LangChain Interview Multi-Agents Flow is a LangGraph-powered system that orchestrates nine specialized AI agents to automate every stage of the technical hiring process. Feed it a resume PDF and a job description, and it produces structured candidate profiles, multi-round interview questions (HR, Technical, and CEO rounds), a final hiring evaluation, and personalized interviewer emails — all driven by a local LLM.Documentation Index
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Quickstart
Get the workflow running locally in minutes with your resume and job description.
Architecture overview
Understand how the supervisor, agents, and LangGraph state connect together.
Agent reference
Explore every agent, its inputs, outputs, and role in the pipeline.
Guides
Learn how to run, customize, and extend the workflow for your needs.
How it works
The system uses a supervisor-agent pattern built on LangGraph’sStateGraph. A central supervisor LLM decides which specialist agent to invoke next, routing based on what data is already available in the shared HiringState. Each agent completes one focused task and returns control to the supervisor, which continues until the full pipeline is done.
Parse inputs
The resume parser and JD analysis agents extract structured data from your PDF resume and plain-text job description.
Research and match
The matching agent scores the candidate’s fit against the JD across four dimensions. The candidate research agent generates strategic interview insights.
Generate interview questions
Three interview agents produce tailored question sets: HR (15 questions), Technical/Principal level (20 questions with expected answers), and CEO/culture-fit (10 questions with behavioral indicators).
Key capabilities
Supervisor orchestration
A central LLM supervisor with loop-detection guardrails routes between agents based on available data.
PDF resume parsing
Extracts name, contact details, skills, frameworks, experience, projects, education, and seniority level.
Multi-round interview generation
Produces HR, technical, and CEO question sets with evaluation criteria and red flags.
Fit scoring
Scores technical fit, experience fit, leadership fit, and communication fit with strengths and gap analysis.
Final hiring evaluation
Generates an overall recommendation, confidence score, concerns, hiring risks, and final verdict.
Local LLM support
Works with any OpenAI-compatible local model via LM Studio — no cloud API required.