A sophisticated multi-agent system designed to handle customer queries related to InfinitePay services through intelligent routing and specialized agent responses.

• Purpose: Main orchestration agent that analyzes incoming queries
• Function: Routes queries to appropriate specialized agents based on content analysis
• Technology: Built using Agno Framework's Team and Workflow components

• Purpose: Handles InfinitePay product-related inquiries
• Knowledge Source: Vector database (ChromaDB) containing InfinitePay documentation
• Process:
  • Generates efficient embedding queries using LLM
  • Retrieves most relevant chunks from vector database
  • Provides accurate product information

• Purpose: Assists with customer issues and support requests
• Capabilities:
  • Accesses customer information from database
  • Reviews previous interaction history
  • Creates support tickets when necessary
  • Provides personalized solutions

• Purpose: Final response enhancement layer
• Function: Transforms technical responses into human-friendly, conversational format
• Benefit: Improves user experience and engagement

The system returns structured responses using Pydantic models:

{
  "response": "Enhanced human-friendly response from Personality Layer",
  "source_agent_response": "Raw response from the originating agent",
  "agent_workflow": {
    "agent_name": "product_knowledge_specialist | customer_support_agent",
    "tool_calls": {
      "tool_name": "parameters_and_metadata"
    }
  }
}

• Framework: Agno Framework for agent orchestration
• API: FastAPI with Uvicorn server
• Vector Database: ChromaDB for efficient similarity search
• Knowledge Base: WebSiteKnowledgeBase for chunk retrieval
• LLM Integration: Mistral API for natural language processing
• Search Enhancement: Tavily API for additional context
• Validation: Pydantic models for response structure

• Python 3.11+
• Docker (optional)
• Mistral API Key
• Tavily API Key

1. Clone the repository

   git clone https://github.com/manthan89-py/Multi-Agent-Query-Resolution.git
   cd <directory>

2. Environment Setup

   # Rename environment file
   cp .env.example .env
   
   # Edit .env file with your API keys
   MISTRAL_API_KEY=your_mistral_api_key_here
   TAVILY_API_KEY=your_tavily_api_key_here

3. Virtual Environment Setup

   # Create virtual environment using uv
   uv venv --python=3.11
   
   # Activate virtual environment
   source .venv/bin/activate  # Linux/Mac
   # or
   .venv\Scripts\activate     # Windows

4. Install Dependencies

   uv pip install -r requirements.txt

5. Run the Application

   python api.py

1. Build Docker Image

   make build

2. Run Docker Container

   make run

3. View Available Commands

   make help

Once the server is running, access the interactive API documentation:

• Swagger UI: http://localhost:8000/docs
• Make sure to run /load_datababse first to populate the database.

• Intelligent Routing: Automatically directs queries to the most appropriate agent
• RAG Pipeline: Efficient retrieval-augmented generation for accurate responses
• Customer Context: Leverages customer history for personalized support
• Scalable Architecture: Built with Agno framework for easy agent addition
• Human-Friendly Output: Personality layer ensures natural conversation flow
• Structured Responses: Consistent JSON output format for easy integration

multi-agent-customer-resolution/
├── api.py                 # FastAPI application entry point
├── requirements.txt       # Python dependencies
├── .env.example          # Environment variables template
├── Dockerfile            # Docker configuration
├── Makefile             # Build and run commands
├── results.json         # Sample responses for testing
├── agents/              # Agent implementations
│   ├── router.py       # Router agent
│   ├── knowledge_agent.py  # Product knowledge agent
│   ├── customer_support_agent.py  # Customer support agent
│   └── workflow.py  # Workflow orchestration 
└── utils/                  
    ├── instructions.py     # Prompts
    ├── logger.py            # logging functions
    └── models.py           # Data/Response models

Sample results and test cases are available in results.json. This file contains:

• Example queries
• Expected responses
• Agent workflow traces

• Add New Agents: Extend the system by implementing new agent classes
• Modify Routing Logic: Update router agent for different query categorization
• Enhance Knowledge Base: Add more documents to the vector database
• Customize Personality: Adjust the personality layer for different brand voices

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-agent)
3. Commit your changes (git commit -am 'Add new agent functionality')
4. Push to the branch (git push origin feature/new-agent)
5. Create a Pull Request

curl -X POST "http://localhost:8000/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "message": "What are the fees of the Maquininha Smart",
    "user_id": "client789"
    }'

    {
        "response": "Sure thing! You can get the Maquininha Smart on a payment plan of 12 installments of R$ 16.58 each. Just a heads-up, the tax rates vary based on how you pay and your monthly revenue. For debit transactions, the tax starts at 1.37%. If you're paying with credit in one go, it's 3.15%, and if you split it into 12 installments, the rate is 12.40%. Oh, and Pix transactions? No tax at all! The good news is, the more you earn monthly, the lower these rates get. For all the nitty-gritty details, you can check out InfinitePay's official website. I'd be happy to help with any other questions you might have!",
        "source_agent_response": "The Maquininha Smart is available for a payment plan of 12 installments of R$ 16.58. Different tax rates apply depending on the payment method and monthly revenue. For example, for debit transactions, the tax rate starts at 1.37%, while for credit transactions paid in one installment, the rate starts at 3.15%, and for 12 installments, it is 12.40%. Pix transactions have a tax rate of zero. The tax rates decrease as the monthly revenue increases. For more detailed information, you can refer to the official website of InfinitePay.",
        "agent_workflow": {
            "agent_name": "Product Knowledge Specialist",
            "tool_calls": {
                "search_knowledge_base": [
                    {
                        "query": "fees of the Maquininha Smart"
                    }
                ]
            }
        }
    }

1. API Keys Not Working

   • Ensure .env file is properly configured
   • Verify API keys are valid and have sufficient quota

2. Vector Database Issues

   • Check ChromaDB installation and permissions
   • Verify knowledge base is properly indexed

3. Agent Routing Problems

   • Review router agent logic
   • Check query classification accuracy

• Check the API documentation at /docs
• Review sample responses in results.json
• Enable debug logging for detailed troubleshooting

• Multi-language support
• Voice query processing
• Advanced analytics dashboard
• Integration with more knowledge sources
• Real-time learning capabilities

Built with ❤️ using Agno Framework, FastAPI, and ChromaDB