# Website to Markdown – Clean LLM & RAG Content Extractor (`dataquarry/website-to-markdown`) Actor Convert any public web page to clean, LLM-ready Markdown with metadata — by URL, a list of URLs, or a whole-site crawl. Strips nav/ads/boilerplate, keeps headings/lists/tables/code. Respects robots.txt. No API key. - **URL**: https://apify.com/dataquarry/website-to-markdown.md - **Developed by:** [Daniel Brenner](https://apify.com/dataquarry) (community) - **Categories:** Developer tools, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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 ## Website to Markdown — Clean LLM & RAG Content Extractor **Convert any public web page to clean, LLM-ready Markdown — by URL, a list of URLs, or a whole-site crawl.** No API key, no browser to manage. The tool fetches the page, strips navigation, ads and boilerplate, and returns tidy Markdown plus structured metadata — ideal for feeding LLMs, building RAG pipelines, archiving articles, or training datasets. It's the no-fuss answer to "**how do I turn a website into Markdown for ChatGPT / a vector database / an AI agent?**" — give it URLs, get Markdown back as JSON, CSV or Excel. ### What it does - **HTML → clean Markdown**, with headings, lists, tables, code blocks and links preserved (GitHub-flavored). - **Main-content extraction** — removes menus, headers, footers, sidebars, cookie banners and ads so the Markdown is just the article, not the chrome. (Or keep the full page if you prefer.) - **Structured metadata** per page — title, description, author, published date, language, site name, canonical URL. - **RAG-ready extras** — optional token-count estimate and overlapping chunks for retrieval-augmented generation. - **Three modes** — a single page, a list of pages, or crawl a site (same-domain link following, depth- and page-limited). - **Respects `robots.txt`** and sends a descriptive User-Agent. Public pages only — no logins, no paywalls. ### What you get (per page) | field | description | |-------|-------------| | `url` / `finalUrl` | requested URL, and the final URL after redirects (if different) | | `title`, `description`, `author`, `publishedDate`, `lang`, `siteName` | page metadata (honest-null when not present) | | `canonicalUrl` | the page's canonical link, if declared | | `markdown` | the clean, LLM-ready Markdown | | `wordCount`, `tokenEstimate` | length, plus an **approximate** LLM token count (a heuristic estimate, not exact) | | `chunks` | optional RAG chunks (`{index, text}`) when "Chunk for RAG" is on | | `links`, `images` | optional lists of absolute links / image URLs on the page | | `httpStatus`, `fetchedAt`, `robotsAllowed` | response status, fetch time, robots check result | Anything a page doesn't expose comes back as `null` — never guessed. ### How to use it **Single page or a list:** ```json { "startUrls": ["https://example.com/blog/post"], "mode": "single" } ```` ```json { "startUrls": ["https://a.com/p1", "https://a.com/p2"], "mode": "list" } ``` **Crawl a site for a RAG dataset:** ```json { "startUrls": ["https://docs.example.com/"], "mode": "crawl", "maxCrawlDepth": 2, "maxPages": 100, "sameDomainOnly": true, "chunkForRag": true, "chunkSize": 2000, "chunkOverlap": 200 } ``` Set `contentMode` to `main` (default — just the article) or `full` (the whole page). Toggle `includeLinks` / `includeImages` to also collect a page's links and images. ### Why this tool - **No API key, no headless browser to babysit** — give it URLs, get Markdown. - **Clean output** — boilerplate-stripped main content, not a raw HTML dump. - **LLM-first** — Markdown + metadata + optional chunking and token estimate, the exact shape RAG and fine-tuning pipelines want. - **Polite & transparent** — respects `robots.txt`, identifies itself, fetches only public pages one at a time. Pricing is **pay-per-result: $2 per 1,000 pages** — you only pay for pages successfully converted. Export as JSON, CSV or Excel. ### Responsible use This tool reformats the public pages you point it at — the same content a browser would load. You're responsible for how you use content from sites you don't own (respect each site's terms and applicable copyright). The tool honors `robots.txt` and never bypasses logins or paywalls. ### FAQ **Do I need an API key?** No. Give it one or more URLs and run it — no key, no quota. **Can it convert a whole website, not just one page?** Yes — use `crawl` mode with a start URL. It follows same-domain links up to the depth and page limits you set, converting each page to Markdown. **Is the Markdown ready for an LLM / RAG?** Yes — that's the point. You get clean Markdown plus metadata, an approximate token count, and optional overlapping chunks for retrieval-augmented generation. **How much does it cost?** Pay-per-result: $2 per 1,000 pages — you only pay for the pages you actually get. # Actor input Schema ## `startUrls` (type: `array`): Web page URLs to convert to Markdown. In crawl mode these are the starting pages. ## `mode` (type: `string`): single = first URL only; list = every URL given; crawl = follow same-domain links from each start URL. ## `contentMode` (type: `string`): main = extract the primary article/content and strip nav, ads and boilerplate; full = convert the whole page. ## `maxResults` (type: `integer`): Maximum number of pages to return. ## `maxCrawlDepth` (type: `integer`): Crawl mode only: how many link-hops from each start URL to follow. ## `maxPages` (type: `integer`): Crawl mode only: hard cap on pages visited. ## `sameDomainOnly` (type: `boolean`): Crawl mode only: only follow links on the same domain as the start URL. ## `includeLinks` (type: `boolean`): Also output the list of links found on each page. ## `includeImages` (type: `boolean`): Also output the list of image URLs found on each page. ## `computeTokenEstimate` (type: `boolean`): Add an approximate LLM token count for each page's Markdown (a heuristic estimate, not exact). ## `chunkForRag` (type: `boolean`): Also split each page's Markdown into overlapping chunks for retrieval-augmented generation. ## `chunkSize` (type: `integer`): Target chunk size in characters when chunking for RAG. ## `chunkOverlap` (type: `integer`): Overlap between consecutive chunks when chunking for RAG. ## `respectRobotsTxt` (type: `boolean`): Skip URLs disallowed by the site's robots.txt for our user-agent. ## `requestDelayMs` (type: `integer`): Optional politeness delay between pages, in milliseconds (pages are fetched one at a time). ## Actor input object example ```json { "startUrls": [ "https://en.wikipedia.org/wiki/OpenStreetMap" ], "mode": "list", "contentMode": "main", "maxResults": 50, "maxCrawlDepth": 1, "maxPages": 50, "sameDomainOnly": true, "includeLinks": false, "includeImages": false, "computeTokenEstimate": true, "chunkForRag": false, "chunkSize": 2000, "chunkOverlap": 200, "respectRobotsTxt": true, "requestDelayMs": 0 } ``` # 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://en.wikipedia.org/wiki/OpenStreetMap" ] }; // Run the Actor and wait for it to finish const run = await client.actor("dataquarry/website-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 = { "startUrls": ["https://en.wikipedia.org/wiki/OpenStreetMap"] } # Run the Actor and wait for it to finish run = client.actor("dataquarry/website-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 '{ "startUrls": [ "https://en.wikipedia.org/wiki/OpenStreetMap" ] }' | apify call dataquarry/website-to-markdown --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dataquarry/website-to-markdown", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Website to Markdown – Clean LLM & RAG Content Extractor", "description": "Convert any public web page to clean, LLM-ready Markdown with metadata — by URL, a list of URLs, or a whole-site crawl. Strips nav/ads/boilerplate, keeps headings/lists/tables/code. Respects robots.txt. No API key.", "version": "0.0", "x-build-id": "xVZvLslVTu6RrYQDy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dataquarry~website-to-markdown/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dataquarry-website-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dataquarry~website-to-markdown/runs": { "post": { "operationId": "runs-sync-dataquarry-website-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dataquarry~website-to-markdown/run-sync": { "post": { "operationId": "run-sync-dataquarry-website-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "startUrls": { "title": "URLs", "type": "array", "description": "Web page URLs to convert to Markdown. In crawl mode these are the starting pages.", "items": { "type": "string" } }, "mode": { "title": "Mode", "enum": [ "single", "list", "crawl" ], "type": "string", "description": "single = first URL only; list = every URL given; crawl = follow same-domain links from each start URL.", "default": "list" }, "contentMode": { "title": "Content", "enum": [ "main", "full" ], "type": "string", "description": "main = extract the primary article/content and strip nav, ads and boilerplate; full = convert the whole page.", "default": "main" }, "maxResults": { "title": "Max results (pages)", "minimum": 1, "type": "integer", "description": "Maximum number of pages to return.", "default": 50 }, "maxCrawlDepth": { "title": "Max crawl depth", "minimum": 0, "type": "integer", "description": "Crawl mode only: how many link-hops from each start URL to follow.", "default": 1 }, "maxPages": { "title": "Max pages to crawl", "minimum": 1, "type": "integer", "description": "Crawl mode only: hard cap on pages visited.", "default": 50 }, "sameDomainOnly": { "title": "Same domain only", "type": "boolean", "description": "Crawl mode only: only follow links on the same domain as the start URL.", "default": true }, "includeLinks": { "title": "Include links list", "type": "boolean", "description": "Also output the list of links found on each page.", "default": false }, "includeImages": { "title": "Include images list", "type": "boolean", "description": "Also output the list of image URLs found on each page.", "default": false }, "computeTokenEstimate": { "title": "Estimate token count", "type": "boolean", "description": "Add an approximate LLM token count for each page's Markdown (a heuristic estimate, not exact).", "default": true }, "chunkForRag": { "title": "Chunk for RAG", "type": "boolean", "description": "Also split each page's Markdown into overlapping chunks for retrieval-augmented generation.", "default": false }, "chunkSize": { "title": "Chunk size (characters)", "minimum": 200, "type": "integer", "description": "Target chunk size in characters when chunking for RAG.", "default": 2000 }, "chunkOverlap": { "title": "Chunk overlap (characters)", "minimum": 0, "type": "integer", "description": "Overlap between consecutive chunks when chunking for RAG.", "default": 200 }, "respectRobotsTxt": { "title": "Respect robots.txt", "type": "boolean", "description": "Skip URLs disallowed by the site's robots.txt for our user-agent.", "default": true }, "requestDelayMs": { "title": "Delay between requests (ms)", "minimum": 0, "type": "integer", "description": "Optional politeness delay between pages, in milliseconds (pages are fetched one at a time).", "default": 0 } } }, "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 } } } } } } } } } } ```