# AI Web Scraper: Extract Any Data From Any Page in Plain English (`lanky_quantifier/ai-web-scraper`) Actor - **URL**: https://apify.com/lanky\_quantifier/ai-web-scraper.md - **Developed by:** [Vhub Systems](https://apify.com/lanky_quantifier) (community) - **Categories:** Developer tools, AI, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 ## AI Web Scraper — Extract Any Data From Any Page in Plain English **Stop writing CSS selectors.** Point this actor at any URL, describe the fields you want in plain English, and get clean structured JSON back. Powered by an LLM, so it keeps working even when the site changes its layout. ### Why this over a normal scraper | Normal scraper | AI Web Scraper | |----------------|----------------| | You write & maintain CSS/XPath selectors | You write one sentence | | Breaks on every redesign | Survives redesigns (reads the page like a human) | | One scraper per site | One actor for **any** site | | Needs a developer | Anyone can use it | ### How to use 1. **URLs to scrape** — paste one or more page URLs. 2. **What to extract** — describe it: *"For each product: name, price, rating, in_stock"*. 3. **List mode** — ON for repeated items (products, posts, rows), OFF for one object per page. 4. Run. Results land in the dataset as clean JSON, ready for Excel/Sheets/API. ### Example **Input** ```json { "startUrls": [{ "url": "https://news.ycombinator.com/" }], "extractionPrompt": "For each story: title, points, author, number_of_comments, url", "listMode": true } ```` **Output** (verified live) ```json [ { "title": "SearXNG: A free internet metasearch engine", "points": 101, "author": "theanonymousone", "number_of_comments": 24 }, { "title": "Giant trees have no trouble pumping water to top branches", "points": 29, "author": "hhs", "number_of_comments": 11 } ] ``` ### Use cases - **E-commerce** — product name, price, rating, availability across any shop. - **Lead-gen** — names, titles, emails, companies from directory pages. - **Real estate / jobs / listings** — structured rows from any listing site. - **News / research** — headlines, authors, dates, summaries. - **Anything with repeated items** — no selector, just describe it. ### Pricing **Pay per result.** You're charged per extracted item — no monthly fee, no charge for empty pages. Cheap on small jobs, scales linearly on big ones. ### Notes - Reads visible page text (not raw HTML), so it's robust to markup changes. - Returns `null` for fields it can't find — it never invents data. - Large pages are trimmed before extraction to keep runs fast and cheap. - Each item includes `_sourceUrl` so you always know where it came from. Built by **Vhub Systems**. # Actor input Schema ## `startUrls` (type: `array`): One or more page URLs to extract data from. ## `extractionPrompt` (type: `string`): Describe the fields you want, e.g. 'For each story: title, points, author, number of comments, url'. ## `listMode` (type: `boolean`): ON = return an array of repeated items (products, posts, rows). OFF = return a single object for the whole page. ## `maxItems` (type: `integer`): Cap on items returned per page (list mode). ## `maxCharsToLLM` (type: `integer`): Trims the cleaned page text before sending to the LLM to control cost/latency. ## Actor input object example ```json { "startUrls": [ { "url": "https://news.ycombinator.com/" } ], "extractionPrompt": "For each story on the page: title, points, author, number_of_comments, url", "listMode": true, "maxItems": 50, "maxCharsToLLM": 24000 } ``` # 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://news.ycombinator.com/" } ], "extractionPrompt": "For each story on the page: title, points, author, number_of_comments, url" }; // Run the Actor and wait for it to finish const run = await client.actor("lanky_quantifier/ai-web-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 = { "startUrls": [{ "url": "https://news.ycombinator.com/" }], "extractionPrompt": "For each story on the page: title, points, author, number_of_comments, url", } # Run the Actor and wait for it to finish run = client.actor("lanky_quantifier/ai-web-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 '{ "startUrls": [ { "url": "https://news.ycombinator.com/" } ], "extractionPrompt": "For each story on the page: title, points, author, number_of_comments, url" }' | apify call lanky_quantifier/ai-web-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lanky_quantifier/ai-web-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Web Scraper: Extract Any Data From Any Page in Plain English", "version": "0.1", "x-build-id": "9ydJnFRbYIKBprsQ7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lanky_quantifier~ai-web-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lanky_quantifier-ai-web-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/lanky_quantifier~ai-web-scraper/runs": { "post": { "operationId": "runs-sync-lanky_quantifier-ai-web-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/lanky_quantifier~ai-web-scraper/run-sync": { "post": { "operationId": "run-sync-lanky_quantifier-ai-web-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": [ "startUrls", "extractionPrompt" ], "properties": { "startUrls": { "title": "URLs to scrape", "type": "array", "description": "One or more page URLs to extract data from.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "extractionPrompt": { "title": "What to extract (plain English)", "type": "string", "description": "Describe the fields you want, e.g. 'For each story: title, points, author, number of comments, url'." }, "listMode": { "title": "Extract a list of items?", "type": "boolean", "description": "ON = return an array of repeated items (products, posts, rows). OFF = return a single object for the whole page.", "default": true }, "maxItems": { "title": "Max items per page", "minimum": 1, "maximum": 500, "type": "integer", "description": "Cap on items returned per page (list mode).", "default": 50 }, "maxCharsToLLM": { "title": "Max page characters sent to the model", "minimum": 2000, "maximum": 100000, "type": "integer", "description": "Trims the cleaned page text before sending to the LLM to control cost/latency.", "default": 24000 } } }, "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 } } } } } } } } } } ```