89ca9ad647
On-demand Scout service (replaces the nightly aggregator): - FastAPI agent-mesh service; card name "matchmaking-service" (HTTP /a2a/tasks + Redis-stream worker matching the sibling-service pattern) - run_search: parallel multi-board sweep (Naukri/blackfalcondata + Foundit + LinkedIn), city-filtered at the board, cheapest-per-result first - per-board adapters + normalizers -> ScoutJob with rich read-only details and offsite apply links; recall mode + MVQ guard - result cache for dev replay ($0); per-board cost knobs; retry-on-5xx - research/: engine design, board cost economics, India-actor shortlist, POC
78 lines
3.5 KiB
Markdown
78 lines
3.5 KiB
Markdown
# browser-apply — browser-use (open source) POC
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The pivot away from heuristics. Instead of mocking an apply flow, an **AI agent drives a
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real Chrome browser** — it looks at the page and clicks/types like a human. No cross-origin
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wall, because it *is* the browser.
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## The moving parts (what's actually running)
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```
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your task (plain English)
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│
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▼
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┌─────────┐ "what do I click next?" ┌──────────────┐
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│ Agent │ ───────────────────────────────► │ OpenAI LLM │
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│(browser-│ ◄─────────────────────────────── │ (the brain) │
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│ use) │ "click element #3, type X" └──────────────┘
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│
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│ Chrome DevTools Protocol (cdp-use)
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▼
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┌──────────────┐
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│ real Chrome │ ← navigates, reads the page, clicks, types, uploads
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└──────────────┘
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```
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- **browser-use** (v0.13.1) — the library. Runs the loop: screenshot/read page → ask the
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LLM what to do → do it → repeat until the task is done.
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- **The LLM** (OpenAI here) — the decision-maker. Sees the page, decides the next action.
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It's swappable: OpenAI, Anthropic, Google, or local (Ollama).
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- **Chrome** — your installed Google Chrome, driven over the DevTools Protocol. No separate
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browser download needed. browser-use auto-adds ad/cookie-blocker extensions.
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## Setup (already done once)
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```bash
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python3 -m venv venv # Python >= 3.11 (you have 3.13.2)
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source venv/bin/activate
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pip install browser-use # pulls openai/anthropic/google SDKs + cdp-use
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# Chrome is reused from /Applications — nothing else to install
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```
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Secrets live in `.env` (git-ignored):
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```
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OPENAI_API_KEY=sk-...
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```
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## Run the smoke test
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```bash
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source venv/bin/activate
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python verify.py # headless (no window)
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HEADLESS=0 python verify.py # SEE Chrome open and the agent drive it
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```
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Expected: it visits example.com and prints `The main heading on the page is 'Example Domain'.`
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## What's proven so far
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- ✅ The full stack works: browser launches, LLM reasons, agent reads a live page (`verify.py`).
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- ✅ Vision résumé parsing works on the real `Resume.pdf` (`parse_resume.py`).
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## Real job-apply flow (built)
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Applies to one real posting: LinkedIn job → hand-off to OpenAI's official job board → fill & submit.
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| File | Role |
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|---|---|
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| `parse_resume.py` | Vision model (gpt-4o-mini + PyMuPDF) → structured `profile` from `Resume.pdf`. Mirrors resume-builder's `parse_service.py`. |
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| `browser_config.py` | Shared `BrowserProfile` (dedicated `chrome-profile/`, real Chrome, visible) so login persists into apply. |
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| `login_once.py` | One-time: open LinkedIn, you log in yourself, session saved. |
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| `apply.py` | Parses résumé → drives the LinkedIn→OpenAI-board apply → auto-submits → structured result. |
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```bash
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source venv/bin/activate
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python login_once.py # one time: log into LinkedIn in the window
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python apply.py # parses résumé, applies, auto-submits
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AUTO_SUBMIT=0 python apply.py # fill everything but STOP before submit (human review)
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```
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Honesty rules kept: a **liveness gate** (dead posting → report, never fake a submit), résumé is
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the source of truth (no invented data), and auto-submit is one flag away from review-first.
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