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AI/TLDR

JustHireMe

A local-first desktop workbench that scrapes job leads, ranks your fit and drafts tailored applications

Assistants & ChatbotsOpen source
Latest
v1.7.0
Updated
1 Sep 2026
Language
Python
License
AGPL-3.0

What's new

v1.7.01 Sep 2026

v1.7.0 shipped Windows, macOS and Linux installers built by GitHub Actions, with SHA256 checksums for every asset and the runtime packs.

Overview

JustHireMe is a local-first desktop workbench for job hunting. It collects leads from ATS and company boards, RSS feeds, Hacker News, GitHub, Reddit and other configured sources, rejects stale, thin, spammy or senior-only postings with a deterministic quality gate, ranks what remains against your profile, and generates a tailored resume PDF, cover letter PDF and outreach drafts for the roles worth pursuing. Profile data, lead history, generated documents, the graph, vectors and settings stay on your machine by default.

The JustHireMe dashboard: an agent status panel reporting scanned, scored and tailored leads, a plain-English box for the role you want, counters for active, scored, ready and applied leads, and a Best leads list with fit scores from 84 to 91
The dashboard opens on a filtered shortlist rather than a raw job feed.JustHireMe website ↗
The knowledge graph view titled Your profile, connected, showing a You node linked to projects, experience and skill nodes, with filters for projects, skills, experience and credentials
The profile graph that backs every fit score.JustHireMe website ↗

Ranking is meant to be explainable rather than a black-box score. It combines source signal, lead quality, a deterministic fit rubric, seniority caps, project and stack evidence, semantic similarity and feedback learning, with optional LLM evaluation on top. Your resume is turned into a Kùzu graph of skills, roles and projects plus LanceDB vectors, so a fit score comes with GraphRAG proof from your own experience. Embeddings use a bundled ONNX model (`all-MiniLM-L6-v2`), so semantic matching works offline without an API key.

The app is a Tauri 2 shell around a React UI and a Python FastAPI sidecar, with installers for Windows, macOS and Linux that update themselves from GitHub releases. LLM steps can run keyless through Ollama or an existing Claude Code or Codex CLI subscription, or through keyed providers such as OpenAI, Anthropic, Gemini, Groq and DeepSeek. It is field- and location-agnostic, and a stdio MCP server exposes fit scoring, the lead-quality gate and lead extraction to other agents. Browser automation and auto-apply code exists but is experimental and disabled by default — generated material is a draft for you to review, not a submission.

What it does

  • Source adapters for ATS boards, RSS/Atom, Hacker News, GitHub, Reddit, custom JSON and a Playwright web fallback, normalised and deduplicated into a local CRM
  • A deterministic quality gate that filters stale, thin, senior-only, unpaid or context-free postings and records the reason
  • Explainable fit ranking backed by a Kùzu profile graph, LanceDB vectors and local ONNX embeddings, with optional LLM evaluation
  • Tailored resume and cover letter PDFs plus founder message, LinkedIn note and cold email drafts, with keyword coverage and selected-project rationale
  • Keyless LLM options (Ollama, Claude Code CLI, Codex CLI) alongside 15+ keyed providers
  • An MCP server exposing `score_job_fit`, `evaluate_lead_quality` and `extract_lead_intel`, and an agent-neutral skill file
The pipeline view listing job leads with company, source board, seniority, a status badge such as Discovered, Tailoring, Approved, Applied or Discarded, and Fit and Signal scores, with source, level and sort filters
One reviewable queue, with fit and signal scores on every row.
The profile page showing structured candidate data extracted from a resume: counts of skills, experience and projects, and a grid of technical skills such as TypeScript, Python, React and PostgreSQL
Resume import turns a profile into structured skills, roles and projects.
The activity view headed What is the agent thinking?, a live log of scout, eval, apply and graphrag events such as listings fetched, match scores and resume PDFs generated
A live stream of every scrape, score and draft, including throttling and fallbacks.

Getting started

Non-developers install the desktop app from GitHub Releases; contributors run it from source with Node.js 24, Python 3.13+, Rust and uv.

Install the desktop app

Download the asset for your platform from the project's GitHub Releases page:`JustHireMe_*_x64-setup.exe` on Windows, `JustHireMe_*_aarch64.dmg` on Apple Silicon, or the `.deb` / AppImage on Linux. Windows and macOS builds are not yet signed or notarized, so you may need More info → Run anyway or Open Anyway. The first launch downloads a one-time runtime pack (browser, vector libraries and embedding model).

bashbash
sudo dpkg -i JustHireMe_*_amd64.deb      # Debian/Ubuntu
chmod +x JustHireMe_*_amd64.AppImage      # or the portable AppImage

Or run the full desktop app from source

This starts the Tauri shell, the frontend and the Python backend sidecar.

bashbash
git clone https://github.com/vasu-devs/JustHireMe.git
cd JustHireMe
npm ci
cd backend
uv sync --dev
cd ..
npm run tauri dev

Import your profile and scan

Import a resume (PDF, DOCX, TXT, MD, JSON Resume, LinkedIn export, GitHub or portfolio URL), describe the role you want, then scan sources and review the ranked leads in the pipeline. API keys for keyed LLM providers are set inside the app; Ollama or a Claude Code / Codex CLI subscription work without one.

Expose it to other agents over MCP

After `uv sync --dev`, point an MCP client at the backend's server script (on Windows use `backend\.venv\Scripts\python.exe`).

jsonjson
{
  "mcpServers": {
    "justhireme": {
      "command": "/absolute/path/to/JustHireMe/backend/.venv/bin/python",
      "args": ["/absolute/path/to/JustHireMe/backend/mcp_server.py"],
      "cwd": "/absolute/path/to/JustHireMe"
    }
  }
}

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

When to use it

  • Replace scrolling several job boards with one local shortlist that has stale and spammy postings already filtered out
  • See why a role scores well against your experience before spending time on an application
  • Draft a tailored resume, cover letter and outreach message for a role while keeping your personal data off third-party servers
  • Give another agent job-fit scoring and lead-quality checks through MCP

How JustHireMe compares

JustHireMe alongside other open-source assistants & chatbots tools AI/TLDR tracks, ranked by GitHub stars.

ToolStarsWhat it does
OpenClaw★ 391kOpenClaw is a self-hosted personal AI assistant that answers you on WhatsApp, Telegram, Slack, Discord, and many other channels, with voice and a live visual canvas.
Hermes Agent★ 251kA self-improving personal AI agent from Nous Research that builds skills from experience, remembers across sessions, and reaches you on Telegram, Discord, Slack, and more.
Odysseus★ 88.9kA self-hosted AI workspace that puts chat, agents, deep research, documents, email, notes, tasks and calendar behind one Docker Compose stack, over local or API models.
CowAgent★ 47.2kA self-hosted assistant that plans and executes tasks with built-in file, terminal, browser and search tools, and answers across a web console plus a dozen messaging platforms.
AstrBot★ 41.3kAn all-in-one agent chatbot platform that puts LLM conversations, tools, knowledge bases and a plugin marketplace inside messaging apps like Telegram, Slack, Discord, QQ and Feishu.
OpenHuman★ 40.5kA local-first desktop personal AI for macOS, Windows and Linux that keeps a compressed memory tree on your machine and orchestrates checkpointed research and automation workflows.
MindsHub★ 39.8kAn agent workspace for knowledge work and software development that runs swappable open-source agent harnesses against your choice of frontier or open models.
JustHireMe★ 2.3kA local-first desktop workbench that scrapes job leads, ranks your fit and drafts tailored applications