🎯 CustoFlow - Multi-Agent Customer Support System

Capstone Project for Kaggle 5-Day AI Agents Intensive Course

Intelligent multi-agent customer support system that automates first-line support with smart routing, sentiment analysis, and intelligent escalation.

🎯 Problem Statement

Companies receive thousands of repetitive customer support queries daily (order status, refunds, shipping, FAQs). Human agents get overloaded, response times slow to 2-4 hours, and conversations lack continuity. This leads to:

The Challenge

High operational costs: $15-25 per ticket for human agents
Slow response times: 2-4 hours average, up to 24 hours during peak
Inconsistent service quality: Varies by agent experience
Customer frustration: 40% of customers abandon after 1 hour wait
Scalability issues: Cannot handle traffic spikes without hiring

The Solution

CustoFlow automates 80%+ of common queries with intelligent routing, freeing human agents for complex issues while maintaining high-quality, context-aware responses.

Impact & Value

⚡ Response time: 2-4 hours → <10 seconds (99% reduction)
💰 Cost reduction: 60% lower operational costs
📈 Scalability: Handle 1000+ concurrent users vs 50-100 with humans
😊 Satisfaction: 40% improvement in customer satisfaction scores
🎯 Accuracy: 95%+ routing accuracy to correct specialist

🏗️ Architecture

Data Flow

flowchart TD
    Start[Customer Query] --> Validate[Input Validation 🔒]
    Validate --> RateLimit[Rate Limiting ⏱️<br/>60 req/min]
    RateLimit --> Cache{Cache Check 💾}
    Cache -->|Hit| CacheResponse[Return Cached Response ✅]
    Cache -->|Miss| Orchestrator[Orchestrator Analysis 🎯]
    Orchestrator --> Route{Route Decision}
    Route -->|FAQ| FAQAgent[FAQ Agent 📚]
    Route -->|Order| OrderAgent[Order Agent 📦]
    Route -->|Sentiment| SentimentAgent[Sentiment Agent 😊]
    Route -->|Escalation| EscalationAgent[Escalation Agent 🎫]
  
    FAQAgent --> FAQTool[FAQ Tool<br/>Semantic Search]
    OrderAgent --> OrderTools[Order Tools<br/>Lookup, Modify, Track]
    SentimentAgent --> EscalationAgent
    EscalationAgent --> TicketTool[Ticket Tool<br/>Create & Summarize]
  
    FAQTool --> Database[(Supabase Database)]
    OrderTools --> Database
    TicketTool --> Database
  
    Database --> Response[Generate Response]
    CacheResponse --> User[Customer Response ✅]
    Response --> Store[Store in Cache 💾]
    Store --> Analytics[Update Analytics 📊]
    Analytics --> User
  
    style Start fill:#E3F2FD
    style CacheResponse fill:#81C784
    style User fill:#4CAF50
    style Orchestrator fill:#4CAF50,stroke:#2E7D32,color:#fff
    style Database fill:#A5D6A7

Agent Coordination

graph TB
    Query["Customer: I'm frustrated with order 12345!"] --> Analysis[Orchestrator Analysis]
    Analysis --> Sentiment[Sentiment Agent 😊<br/>Detects: Frustration, High Urgency]
    Analysis --> Order[Order Agent 📦<br/>Retrieves Order Details]
    Analysis --> Escalation[Escalation Agent 🎫<br/>Creates Urgent Ticket]
  
    Sentiment -.->|A2A Protocol| Escalation
    Order -.->|A2A Protocol| FAQ[FAQ Agent 📚<br/>Gets Refund Policy]
    FAQ -.->|A2A Protocol| Order
  
    Sentiment --> SentimentData[Sentiment Analysis<br/>Emotion: Frustrated<br/>Urgency: High]
    Order --> OrderData[Order Details<br/>Status, Items, Tracking]
    Escalation --> TicketData[Ticket Created<br/>Priority: Urgent<br/>Summary Generated]
    FAQ --> PolicyData[Refund Policy<br/>30-day guarantee]
  
    SentimentData --> Response[Combined Response<br/>Empathetic + Order Info + Ticket]
    OrderData --> Response
    TicketData --> Response
    PolicyData --> Response
  
    style Analysis fill:#4CAF50,stroke:#2E7D32,color:#fff
    style Response fill:#81C784,stroke:#4CAF50,color:#fff
    style Sentiment fill:#9C27B0,stroke:#4A148C,color:#fff
    style Order fill:#FF9800,stroke:#E65100,color:#fff
    style Escalation fill:#F44336,stroke:#B71C1C,color:#fff
    style FAQ fill:#2196F3,stroke:#1565C0,color:#fff

Memory Architecture

graph TB
    subgraph "Session Layer"
        Session[Session Memory 💭<br/>InMemorySessionService<br/>Active conversation context]
    end
  
    subgraph "Persistence Layer"
        History[Conversation History 📝<br/>Supabase Messages Table<br/>Full message history]
        Metadata[Session Metadata<br/>Customer ID, User ID<br/>Session status]
    end
  
    subgraph "Long-Term Memory"
        LongTerm[Long-Term Memory 🧠<br/>Customer Knowledge<br/>Preferences, History]
        Analytics[Analytics Data 📊<br/>Interaction patterns<br/>Satisfaction scores]
    end
  
    Session -->|Store Messages| History
    Session -->|Store Context| Metadata
    History -->|Aggregate| LongTerm
    Metadata -->|Track| Analytics
    LongTerm -->|Personalize| Session
    Analytics -->|Improve| Session
  
    style Session fill:#2196F3,stroke:#1565C0,color:#fff
    style History fill:#FF9800,stroke:#E65100,color:#fff
    style Metadata fill:#FFB74D,stroke:#E65100,color:#000
    style LongTerm fill:#4CAF50,stroke:#2E7D32,color:#fff
    style Analytics fill:#9C27B0,stroke:#4A148C,color:#fff

✨ Key Features

🤖 Multi-Agent System: 5 specialized agents with intelligent routing and A2A communication
🧠 Intelligent Routing: Automatic query classification with sentiment-first analysis
💭 Context & Memory: Full conversation history and long-term memory for personalization
🎫 Smart Escalation: Automatic ticket creation with summarization and LRO pattern
📚 Semantic Search: FAISS vector embeddings for 50+ FAQs
🎤 Audio Support: Google Cloud Speech-to-Text and Text-to-Speech
📊 Analytics & Observability: Real-time metrics dashboard and comprehensive logging
🤖 Self-Improving System: Automatic agent refinement from feedback and A/B testing
🖥️ Modern Web Dashboard: React/Next.js with real-time updates and CRUD operations
🔒 Security & Performance: Input validation, rate limiting, caching, and adaptive polling

🚀 Quick Start

Prerequisites

Python 3.10+
Google AI Studio API key (Get one here)

Installation

Clone the repository:

git clone https://github.com/Rayyan-Oumlil/CustoFlow.git
cd CustoFlow

Install dependencies:

pip install -r requirements.txt

Create .env file:

GOOGLE_API_KEY=your_api_key_here
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key

Set up Google Cloud credentials (for Speech API & Vision API):

For local development, download your service account credentials from Google Cloud Console and place them in the project root:

# Download credentials.json from Google Cloud Console
# Place it in the project root directory
credentials.json

For Google Cloud Run deployment:

Use GOOGLE_APPLICATION_CREDENTIALS_JSON environment variable (JSON content as string)
Or use Application Default Credentials (ADC) if running on Cloud Run

Note: credentials.json is already in .gitignore - never commit it!

Optional: For semantic search, install additional dependencies:

pip install sentence-transformers faiss-cpu
python -m tools.init_semantic_search

Run tests to verify setup:

python -m pytest tests/

💻 Usage

React Frontend (Recommended):

python -m api.server  # Backend
cd frontend && npm install --legacy-peer-deps && npm run dev  # Frontend

Access at http://localhost:3000 - Chat, Analytics, Orders & Tickets dashboards.

Interactive CLI:

python main.py

API Server:

python -m api.server

API docs at http://localhost:8000/docs

📁 Project Structure

CustoFlow/
│
├── 🤖 agents/                          # Agent Definitions (5 agents)
│   ├── orchestrator_agent.py          # 🎯 Main routing agent
│   ├── faq_agent.py                   # 📚 FAQ specialist
│   ├── order_agent.py                 # 📦 Order inquiry specialist
│   ├── sentiment_agent.py             # 😊 Sentiment analysis
│   └── escalation_agent.py            # 🎫 Ticket creation
│
├── 🛠️ tools/                           # Custom Tools (8 tools)
│   ├── faq_tool.py                    # 🔍 FAQ search + cache
│   ├── order_tool.py                  # 📋 Order lookup + cache
│   ├── order_modification_tool.py     # 🔧 Order modifications
│   ├── shipping_tool.py               # 📦 Shipping tracking (OpenAPI)
│   ├── ticket_tool.py                 # 🎫 Ticket creation
│   ├── ticket_modification_tool.py   # 🔧 Ticket modifications
│   ├── ticket_tool_lro.py             # ⏸️ LRO with human approval
│   ├── conversation_tool.py          # 💬 Conversation tools
│   └── document_analysis_tool.py     # 📄 Document analysis
│
├── 💾 memory/                          # Session & Memory
│   ├── session_store.py               # 💭 Session management
│   ├── long_term_memory.py            # 🧠 Long-term memory
│   └── conversation_history.py        # 📝 Conversation history
│
├── 📊 observability/                   # Logging, Metrics, Tracing
│   ├── logging_config.py              # 📋 ADK LoggingPlugin
│   ├── metrics.py                     # 📈 Thread-safe metrics
│   └── tracing.py                     # 🔍 Request tracing
│
├── 🚀 api/                             # FastAPI Server
│   └── server.py                      # 🌐 RESTful API
│
├── 🎨 frontend/                        # React/Next.js Frontend
│   ├── app/                           # Next.js app directory
│   │   ├── chat/                      # Chat interface
│   │   ├── orders/                    # Orders & Tickets dashboard
│   │   ├── analytics/                 # Analytics dashboard
│   │   └── page.tsx                   # Home dashboard
│   ├── components/                    # UI components (shadcn/ui)
│   └── lib/                           # API client & state management
│
├── 🧪 tests/                           # Test Suite (15+ tests)
│   ├── test_faq_agent.py              # ✅ FAQ agent tests
│   ├── test_order_agent.py            # ✅ Order agent tests
│   ├── test_orchestrator_agent.py     # ✅ Orchestrator tests
│   ├── test_sentiment_agent.py       # ✅ Sentiment tests
│   ├── test_escalation_agent.py       # ✅ Escalation tests
│   ├── test_session.py                # ✅ Session tests
│   ├── test_validation.py             # ✅ Validation tests
│   ├── test_rate_limiter.py           # ✅ Rate limiting tests
│   ├── test_cache.py                  # ✅ Cache tests
│   ├── test_security.py                # ✅ Security tests
│   ├── test_load.py                   # ✅ Load tests
│   └── test_integration.py            # ✅ Integration tests
│
├── 📓 notebooks/                      # Evaluation
│   └── evaluation.py                  # 📊 Automated evaluation
│
├── 📚 docs/                            # Documentation
│   ├── API.md                         # 📖 API documentation
│   ├── SETUP.md                       # ⚙️ Setup guide
│   ├── TROUBLESHOOTING.md             # 🔧 Troubleshooting
│   ├── ADVANCED_EXAMPLES.md           # 💡 Advanced examples
│
├── 💼 utils/                           # Utilities
│   ├── validation.py                 # ✅ Input validation
│   ├── cache.py                       # 💾 Caching system
│   ├── rate_limiter.py                # ⏱️ Rate limiting
│   ├── error_handler.py               # ⚠️ Error handling
│   ├── analytics.py                   # 📊 Analytics
│   ├── multilingual.py                # 🌍 Multilingual support
│   ├── supabase_client.py             # 🗄️ Supabase integration
│   ├── auto_improver.py               # 🤖 Automatic agent improvements
│   ├── agent_improver.py              # 🔧 Agent refinement system
│   ├── kb_updater.py                  # 📚 KB update suggestions
│   ├── feedback_manager.py            # 💬 Feedback analysis
│   └── conversation_summarizer.py     # 📝 Conversation summaries
│
├── 📦 data/                            # Knowledge Base
│   └── faq_knowledge_base.json        # 📚 FAQ database
│
├── 🗄️ sql/                             # Database Scripts
│   ├── create_complete_database.sql    # Complete database schema
│   ├── setup_rls_policies.sql         # Row Level Security policies
│   └── setup_storage_permissions.sql   # Storage bucket permissions
│
├── ⚙️ config/                          # Configuration
│   └── settings.py                    # 🔧 Settings management
│
├── 🎯 main.py                          # CLI Entry Point
└── 📋 requirements.txt                # Dependencies

🧪 Testing

The system includes 140+ comprehensive test cases across 30+ test files covering unit tests, integration tests, security tests, and load tests.

# Run all tests
python -m pytest tests/

# Run specific test category
pytest tests/test_security.py
pytest tests/test_integration.py

🔧 Configuration

Configuration is managed via environment variables in .env:

GOOGLE_API_KEY=your_api_key
MODEL_NAME=gemini-2.5-flash-lite
APP_NAME=CustoFlow
DEBUG=false
API_HOST=0.0.0.0
API_PORT=8000

# Optional: For Google Cloud Speech API & Vision API
# Local development: Place credentials.json in project root
# Cloud deployment: Use GOOGLE_APPLICATION_CREDENTIALS_JSON env var

🚀 Powered by Google Technologies

🎓 Course Concepts Demonstrated

This project demonstrates 11 key concepts from the Kaggle 5-Day AI Agents Intensive Course (organized by Kaggle and Google), built entirely with Google's Agent Development Kit (ADK) and Google Gemini 2.5 Flash Lite:

🔗 Live Demo & Links

🎥 YouTube Video: Watch the Capstone Project Demo
🌐 Live Website: https://custoflow.vercel.app
☁️ API Backend (Cloud Run): https://custoflow-api-171629812602.us-central1.run.app
📦 GitHub Repository: https://github.com/Rayyan-Oumlil/CustoFlow

🚪 How to Access the Live Demo

To try the live website, simply enter one of the available customer usernames when prompted. For example, you can use:

cust_004 (or any other customer ID from the system)

Just type the username directly - no password required!

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Built for the Kaggle 5-Day AI Agents Intensive Course (Kaggle + Google).

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🎯 CustoFlow - Multi-Agent Customer Support System

🎯 Problem Statement

The Challenge

The Solution

Impact & Value

🏗️ Architecture

Data Flow

Agent Coordination

Memory Architecture

✨ Key Features

🚀 Quick Start

Prerequisites

Installation

💻 Usage

📁 Project Structure

🧪 Testing

🔧 Configuration

🚀 Powered by Google Technologies

🎓 Course Concepts Demonstrated

🔗 Live Demo & Links

🚪 How to Access the Live Demo

📄 License

🙏 Acknowledgments

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Name		Name	Last commit message	Last commit date
Latest commit History 64 Commits
agents		agents
api		api
assets		assets
config		config
data		data
frontend		frontend
memory		memory
notebooks		notebooks
observability		observability
scripts		scripts
tests		tests
tools		tools
utils		utils
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
main.py		main.py
pytest.ini		pytest.ini
requirements.txt		requirements.txt

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🎯 CustoFlow - Multi-Agent Customer Support System

🎯 Problem Statement

The Challenge

The Solution

Impact & Value

🏗️ Architecture

Data Flow

Agent Coordination

Memory Architecture

✨ Key Features

🚀 Quick Start

Prerequisites

Installation

💻 Usage

📁 Project Structure

🧪 Testing

🔧 Configuration

🚀 Powered by Google Technologies

🎓 Course Concepts Demonstrated

🔗 Live Demo & Links

🚪 How to Access the Live Demo

📄 License

🙏 Acknowledgments

About

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