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Mockly: Agentic AI Mock Interviewer ⚡

Mockly is an AI-powered Mock Interview platform designed to help developers ace their technical interviews. By leveraging an agentic workflow, real-time web research, and resume parsing, Mockly generates tailored, context-aware interview questions and provides detailed, structured feedback.

🚀 Key Features

  • 📄 Intelligent Resume Parsing: Extract skills, experience level, projects, and education directly from uploaded PDF and Word (.docx) documents to personalize the interview experience.
  • 🔍 Web-Augmented Research: Performs live web searches (via Tavily) to fetch the latest industry-relevant technical questions and trends.
  • ⚡ Smart-Fast LLM Routing: Dynamic model routing optimizes latency and costs:
    • Fast Model: Summarization and search query generation.
    • Smart Model: Complex question generation and detailed answer evaluations.
  • 💾 Query Caching: In-memory caching for web research results to save Tavily API credits and improve API response times.
  • 📝 Interactive Chat Sandbox: Responsive, sticky chat interface with isolated scroll areas for questions and evaluations.
  • 🔐 NextAuth Integration: Out-of-the-box support for Google & GitHub social logins.
  • 📊 Database Integration: Prisma Client connected to a PostgreSQL database for session management.

🛠️ Tech Stack

Frontend

  • Framework: Next.js (App Router, React 19)
  • Styling: Tailwind CSS, Shadcn UI
  • Icons: Phosphor Icons
  • ORM: Prisma with PostgreSQL

Backend

  • Framework: FastAPI (Python 3.10+)
  • Agentic Orchestration: LangGraph & LangChain
  • Models: NVIDIA NIMs (Llama 3.1 8B/70B Instruct)
  • Search Engine: Tavily Search API
  • Text Extraction: PyMuPDF (fitz) & python-docx
  • Parsing Resiliency: json-repair

📂 Project Structure

Mockly/
├── backend/                # FastAPI backend & LangGraph agents
│   ├── app/
│   │   ├── agents/         # Graph definitions, nodes, and prompts
│   │   ├── api/            # API endpoints (resume parsing, interview)
│   │   ├── tools/          # Web search cache, resume text extraction
│   │   └── utils/          # State serialization helpers
│   ├── main.py             # FastAPI entrypoint
│   └── requirements.txt
├── frontend/               # Next.js web application
│   ├── app/                # App router pages (dashboard, sandbox)
│   ├── components/         # React components (interview panel, inputs)
│   ├── prisma/             # Schema definitions and migrations
│   └── package.json
└── README.md               # Project documentation (this file)

⚙️ Getting Started

Prerequisites

  • Node.js 18+ & pnpm (or npm/yarn)
  • Python 3.10+
  • PostgreSQL database (or Prisma Postgres/Accelerate connection string)

1. Backend Setup

  1. Navigate to the backend directory: 
    cd backend
  2. Create and activate a virtual environment: 
    python3 -m venv .venv
source .venv/bin/activate
  3. Install dependencies: 
    pip install -r requirements.txt
  4. Configure environment variables in a .env file inside the backend/ directory: 
    NVIDIA_BASE_URL="https://integrate.api.nvidia.com/v1"
NVIDIA_API_KEY="your-nvidia-api-key"
NVIDIA_FAST_MODEL="meta/llama-3.1-8b-instruct"
NVIDIA_SMART_MODEL="meta/llama-3.1-70b-instruct"
TAVILY_API_KEY="your-tavily-api-key"
CORS_ORIGINS="http://localhost:3000"
  5. Start the FastAPI server: 
    fastapi dev main.py
# or
uvicorn main:app --reload --port 8000

2. Frontend Setup

  1. Navigate to the frontend directory: 
    cd frontend
  2. Install packages: 
    pnpm install
# or npm install
  3. Configure environment variables in a .env file inside the frontend/ directory: 
    DATABASE_URL="postgresql://username:password@localhost:5432/mockly"
AUTH_SECRET="your-generated-secret"

# Social Login OAuth Credentials
AUTH_GOOGLE_ID="google-client-id"
AUTH_GOOGLE_SECRET="google-client-secret"
AUTH_GITHUB_ID="github-client-id"
AUTH_GITHUB_SECRET="github-client-secret"

NEXT_PUBLIC_API_URL="http://localhost:8000"
  4. Run Prisma migrations & database setup: 
    pnpx prisma db push
# optionally run the studio to view your tables:
pnpx prisma studio
  5. Start the development server: 
    pnpm dev
# or npm run dev

Open http://localhost:3000 to start preparing with Mockly!