AI Coding Interview Platform

An interactive platform that uses AI to conduct coding interviews, providing real-time feedback and evaluation through a multi-agent architecture.

Features

• Multi-agent AI system with specialized agents:
  • Interviewer Agent - Conducts the interview and communicates with the user
  • Code Evaluator Agent - Evaluates code submissions
  • Final Evaluator Agent - Provides comprehensive candidate assessment
  • Coordinator Agent - Orchestrates the interview process and agent communication
• Real-time code editor with syntax highlighting
• Interactive chat interface
• Multiple programming language support
• Adaptive difficulty levels
• Comprehensive interview feedback
• Session persistence

Tech Stack

Backend

• Python with FastAPI
• LangChain for AI interaction
• Postgres Supabase for prod/ SQLite database for local dev
• Chroma vector store
• OpenAI compatible endpoint(incl. Ollama on local dev)

Frontend

• React with TypeScript
• Material-UI for components
• Monaco Editor for code editing
• React Router for navigation
• Axios for API calls

Multi-Agent Architecture

The platform is built with a specialized multi-agent architecture:

1. Interviewer Agent:

   • Conducts the interview directly with the candidate
   • Handles introductions, technical questions, and problem presentation
   • Only agent that communicates with the user

2. Code Evaluator Agent:

   • Specializes in evaluating code submissions
   • Analyzes code for correctness, efficiency, and style
   • Provides structured feedback on submissions

3. Final Evaluator Agent:

   • Provides comprehensive assessment of the candidate
   • Evaluates technical skills, problem-solving ability, and communication
   • Makes hiring recommendations

4. Coordinator Agent:

   • Orchestrates the entire interview process
   • Manages communication between specialized agents
   • Maintains context and ensures smooth transitions between interview stages

This architecture provides a seamless and natural interview experience while leveraging specialized capabilities of each agent behind the scenes.

Setup Instructions

Backend Setup

1. Navigate to the backend directory:

cd backend

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

• Copy .env.example to .env
• Add your OpenAI API key and other configurations

5. Start the backend server:

uvicorn app.main:app --reload

The backend server will run on http://localhost:8000

Frontend Setup

1. Navigate to the frontend directory:

cd frontend

2. Install dependencies:

npm install

3. Start the development server:

npm start

The frontend application will run on http://localhost:3000

API Documentation

Once the backend is running, visit http://localhost:8000/docs for complete API documentation.

Main Endpoints

• POST /api/v1/interviews/sessions - Create new interview session
• POST /api/v1/interviews/sessions/{session_id}/respond - Send candidate response
• POST /api/v1/interviews/sessions/{session_id}/code - Submit code for evaluation
• GET /api/v1/interviews/sessions/{session_id}/final-evaluation - Get final evaluation

Interview Flow

1. User starts a new interview session by selecting role and difficulty
2. Interviewer Agent conducts initial introduction
3. Technical discussion phase with adaptive questions
4. Coding challenge presentation
5. Code Evaluator Agent assesses code submissions
6. Final Evaluator Agent provides comprehensive evaluation

Project Structure

.
├── backend/
│   ├── app/
│   │   ├── agent/
│   │   │   ├── base_agent.py
│   │   │   ├── interviewer_agent.py
│   │   │   ├── evaluator_agent.py
│   │   │   ├── final_evaluator_agent.py
│   │   │   ├── coordinator_agent.py
│   │   │   └── prompts.py
│   │   ├── core/
│   │   │   ├── config.py
│   │   │   └── database.py
│   │   ├── models/
│   │   │   └── interview.py
│   │   ├── routers/
│   │   │   └── interview.py
│   │   └── main.py
│   ├── requirements.txt
│   └── .env
├── frontend/
│   ├── public/
│   │   ├── index.html
│   │   └── manifest.json
│   ├── src/
│   │   ├── components/
│   │   │   ├── ChatInterface.tsx
│   │   │   └── CodeEditor.tsx
│   │   ├── pages/
│   │   │   ├── HomePage.tsx
│   │   │   └── InterviewPage.tsx
│   │   ├── services/
│   │   │   └── api.ts
│   │   ├── App.tsx
│   │   ├── index.tsx
│   │   └── index.css
│   ├── package.json
│   └── tsconfig.json
└── README.md

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License - feel free to use this project for any purpose.

Support

For support or questions, please open an issue in the repository.