🤖 AI CV Reviewer

A multi-agent AI system that provides comprehensive CV analysis, feedback, and recommendations using Python, LangChain, LangGraph, and Streamlit with Anthropic's Claude.

• Multi-format Support: Upload PDF, DOCX, or TXT files
• Intelligent Data Extraction: Automatically extract structured information from CVs
• Comprehensive Analysis: AI-powered analysis of experience, skills, and education
• Constructive Feedback: Detailed feedback on CV strengths and areas for improvement
• Actionable Recommendations: Specific suggestions for career development
• Beautiful UI: Modern, responsive Streamlit interface
• Export Results: Download comprehensive reports in JSON format

The application uses a multi-agent architecture with four specialized agents:

1. Extraction Agent: Extracts structured data from CV text
2. Analysis Agent: Analyzes CV content and provides insights
3. Feedback Agent: Generates constructive feedback
4. Recommendation Agent: Provides improvement suggestions and career guidance

CV Upload → Text Extraction → Data Analysis → Feedback Generation → Recommendations → Results

• Python 3.8+
• Streamlit: Web interface
• LangChain: LLM framework
• LangGraph: Multi-agent orchestration
• Anthropic Claude: AI language model
• Pydantic v2: Data validation and serialization
• PyPDF2: PDF processing
• python-docx: DOCX processing

1. Clone the repository:

   git clone <repository-url>
   cd cv-reviewer

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:

   cp env.example .env

   Edit .env and add your Anthropic API key:

   ANTHROPIC_API_KEY=your_anthropic_api_key_here
   

1. Start the application:

   streamlit run main.py

2. Open your browser and navigate to http://localhost:8501

3. Upload your CV (PDF, DOCX, or TXT format)

4. Click "Start CV Review" to begin the analysis

5. Review the results:

   • Extracted information
   • Analysis results with scores
   • Detailed feedback
   • Improvement recommendations

6. Download the full report as JSON for further analysis

cv-reviewer/
├── app/
│   ├── __init__.py
│   ├── main.py                 # Streamlit main app
│   ├── models.py               # Pydantic data models
│   ├── agents/
│   │   ├── __init__.py
│   │   ├── extraction_agent.py # CV data extraction
│   │   ├── analysis_agent.py   # Data analysis
│   │   ├── feedback_agent.py   # Feedback generation
│   │   └── recommendation_agent.py # Improvement suggestions
│   ├── graph/
│   │   ├── __init__.py
│   │   └── workflow.py         # LangGraph workflow
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── file_processor.py   # File handling
│   │   └── llm_config.py       # LLM configuration
│   └── ui/
│       ├── __init__.py
│       └── components.py       # Streamlit UI components
├── requirements.txt
├── env.example
├── .gitignore
└── README.md

• ANTHROPIC_API_KEY: Your Anthropic API key (required)

The application generates comprehensive reports including:

• Personal information (name, email, phone, location)
• Work experience with details
• Education background
• Skills with proficiency levels
• Certifications and languages

• Overall CV score (0-100)
• Strengths and weaknesses
• Experience analysis
• Skills analysis
• Education analysis
• Market alignment assessment

• General feedback
• Experience feedback
• Skills feedback
• Education feedback
• Presentation feedback
• Specific improvements
• Positive aspects

• Skill development suggestions
• Experience gap identification
• Career path suggestions
• Immediate actions
• Long-term goals
• Industry trends

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

This project is licensed under the MIT License.

If you encounter any issues:

1. Check that your Anthropic API key is correctly configured
2. Ensure your CV file is in a supported format
3. Verify the file size is within limits
4. Check the console for error messages

• Support for more file formats
• Integration with job boards
• Resume template suggestions
• ATS optimization scoring
• Multi-language support
• Batch processing capabilities
• Advanced analytics dashboard