A production hierarchical multi-agent system that automates technical hiring screening. Built with LangGraph, LangChain, 2 custom MCP servers, and a real-time LiveKit voice agent — reducing screening time from 15 minutes to under 1 minute.
Hire a Human uses a hierarchical supervisor-subagent pattern implemented in LangGraph's StateGraph. A supervisor agent routes control to specialized sub-agents based on the hiring stage, collecting outputs and making structured decisions.
Orchestrates the hiring pipeline. Routes between sub-agents using conditional edges in a StateGraph. Manages shared state — candidate profile, scores, and decisions — across the entire flow.
Parses and evaluates candidate resumes. Extracts skills, experience, and education. Scores candidates against role requirements. Passes structured profile to the next agent.
Custom FastMCP server that evaluates a candidate's GitHub profile. Analyzes repository languages, commit frequency, code quality signals, and contribution patterns through structured tool calls.
Custom FastMCP server that evaluates LeetCode performance. Reports total problems solved, difficulty distribution (Easy/Medium/Hard), and consistency — giving objective technical signal without live coding tests.
Real-time AI voice agent that conducts structured technical interviews. Achieves ~700ms time-to-first-audio. Supports 11 languages. Uses Silero VAD for voice activity detection and Twilio for telephony.
Full-stack React + TypeScript frontend. Recruiters view structured candidate reports, screening scores, voice interview transcripts, and GitHub/LeetCode analysis — all in one place.
The system reduces a 15-minute manual process to under 60 seconds through automated multi-agent coordination.
Candidate submits resume, GitHub username, and LeetCode profile through the React frontend. Data is stored in PostgreSQL via FastAPI backend.
The LangGraph supervisor initializes the StateGraph and begins routing. Resume screening agent runs first, extracting and scoring profile data. Output is added to shared state.
The GitHub and LeetCode MCP servers are called as tools. They return structured evaluation data — repository analysis and problem-solving metrics — which the supervisor adds to candidate state.
If the candidate passes initial screening, the LiveKit voice agent initiates a structured technical interview in the candidate's preferred language. Transcript is generated and scored.
The supervisor consolidates all signals and generates a structured hiring recommendation. Recruiter sees complete candidate profile, all scores, and interview transcript in the dashboard — in under 1 minute total.
Sanskar Agrawal builds production agentic AI systems using LangGraph, LangChain, and custom MCP servers. Based in Indore, India — available for remote projects globally.