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How to scrape Google AI Overview responses with minimal effort

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#AIOverview#Scraping
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Google AI Overview represents Google’s most advanced AI-powered search integration with sophisticated citation systems, dynamic content loading, and seamless integration with traditional search results that provides unparalleled insights into AI-generated content.

The challenge: Google AI Overview wasn’t built for programmatic access. The platform uses multiple layout variations, dynamic citation interaction, and complex content rendering that standard scraping tools can’t handle.

After analyzing thousands of Google AI Overview interactions, we’ve reverse-engineered the complete process. This guide will show you exactly how to scrape Google AI Overview and extract the structured data that makes it valuable for search intelligence and content analysis.

Table of contents

Why scrape Google AI Overview responses?

Google AI Overview provides unique AI-generated summaries that enhance traditional search results.

What makes Google AI Overview responses valuable:

  • AI-generated summary content with advanced formatting and structure
  • Dynamic citation system with interactive source linking and metadata
  • Integration with traditional search results providing comprehensive context
  • Multi-layout page variations that adapt to different query types
  • Detailed source attribution with descriptions and relevance rankings

Why it matters: Google AI Overview represents a significant shift in how search information is presented, combining AI-generated summaries with traditional search results in a way that can’t be accessed through standard search APIs.

Use cases:

  • Search Intelligence: Analyze AI-generated content patterns
  • Content Strategy: Understand how AI synthesizes information
  • SEO Analysis: Monitor source attribution and citation patterns
  • Brand Monitoring: Track how AI Overview represents your content

Understanding AI Overview is critical for Answer Engine Optimization (AEO) strategies.

Understanding Google AI Overview’s architecture

Google AI Overview uses a sophisticated multi-layered architecture that makes scraping challenging:

Request Flow

  1. Initial request: User searches via google.com (no special parameters needed)
  2. AI detection: Google determines if AI Overview is relevant
  3. Content generation: AI generates summary with citations
  4. Dynamic rendering: JavaScript loads interactive citation system
  5. Layout selection: Different page structures based on content type

Response Structure

Google AI Overview returns complex content with multiple data types:

  • AI Summary Text: Main AI-generated content with integrated citations
  • Dynamic Citations: Interactive buttons that reveal source information
  • Source Links: Structured attribution with descriptions
  • Search Integration: Combined with organic results, People Also Ask, and related searches
  • Multi-layout Support: Different DOM structures for various content types

Technical Challenges

  • Multiple Layout Variations: Different selectors for different page types
  • Dynamic Citation Loading: Citations revealed via JavaScript interaction
  • Content Integration: AI Overview embedded within search results
  • Anti-bot Detection: Advanced behavioral analysis and CAPTCHA challenges
  • Geolocation Variation: Different content based on user location

The dynamic citation system challenge

Google AI Overview’s citation system is uniquely sophisticated, requiring specialized handling:

Citation Architecture Variations

SV6KPE Layout Version (AI Mode-like):

# Similar to AI Mode structure
SV6KPE_LOCATOR = "#m-x-content [data-container-id='main-col']"

# Uses HTML comment-based citations
sources = await extract_aimode_sources(page)
citations = await extract_aimode_citation_pills(page)

Alternative Layout Version:

# Different page structure
NON_SV6KPE_LOCATOR = "#m-x-content [data-rl]"

# Requires interactive citation extraction
sources = await _extract_aioverview_sources(page)
citations = await _extract_aioverview_citation_pills(page, main_content_div)

Interactive Citation Extraction

Dynamic Citation Pills:

# Click citation buttons to reveal sources
elements = await main_content_div.locator('[jsname="HtgYJd"]').all()

for el in elements:
    current_pill = []

    # Click to reveal citation sources
    await el.dispatch_event("click")
    await sleep(100)  # Wait for content to load

    # Extract revealed links
    links_locator = page.locator('ul[jsname="Z3saHd"]').locator("a")
    links = await links_locator.all()

    for link in links:
        if await link.is_visible():
            url = await link.get_attribute("href")
            label = await link.get_attribute("aria-label")
            # Process citation data

Building the scraping infrastructure

Here’s the complete infrastructure needed for reliable Google AI Overview scraping:

Core Components

import asyncio
from playwright.async_api import Page, Browser
from services.cookie_stash import cookie_stash
from services.page_interceptor import PlaywrightInterceptor
from services.captchas.solve import solve_captcha
from bs4 import BeautifulSoup

AIOVERVIEW_URL = "https://www.google.com/search"

Request Configuration

class AiOverviewRequest(TypedDict):
    prompt: str  # Search query
    country: str  # Country code
    include: Dict[str, bool]  # Content options (markdown, html)

URL Construction and Navigation

# Standard Google Search URL (AI Overview appears automatically)
search_url = build_url_with_params(
    AIOVERVIEW_URL,
    {
        "q": prompt,  # Search query
        "hl": google_params["hl"],  # Language
        "gl": google_params["gl"],  # Country
    },
)

# Navigate to search results
response = await page.goto(search_url, timeout=20_000)

if not is_http_success(response.status):
    # Handle CAPTCHA if needed
    solved_captcha = await solve_captcha(page, page_interceptor)
    if not solved_captcha:
        raise Exception(f"HTTP error: {response.status}")

Layout Detection and Selection

async def wait_for_ai_overview(page: Page, timeout: int = 10_000) -> str:
    """Wait for AI Overview div and detect layout version."""

    # Wait for either AI Overview version
    await page.wait_for_selector(
        "#m-x-content [data-container-id='main-col'], #m-x-content [data-rl]",
        timeout=timeout,
        state="visible"
    )

    # Check which selector actually matched
    if await page.locator("#m-x-content [data-container-id='main-col']").count() > 0:
        return "#m-x-content [data-container-id='main-col']"  # SV6KPE version
    else:
        return "#m-x-content [data-rl]"  # Alternative version

Handling multi-layout page variations

Google AI Overview uses different DOM structures based on content type and layout:

Layout Version Detection

# Detect which layout version is present
selector_found = await wait_for_ai_overview(page)
is_Sv6kpe_version = selector_found == SV6KPE_LOCATOR

main_content_div = page.locator(MAIN_COL_LOCATOR).first
aioverview_section_html = await main_content_div.evaluate("el => el.outerHTML")
text = await main_content_div.inner_text()

Adaptive Parsing Strategy

SV6KPE Version Processing:

if is_Sv6kpe_version:
    # Use AI Mode-style parsing
    sources = await extract_aimode_sources(page)
    citations = await extract_aimode_citation_pills(page)

    if not len(sources):
        raise Exception("no sources")

    markdown = convert_aimode_html_to_markdown(aioverview_section_html, citations)

Alternative Version Processing:

else:
    # Handle cookies popup first
    try:
        await page.click("#L2AGLb", timeout=500)  # Accept cookies
    except Exception:
        pass  # Ignore if cookie button not found

    # Extract sources directly
    sources = await _extract_aioverview_sources(page)

    # Interactive citation extraction if markdown needed
    if include_markdown:
        citations = await _extract_aioverview_citation_pills(
            page=page, main_content_div=main_content_div
        )
        if not len(citations):
            raise Exception("no citations")

        markdown = convert_html_to_markdown_with_links(
            aioverview_section_html, citations, '[jsname="HtgYJd"]'
        )

Parsing AI Overview responses and citations

Google AI Overview requires sophisticated parsing due to its dynamic citation system:

Source Extraction (Alternative Layout)

async def _extract_aioverview_sources(page: Page) -> List[LinkData]:
    """Extract sources from AI Overview sources section."""
    sources = []
    seen_urls = set()
    position = 1

    # AI Overview sources selector
    ai_overview_sources = await page.locator(
        "#m-x-content ul > li > a, #m-x-content ul > li > div > a"
    ).all()

    for source_elem in ai_overview_sources:
        url = await source_elem.get_attribute("href")
        label = await source_elem.get_attribute("aria-label")

        if url and label and url not in seen_urls:
            # Extract description from parent element
            description = await _extract_aioverview_source_description(source_elem)

            sources.append(LinkData(
                position=position,
                label=str(label),
                url=str(url),
                description=description,
            ))
            seen_urls.add(url)
            position += 1

    return sources

async def _extract_aioverview_source_description(element: Locator) -> str | None:
    """Extract description from source element."""
    try:
        parent = element.locator("xpath=..")
        description_div = parent.locator(".gxZfx").first
        return await description_div.inner_text(timeout=1000)
    except Exception:
        pass
    return None

Dynamic Citation Extraction

async def _extract_aioverview_citation_pills(
    page: Page, main_content_div: Locator
) -> List[List[LinkData]]:
    """Extract citation pills by clicking interactive buttons."""
    citation_pills = []

    # Find citation buttons
    elements = await main_content_div.locator('[jsname="HtgYJd"]').all()

    for el in elements:
        current_pill = []

        # Click to reveal citation sources
        await el.dispatch_event("click")
        await sleep(100)  # Wait for dropdown

        # Extract revealed links
        links_locator = page.locator('ul[jsname="Z3saHd"]').locator("a")
        links = await links_locator.all()

        position = 1
        for link in links:
            # Ignore hidden links
            if not await link.is_visible():
                continue

            url = await link.get_attribute("href")
            label = await link.get_attribute("aria-label")

            current_pill.append(LinkData(
                position=position,
                label=str(label),
                url=str(url),
                description=None,
            ))
            position += 1

        citation_pills.append(current_pill)

    return citation_pills

HTML to Markdown Conversion

def convert_html_to_markdown_with_links(
    html_content: str, citations: List[List[LinkData]], citation_pill_locator: str
) -> str:
    """Convert AI Overview HTML to markdown with proper citation links."""

    soup = BeautifulSoup(html_content, "html.parser")

    # Find citation buttons
    buttons = soup.select(citation_pill_locator)

    for i, button in enumerate(buttons):
        if i < len(citations):
            # Replace citation button with actual links
            pill_links = citations[i]

            for link_data in pill_links:
                new_anchor = soup.new_tag("a", href=link_data["url"])
                new_anchor.string = link_data["label"]
                button.insert_after(new_anchor)

            button.decompose()  # Remove the button

    # Convert to markdown
    h = html2text.HTML2Text()
    h.ignore_links = False
    h.body_width = 0
    markdown = h.handle(str(soup))

    return markdown.strip()

Extracting structured data from search integration

Google AI Overview is often integrated with traditional search results:

Complete Response Processing

async def parse_aioverview_response(
    page: Page, request_data: ScrapeRequest, is_Sv6kpe_version: bool
) -> ScrapeAiOverviewResult:
    """Complete AI Overview response processing."""
    include_markdown = request_data.get("include", {}).get("markdown", False)
    include_html = request_data.get("include", {}).get("html", False)

    # Extract AI Overview content
    main_content_div = page.locator(MAIN_COL_LOCATOR).first
    aioverview_section_html = await main_content_div.evaluate("el => el.outerHTML")
    text = await main_content_div.inner_text()

    sources = []
    markdown = ""

    # Process based on layout version
    if is_Sv6kpe_version:
        # Use AI Mode parsing approach
        sources = await extract_aimode_sources(page)
        citations = await extract_aimode_citation_pills(page)

        if not len(sources):
            raise Exception("no sources")

        markdown = convert_aimode_html_to_markdown(aioverview_section_html, citations)
    else:
        # Use interactive extraction
        sources = await _extract_aioverview_sources(page)

        if include_markdown:
            citations = await _extract_aioverview_citation_pills(
                page=page, main_content_div=main_content_div
            )
            if not len(citations):
                raise Exception("no citations")

            markdown = convert_html_to_markdown_with_links(
                aioverview_section_html, citations, '[jsname="HtgYJd"]'
            )

    if not len(sources):
        raise Exception("no sources")

    result: ScrapeAiOverviewResult = {
        "text": text,
        "sources": sources,
    }

    if include_markdown:
        result["markdown"] = markdown

    if include_html:
        result["html"] = await upload_html(
            request_data["requestId"], await page.content()
        )

    return result

Search Integration Handling

The AI Overview scraper can also be integrated with Google Search to provide comprehensive results:

# Integration with Google Search scraper
if include_aioverview:
    selector_found = await wait_for_ai_overview(page)
    is_Sv6kpe_version = selector_found == SV6KPE_LOCATOR
    aioverview = await parse_aioverview_response(page, request_data, is_Sv6kpe_version)

# Combined result with organic results and AI Overview
google_result = {
    "organicResults": organic_results,
    "relatedSearches": related_searches,
    "peopleAlsoAsk": people_also_ask,
    "aioverview": aioverview,  # AI Overview data
}

Managing dynamic content and session handling

Google AI Overview requires sophisticated session management and dynamic content handling:

# Handle cookie consent dialog (alternative layout)
try:
    await page.click("#L2AGLb", timeout=500)  # Accept cookies button
except Exception:
    pass  # Ignore if cookie button not present or already accepted

Dynamic Content Waiting

# Wait for AI Overview content to appear
async def wait_for_ai_overview(page: Page, timeout: int = 10_000) -> str:
    """Wait for AI Overview with timeout and layout detection."""

    main_col_locator = "#m-x-content [data-container-id='main-col'], #m-x-content [data-rl]"

    await page.wait_for_selector(
        main_col_locator,
        timeout=timeout,
        state="visible"
    )

    # Determine which layout version is present
    if await page.locator("#m-x-content [data-container-id='main-col']").count() > 0:
        detected_selector = "#m-x-content [data-container-id='main-col']"
    else:
        detected_selector = "#m-x-content [data-rl]"

    logger.info(f"AI Overview content found with selector: {detected_selector}")
    return detected_selector

Error Handling and Recovery

# Comprehensive error handling
try:
    response = await page.goto(search_url, timeout=20_000)

    if response is None:
        raise Exception("Navigation failed - no response received")

    # Handle HTTP errors (potentially CAPTCHA)
    if not is_http_success(response.status):
        solved_captcha = await solve_captcha(page, page_interceptor)
        metadata["solved_captcha"] = solved_captcha

        if not solved_captcha:
            raise Exception(f"HTTP error: {response.status} (probably captcha)")

except Exception as e:
    raise Exception(f"Proxy timed out or navigation failed: {str(e)}")

Using cloro’s managed Google AI Overview scraper

Building and maintaining a reliable Google AI Overview scraper requires significant engineering resources:

Infrastructure Requirements

AI Overview-Specific Challenges:

  • Multi-layout detection and adaptive parsing
  • Dynamic citation system interaction
  • Integrated search result processing
  • Browser automation with JavaScript execution
  • Complex error handling and recovery

Anti-Bot Evasion:

  • Browser fingerprinting rotation
  • CAPTCHA solving integration
  • Proxy pool management
  • Rate limiting and behavioral simulation
  • Cookie session persistence

Performance Optimization:

  • Layout detection algorithms
  • Interactive content handling
  • Multi-format output generation
  • Error recovery mechanisms
  • Geographic distribution

Managed Solution API

import requests

# Simple API call - no layout management needed
response = requests.post(
    "https://api.cloro.dev/v1/monitor/aioverview",
    headers={
        "Authorization": "Bearer sk_live_your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "prompt": "What do you know about Tesla's latest updates?",
        "country": "US",
        "include": {
            "markdown": True
        }
    }
)

result = response.json()
print(f"AI Overview: {result['result']['aioverview']['text'][:100]}...")
print(f"Sources: {len(result['result']['aioverview']['sources'])} citations")
print(f"Organic Results: {len(result['result']['organicResults'])} found")
print(f"Markdown: {'Yes' if result['result']['aioverview'].get('markdown') else 'No'}")

Response Structure

{
  "success": true,
  "result": {
    "organicResults": [
      {
        "position": 1,
        "title": "Tesla Updates 2024",
        "link": "https://tesla.com/updates",
        "displayedLink": "tesla.com",
        "snippet": "Latest Tesla updates and improvements...",
        "page": 1
      }
    ],
    "peopleAlsoAsk": [
      {
        "question": "What are Tesla's latest features?",
        "type": "LINK",
        "title": "Tesla Feature Updates",
        "link": "https://example.com/tesla-features"
      }
    ],
    "relatedSearches": [
      {
        "query": "Tesla software updates 2024",
        "link": "https://google.com/search?q=tesla+software+updates+2024"
      }
    ],
    "aioverview": {
      "text": "Tesla's recent updates include significant improvements to their Full Self-Driving capability...",
      "sources": [
        {
          "position": 1,
          "url": "https://tesla.com/updates/fsd",
          "label": "Tesla FSD Updates",
          "description": "Latest Full Self-Driving improvements and capabilities"
        }
      ],
      "html": "https://storage.googleapis.com/aioverview-response.html",
      "markdown": "**Tesla's recent updates** include significant improvements..."
    }
  }
}

Key Benefits

  • P50 latency < 8s vs. manual scraping that takes minutes
  • No infrastructure costs - we handle browsers, proxies, and layout detection
  • Structured data - automatic citation system parsing and layout adaptation
  • Search integration - combined AI Overview with organic results
  • Compliance - ethical scraping practices and rate limiting
  • Scalability - handle thousands of requests without breaking Google’s terms

Start scraping Google AI Overview today.

The insights from Google AI Overview data are too valuable to ignore. Whether you’re a search intelligence analyst studying AI content patterns, a content strategist optimizing for AI, or a business monitoring your AI presence, access to structured Google AI Overview data provides incredible opportunities.

For most developers and businesses, we recommend using cloro’s Google AI Overview scraper. You get:

  • Immediate access to reliable scraping infrastructure
  • Automatic layout detection and adaptive parsing
  • Built-in dynamic citation system handling
  • Comprehensive error handling and CAPTCHA solving
  • Structured JSON output with search integration
  • Multi-format support (text, markdown, HTML)

The cost of building and maintaining this infrastructure yourself typically runs $5,000-10,000/month in development time, browser instances, proxy services, and layout management.

For advanced users needing custom solutions, the technical approach outlined above provides the foundation for building your own scraping system. Be prepared for ongoing maintenance as Google frequently updates its AI Overview layouts and citation systems.

The window of opportunity is closing. As more businesses discover the value of AI search intelligence, competition for understanding AI behavior intensifies. Companies that start monitoring and analyzing AI Overview responses now will build advantages that become increasingly difficult to overcome.

Ready to unlock Google AI Overview data for your business? Get started with cloro’s API to start accessing AI-generated search summaries.

Don’t let your competitors dominate AI search results. Start scraping Google AI Overview today.

Check what AI says about your brand Read our docs