# AI Web Content Crawler - Markdown for LLMs (`intelscrape/ai-web-content-crawler`) Actor Crawl any website and extract clean Markdown optimized for LLM training, RAG pipelines, and AI knowledge bases - removes boilerplate and outputs structured JSON with URL, title, markdown, and metadata. - **URL**: https://apify.com/intelscrape/ai-web-content-crawler.md - **Developed by:** [IntelScrape](https://apify.com/intelscrape) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 Crawler — Clean Markdown Output for LLM & RAG Systems Crawl any website and extract clean, structured markdown optimized for LLM training, RAG pipelines, vector databases, and AI knowledge bases. Removes navigation, ads, footers, and boilerplate. Built for AI engineers and developers. ### What you get - Page URL, title, meta description - **Clean markdown content** (no HTML noise) - Word count, reading time estimate - Internal/external links - Headings structure (H1–H6) - Open Graph metadata - Last modified date (if available) ### Output formats - **Markdown** (default, best for LLMs) - **HTML** (raw page) - **Plain text** (stripped) ### Use cases - **RAG pipeline data ingestion** — crawl docs sites, wikis, knowledge bases - **LLM fine-tuning datasets** — extract clean web text at scale - **AI chatbot knowledge bases** — feed your chatbot with fresh website content - **Competitive intelligence** — scrape competitor sites for structured content - **Documentation archiving** — bulk-export technical docs as markdown ### Sample output ```json { "url": "https://docs.stripe.com/api/charges", "title": "Charges | Stripe API Reference", "markdown": "## Charges\n\nTo charge a credit or debit card, you create a `Charge` object...", "wordCount": 1247, "headings": ["Charges", "Create a charge", "Parameters", "Returns"], "internalLinks": ["https://docs.stripe.com/api/charges/create"], "scrapedAt": "2026-03-13T00:00:00Z" } ```` ### Integrations - **LangChain** — use as document loader - **LlamaIndex** — feed as document nodes - **Pinecone / Weaviate / Chroma** — embed and store - **OpenAI fine-tuning** — convert output to JSONL training format - **Make.com / Zapier / n8n** — schedule crawls, send to Google Docs ### Keywords web scraper for AI, RAG data collection, LLM training data scraper, website to markdown, web crawler markdown, AI knowledge base builder, LangChain web scraper, RAG pipeline tool, website content extraction, documentation crawler, AI dataset scraper ### Schedule Run when website content updates. Set up weekly crawls to keep your RAG system fresh. *** ### ?? More Actors by This Developer | Actor | What it does | |-------|-------------| | **Content Freshness Auditor** | Audit the sites you crawl for stale content before ingesting | | **Amazon Scraper** | Crawl Amazon product pages and reviews for AI training data | | **LinkedIn Lead Scraper** | Find AI engineers and LLM teams who need training data | | **Gov Contract Scraper** | Find government AI/ML contracts � target customers for your RAG tools | > ?? **Power combo**: AI Web Crawler (collect) ? Content Freshness Auditor (validate quality) ? ingest only fresh, clean content into your LLM. # Actor input Schema ## `startUrls` (type: `array`): Starting URLs for the crawl ## `maxCrawlPages` (type: `integer`): Max pages to crawl ## `maxDepth` (type: `integer`): Crawl depth ## `outputFormat` (type: `string`): Output format ## `excludeUrlPatterns` (type: `array`): Patterns to skip e.g. \['login', 'cart', 'signup'] ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.example.com" } ], "maxCrawlPages": 100, "maxDepth": 3, "outputFormat": "markdown", "excludeUrlPatterns": [] } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("intelscrape/ai-web-content-crawler").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("intelscrape/ai-web-content-crawler").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 intelscrape/ai-web-content-crawler --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=intelscrape/ai-web-content-crawler", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Web Content Crawler - Markdown for LLMs", "description": "Crawl any website and extract clean Markdown optimized for LLM training, RAG pipelines, and AI knowledge bases - removes boilerplate and outputs structured JSON with URL, title, markdown, and metadata.", "version": "1.0", "x-build-id": "CiwTtyAGgAmdKusL2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/intelscrape~ai-web-content-crawler/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-intelscrape-ai-web-content-crawler", "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/intelscrape~ai-web-content-crawler/runs": { "post": { "operationId": "runs-sync-intelscrape-ai-web-content-crawler", "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/intelscrape~ai-web-content-crawler/run-sync": { "post": { "operationId": "run-sync-intelscrape-ai-web-content-crawler", "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", "properties": { "startUrls": { "title": "URLs to crawl", "type": "array", "description": "Starting URLs for the crawl", "items": { "type": "object", "properties": { "url": { "type": "string", "title": "URL", "description": "URL to crawl" } } }, "default": [ { "url": "https://docs.example.com" } ] }, "maxCrawlPages": { "title": "Max pages to crawl", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Max pages to crawl", "default": 100 }, "maxDepth": { "title": "Crawl depth", "minimum": 1, "maximum": 20, "type": "integer", "description": "Crawl depth", "default": 3 }, "outputFormat": { "title": "Output format", "enum": [ "markdown", "html", "text" ], "type": "string", "description": "Output format", "default": "markdown" }, "excludeUrlPatterns": { "title": "URL patterns to exclude", "type": "array", "description": "Patterns to skip e.g. ['login', 'cart', 'signup']", "items": { "type": "string" }, "default": [] } } }, "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 } } } } } } } } } } ```