# AI Universal Scraper — Extract Anything from Any Page (`flash_scraper/ai-universal-scraper`) Actor Give any URL + the fields you want; an LLM (OpenAI or Anthropic) extracts clean structured JSON from the page. Works on any site. - **URL**: https://apify.com/flash\_scraper/ai-universal-scraper.md - **Developed by:** [Flash Scrape](https://apify.com/flash_scraper) (community) - **Categories:** Automation, AI, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $1.00 / 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 ## AI Universal Scraper — scrape any website with GPT or Claude, no selectors needed **An AI web scraper that turns any page into clean, structured JSON.** Paste a URL, list the fields you want (`title`, `price`, `author`…), plug in your own OpenAI or Anthropic API key, and a language model reads the page like a human and returns exactly those fields. No CSS selectors to maintain, no per-site configuration, no broken parser every time a site changes its HTML — which is exactly where traditional scrapers fall over. ### What it does - **Extracts structured data from any URL** — you provide a list of pages and either a list of field names, a plain-English instruction, or both. - **Uses your own LLM key** — supports **OpenAI** (default model `gpt-4o-mini`) and **Anthropic** (default model `claude-haiku-4-5-20251001`), with an optional model override so you can trade cost for quality. - **Cleans pages before sending them to the model** — scripts, styles and markup are stripped so you only pay tokens for actual content, capped at a character limit you control (`maxChars`, default 12,000). - **Returns strict JSON** — the model is instructed to answer with pure JSON, and the reply is parsed defensively (code fences and stray prose are handled). - **One row per item** — a single-object answer becomes one dataset row per URL; when the page clearly lists multiple items (a news feed, a product listing), the model returns an array and you get one row per item, each tagged with the source `url`. - **Fails soft** — an unreachable URL or a failed extraction is pushed as a row with an `error` field and the run continues with the remaining URLs. ### Use cases - **Competitor product monitoring** — pull `name`, `price`, `availability` from competitor product pages without writing a scraper per site. - **Content and news pipelines** — turn any article or feed page into `{title, summary, author}` rows, ready for a newsletter digest or a research database. - **AI data extraction pipelines** — use it as the "scrape any website with GPT" step in a larger workflow: feed the dataset into Zapier, Make, or your own code via the Apify API. - **Lead and directory extraction** — extract names, roles and links from listing pages that have no API and no purpose-built scraper. - **One-off datasets** — when a client asks for structured data from an obscure site, describe the fields instead of reverse-engineering the HTML. ### Input | Field | Type | Default | Description | |---|---|---|---| | `startUrls` | array | — (required) | One or more page URLs to extract data from. | | `fields` | array | — | The data points you want, e.g. `"title"`, `"price"`, `"author"`, `"rating"`. Leave empty if you use Instructions instead. | | `instructions` | string | — | Natural-language guidance, e.g. "Extract every news item as a separate object with title and points." | | `llmProvider` | string | `openai` | Which model provider to use: `openai` or `anthropic`. | | `apiKey` | string | — (optional) | Your OpenAI or Anthropic API key. Stored encrypted; used only to call the model. Leave empty to run in no-LLM preview mode. | | `model` | string | — | Override the model. Defaults: OpenAI → `gpt-4o-mini`, Anthropic → `claude-haiku-4-5`. | | `maxChars` | integer | `12000` | How much page text to send to the model (controls cost). Minimum 1,000. | You must set `startUrls`; for full AI extraction also provide `apiKey` plus at least one of `fields` or `instructions`. The `apiKey` is optional — without it the Actor runs in a **no-LLM preview mode** that uses built-in heuristics to return each page's title, meta description, and a short text preview. That's handy for a free test drive, but the structured field extraction described below needs your OpenAI or Anthropic key. ```json { "startUrls": ["https://news.ycombinator.com"], "fields": ["title", "summary", "author"], "llmProvider": "openai", "apiKey": "sk-...", "maxChars": 12000 } ```` #### Where to get an API key - **OpenAI** — create a key at [platform.openai.com/api-keys](https://platform.openai.com/api-keys). - **Anthropic** — create a key at [console.anthropic.com](https://console.anthropic.com/) under API Keys. The key is marked secret in the input schema, so Apify stores it encrypted and it never appears in logs or shared run links. ### Output Each dataset row is the JSON object the model extracted, plus the source `url`. The field names are the ones **you** asked for — the schema of the output is effectively yours to define. With the example input above, a row looks like: ```json { "url": "https://news.ycombinator.com", "title": "Show HN: I built a self-hosted analytics tool in a weekend", "summary": "A lightweight, privacy-first alternative to hosted analytics, written in Go.", "author": "jdoe42" } ``` If a URL can't be fetched or the model's reply can't be parsed, you get a row like `{"url": "https://…", "error": "extraction failed: …"}` instead, so nothing disappears silently. Export everything as JSON, CSV, or Excel from the run's Dataset tab, or pull it via the Apify API. ### Pricing This actor uses **Apify pay-per-event pricing charged per `result`** — you pay only for the rows actually delivered to your dataset, never for setup or empty runs. See the actor's Pricing tab for the current per-result rate. A **free Apify plan is enough to try it**. Separately, **you pay your LLM provider directly for tokens** on your own key. With the default `gpt-4o-mini` or `claude-haiku-4-5` and the 12,000-character cap, a page typically costs a fraction of a cent; lower `maxChars` for cheaper runs or raise it for long pages. ### Tips / FAQ **Does it work on JavaScript-heavy sites?** It fetches the raw HTML over HTTP (no headless browser). Server-rendered pages — most articles, listings, product pages, docs — work well. A single-page app that renders everything client-side may return little usable text; check the row's content before scaling up. **Which provider and model should I pick?** Extraction quality depends on the model you choose. The defaults (`gpt-4o-mini`, `claude-haiku-4-5`) are fast and cheap and handle most pages. For messy or ambiguous pages, set `model` to a stronger one (e.g. `gpt-4o` or a Claude Sonnet model) — same input, better judgment, higher token cost. **How do I get multiple rows from one page?** Use `instructions`, e.g. "Extract every news item as a separate object with title and points." When the model returns an array, each object becomes its own dataset row. **How do I control cost?** Only the first `maxChars` characters of cleaned page text are sent to the model (default 12,000). That cap, plus your choice of model, is your cost lever. **Does it follow links or crawl a whole site?** No — it processes exactly the URLs you provide, one LLM call per page. For crawling, generate the URL list first (e.g. from a sitemap) and pass it in. **What happens on errors?** Failed fetches and failed extractions are logged and pushed as `{url, error}` rows; the run continues with the next URL rather than aborting. ### Related actors - [Smart Scraper by Example](https://apify.com/flash_scraper/smart-scraper-by-example) — show it one example of the data you want and it scrapes the rest of the site to match. - [AI Web Scraper](https://apify.com/flash_scraper/ai-web-scraper) — a sibling AI extraction actor for structured web data. - [Company & Domain Enricher](https://apify.com/flash_scraper/company-domain-enricher) — enrich the domains you scrape into full company records. *** Found a bug or need a feature? Open an Issue on this actor's **Issues** tab — typical response within 1 business day. # Actor input Schema ## `startUrls` (type: `array`): One or more page URLs to extract data from. ## `fields` (type: `array`): The data points you want, e.g. "title", "price", "author", "rating". Leave empty if you use Instructions instead. ## `instructions` (type: `string`): Natural-language guidance, e.g. "Extract every news item as a separate object with title and points." ## `llmProvider` (type: `string`): AI provider used for extraction — OpenAI (GPT) or Anthropic (Claude). Must match the API key you provide below. ## `apiKey` (type: `string`): Optional — without a key the Actor runs in no-LLM preview mode using built-in heuristic extraction. Your OpenAI or Anthropic API key. Stored encrypted; used only to call the model. ## `model` (type: `string`): Override the model. Defaults: OpenAI → gpt-4o-mini, Anthropic → claude-haiku-4-5. ## `maxChars` (type: `integer`): How many characters of page text to send to the model per page — higher captures more of the page but costs more tokens, e.g. 12000. ## Actor input object example ```json { "startUrls": [ "https://example.com/product/123" ], "fields": [ "title", "summary", "author" ], "llmProvider": "openai", "maxChars": 12000 } ``` # 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": [ "https://news.ycombinator.com" ], "fields": [ "title", "summary", "author" ] }; // Run the Actor and wait for it to finish const run = await client.actor("flash_scraper/ai-universal-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": ["https://news.ycombinator.com"], "fields": [ "title", "summary", "author", ], } # Run the Actor and wait for it to finish run = client.actor("flash_scraper/ai-universal-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": [ "https://news.ycombinator.com" ], "fields": [ "title", "summary", "author" ] }' | apify call flash_scraper/ai-universal-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=flash_scraper/ai-universal-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Universal Scraper — Extract Anything from Any Page", "description": "Give any URL + the fields you want; an LLM (OpenAI or Anthropic) extracts clean structured JSON from the page. Works on any site.", "version": "0.1", "x-build-id": "iUAwCnIlyDvdNa1Ed" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/flash_scraper~ai-universal-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-flash_scraper-ai-universal-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/flash_scraper~ai-universal-scraper/runs": { "post": { "operationId": "runs-sync-flash_scraper-ai-universal-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/flash_scraper~ai-universal-scraper/run-sync": { "post": { "operationId": "run-sync-flash_scraper-ai-universal-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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" ], "properties": { "startUrls": { "title": "URLs to scrape", "type": "array", "description": "One or more page URLs to extract data from.", "items": { "type": "string" } }, "fields": { "title": "Fields to extract", "type": "array", "description": "The data points you want, e.g. \"title\", \"price\", \"author\", \"rating\". Leave empty if you use Instructions instead.", "items": { "type": "string" } }, "instructions": { "title": "Extra instructions (optional)", "type": "string", "description": "Natural-language guidance, e.g. \"Extract every news item as a separate object with title and points.\"" }, "llmProvider": { "title": "LLM provider", "enum": [ "openai", "anthropic" ], "type": "string", "description": "AI provider used for extraction — OpenAI (GPT) or Anthropic (Claude). Must match the API key you provide below.", "default": "openai" }, "apiKey": { "title": "LLM API key", "type": "string", "description": "Optional — without a key the Actor runs in no-LLM preview mode using built-in heuristic extraction. Your OpenAI or Anthropic API key. Stored encrypted; used only to call the model." }, "model": { "title": "Model (optional)", "type": "string", "description": "Override the model. Defaults: OpenAI → gpt-4o-mini, Anthropic → claude-haiku-4-5." }, "maxChars": { "title": "Max characters per page", "minimum": 1000, "type": "integer", "description": "How many characters of page text to send to the model per page — higher captures more of the page but costs more tokens, e.g. 12000.", "default": 12000 } } }, "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 } } } } } } } } } } ```