = "What are the most recent developments in quantum computing breakthroughs in 2024?"
       print(f"Analysis Question: {question}")
      
       strive:
           response_text = await self.run_agent_with_input(
               agent=self.brokers['researcher'],
               user_input=question
           )
           abstract = response_text[:300] + "..." if len(response_text) > 300 else response_text
          
           task_result = TaskResult(
               agent_name="researcher",
               job="Quantum Computing Analysis",
               consequence=abstract
           )
           self.outcomes.append(task_result)
          
           print(f"✓ Analysis Full: {abstract}")
           return response_text
          
       besides Exception as e:
           error_msg = f"Analysis failed: {str(e)}"
           print(f"❌ {error_msg}")
           return error_msg
  
   async def demonstrate_calculator_agent(self):
       """Display mathematical calculation capabilities"""
       print("n=== Calculator Agent Demo ===")
      
       calc_problem = """Calculate the compound annual development fee (CAGR) for an funding
       that grows from $50,000 to $125,000 over 8 years. Use the system:
       CAGR = (Ending Worth / Starting Worth)^(1/variety of years) - 1
       Specific the consequence as a share."""
      
       print("Math Downside: CAGR Calculation")
      
       strive:
           response_text = await self.run_agent_with_input(
               agent=self.brokers['calculator'],
               user_input=calc_problem
           )
           abstract = response_text[:250] + "..." if len(response_text) > 250 else response_text
          
           task_result = TaskResult(
               agent_name="calculator",
               job="CAGR Calculation",
               consequence=abstract
           )
           self.outcomes.append(task_result)
          
           print(f"✓ Calculation Full: {abstract}")
           return response_text
          
       besides Exception as e:
           error_msg = f"Calculation failed: {str(e)}"
           print(f"❌ {error_msg}")
           return error_msg
  
   async def demonstrate_data_analysis(self):
       """Display knowledge evaluation capabilities"""
       print("n=== Information Evaluation Agent Demo ===")
      
       data_task = """Analyze this quarterly gross sales knowledge for a tech startup (in hundreds USD):
       Q1 2023: $125K, Q2 2023: $143K, Q3 2023: $167K, This autumn 2023: $152K
       Q1 2024: $187K, Q2 2024: $214K, Q3 2024: $239K, This autumn 2024: $263K
      
       Calculate development traits, determine patterns, and supply enterprise insights."""
      
       print("Information Evaluation: Quarterly Gross sales Traits")
      
       strive:
           response_text = await self.run_agent_with_input(
               agent=self.brokers['analyst'],
               user_input=data_task
           )
           abstract = response_text[:250] + "..." if len(response_text) > 250 else response_text
          
           task_result = TaskResult(
               agent_name="analyst",
               job="Gross sales Information Evaluation",
               consequence=abstract
           )
           self.outcomes.append(task_result)
          
           print(f"✓ Evaluation Full: {abstract}")
           return response_text
          
       besides Exception as e:
           error_msg = f"Evaluation failed: {str(e)}"
           print(f"❌ {error_msg}")
           return error_msg
  
   async def demonstrate_content_creation(self):
       """Display content material creation capabilities"""
       print("n=== Content material Creation Agent Demo ===")
      
       writing_task = """Create a quick government abstract (2-3 paragraphs) for a board presentation
       that mixes the important thing findings from:
       1. Latest quantum computing developments
       2. Robust monetary development traits exhibiting 58% year-over-year development
       3. Suggestions for strategic planning
      
       Use skilled enterprise language appropriate for C-level executives."""
      
       print("Content material Creation: Government Abstract")
      
       strive:
           response_text = await self.run_agent_with_input(
               agent=self.brokers['writer'],
               user_input=writing_task
           )
           abstract = response_text[:250] + "..." if len(response_text) > 250 else response_text
          
           task_result = TaskResult(
               agent_name="author",
               job="Government Abstract",
               consequence=abstract
           )
           self.outcomes.append(task_result)
          
           print(f"✓ Content material Created: {abstract}")
           return response_text
          
       besides Exception as e:
           error_msg = f"Content material creation failed: {str(e)}"
           print(f"❌ {error_msg}")
           return error_msg
  
   def display_comprehensive_summary(self):
       """Show complete tutorial abstract and outcomes"""
       print("n" + "="*70)
       print("🚀 ADVANCED ADK TUTORIAL - COMPREHENSIVE SUMMARY")
       print("="*70)
      
       print(f"n📊 EXECUTION STATISTICS:")
       print(f"   • Complete brokers created: {len(self.brokers)}")
       print(f"   • Complete duties accomplished: {len(self.outcomes)}")
       print(f"   • Mannequin used: {self.mannequin} (Free Tier)")
       print(f"   • Runner sort: Direct Agent Execution")
      
       print(f"n🤖 AGENT CAPABILITIES DEMONSTRATED:")
       print("   • Superior net analysis with Google Search integration")
       print("   • Complicated mathematical computations and monetary evaluation")
       print("   • Statistical knowledge evaluation with enterprise insights")
       print("   • Skilled content material creation and documentation")
       print("   • Asynchronous agent execution and error dealing with")
      
       print(f"n🛠️ KEY ADK FEATURES COVERED:")
       print("   • Agent() class with specialised directions")
       print("   • Constructed-in software integration (google_search)")
       print("   • InMemoryRunner for agent execution")
       print("   • Async/await patterns for concurrent operations")
       print("   • Skilled error dealing with and logging")
       print("   • Modular, scalable agent structure")
      
       print(f"n📋 TASK RESULTS SUMMARY:")
       for i, lead to enumerate(self.outcomes, 1):
           print(f"   {i}. {consequence.agent_name.title()}: {consequence.job}")
           print(f"      End result: {consequence.consequence[:100]}...")
      
       print(f"n🎯 PRODUCTION READINESS:")
       print("   • Code follows ADK greatest practices")
       print("   • Prepared for deployment on Cloud Run")
       print("   • Suitable with Vertex AI Agent Engine")
       print("   • Scalable multi-agent structure")
       print("   • Enterprise-grade error dealing with")
      
       print(f"n🔗 NEXT STEPS:")
       print("   • Discover sub-agent delegation with LlmAgent")
       print("   • Add customized instruments and integrations")
       print("   • Deploy to Google Cloud for manufacturing use")
       print("   • Implement persistent reminiscence and periods")
      
       print("="*70)
       print("✅ Tutorial accomplished efficiently! Comfortable Agent Constructing! 🎉")
       print("="*70)

async def predominant():
   """Most important tutorial execution operate"""
   print("🚀 Google ADK Python - Superior Tutorial")
   print("=" * 50)
  
   strive:
       api_key = get_api_key()
       print("✅ API key configured efficiently")
   besides Exception as e:
       print(f"❌ Error: {e}")
       return
  
   tutorial = AdvancedADKTutorial()
  
   tutorial.create_specialized_agents()
  
   print(f"n🎯 Operating complete agent demonstrations...")
  
   await tutorial.demonstrate_single_agent_research()
   await tutorial.demonstrate_calculator_agent()
   await tutorial.demonstrate_data_analysis()
   await tutorial.demonstrate_content_creation()
  
   tutorial.display_comprehensive_summary()


def run_tutorial():
   """Run the tutorial in Jupyter/Colab atmosphere"""
   import asyncio
  
   strive:
       from IPython import get_ipython
       if get_ipython() isn't None:
           return asyncio.create_task(predominant())
   besides ImportError:
       cross
  
   return asyncio.run(predominant())


if __name__ == "__main__":
   strive:
       loop = asyncio.get_running_loop()
       print("Detected Pocket book atmosphere. Please run: await predominant()")
   besides RuntimeError:
       asyncio.run(predominant())


await predominant()