# Google Trends Scraper — Interest Over Time & Rising Queries (`datascoutlab/google-trends-reliable`) Actor Get Google Trends data that actually arrives: interest over time, interest by region, rising related queries for any keyword, country & time range. Built to survive Google's rate limits — no empty datasets, no endless runs, no API key. Clean rows ready for Sheets, dashboards & AI agents. - **URL**: https://apify.com/datascoutlab/google-trends-reliable.md - **Developed by:** [Anton DataScout](https://apify.com/datascoutlab) (community) - **Categories:** AI, Developer tools, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.70 / 1,000 data 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/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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Reliable Google Trends data — interest over time, interest by region, and rising related queries — for any keywords, any country, up to 5 years back. No API key. Google aggressively rate-limits Trends (HTTP 429), and most scrapers built on top of it just fall over. Their issue trackers say it out loud: *"Runs with infinite loop and no result"*, *"Too much time and no output"*, *"It never worked."* You start a run, wait, pay for compute, and get an empty dataset with no explanation. The official Apify Google Trends actor sits at 3.1 stars for exactly this reason. This scraper is built around that failure mode instead of ignoring it. The full API dance — cookies, explore, widget data — rides a single **sticky residential-proxy session**. The moment Google returns a 429, the session is thrown away and the batch restarts on a fresh IP, with bounded retries and backoff. The run either delivers real data or fails fast and loud with a clear message. It never hangs, and it never quietly charges you for an empty result. | You give it | You get back | | --- | --- | | Keywords (up to 5 on a shared 0–100 scale) | Interest-over-time timeline per keyword | | A country or region (or worldwide) | Interest-by-region breakdown with geo codes | | A time range (7 days → 5 years) | Top and rising related queries and topics | | Toggles for each data type | Clean, long-format rows ready for CSV, Sheets, or an LLM | ### What does the Google Trends Scraper do? It pulls the same numbers you see on trends.google.com — normalized 0–100 search interest — straight from Google Trends' own internal API, and returns them as flat dataset rows you can sort, filter, and export. For each keyword you can collect four data types, each an opt-in toggle so you only pay for the rows you actually want: - **Interest over time** — the search-interest timeline (0–100) for every keyword across your chosen window. - **Interest by region** — how interest breaks down by country or subregion, with geo codes. - **Related queries** — the top and *rising* searches people run alongside your keyword. - **Related topics** — the top and rising topics associated with your keyword. Up to 5 keywords are compared per batch on one shared 0–100 scale. Larger keyword lists are processed in additional batches automatically. ### Who is it for? | Audience | What they pull | | --- | --- | | SEO specialists | Rising related queries to target topics before they get competitive | | Content marketers | Interest-over-time curves to time articles, videos, and campaigns | | Product teams | Demand trajectory for a niche before committing to build it | | Market researchers | Interest-by-region maps to size and compare geographic demand | | AI / trend pipelines | Clean typed rows feeding dashboards, alerts, and LLM analysis | ### Why use this scraper? - **Reliability over empty datasets.** Sticky sessions and residential proxies beat the 429 wall that makes other Trends scrapers hang or return nothing. - **Rising related queries = the best early-trend signal Google gives away.** Every rising query comes flagged with its growth percentage. - **Up to 5 keywords on a shared 0–100 scale.** Directly comparable numbers, so you can rank demand head-to-head. - **Any country or region.** Worldwide, a single country (`US`, `GB`, `DE`), or a subregion (`US-CA`). - **Up to 5 years of history.** From the past 7 days to the past 5 years, in one toggle. - **No API key, no login, no quota.** Just keywords in, rows out. ### What data do you get? Every row is long-format and carries a `type` field so you can split the dataset by data type. Here are real samples of each row. **Interest over time** ```json { "type": "interestOverTime", "date": "Jul 22, 2026", "timestamp": 1753142400, "keyword": "ai agents", "value": 14, "isPartial": false } ```` **Interest by region** ```json { "type": "interestByRegion", "region": "California", "geoCode": "US-CA", "keyword": "ai agents", "value": 100 } ``` **Related queries** (and `relatedTopics`, same shape) ```json { "type": "relatedQueries", "keyword": "ai agents", "query": "ai agent frameworks", "value": 3600, "isRising": true, "change": "+3,600%" } ``` Rows where `isRising` is `true` come with a growth figure in `change` (for example `+3,600%` or `Breakout`). These rising queries are the single most valuable output for early-trend detection — they surface the searches gaining momentum right now, before they show up in any keyword tool. ### How to scrape Google Trends data 1. Add your **keywords** (up to 5 share one comparable 0–100 scale). 2. Set a **region** — a country code like `US`, a subregion like `US-CA`, or leave it empty for worldwide. 3. Pick a **time range** from the past 7 days up to the past 5 years. 4. Toggle the **data types** you want: interest over time, interest by region, related queries, related topics. 5. Run the actor and export the dataset as JSON, CSV, or Excel, or read it from the Apify API. No account or API key is required on Google's side — the scraper handles cookies, sessions, and proxy rotation for you. ### How to find rising search trends Enable **Related queries** (and optionally **Related topics**). In the output, filter for rows where `isRising` is `true`. The `change` field tells you how fast each query is climbing — a percentage jump or `Breakout` for surges too large to quantify. Because rising queries reflect momentum rather than absolute volume, they are the earliest public signal that a topic is heating up. Pull them on a short time range (past 7 days or past month) to catch trends while they are still forming. ### How much does it cost to scrape Google Trends You pay only for Apify platform usage (compute + proxy) — there is no Google API fee, because there is no API key involved. Cost scales with how many rows you collect, which is why every data type is an opt-in toggle: enable only interest over time for a lean run, or turn on regions and related data when you need the full picture. If a run collects zero rows because every batch was rate-limited, the actor stops with an error instead of silently returning an empty dataset, so you are not left paying for nothing. ### How to track keyword interest over time Keep **Interest over time** enabled and choose your window. Each row is one keyword at one point in time, with a normalized `value` from 0 to 100 and a `date`. Sort by `keyword` then `timestamp` to reconstruct the full curve, or pivot on `date` to compare several keywords side by side. The most recent data point is marked `isPartial: true` because its period is still in progress — exclude those rows when you need only finalized values. ### How to do SEO keyword research with Google Trends 1. Put your seed keywords (up to 5) in one batch so their interest values are directly comparable on the shared 0–100 scale. 2. Enable **Related queries** to harvest the searches Google associates with each seed — these are ready-made topic and long-tail ideas. 3. Filter rising queries to find low-competition topics you can publish on before the rest of the market catches up. 4. Add **Interest by region** to prioritize the countries or states where demand is strongest. ### How to export Google Trends data to CSV or Google Sheets After a run, open the dataset and export in one click as **CSV**, **Excel**, or **JSON**, or pull it programmatically from the Apify Dataset API. The dataset ships with three ready-made views — **Interest over time**, **Related queries & topics**, and **Interest by region** — so each data type lands in its own clean table. Because rows are long-format, they drop straight into Google Sheets, Looker Studio, or a pandas DataFrame without reshaping. ### Google Trends API alternative Google does not offer an official public Google Trends API. This actor is a drop-in alternative: it returns the same 0–100 normalized data the Trends website shows, as structured rows, over a simple input — no key, no OAuth, no daily quota. Call it on a schedule via the Apify API or MCP and treat it as the Trends endpoint Google never shipped. ### Is it legal to scrape Google Trends? This actor collects only publicly available Google Trends data — the same information any visitor can see on trends.google.com without logging in. It does not access private accounts or personal data. As always, review Google's terms and your own local regulations, and use the data responsibly. See the disclaimer below. ### Input configuration | Field | Type | Default | Description | | --- | --- | --- | --- | | `keywords` | array (required) | `["ai agents"]` | Search terms to analyze. Up to 5 are compared on a shared 0–100 scale per batch; more are split into separate batches. | | `geo` | string | `""` (worldwide) | Country/region code like `US`, `GB`, `DE`, or a subregion like `US-CA`. | | `timeRange` | string | `today 12-m` | Look-back window: `now 7-d`, `today 1-m`, `today 3-m`, `today 12-m`, or `today 5-y`. | | `category` | integer | `0` | Google Trends category ID to narrow results (`0` = all; e.g. `5` = Computers & Electronics). | | `includeTimeline` | boolean | `true` | Interest-over-time values (0–100) per keyword. | | `includeRegions` | boolean | `false` | Interest breakdown by country or subregion. | | `includeRelatedQueries` | boolean | `false` | Top and rising queries related to each keyword. | | `includeRelatedTopics` | boolean | `false` | Top and rising topics related to each keyword. | ### FAQ **Why is my run sometimes slower?** When Google rate-limits an IP, the actor rotates to a fresh sticky session and retries (each attempt is logged). Delivering complete data in 60 seconds beats "finishing" in 5 seconds with an empty dataset — reliability is the whole point. **Do the values match trends.google.com?** Yes. The actor uses the same internal API the website uses, with the same 0–100 normalization applied per batch. Numbers within a batch are directly comparable; numbers from different batches are each normalized independently. **Do I need an API key?** No. No key, no login, and no quota — just provide keywords. **Why is the latest data point marked partial?** Google flags the trailing period as incomplete because it is still in progress. Those rows carry `isPartial: true` so you can exclude them when you need only settled values. ### Integrations Connect the Actor to the tools you already use through Apify integrations: - **Trends → Google Sheets** — auto-append interest-over-time and rising queries into a live trend dashboard. - **Trends → Slack / Discord** — get alerted when a tracked keyword's rising queries spike. - **Trends → Make / Zapier** — trigger content or SEO workflows off fresh trend data. - **Trends → BigQuery / Snowflake** — warehouse trend series via Airbyte for forecasting and analysis. - **Scheduled runs & webhooks** — run daily/weekly to build a keyword time series, or POST results on completion. ### Use with AI agents via MCP The actor exposes clean, typed rows that plug straight into LLM pipelines. Run it through Apify's MCP integration so an AI agent can pull live Google Trends data — interest curves, regional demand, and rising queries — as a tool call, then reason over the results or trigger alerts on emerging trends. ### Related scrapers - **App Store Reviews Scraper** — mine iOS app reviews for pain points and feature requests. - **Google Play Reviews Scraper** — the same review intelligence for Android apps. - **YouTube Outlier Finder** — surface breakout videos on small channels before they blow up. - **Telegram Channel Scraper** — pull posts and signals from public Telegram channels. ### Disclaimer This actor is not affiliated with, endorsed by, or sponsored by Google. Google Trends is a trademark of Google LLC. The actor accesses only publicly available data and is intended for lawful use in compliance with Google's terms and applicable regulations. ### SEO Keywords google trends scraper, google trends api, scrape google trends, google trends data, interest over time, interest by region, related queries, rising search trends, trending keywords, keyword research tool, seo keyword research, search trend analysis, google trends alternative, trends data export, google trends csv, trend detection, breakout keywords, search interest data, market trend research, google trends actor, apify google trends, no api key trends scraper # Actor input Schema ## `keywords` (type: `array`): Search terms to analyze, e.g. `ai agents`, `standing desk`. Up to 5 keywords are compared on a shared 0–100 scale per batch; more keywords are processed in separate batches. ## `geo` (type: `string`): Country/region code like `US`, `GB`, `DE`, or a region like `US-CA`. Leave empty for worldwide. ## `timeRange` (type: `string`): How far back to look. ## `category` (type: `integer`): Google Trends category ID to narrow results (0 = all categories). E.g. 5 = Computers & Electronics. ## `includeTimeline` (type: `boolean`): Weekly/daily interest values (0–100) for each keyword. ## `includeRegions` (type: `boolean`): Interest breakdown by country or subregion. ## `includeRelatedQueries` (type: `boolean`): Top and rising search queries related to each keyword — rising queries are early trend signals. ## `includeRelatedTopics` (type: `boolean`): Top and rising topics related to each keyword. ## Actor input object example ```json { "keywords": [ "ai agents" ], "geo": "", "timeRange": "today 12-m", "category": 0, "includeTimeline": true, "includeRegions": false, "includeRelatedQueries": false, "includeRelatedTopics": false } ``` # 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": [ "ai agents" ] }; // Run the Actor and wait for it to finish const run = await client.actor("datascoutlab/google-trends-reliable").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": ["ai agents"] } # Run the Actor and wait for it to finish run = client.actor("datascoutlab/google-trends-reliable").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": [ "ai agents" ] }' | apify call datascoutlab/google-trends-reliable --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datascoutlab/google-trends-reliable", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends Scraper — Interest Over Time & Rising Queries", "description": "Get Google Trends data that actually arrives: interest over time, interest by region, rising related queries for any keyword, country & time range. Built to survive Google's rate limits — no empty datasets, no endless runs, no API key. Clean rows ready for Sheets, dashboards & AI agents.", "version": "1.0", "x-build-id": "uxMt8joCz4vzLn5T9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datascoutlab~google-trends-reliable/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datascoutlab-google-trends-reliable", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datascoutlab~google-trends-reliable/runs": { "post": { "operationId": "runs-sync-datascoutlab-google-trends-reliable", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datascoutlab~google-trends-reliable/run-sync": { "post": { "operationId": "run-sync-datascoutlab-google-trends-reliable", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "Search terms to analyze, e.g. `ai agents`, `standing desk`. Up to 5 keywords are compared on a shared 0–100 scale per batch; more keywords are processed in separate batches.", "items": { "type": "string" } }, "geo": { "title": "Region", "type": "string", "description": "Country/region code like `US`, `GB`, `DE`, or a region like `US-CA`. Leave empty for worldwide.", "default": "" }, "timeRange": { "title": "Time range", "enum": [ "now 7-d", "today 1-m", "today 3-m", "today 12-m", "today 5-y" ], "type": "string", "description": "How far back to look.", "default": "today 12-m" }, "category": { "title": "Category ID", "type": "integer", "description": "Google Trends category ID to narrow results (0 = all categories). E.g. 5 = Computers & Electronics.", "default": 0 }, "includeTimeline": { "title": "Interest over time", "type": "boolean", "description": "Weekly/daily interest values (0–100) for each keyword.", "default": true }, "includeRegions": { "title": "Interest by region", "type": "boolean", "description": "Interest breakdown by country or subregion.", "default": false }, "includeRelatedQueries": { "title": "Related queries", "type": "boolean", "description": "Top and rising search queries related to each keyword — rising queries are early trend signals.", "default": false }, "includeRelatedTopics": { "title": "Related topics", "type": "boolean", "description": "Top and rising topics related to each keyword.", "default": false } } }, "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 } } } } } } } } } } ```