Next.js Azure AI Agent Starter: Build Intelligent Chat Applications Quickly

Project Overview

This starter template provides everything you need to build sophisticated AI-powered chat applications using Next.js and Azure OpenAI. Whether you’re creating a customer support bot, a technical assistant, or a general-purpose chat interface, this template gives you a production-ready foundation with intelligent features built in.

Chat Interface Preview

Key Features

  • 🤖 Intelligent Query Classification - Automatically categorizes user queries into general, refund, or technical support types
  • 🔄 Streaming Responses - Real-time, token-by-token response generation for a natural conversation flow
  • 📱 Responsive Design - Modern UI that works seamlessly across desktop, tablet, and mobile devices
  • 🧠 Adaptive Model Selection - Intelligently selects the appropriate model based on query complexity
  • 🎭 Dynamic Agent Personalities - Tailors the AI’s communication style based on the query type

Getting Started

Want to try it yourself? The setup process is straightforward:

# Clone the repository
git clone https://github.com/cihancinar/nextjs-azure-ai-starter

# Set up environment variables
# Rename .example.env.local to .env.local and add your Azure OpenAI credentials

# Install dependencies and start the dev server
npm install
npm run dev

Once running, open http://localhost:3000 in your browser to interact with your new AI agent.

Project Structure

The template is organized for clarity and easy customization:

  • Main Chat Interface: app/(chat)/page.tsx
  • Agent Logic: app/(chat)/api/chat/route.ts
  • UI Components: components/ directory
  • Static Assets: public/ directory

Customization Options

You can easily adapt this template for your specific needs:

  • Modify the agent logic to handle different types of queries
  • Adjust classification categories and system prompts
  • Customize the UI appearance and behavior
  • Integrate with other Azure AI services

How It Works

The application uses a sophisticated approach to handling user interactions:

  1. When a user submits a query, it’s first classified to determine the intent
  2. Based on the classification, the appropriate model and personality are selected
  3. The query is processed with contextual understanding
  4. Responses are streamed back to the user in real-time

This approach ensures efficient use of AI resources while providing a personalized experience for different types of queries.

Resources

License

This project is available under the MIT license, making it freely available for both personal and commercial use.