# Semrush Keyword (`pnda/semrush-keyword`) Actor Extract Semrush keyword data at scale: search volume, CPC, keyword difficulty, competition, search intent, SERP features and 12-month trends. Phrase, exact, broad or related match across 47 country databases โ€” up to 10,000 keywords per run, straight into a clean Apify dataset. - **URL**: https://apify.com/pnda/semrush-keyword.md - **Developed by:** [PNDA](https://apify.com/pnda) (community) - **Categories:** Automation, E-commerce, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $6.00 / 1,000 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 ## ๐Ÿ” Semrush Keyword Scraper โ€” Volume, CPC, Difficulty & Intent at Scale Turn any seed keyword into a full, ranked list of high-opportunity keywords โ€” **search volume, CPC, competition, keyword difficulty, SERP features and search intent** โ€” across **47 regional databases**. This Actor plugs straight into Apify, accepts a single keyword, and returns Semrush keyword metrics in a clean dataset you can export to **JSON, CSV, Excel, HTML or RSS** โ€” or pipe directly into your scraping, n8n, Make, Zapier, Looker Studio or GPT workflows. No Semrush login required. --- ### ๐Ÿ’ก Who is it for? - **SEO agencies** building keyword lists for clients at scale. - **Content teams** running bulk topic discovery for blogs, landing pages and programmatic SEO. - **PPC & paid search managers** hunting low-CPC, low-competition keywords before bidding. - **Affiliate marketers & dropshippers** validating niches and product demand in any market. - **E-commerce founders** sizing a market before launching a new category or region. - **Data teams** enriching a CRM, ad platform or internal dashboard with keyword metrics. --- ### โš™๏ธ How it works You give the Actor a keyword, a country database, and a match type. It pulls the full keyword ideas report from Semrush, paginates through as many pages as you need (100 keywords per page), and pushes every keyword as a structured row into the Apify dataset. #### Match types supported | Match type | What it returns | |---|---| | **Phrase** | Keywords containing the exact phrase in any order | | **Exact** | Keywords containing the exact phrase as-is | | **Broad** | Broad variations including synonyms and related terms | | **Related** | Semantically related keywords (perfect for topical clusters) | #### Coverage 47 country databases: ๐Ÿ‡บ๐Ÿ‡ธ US ยท ๐Ÿ‡ฌ๐Ÿ‡ง UK ยท ๐Ÿ‡จ๐Ÿ‡ฆ CA ยท ๐Ÿ‡ฆ๐Ÿ‡บ AU ยท ๐Ÿ‡ซ๐Ÿ‡ท FR ยท ๐Ÿ‡ฉ๐Ÿ‡ช DE ยท ๐Ÿ‡ฎ๐Ÿ‡น IT ยท ๐Ÿ‡ช๐Ÿ‡ธ ES ยท ๐Ÿ‡ง๐Ÿ‡ท BR ยท ๐Ÿ‡ณ๐Ÿ‡ฑ NL ยท ๐Ÿ‡ฏ๐Ÿ‡ต JP ยท ๐Ÿ‡ฎ๐Ÿ‡ณ IN ยท ๐Ÿ‡ธ๐Ÿ‡ฌ SG ยท ๐Ÿ‡ฒ๐Ÿ‡ฝ MX ยท ๐Ÿ‡ฆ๐Ÿ‡ช AE ยท and 32 more. --- ### ๐Ÿ“ฅ Input | Field | Type | Required | Description | |---|---|---|---| | `q` | string | โœ… | Seed keyword (e.g. `digital marketing`, `running shoes`) | | `db` | string | โœ… | Country database code (`us`, `uk`, `fr`, `de`, โ€ฆ) | | `type` | string | โ€” | Match type: `phrase` (default), `exact`, `broad`, `related` | | `pages` | integer | โ€” | Pages to fetch โ€” 100 keywords per page (default `1`, max `100` = 10,000 keywords) | #### Example input ```json { "q": "digital marketing", "db": "us", "type": "phrase", "pages": 5 } ```` *** ### ๐Ÿ“ค Output Each keyword is pushed as one row in the dataset. Example item: ```json { "phrase": "digital marketing agency", "database": "us", "volume": 18100, "cpc": 24.35, "competition_level": 0.87, "difficulty": 71, "results": 2140000000, "intents": [1, 3], "serp_features": [7, 9, 16, 21], "trends": [54, 54, 66, 81, 66, 54, 54, 54, 81, 54, 54, 54] } ``` #### Field reference | Field | Meaning | |---|---| | `phrase` | The keyword | | `volume` | Average monthly search volume | | `cpc` | Cost per click in USD (paid search benchmark) | | `competition_level` | Paid competition index (0.00 โ†’ 1.00) | | `difficulty` | Organic keyword difficulty score (0 โ†’ 100) | | `results` | Total number of indexed results on Google | | `intents` | Search intent flags (informational, navigational, commercial, transactional) | | `serp_features` | SERP feature codes (featured snippet, People Also Ask, local pack, video, images, etc.) | | `trends` | 12-month relative volume trend | *** ### ๐Ÿš€ Why use this Actor - โœ… **Semrush keyword metrics** โ€” volume, CPC, difficulty, competition, intent, SERP features and trends in one row. - โœ… **Up to 10,000 keywords per run** โ€” in one click, pushed straight into a dataset. - โœ… **47 markets** โ€” international SEO, localization and multi-country PPC in the same workflow. - โœ… **Clean, flat schema** โ€” every field typed and ready for BigQuery, Postgres, Sheets or GPT. - โœ… **All match types** โ€” phrase, exact, broad, and semantic (related) in a single input. - โœ… **No login, no scraping headaches** โ€” authentication and pagination handled for you. - โœ… **Pipeline-ready** โ€” trigger from n8n, Make, Zapier, Apify Schedules or the Apify API. *** ### ๐Ÿ’ผ Use cases - **Programmatic SEO** โ€” generate thousands of landing-page keywords per vertical and region. - **Content calendars** โ€” cluster `related` keywords into topic pillars automatically. - **PPC bid lists** โ€” pull `volume` + `cpc` + `competition_level` before uploading to Google Ads. - **Niche research** โ€” compare volume vs. difficulty across countries in minutes. - **Competitor gap analysis** โ€” combine with the [Semrush Domain](https://apify.com/pnda/semrush-domain) and [Semrush Competitor](https://apify.com/pnda/semrush-competitor) Actors. - **AI content pipelines** โ€” feed structured keyword data into GPT for briefs and outlines. *** ### ๐Ÿงฉ Part of the Semrush suite Chain this Actor with the rest of the suite for a full SEO workflow: | Actor | What it does | |---|---| | ๐Ÿ” [Semrush Keyword](https://apify.com/pnda/semrush-keyword) | Keyword ideas โ€” volume, CPC, difficulty, intent (this Actor) | | ๐ŸŒ [Semrush Domain](https://apify.com/pnda/semrush-domain) | Full domain audit โ€” authority, traffic, top keywords | | ๐Ÿ”— [Semrush Backlink Analytics](https://apify.com/pnda/semrush-backlink-analytics) | Authority-scored backlinks, anchors, referring domains | | ๐ŸฅŠ [Semrush Competitor](https://apify.com/pnda/semrush-competitor) | Up to 200 organic competitors and keyword overlap | *** ### ๐Ÿ’ฐ Pricing **Pay-per-result** โ€” you're charged **$0.006 per keyword returned**, plus a small per-run start fee. You only pay for keywords you actually receive. | Run | Keywords | Approx. cost | |---|---|---| | `pages = 1` | 100 | ~$0.60 | | `pages = 10` | 1,000 | ~$6 | | `pages = 100` | 10,000 | ~$60 | New users get free result credits to try the Actor before spending anything. *** ### โ–ถ๏ธ Start now Drop a keyword. Pick a country. Click **Run**. Export your dataset. That's it โ€” you have Semrush keyword intelligence on tap. # Actor input Schema ## `q` (type: `string`): Keyword to search ## `db` (type: `string`): Database/region code ## `type` (type: `string`): Keyword match type ## `pages` (type: `integer`): Number of pages to fetch (each page contains up to 100 results) ## Actor input object example ```json { "q": "digital marketing", "db": "us", "type": "phrase", "pages": 1 } ``` # Actor output Schema ## `dataset` (type: `string`): All keywords returned by this run (JSON, CSV, Excel, HTML, RSS). # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "q": "digital marketing", "db": "us", "type": "phrase", "pages": 1 }; // Run the Actor and wait for it to finish const run = await client.actor("pnda/semrush-keyword").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "q": "digital marketing", "db": "us", "type": "phrase", "pages": 1, } # Run the Actor and wait for it to finish run = client.actor("pnda/semrush-keyword").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "q": "digital marketing", "db": "us", "type": "phrase", "pages": 1 }' | apify call pnda/semrush-keyword --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=pnda/semrush-keyword", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Semrush Keyword", "description": "Extract Semrush keyword data at scale: search volume, CPC, keyword difficulty, competition, search intent, SERP features and 12-month trends. Phrase, exact, broad or related match across 47 country databases โ€” up to 10,000 keywords per run, straight into a clean Apify dataset.", "version": "0.0", "x-build-id": "gBfWtmtZpj8dSYcGv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/pnda~semrush-keyword/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-pnda-semrush-keyword", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/pnda~semrush-keyword/runs": { "post": { "operationId": "runs-sync-pnda-semrush-keyword", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/pnda~semrush-keyword/run-sync": { "post": { "operationId": "run-sync-pnda-semrush-keyword", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "q", "db" ], "properties": { "q": { "title": "Query", "type": "string", "description": "Keyword to search" }, "db": { "title": "Database", "enum": [ "us", "uk", "ca", "au", "br", "fr", "de", "it", "es", "nl", "se", "dk", "be", "no", "ie", "ar", "mx", "co", "cl", "pe", "in", "sg", "id", "ph", "my", "th", "vn", "jp", "hk", "cn", "tr", "za", "il", "sa", "ae", "pl", "cz", "ro", "hu", "at", "ch", "pt", "fi", "gr", "nz", "ru", "ua" ], "type": "string", "description": "Database/region code", "default": "us" }, "type": { "title": "Match Type", "enum": [ "phrase", "exact", "broad", "related" ], "type": "string", "description": "Keyword match type", "default": "phrase" }, "pages": { "title": "Number of Pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Number of pages to fetch (each page contains up to 100 results)", "default": 1 } } }, "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 } } } } } } } } } } ```