JobMatchCV is an innovative multi-agent system that leverages the AutoGen framework to enhance CVs based on specific job advertisements, providing a tailored markdown output.
JobMatchCV employs a sophisticated multi-agent system to analyze, enhance, and format CVs:
- User Input Agent: Manages initial input and text extraction from CVs and job ads.
- CV Analysis Agent: Examines CV structure, content, and formatting.
- Job Analysis Agent: Dissects job requirements and desired qualifications.
- ATS Standards Agent: Provides expertise on Applicant Tracking System standards.
- CV Enhancement Agent: Generates improvement suggestions based on job-CV comparison.
- User Output Agent: Presents enhancement suggestions clearly and actionably.
- Markdown Conversion Agent: Transforms the enhanced CV into a professional markdown document.
- Multi-agent collaboration for comprehensive CV optimization
- Support for PDF and DOCX input formats
- Web scraping for job description extraction
- ATS-compliant enhancement suggestions
- Automatic conversion to markdown format
- Final output saved as
cv.md
- Open the
JobMatchCV.ipynbJupyter notebook. - Follow the step-by-step instructions to:
- Set up your environment and API keys
- Upload your CV (PDF or DOCX)
- Provide the job advertisement link
- Initiate the CV enhancement process
- Retrieve your optimized CV as a markdown file (
cv.md)
JobMatchCV offers flexibility in model selection:
- Default: GPT-4 for analysis, Claude 3.5 Sonnet for enhancements
- Alternative options: Gemini, Mistral, and Codestral
Adjust the llm_config settings in the notebook to experiment with different models.
We welcome contributions! For suggestions or new features, please open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.