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AI Job Application Agent

CI License: MIT Live App

AI Job Application Agent is a grounded job-application copilot with three first-class systems: integrated job discovery across Greenhouse and Lever boards, a five-agent tailoring pipeline built on deterministic baselines plus LLM refinement, and grounded review that revises or rejects unsupported claims before export.

The result is more than a paste-a-JD resume tool. It can search indexed job boards, match postings, tailor a resume and cover letter through supervised agents, and keep the final package anchored to evidence from the source profile.

Architecture diagram

Live landing page: https://job-application-copilot.xyz; Workspace app: https://app.job-application-copilot.xyz

Job Discovery

The app can search configured Greenhouse and Lever boards before the agent workflow starts. The default local configuration indexes 12 Greenhouse boards and 3 Lever sites, normalizes postings through provider-specific clients, and ranks results through the matching layer before a selected JD enters the tailoring pipeline.

Agent Pipeline

The workflow runs through ApplicationOrchestrator with progress callbacks, per-stage duration logging, JSON-contracted agent outputs, and deterministic fallback if assisted generation is unavailable.

  1. FitAgent compares the candidate profile against the job description.
  2. TailoringAgent rewrites the deterministic baseline into role-specific resume guidance.
  3. ReviewAgent checks grounding, reports unsupported claims, and returns corrected tailoring when repairs are possible.
  4. ResumeGenerationAgent builds the final tailored resume artifact from the reviewed output.
  5. CoverLetterAgent runs only after review approval and creates a role-specific cover letter.

Each agent follows the same operating shape: deterministic baseline first, LLM-assisted refinement second, structured JSON output, and grounded fallback behavior when assisted execution is unavailable.

Grounding And Fallbacks

  • Deterministic services build the candidate profile, JD summary, fit analysis, and first-pass tailored draft before the agent layer runs.
  • ReviewAgent returns grounding_issues, unresolved_issues, revision_requests, and an optional corrected_tailoring payload.
  • The orchestrator uses corrected_tailoring as the downstream source of truth when review repairs the draft.
  • Cover-letter generation is gated on review approval.
  • The fallback review path checks whether the output references missing hard skills that are not evidenced in the source profile.

Prompt And Runtime Discipline

  • Prompt builders compact large JSON sections through escalating string and list caps before falling back to a section summary.
  • Prompt metadata records estimated input size, compacted section count, compacted labels, and budget mode.
  • The OpenAI wrapper routes by task, tracks usage, enforces quota checks, records response metadata, and retries incomplete JSON responses with a higher output budget when appropriate.

Product Surface

  1. Sign in with Google through Supabase-backed auth
  2. Upload a resume and build a normalized candidate profile
  3. Search Greenhouse and Lever boards, import a supported posting, or paste a JD manually
  4. Review a structured JD summary
  5. Run the grounded agentic workflow
  6. Review the tailored resume and cover letter
  7. Ask grounded follow-up questions in the workspace assistant
  8. Export Markdown or WeasyPrint-backed PDF documents

Engineering Notes

  • 37 focused Python test files cover parsing, normalization, fitting, tailoring, orchestration, builders, exports, auth, quotas, persistence, and backend routes.
  • 12 ADRs in docs/adr/ record product and architecture decisions, including the Streamlit-first to Next.js + FastAPI transition.
  • Architecture details live in docs/architecture.md.
  • Deployment notes live in docs/next-fastapi-transition.md.

Deployment Shape

  • app.job-application-copilot.xyz -> Vercel frontend
  • api.job-application-copilot.xyz -> VPS FastAPI backend
  • deploy/vps/ -> Docker Compose + Caddy bundle for the backend stack