feat: Introduce Sequential Agent tutorial for google ADK
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Shubhamsaboo
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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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# 🎯 Tutorial 9.1: Sequential Agents - Business Implementation Plan Generator
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## 🎯 What You'll Learn
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- **Sequential Agent Composition**: How to orchestrate multiple specialized agents in sequence
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- **AgentTool Integration**: Wrapping agents as tools for enhanced capabilities
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- **Web Search Integration**: Real-time market intelligence through search agents
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- **Business Analysis Pipeline**: From market research to implementation planning
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- **Streamlit Web Interface**: User-friendly application for business planning
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## 🧠 Core Concept: Sequential Agent with Search Capabilities
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According to the [ADK workflow agents documentation](https://google.github.io/adk-docs/agents/workflow-agents/), **Sequential Agents** execute sub-agents one after another, in sequence. This tutorial demonstrates a **Business Implementation Plan Generator** that combines web search capabilities with sequential analysis:
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```
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Business Topic → SequentialAgent → 4 Sub-agents (Sequential Execution)
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↓
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[Market Research + Web Search] → [SWOT Analysis] → [Strategy] → [Implementation]
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↓
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Complete Business Implementation Plan
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```
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**Key Innovation**: The Market Research Agent uses a specialized Search Agent (wrapped as AgentTool) to access real-time web search capabilities for current market intelligence.
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## 📁 Project Structure
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```
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9_1_sequential_agent/
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├── agent.py # Business implementation plan generator with search capabilities
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├── app.py # Streamlit web interface for business planning
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├── requirements.txt # Python dependencies
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└── README.md # This documentation
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```
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## 🚀 Getting Started
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### 1. Install Dependencies
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```bash
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cd 9_1_sequential_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 with your Google API key:
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```bash
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echo "GOOGLE_API_KEY=your_ai_studio_key_here" > .env
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```
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**Important**: Get your API key from [Google AI Studio](https://aistudio.google.com/)
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### 3. Run the Streamlit App
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```bash
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streamlit run app.py
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```
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This will launch the **Business Implementation Plan Generator Agent** web interface!
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## 🧪 How It Works
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### **Business Implementation Plan Generation Pipeline**
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The agent processes business opportunities through a sophisticated 4-step sequential workflow:
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1. **🔍 Market Analysis** - Uses web search for current market information and competitive research
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2. **📊 SWOT Analysis** - Strategic assessment of strengths, weaknesses, opportunities, and threats
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3. **🎯 Strategy Development** - Strategic objectives and action plans
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4. **📋 Implementation Planning** - Detailed execution roadmap and resource requirements
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**Key Innovation**: The Market Analysis Agent has access to a specialized Search Agent (wrapped as AgentTool) that can perform real-time web searches using the `google_search` tool. This provides current market intelligence that feeds into the sequential analysis pipeline.
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The `SequentialAgent` ensures each step builds upon the previous step's output, creating a comprehensive business implementation plan ready for execution.
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**Result**: A complete business implementation plan with market research, strategic analysis, and execution roadmap.
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## 🔧 ADK Concepts Demonstrated
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### **1. SequentialAgent Pattern**
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The core workflow orchestrator that executes sub-agents in sequence, ensuring each step builds upon the previous step's output.
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### **2. AgentTool Integration**
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Advanced pattern where one agent (Search Agent) is wrapped as a tool and used by another agent (Market Researcher) to enhance capabilities.
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### **3. Web Search Capabilities**
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Real-time market intelligence through integrated search functionality, providing current data rather than relying on training data.
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### **4. Sub-agent Specialization**
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Each sub-agent specializes in a specific business analysis phase, creating a modular and maintainable system.
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### **5. Session Management**
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Maintains conversation state across the entire analysis pipeline, ensuring context flows between agents.
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### **6. Runner Execution**
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Processes the complete business implementation workflow with proper error handling and response management.
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## 🧪 Sample Topics to Try
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- **Electric vehicle charging stations** in urban areas
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- **AI-powered healthcare diagnostics** and patient care
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- **Sustainable food delivery** services and packaging
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- **Remote work collaboration** tools and platforms
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- **Renewable energy storage** solutions
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## 📊 Expected Output
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The sequential agent will provide:
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1. **Market Research**: Competitive analysis and market trends
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2. **SWOT Analysis**: Strategic assessment with actionable insights
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3. **Strategy Plan**: Clear objectives and implementation steps
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4. **Implementation Roadmap**: Practical execution guidance
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## 🎯 Learning Objectives
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- ✅ Understand how `SequentialAgent` orchestrates sub-agents
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- ✅ Learn to execute sequential agents with Runner and Session management
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- ✅ See how sub-agents can build upon each other's output
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- ✅ Experience a working, executable sequential workflow
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- ✅ Understand AgentTool integration for enhanced capabilities
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## 🚀 Next Steps
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- Try different business topics to see the sequential workflow in action
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- Experiment with reordering the sub-agents
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- Add more specialized agents to the pipeline
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- Explore other ADK workflow patterns (Parallel, Branching)
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## 🔧 Troubleshooting
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**Common Issues:**
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- **API Key Error**: Ensure `GOOGLE_API_KEY` is set in `.env`
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- **Import Errors**: Make sure you're in the correct directory
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- **Search Tool Errors**: Verify your API key has access to search capabilities
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**Pro Tips:**
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- Start with simple topics to understand the flow
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- Use the Streamlit app for easy testing and visualization
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- The sequential pattern is great for predictable, step-by-step processes
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- Web search integration provides real-time market intelligence
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## 📚 Key Takeaways
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- **SequentialAgent** is perfect for workflows that must happen in order
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- **AgentTool integration** allows agents to enhance each other's capabilities
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- **Web search capabilities** provide current market intelligence
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- **Sub-agents** can be simple `LlmAgent` instances or complex tool-enabled agents
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- **Clean, readable code** makes it easy to understand and modify
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- **Streamlit interface** provides user-friendly access to complex agent workflows
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import os
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import asyncio
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from dotenv import load_dotenv
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from google.adk.agents import LlmAgent, SequentialAgent
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from google.adk.tools import google_search
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from google.adk.tools.agent_tool import AgentTool
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from google.adk.sessions import InMemorySessionService
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from google.adk.runners import Runner
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from google.genai import types
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# Load environment variables
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load_dotenv()
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# --- Search Agent (Wrapped as AgentTool) ---
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search_agent = LlmAgent(
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name="search_agent",
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model="gemini-2.0-flash",
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description="Conducts web search for current market information and competitive analysis",
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instruction=(
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"You are a web search specialist. When given a business topic:\n"
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"1. Use web search to find current market information\n"
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"2. Identify key competitors and their market position\n"
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"3. Gather recent industry trends and market data\n"
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"4. Find market size estimates and growth projections\n"
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"5. Provide comprehensive, up-to-date market analysis\n\n"
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"Always use web search to get the most current information available."
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),
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tools=[google_search]
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)
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# --- Simple Sub-agents ---
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market_researcher = LlmAgent(
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name="market_researcher",
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model="gemini-2.5-flash",
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description="Conducts market research and competitive analysis using search capabilities",
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instruction=(
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"You are a market research specialist. Given a business topic:\n"
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"1. Use the search_agent to gather current market information\n"
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"2. Identify key competitors and their market position\n"
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"3. Analyze current market trends and opportunities\n"
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"4. Provide industry insights and market size estimates\n"
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"5. Synthesize search results into comprehensive market analysis\n\n"
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"Provide a comprehensive analysis in clear, structured format based on current web research."
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),
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tools=[AgentTool(search_agent)]
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)
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swot_analyzer = LlmAgent(
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name="swot_analyzer",
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model="gemini-2.5-flash",
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description="Performs SWOT analysis based on market research",
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instruction=(
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"You are a strategic analyst. Given market research findings:\n"
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"1. Identify internal strengths and competitive advantages\n"
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"2. Assess internal weaknesses and limitations\n"
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"3. Identify external opportunities in the market\n"
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"4. Evaluate external threats and challenges\n\n"
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"Provide a clear SWOT analysis with actionable insights."
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)
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)
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strategy_formulator = LlmAgent(
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name="strategy_formulator",
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model="gemini-2.5-flash",
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description="Develops strategic objectives and action plans",
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instruction=(
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"You are a strategic planner. Given SWOT analysis results:\n"
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"1. Define 3-5 key strategic objectives\n"
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"2. Create specific action items for each objective\n"
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"3. Recommend realistic timeline for implementation\n"
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"4. Define success metrics and KPIs to track\n\n"
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"Provide a clear strategic plan with actionable steps."
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)
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)
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implementation_planner = LlmAgent(
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name="implementation_planner",
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model="gemini-2.5-flash",
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description="Creates detailed implementation roadmap",
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instruction=(
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"You are an implementation specialist. Given the strategy plan:\n"
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"1. Identify required resources (human, financial, technical)\n"
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"2. Define key milestones and checkpoints\n"
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"3. Develop risk mitigation strategies\n"
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"4. Provide final recommendations with confidence level\n\n"
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"Create a practical implementation roadmap."
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)
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)
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# --- Sequential Agent (Pure Sequential Pattern) ---
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business_intelligence_team = SequentialAgent(
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name="business_intelligence_team",
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description="Sequentially processes business intelligence through research, analysis, strategy, and planning",
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sub_agents=[
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market_researcher, # Step 1: Market research (with search capabilities)
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swot_analyzer, # Step 2: SWOT analysis
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strategy_formulator, # Step 3: Strategy development
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implementation_planner # Step 4: Implementation planning
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]
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)
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# --- Runner Setup for Execution ---
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session_service = InMemorySessionService()
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runner = Runner(
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agent=business_intelligence_team,
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app_name="business_intelligence",
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session_service=session_service
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)
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# --- Simple Execution Function ---
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async def analyze_business_intelligence(user_id: str, business_topic: str) -> str:
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"""Process business intelligence through the sequential pipeline"""
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session_id = f"bi_session_{user_id}"
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# Create or get session
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session = await session_service.get_session(
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app_name="business_intelligence",
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user_id=user_id,
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session_id=session_id
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)
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if not session:
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session = await session_service.create_session(
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app_name="business_intelligence",
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user_id=user_id,
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session_id=session_id,
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state={"business_topic": business_topic, "conversation_history": []}
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)
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# Create user content
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user_content = types.Content(
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role='user',
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parts=[types.Part(text=f"Please analyze this business topic: {business_topic}")]
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)
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# Run the sequential pipeline
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response_text = ""
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async for event in runner.run_async(
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user_id=user_id,
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session_id=session_id,
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new_message=user_content
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):
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if event.is_final_response():
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if event.content and event.content.parts:
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response_text = event.content.parts[0].text
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return response_text
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import streamlit as st
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import asyncio
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from agent import business_intelligence_team, analyze_business_intelligence
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# Page configuration
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st.set_page_config(
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page_title="Sequential Agent Demo",
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page_icon=":arrow_right:",
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layout="wide"
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)
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# Title and description
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st.title("🚀 Business Implementation Plan Generator Agent")
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st.markdown("""
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This **Business Implementation Plan Generator Agent** analyzes business opportunities through a comprehensive 4-step process:
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1. **🔍 Market Analysis** - Researches market, competitors, and trends using web search
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2. **📊 SWOT Analysis** - Identifies strengths, weaknesses, opportunities, and threats
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3. **🎯 Strategy Development** - Creates strategic objectives and action plans
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4. **📋 Implementation Planning** - Generates detailed business implementation roadmap
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**Result**: A complete business implementation plan ready for execution.
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""")
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# This is a placeholder user_id for demo purposes.
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# In a real app, you might use authentication or session state to set this.
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user_id = "demo_user"
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# Sample business topics
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sample_topics = [
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"Electric vehicle charging stations in urban areas",
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"AI-powered healthcare diagnostics",
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"Sustainable food delivery services",
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"Remote work collaboration tools",
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"Renewable energy storage solutions"
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]
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# Main content
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st.header("Generate Your Business Implementation Plan")
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# Topic input
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business_topic = st.text_area(
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"Enter a business opportunity to analyze:",
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value=sample_topics[0],
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height=100,
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placeholder="Describe a business opportunity, industry, or market you'd like to analyze for implementation planning..."
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)
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# Sample topics
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st.subheader("Or choose from sample business opportunities:")
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cols = st.columns(len(sample_topics))
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for i, topic in enumerate(sample_topics):
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if cols[i].button(topic, key=f"topic_{i}"):
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business_topic = topic
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st.rerun()
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# Analysis button
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if st.button("🚀 Generate Business Implementation Plan", type="primary"):
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if business_topic.strip():
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st.info("🚀 Starting business analysis... This will research the market, perform SWOT analysis, develop strategy, and create an implementation plan.")
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# Display the workflow
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st.subheader("Business Analysis Workflow")
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.markdown("**1. Market Analysis**")
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st.markdown("🔍 Web search + competitive research")
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with col2:
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st.markdown("**2. SWOT Analysis**")
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st.markdown("📊 Strengths, Weaknesses, Opportunities, Threats")
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with col3:
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st.markdown("**3. Strategy Development**")
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st.markdown("🎯 Strategic objectives and action plans")
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with col4:
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st.markdown("**4. Implementation Planning**")
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st.markdown("📋 Detailed roadmap and execution plan")
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# Run the actual analysis
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with st.spinner("Generating comprehensive business implementation plan..."):
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try:
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result = asyncio.run(analyze_business_intelligence(user_id, business_topic))
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st.success("✅ Business Implementation Plan Generated!")
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st.subheader("Your Business Implementation Plan")
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st.markdown(result)
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except Exception as e:
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st.error(f"❌ Error during analysis: {str(e)}")
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st.info("Make sure you have set up your GOOGLE_API_KEY in the .env file")
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else:
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st.error("Please enter a business opportunity to analyze.")
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# How it works (in sidebar)
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with st.sidebar:
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st.header("How It Works")
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st.markdown("""
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The **Business Implementation Plan Generator Agent** uses a sophisticated sequential workflow to create comprehensive business plans:
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1. **🔍 Market Analysis Agent**: Uses web search to research current market conditions, competitors, and trends
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2. **📊 SWOT Analysis Agent**: Analyzes the market research to identify strategic insights
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3. **🎯 Strategy Development Agent**: Creates strategic objectives and action plans based on SWOT analysis
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4. **📋 Implementation Planning Agent**: Develops detailed execution roadmaps and resource requirements
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**Key Innovation**: The Market Analysis Agent has access to a specialized Search Agent (wrapped as AgentTool) that can perform real-time web searches for current market intelligence.
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Each agent builds upon the previous agent's output, creating a comprehensive business implementation plan ready for execution.
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""")
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google-adk>=1.9.0
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streamlit>=1.28.0
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python-dotenv>=1.1.1
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pydantic>=2.0.0
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@ -54,7 +54,14 @@ This crash course covers the essential concepts of Google ADK through hands-on t
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- Error handling and logging
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- Usage analytics and monitoring
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8. **More tutorials coming soon!**
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8. **[8_simple_multi_agent](./8_simple_multi_agent/README.md)** - Multi-agent orchestration
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- **[8.1 Multi-Agent Researcher](./8_simple_multi_agent/multi_agent_researcher/README.md)** - Research pipeline with specialized agents
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- Coordinator agent with sub-agents
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- Sequential workflow: Research → Summarize → Critique
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- Web search integration and comprehensive analysis
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9. **[9_multi_agent_patterns](./9_multi_agent_patterns/README.md)** - Multi-Agent Patterns
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- **[9.1 Sequential Agent](./9_multi_agent_patterns/9_1_sequential_agent/README.md)** — Deterministic pipeline of sub-agents (e.g., Draft → Critique → Improve)
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## 🛠️ Prerequisites
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