Headhunter-Agent: Full Tech Stack of a Local-First Multi-Agent System

Headhunter-Agent is a Local-First, Privacy-Preserving Multi-Agent System (MAS) — a native Clojure desktop console that orchestrates a pipeline of AI agents to automate the entire job-hunting workflow: profiling, evaluation, interview prep, resume tailoring, and decentralized submission.

This post walks through the full tech stack end-to-end, from the JavaFX desktop shell down to the Ed25519 signing keys.

Why Build This?

The job market runs on manual, browser-dependent workflows: paste your resume into portals, solve CAPTCHAs, re-type the same information across 50 sites, track applications in a spreadsheet. Headhunter-Agent inverts this:

• Your data stays local. No cloud, no analytics, no third-party storage.
• Agents do the work. A pipeline of Gemini-powered agents evaluates fit, generates strategy, and tailors outputs.
• Machine-to-machine submission. The M2M protocol lets agents apply directly — no browser, no portal.

It is designed to run entirely locally as a native Clojure Desktop GUI using cljfx (JavaFX). No web browsers, no local web servers, and zero JavaScript.

System Architecture

The system is organized into three interface layers (CLI, GUI, M2M) feeding into a shared pipeline of five modules, all backed by local data files:

Component Breakdown

1. Interface Layer — Desktop GUI (cljfx)

The primary interface is a native JavaFX window built with cljfx (878 lines). It provides four tabs in a single-window layout with a fixed sidebar:

• Data Vault — Paste raw LinkedIn/CV text, extract structured master profile and 8-12 STAR stories
• JD Evaluator — 3-stage MAS pipeline with color-coded score visualization (green >= 4.0, yellow >= 3.0, red < 3.0)
• Interview Prep — Generate STAR-mapped prep sheets
• Pipeline Tracker — Visual cards for each application with status badges

The GUI uses a Nord Dark theme (294 lines CSS) with slate-900 backgrounds, emerald/orange/red badge systems, and custom scrollbars. All heavy operations run in future threads with Platform/runLater for JavaFX thread safety.

2. Interface Layer — CLI (Babashka)

The CLI is defined in bb.edn and routes through core.clj:

bb profile   --extract /path/to/linkedin-dump.txt
bb evaluate  --file ./jds/defence-collective.txt
bb interview --file ./jds/defence-collective.txt
bb pdf       --file ./jds/defence-collective.txt
bb tracker   list
bb daemon    serve --port 8081

Same source code runs in both Babashka (native binary, millisecond startup) and JVM Clojure.

3. Multi-Agent Evaluator Pipeline

The core intelligence is a 3-stage sequential MAS pipeline in evaluator.clj:

Each agent calls Google Gemini via HTTP POST with a carefully crafted system prompt: - Agent 1 — Parses JD, checks Fair Consideration Framework (FCF) legitimacy, identifies red flags - Agent 2 — Brutal comparison of Master Profile vs JD, exact gaps/strengths, GO/NO-GO - Agent 3 — Pre-interview cheat sheet: business model, tech stack, cold outreach strategy

Temperature varies by stage: 0.5 for evaluation, 0.2 for extraction and PDF generation.

4. M2M Protocol — Decentralized Job Applications

The M2M protocol is a machine-to-machine job application pipeline that eliminates browsers, CAPTCHAs, and manual portals. It defines four sub-protocols:

The cryptography uses Ed25519 (Java java.security) with: - Every application signed by the candidate’s private key - Every attachment has its own SHA-256 digest + signature - Employers publish their public key in DNS TXT records - Signed acknowledgments from employers with application IDs

5. Daemon MCP Server

The Daemon is a Model Context Protocol (MCP) server — a personal API for the headhunter-agent system. It implements JSON-RPC 2.0 over HTTP using the babashka HTTP server:

Query the daemon with any MCP-compatible client:

curl -X POST http://localhost:8081 \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","method":"tools/list","id":1}'

curl -X POST http://localhost:8081 \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"get_architecture"},"id":2}'

6. PDF Resume Pipeline

Resumes are compiled through a multi-stage pipeline:

7. Application Pipeline Tracker

All applications are tracked in a markdown file at data/applications.md:

Technology Stack

Layer	Technology	Purpose
Language	Clojure 1.12	Core logic, all modules
Runtime	JVM + Babashka	Desktop GUI + CLI automation
GUI	cljfx (JavaFX)	Native desktop window
AI	Google Gemini API	All agent intelligence
PDF	Typst	Resume compilation
JSON	Cheshire 5.13	JSON parsing/generation
HTTP	babashka http-client 0.4.23	API calls + M2M fetch/submit
Server	babashka http-server 0.1.7	Daemon MCP + Directory service
Crypto	Ed25519 (java.security)	M2M signing + verification
Discovery	DNS TXT + Java InitialDirContext	M2M endpoint discovery
Data	.edn files, Markdown	Local storage

Key Design Decisions

1. Privacy-first — All data stays local as .edn / .md files. No cloud, no analytics, no telemetry.
2. No web stack — Native JavaFX desktop GUI (cljfx). No browsers, no JS, no npm.
3. Dual interface — Full GUI desktop app + terminal CLI for automation. Same source code in both.
4. Babashka compatible — Same source runs in JVM Clojure and Babashka native binary.
5. Gemini-powered — All ML through Google Gemini API (model-agnostic — swap to any provider).
6. Markdown-based tracker — Simple, human-readable, git-friendly application database.
7. Typst for PDF — Modern typesetting replacing LaTeX / HTML-to-PDF. Fast, deterministic.
8. Ed25519 for M2M — Modern post-quantum-ready cryptography for decentralized identity.
9. Data Contract — Two-layer file ownership model. User layer is NEVER touched by updates.
10. MCP protocol — Daemon exposes the system’s knowledge through a standard interface consumable by any AI agent.

Repository Structure

headhunter-agent/
├── src/career_ops/
│   ├── core.clj              CLI entry point & command routing
│   ├── gui.clj               JavaFX desktop GUI (878 lines)
│   ├── profiler.clj          Data Vault extraction from raw text
│   ├── evaluator.clj         3-stage MAS pipeline
│   ├── interview.clj         STAR story interview prep
│   ├── pdf.clj               ATS-tailored PDF resume generator
│   ├── tracker.clj           Application pipeline tracker
│   ├── style.css             Nord Dark GUI theme
│   ├── m2m/                  M2M Protocol module
│   │   ├── core.clj          CLI routing
│   │   ├── crypto.clj        Ed25519 keygen, sign, verify
│   │   ├── schema.clj        JSON-LD validation
│   │   ├── registry.clj      DNS + directory discovery
│   │   ├── directory.clj     Directory server (optional)
│   │   ├── fetch.clj         Job posting HTTP fetcher
│   │   ├── submit.clj        Signed application builder
│   │   └── verify.clj        Inbound signature verification
│   └── daemon/
│       ├── core.clj          MCP server entry point
│       └── data.clj          Tool content repository
├── config/profile.example.yml
├── modes/
│   ├── _shared.md            Scoring system, rules, archetypes
│   ├── _profile.example.md   User archetypes, narrative
│   └── oferta.md             A-G evaluation mode instructions
├── docs/m2m-protocol/
│   ├── SPECIFICATION.md      Full protocol spec (522 lines)
│   └── ARCHITECTURE.md       M2M system architecture
├── deps.edn                  Clojure dependencies
├── bb.edn                    Babashka task definitions
├── resume.typ                Typst resume entry point
├── resume_template.typ       Typst layout template
├── DATA_CONTRACT.md          User vs System layer ownership
└── setup.sh / setup.bat      Platform setup scripts

Getting Started

# Prerequisites: JDK 11+, Clojure CLI, Babashka (optional)
git clone https://gitlab.com/nurazhar/headhunter-agent.git
cd headhunter-agent

# Setup
cp .env.example .env   # Add your GEMINI_API_KEY
bash setup.sh          # Creates config files from examples

# Launch desktop GUI
clj -M:run

# Or use CLI
bb profile --extract /path/to/linkedin-dump.txt
bb evaluate --file ./jds/sample-jd.txt
bb interview --file ./jds/sample-jd.txt
bb pdf --file ./jds/sample-jd.txt
bb tracker list

# Start Daemon MCP server
bb daemon serve --port 8081

# M2M protocol (experimental)
bb bb-m2m keygen
bb bb-m2m discover employer.example
bb bb-m2m fetch https://employer.example/jobs/42

What’s Next

The priority items before production readiness:

1. Test suite — 2,400+ lines of Clojure with zero test coverage is the critical gap
2. CI test runner — GitLab CI pipeline with automated testing
3. Versioning — Semantic versioning with release tags
4. Linter — clj-kondo integration for consistent code style
5. Binary attachments — Fix the placeholder binary data in M2M submit.clj
6. Employer-side verification library — P5 of M2M roadmap

The full source is at gitlab.com/nurazhar/headhunter-agent. The Daemon MCP server runs on bb daemon serve and exposes all system documentation through the Model Context Protocol — consumable by any MCP-compatible AI agent.