⚡ Tutorial 9.3: Parallel Agents - Market Snapshot Team

🎯 What You'll Learn

• Parallel Agent Composition: How to orchestrate multiple specialized agents concurrently
• Shared State: How parallel children write to a common session.state safely
• Branching Context: Invocation branches for clean, isolated tool/memory context
• Streamlit Interface: A simple UI to run and visualize parallel results

🧠 Core Concept: ParallelAgent with Shared State

According to the ADK docs, Parallel Agents execute their sub-agents concurrently. Each child runs on its own invocation branch but shares the same session.state.

Topic → ParallelAgent → 3 Sub-agents (Concurrent Execution)
             ↓
   [Market Trends] + [Competitors] + [Funding News]
             ↓
            Snapshot in state

Each child agent writes results to a distinct key in shared state to avoid overwrites: market_trends, competitors, funding_news.

📁 Project Structure

9_3_parallel agent/
├── agent.py              # Parallel workflow (3 research agents + ParallelAgent)
├── app.py                # Streamlit UI to run and view snapshot
├── requirements.txt      # Python dependencies
├── README.md             # This documentation
└── .env.example          # Example environment variables

🚀 Getting Started

1. Install Dependencies

cd "9_3_parallel agent"
pip install -r requirements.txt

2. Set Up Environment

Create a .env file with your Google API key:

echo "GOOGLE_API_KEY=your_ai_studio_key_here" > .env

Get your key from Google AI Studio.

3. Run the Streamlit App

streamlit run app.py

🧪 How It Works

• ParallelAgent executes market_trends_agent, competitor_intel_agent, and funding_news_agent concurrently.
• Each child uses web search and writes to a unique output_key in session.state.
• The UI reads session.state and displays a 3-column snapshot.

🔧 ADK Concepts Demonstrated

• ParallelAgent pattern and event interleaving
• Shared session.state with distinct keys per child
• Invocation branches for contextual separation
• Runner + Session services for execution

📚 Key Takeaways

• Parallel fan-out is ideal for independent data gathering
• Keep output keys distinct to avoid overwrites in shared state
• Combine with a downstream synthesizer agent if you need a single report