# AI Web Crawler → Markdown for LLM & RAG Knowledge Bases (`lukas459/ai-web-to-markdown-crawler-llm-rag-optimized`) Actor Website crawler that extracts clean Markdown for LLMs, RAG systems, and AI agents. Supports sitemap crawling, bulk URLs, and large websites while removing ads, navigation, and cookie noise. Create AI-ready datasets for chatbots, knowledge bases, and vector search. - **URL**: https://apify.com/lukas459/ai-web-to-markdown-crawler-llm-rag-optimized.md - **Developed by:** [lukas schmeck](https://apify.com/lukas459) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing $3.00 / 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 ## AI-Ready Web to Markdown Crawler 🚀 Convert any website into clean, noise-free Markdown optimized perfectly for LLMs (ChatGPT, Claude, Llama), RAG systems, and AI Agents. ### Why use this Actor? 🤔 Standard web scrapers dump raw HTML full of cookie banners, navigation menus, ads, and footers. This wastes valuable context tokens and confuses AI models. This Actor automatically filters out the background noise, extracts only the **main article or text content**, and formats it into pristine Markdown. #### Key Features ✨ - **Main Content Extraction:** Strips headers, footers, sidebars, and ads automatically. - **LLM-Optimized:** Outputs clean Markdown (`# Headings`, `* Lists`, `**Bold text**`) that AIs love. - **Bulk Processing:** Drop a list of multiple URLs to process them all in one single run. - **Ultra Fast & Cost-Effective:** Lightweight processing that saves your compute budget. ### Input Example 📥 Simply provide one or multiple URLs: - `https://en.wikipedia.org/wiki/Python_(programming_language)` ### Output Example 📤 You will get a structured JSON dataset containing: - `source_url`: The original link. - `markdown_content`: The clean text ready to be fed into your AI application or vector database. # Actor input Schema ## `startUrls` (type: `array`): Füge hier normale URLs oder Links zu sitemap.xml ein. ## `maxPages` (type: `integer`): Wie viele Unterseiten maximal gecrawlt werden sollen. ## `chunkSize` (type: `integer`): Maximale Anzahl an Zeichen pro Text-Abschnitt für die KI. ## `chunkOverlap` (type: `integer`): Wie viele Zeichen sollen sich zwischen zwei Chunks überschneiden? ## Actor input object example ```json { "startUrls": [ { "url": "" } ], "maxPages": 50, "chunkSize": 1000, "chunkOverlap": 200 } ```` # 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 = { "startUrls": [ { "url": "" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("lukas459/ai-web-to-markdown-crawler-llm-rag-optimized").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": [{ "url": "" }] } # Run the Actor and wait for it to finish run = client.actor("lukas459/ai-web-to-markdown-crawler-llm-rag-optimized").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": [ { "url": "" } ] }' | apify call lukas459/ai-web-to-markdown-crawler-llm-rag-optimized --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lukas459/ai-web-to-markdown-crawler-llm-rag-optimized", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Web Crawler → Markdown for LLM & RAG Knowledge Bases", "description": "Website crawler that extracts clean Markdown for LLMs, RAG systems, and AI agents. Supports sitemap crawling, bulk URLs, and large websites while removing ads, navigation, and cookie noise. Create AI-ready datasets for chatbots, knowledge bases, and vector search.", "version": "0.0", "x-build-id": "cADzWN46d3IsIsqQH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lukas459~ai-web-to-markdown-crawler-llm-rag-optimized/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lukas459-ai-web-to-markdown-crawler-llm-rag-optimized", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lukas459~ai-web-to-markdown-crawler-llm-rag-optimized/runs": { "post": { "operationId": "runs-sync-lukas459-ai-web-to-markdown-crawler-llm-rag-optimized", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lukas459~ai-web-to-markdown-crawler-llm-rag-optimized/run-sync": { "post": { "operationId": "run-sync-lukas459-ai-web-to-markdown-crawler-llm-rag-optimized", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Start URLs oder Sitemaps", "type": "array", "description": "Füge hier normale URLs oder Links zu sitemap.xml ein.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxPages": { "title": "Maximale Anzahl an Seiten", "type": "integer", "description": "Wie viele Unterseiten maximal gecrawlt werden sollen.", "default": 50 }, "chunkSize": { "title": "Chunk Größe (Zeichen)", "type": "integer", "description": "Maximale Anzahl an Zeichen pro Text-Abschnitt für die KI.", "default": 1000 }, "chunkOverlap": { "title": "Chunk Überlappung (Zeichen)", "type": "integer", "description": "Wie viele Zeichen sollen sich zwischen zwei Chunks überschneiden?", "default": 200 } } }, "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 } } } } } } } } } } ```