Multi-Agent Interview System

A sophisticated AI-powered mock interview system that uses 4 specialized agents to conduct realistic technical interviews. The system follows a router/orchestrator architecture pattern where agents work together to provide comprehensive interview experiences.

System Architecture

2025-09-30.12-07-36.mp4

Core Agents

1. InterviewerAgent: Generates contextual questions based on resume, job description, and current topic
2. TopicManagerAgent: Controls topic flow and interview depth throughout the session
3. EvaluatorAgent: Evaluates candidate responses in real-time with detailed feedback
4. OrchestratorAgent: Coordinates all agents and manages the overall interview flow

System Flow

Query + User Input -> Router/Orchestrator -> Specialized Agents
                                    |
                            InterviewerAgent (Questions)
                            TopicManagerAgent (Topic Control)  
                            EvaluatorAgent (Evaluation)

Features

• Contextual Question Generation: Questions are tailored to the candidate's resume and job requirements
• Dynamic Topic Management: System adapts interview topics and depth based on candidate performance
• Real-time Evaluation: Immediate feedback and scoring for candidate responses
• Intelligent Orchestration: Seamless coordination between all agents
• Interactive Interface: User-friendly command-line interface
• Configurable Parameters: Customizable interview settings and agent behavior

Prerequisites

• Python 3.10+
• OpenAI API key
• Required Python packages (see requirements.txt)

Usage

Interactive Mode

Run the main interview system:

python main.py

Available commands:

• start - Start a new interview
• answer <response> - Answer the current question
• status - Check interview status
• end - End the current interview
• help - Show help information
• quit - Exit the program

Project Structure

finalround-assignment/
|-- main.py                 # Main entry point with interactive interface
|-- agents.py              # Core agent implementations
|-- interview_system.py    # Router/orchestrator system
|-- config.py              # Configuration settings
|-- demo.py                # Demo script
|-- sample_resume.txt      # Sample candidate resume
|-- sample_job_description.txt  # Sample job description
|-- requirements.txt       # Python dependencies
|-- pyproject.toml        # Project configuration

Configuration

The system can be configured through environment variables:

• OPENAI_API_KEY: Your OpenAI API key (required)
• OPENAI_MODEL: OpenAI model to use (default: gpt-3.5-turbo)
• MAX_INTERVIEW_ROUNDS: Maximum interview rounds (default: 5)
• DEFAULT_INTERVIEW_DEPTH: Default interview depth (default: 2)
• INTERVIEWER_TEMPERATURE: Temperature for question generation (default: 0.7)
• TOPIC_MANAGER_TEMPERATURE: Temperature for topic management (default: 0.3)
• EVALUATOR_TEMPERATURE: Temperature for evaluation (default: 0.2)

Agent Details

InterviewerAgent

• Generates contextual questions based on resume and job description
• Adapts question difficulty based on interview depth
• Avoids repeating previously asked questions
• Focuses on job-relevant technical skills

TopicManagerAgent

• Manages interview topic progression
• Adjusts interview depth based on candidate performance
• Ensures comprehensive coverage of job requirements
• Balances technical and behavioral questions

EvaluatorAgent

• Provides real-time evaluation of candidate responses
• Scores responses on a 1-10 scale
• Offers detailed feedback and improvement suggestions
• Considers technical accuracy, communication, and relevance

OrchestratorAgent

• Coordinates all other agents
• Manages interview session state
• Handles the overall interview flow
• Provides session summaries and progress tracking

Interview Topics

The system covers various interview topics:

• Technical Skills: Programming languages, frameworks, tools
• Problem Solving: Algorithm design, debugging, optimization
• System Design: Architecture, scalability, performance
• Leadership/Management: Team leadership, mentoring, project management
• Behavioral: Past experiences, conflict resolution, growth mindset
• Company Culture Fit: Values alignment, work style, collaboration

Example Usage

1. Start an interview:

   > start
   

2. Answer questions:

   > answer I have 5+ years of Python experience with Django and Flask frameworks...
   

3. Check status:

   > status
   

4. End interview:

   > end
   

Sample Data

The system includes sample data files:

• sample_resume.txt: Contains a sample software engineer resume
• sample_job_description.txt: Contains a sample senior Python developer job description

You can replace these with your own resume and job description files.

• Built with LangChain and OpenAI
• Implements multi-agent architecture patterns
• Designed for realistic interview simulation