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

JobRadar, an open-source command-line job search agent, searches 8 free job sources simultaneously and uses a local LLM to score listings 0-100 on skills, experience, salary, and remote fit, with the AI serving as a guide rather than a decision-maker. The tool, which supports Ollama or llama.cpp backends and works fully offline, includes a web dashboard at localhost:3000 and is available via GitHub at ANIRudH-lab-life/job-radar.

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 or 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 downloading: | Step | Checks for | Downloads if missing | |---|---|---| | 1. Python | 3.9+ in PATH | — shows install link | | 2. pip packages | Each import individually | Only missing packages | | 3. LLM Backend | You choose: Ollama or llama.cpp | Ollama install + model pull, or llama.cpp binary | | 4. LLM Model | Only if llama.cpp chosen | qwen3-1.7b Q4 K M ~1.1GB GGUF | | 5. Config | profile.yaml | Creates defaults | 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 | Platform | Installer | llama-server source | |---|---|---| | Linux x64 | bash setup.sh | Ollama recommended or GitHub release zip | | macOS ARM | bash setup.sh | Ollama recommended or brew install llama.cpp | | macOS Intel | bash setup.sh | Ollama recommended or brew install llama.cpp | | Windows x64 | .\setup.ps1 | Ollama recommended or GitHub release zip | cd job-radar source .venv/bin/activate Windows: .venv\Scripts\activate Quick search no AI — fast python -m jobradar -q "python developer" --no-ai Search with AI rating needs a local LLM running python -m jobradar -q "python developer" -p profile.yaml Interactive mode — just run it and type queries python -m jobradar Web dashboard cd dashboard && bash run.sh Windows: python -m uvicorn app:app --port 3000 Open http://localhost:3000 Start the LLM server for AI scoring Option A: Ollama recommended ollama serve & if not already running python -m jobradar -q "python dev" -p profile.yaml Option B: llama.cpp faster llama-server --model models/qwen3-1.7b-q4 k m.gguf --port 8080 python -m jobradar -q "python dev" -p profile.yaml | Source | Type | Notes | |---|---|---| | Remotive | Job board API | Remote jobs | | Arbeitnow | Job board API | Worldwide, paginated | | RemoteOK | Job board API | Remote jobs, good volume | | Jobicy | Job board API | Remote jobs with salary data | | Himalayas | Job board API | Remote jobs with seniority levels | | Greenhouse | Direct ATS | 15 curated tech companies Gitlab, Figma, Stripe, etc. | | Ashby | Direct ATS | 15 curated tech companies OpenAI, Anthropic, Linear, etc. | | Web scraping | 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 . Change cache duration to 30 days python -m jobradar -q "python" --cache-days 30 Skip the cache entirely python -m jobradar -q "python" --no-cache Clear the 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 cards with 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 or python -m uvicorn app:app --port 3000 Windows Then open http://localhost:3000 http://localhost:3000 . | Flag | Default | What it does | |---|---|---| -q , --query | — | Your search query | -l , --location | — | Filter by location | -p , --profile | profile.yaml | Path to your profile | --no-ai | off | Skip AI rating faster | --export | — | Save results to .json or .csv | --limit | 50 | Max jobs per source | --max-pages | 3 | Max pages per source | --max-concurrency | 3 | Concurrent AI rating calls | --companies | companies.yaml | Company list for ATS sources | --cache-days | 7 | Days to remember seen jobs | --no-cache | off | Disable the cache | --clear-cache | — | Clear cache and exit | --list-ats-companies | — | Show configured ATS companies | --enable-linkedin | off | | --llm-url | auto-detect | LLM server URL auto-scans 11434, 8080, 1234 | --llm-model | qwen3:1.7b | LLM model name override auto-detection | JobRadar defaults to qwen3:1.7b 1.1 GB, fast on CPU . If you want a different model: Pull a different model ollama pull qwen3:8b larger, better reasoning, slower ollama pull qwen2.5:1.5b smaller, faster, less accurate Use it with JobRadar 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 /ANIRudH-lab-life/job-radar/blob/main/LICENSE