JobRadar: Open-source job search agent that scores listings with a local LLM

A CLI job search agent that hunts across 8 free sources at once and helps you figure out which ones are actually worth your time.

It uses a local LLM to score jobs against your profile — but the AI is just a guide, not a decision-maker. You're the one who decides what fits. That's the whole point.

Most job boards show you hundreds of listings and leave you drowning in tabs. JobRadar pulls from multiple sources at once, filters out the noise, and gives you a ranked list with AI-generated notes on why each job might (or might not) be a fit.

But here's the thing: we believe the best job search tool puts a human in the loop. The AI can score and summarize, but it can't understand your gut feeling about a company culture, or that you'd rather work somewhere with a smaller team even if the salary is lower. That part is yours.

So use the scores as a starting point, not a verdict.

Searches 8 sources at once— Remotive, Arbeitnow, RemoteOK, Jobicy, Himalayas, Greenhouse (direct ATS), Ashby (direct ATS), and optionally LinkedIn** Rates jobs with a local LLM**— auto-detects Ollama or llama.cpp, scores each job 0-100 on skills match, experience fit, salary fit, and remote fit** Remembers what you've seen**— persistent cache so you don't re-review the same jobs every run** Works completely offline**— all AI runs locally on your machine, no cloud APIs, no subscriptions** Looks good in the terminal**— color-coded scores, progress bars, clean tables** Web dashboard**— dark-mode SPA at localhost:3000 with Kanban pipeline, filters, and config editor

curl -fsSL https://raw.githubusercontent.com/ANIRudH-lab-life/job-radar/main/setup.sh | bash
npx jobradar-setup
git clone https://github.com/ANIRudH-lab-life/job-radar.git
cd job-radar
npm run setup
git clone https://github.com/ANIRudH-lab-life/job-radar.git
cd job-radar
.\setup.ps1
git clone https://github.com/ANIRudH-lab-life/job-radar.git
cd job-radar
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
pip install -r dashboard/requirements.txt

The smart installer (setup.sh

/ setup.ps1

) checks each dependency before down:

Safe to re-run — second run is instant, nothing re-downloaded.

The installer asks which LLM backend you prefer:

Ollama (recommended)— auto-installs the model, easy to manage, works out of the box** llama.cpp (faster)**— raw performance, downloads GGUF model manually

cd job-radar
source .venv/bin/activate   # Windows: .venv\Scripts\activate

python -m jobradar -q "python developer" --no-ai

python -m jobradar -q "python developer" -p profile.yaml

python -m jobradar

cd dashboard && bash run.sh   # Windows: python -m uvicorn app:app --port 3000

ollama serve &                              # if not already running
python -m jobradar -q "python dev" -p profile.yaml

llama-server --model models/qwen3-1.7b-q4_k_m.gguf --port 8080
python -m jobradar -q "python dev" -p profile.yaml

The Greenhouse and Ashby sources pull directly from company career pages via their public APIs. No login, no API keys. You can edit the company list in companies.yaml

.

Create a profile.yaml

with your details so the AI can score jobs against your actual background:

name: "Your Name"
title: "Software Engineer"
experience_years: 5
skills: [Python, Docker, AWS, React]
desired_roles: [Backend Engineer, SRE]
salary_min: 100000
salary_max: 160000
location_preference: "Remote"
remote_ok: true
industries: [Fintech, SaaS]

The more detail you put in, the better the scoring. But remember — the AI's score is a suggestion, not a ranking you have to follow.

Edit companies.yaml

to add or remove companies for the Greenhouse and Ashby sources. Just use the company slug (the part of the URL on their careers page):

greenhouse:
  - gitlab
  - figma
  - discord
  - shopify
  - stripe

ashby:
  - openai
  - anthropic
  - linear
  - resend
  - clerk

JobRadar remembers jobs you've already seen so you don't re-review them on every run. By default, it keeps a 7-day cache in ~/.jobradar/seen_jobs.db

.

python -m jobradar -q "python" --cache-days 30

python -m jobradar -q "python" --no-cache

python -m jobradar --clear-cache

The dashboard gives you a visual interface for everything:

Header stats— total discovered, high match, pending review, applied** Job cardswith color-coded match scores (green/yellow/red) Inspector panel**— full description, matched keywords, LLM reasoning** Kanban pipeline**— drag jobs from Discovered → Reviewing → Applied → Interviewing** Config editor**— edit profile.yaml and companies.yaml in the UI** Live activity log**— terminal-style console showing LLM scoring progress** Keyboard shortcuts**— J/K to scroll, E to edit, A to mark applied

Start it with:

cd dashboard
bash run.sh          # Linux/macOS
python -m uvicorn app:app --port 3000   # Windows

Then open http://localhost:3000.

JobRadar defaults to qwen3:1.7b

(1.1 GB, fast on CPU). If you want a different model:

ollama pull qwen3:8b          # larger, better reasoning, slower
ollama pull qwen2.5:1.5b      # smaller, faster, less accurate

python -m jobradar -q "python dev" --llm-model qwen3:8b

Or set it permanently in your environment:

export LLM_MODEL="qwen3:8b"
python -m jobradar -q "python dev"
job-radar/
├── setup.sh              # Smart installer (Linux/macOS)
├── setup.ps1             # Smart installer (Windows)
├── package.json          # npm wrapper
├── profile.yaml          # Your profile (edit this)
├── companies.yaml        # ATS company slugs (edit this)
├── jobradar/
│   ├── models.py         # Job and Profile dataclasses
│   ├── rating.py         # Local LLM rating with retry logic
│   ├── cache.py          # SQLite seen-jobs cache
│   ├── display.py        # Rich terminal UI
│   ├── cli.py            # Search pipeline + argparse
│   └── sources/
│       ├── remotive.py   # Remotive API
│       ├── arbeitnow.py  # Arbeitnow API (paginated)
│       ├── remoteok.py   # RemoteOK API
│       ├── jobicy.py     # Jobicy API
│       ├── himalayas.py  # Himalayas API
│       ├── greenhouse.py # Greenhouse ATS (direct API)
│       ├── ashby.py      # Ashby ATS (direct API)
│       └── linkedin.py   # LinkedIn scraping (opt-in)
└── dashboard/
    ├── app.py            # FastAPI backend
    ├── database.py       # SQLite storage
    ├── run.sh            # Dashboard launcher
    └── static/
        └── index.html    # Dark-mode SPA

We built JobRadar because job searching is exhausting and the tools out there either dump too many listings on you or try to automate the whole thing. We think the sweet spot is: let the machine do the grunt work (searching, filtering, summarizing) and keep the human making the actual decisions.

The AI scoring is there to save you time reading through listings, not to tell you what to apply for. A 95/100 score doesn't mean "apply immediately" — it means "this one looks relevant, worth a closer look." A 40/100 doesn't mean "skip it" — it might be a role you'd love that the AI just doesn't have enough context for.

You are the loop. The AI is just the filter.

LinkedIn scraping is off by default. It depends on undocumented HTML that breaks constantly and might violate their ToS. We keep it around because sometimes it's useful, but we'd rather you know the tradeoff:

python -m jobradar -q "python dev" --enable-linkedin
pip install pytest
pytest tests/ -v

MIT — see LICENSE