Clear Purpose and Objectives

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:

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:

1. User submits JD and GitHub username (UI/CLI)
2. Inputs validated and sanitized
3. Multi-agent workflow orchestrated (LangGraph)
4. 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
  • Input: Job description (editable), GitHub username
  • Output: Score, match level, detailed report
  • Error handling and user guidance throughout
• CLI:
  • Interactive prompts for JD and GitHub username
  • Prints evaluation report to console

Safety & Security

• Input validation (GitHub username, JD)
• Output filtering and type/range checks
• Prompt injection and toxicity checks
• Error handling

Example

• GitHub Username:
  • Regex and API existence check
  • Error message for invalid/blocked usernames
• Job Description:
  • Detoxify model for toxicity
  • Prompt injection keyword blacklist
  • Length and content checks
• Output:
  • Score must be float in [0,1]
  • Report must be non-empty string
  • All failures logged and surfaced to user

Testing & Quality Assurance

• Comprehensive Test Suite:
  • Unit tests for agents and tools
  • Integration tests for agent workflows
  • Performance tests
• Coverage:
  • greater than 80% for core functionality
• How to Run:
  • pytest tests/ -v --cov=src --cov-report=html
  • here is the coverage output
    

Logging, Monitoring & Resilience

• Centralized logging for all agent/tool actions
• Health monitoring via Streamlit UI
• Easy configuration and environment management
• with_retry utility wraps all network/API calls
• Handles timeouts, connection errors, and rate limits
• Exponential backoff and max retry limits
• All retry/failure events logged for traceability

Installation and Usage Instructions

1. Clone the repository
2. Create and activate a Python virtual environment
3. Install dependencies: pip install -r requirements.txt
4. Configure .env with API keys and parameters
5. Run the web UI: streamlit run frontend.py
6. Or run CLI: python -m src.app
7. For Docker: build and run as described in the README

• Build: docker build -t profile-evaluator .
• Run: docker run -p 8501:8501 --env-file .env profile-evaluator

Screenshots

Toxic JD error:

Visual Demo

Jump here for the demo.

Scoring & Evaluation Logic

• Stages:
  1. Repository Language Match
  2. Activity Scoring (last year)
  3. Advanced Scoring (stars, issues)
  4. Aggregation: Overall Score = Repo Match Score * 0.5 + Activity Score * 0.3 + Advanced Score * 0.2
• Result Levels:
  • greater than 80%: Excellent
  • 50–80%: Good
  • less than 50%: Partial Match

Troubleshooting & FAQ

• Common Issues:
  • API key/configuration errors: Check .env and logs
  • Low coverage: Run pytest and review reports
  • 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.

Features and Benefits Analysis

• Automated, objective JD-to-GitHub profile matching
• Multi-agent, extensible architecture
• Secure input/output validation and toxicity detection
• Robust error handling and monitoring
• User-friendly web and CLI interfaces
• High test coverage and reproducibility

Competitive Differentiation

• Production-grade security and validation guardrails
• Explainable, human-readable evaluation reports
• Modular, extensible agent and tool design
• Dockerized deployment and health monitoring

Originality of Work

• First open-source, production-ready multi-agent system for JD-driven GitHub evaluation
• Combines LLM, static analysis, and advanced validation in a single workflow

Innovation in Methods/Approaches

• Hybrid LLM + static fallback for skill extraction
• Prompt injection and toxicity detection in all user/LLM inputs
• Exponential backoff retry logic for all network calls
• Output validation and explainability at every step

Advancement of Knowledge or Practice

• Demonstrates best practices for productionizing AI agentic workflows
• Provides a template for secure, explainable, and robust AI system design

Real-World Applications

• Automated technical candidate screening
• AI-driven talent analytics
• Research in explainable AI for HR
• Open-source contribution analysis

Technical Asset Access Links

• GitHub Repository
• ReadyTensor Publication Example

Performance Characteristics and Requirements

• Handles large GitHub profiles and complex JDs efficiently
• Workflow step timings and throughput tested in integration suite
• Designed for reliability under API/network failures

Maintenance and Support Status

• Actively maintained
• Issues and feature requests via GitHub
• Contributions welcome

Access and Availability Status

• Open-source, public repository
• Docker image build instructions provided

License and Usage Rights of the Technical Asset

• MIT License (see LICENSE file)

Contact Information of Asset Creators

• Author: TechBrij
• Issues: GitHub Issues