# Google AI Mode Scraper — Generative Answers (`scrape.badger/google-ai-mode-scraper`) Actor
Scrape Google AI Mode (generative answer responses from udm=50): structured text blocks, citations with links and titles. Accepts one or more queries. Ideal for building AI-vs-AI comparisons, answer-engine monitoring, and SEO research on how Google's AI summarises topics.
- **URL**: https://apify.com/scrape.badger/google-ai-mode-scraper.md
- **Developed by:** [Scrape Badger](https://apify.com/scrape.badger) (community)
- **Categories:** AI, Developer tools, SEO tools
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $5.00 / 1,000 ai mode queries
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
### What does Google AI Mode Scraper do?
Scrape [Google AI Mode](https://www.google.com/search?udm=50) — Google's generative answer responses (udm=50). Returns structured text blocks and source citations.
### Why use Google AI Mode Scraper?
- **Structured generative answers.** Google's AI-generated responses broken into `text_blocks` + `references`.
- **Source citations.** Every reference carries title, URL, and snippet.
- **Batch queries.** One run = many queries (newline or comma separated).
- **Answer-engine monitoring.** Track how Google's AI summarises your topic over time.
- **Not the AI Overview sub-block.** This is the dedicated `udm=50` endpoint — stronger signal than AI Overview on a standard SERP.
### What data can Google AI Mode Scraper extract?
| Field | Type | Description |
|---|---|---|
| query | string | Input query |
| text_blocks | array | Generated answer blocks (`type`, `snippet`) |
| references | array | Source citations (`title`, `link`, `snippet`) |
### How to scrape Google AI Mode (udm=50)
1. Click **Try for free**.
2. Paste one or many queries into **Queries** (one per line or comma-separated).
3. Optional: set `gl` / `hl`.
4. Click **Start** — each query produces one dataset record with text_blocks + references.
### How much will it cost?
**$0.005 per query (≈ $5 per 1,000 queries).** One call per query. 100-query run = $0.50.
#### Competitor benchmark
| Actor | Author | Price | Notes |
|---|---|---|---|
| apify/google-search-scraper | Apify | ~$3.50 / 1k pages | AI Overview as SERP sub-block |
| compass/crawler-google-places | Compass | ~$9 / 1k | Different vertical |
| **scrape-badger/google-ai-mode-scraper** | **ScrapeBadger** | **$5 / 1k queries** | **Dedicated udm=50 endpoint** |
### Input
Configure the run in the **Input** tab above, or pass a JSON object matching the fields below when calling the Actor via the Apify API.
| Field | Required | Description |
|---|---|---|
| queries | ✅ | One query per line, or comma-separated. |
| gl | — | Country code (default `us`). |
| hl | — | Language code (default `en`). |
### Output
Every successful run streams records into the run's dataset. Download as JSON, CSV, XML, Excel, or HTML from the **Dataset** tab; consume programmatically via the Apify API or webhooks.
Example record:
```json
{
"query": "what is kubernetes?",
"text_blocks": [
{
"type": "paragraph",
"snippet": "Kubernetes is an open-source container orchestration system\u2026"
},
{
"type": "list_item",
"snippet": "Automates deployment, scaling, and management\u2026"
}
],
"references": [
{
"title": "Kubernetes \u2014 Wikipedia",
"link": "https://en.wikipedia.org/wiki/Kubernetes",
"snippet": "\u2026"
},
{
"title": "kubernetes.io",
"link": "https://kubernetes.io",
"snippet": "Production-Grade Container Orchestration"
}
]
}
````
### Tips / Advanced options
- **Track AI answer drift over time.** Cron a daily run with the same queries — watch how Google's AI updates its answer.
- **Compare AI answers across engines.** Pair with ChatGPT, Claude, and Perplexity for an answer-engine comparison dashboard.
- **Reference extraction for SEO.** The `references` field is gold for SEO — these are the sources Google trusts most.
- **Not every query gets an AI answer.** Google gates udm=50 on confidence. Simple factual queries always work; ambiguous queries may fall back to standard SERP.
### FAQ, Disclaimers, Support
#### Is this the same as AI Overview?
No — AI Overview is a sub-block on a standard SERP. This actor hits the dedicated `udm=50` endpoint which is the full-page generative answer experience.
#### Why are some queries empty?
Google doesn't generate AI answers for every query — low-confidence or sensitive topics fall back to standard SERP. Empty records are emitted with `status: empty`.
#### How do I compare with ChatGPT / Claude?
Run the same query through all providers, compare `text_blocks` quality and `references` overlap.
#### Will this break when Google updates udm=50?
ScrapeBadger adapts on our side — the actor API remains stable even when Google restructures.
#### Disclaimer
This Actor scrapes public Google data only. You're responsible for compliance with Google's Terms of Service and any applicable data-protection laws (GDPR, CCPA, etc.) in your jurisdiction. ScrapeBadger does not store the scraped results — they are delivered directly to your Apify dataset.
#### Support
Something not working? Open a ticket in the **Issues** tab above — we triage within one business day. Full API reference: [docs.scrapebadger.com](https://docs.scrapebadger.com).
#### Powered by
[ScrapeBadger](https://scrapebadger.com) — Google-optimised residential proxy pool + browser-farm fallback, 99.7% uptime, unmetered bandwidth. No CAPTCHAs reach you.
# Actor input Schema
## `queries` (type: `string`):
One query per line, or comma-separated. Each query runs one AI Mode call. Example: what is kubernetes?
## `gl` (type: `string`):
ISO 3166-1 alpha-2 country code (lowercase).
## `hl` (type: `string`):
Language code for the response (BCP-47, e.g. 'en', 'en-GB', 'de'). Affects result language and locale-specific labels.
## Actor input object example
```json
{
"queries": "what is kubernetes?",
"gl": "us",
"hl": "en"
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"queries": "what is kubernetes?"
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrape.badger/google-ai-mode-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "queries": "what is kubernetes?" }
# Run the Actor and wait for it to finish
run = client.actor("scrape.badger/google-ai-mode-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"queries": "what is kubernetes?"
}' |
apify call scrape.badger/google-ai-mode-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrape.badger/google-ai-mode-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Google AI Mode Scraper — Generative Answers",
"description": "Scrape Google AI Mode (generative answer responses from udm=50): structured text blocks, citations with links and titles. Accepts one or more queries. Ideal for building AI-vs-AI comparisons, answer-engine monitoring, and SEO research on how Google's AI summarises topics.",
"version": "1.0",
"x-build-id": "UX5xbnXegom1flJ5H"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrape.badger~google-ai-mode-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrape.badger-google-ai-mode-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrape.badger~google-ai-mode-scraper/runs": {
"post": {
"operationId": "runs-sync-scrape.badger-google-ai-mode-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrape.badger~google-ai-mode-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrape.badger-google-ai-mode-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"queries"
],
"properties": {
"queries": {
"title": "Queries",
"type": "string",
"description": "One query per line, or comma-separated. Each query runs one AI Mode call. Example: what is kubernetes?"
},
"gl": {
"title": "Country",
"type": "string",
"description": "ISO 3166-1 alpha-2 country code (lowercase).",
"default": "us"
},
"hl": {
"title": "Language",
"type": "string",
"description": "Language code for the response (BCP-47, e.g. 'en', 'en-GB', 'de'). Affects result language and locale-specific labels.",
"default": "en"
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```