Who hiring-agent is for#
Developer resume pre-screening
Use Hiring Agent to create a structured first-pass evaluation before deciding which candidates deserve deeper review.
Skip if:
You need legal-grade automated hiring decisions with audited compliance controls.
GitHub-backed project review
Engineering managers can use the GitHub enrichment path to inspect project evidence alongside resume claims.
Skip if:
Your candidates rarely provide public GitHub profiles.
Local LLM evaluation experiments
Teams can test resume scoring with Ollama when data privacy prevents hosted model use.
Skip if:
You cannot run or configure local models or a Gemini API key.
The problem it solves#
Technical resume review is hard to keep consistent across reviewers and candidates. Hiring Agent addresses that by turning PDFs and GitHub activity into structured evidence, then applying explicit scoring criteria that humans can inspect before making interview decisions.
How it solves it#
Resume PDF extraction
The pipeline converts a resume PDF to markdown before extracting structured sections with prompt templates.
GitHub signal enrichment
The README describes fetching GitHub profiles and repositories, classifying projects, and selecting seven meaningful projects for review.
Explainable scoring
Evaluations include category scores, evidence, bonus points, and deductions rather than a single unexplained number.
Local or hosted model providers
Teams can run with Ollama for local models or use Google Gemini when a hosted model is acceptable.
CSV development output
When development mode is enabled, score.py appends key evaluation fields to resume_evaluations.csv and caches intermediate JSON.
Strengths and trade-offs#
Strengths
- Technical evidence focusGitHub enrichment helps reviewers evaluate candidate projects rather than relying only on resume phrasing.
- Local model optionOllama support gives teams a path to keep sensitive resume data off hosted LLM services.
- Readable pipelineSeparate modules for PDF parsing, GitHub enrichment, evaluation, and orchestration make the workflow easier to audit.
Trade-offs
- -Human review still requiredResume scoring can introduce bias or miss context, so Hiring Agent should support a human process rather than decide outcomes alone.
- -Requires candidate GitHub signalThe GitHub enrichment path is less useful for candidates whose strongest work is private, employer-owned, or not tied to a GitHub profile.
- -Not an ATSHiring Agent does not manage candidate stages, recruiter collaboration, interview scheduling, or approvals.
hiring-agent vs alternatives#
Compared to Greenhouse
Greenhouse is an applicant tracking system for managing candidates, interview stages, recruiter collaboration, approvals, and hiring operations. Hiring Agent is a focused resume evaluation pipeline that parses PDFs, enriches GitHub activity, and prints explainable technical scores. Use Greenhouse to run the hiring process; use Hiring Agent as a review aid for technical resume evidence.
What it's built on#
- Languages
- Python
FAQ#
What does Hiring Agent evaluate?
Hiring Agent evaluates technical resumes by parsing PDFs, extracting structured candidate data, enriching GitHub signals, and producing explainable category scores.
Can Hiring Agent run locally?
Yes. The README says it can run fully local with Ollama, or use Google Gemini when a hosted provider is configured.
What license does Hiring Agent use?
GitHub metadata reports Hiring Agent as MIT licensed.
Does Hiring Agent replace recruiters?
No. It produces evidence and scores for review, but humans should make hiring decisions and check fairness, context, and compliance.
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