Data Analyst - AI Agent

An agentic conversational data analysis platform powered by LangGraph. Analyze CSV data through natural language conversations with real-time preview updates.

Overview

Full-stack application that combines an intelligent agent architecture with a modern web interface. The system uses LangGraph to orchestrate a multi-step workflow that understands user intent, processes data, and generates insights—all while streaming updates to the frontend in real-time.

Agent Architecture

The core of the system is a LangGraph state machine that processes user queries through three sequential nodes:

1. Understand Intent - Uses OpenAI GPT-4o to analyze user messages and extract analysis requirements
2. Process Data - Executes data operations (loading, filtering, summarizing) using Pandas tools
3. Generate Response - Synthesizes results into natural language responses

The agent maintains conversation context and streams status updates via Server-Sent Events (SSE) for real-time UI feedback.

Technology Stack

Frontend

• React 18 - UI framework
• TypeScript - Type safety
• Vite - Build tool and dev server
• Tailwind CSS - Styling
• SSE (Server-Sent Events) - Real-time streaming updates

Backend

• FastAPI - REST API framework
• LangGraph - Agent orchestration and state management
• LangChain - LLM integration framework
• OpenAI GPT-3.5-turbo - Natural language understanding and generation
• Pandas - Data analysis and manipulation
• SSE-Starlette - Server-Sent Events streaming

Project Structure

ai-datalab/
├── backend/
│   ├── agent/
│   │   ├── graph.py      # LangGraph state machine definition
│   │   └── nodes.py      # Agent nodes (understand, process, respond)
│   ├── tools/
│   │   └── data_analysis.py  # Pandas-based data operations
│   ├── data/             # CSV files for analysis
│   └── main.py           # FastAPI application
├── frontend/
│   ├── src/
│   │   ├── components/   # React components
│   │   ├── hooks/        # Custom hooks (useSSE)
│   │   └── App.tsx       # Main application
│   ├── package.json      # Frontend dependencies
│   └── vite.config.ts    # Vite configuration
└── ...

Setup

Prerequisites

• Node.js 18+
• Python 3.8+
• OpenAI API key

Frontend

cd frontend
npm install
npm run dev

Frontend runs on http://localhost:5175

Backend

cd backend
pip install -r requirements.txt
cp .env.example .env
# Add your OPENAI_API_KEY to .env
python main.py

Backend runs on http://localhost:8000

Usage

1. Start the backend server
2. Start the frontend dev server
3. Place CSV files in backend/data/
4. Chat with the agent to analyze your data
5. View real-time analysis results in the preview panel

API Endpoints

• POST /api/chat - Process chat messages through the agent
• GET /api/stream/{session_id} - SSE stream for real-time updates
• GET /api/health - Health check

Build

cd frontend
npm run build