# Smart Scraper by Example — no selectors, no code (`oriented_wallpaper/smart-scraper-by-example`) Actor Give a URL + an example value you can see on the page (a price, a title, a name). The actor learns the surrounding HTML pattern and extracts every similar item as structured rows. No CSS selectors, no API key. - **URL**: https://apify.com/oriented\_wallpaper/smart-scraper-by-example.md - **Developed by:** [Flash Scrape](https://apify.com/oriented_wallpaper) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Smart Scraper by Example **No CSS selectors. No code. No API key.** Give a URL and an example value you can *see* on the page — the actor figures out the HTML pattern around it and pulls every similar item. Inspired by [autoscraper](https://github.com/alirezamika/autoscraper) (MIT) — re-implemented natively in Node. ### How it works 1. You provide one or more **example values** (a price, a title, a name) copied from the page. 2. For each example, the actor finds it in the HTML and learns the wrapping tag + class. 3. It extracts every element matching that rule and **zips the fields into rows**. ### Input | Field | Description | |---|---| | **startUrls** | Pages to scrape (list/category/search pages with repeating items). | | **examples** | Values visible on the page, one per line. Label them `author: Albert Einstein` to name columns. | | **maxItems** | Stop after N rows (0 = no limit). | #### Example ```json { "startUrls": [{ "url": "https://quotes.toscrape.com/" }], "examples": ["quote: process of our thinking", "author: Albert Einstein"] } ```` → returns one row per quote: `{ quote, author, sourceUrl }`. ### Output One dataset item per extracted row — export to **CSV / JSON / Excel** from the run's dataset. ### Tips - Works best when each value sits in **its own element** (a `` price, an `

`/`` title). - Copy an **exact** value from the page's visible text (not from an image or a dropdown). ### Pay-per-event The actor calls `Actor.charge({ eventName: "item" })` once per extracted row. Define the **`item`** event in **Apify Console → your actor → Publication → Monetization → Pay per event** (suggested \~$0.001/item). Guarded so it runs fine while monetization is unconfigured. ### Run / publish ```bash apify run # local apify push # upload + build on Apify ``` > Scrapes public web content. Use responsibly and within each site's terms. # Actor input Schema ## `startUrls` (type: `array`): Pages to scrape — typically list/category/search pages with repeating items. ## `examples` (type: `array`): Values you can SEE on the page, one per line. Optionally label them as 'label: value' to name the columns (e.g. 'author: Albert Einstein'). The actor finds every similar item. ## `maxItems` (type: `integer`): Stop after this many rows across all URLs. Use 0 for no limit. ## Actor input object example ```json { "startUrls": [ { "url": "https://quotes.toscrape.com/" } ], "examples": [ "quote: process of our thinking", "author: Albert Einstein" ], "maxItems": 0 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://quotes.toscrape.com/" } ], "examples": [ "quote: process of our thinking", "author: Albert Einstein" ] }; // Run the Actor and wait for it to finish const run = await client.actor("oriented_wallpaper/smart-scraper-by-example").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "startUrls": [{ "url": "https://quotes.toscrape.com/" }], "examples": [ "quote: process of our thinking", "author: Albert Einstein", ], } # Run the Actor and wait for it to finish run = client.actor("oriented_wallpaper/smart-scraper-by-example").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 '{ "startUrls": [ { "url": "https://quotes.toscrape.com/" } ], "examples": [ "quote: process of our thinking", "author: Albert Einstein" ] }' | apify call oriented_wallpaper/smart-scraper-by-example --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=oriented_wallpaper/smart-scraper-by-example", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Smart Scraper by Example — no selectors, no code", "description": "Give a URL + an example value you can see on the page (a price, a title, a name). The actor learns the surrounding HTML pattern and extracts every similar item as structured rows. No CSS selectors, no API key.", "version": "0.1", "x-build-id": "teUzHisPJMp0X0Iuh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/oriented_wallpaper~smart-scraper-by-example/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-oriented_wallpaper-smart-scraper-by-example", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/oriented_wallpaper~smart-scraper-by-example/runs": { "post": { "operationId": "runs-sync-oriented_wallpaper-smart-scraper-by-example", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/oriented_wallpaper~smart-scraper-by-example/run-sync": { "post": { "operationId": "run-sync-oriented_wallpaper-smart-scraper-by-example", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls", "examples" ], "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "Pages to scrape — typically list/category/search pages with repeating items.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "examples": { "title": "Example values", "type": "array", "description": "Values you can SEE on the page, one per line. Optionally label them as 'label: value' to name the columns (e.g. 'author: Albert Einstein'). The actor finds every similar item.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Stop after this many rows across all URLs. Use 0 for no limit.", "default": 0 } } }, "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 } } } } } } } } } } ```