feat: Add Simple Multi-Agent Researcher tutorial for google ADK course
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# 🎯 Tutorial 8: Simple Multi‑Agent Researcher (Runs with ADK)
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## 🎯 What You'll Learn
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- **Multi‑agent orchestration** using a coordinator agent with specialized sub‑agents
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- **Sequential workflow** where agents build upon each other's outputs
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- **Web search integration** for real-time research capabilities
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- **Running with ADK Web** to interactively test the multi‑agent system
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## 🧠 Core Concept: Multi-Agent Research Pipeline
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A coordinator `LlmAgent` orchestrates three specialized agents in a sequential workflow: Research → Summarize → Critique. Each agent contributes to building a comprehensive research report.
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```
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User Query → Coordinator Agent
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│
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├──▶ Research Agent (web search + analysis)
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│ │
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│ └──▶ Research Findings
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│
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├──▶ Summarizer Agent (synthesis)
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│ │
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│ └──▶ Key Insights
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│
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└──▶ Critic Agent (quality analysis)
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│
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└──▶ Final Report with Recommendations
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```
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## 📁 Project Structure
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```
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8_simple_multi_agent/
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├── README.md # This file
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├── requirements.txt # Dependencies
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├── multi_agent_researcher/ # Main implementation
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│ ├── agent.py # Multi-agent system (exports root_agent)
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└── .env # Environment variables (create this)
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```
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## 🚀 Getting Started
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### 1. Install Dependencies
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Navigate to the `8_simple_multi_agent` folder and install the required libraries:
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```bash
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cd 8_simple_multi_agent
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pip install -r requirements.txt
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```
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### 2. Set Up Environment
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Create a `.env` file in the `8_simple_multi_agent` folder:
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```bash
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# Create .env file
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echo "GOOGLE_API_KEY=your_ai_studio_key_here" > .env
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```
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**Important**: Replace `your_ai_studio_key_here` with your actual Google AI Studio API key from [https://aistudio.google.com/](https://aistudio.google.com/)
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### 3. Run with ADK Web (Recommended)
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From the `8_simple_multi_agent` folder:
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```bash
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adk web
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```
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**ADK Web Setup:**
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- Open the local URL printed in the terminal
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- In the import section, use this path:
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```
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ai_agent_framework_crash_course.google_adk_crash_course.8_simple_multi_agent.multi_agent_researcher
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```
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- Select the `root_agent` object
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- Start chatting with your multi-agent researcher!
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## 🧪 Sample Prompts to Try
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### **Comprehensive Research Query:**
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```
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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.
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```
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### **Other Test Queries:**
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```
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"Research the current state of AI regulation in the European Union and its impact on business innovation"
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```
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```
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"Investigate the latest developments in CRISPR gene editing technology and its potential applications in medicine"
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```
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```
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"Research the effectiveness of personalized learning platforms in K-12 education, including current implementations and learning outcomes"
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```
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## 🔍 How It Works
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### **Research Agent:**
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- Conducts comprehensive web research using Google Search
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- Gathers current information, trends, and developments
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- Provides structured findings with sources and outlines
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### **Summarizer Agent:**
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- Synthesizes research into clear, actionable insights
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- Creates executive summaries and key bullet points
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- Identifies critical patterns and takeaways
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### **Critic Agent:**
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- Performs quality analysis and gap identification
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- Provides risk assessment and opportunity analysis
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- Gives actionable recommendations and next steps
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### **Coordinator:**
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- Orchestrates the entire research workflow
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- Ensures proper sequence: Research → Summarize → Critique
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- Integrates all outputs into a cohesive final report
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## 📝 Tips for Best Results
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- **Be specific** in your research queries for better agent coordination
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- **Allow completion** of the full workflow for comprehensive results
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- The system automatically follows the research pipeline for thorough analysis
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- Each agent builds upon the previous agent's work for better insights
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## 🔗 Next Steps
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After mastering this tutorial, explore:
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- **Tutorial 9**: Workflow Agents (Sequential, Parallel, Branching)
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- **Advanced Patterns**: Custom tools and agent communication
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- **Integration**: Connect with external data sources and APIs
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## 🚨 Troubleshooting
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- **API Key Issues**: Ensure your `.env` file is in the correct location and contains a valid `GOOGLE_API_KEY`
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- **Import Errors**: Make sure you're using the exact import path shown above
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- **Agent Not Found**: Verify that `root_agent` is properly exported from the module
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# If using Gemini via Google AI Studio
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GOOGLE_GENAI_USE_VERTEXAI=False
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GOOGLE_API_KEY="your-api-key"
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from .agent import root_agent
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import os
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from dotenv import load_dotenv
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from google.adk.agents import LlmAgent
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from google.adk.tools.agent_tool import AgentTool
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from google.adk.tools import google_search
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# Load environment variables
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load_dotenv()
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# --- Sub-agents ---
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research_agent = LlmAgent(
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name="research_agent",
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model="gemini-2.0-flash",
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description="Finds key information and outlines for a given topic.",
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instruction=(
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"You are a focused research specialist. Given a user topic or goal, "
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"conduct thorough research and produce:\n"
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"1. A comprehensive bullet list of key facts and findings\n"
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"2. Relevant sources and references (when available)\n"
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"3. A structured outline for approaching the topic\n"
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"4. Current trends or recent developments\n\n"
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"Keep your research factual, well-organized, and comprehensive. "
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"Use the google_search tool to find current information when needed."
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),
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tools=[google_search]
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)
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summarizer_agent = LlmAgent(
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name="summarizer_agent",
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model="gemini-2.5-flash",
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description="Summarizes research findings clearly and concisely.",
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instruction=(
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"You are a skilled summarizer. Given research findings, create:\n"
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"1. A concise executive summary (2-3 sentences)\n"
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"2. 5-7 key bullet points highlighting the most important information\n"
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"3. A clear takeaway message\n"
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"4. Any critical insights or patterns you notice\n\n"
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"Focus on clarity, relevance, and actionable insights. "
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"Avoid repetition and maintain the logical flow of information."
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),
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)
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critic_agent = LlmAgent(
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name="critic_agent",
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model="gemini-2.5-flash",
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description="Provides constructive critique and improvement suggestions.",
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instruction=(
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"You are a thoughtful analyst and critic. Given research and summaries, provide:\n"
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"1. **Gap Analysis**: Identify missing information or areas that need more research\n"
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"2. **Risk Assessment**: Highlight potential risks, limitations, or biases\n"
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"3. **Opportunity Identification**: Suggest areas for further exploration or improvement\n"
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"4. **Quality Score**: Rate the overall research quality (1-10) with justification\n"
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"5. **Actionable Recommendations**: Provide specific next steps or improvements\n\n"
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"Be constructive, thorough, and evidence-based in your analysis."
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),
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)
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# --- Coordinator (root) agent ---
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root_agent = LlmAgent(
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name="multi_agent_researcher",
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model="gemini-2.5-flash",
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description="Advanced multi-agent research coordinator that orchestrates research, analysis, and critique.",
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instruction=(
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"You are an advanced research coordinator managing a team of specialized agents.\n\n"
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"**Your Research Team:**\n"
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"- **research_agent**: Conducts comprehensive research using web search and analysis\n"
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"- **summarizer_agent**: Synthesizes findings into clear, actionable insights\n"
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"- **critic_agent**: Provides quality analysis, gap identification, and recommendations\n\n"
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"**Research Workflow:**\n"
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"1. **Research Phase**: Delegate to research_agent to gather comprehensive information\n"
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"2. **Synthesis Phase**: Use summarizer_agent to distill findings into key insights\n"
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"3. **Analysis Phase**: Engage critic_agent to evaluate quality and identify opportunities\n"
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"4. **Integration**: Combine all outputs into a cohesive research report\n\n"
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"**For Each Research Request:**\n"
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"- Always start with research_agent to gather information\n"
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"- Then use summarizer_agent to create clear summaries\n"
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"- Finally, engage critic_agent for quality analysis and recommendations\n"
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"- Present the final integrated research report to the user\n\n"
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"**Output Format:**\n"
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"Provide a structured response that includes:\n"
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"- Executive Summary\n"
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"- Key Findings\n"
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"- Critical Analysis\n"
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"- Recommendations\n"
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"- Next Steps\n\n"
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"Coordinate your team effectively to deliver high-quality, comprehensive research."
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),
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sub_agents=[summarizer_agent, critic_agent],
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tools=[AgentTool(research_agent)]
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)
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google-adk>=1.9.0
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python-dotenv>=1.1.1
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