# AI Web Scraper - Webscraper with AI based Summery or answer (`sidjain/apify-webscrap`) Actor Web Page Scraper + AI Summary/Answer: Scrapes any URL, extracts content (text, links, images, tables, lists,raw html,tech stack), auto-falls back to headless browser for JS sites, and optionally generates an AI summary/answer from your prompt. Try with frontend at-https://aiscraperweb.netlify.app/ - **URL**: https://apify.com/sidjain/apify-webscrap.md - **Developed by:** [Siddharth Jain](https://apify.com/sidjain) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 48 total users, 4 monthly users, 55.0% 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 ## Web Scraper AI (Apify Actor) Scrape a web page (HTTP first, browser fallback for JS-heavy pages) and optionally generate an AI summary via Pollinations. ### What it does - Validates the input URL (`http` / `https` only) - Tries a fast HTTP scrape first (Axios + Cheerio) - If the page looks blocked / empty / JS-rendered, falls back to a headless browser scrape (Puppeteer + `@sparticuz/chromium`) - Optionally calls Pollinations (`https://text.pollinations.ai/`) to produce a summary using your `prompt` ### Input This Actor expects a JSON input with: - `url` (required): page URL to scrape - `prompt` (optional): if provided, the Actor will request an AI summary Example: ```json { "url": "https://example.com", "prompt": "Summarize the page in 5 bullets" } ```` ### Output The Actor writes results to: - Default dataset (one item per run) - Key-value store as `OUTPUT` Fields include: - `url`, `methodUsed`, `scrapedAt` - `title`, `description`, `paragraphs`, `images`, `links` - `tables`, `lists`, `uniqueComponents`, `rawHTML`, `techStack` - `summary/ai answer` (only if `prompt` is provided) ### Run locally (Windows) Install deps: ```bash npm install ``` Quick smoke test: ```bash npm run test:local ``` Run with your own URL: ```bash npm run set-input -- --url https://example.com --prompt "Summarize this page" npm start ``` Where to find output: - `storage/datasets/default/000000001.json` - `storage/key_value_stores/default/OUTPUT.json` ### Deploy / host on Apify #### Option A: Apify Console (UI) — easiest 1. Zip the project (or connect Git repo) 2. In Apify Console → **Actors** → **Create new** → **Source code** 3. Upload the code 4. Build the Actor image 5. Run it with an input JSON (see **Input** section) #### Option B: Apify CLI If you use the Apify CLI: 1. Install/login: ```bash npm i -g apify-cli apify login ``` 2. From this project folder: ```bash apify push ``` 3. Then run it from Apify Console or via CLI. ### Will I get the “form layout” like other Actors? Yes. Apify shows an input **form UI** automatically when your Actor provides an input schema. This project includes: - `INPUT_SCHEMA.json` (defines the fields) - `actor.json` references it via the `input` property That’s what generates the “every actor has to fill details” form. If you want more fields (proxy, cookies, max pages, etc.), you extend `INPUT_SCHEMA.json` and update the code to read them. ### Notes / limitations - Some sites block scraping (bot protection, captchas, login walls). In those cases, the Actor may return `BLOCKED` / `LOGIN_REQUIRED`. - AI summary depends on Pollinations availability/rate limits. # Actor input Schema ## `url` (type: `string`): Page URL to scrape (http/https). ## `prompt` (type: `string`): If provided, the Actor will request a summary from Pollinations using this prompt. ## Actor input object example ```json {} ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("sidjain/apify-webscrap").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 = {} # Run the Actor and wait for it to finish run = client.actor("sidjain/apify-webscrap").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 '{}' | apify call sidjain/apify-webscrap --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sidjain/apify-webscrap", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Web Scraper - Webscraper with AI based Summery or answer", "description": "Web Page Scraper + AI Summary/Answer: Scrapes any URL, extracts content (text, links, images, tables, lists,raw html,tech stack), auto-falls back to headless browser for JS sites, and optionally generates an AI summary/answer from your prompt.\nTry with frontend at-https://aiscraperweb.netlify.app/", "version": "0.0", "x-build-id": "zJer1K9bJKcNsZMdI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sidjain~apify-webscrap/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sidjain-apify-webscrap", "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/sidjain~apify-webscrap/runs": { "post": { "operationId": "runs-sync-sidjain-apify-webscrap", "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/sidjain~apify-webscrap/run-sync": { "post": { "operationId": "run-sync-sidjain-apify-webscrap", "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": [ "url" ], "properties": { "url": { "title": "URL", "type": "string", "description": "Page URL to scrape (http/https)." }, "prompt": { "title": "AI Prompt (optional)", "type": "string", "description": "If provided, the Actor will request a summary from Pollinations using this prompt." } } }, "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 } } } } } } } } } } ```