# Pinterest Search Scraper (`crw/pinterest-search-scraper`) Actor Search Pinterest by keyword and collect structured pin data — images, save counts, pinner profiles, board details, dominant colors, and more. Spot trending content, discover influential creators, and export to JSON, CSV, or Excel instantly. - **URL**: https://apify.com/crw/pinterest-search-scraper.md - **Developed by:** [CRW](https://apify.com/crw) (community) - **Categories:** Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.99 / 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 ## Pinterest Search Scraper Pinterest has billions of pins. Finding what's actually trending — and who's driving it — means endless scrolling with no way to export anything. This scraper searches Pinterest by keyword and gives you a clean, structured dataset of pins. Images, descriptions, save counts, pinner profiles, board info — all in one place, ready to filter and analyze. ### Why use this? Pinterest is one of the best signals for visual trends, but the platform gives you no data export. You can see what's popular, but you can't measure it, compare it, or build on it. This Actor changes that. **Great for:** - 🎨 **Designers & creative teams** — Know which aesthetics are trending before your competitors do - 📦 **E-commerce brands** — Find out which products and styles are getting saved the most in your niche - 📊 **Marketers & analysts** — Back your creative decisions with real engagement data, not gut feeling - 🤖 **AI/ML teams** — Collect labeled image datasets from real-world visual content at scale - 🔍 **Researchers** — Identify the influential pinners shaping any topic area ### What you get Pinterest doesn't tell you why something is trending — but the data does. Each pin comes with: #### The pin itself | Field | Description | |---|---| | **Image URL** | Direct link to the image at original resolution | | **Title & Description** | The pin's text content | | **Save Count** | How many times this pin has been saved — the closest thing to an engagement metric on Pinterest | | **Dominant Color** | Hex color extracted from the image — useful for palette research | | **Domain & Link** | Where the pin originates and links to | | **Created At** | When the pin was posted | | **Is Video** | Whether it's a video pin, and its duration in seconds | | **Is Promoted** | Whether it's a paid ad | | **Shopping Flags** | Product tags if the pin is shoppable | #### The pinner | Field | Description | |---|---| | **Username & Full Name** | Who saved this pin | | **Follower Count** | Their audience size — a quick proxy for influence | | **Profile URL** | Direct link to their Pinterest profile | #### The board | Field | Description | |---|---| | **Board Name & URL** | Which board this pin was saved to | | **Pin Count** | How curated and active that board is | | **Board Owner** | Username of whoever created the board | ### How to run 1. Enter a keyword — anything you'd type into Pinterest's search bar 2. Set how many pins you want (up to 200) 3. Click **Start** Pins appear in the dataset page by page as they're collected. No waiting for the full run to finish — data starts flowing immediately. ### Example **Input:** ```json { "query": "minimal desk setup", "max_results": 100 } ```` **Output (one pin):** ```json { "title": "Morning Study Setup", "description": "Minimal desk setup for productive mornings", "image_url": "https://i.pinimg.com/originals/ab/cd/ef/abcdef.jpg", "save_count": 4821, "dominant_color": "#c8c5b8", "domain": "notion.so", "link": "https://notion.so/templates/study", "pinner_username": "jane_doe", "pinner_follower_count": 2301, "pinner_url": "https://www.pinterest.com/jane_doe", "board_name": "Study Aesthetic", "board_url": "https://www.pinterest.com/jane_doe/study-aesthetic", "is_promoted": false, "is_video": false } ``` ### Notes - Up to **200 pins** per run — run multiple times with different keywords to build larger datasets - Promoted pins are included and flagged with `is_promoted: true`, so you can filter them out if needed *** ### You Might Also Need Found a board worth exploring? Once you identify a board from your search results, you can scrape its full contents — every pin, save count, and creator detail — with the board scraper. #### 📌 [Pinterest Board Scraper](https://apify.com/crw/pinterest-board-scraper) > Collect all pins from any Pinterest board or user's created feed — images, save counts, pinner profiles, and creator info in one dataset. | | Pinterest Search Scraper | Pinterest Board Scraper | |---|---|---| | **Starting point** | You have a keyword or topic | You have a specific board URL | | **Use case** | Discover trending pins across all of Pinterest | Deep-dive into one board's full pin history | | **Coverage** | Up to 200 pins per keyword search | Up to 500 pins per board | | **Best for** | Trend research, finding influential pinners | Auditing a board, building curated image datasets | → **[Try Pinterest Board Scraper](https://apify.com/crw/pinterest-board-scraper)** # Actor input Schema ## `query` (type: `string`): The keyword to search for on Pinterest (e.g. "study motivation"). ## `max_results` (type: `integer`): Maximum number of pins to collect. Must be between 10 and 200. ## Actor input object example ```json { "query": "study motivation", "max_results": 100 } ``` # Actor output Schema ## `api` (type: `string`): REST API endpoint to download the collected pins programmatically. # 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 = { "query": "study motivation", "max_results": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("crw/pinterest-search-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 = { "query": "study motivation", "max_results": 100, } # Run the Actor and wait for it to finish run = client.actor("crw/pinterest-search-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 '{ "query": "study motivation", "max_results": 100 }' | apify call crw/pinterest-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crw/pinterest-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pinterest Search Scraper", "description": "Search Pinterest by keyword and collect structured pin data — images, save counts, pinner profiles, board details, dominant colors, and more. Spot trending content, discover influential creators, and export to JSON, CSV, or Excel instantly.", "version": "0.0", "x-build-id": "hDO5kHGNfK0qApuv8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crw~pinterest-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crw-pinterest-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crw~pinterest-search-scraper/runs": { "post": { "operationId": "runs-sync-crw-pinterest-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crw~pinterest-search-scraper/run-sync": { "post": { "operationId": "run-sync-crw-pinterest-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Search query", "type": "string", "description": "The keyword to search for on Pinterest (e.g. \"study motivation\")." }, "max_results": { "title": "Maximum number of pins", "minimum": 10, "maximum": 200, "type": "integer", "description": "Maximum number of pins to collect. Must be between 10 and 200.", "default": 100 } } }, "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 } } } } } } } } } } ```