🎯 Tutorial 8: Simple Multi‑Agent Researcher (Runs with ADK)

🎯 What You'll Learn

• Multi‑agent orchestration using a coordinator agent with specialized sub‑agents
• Sequential workflow where agents build upon each other's outputs
• Web search integration for real-time research capabilities
• Running with ADK Web to interactively test the multi‑agent system

🧠 Core Concept: Multi-Agent Research Pipeline

A coordinator LlmAgent orchestrates three specialized agents in a sequential workflow: Research → Summarize → Critique. Each agent contributes to building a comprehensive research report.

User Query → Coordinator Agent
                │
                ├──▶ Research Agent (web search + analysis)
                │           │
                │           └──▶ Research Findings
                │
                ├──▶ Summarizer Agent (synthesis)
                │           │
                │           └──▶ Key Insights
                │
                └──▶ Critic Agent (quality analysis)
                            │
                            └──▶ Final Report with Recommendations

📁 Project Structure

8_simple_multi_agent/
├── README.md                    # This file
├── requirements.txt             # Dependencies
├── multi_agent_researcher/      # Main implementation
│   ├── agent.py                # Multi-agent system (exports root_agent)
└── .env                        # Environment variables (create this)

🚀 Getting Started

1. Install Dependencies

Navigate to the 8_simple_multi_agent folder and install the required libraries:

cd 8_simple_multi_agent
pip install -r requirements.txt

2. Set Up Environment

Create a .env file in the 8_simple_multi_agent folder:

# Create .env file
echo "GOOGLE_API_KEY=your_ai_studio_key_here" > .env

Important: Replace your_ai_studio_key_here with your actual Google AI Studio API key from https://aistudio.google.com/

3. Run with ADK Web (Recommended)

From the 8_simple_multi_agent folder:

adk web

ADK Web Setup:

• Open the local URL printed in the terminal
• In the import section, use this path:

  ai_agent_framework_crash_course.google_adk_crash_course.8_simple_multi_agent.multi_agent_researcher
  

• Select the root_agent object
• Start chatting with your multi-agent researcher!

🧪 Sample Prompts to Try

Comprehensive Research Query:

Research the future of renewable energy integration in smart cities, including current technologies, implementation challenges, economic feasibility, and policy requirements. Provide a critique and suggestions.

Other Test Queries:

"Research the current state of AI regulation in the European Union and its impact on business innovation"

"Investigate the latest developments in CRISPR gene editing technology and its potential applications in medicine"

"Research the effectiveness of personalized learning platforms in K-12 education, including current implementations and learning outcomes"

🔍 How It Works

Research Agent:

• Conducts comprehensive web research using Google Search
• Gathers current information, trends, and developments
• Provides structured findings with sources and outlines

Summarizer Agent:

• Synthesizes research into clear, actionable insights
• Creates executive summaries and key bullet points
• Identifies critical patterns and takeaways

Critic Agent:

• Performs quality analysis and gap identification
• Provides risk assessment and opportunity analysis
• Gives actionable recommendations and next steps

Coordinator:

• Orchestrates the entire research workflow
• Ensures proper sequence: Research → Summarize → Critique
• Integrates all outputs into a cohesive final report

📝 Tips for Best Results

• Be specific in your research queries for better agent coordination
• Allow completion of the full workflow for comprehensive results
• The system automatically follows the research pipeline for thorough analysis
• Each agent builds upon the previous agent's work for better insights

🔗 Next Steps

After mastering this tutorial, explore:

• Tutorial 9: Workflow Agents (Sequential, Parallel, Branching)
• Advanced Patterns: Custom tools and agent communication
• Integration: Connect with external data sources and APIs

🚨 Troubleshooting

• API Key Issues: Ensure your .env file is in the correct location and contains a valid GOOGLE_API_KEY
• Import Errors: Make sure you're using the exact import path shown above
• Agent Not Found: Verify that root_agent is properly exported from the module