A full-stack job search assistant that crawls postings from major tech companies, analyzes them with LLMs, parses your resume, and runs AI mock interviews — so you can focus on preparing, not sifting through job boards.
The whole loop in one terminal run, offline: 54 sample postings crawled and structured, resume×job skill gaps, and a mock interview with a feedback report. Replay it with python examples/full_journey_demo.py.
No API key? No problem. Demo mode runs the whole app offline:
pip install -r requirements.txt
FINDJOBS_DEMO=1 python api_server.py # backend on :5000, seeds sample jobs on first run
cd FrontEnd && npm install && npm run dev # frontend on :8080With FINDJOBS_DEMO=1, the server fills an empty jobs.db from data/sample_jobs.json (54 hand-written postings from the companies the crawlers target) and routes every LLM call through demo_llm.py, a deterministic stub that answers from the prompt content and never touches the network, even if a key is configured. Upload the bundled data/sample_resume.pdf on the resume page to walk the full loop: parsing, match scores, a 3-stage mock interview, and the tracking board. While the backend runs in demo mode, a small "Demo" badge shows in the navbar.
Both fixtures can be regenerated with python scripts/seed_demo_data.py and python scripts/make_sample_resume.py.
Four pieces wired into one flow: a crawler pulls postings from company career sites, an LLM reads each one for requirements and skills, your resume gets parsed and scored against them, and any posting can drive an AI mock interview straight off its job description. The React frontend ties it together, so you go from "what's out there" to "let me practice for this one" without leaving the app.
- Job crawler — pulls postings from Tencent, NetEase, ByteDance, Amazon and more, via API or Selenium, with automatic cleaning and normalization.
- LLM analysis — extracts education/major requirements, scores skill tags (1–5), and classifies each posting into a job taxonomy.
- Resume parsing & matching — parses PDF/Word resumes, scores skills, and computes a case-insensitive job-resume match percentage.
- AI mock interview — generates questions from any job description and runs a multi-turn interview with real-time feedback.
- Demo mode: set
FINDJOBS_DEMO=1to run the full app offline with seeded sample jobs, a bundled sample resume, and a deterministic stub LLM instead of a paid key. - SQLite persistence — analyzed postings are stored in a local
jobs.db; existing CSV data is migrated automatically on first run, with CSV/JSON as fallback. Uploaded resumes and mock-interview transcripts live in the same database, so restarting the API server no longer wipes them.
FindJobs-Agent/
├── FrontEnd/ # React frontend
│ ├── src/
│ │ ├── components/ # Page components
│ │ │ ├── JobsPage.tsx # Job browsing
│ │ │ ├── ResumePage.tsx # Resume analysis
│ │ │ └── InterviewPage.tsx # AI interview
│ │ └── App.tsx
│ └── package.json
├── findjobs/ # The package: crawler, matcher, resume, interview, pipeline
│ ├── pipeline.py # Main pipeline; also the `findjobs` CLI entry
│ ├── job_agent.py # LLM job analysis agent
│ ├── api_server.py # Flask API server
│ ├── storage.py # SQLite store: jobs, applications, resumes, interviews (jobs.db)
│ ├── interview_agent.py # AI interview module
│ ├── resume_parser.py # Resume parser
│ └── ... # crawlers, scoring, LLM client, demo stub
├── data/ # Label library + taxonomy + demo fixtures
├── config/ # llm_config.json and deployment configs
├── docs/ # API reference and project notes
├── scripts/ # Helpers: demo seeding, sample resume generator, crawler smoke test
├── tests/ # pytest suite
├── pyproject.toml
└── requirements.txt
- Python 3.9+
- Node.js 18+
- Chrome (required for Selenium crawler)
git clone https://github.com/he-yufeng/FindJobs-Agent.git
cd FindJobs-Agentpip install -r requirements.txtCreate an API_key.md file with your OpenAI API key:
sk-your-api-key-here
python api_server.pycd FrontEnd
npm install
npm run devVisit http://localhost:8080 in your browser.
pipeline.py chains crawl → analyze → score → serve. Run the whole thing, or a single stage:
python pipeline.py # crawl + analyze + build site data
python job_crawler_v2.py -c tencent netease amazon -m 300 # crawl only (--list shows companies)
python pipeline.py --analyze-only --max-jobs 50 # analyze only (for testing)An optional aggregator source is available via freehire.me, an open IT job board whose public API needs no key. Each result already carries the full markdown JD and the employer's own ATS apply link, so there is no detail page to re-fetch. It stays off by default; flip it on with --freehire, or run it standalone:
python pipeline.py --freehire --freehire-query "backend" # company crawlers + freehire
python freehire_source.py -q "ml engineer" --skills python,pytorch --countries us,de -m 100
python freehire_source.py --list-facets skills # live filter vocabulary (skill slugs, country codes)Pluggable job sources. Crawlers, third-party APIs, and local JSON files all enter through one JobSource interface (findjobs/job_source.py): implement name + fetch() returning canonical job dicts, then register_source(...) — no pipeline edits. The pipeline walks the registry (company crawlers by default, freehire when enabled), dedupes across sources, and pipeline.step1_crawl_jobs(sources=[...]) accepts a full replacement list for custom feeds.
A third bundled source, Remotive, widens coverage to general remote jobs (their public API also needs no key). Unlike the aggregator adapters it is a direct JobSource with no subprocess: instantiate it yourself or swap it into the registry:
from findjobs.job_source import register_source
from findjobs.remotive_source import RemotiveSource
register_source(RemotiveSource(search="llm", category="software-dev", limit=100))
# or take the whole pipeline over with a custom list:
# pipeline.step1_crawl_jobs(sources=[RemotiveSource(limit=100)])Filter vocabularies are read from /api/v1/jobs/facets at runtime instead of being hardcoded.
The crawler knows 32 companies (--list), all fetched through the public job APIs that each careers site serves to job seekers. No login, no key, no page scraping behind a wall.
As of the last refresh (2026-09), four targeted crawlers are live; the Remotive remote-jobs source was verified live on 2026-09-17:
| Source | Endpoint | Notes |
|---|---|---|
| Tencent (腾讯) | careers.tencent.com query API |
up to 400 per pull |
| ByteDance (字节跳动) | jobs.bytedance.com portal API |
moved to the current POST payload; the old GET form is gone |
| NetEase (网易) | public search API | social + campus |
| Amazon | amazon.jobs search API |
global, CN filter supported |
| Remotive | remotive.com/api/remote-jobs |
key-less remote-jobs API; full JD (HTML stripped to text) and apply links included |
data/latest_jobs.json holds the latest snapshot: 1,187 postings across these four, committed so the pipeline has real data to chew on without a fresh crawl. data/latest_enriched.jsonl is the same set after LLM analysis (degree, major requirement, scored skill tags, job family), so the site and the matcher work out of the box with zero API spend.
The other adapters (Baidu, Kuaishou, Xiaomi, Bilibili, DiDi, Pinduoduo, Huawei, Ctrip, DJI, NIO, XPeng, Li Auto, OPPO, VIVO, SenseTime, MiHoYo, SHEIN, Shopee, KE, Yuanfudao, Zuoyebang, Zhilian, Lagou, Microsoft, Google) were written against 2023-era endpoints that have since changed shape or started blocking plain requests; they currently return empty and need an adapter refresh. Alibaba, Meituan and JD go through job_crawler_selenium.py and want a local browser driver. If you rely on one of these, open an issue and it moves up the queue.
| Endpoint | Method | Description |
|---|---|---|
/api/jobs |
GET | List job postings |
/api/jobs/<id> |
GET | Get job details |
/api/resume/upload |
POST | Upload resume |
/api/resume/analyze |
POST | Analyze resume |
/api/interview/start |
POST | Start mock interview |
/api/interview/answer |
POST | Submit interview answer |
Crawl, analyze, resume match, and mock interview work end to end. The next steps widen the funnel and follow the hunt past the match:
- More job sources — extend the crawler beyond the current company set to job boards and aggregators, so matching isn't limited to a fixed list.
- Incremental crawls — track which postings were already seen and fetch only new ones, instead of re-crawling and re-analyzing the full set each run.
- Application tracking — a board page in the frontend tracks each job through bookmarked / applied / replied / interview / offer / rejected, backed by
/api/applicationsin SQLite. Status changes and notes save inline. - Voice mock interviews — speech in and out for the AI interviewer, closer to a real screen than a text chat.
FindJobs-Agent is one of the applied agents I've built. A few others you might find useful:
- CoreCoder — want to understand how a coding agent really works? Read the whole ~1k-line engine end to end, not a black box.
- RepoWiki — dropped into an unfamiliar codebase? It gives you a guided wiki and a where-to-start reading path, a self-hostable DeepWiki alternative.
- ContractGuard — catch the risky clauses before you sign: it reads contracts and flags the dangerous bits.
- GitSense — want to contribute to open source? It finds issues worth your time and gauges whether your PR will get merged.
- CodeABC — understand any codebase even if you don't code, built for non-programmers.
Issues and pull requests are welcome!
MIT License