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There is no documented Google endpoint that returns the consumer AI Mode page as a stable JSON object. The dependable choice depends on what you actually need: use Gemini API Grounding with Google Search for model-generated answers with citation spans; use the Search Researcher Result API when you qualify for its research program and need browser-style Search HTML; or use a commercial, vendor-specific extractor when you must parse the consumer interface.
This distinction matters because the three routes have different output formats, eligibility rules, commercial terms and maintenance costs. The guide below shows how to produce citation-aware JSON, how to capture the rendered interface when that is unavoidable, and how to make your parser tolerate missing or changing fields.
Choose the right meaning of “scrape AI Mode”
Google describes AI Mode as a system for nuanced questions that may explore several subtopics and supporting sources. Google has also said that AI Mode and AI Overviews can use different models and techniques, so the answer and links can vary between responses. Treat every citation set as the sources shown for that response, not as a permanent bibliography.
| Approach | What you receive | Access and commercial limits | Stability | Best use |
|---|---|---|---|---|
| Gemini API with Google Search grounding | Model text plus URL citation annotations, titles and start/end offsets; response steps can include executed search queries. | Official API feature; use the models and tools currently available to your project. | Structured API response, but model and field availability can change. | Answer text with source links and precise answer-to-source mapping. |
| Search Researcher Result API | The HTML Google would return for an eligible Search URL. | Eligibility and application required; rolling request limits; Google restricts the program to non-commercial use. | Browser HTML, not a promised AI Mode JSON schema. | Research projects that need Search-page HTML. |
| Commercial third-party interface extractor | Vendor-parsed answer blocks, references and sometimes the retrieved HTML. | Vendor terms and pricing apply. | Fields are optional; raw markup and class names can change. | Production extraction when you accept vendor-specific maintenance. |
Path 1: return an answer and citations through Gemini grounding
If your requirement is “give me an answer, the URLs that support it, and the character range each URL supports,” grounding with Google Search is the documented route. It is not the consumer AI Mode page and Google’s documentation does not establish that the generated answer will match AI Mode for the same question.
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Python example: emit citation-aware JSON
Install the current Google Gen AI SDK, set GEMINI_API_KEY, and verify the model name and grounding-tool syntax in the current Gemini documentation before deployment. The parser below deliberately walks the returned object instead of assuming one permanent nesting layout.
import json
import os
from google import genai
from google.genai import types
question = os.environ.get("QUESTION", "What are the main trade-offs of passkeys?")
model = os.environ.get("GEMINI_MODEL", "gemini-2.5-flash")
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
response = client.models.generate_content(
model=model,
contents=question,
config=types.GenerateContentConfig(
tools=[types.Tool(google_search=types.GoogleSearch())]
),
)
data = json.loads(response.to_json())
text_parts = []
citations = []
search_steps = []
def walk(node):
if isinstance(node, dict):
citation = node.get("url_citation")
if isinstance(citation, dict):
citations.append({
"url": citation.get("url"),
"title": citation.get("title"),
"start_index": citation.get("start_index"),
"end_index": citation.get("end_index"),
})
if "text" in node and isinstance(node["text"], str):
text_parts.append(node["text"])
for key, value in node.items():
if "search" in key.lower() and key not in ("search_query",):
search_steps.append(value)
walk(value)
elif isinstance(node, list):
for item in node:
walk(item)
walk(data)
result = {
"answer": "".join(text_parts).strip(),
"citations": citations,
"search_steps": search_steps,
}
print(json.dumps(result, ensure_ascii=False, indent=2))The important fields are the text block and each url_citation: URL, title, start_index and end_index. Offsets let your renderer attach a link to the exact answer span rather than placing every source in a footnote. Keep the raw response as well as your normalized object so you can reprocess it when the SDK adds fields.
Validate offsets before inserting links
Offsets should be treated as data, not trusted blindly. Check that they are integers, that 0 ≤ start_index ≤ end_index ≤ len(answer), and that the URL uses an allowed scheme. Sort non-overlapping ranges from the end of the string toward the beginning before inserting HTML; otherwise an earlier insertion changes later offsets. If ranges overlap, preserve the text once and attach a list of sources to that span.
cURL and Node.js requests
The exact REST endpoint and request schema depend on the Gemini API version enabled for your project. Use Google’s current grounding documentation to generate the request, then apply the same normalization rules: retain every text block, every URL citation annotation and any search-query step. Do not hard-code a field path copied from an older response. A robust implementation records the API version, model, capture time and the unmodified response.
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Path 2: use the Search Researcher Result API for browser-style HTML
Google’s Search Researcher Result API returns the HTML Google would return to a browser for an accepted Search URL. It is an eligibility-gated research program: you must apply, requests are subject to project limits on a rolling 24-hour basis, some parameters are rejected, and non-search URLs produce errors.
Most importantly, Google’s terms restrict this program to non-commercial use. It is therefore not a general-purpose commercial AI Mode scraper, and its documentation does not promise a stable JSON object for an AI Mode answer. If you use it, store the returned HTML and parse defensively. Detect an absent AI Mode region as a valid “no answer returned” result rather than throwing an exception.
HTML-to-JSON normalization
- Record the exact Search URL, timestamp, HTTP status and response headers.
- Parse visible text and links from the returned document while preserving their DOM order.
- Identify the answer container using multiple signals rather than one CSS class.
- For every extracted link, retain its displayed text, resolved URL and the nearest answer text range.
- Emit
answer: nulland an emptycitationsarray when no AI Mode content is present. - Archive the original HTML so a parser update can be replayed without another request.
Path 3: commercial interface extraction services
Some vendors document endpoints that retrieve Google’s AI Mode interface and return parsed text blocks and references, with an option to include HTML. One documented implementation says response fields are optional because answer shape, references and shopping cards vary. It also warns that Google’s raw markup class names may change without warning.
That is a vendor contract, not a Google contract. Before choosing one, confirm current terms, data handling, geographic availability and response behavior. In production, treat every field as nullable, accept an empty answer, and log the vendor response version. Validate that each returned citation actually appears in the captured response; never assume a non-empty references array proves that the answer cited those pages.
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Design a JSON contract that survives change
Keep your public schema small and explicit. A practical normalized record looks like this:
{
"query": "string",
"answer": "string or null",
"citations": [
{
"url": "https://example.com/page",
"title": "Example title or null",
"start_index": 0,
"end_index": 120,
"source": "gemini_grounding | researcher_html | vendor"
}
],
"search_queries": [],
"captured_at": "2026-09-29T00:00:00Z",
"raw_available": true,
"warnings": []
}Use null for unknown titles or offsets; do not substitute zero, an empty string or a fabricated URL. Preserve duplicate citations when they support different spans, or add a stable internal ID and a separate span list. Include warnings such as answer_missing, citation_offsets_invalid or markup_changed so downstream jobs can distinguish “no answer” from “parser failed.”
Capturing the literal rendered page with a browser
Use browser automation only when the visible consumer interface itself is your requirement. The interface may require an ordinary browser session, may show bot checks, and can change without notice. Do not bypass access controls. Supply a Search URL that you are authorized to visit and keep extraction selectors configurable.
Minimal Playwright capture in Python
import asyncio, json, os
from playwright.async_api import async_playwright
async def main():
url = os.environ["AI_MODE_URL"]
async with async_playwright() as p:
browser = await p.chromium.launch(headless=True)
page = await browser.new_page()
await page.goto(url, wait_until="domcontentloaded", timeout=60000)
await page.wait_for_timeout(3000)
payload = await page.evaluate("""() => ({
text: document.body.innerText || "",
links: Array.from(document.querySelectorAll('a[href]')).map(a => ({
text: (a.innerText || '').trim(), url: a.href
}))
})""")
print(json.dumps(payload, ensure_ascii=False))
await browser.close()
asyncio.run(main())This intentionally returns raw visible text and links rather than pretending that a particular AI Mode CSS class is permanent. Add your own answer-region detector, capture screenshots for debugging, and retry only transient navigation failures. Respect Google’s terms, robots and applicable privacy rules; do not collect signed-in personal results unless you have a documented lawful basis.
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Reliability, cost and maintenance checklist
- Cache by normalized query, locale, device and capture time, but label cached records clearly.
- Store raw API JSON or HTML alongside normalized JSON for replay and audits.
- Monitor answer-missing rates, invalid offsets, duplicate URLs and sudden schema changes.
- Expect different links for different runs because AI Mode can fan out into multiple related searches.
- Separate commercial production traffic from the Researcher Result API, whose program is non-commercial.
- Pin SDK versions in deployments, but schedule compatibility checks because model and tool availability changes.
Common failures and fixes
No citation annotations
The request may not have enabled Google Search grounding, the selected model may not support the tool, or the answer may contain no cited span. Confirm the current model/tool combination and preserve the uncited answer with a warning.
Empty AI Mode container
Google may have returned no AI Mode content for that query, or the vendor/parser may have missed a changed structure. Treat it as an empty result, save the raw response, and update detection rules only after inspecting a new capture.
Offsets do not match visible text
Offsets apply to the API’s text block, not to text after Markdown or HTML rendering. Validate against the exact unmodified block, then render links in a second step.
Researcher API rejection
Check that the URL is a supported Google Search URL, that your project is approved, and that you have not exceeded the rolling limit. A commercial workload is outside the program’s stated use.
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Browser timeout or bot check
Increase navigation time only for slow pages, capture the resulting HTML for diagnosis, and stop when a bot check appears. Do not attempt to defeat the challenge.
Or skip the browser setup
If you only need a visual capture of a rendered page for QA or records—not citation-aware JSON—ScreenshotNeo provides a one-request website screenshot API. It is separate from Google’s answer and citation APIs, so it will not turn AI Mode into structured JSON.
Using the API documented at https://screenshotneo.com/docs/:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://www.google.com -o shot.webpBefore capture, ScreenshotNeo can accept cookie or consent banners and remove more than 60 known consent platforms, newsletter popups and chat widgets; each cleanup step can be disabled. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads and cache hits are not billed, and the response identifies the page verdict and billing status in X-Page-Verdict and X-Billed headers. Its MCP server exposes take_screenshot, get_page_info and capture_pdf for Claude, Cursor and other MCP clients. The Free plan includes 1,000 screenshots per month without a card; paid plans start at $5 for 3,000 shots. Sign up free.
Frequently Asked Questions
Does Gemini grounding return the exact consumer AI Mode answer?
No. It is an official Gemini API response grounded with Google Search; the reviewed documentation does not establish identical wording, model behavior or links for the consumer AI Mode experience.
Can I use the Search Researcher Result API for a paid scraping service?
Google’s Researcher Program terms describe non-commercial use, so it should not be presented as a commercial scraping route.
What should my parser do when Google changes its markup?
Keep selectors configurable, detect missing fields explicitly, archive raw HTML, and emit a warning or empty result instead of failing silently.
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