The primary objective of this project is to automate the evaluation of a candidate’s public GitHub profile against a job description (JD) using a secure, robust, and explainable multi-agent system. The system aims to streamline technical hiring by providing objective, reproducible, and scalable candidate assessments.
Target Audience Definition
Technical recruiters and hiring managers
Engineering and AI/ML teams
HR technology solution providers
Researchers in AI-driven talent evaluation
Problem Definition
Manual screening of open-source contributions is time-consuming, subjective, and error-prone. There is a need for an automated, transparent, and secure solution to match candidate skills and activity with job requirements.
Current State Gap Identification
Lack of automation in JD-to-GitHub profile matching
Insufficient security and validation in existing tools
Limited explainability and reproducibility in candidate evaluation
Absence of robust error handling and monitoring in demo systems
Context Establishment
This work builds on recent advances in multi-agent orchestration, LLM integration, and secure AI system design. It addresses the need for production-grade, enterprise-ready solutions in technical hiring.
Architecture
The system is composed of four main agents, each responsible for a distinct stage in the evaluation pipeline:
Agent Name
Role & Functionality
JD Analyzer Agent
Extracts programming languages and skills from the job description using LLM with static analysis fallback.
Repo Match Agent
Matches candidate's GitHub repositories to required skills.
Activity Agent
Analyzes commit activity in relevant repositories over the past year.
Evaluation Agent
Scores the match and generates a human-readable evaluation report.
Key Tools:
SafetyValidator: Detects toxicity and prompt injection in JD
Retry Utility: Robust retry with exponential backoff for all network calls
Logger: Centralized, structured logging for compliance and debugging
Workflow:
User submits JD and GitHub username (UI/CLI)
Inputs validated and sanitized
Multi-agent workflow orchestrated (LangGraph)
Results validated and presented to user
Clear Prerequisites and Requirements
Python 3.9+
Internet access for GitHub API and LLM
Groq API key (for LLM-based JD analysis)
Docker (optional, for containerized deployment)
Streamlit (for web UI)
Tools, Frameworks, & Services
LangGraph: Multi-agent orchestration
Streamlit: Web user interface
Detoxify: Toxicity detection in JD
Pytest: Testing framework
Docker: Containerization
GitHub API: Data source
Groq LLM: Language model integration
Module 3 Production Improvements
User Interface
Web App: [NEW]
Built with Streamlit for intuitive, guided workflows
UI not loading: Ensure dependencies and port availability
Support:
GitHub Issues for bug reports and feature requests
Significance and Implications of Work
This project demonstrates how to move from a research demo to a production-ready, secure, and explainable AI system for technical hiring. It sets a new standard for automation, safety, and transparency in candidate evaluation.