# Google Trends Scraper (`maged120/google-trends-scraper`) Actor Extract trend data from Google Trends: interest over time, interest by region, daily trending searches, and real-time trends — no login required. - **URL**: https://apify.com/maged120/google-trends-scraper.md - **Developed by:** [Maged](https://apify.com/maged120) (community) - **Categories:** Automation, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Trends Scraper do? **Google Trends Scraper** extracts structured trend data from [Google Trends](https://trends.google.com/) and delivers it as clean, flat records ready for analysis. It collects **interest over time** and **geographic breakdowns** for up to 5 keywords, worldwide or filtered by country, category, and search type. Run it on Apify with scheduling, webhook triggers, and full API access to integrate trend signals directly into your data pipelines. ### Why use Google Trends Scraper? - **Market research** — Track rising interest in products, brands, or technologies before competitors notice. - **SEO & content strategy** — Identify when and where search interest peaks to time content releases. - **Academic research** — Extract longitudinal data for up to 5 comparison keywords across custom date ranges. - **Competitive intelligence** — Compare up to 5 brands or keywords side-by-side with indexed values and regional breakdowns. - **Investment signals** — Monitor sentiment shifts in finance, tech, or health categories. ### How to use Google Trends Scraper 1. Open the actor and click **Try for free**. 2. Enter up to **5 keywords** to compare (e.g. `["ChatGPT", "Gemini", "Claude"]`). 3. Choose which **data types** to fetch: interest over time, interest by region, or both. 4. Set your **country** (or leave blank for worldwide), **timeframe**, **category**, and **search type**. 5. Click **Start** and download results as JSON, CSV, or Excel from the **Output** tab. ### Input All fields are optional — the actor ships with sensible defaults. Configure via the **Input** tab or pass JSON directly. ```json { "keywords": ["artificial intelligence", "machine learning"], "dataTypes": ["interestOverTime", "interestByRegion"], "geo": "US", "timeframe": "past_12_months", "category": 0, "gprop": "web", "regionResolution": "COUNTRY", "language": "en-US", "timezoneOffset": 0, "maxRetries": 3, "retryDelayMs": 2000, "requestDelayMs": 1000 } ```` #### Key input fields | Field | Description | Default | |-------|-------------|---------| | `keywords` | 1–5 search terms to analyze | `["artificial intelligence"]` | | `dataTypes` | Which data to collect (see below) | Both types | | `geo` | ISO country code (`US`, `GB`, `DE`) or region (`US-NY`). Empty = worldwide | `""` (worldwide) | | `timeframe` | Time range preset | `past_12_months` | | `customTimeframe` | Exact range `YYYY-MM-DD YYYY-MM-DD` (requires `"timeframe": "custom"`) | — | | `category` | Category to filter results (0 = All categories, 7 = Finance, 174 = Health, 328 = Technology, etc.) | `0` | | `gprop` | `web`, `images`, `news`, `youtube`, or `shopping` | `web` | | `regionResolution` | `COUNTRY`, `REGION`, `DMA`, or `CITY` | `COUNTRY` | | `language` | BCP 47 tag (`en-US`, `de-DE`, `fr-FR`, `zh-CN`) | `en-US` | | `timezoneOffset` | UTC offset in minutes (e.g. `-300` = EST) | `0` | | `requestDelayMs` | Delay between requests in ms | `1000` | #### Data types | Value | Description | |-------|-------------| | `interestOverTime` | Indexed search interest timeline (one item per weekly/daily data point) | | `interestByRegion` | Geographic breakdown by country/region/city (one item per region) | ### Output Each piece of data is its own dataset item — results are flat rows, not nested objects. You can download the dataset in various formats such as JSON, CSV, or Excel. #### Interest over time item One item per keyword per weekly or daily data point: ```json { "type": "interestOverTime", "keyword": "artificial intelligence", "keywords": ["artificial intelligence", "machine learning"], "geo": "US", "timeframe": "today 12-m", "category": 0, "gprop": "web", "language": "en-US", "fetchedAt": "2025-05-09T14:22:00.000Z", "date": "2024-05-12T00:00:00", "value": 72, "partial": false } ``` #### Interest by region item One item per keyword per country or sub-region: ```json { "type": "interestByRegion", "keyword": "artificial intelligence", "keywords": ["artificial intelligence", "machine learning"], "geo": "Worldwide", "resolution": "COUNTRY", "timeframe": "today 12-m", "category": 0, "gprop": "web", "language": "en-US", "fetchedAt": "2025-05-09T14:22:00.000Z", "geoCode": "IN", "geoName": "India", "value": 100 } ``` ### Data fields reference | Field | Format | Description | |-------|--------|-------------| | `type` | text | Item type: `interestOverTime` or `interestByRegion` | | `keyword` | text | The specific keyword this item belongs to | | `keywords` | array | All keywords from the analysis session | | `geo` | text | Country/region code or "Worldwide" | | `resolution` | text | Geographic resolution for region items | | `timeframe` | text | Google Trends timeframe string used | | `date` | text | ISO timestamp for interest-over-time data points | | `value` | number | Indexed interest score (0–100) | | `partial` | boolean | Whether this data point covers a partial period (interest over time only) | | `geoCode` | text | ISO country/region code for region items | | `geoName` | text | Human-readable region name | | `fetchedAt` | date | ISO 8601 timestamp of data collection | **Estimated costs on Apify platform:** - Single keyword analysis run: < $0.01 - 100 keyword runs/day: ~$0.50–$1.00 per month The free tier includes enough compute units to run hundreds of analyses per month. ### Tips & advanced options - **Compare up to 5 keywords** — Results are indexed 0–100 relative to the highest-scoring term. Use all 5 slots for meaningful brand or topic comparisons. - **Custom date ranges** — Set `"timeframe": "custom"` and `"customTimeframe": "2018-01-01 2023-12-31"` for precise historical windows. - **DMA resolution** — Set `regionResolution: "DMA"` with `geo: "US"` for Nielsen Designated Market Area breakdowns. - **Category filtering** — Use the Category dropdown to isolate intent. Selecting Health filters "apple" to nutrition rather than the tech company; Finance captures investor-driven searches rather than general interest. - **Increase request delay** — Set `requestDelayMs: 2000+` if you encounter rate-limit errors on high-frequency runs. - **Language localization** — Change `language` to `de-DE`, `fr-FR`, or `zh-CN` to receive regional labels in local languages. - **Filter by type in CSV exports** — Every item has a `type` field, so you can filter rows after downloading CSV to get clean per-type exports. ### FAQ, disclaimers, and support **Is this legal?** Google Trends data is publicly available. Always respect Google's Terms of Service and rate limits. **Why are values 0–100?** Google Trends reports relative search interest, not absolute volumes. 100 = peak popularity for the highest-interest location or time in the selected context. **My keyword returned no data.** Try broadening the timeframe, removing the geo filter, or checking spelling. Niche terms may lack sufficient data for Google to return results. **Rate limiting.** Use `requestDelayMs: 2000` for high-frequency runs to stay within Google's rate limits. For bugs or feature requests, use the **Issues** tab. Custom enterprise solutions available on request. # Actor input Schema ## `keywords` (type: `array`): Search terms to analyze (max 5 for comparison). ## `dataTypes` (type: `array`): Select which Google Trends data types to collect. ## `geo` (type: `string`): ISO 3166-1 alpha-2 country code (e.g. US, GB, DE). Leave empty for worldwide data. Use ISO 3166-2 for sub-regions (e.g. US-NY for New York). ## `timeframe` (type: `string`): Time range for trend data. Use 'custom' to specify an exact date range below. ## `customTimeframe` (type: `string`): Custom date range in 'YYYY-MM-DD YYYY-MM-DD' format (e.g. '2020-01-01 2023-12-31'). Only applies when Timeframe is set to 'Custom date range'. ## `category` (type: `string`): Filter results to a specific Google Trends category. Defaults to All categories. ## `gprop` (type: `string`): Google property to filter results by search type. ## `regionResolution` (type: `string`): Geographic resolution for Interest by Region data. COUNTRY works worldwide; REGION/DMA/CITY require a specific geo. ## `language` (type: `string`): Language for results as a BCP 47 tag (e.g. en-US, de-DE, fr-FR, ja-JP, zh-CN). Affects labels and related content language. ## `timezoneOffset` (type: `integer`): UTC offset in minutes. Affects timeframe boundaries. Examples: -300 = EST (UTC-5), 0 = UTC, 60 = CET (UTC+1), 330 = IST (UTC+5:30). ## `maxRetries` (type: `integer`): Maximum number of retry attempts for failed API requests. ## `retryDelayMs` (type: `integer`): Base delay in milliseconds between retry attempts. Each retry multiplies this by the attempt number (exponential backoff). ## `requestDelayMs` (type: `integer`): Delay in milliseconds between consecutive API calls to avoid rate limiting. ## Actor input object example ```json { "keywords": [ "artificial intelligence" ], "dataTypes": [ "interestOverTime", "interestByRegion" ], "geo": "", "timeframe": "past_30_days", "customTimeframe": "2026-01-01 2026-03-03", "category": "0", "gprop": "web", "regionResolution": "COUNTRY", "language": "en-US", "timezoneOffset": 0, "maxRetries": 3, "retryDelayMs": 2000, "requestDelayMs": 1000 } ``` # 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 = { "keywords": [ "artificial intelligence" ], "dataTypes": [ "interestOverTime", "interestByRegion" ], "geo": "", "customTimeframe": "2026-01-01 2026-03-03", "language": "en-US" }; // Run the Actor and wait for it to finish const run = await client.actor("maged120/google-trends-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 = { "keywords": ["artificial intelligence"], "dataTypes": [ "interestOverTime", "interestByRegion", ], "geo": "", "customTimeframe": "2026-01-01 2026-03-03", "language": "en-US", } # Run the Actor and wait for it to finish run = client.actor("maged120/google-trends-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 '{ "keywords": [ "artificial intelligence" ], "dataTypes": [ "interestOverTime", "interestByRegion" ], "geo": "", "customTimeframe": "2026-01-01 2026-03-03", "language": "en-US" }' | apify call maged120/google-trends-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=maged120/google-trends-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends Scraper", "description": "Extract trend data from Google Trends: interest over time, interest by region, daily trending searches, and real-time trends — no login required.", "version": "1.0", "x-build-id": "9bgGEO7wuOXScK635" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/maged120~google-trends-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-maged120-google-trends-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/maged120~google-trends-scraper/runs": { "post": { "operationId": "runs-sync-maged120-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/maged120~google-trends-scraper/run-sync": { "post": { "operationId": "run-sync-maged120-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "Search terms to analyze (max 5 for comparison).", "default": [ "artificial intelligence" ], "items": { "type": "string" } }, "dataTypes": { "title": "Data Types to Fetch", "type": "array", "description": "Select which Google Trends data types to collect.", "items": { "type": "string", "enum": [ "interestOverTime", "interestByRegion" ] }, "default": [ "interestOverTime", "interestByRegion" ] }, "geo": { "title": "Country / Region", "type": "string", "description": "ISO 3166-1 alpha-2 country code (e.g. US, GB, DE). Leave empty for worldwide data. Use ISO 3166-2 for sub-regions (e.g. US-NY for New York).", "default": "" }, "timeframe": { "title": "Timeframe", "enum": [ "past_hour", "past_4_hours", "past_day", "past_7_days", "past_30_days", "past_90_days", "past_12_months", "past_5_years", "2004_to_present", "custom" ], "type": "string", "description": "Time range for trend data. Use 'custom' to specify an exact date range below.", "default": "past_30_days" }, "customTimeframe": { "title": "Custom Date Range", "type": "string", "description": "Custom date range in 'YYYY-MM-DD YYYY-MM-DD' format (e.g. '2020-01-01 2023-12-31'). Only applies when Timeframe is set to 'Custom date range'.", "default": "" }, "category": { "title": "Category", "enum": [ "0", "57", "71", "13", "903", "7", "543", "8", "174", "766", "11", "31", "958", "275", "179", "65", "276", "328", "19" ], "type": "string", "description": "Filter results to a specific Google Trends category. Defaults to All categories.", "default": "0" }, "gprop": { "title": "Search Type", "enum": [ "web", "images", "news", "youtube", "shopping" ], "type": "string", "description": "Google property to filter results by search type.", "default": "web" }, "regionResolution": { "title": "Regional Breakdown", "enum": [ "COUNTRY", "REGION", "DMA", "CITY" ], "type": "string", "description": "Geographic resolution for Interest by Region data. COUNTRY works worldwide; REGION/DMA/CITY require a specific geo.", "default": "COUNTRY" }, "language": { "title": "Language", "type": "string", "description": "Language for results as a BCP 47 tag (e.g. en-US, de-DE, fr-FR, ja-JP, zh-CN). Affects labels and related content language.", "default": "en-US" }, "timezoneOffset": { "title": "Timezone Offset (minutes)", "minimum": -720, "maximum": 840, "type": "integer", "description": "UTC offset in minutes. Affects timeframe boundaries. Examples: -300 = EST (UTC-5), 0 = UTC, 60 = CET (UTC+1), 330 = IST (UTC+5:30).", "default": 0 }, "maxRetries": { "title": "Max Retries per Request", "minimum": 0, "maximum": 10, "type": "integer", "description": "Maximum number of retry attempts for failed API requests.", "default": 3 }, "retryDelayMs": { "title": "Retry Base Delay (ms)", "minimum": 0, "maximum": 30000, "type": "integer", "description": "Base delay in milliseconds between retry attempts. Each retry multiplies this by the attempt number (exponential backoff).", "default": 2000 }, "requestDelayMs": { "title": "Request Delay (ms)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Delay in milliseconds between consecutive API calls to avoid rate limiting.", "default": 1000 } } }, "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 } } } } } } } } } } ```