google-browser-scraper

Google Search results from a real browser, through your own sticky proxy.

Plain HTTP is no longer a reliable way to scrape Google: it often expects a browser that runs JavaScript, and a fresh browser on a fresh proxy exit can be refused quickly. This tool takes a browser path instead. It warms a sticky proxy session with ordinary browsing, types the query into Google's search box, checks whether Google served results or refused, and writes one JSON record per results page. Captchas, blocks and failures are reported as such, never as "no results".

Install

pip install google-browser-scraper
patchright install chromium

Quickstart

You need a residential proxy with sticky sessions. Put {session} where your provider expects a session id in the username:

google-browser-scraper search "best running shoes" \
  --proxy "http://USER-session-{session}:PASS@gate.example.com:7000" \
  -o results.jsonl

Success is a results.jsonl with one JSON object per results page:

{
  "search_metadata": {"status": "Success", "page_verdict": "ok"},
  "search_parameters": {"engine": "google", "q": "best running shoes", "page": 1},
  "organic_results": [
    {"position": 1, "title": "...", "link": "https://...", "snippet": "..."}
  ],
  "related_questions": [{"question": "..."}],
  "exit": {"session": "...", "queries_on_exit": 1, "bytes": "..."}
}

The first query takes a few minutes, because a new exit is warmed up first, and the warm-up costs noticeably more traffic than a search. Later queries on the same exit take seconds. A summary of pages served and traffic used is printed at the end of every run.

With NodeMaven, the session id is handled for you and the credentials come from the environment:

pip install "google-browser-scraper[nodemaven]"
export NODEMAVEN_LOGIN=... NODEMAVEN_PASSWORD=...
google-browser-scraper search -f queries.txt --nodemaven --country us -o results.jsonl

From Python:

from google_browser_scraper import ProxyTemplate, Scraper, Settings

proxy = ProxyTemplate("http://USER-session-{session}:PASS@gate.example.com:7000")
for page in Scraper(proxy, Settings(pages=2)).run(["best running shoes"]):
    for result in page.get("organic_results", []):
        print(result["position"], result["title"], result["link"])

What you get

The schema is intentionally SerpApi-like: organic_results, ads, related_questions, related_searches, pagination and search_metadata, so simple consumers can often switch with minimal mapping. Knowledge panels, shopping, images and other special blocks are not parsed.

Each record also has an exit block: which proxy session answered and how many bytes the page cost. A query that failed is a record too, with an error. --format csv writes the organic results as a table, and --price-per-gb adds the cost per 1000 pages to the summary.

How it works

1. New identity: a sticky proxy session and a fresh browser profile.
2. Warm-up: a few ordinary pages, then Google's front page.
3. Search: the query is typed into the box and submitted.
4. Check before parsing: the page is classified first, so a captcha or a block is reported as one and never parsed as an empty result.
5. Keep or drop: an exit Google answers is reused for the next queries; an exit it refuses is dropped and the query is retried on a new one. If exit after exit is refused, the run stops instead of spending more traffic.

Google wraps result links in encrypted /goto redirects. Each one is resolved with a small request through a separate short-lived proxy session, so link resolution does not touch the warmed browser session. --no-resolve-links skips it.

Traffic goes through a small local relay that counts bytes and blocks a large model download Chrome otherwise makes on every fresh profile.

The defaults come from measurements in proxy-benchmark.

Proxies

Any provider with sticky sessions works through --proxy and a {session} placeholder. A proxy without one is accepted with a warning: if the exit changes on every request, warming it does nothing. SOCKS5 needs an IP-whitelisted endpoint, because Chrome cannot send a SOCKS5 username and password.

Run it as an API

google-browser-scraper serve --nodemaven --country us --prewarm
curl "http://127.0.0.1:8000/search.json?q=best+running+shoes"

Same record format as above. Requests run one at a time on one held identity. Set GBS_API_KEY to require a key; without one the server only listens on localhost.

Use it from an AI agent (MCP)

{
  "mcpServers": {
    "google-search": {
      "command": "google-browser-scraper",
      "args": ["mcp", "--nodemaven", "--country", "us", "--prewarm"],
      "env": {"NODEMAVEN_LOGIN": "...", "NODEMAVEN_PASSWORD": "..."}
    }
  }
}

It exposes one tool, google_search(query, pages).

Docker

docker build -t google-browser-scraper .
docker run --rm google-browser-scraper doctor
docker run --rm -p 8000:8000 -e GBS_API_KEY=change-me \
  -e GBS_PROXY='http://USER-session-{session}:PASS@gate.example.com:7000' \
  google-browser-scraper serve --host 0.0.0.0

Check your machine

google-browser-scraper doctor

Starts the browser on a blank page, sends nothing, and reports what a website would see: whether the browser looks headless, whether WebGL works, the screen size. Google answers some machines much less than others, so run this first.

What this is not

Not a free Google Search API, and not a guarantee that every proxy exit will work. It is a browser-based collector that makes failures explicit, drops exits Google refuses, and tells you what each page cost.

Status

Early. Tested on Windows. Linux and Docker are experimental: on a machine without a GPU, Chrome has no WebGL, which a normal desktop always has; the Docker image turns on a software renderer for it. The cloak engine (--engine cloak) is experimental too; patchright is the default.

Google changes its pages often. If results come back empty or wrong, run with --save-html DIR and open an issue with what you see.

You are responsible for using this within Google's terms and the law where you run it.

Adding an engine

An engine joins this tool only after it has been measured on proxy-benchmark: add it there, run the warm-up ladder against Google -

python scripts/run_ladder.py --engines your-engine

• and open a pull request here with the run file. The ladder runs identities with and without the warm-up in the same hours, so the result shows whether the engine actually gets served, not whether it got lucky once.

License

MIT