# Google Trends Scraper — Interest, Regions & Trending Searches (`khadinakbar/google-trends-scraper`) Actor Extract all 5 Google Trends data types: keyword interest over time, by region, related queries, related topics & daily trending searches. No API key. PAY\_PER\_EVENT. - **URL**: https://apify.com/khadinakbar/google-trends-scraper.md - **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community) - **Categories:** Marketing, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 result items 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 Trends Scraper — Interest, Regions & Trending Searches Extract all 5 types of Google Trends data in a single run — no API key, no browser required. Compare up to 5 keywords, drill into regional breakdowns, discover rising queries, and pull daily trending searches from any country. Output is structured JSON ready for AI pipelines, spreadsheets, or business intelligence dashboards. ### What This Actor Does Google Trends Scraper hits the unofficial Google Trends JSON API directly, using session-based requests to retrieve clean, structured data without Playwright or a headless browser. This makes it fast (typically 20–60 seconds per run), low-cost, and highly reliable. It supports all five of Google Trends' core data surfaces: - **Interest Over Time** — weekly or daily relative interest scores (0–100) for your keywords, across any timeframe from the past hour to all time since 2004. - **Interest by Region** — which countries, states, or cities show the most interest in your keyword. Great for geo-targeting and market sizing. - **Related Queries** — what other searches are rising or consistently popular alongside your keyword. The "rising" ones are early trend signals. - **Related Topics** — Knowledge Graph entities related to your keyword, with entity IDs (MIDs) for structured data and entity linking. - **Trending Searches** — today's hottest searches in any country. Includes search volume estimates, related queries, and linked news articles. You can request any combination of these five data types in a single run. ### Features - **No API key required** — works entirely through Google's unofficial JSON endpoints - **Compare up to 5 keywords** — same as the Google Trends UI, side by side in the same dataset - **All 5 data types in one run** — no need to run multiple actors or make multiple API calls - **Custom date ranges** — use preset timeframes or specify your own `YYYY-MM-DD YYYY-MM-DD` range - **All Google properties** — Web Search, Google News, Google Images, YouTube Search, Google Shopping - **Category filtering** — filter trends by any of Google's 1,000+ topic categories - **RSS fallback for trending searches** — if the JSON API is blocked, falls back to Google's Trending RSS feed automatically - **PAY_PER_EVENT pricing** — you only pay for actual results returned, not compute time - **MCP-ready output** — flat JSON with semantic field names, ready for Claude, GPT-4, and other LLM pipelines via Apify MCP ### Use Cases **Content Marketing & SEO** — Track keyword interest trends over time to time your content publishing. Find rising related queries before your competitors target them. Identify which regions are most interested in your niche for geo-targeted campaigns. **Market Research** — Validate product ideas by measuring search interest growth. Compare competitor brand searches to see who's gaining momentum. Use interest by region to prioritize market expansion decisions. **News & Media Monitoring** — Pull daily trending searches in any country to know what topics are capturing public attention right now. Get linked news articles and search volume estimates for each trend. **AI & LLM Pipelines** — Feed structured trends data directly into Claude, GPT-4, or other AI agents via the Apify MCP server. The flat output format with semantic field names is optimized for LLM comprehension without hallucination risk. **Investment Research** — Track search interest for company names, products, or technologies as a leading indicator of consumer demand. Rising queries signal emerging opportunities before traditional data sources pick them up. ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `keywords` | array | `[]` | 1–5 search terms to analyze. Leave empty for trending_searches only. | | `geo` | string | `"US"` | Two-letter country code. Leave blank for worldwide. | | `timeframe` | string | `"today 12-m"` | Preset range or custom. See options below. | | `dataTypes` | array | `["interest_over_time","related_queries"]` | Which of the 5 data types to extract. | | `trendingSearchesGeo` | string | same as geo | Country for trending searches (separate from keyword geo). | | `maxResults` | number | `500` | Cap on total records returned. Controls cost. | | `customTimeRange` | string | — | Overrides timeframe. Format: `"2023-01-01 2024-01-01"`. | | `outputFormat` | string | `"flat"` | `flat` = one record per data point. `summary` = logged summary. | **Timeframe options:** `now 1-H` (1 hour), `now 4-H` (4 hours), `now 1-d` (1 day), `now 7-d` (7 days), `today 1-m` (30 days), `today 3-m` (90 days), `today 12-m` (12 months), `today 5-y` (5 years), `all` (2004–present). ### Output Fields Every record has a `type` field indicating which data surface it came from. Fields are populated based on type: | Field | Type | Present In | Description | |-------|------|-----------|-------------| | `type` | string | All | interest_over_time, interest_by_region, related_query, related_topic, or trending_search | | `keyword` | string | All except trending_search | The search keyword this data belongs to | | `value` | number | interest_over_time, interest_by_region, related_* | Relative interest score 0–100 | | `date` | string | interest_over_time, trending_search | Human-readable date label | | `period` | string | interest_over_time | Chart axis period label e.g. "Mar 17–23, 2024" | | `is_partial` | boolean | interest_over_time | True if the period hasn't ended yet | | `geo` | string | Most types | Country code or "worldwide" | | `geo_code` | string | interest_by_region | ISO subdivision code e.g. "US-CA" | | `geo_name` | string | interest_by_region | Human-readable region name e.g. "California" | | `related_query` | string | related_query | The related search term | | `is_rising` | boolean | related_query, related_topic | true = rising trend, false = top/sustained trend | | `formatted_value` | string | related_query, related_topic | "Breakout" or "+950%" for rising queries | | `topic_title` | string | related_topic | Knowledge Graph entity display name | | `topic_type` | string | related_topic | Entity category e.g. "Software", "Person", "Topic" | | `topic_mid` | string | related_topic | Google Knowledge Graph MID for entity linking | | `title` | string | trending_search | The trending search query | | `traffic` | string | trending_search | Estimated search volume e.g. "2M+" | | `traffic_value` | number | trending_search | Raw numeric traffic estimate | | `related_queries` | array | trending_search | List of related search strings | | `articles` | array | trending_search | Up to 3 news articles (title, url, source, snippet) | | `image_url` | string | trending_search | Representative image URL | | `scraped_at` | string | All | ISO 8601 timestamp of extraction | | `source_url` | string | All | Google Trends URL for verification | ### Example Output **Interest Over Time record:** ```json { "type": "interest_over_time", "keyword": "ChatGPT", "date": "Mar 24, 2024", "period": "Mar 17 – 23, 2024", "value": 87, "is_partial": false, "geo": "US", "timeframe": "today 12-m", "scraped_at": "2024-03-31T14:22:00.000Z", "source_url": "https://trends.google.com/trends/explore?q=ChatGPT&geo=US&date=today%2012-m" } ```` **Related Query record:** ```json { "type": "related_query", "keyword": "ChatGPT", "related_query": "chatgpt login", "value": 100, "formatted_value": "100", "is_rising": false, "geo": "US", "timeframe": "today 12-m", "scraped_at": "2024-03-31T14:22:00.000Z" } ``` **Trending Search record:** ```json { "type": "trending_search", "title": "Super Bowl", "traffic": "2M+", "traffic_value": 2000000, "geo": "US", "date": "Feb 11, 2024", "related_queries": ["Super Bowl 2024", "Super Bowl halftime show"], "articles": [ { "title": "Super Bowl LVIII: Everything you need to know", "url": "https://espn.com/nfl/story/_/id/...", "source": "ESPN", "snippet": "Everything fans need to know heading into the big game..." } ], "image_url": "https://t2.gstatic.com/images?q=tbn:...", "scraped_at": "2024-02-11T20:00:00.000Z" } ``` ### Cost & Performance Pricing is **PAY\_PER\_EVENT** at **$0.005 per result record**. You only pay for actual data returned — not compute time, not failed runs. | Run Type | Approx. Records | Approx. Cost | |----------|----------------|--------------| | Interest over time, 1 keyword, 12 months | ~52 | $0.26 | | Interest by region, 1 keyword, worldwide | ~200 | $1.00 | | Related queries + topics, 1 keyword | ~50 | $0.25 | | Trending searches, 1 country | ~20–25 | $0.10–$0.13 | | Full 5-keyword run, all 5 data types | ~500–700 | $2.50–$3.50 | Typical run time is **20–60 seconds** depending on which data types you select. ### Limitations - Google Trends enforces a **5-keyword maximum** per comparison — this is a Google platform limit, not an actor limit. - Interest scores are **relative, not absolute** — 100 means peak popularity within the selected timeframe, not a specific search volume. Values for different keywords are only comparable within the same run. - The `trending_searches` JSON endpoint may occasionally be blocked by Google's anti-bot detection. The actor automatically falls back to Google's public Trending RSS feed in this case. - Very short timeframes (past hour, 4 hours) return fewer data points than longer ranges. - Historical data before 2004 is unavailable through Google Trends. ### No Login Required This actor works entirely through Google's unofficial JSON API and public RSS feeds. No Google account, cookies, OAuth tokens, or browser automation required. Data is extracted via direct HTTP requests with session management. ### Support & Troubleshooting **Empty results for trending searches** — Try a well-supported country code: `US`, `GB`, `IN`, `AU`, `CA`, `DE`, `FR`, `BR`, `JP`. Not all countries have sufficient data. **"No keywords provided" error** — Keywords are only required for `interest_over_time`, `interest_by_region`, `related_queries`, and `related_topics`. For `trending_searches` only, leave the keywords array empty. **Rate limited or blocked** — Enable Apify residential proxies in the actor's proxy configuration. Free shared datacenter IPs are more likely to be blocked by Google. **Value is 0 for all data points** — This may mean the keyword had very low interest in the selected region/timeframe. Try broadening the geo to worldwide or extending the timeframe. If you encounter bugs or want to request features, use the **Issues** tab on this actor's Apify Store page. ### Changelog **v1.0.0** — Initial release. All 5 Google Trends data types supported. PAY\_PER\_EVENT pricing ($0.005/record). Automatic RSS fallback for trending searches. Compare up to 5 keywords. Custom date ranges supported. # Actor input Schema ## `keywords` (type: `array`): List of 1–5 search terms to analyze on Google Trends. Use this when the user provides specific keywords, topics, or brand names to compare. For keyword comparison (e.g. \['ChatGPT', 'Gemini', 'Copilot']), provide up to 5 terms. Do NOT provide more than 5 — Google Trends enforces this limit. ## `geo` (type: `string`): Two-letter country code to filter trends by location. Use 'US' for United States, 'GB' for United Kingdom, 'IN' for India, or '' (empty string) for worldwide data. ## `timeframe` (type: `string`): Date range for trend data. ## `property` (type: `string`): Which Google property to analyze. ## `category` (type: `integer`): Google Trends category to filter results. Use 0 for all categories (default). ## `dataTypes` (type: `array`): Which Google Trends data to fetch. 'interest\_over\_time', 'interest\_by\_region', 'related\_queries', 'related\_topics', 'trending\_searches' (no keywords needed for this one). ## `trendingSearchesGeo` (type: `string`): Country code for daily trending searches (only used when 'trending\_searches' is in dataTypes). Example: 'US', 'GB', 'IN'. ## `maxResults` (type: `integer`): Maximum total number of records to return. ## `customTimeRange` (type: `string`): Override timeframe with a specific date range. Format: 'YYYY-MM-DD YYYY-MM-DD' (e.g. '2023-01-01 2024-01-01'). ## `outputFormat` (type: `string`): How to structure output. 'flat' = one record per data point (default). 'summary' = one record per keyword with nested arrays. ## Actor input object example ```json { "keywords": [ "ChatGPT" ], "geo": "US", "timeframe": "today 12-m", "property": "web", "category": 0, "dataTypes": [ "interest_over_time", "related_queries" ], "trendingSearchesGeo": "US", "maxResults": 500, "outputFormat": "flat" } ``` # Actor output Schema ## `results` (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 = { "keywords": [ "ChatGPT" ], "geo": "US", "dataTypes": [ "interest_over_time", "related_queries" ], "trendingSearchesGeo": "US" }; // Run the Actor and wait for it to finish const run = await client.actor("khadinakbar/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": ["ChatGPT"], "geo": "US", "dataTypes": [ "interest_over_time", "related_queries", ], "trendingSearchesGeo": "US", } # Run the Actor and wait for it to finish run = client.actor("khadinakbar/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": [ "ChatGPT" ], "geo": "US", "dataTypes": [ "interest_over_time", "related_queries" ], "trendingSearchesGeo": "US" }' | apify call khadinakbar/google-trends-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=khadinakbar/google-trends-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends Scraper — Interest, Regions & Trending Searches", "description": "Extract all 5 Google Trends data types: keyword interest over time, by region, related queries, related topics & daily trending searches. No API key. PAY_PER_EVENT.", "version": "1.0", "x-build-id": "ovFxbajRC359klvU6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/khadinakbar~google-trends-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-khadinakbar-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/khadinakbar~google-trends-scraper/runs": { "post": { "operationId": "runs-sync-khadinakbar-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/khadinakbar~google-trends-scraper/run-sync": { "post": { "operationId": "run-sync-khadinakbar-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": "List of 1–5 search terms to analyze on Google Trends. Use this when the user provides specific keywords, topics, or brand names to compare. For keyword comparison (e.g. ['ChatGPT', 'Gemini', 'Copilot']), provide up to 5 terms. Do NOT provide more than 5 — Google Trends enforces this limit.", "items": { "type": "string" } }, "geo": { "title": "Geographic Region", "type": "string", "description": "Two-letter country code to filter trends by location. Use 'US' for United States, 'GB' for United Kingdom, 'IN' for India, or '' (empty string) for worldwide data." }, "timeframe": { "title": "Timeframe", "enum": [ "now 1-H", "now 4-H", "now 1-d", "now 7-d", "today 1-m", "today 3-m", "today 12-m", "today 5-y", "all" ], "type": "string", "description": "Date range for trend data.", "default": "today 12-m" }, "property": { "title": "Property / Search Type", "enum": [ "web", "news", "images", "youtube", "froogle" ], "type": "string", "description": "Which Google property to analyze.", "default": "web" }, "category": { "title": "Category ID", "type": "integer", "description": "Google Trends category to filter results. Use 0 for all categories (default).", "default": 0 }, "dataTypes": { "title": "Data Types to Extract", "type": "array", "description": "Which Google Trends data to fetch. 'interest_over_time', 'interest_by_region', 'related_queries', 'related_topics', 'trending_searches' (no keywords needed for this one).", "items": { "type": "string", "enum": [ "interest_over_time", "interest_by_region", "related_queries", "related_topics", "trending_searches" ] } }, "trendingSearchesGeo": { "title": "Trending Searches Country", "type": "string", "description": "Country code for daily trending searches (only used when 'trending_searches' is in dataTypes). Example: 'US', 'GB', 'IN'." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Maximum total number of records to return.", "default": 500 }, "customTimeRange": { "title": "Custom Date Range", "type": "string", "description": "Override timeframe with a specific date range. Format: 'YYYY-MM-DD YYYY-MM-DD' (e.g. '2023-01-01 2024-01-01')." }, "outputFormat": { "title": "Output Format", "enum": [ "flat", "summary" ], "type": "string", "description": "How to structure output. 'flat' = one record per data point (default). 'summary' = one record per keyword with nested arrays.", "default": "flat" } } }, "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 } } } } } } } } } } ```