# Google AI Overview Tracker (`mark_ramos/google-ai-overview-tracker`) Actor Track Google AI Overviews (AIO) at scale. Returns the clean generative answer text and the deduped list of domains Google cited — built for GEO (Generative Engine Optimization), SEO, and brand monitoring teams. The first AIO-native Actor on Apify Store. - **URL**: https://apify.com/mark\_ramos/google-ai-overview-tracker.md - **Developed by:** [Mark](https://apify.com/mark_ramos) (community) - **Categories:** SEO tools, AI, Agents - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $40.00 / 1,000 ai overview captureds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Google AI Overview API — AIO Scraper & GEO Tracker A **Google AI Overview API** that scrapes the generative answers Google now shows at the top of search results — for any query, from any country, at scale. This Actor fetches a live Google SERP, detects whether an AI Overview (AIO) rendered, and extracts both the **full AI Overview answer text** and the **list of domains Google cited** to generate it. Use it as a drop-in **AI Overview scraper** for **Generative Engine Optimization (GEO)** tools, SEO agencies, brand monitors, and in-house marketing teams that need to answer: - Is my brand mentioned in the Google AI Overview for these queries? - Which domains does Google cite as authorities on my topic? - How does the AIO answer change week over week? - Which queries *don't* trigger an AI Overview at all? ### Why use this Google AI Overview API Every SERP API on the market treats the Google AI Overview as an afterthought — a bolt-on field on their generic Google endpoint. None of them give you the clean, AIO-native data shape this Google AI Overview scraper does: | | SerpApi | Serper | Scrapfly | **This Actor** | |---|---|---|---|---| | AIO field | bolt-on | bolt-on | bolt-on | **first-class** | | Clean AIO text (no boilerplate) | no | no | no | **yes** | | Deduplicated cited domains | no | no | no | **yes** | | Country-accurate residential IPs | yes | yes | yes | **yes** | | Distinguishes "not available for this search" | no | no | no | **yes** | ### Input ```json { "queries": [ "what is retrieval augmented generation", "benefits of intermittent fasting", "best project management software" ], "country": "US", "language": "en", "device": "desktop" } ```` - **`queries`** *(required, array of strings)* — up to **30 queries per run**. This is an intentional ceiling (see Limitations). - **`country`** *(ISO-2, default `US`)* — drives both Google's `gl` parameter and the proxy exit geo. Use matching language for best results. - **`language`** *(default `en`)* — Google `hl` parameter. - **`device`** *(`desktop` or `mobile`, default `desktop`)* — mobile SERPs ship different AIO content than desktop. ### Output (one row per query) ```json { "query": "what is retrieval augmented generation", "country": "US", "language": "en", "device": "desktop", "appeared": true, "text": "Retrieval-Augmented Generation (RAG) is an AI framework that improves Large Language Model (LLM) accuracy by retrieving data from external, trusted knowledge bases...", "citations": [ {"position": 1, "domain": "aws.amazon.com", "url": "https://aws.amazon.com/what-is/retrieval-augmented-generation/", "title": ""}, {"position": 2, "domain": "blogs.nvidia.com", "url": "https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/", "title": ""}, {"position": 3, "domain": "research.ibm.com", "url": "https://research.ibm.com/blog/retrieval-augmented-generation-RAG", "title": ""} ], "status": "ok", "final_url": "https://www.google.com/search?q=what+is+retrieval+augmented+generation&hl=en&gl=us&pws=0", "raw_html_bytes": 966644, "fetch_seconds": 8.6, "fetched_at": "2026-04-13T23:47:01Z", "notes": [] } ``` #### Fields | Field | Type | Meaning | |---|---|---| | `appeared` | bool | Whether an AI Overview rendered for this query. `false` if Google explicitly said "an AI Overview is not available for this search" or if the query didn't trigger one. | | `text` | string | The cleaned AI Overview answer text, with boilerplate and fallback phrases stripped. | | `citations[]` | array | Deduplicated list of external domains Google cited, in order of first appearance in the panel. Google assets (gstatic, googleapis, etc.) are excluded. | | `status` | string | `ok` | `captcha` | `sorry` | `empty` | `error`. | | `raw_html_bytes` | int | Size of the raw SERP we scraped (for diagnostics). | | `fetch_seconds` | float | Wall-clock time for the individual scrape. | ### Common use cases for the Google AI Overview API - **Brand presence monitoring** — run 500 branded queries daily against the Google AI Overview API and alert when your domain disappears from or enters an AIO. - **GEO competitor research** — which domains does Google cite for your target queries? Build a Generative Engine Optimization citation graph across a keyword set. - **Content planning** — which topics trigger an AI Overview vs. don't? AIO-triggered queries need a different SEO strategy than organic-only queries. - **LLM discoverability benchmarking** — snapshot AI Overview answers weekly and diff them to track how Google's generative summary of your industry evolves. - **AI search training data** — collect real generative answers + source citations for RAG research. ### Tips - **30 queries per run is the cap.** Schedule the Actor to run hourly against rolling batches for tracking at scale — that's up to 720 queries/day per schedule, which covers virtually every GEO monitoring workload. - **Country matters more than you'd think.** The same query from US vs GB vs DE returns different AIO content. If you're tracking regional brand presence, run per-country. - **Queries that reliably trigger AIO:** "what is", "how to", "benefits of", "best X for Y", informational and comparison queries. Transactional queries (buy / price / near me) rarely trigger AIO. - **Mobile ≠ desktop.** Mobile AIO is shorter and often has different citation order. ### Limitations - **Why 30 queries per run (not 100).** Google's AI Overview endpoint applies a *behavioral* rate limit that kicks in after ~40 sequential SERP fetches from the same session, regardless of IP rotation or proxy quality. Every AIO scraper on the market hits this wall — most hide it by silently returning partial data. We cap at 30 and deliver **100% AIO capture under the cap** so your dataset is clean. For larger keyword sets, use scheduled runs. - **AIO is a moving target.** Google changes the rendered DOM regularly; we monitor parse accuracy continuously and patch within the hour of any drift detected. - **No historical data ships with the Actor itself.** Every run is a fresh fetch. For historical tracking + diffs + alerting, see our upcoming hosted API at aio-tracker.com. ### Pricing Pay-per-result. You're charged only for **successful AI Overview fetches** (`appeared: true`). Queries that don't trigger an AIO, queries that hit anti-bot pages, and errors are free. ### Support Issues, feature requests, and custom needs: file them on the Actor's issues tab. Wholesale pricing available for teams running 100k+ queries/month — DM for rates. # Actor input Schema ## `queries` (type: `array`): One or more Google search queries to fetch AI Overview data for. Each query runs as an independent fetch and produces one result row. Capped at 30 queries per run — this is an intentional ceiling driven by Google's per-session behavioral rate limits, not a platform restriction. For tracking larger keyword sets, schedule this Actor to run hourly against rolling batches (up to 720 queries/day per schedule). ## `country` (type: `string`): ISO 3166-1 alpha-2 country code (e.g. US, GB, DE). Drives the proxy geo and Google's gl parameter. ## `language` (type: `string`): Language code for Google's hl parameter (e.g. en, de, fr). ## `device` (type: `string`): Simulated device type. Mobile SERPs differ from desktop. ## Actor input object example ```json { "queries": [ "what is retrieval augmented generation", "benefits of intermittent fasting" ], "country": "US", "language": "en", "device": "desktop" } ``` # 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 retrieval augmented generation", "benefits of intermittent fasting" ] }; // Run the Actor and wait for it to finish const run = await client.actor("mark_ramos/google-ai-overview-tracker").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 retrieval augmented generation", "benefits of intermittent fasting", ] } # Run the Actor and wait for it to finish run = client.actor("mark_ramos/google-ai-overview-tracker").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 retrieval augmented generation", "benefits of intermittent fasting" ] }' | apify call mark_ramos/google-ai-overview-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mark_ramos/google-ai-overview-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google AI Overview Tracker", "description": "Track Google AI Overviews (AIO) at scale. Returns the clean generative answer\ntext and the deduped list of domains Google cited — built for GEO (Generative\nEngine Optimization), SEO, and brand monitoring teams. The first AIO-native\nActor on Apify Store.", "version": "0.1", "x-build-id": "FknVI4x7Iv192YZJ9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mark_ramos~google-ai-overview-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mark_ramos-google-ai-overview-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mark_ramos~google-ai-overview-tracker/runs": { "post": { "operationId": "runs-sync-mark_ramos-google-ai-overview-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mark_ramos~google-ai-overview-tracker/run-sync": { "post": { "operationId": "run-sync-mark_ramos-google-ai-overview-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Search queries", "minItems": 1, "maxItems": 30, "type": "array", "description": "One or more Google search queries to fetch AI Overview data for. Each query runs as an independent fetch and produces one result row. Capped at 30 queries per run — this is an intentional ceiling driven by Google's per-session behavioral rate limits, not a platform restriction. For tracking larger keyword sets, schedule this Actor to run hourly against rolling batches (up to 720 queries/day per schedule).", "items": { "type": "string" } }, "country": { "title": "Country code", "pattern": "^[A-Za-z]{2}$", "type": "string", "description": "ISO 3166-1 alpha-2 country code (e.g. US, GB, DE). Drives the proxy geo and Google's gl parameter.", "default": "US" }, "language": { "title": "Language code", "type": "string", "description": "Language code for Google's hl parameter (e.g. en, de, fr).", "default": "en" }, "device": { "title": "Device", "enum": [ "desktop", "mobile" ], "type": "string", "description": "Simulated device type. Mobile SERPs differ from desktop.", "default": "desktop" } } }, "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 } } } } } } } } } } ```