# Google Trends Scraper - 50 Terms, Trending Now, No API Key (`locaihost/google-trends`) Actor Google Trends scraper with no API key: interest over time and by region, related queries, and every Trending Now search per country with volume and growth. Compare up to 50 terms on one 0-100 scale. New-only mode. $2 per 1,000 rows; the most used Google Trends scraper charges $3. - **URL**: https://apify.com/locaihost/google-trends.md - **Developed by:** [locaihost data](https://apify.com/locaihost) (community) - **Categories:** SEO tools, Automation, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.10 / 1,000 trends rows 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/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. 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. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## 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. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## Google Trends Scraper & API: Compare 50 Terms, Trending Now Google Trends data as clean, typed rows: interest over time, interest by region, related queries, and the full Trending Now list for any country. Compare up to 50 search terms on one 0–100 scale, not just 5. Built for SEO and content teams, market researchers, analysts and anyone who wants Trends data in a spreadsheet or pipeline without fighting rate limits. ### Quick start Paste this into the **JSON** input tab and click **Start**: ```json {"searchTerms": ["coffee", "tea", "matcha"], "geo": "US", "includeRelatedQueries": true, "trendingCountries": ["US"], "maxTrendsPerCountry": 10} ``` You get one row per term (12 months of weekly interest, average, peak, latest value, related queries) and today's 10 top trending searches in the US. ### What you get - **Interest over time** for each term: ISO dates, values 0–100, the step Google used (hour, day, week, month) and a flag on the current unfinished period. - **More than 5 terms on one scale.** Google compares at most 5 terms at a time and scales each comparison on its own, so two comparisons can't be put side by side. Give this Actor up to 50 terms and it chains the comparisons through a shared anchor term and rescales them, so every term is on the same 0–100 scale and you can rank all of them. - **Interest by region**: countries, regions or states, US metros or cities. - **Related queries**, top and rising (with Google's "Breakout" flag), with Google's filler removed (see below). - **Trending Now**: every trending search Google lists for a country in the past 4 hours, 24 hours, 48 hours or 7 days, often several hundred a day, with search volume, growth, start time, whether it is still rising, category and the searches behind it. Not just the RSS feed's newest 10. - **Only new trends** for daily alerts: a schedule returns, and charges for, only trends it hasn't returned before. - **Friendly inputs**: "United States" or "US", "past-90-days" or your own start and end dates, a category from a list or any of Google's ~1,400 category ids, and web, image, news, shopping or YouTube search. - A link on every row to the same view on trends.google.com, so you can check our numbers. ### Price **$2 per 1,000 rows** on the Free and Starter plans ($1.50 on Scale, $1.10 on Business), plus $0.00005 per run start. You don't pay for Apify platform usage on top. What a row is depends on the output format you pick: | Output format | One row is | Example cost | |---|---|---| | `series` (default) | one search term, with its whole timeline, regions and related queries inside | 10 terms = 10 rows = $0.02 | | `tidy` | one date, one region or one related query | 10 terms × 53 weeks = 530 rows = $1.06 | | Trending Now (either format) | one trending search | 100 trends = $0.20 | Runs that return nothing because of the input, or because nothing is new, cost nothing. Free-plan runs stop at 500 rows. ### Input | Field | What it does | |---|---| | `searchTerms` | Up to 50 terms. 5 or fewer are compared directly; more are chained through an anchor term | | `geo` | Empty for worldwide; a country name or code (`"Germany"`, `"DE"`), a region (`"US-CA"`) or a US metro (`"US-NY-501"`) | | `timeRange` | `past-hour`, `past-4-hours`, `past-day`, `past-7-days`, `past-30-days`, `past-90-days`, `past-12-months` (default), `past-5-years`, `since-2004`. Or set `startDate` and `endDate` (YYYY-MM-DD) | | `category`, `categoryId` | A top-level category such as `food-drink`, or any Google Trends category id | | `searchType` | `web` (default), `images`, `news`, `shopping`, `youtube` | | `includeTimeline`, `includeRegions`, `includeRelatedQueries`, `includeRelatedTopics` | What to fetch for each term. Only the timeline is on by default | | `regionLevel` | `auto`, `country`, `region`, `metro` (US only), `city` | | `anchorTerm` | The term shared by every comparison when there are more than 5. Default: your first term | | `outputFormat` | `series` (one row per term) or `tidy` (one row per data point) | | `trendingCountries`, `trendingWindow`, `trendingCategory`, `onlyActiveTrends`, `maxTrendsPerCountry` | Trending Now | | `onlyNew` | Only trends not returned by an earlier run of the same setup | | `maxRows` | Stop after this many rows | ### Sample output Twelve drinks on one scale, United States, past 5 years, weekly (a real run on 2026-10-13, `outputFormat: "series"`; the `timeline` column is left out). Google allows 5 terms per comparison, so this took three comparisons chained through the anchor `coffee`: | term | averageValue | peakValue | peakDate | latestValue | compared in the same request | |---|---|---|---|---|---| | coffee | 59.89 | 100 | 2026-04-12 | 71 | coffee, tea, matcha, kombucha, energy drink | | tea | 32.92 | 47 | 2026-04-12 | 31 | coffee, tea, matcha, kombucha, energy drink | | smoothie | 7.62 | 11.09 | 2026-05-31 | 6.05 | coffee, green tea, boba, smoothie | | espresso | 6.39 | 16 | 2026-04-12 | 8 | coffee, hot chocolate, espresso, latte, cold brew | | boba | 5.07 | 20.17 | 2022-02-06 | 3.03 | coffee, green tea, boba, smoothie | | latte | 3.90 | 7 | 2025-11-09 | 5 | coffee, hot chocolate, espresso, latte, cold brew | | matcha | 3.68 | 9 | 2026-04-12 | 5 | coffee, tea, matcha, kombucha, energy drink | | green tea | 2.84 | 6.05 | 2026-04-05 | 2.02 | coffee, green tea, boba, smoothie | | energy drink | 2.45 | 8 | 2026-04-12 | 4 | coffee, tea, matcha, kombucha, energy drink | One `series` row, shortened: ```json { "type": "term", "term": "coffee", "geo": "US", "geoName": "United States", "timeRange": "past-12-months", "resolution": "WEEK", "averageValue": 79.06, "peakValue": 100, "peakDate": "2026-04-12", "latestValue": 71, "latestDate": "2026-10-04", "timeline": [{"date": "2025-10-12", "value": 61, "isPartial": false}], "regions": [{"code": "US-WY", "name": "Wyoming", "value": 100}], "relatedQueries": {"top": [{"query": "coffee near me", "value": 100}], "rising": [{"query": "how to brew pour over coffee", "risePercent": 3350, "breakout": false}]}, "notes": [], "trendsUrl": "https://trends.google.com/trends/explore?q=coffee%2Ctea%2Cmatcha&geo=US&date=today%2012-m" } ``` One `trending-now` row (US, past 24 hours): ```json { "type": "trending-now", "country": "US", "title": "harrison mevis", "searchVolume": 1000000, "searchVolumeText": "1,000,000+", "increasePercent": 1000, "startedAt": "2026-10-13T00:30:00Z", "endedAt": null, "active": true, "categories": ["Sports"], "relatedQueries": ["rams kicker", "bills", "buffalo bills"], "window": "past-24-hours", "source": "trending-now" } ``` With `outputFormat: "tidy"` the same data comes as `interest-over-time` rows (`term`, `date`, `value`, `isPartial`, `rawValue`), `interest-by-region` rows (`regionCode`, `regionName`, `value`) and `related-query` rows (`list`, `rank`, `query`, `value`, `risePercent`, `breakout`). The dataset has a view for each row type, and the run's `SUMMARY` record lists the comparisons made, the anchor, any filler removed and anything that failed. ### How the 0–100 numbers work Google never publishes search counts. Each value is search interest relative to the highest point in what you asked for: 100 is the peak, 50 is half as popular, 0 means too little data. Region maps are on each term's own scale (100 = the region where that term is most popular). Values from different runs are on different scales; compare terms inside one run. With more than 5 terms, the anchor term appears in every comparison. The ratio of the anchor's totals between two comparisons converts one scale into the other, and the result is renormalised so the highest point of all terms is 100. `rawValue` keeps Google's own number. Google rounds each comparison to whole numbers relative to its biggest term, so a small term that shares a comparison with a giant one gets coarse values (kombucha reads 1 to 2 next to coffee above). The anchor sits in every comparison, so a giant anchor flattens all of them: pick a term of middling popularity as `anchorTerm` (here `espresso` rather than `coffee`) and the small terms keep their detail. If the anchor reads 0 to 9 in a comparison, that row's `scaleNote` says its conversion is approximate. ### Honest limits - **Google rate-limits Trends hard.** This Actor spaces requests, backs off on "too many requests", rotates IPs and moves to residential proxies only when it must. A very large request can still run out of time; it then stops with what it has and a message saying what to narrow, instead of failing. - **Filler in related queries.** Google labels API clients like this one as automated and, for some terms, pads related lists with stock phrases ("gardening", "hospitals", "how to cook pasta" for "tea"). By default clusters of those are removed and listed in the run's SUMMARY record. Turn off `filterNoise` to get Google's lists exactly as returned. A real but generic related query can occasionally be removed with them. - **Related topics** currently come back empty from Google for most terms when requested by an automated tool. The option is there and the run tells you when Google returned nothing. - **Trending Now** search volumes are Google's own lower bounds ("20K+" becomes 20000). If the full list is unavailable, the Actor falls back to the public RSS feed (newest 10 per country, with news headlines) and says so. - Data comes from Google's public Trends pages. Cite it as "Data source: Google Trends (https://www.google.com/trends)" when you publish it, as Google asks. ### FAQ **Can I compare more than 5 terms?** Yes, up to 50 in one run, on one scale. See "How the 0–100 numbers work". **Why do my numbers differ slightly from trends.google.com?** Google samples searches, so the same query can return slightly different values from one request to the next. Use the `trendsUrl` on each row to compare. **Can I schedule daily trend alerts?** Yes. Set `trendingCountries`, turn on `onlyNew`, and schedule the Actor. Each run returns only trends it hasn't returned before. **Is there a free trial?** Apify's free plan includes monthly credit that covers thousands of rows here. # Actor input Schema ## `searchTerms` (type: `array`): Words or phrases to look up, e.g. "coffee", "iphone 17". Up to 5 are compared directly, like on trends.google.com. Add more (up to 50) and they are put on one shared 0–100 scale through an anchor term, so you can rank them all against each other. ## `geo` (type: `string`): Leave empty for worldwide. A country name or code ("United States", "US", "Germany", "DE"), a region code ("US-CA", "GB-SCT") or a US metro ("US-NY-501"). ## `timeRange` (type: `string`): Google picks the step from the range: minutes or hours up to a week, days up to 9 months, weeks up to 5 years, months beyond. Set a start and end date below for any other window. ## `startDate` (type: `string`): YYYY-MM-DD, from 2004-01-01. With an end date, it replaces the time range. ## `endDate` (type: `string`): YYYY-MM-DD. Empty means today. ## `category` (type: `string`): Narrows an ambiguous term, e.g. "jaguar" in Autos & Vehicles. For any of Google's ~1,400 subcategories, put its id in Category id. ## `categoryId` (type: `integer`): Any Google Trends category id, e.g. 916 for Coffee & Tea. Overrides Category. ## `searchType` (type: `string`): Which Google search the interest is measured on. ## `includeTimeline` (type: `boolean`): The 0–100 timeline for each term. ## `includeRegions` (type: `boolean`): Where each term is searched most: countries (worldwide), regions or states (inside a country). Each term's map is on its own 0–100 scale. ## `regionLevel` (type: `string`): Automatic means countries for a worldwide search and regions inside a country. ## `includeLowVolumeRegions` (type: `boolean`): Also list regions where Google has too little data to give a value (they come back as 0). ## `includeRelatedQueries` (type: `boolean`): Top and rising searches people make with each term (up to 25 of each). ## `includeRelatedTopics` (type: `boolean`): Top and rising topics. Google currently returns these lists empty to automated tools for most terms; the run says so when that happens. ## `filterNoise` (type: `boolean`): Google sometimes pads related lists with stock phrases unrelated to the term ("gardening" and "hospitals" for "tea"). On: clusters of those are removed and listed in the run's SUMMARY. Off: Google's lists exactly as returned. ## `anchorTerm` (type: `string`): With more than 5 search terms, every comparison includes this term so they can be put on one scale. Empty = your first term. Pick one of medium popularity: one far smaller than the rest gives rougher numbers. ## `outputFormat` (type: `string`): Each dataset row is one charged result. One row per term is cheapest. One row per data point loads straight into a spreadsheet or database, but a 12-month weekly timeline is 53 rows per term. ## `maxRows` (type: `integer`): Stop after this many rows. 0 = no limit. ## `trendingCountries` (type: `array`): Today's trending searches for each country ("US", "United Kingdom", "IN"): every trend Google lists, with search volume, growth, start time, category and the queries behind it. ## `trendingWindow` (type: `string`): How far back the Trending Now list goes. ## `trendingCategory` (type: `string`): Only trends in this category. ## `onlyActiveTrends` (type: `boolean`): Skip trends Google marks as ended. ## `maxTrendsPerCountry` (type: `integer`): 0 = every trend Google lists (often several hundred a day in big countries). ## `onlyNew` (type: `boolean`): For daily alerts: return only trends this setup has not returned before, so a schedule never pays twice for the same trend. Remembered per set of countries and window. ## Actor input object example ```json { "searchTerms": [ "coffee", "tea", "matcha", "energy drink", "kombucha", "yerba mate" ], "geo": "US", "timeRange": "past-12-months", "category": "all", "searchType": "web", "includeTimeline": true, "includeRegions": false, "regionLevel": "auto", "includeLowVolumeRegions": false, "includeRelatedQueries": false, "includeRelatedTopics": false, "filterNoise": true, "outputFormat": "series", "maxRows": 0, "trendingCountries": [ "US", "GB", "IN" ], "trendingWindow": "past-24-hours", "trendingCategory": "all", "onlyActiveTrends": false, "maxTrendsPerCountry": 10, "onlyNew": false } ``` # Actor output Schema ## `terms` (type: `string`): One row per search term with its timeline, regions and related queries (series format). ## `timeline` (type: `string`): One row per term and date (tidy format). ## `trending` (type: `string`): One row per trending search. ## `summary` (type: `string`): Comparisons made, anchor used, filler removed, failures and cost. # 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 = { "searchTerms": [ "coffee", "tea", "matcha" ], "geo": "US", "trendingCountries": [ "US" ], "maxTrendsPerCountry": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("locaihost/google-trends").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 = { "searchTerms": [ "coffee", "tea", "matcha", ], "geo": "US", "trendingCountries": ["US"], "maxTrendsPerCountry": 10, } # Run the Actor and wait for it to finish run = client.actor("locaihost/google-trends").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchTerms": [ "coffee", "tea", "matcha" ], "geo": "US", "trendingCountries": [ "US" ], "maxTrendsPerCountry": 10 }' | apify call locaihost/google-trends --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,locaihost/google-trends" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/TNA6SaBPSd9Pm4NgH/builds/Ax3egm52FOSkBgpYN/openapi.json