<p align="center"> <img src="https://raw.githubusercontent.com/nodemaven/google-browser-scraper/main/assets/banner.png" alt="Google Browser Scraper"> </p>

google-browser-scraper

Self-hosted Google search scraper for Python. Scrape Google search results (SERP) with a real Chrome browser and your own residential proxy, and get clean JSON back. Use it as a CLI, a Python library, a local HTTP API, or an MCP server that gives AI agents live Google search.

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, opens Google's results page for each query in that browser, 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".

google-browser-scraper doctor shows what your machine looks like to a website.

Features

• Real browser scraping: real Chrome via Patchright, with a warm-up before every new identity
• Bring your own proxy: any residential proxy with sticky sessions, built-in NodeMaven support
• Self-hosted SERP API alternative with JSON: organic_results, ads, related_questions, related_searches, pagination
• Honest failures: captchas and blocks come back labeled, not as empty results
• Cost tracking: bytes per page and cost per 1000 pages in every run summary
• Four ways to run it: CLI, Python library, HTTP API, MCP server for AI agents
• Docker image for self-hosted deployment

Use cases

• SEO rank tracking and SERP monitoring for your own keywords
• Keyword and competitor research from live Google results
• Web search for AI agents and LLM apps through MCP
• A self-hosted alternative to paid Google search APIs when you already pay for proxies

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, with the hosts that took most of it, 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

To try it without a proxy, use your own connection:

google-browser-scraper search "best running shoes" --no-proxy

Google then sees one address for every query, so it starts answering with captchas sooner than it would across a pool of exits. Those pages come back as captcha in page_verdict, not as empty results.

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. search_metadata.timings says where the page's time went, in seconds per step (type, load, ready, read, parse, and links, which overlaps the next query and is not part of total_time_taken). 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.
3. Search: each query opens Google's results page directly, which is faster than the search box and skips the autocomplete traffic of typing. --direct follow-ups sends an exit's first query through the box and --direct none every query; --input sets how a query goes into the box (type key by key, insert all at once, words word by word).
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.

A served exit is kept for up to 50 queries (--max-per-exit). Every new exit pays for a warm-up, so a short cap mostly buys more warm-up traffic. A browser error on a search is retried once on the same exit (through Google's front page when queries go through the search box) before the exit is dropped; a network error drops it at once.

--workers N searches on N exits at once, one browser each, sharing one list of queries. Each browser needs memory and CPU of its own. The run stops when exit after exit is refused or unreachable with no worker served in between.

The command line copies the cookies of the first exit Google serves and starts up to 10 new exits from that copy (--clone N, --clone 0 turns it off), on new sticky sessions, instead of warming each one; all workers share the copy. Google's acceptance follows the cookies more than the address, so a copy usually starts out served, and costs a few MB instead of a full warm-up. A copy refused on its first query ends the copying; the copied cookies are used for three hours at most (--clone-max-age). A copy waits 40-60 seconds after its browser starts before the first search.

--spare 1 warms the next exit in the background once the current one is served, so a retired exit is replaced without waiting for a warm-up. A spare costs a warm-up's traffic whether it is used or not.

Google wraps result links in encrypted /goto redirects. Each one is resolved with a small request through a separate short-lived proxy session, a few at a time, so link resolution does not touch the warmed browser session. It runs in the background while the browser goes on to the next query; records still come out in the order each browser searched them. --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. The cloak engine (--engine cloak) is experimental; 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