hiring-agent
AI agent to evaluate and score resumes.
Access complete InterviewStreet Hiring Agent API documentation within the repository. Explore the README and Python modules for essential details and integration guidance.
What Are the Dependencies for Running the Hiring Agent?Discover the essential dependencies for running the Hiring Agent. Learn about required Python versions, key packages like PyMuPDF and Ollama, and environment variable setup for resume scoring.
How to Automate Interview Scheduling with the Hiring Agent: A Config-Driven GuideAutomate interview scheduling with Hiring Agent by configuring providers.json and config.py. Streamline your hiring process and save time with this practical guide.
How to Set Up the Interviewstreet Hiring Agent Locally: Complete Installation GuideSet up the Interviewstreet Hiring Agent locally with our complete installation guide. Clone the repo, create an environment, install dependencies, and configure your .env file for local candidate evaluation.
What Programming Languages Are Used in the Hiring Agent? A Complete Technical BreakdownDiscover the programming languages powering the InterviewStreet Hiring Agent. Explore its Python 3.11+ core, Jinja2 templates, PyMuPDF, and Pydantic integration for efficient resume processing.
How to Integrate the Hiring Agent with Existing HR Tools: A Complete Technical GuideIntegrate the Hiring Agent with your HR tools and ATS using its modular Python functions. This complete technical guide shows you how to seamlessly connect your application stack for efficient hiring.
How to Test Evaluation Logic Without Processing Real Resumes in Hiring-AgentEasily test hiring-agent evaluation logic without real resumes. Inject mock LLM providers and use minimal resume text strings for fast, accurate validation. Avoid live model calls.
Key Differences Between Development Mode and Production Mode in Hiring AgentUnderstand development vs production mode in Hiring Agent. Development uses caching for speed, production ensures fresh candidate data. Learn the key differences for efficient hiring.
How the github.py Module Selects the Top 7 Projects from Candidate RepositoriesDiscover how github.py selects top projects using an LLM pipeline. Learn how it ranks repositories by stars, evaluates impressive unique projects, and fills quotas for the best 7.
What Causes Resume Score Variance Across Multiple Runs in Hiring AgentUnderstand why resume scores vary on Hiring Agent. Explore LLM non-determinism, hidden PDF content, GitHub selection, and caching as key causes for score variance.
How transform.py Normalizes Loose LLM JSON to JSON Resume FormatLearn how transform.py in hiring-agent normalizes loose LLM JSON to JSON Resume format. Discover its pipeline for dates, skills, projects, and personal details.
Common Causes of LLM Extraction Failures and How to Debug Them in Hiring-AgentDebug LLM extraction failures caused by markdown wrappers truncated JSON or schema mismatches in Hiring-Agent. Learn common causes and solutions to improve data extraction accuracy.
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