🧠 Multi-Agent AI System – Support & Analytics Agents

This project implements a multi-agent system using LangChain , where two agents serve distinct but complementary business roles:

• 🎧 Support Agent: Handles client support, course/class info, and order/payment queries.
• 📊 Dashboard Agent: Provides analytics and insights for business decision-making.

Backend is powered by Flask, MongoDB, and LangChain; while the frontend is hosted via Streamlit.

🌐 Hosted Frontend

👉 Live Demo on Streamlit

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🔧 Features

👩‍💼 Support Agent

• Get course/class info (by instructor, status, date)
• Order/payment status by ID or client
• Client info by name/email/phone
• Create new client enquiries and orders
• Multilingual query support (e.g. Hindi)

📈 Dashboard Agent

• Revenue & outstanding dues
• Top courses by enrollment
• Attendance %, drop-off rates
• Active vs inactive clients

🧠 Memory & Multilingual Support

• Retains conversation history for follow-up queries
• Translates non-English input (e.g., Hindi → English) before execution

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📁 Folder Structure

MULTIAGENT-ASSIGNMENT/
├── backend/
│   ├── agents/
│   │   ├── dashboard_agent.py          # Analytics Agent
│   │   └── support_agent.py            # Support & Service Agent
│   ├── models/
│   │   ├── schemas.py                  # MongoDB Schemas & Connection
│   │   └── seed_data.py                # Initial data for testing
│   ├── tools/
│   │   ├── external_api.py             # Simulated client/order creation API
│   │   └── mongodb_tool.py             # CRUD & Aggregation Helpers
│   ├── app.py                          # Main Flask app with API routes
├── frontend.py                         # Optional Streamlit interface (hosted)
├── requirements.txt                    # Python dependencies
├── .env                                # Environment variables (Mongo URI, API keys)
├── screenshots/
│   ├── 1.png, 1.1.png                  # Example 1: Query + Logs
│   ├── 2.png, 2.2.png                  # Example 2: Query + Logs
│   ├── 3.png, 3.3.png                  # Example 3: Query + Logs
│   ├── 4.png, 4.4.png                  # Example 4: Multilingual + Memory

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🖼️ Example Screenshots

🔹 Example 1: Query for classes this week

Query Terminal Logs	Server Terminal

🔹 Example 2: Creating an Order

Query Terminal Logs	Server Terminal

🔹 Example 3: Extracting Total Revenue

Query Terminal Logs	Server Terminal

🔹 Example 4: Multilingual Query + Memory Recall

Query Terminal Logs	Server Terminals

In Screenshot 4, the query was made in Hindi and automatically translated. The next query, "what did I ask earlier?", demonstrates memory recall capability.

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🛠 Setup & Run Instructions

Follow these steps to set up and run the Multi-Agent AI backend + frontend on your local system.

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1️⃣ Clone the Repository

git clone https://github.com/your-username/multiagent-assignment.git
cd multiagent-assignment

2️⃣ Install Python Dependencies

pip install -r requirements.txt

3️⃣ Set Up Environment Variables

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_key
MONGODB_URI=your_mongo_connection_uri

4️⃣ Seed the MongoDB Database

This step populates the database with mock data (courses, clients, payments, etc.).

cd backend/models
python seed_data.py

5️⃣ Start the Flask Server

cd ../../backend
python app.py

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📡 Available Endpoints

Endpoint	Method	Description
/support	POST	Send queries to the Support Agent
/dashboard	POST	Send queries to the Dashboard Agent

🔍 Sample Request Format (JSON)

{
  "query": "What classes are available this week?"
}

🧠 Notes:

-Input should be a natural language query in the query field.

-Non-English queries are automatically translated to English using Google Translate.

-Responses are always returned in English.

-Memory is preserved per agent instance (useful for contextual follow-ups).

🚀 Key Features Recap

• 🔍 Natural Language Querying over MongoDB collections
• 🧠 Memory-enabled Agents for contextual follow-up
• 🌍 Multilingual Input Support (via Google Translate)
• 🔗 External API Integration for simulating orders and client creation
• 📊 Business Analytics like revenue, attendance, and course trends
• 🖼️ Visual frontend using Streamlit for live demos

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🧰 Tech Stack

Layer	Tools Used
Agent Logic	LangChain, OpenAI GPT-4o
Backend	Python, Flask
Database	MongoDB
Agent Memory	ConversationBufferMemory (LangChain)
Frontend	Streamlit
Hosting	Render (Flask backend), Streamlit Cloud

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🙌 Credits

This project was built as part of a multi-agent backend system following modular LangChain-based architecture, designed to simulate real-world agent collaboration between support and analytics roles.

Developed by: Priyanshu Deswal