# ⚡ Fast Google Trends Scraper — 5-Term Compare, No-Charge Fails (`swiftscrape/google-trends-fast-scraper`) Actor Fast Google Trends scraper & API: extract interest over time, related queries and rising searches for up to 5 compared keywords in seconds. Built for SEO, market research and AI agents. Pay only for terms that return data — failed, blocked or empty terms are never charged. - **URL**: https://apify.com/swiftscrape/google-trends-fast-scraper.md - **Developed by:** [swift scrape](https://apify.com/swiftscrape) (community) - **Categories:** SEO tools, News, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 term with data extracteds 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/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 ## ⚡ Fast Google Trends Scraper — 5-Term Compare, No-Charge Fails Extract Google Trends data in seconds, not minutes: interest over time and related queries for up to 5 compared terms (related topics for single-term lookups — see the note below). ### Why this scraper | | This actor | Typical alternatives | |---|---|---| | Speed (2 terms) | **< 10 s** | several minutes | | Failed term | **$0.00 — never charged** | compute billed anyway | | 5-term comparison | **native, one normalized session** | broken or N separate runs | | AI agents (MCP) | **flat, documented input schema** | multi-param bugs | Pricing: **$0.005 per term that returns data.** Blocked, empty or timed-out terms cost you nothing — the incentive to stay reliable is on us. ### Input | Field | Type | Default | Notes | |---|---|---|---| | `terms` | array of strings | — | 1 to 5 terms, compared in one Google Trends session (same normalization as the Trends UI) | | `timeRange` | enum | `today 12-m` | `now 1-H`, `now 1-d`, `now 7-d`, `today 1-m`, `today 3-m`, `today 12-m`, `today 5-y` | | `geo` | string | `""` (worldwide) | ISO country code, e.g. `FR`, `US` — leave empty for worldwide | | `includeRelated` | boolean | `true` | also fetch related queries and related topics per term | Minimal input: ```json { "terms": ["chatgpt", "gemini"] } ```` ### Output example One item per term is pushed to the dataset. This is a genuine output item from a local run with `{"terms":["chatgpt","gemini"],"geo":"FR"}` (`interestOverTime` trimmed to a few points here for readability — a real `today 12-m` run returns ~52-53 weekly points; `relatedQueries` trimmed to a handful of entries out of the full list): ```json { "term": "chatgpt", "geo": "FR", "timeRange": "today 12-m", "interestOverTime": [ { "date": "2025-07-13", "value": 58 }, { "date": "2025-07-20", "value": 63 }, { "date": "2025-07-27", "value": 54 }, { "date": "2025-08-03", "value": 54 }, { "date": "2026-06-21", "value": 70 }, { "date": "2026-06-28", "value": 69 }, { "date": "2026-07-05", "value": 66 }, { "date": "2026-07-12", "value": 57 } ], "relatedQueries": { "top": [ { "query": "chatgpt gratuit", "value": 100, "formattedValue": "100" }, { "query": "chatgpt gpt", "value": 48, "formattedValue": "48" }, { "query": "chatgpt openai", "value": 35, "formattedValue": "35" }, { "query": "ia chatgpt", "value": 28, "formattedValue": "28" }, { "query": "chatgpt image", "value": 22, "formattedValue": "22" }, { "query": "chatgpt logo transparent", "value": 1, "formattedValue": "1" } ], "rising": [ { "query": "how to use chatgpt effectively", "value": 2850, "formattedValue": "+2,850%" }, { "query": "chatgpt atlas", "value": 700, "formattedValue": "+700%" }, { "query": "chatgpt logo transparent", "value": 190, "formattedValue": "+190%" } ] }, "relatedTopics": { "top": [], "rising": [] }, "normalizedWith": ["gemini"], "error": null } ``` Note on `relatedTopics`: Google only returns a "related topics" widget when a single term is explored. As soon as 2+ terms are compared in the same session (this actor's normal mode), Google's own API stops returning that widget at all — so `relatedTopics` comes back as an empty group (`{"top":[],"rising":[]}`) in every multi-term comparison. `relatedQueries` is unaffected and is always populated per term (when `includeRelated` is `true` and the term has data). This is a real, observed characteristic of the Google Trends API, not a bug in this actor. If a term is blocked, returns no data, or times out, its item looks like this instead — and is **never charged**: ```json { "term": "some-term", "geo": "worldwide", "timeRange": "today 12-m", "interestOverTime": [], "relatedQueries": null, "relatedTopics": null, "normalizedWith": ["other-term"], "error": "no-data" } ``` ### Use with AI agents (MCP) This actor works out of the box with the Apify MCP server. Ask your agent: "Compare Google Trends interest for chatgpt and gemini over the past 12 months in France." The flat input schema (`terms`, `timeRange`, `geo`, `includeRelated`) maps directly to natural language — no nested flags. ### Use with n8n / Make No visual builder needed — call the actor's run-sync endpoint directly from an HTTP module. **n8n (HTTP Request node):** 1. Add an **HTTP Request** node. 2. Method: `POST` 3. URL: `https://api.apify.com/v2/acts/YOUR_USERNAME~google-trends-fast-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN` 4. Body content type: `JSON` 5. Body: ```json { "terms": ["chatgpt", "gemini"], "timeRange": "today 12-m", "geo": "FR", "includeRelated": true } ``` 6. Run the node — the output is the array of term items, ready to feed into the next node (Set, Filter, Google Sheets, etc.). **Make (HTTP module):** 1. Add an **HTTP > Make a request** module. 2. URL: `https://api.apify.com/v2/acts/YOUR_USERNAME~google-trends-fast-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN` 3. Method: `POST` 4. Body type: `Raw` / `JSON (application/json)` 5. Request content: same JSON body as above. 6. Map the returned array items in a following **Iterator** module if you need one bundle per term. Swap `YOUR_USERNAME`, the actor name, and `YOUR_APIFY_TOKEN` for your own values (find your token under Apify Console → Settings → Integrations). ### FAQ **Is scraping Google Trends legal?** This actor only accesses publicly available, aggregated, anonymized data — the same data visible to anyone on trends.google.com. Review Google's ToS and your local regulations for your specific use case. **How fresh is the data?** Fetched live at run time, no cache. **What does a 5-term comparison cost?** 5 × $0.005 = $0.025 when all five return data; less if some fail (you only pay for delivered terms). **Why is `relatedTopics` empty when I compare 2+ terms?** See the note in the [Output example](#output-example) section above — it's how Google's own API behaves in comparison mode, not something this actor can add back. **What happens if a term is invalid or blocked?** It's returned with `error` set to `"blocked"`, `"no-data"`, or `"timeout"`, empty results, and is **not charged** — see the second example above. # Actor input Schema ## `terms` (type: `array`): 1 to 5 terms. Multiple terms are compared in ONE Google Trends session, so their values are normalized against each other (like the Google Trends UI). ## `timeRange` (type: `string`): Period to analyze. ## `geo` (type: `string`): Two-letter country code like FR or US. Leave empty for worldwide. ## `includeRelated` (type: `boolean`): Also fetch related queries and related topics (top + rising) for each term. ## Actor input object example ```json { "terms": [ "chatgpt", "gemini" ], "timeRange": "today 12-m", "geo": "", "includeRelated": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "terms": [ "chatgpt", "gemini" ] }; // Run the Actor and wait for it to finish const run = await client.actor("swiftscrape/google-trends-fast-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "terms": [ "chatgpt", "gemini", ] } # Run the Actor and wait for it to finish run = client.actor("swiftscrape/google-trends-fast-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 '{ "terms": [ "chatgpt", "gemini" ] }' | apify call swiftscrape/google-trends-fast-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=swiftscrape/google-trends-fast-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "⚡ Fast Google Trends Scraper — 5-Term Compare, No-Charge Fails", "description": "Fast Google Trends scraper & API: extract interest over time, related queries and rising searches for up to 5 compared keywords in seconds. Built for SEO, market research and AI agents. Pay only for terms that return data — failed, blocked or empty terms are never charged.", "version": "0.1", "x-build-id": "1Dpnbc1Q5ck3cp0dO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/swiftscrape~google-trends-fast-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-swiftscrape-google-trends-fast-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/swiftscrape~google-trends-fast-scraper/runs": { "post": { "operationId": "runs-sync-swiftscrape-google-trends-fast-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/swiftscrape~google-trends-fast-scraper/run-sync": { "post": { "operationId": "run-sync-swiftscrape-google-trends-fast-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "terms" ], "properties": { "terms": { "title": "Search terms", "minItems": 1, "maxItems": 5, "type": "array", "description": "1 to 5 terms. Multiple terms are compared in ONE Google Trends session, so their values are normalized against each other (like the Google Trends UI).", "items": { "type": "string" } }, "timeRange": { "title": "Time range", "enum": [ "now 1-H", "now 1-d", "now 7-d", "today 1-m", "today 3-m", "today 12-m", "today 5-y" ], "type": "string", "description": "Period to analyze.", "default": "today 12-m" }, "geo": { "title": "Country (ISO code)", "maxLength": 2, "type": "string", "description": "Two-letter country code like FR or US. Leave empty for worldwide.", "default": "" }, "includeRelated": { "title": "Include related queries & topics", "type": "boolean", "description": "Also fetch related queries and related topics (top + rising) for each term.", "default": 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 } } } } } } } } } } ```