# Google Trends Scraper (`silentshadow55/google-trends-actor`) Actor Google Trends data as a fast API — no browser, no pytrends. Interest over time, interest by region, related & rising queries for any term, worldwide or by country/state, across Web, YouTube, News, Images & Shopping. Compare up to 5 terms on one scale. Built for scheduled monitoring. - **URL**: https://apify.com/silentshadow55/google-trends-actor.md - **Developed by:** [Esteban Ortega](https://apify.com/silentshadow55) (community) - **Categories:** SEO tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 trend results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 Over Time, Regions & Related Queries Get **Google Trends data as a fast, reliable API**: interest over time, interest by region, related queries, and rising topics for any search term — worldwide or for any country, state, or metro area, across **Web, YouTube, News, Images, and Shopping** search. **No browser. No pytrends. No 429 roulette.** This Actor speaks directly to the same JSON endpoints the Trends website uses, with automatic session rotation and backoff when Google rate-limits — so runs are fast (seconds per term, not minutes) and cheap. ### Why this exists - **Google Trends has no official public API** (the long-promised one is still in limited alpha). - **pytrends, the Python library everyone used for a decade, was abandoned in April 2025** and no longer works reliably. - Browser-based Trends scrapers are slow, expensive, and flaky — a headless Chrome per keyword to fetch what is ultimately a JSON response. This Actor is the missing piece: pytrends' functionality, maintained, as a pay-per-result API. ### What you get per search term - 📈 **Interest over time** — the full 0–100 series at Google's finest granularity for your timeframe (hourly/daily/weekly), with partial-period flags, plus computed `average_interest` and `peak` - 🗺️ **Interest by region** — countries, states/regions, cities, or US metro areas (DMA) - 🔍 **Related queries** — top and **rising** (including "Breakout" markers — the early-signal goldmine) - 🏷️ **Related topics** — top and rising topic entities - ⚖️ **True compare mode** — up to 5 terms normalized on one shared 0–100 scale, exactly like the Trends UI comparison (independent scaling when off) - 🌐 **All five search properties** — Web, YouTube, News, Images, Shopping ### Example output ```json { "term": "chatgpt", "geo": "US", "timeframe": "today 12-m", "property": "web", "average_interest": 78.9, "peak": { "date": "Oct 19, 2025", "value": 100 }, "interest_over_time": [ { "date": "Jul 7, 2025", "timestamp": 1751846400, "value": 71, "is_partial": false } ], "interest_by_region": [ { "geo_code": "US-UT", "geo_name": "Utah", "value": 100 } ], "related_queries": { "top": [ { "query": "chatgpt login", "value": 100 } ], "rising": [ { "query": "chatgpt agents", "formatted_value": "Breakout" } ] } } ```` ### Input ```json { "searchTerms": ["standing desk", "walking pad"], "geo": "US", "timeframe": "today 12-m", "compareAllTerms": true, "dataTypes": ["interestOverTime", "relatedQueries"] } ``` Timeframes: `now 1-H` → `today 5-y`, `all`, or custom ranges like `2025-01-01 2025-06-30`. Geo: country (`US`), region (`US-TX`), or empty for worldwide. ### Who uses this - **SEO & content teams** — seasonality, topic momentum, and rising queries before they're competitive - **E-commerce & product research** — validate demand and spot breakout products by region - **Investors & analysts** — search interest as an alternative data signal - **Journalists & researchers** — quantify attention around events and narratives - **AI agents** — "is interest in X growing?" answered with real data via Apify's MCP integration ### FAQ **Is there an official Google Trends API?** No — Google's announced API remains limited-access alpha. The Trends website is the only public source, and this Actor reads the same JSON its charts read. **Is this a pytrends replacement?** Yes, deliberately. pytrends was archived in April 2025. This Actor covers its core surface (interest over time, interest by region, related queries/topics, categories, properties, compare mode) without you maintaining cookies, retries, or proxies. **What do the 0–100 values mean?** Google normalizes each request: 100 = peak popularity within *your* timeframe/geo/term-set. Values are relative, not absolute search volumes. Use `compareAllTerms` to make multiple terms share one scale. **How does it handle Google's rate limits?** Polite pacing, exponential backoff, automatic cookie-session rotation, and per-session proxy identities via Apify Proxy. Failed sub-requests are reported per-term in an `errors` field rather than failing your whole run. **Can I export CSV/Excel?** Yes — every run's dataset downloads as JSON, CSV, XLSX, or XML, and is reachable via API, webhooks, and integrations (Google Sheets, Make, Zapier, LangChain). **Is this legal?** It reads the same publicly available, aggregated, anonymized statistics Google publishes to every visitor at trends.google.com. ### Roadmap - Daily trending searches / real-time trends endpoint - Long-format output option (one row per data point, Sheets-ready) - Autocomplete/topic-entity resolution (search "mercury" → pick planet vs element) Found a term or geo that misbehaves? Open an issue — fixes ship fast. # Actor input Schema ## `searchTerms` (type: `array`): Terms to get Google Trends data for. Each term produces one result item. Use quotes inside a term for exact-match behavior, or a topic like 'chatgpt'. ## `timeframe` (type: `string`): Time range: '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', or a custom range like '2025-01-01 2025-06-30'. ## `geo` (type: `string`): Two-letter country code ('US', 'GB', 'DE'), a region code ('US-TX'), or empty for worldwide. ## `searchProperty` (type: `string`): Which Google property to measure interest on. ## `category` (type: `integer`): Google Trends category to narrow results (0 = all categories). Find IDs in the Trends UI URL after picking a category, e.g. 71 = Food & Drink. ## `dataTypes` (type: `array`): Which Trends datasets to fetch for each term. Fewer types = faster, cheaper runs. ## `compareAllTerms` (type: `boolean`): When on (2-5 terms), all terms are normalized together on one 0-100 scale so values are directly comparable — exactly like adding comparison terms in the Trends UI. When off, each term is scaled independently. ## `regionResolution` (type: `string`): Granularity for 'interest by region'. Leave unset for automatic (countries for worldwide, states/regions for a country). Forcing a level that doesn't fit the geo can return no data. ## `proxyConfiguration` (type: `object`): Proxies to use. Google rate-limits IPs aggressively, so keep Apify Proxy enabled; switch to residential groups if you run very large term lists. ## Actor input object example ```json { "searchTerms": [ "chatgpt" ], "timeframe": "today 12-m", "geo": "", "searchProperty": "web", "category": 0, "dataTypes": [ "interestOverTime", "interestByRegion", "relatedQueries", "relatedTopics" ], "compareAllTerms": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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": [ "chatgpt" ] }; // Run the Actor and wait for it to finish const run = await client.actor("silentshadow55/google-trends-actor").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": ["chatgpt"] } # Run the Actor and wait for it to finish run = client.actor("silentshadow55/google-trends-actor").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 '{ "searchTerms": [ "chatgpt" ] }' | apify call silentshadow55/google-trends-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=silentshadow55/google-trends-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends Scraper", "description": "Google Trends data as a fast API — no browser, no pytrends. Interest over time, interest by region, related & rising queries for any term, worldwide or by country/state, across Web, YouTube, News, Images & Shopping. Compare up to 5 terms on one scale. Built for scheduled monitoring.", "version": "0.0", "x-build-id": "gWRO1ftxv8P6gtbnu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/silentshadow55~google-trends-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-silentshadow55-google-trends-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/silentshadow55~google-trends-actor/runs": { "post": { "operationId": "runs-sync-silentshadow55-google-trends-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/silentshadow55~google-trends-actor/run-sync": { "post": { "operationId": "run-sync-silentshadow55-google-trends-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchTerms" ], "properties": { "searchTerms": { "title": "Search terms", "type": "array", "description": "Terms to get Google Trends data for. Each term produces one result item. Use quotes inside a term for exact-match behavior, or a topic like 'chatgpt'.", "items": { "type": "string" } }, "timeframe": { "title": "Timeframe", "type": "string", "description": "Time range: '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', or a custom range like '2025-01-01 2025-06-30'.", "default": "today 12-m" }, "geo": { "title": "Location (geo code)", "type": "string", "description": "Two-letter country code ('US', 'GB', 'DE'), a region code ('US-TX'), or empty for worldwide.", "default": "" }, "searchProperty": { "title": "Search property", "enum": [ "web", "images", "news", "youtube", "shopping" ], "type": "string", "description": "Which Google property to measure interest on.", "default": "web" }, "category": { "title": "Category ID", "minimum": 0, "type": "integer", "description": "Google Trends category to narrow results (0 = all categories). Find IDs in the Trends UI URL after picking a category, e.g. 71 = Food & Drink.", "default": 0 }, "dataTypes": { "title": "Data to fetch", "type": "array", "description": "Which Trends datasets to fetch for each term. Fewer types = faster, cheaper runs.", "items": { "type": "string", "enum": [ "interestOverTime", "interestByRegion", "relatedQueries", "relatedTopics" ], "enumTitles": [ "Interest over time", "Interest by region", "Related queries", "Related topics" ] }, "default": [ "interestOverTime", "interestByRegion", "relatedQueries", "relatedTopics" ] }, "compareAllTerms": { "title": "Compare terms on one scale", "type": "boolean", "description": "When on (2-5 terms), all terms are normalized together on one 0-100 scale so values are directly comparable — exactly like adding comparison terms in the Trends UI. When off, each term is scaled independently.", "default": false }, "regionResolution": { "title": "Region breakdown level", "enum": [ "COUNTRY", "REGION", "CITY", "DMA" ], "type": "string", "description": "Granularity for 'interest by region'. Leave unset for automatic (countries for worldwide, states/regions for a country). Forcing a level that doesn't fit the geo can return no data." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxies to use. Google rate-limits IPs aggressively, so keep Apify Proxy enabled; switch to residential groups if you run very large term lists.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```