# Website to Markdown Converter for LLM Training (`pink_comic/website-content-to-markdown`) Actor Convert any web page to clean Markdown. Strips nav, ads, scripts, styling. Preserves headings, lists, tables, code blocks, links. Perfect for LLM training data, RAG pipelines, content migration, documentation archival, and text analysis. Bulk processing with word/link/image counts. - **URL**: https://apify.com/pink\_comic/website-content-to-markdown.md - **Developed by:** [Ava Torres](https://apify.com/pink_comic) (community) - **Categories:** Developer tools, AI - **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 ### Website to Markdown Converter for LLM Training | $0.002/page Convert any web page to clean, structured Markdown optimized for LLM training, RAG pipelines, and content processing. Process hundreds of URLs in a single run with concurrent fetching. #### What it does - Fetches web pages and extracts the main readable content - Strips navigation, ads, footers, scripts, styles, and other non-content elements - Converts HTML to Markdown preserving structure: headings, paragraphs, lists, tables, code blocks, blockquotes, and inline formatting - Optionally includes links and images as Markdown references - Reports metadata per page: word count, character count, link count, image count #### Use cases - **LLM training data** -- Convert web content to clean text for fine-tuning datasets and RAG retrieval pipelines - **AI knowledge bases** -- Build structured Markdown knowledge bases from documentation sites, wikis, and help centers - **Content migration** -- Move content between CMS platforms (WordPress, Ghost, Notion) in portable Markdown format - **Documentation archival** -- Archive web-based docs as version-controlled Markdown files - **SEO content auditing** -- Extract and analyze competitor content structure and word counts at scale - **Text analysis** -- Clean text extraction for NLP processing, sentiment analysis, and topic modeling - **Research data collection** -- Scrape articles, blog posts, and papers into a structured dataset #### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `urls` | string[] | required | List of URLs to convert | | `maxResults` | integer | 100 | Maximum pages to process | | `includeLinks` | boolean | true | Keep hyperlinks in the Markdown output | | `includeImages` | boolean | false | Keep image references in the Markdown output | #### Output fields Each result in the dataset includes: | Field | Description | |-------|-------------| | `url` | Source page URL | | `title` | Page title | | `markdown` | Full Markdown content with structure preserved | | `wordCount` | Number of words in the extracted content | | `characterCount` | Total characters in the Markdown output | | `linkCount` | Number of hyperlinks found | | `imageCount` | Number of images found | #### Example output ```json { "url": "https://en.wikipedia.org/wiki/Web_scraping", "title": "Web scraping - Wikipedia", "markdown": "### Web scraping\n\nWeb scraping is data scraping used for extracting data from websites...", "wordCount": 3847, "characterCount": 24102, "linkCount": 156, "imageCount": 2 } ```` #### Pricing $0.002 per page converted. Convert 500 pages for $1. #### Integrations Works with Apify's API, webhooks, and MCP server integration. Chain with other actors to build automated content pipelines. # Actor input Schema ## `urls` (type: `array`): List of web page URLs to convert to Markdown. Enter one URL per line. ## `maxResults` (type: `integer`): Maximum number of pages to convert. Use this to control costs. ## `includeLinks` (type: `boolean`): Include hyperlinks in the Markdown output. Set to false for plain text. ## `includeImages` (type: `boolean`): Include image references in the Markdown output. ## Actor input object example ```json { "urls": [ "https://en.wikipedia.org/wiki/Web_scraping", "https://docs.apify.com/academy/getting-started" ], "maxResults": 100, "includeLinks": true, "includeImages": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://en.wikipedia.org/wiki/Web_scraping", "https://docs.apify.com/academy/getting-started" ] }; // Run the Actor and wait for it to finish const run = await client.actor("pink_comic/website-content-to-markdown").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "urls": [ "https://en.wikipedia.org/wiki/Web_scraping", "https://docs.apify.com/academy/getting-started", ] } # Run the Actor and wait for it to finish run = client.actor("pink_comic/website-content-to-markdown").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 '{ "urls": [ "https://en.wikipedia.org/wiki/Web_scraping", "https://docs.apify.com/academy/getting-started" ] }' | apify call pink_comic/website-content-to-markdown --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=pink_comic/website-content-to-markdown", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Website to Markdown Converter for LLM Training", "description": "Convert any web page to clean Markdown. Strips nav, ads, scripts, styling. Preserves headings, lists, tables, code blocks, links. Perfect for LLM training data, RAG pipelines, content migration, documentation archival, and text analysis. Bulk processing with word/link/image counts.", "version": "1.0", "x-build-id": "eS4aM3NSRTZalQtco" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/pink_comic~website-content-to-markdown/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-pink_comic-website-content-to-markdown", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/pink_comic~website-content-to-markdown/runs": { "post": { "operationId": "runs-sync-pink_comic-website-content-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/pink_comic~website-content-to-markdown/run-sync": { "post": { "operationId": "run-sync-pink_comic-website-content-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls" ], "properties": { "urls": { "title": "Page URLs", "type": "array", "description": "List of web page URLs to convert to Markdown. Enter one URL per line.", "items": { "type": "string" } }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of pages to convert. Use this to control costs.", "default": 100 }, "includeLinks": { "title": "Include Links", "type": "boolean", "description": "Include hyperlinks in the Markdown output. Set to false for plain text.", "default": true }, "includeImages": { "title": "Include Images", "type": "boolean", "description": "Include image references in the Markdown output.", "default": false } } }, "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 } } } } } } } } } } ```