# Markdown Anything — URL to Markdown (`s-r/markdown-anything`) Actor Convert any URL to clean markdown using a 3-provider fallback chain. Batch input, high concurrency. - **URL**: https://apify.com/s-r/markdown-anything.md - **Developed by:** [SR](https://apify.com/s-r) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 2 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 url converteds 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 ## Free URL to Markdown API — LLM-Ready Web Content Convert any URL to clean markdown — built for **LLM context windows**, **RAG pipelines**, and **AI training data**. Multi-provider fallback chain delivers high success rates on JavaScript-heavy SPAs where single-source URL-to-markdown converters fail. ### What you get Per URL: - `markdown` — clean markdown body (titles, paragraphs, links, code blocks preserved) - `provider` — internal label of which extraction backend succeeded - `length` — character count - `fetched_in_seconds` — wall time - `error` — set only when every backend in the fallback chain fails ### Why use a URL to markdown API Markdown is **70-90% smaller than equivalent HTML** for the same content, which means dramatically lower token costs when you feed pages into an LLM. This URL to markdown converter is built for: - **RAG pipelines** — clean text into your vector store, no HTML noise - **LLM context windows** — fit 4-5× more pages in the same prompt budget - **AI training data** — bulk URL-to-markdown for fine-tuning datasets - **Content migration / archival** — extract the substance, drop the chrome ### Why fallback chain beats single-provider markdown actors Most URL-to-markdown actors call one extraction service. When that service is down, rate-limited, or fails on a particular page, the actor errors out and you pay for nothing. This Actor tries **multiple managed extraction backends in sequence per URL** — the first that returns a valid response wins. You get measurably higher success rates on the same input, especially for: - JavaScript-heavy SPAs (React, Vue, Next.js apps) - Cloudflare-protected pages - Sites with aggressive bot detection - Geo-restricted content ### Input | Field | Default | Description | |---|---|---| | `urls` | required | Array of URLs to convert to markdown | | `concurrency` | 25 | Concurrent fetches (1-100) — set lower if you hit rate limits | ### Output (sample) ```json { "url": "https://en.wikipedia.org/wiki/Apify", "provider": "provider_a", "markdown": "## Apify\n\nApify is a software platform...", "length": 6087, "fetched_in_seconds": 3.45 } ```` ### Use cases - **AI agents that browse the web** — feed them clean markdown, not HTML - **RAG over arbitrary URLs** — bulk-ingest documentation, blogs, news - **Competitive analysis pipelines** — bulk-scrape competitor pages for LLM analysis - **News monitoring** — convert article URLs to clean text for sentiment analysis - **Knowledge base building** — turn a list of URLs into a clean markdown corpus ### Related Actors — Free SEO Suite This actor pairs with two other free tools for a complete SEO research workflow: - **[Free Backlink Checker](https://apify.com/s-r/backlinks-checker)** — domain authority + full backlink list for any website - **[Free Keyword Research Tool for SEO](https://apify.com/s-r/google-keywords)** — volume + CPC + difficulty + intent for any keyword Use the keyword research tool to find what to write about, the backlink checker to score competitors' authority, and this actor to bulk-extract their content for analysis. # Actor input Schema ## `urls` (type: `array`): List of URLs to convert to markdown. ## `concurrency` (type: `integer`): Concurrent URL fetches (1-100). ## Actor input object example ```json { "urls": [ "https://example.com" ], "concurrency": 25 } ``` # 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://example.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("s-r/markdown-anything").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://example.com"] } # Run the Actor and wait for it to finish run = client.actor("s-r/markdown-anything").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://example.com" ] }' | apify call s-r/markdown-anything --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=s-r/markdown-anything", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Markdown Anything — URL to Markdown", "description": "Convert any URL to clean markdown using a 3-provider fallback chain. Batch input, high concurrency.", "version": "0.1", "x-build-id": "FPUTVZaHsTFYgJdcG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/s-r~markdown-anything/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-s-r-markdown-anything", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/s-r~markdown-anything/runs": { "post": { "operationId": "runs-sync-s-r-markdown-anything", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/s-r~markdown-anything/run-sync": { "post": { "operationId": "run-sync-s-r-markdown-anything", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "URLs", "type": "array", "description": "List of URLs to convert to markdown.", "items": { "type": "string" } }, "concurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 100, "type": "integer", "description": "Concurrent URL fetches (1-100).", "default": 25 } } }, "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 } } } } } } } } } } ```