# rag-docs-scraper (`marbled_jury/my-actor`) Actor Extract clean, RAG-optimized Markdown from any technical documentation. Built for LLMs and AI agents. No noise, just high-fidelity data. - **URL**: https://apify.com/marbled\_jury/my-actor.md - **Developed by:** [Hastin S.](https://apify.com/marbled_jury) (community) - **Categories:** AI, Agents, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% 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 Documentation & RAG Scraper 🤖📄 The **AI Documentation & RAG Scraper** is a high-performance tool designed to transform messy technical documentation into clean, structured **Markdown**. It is specifically optimized for **RAG (Retrieval-Augmented Generation)** pipelines, LLM fine-tuning, and AI agents. Stop feeding your AI noisy HTML. Get the clean text you need, instantly. --- ### ✨ Key Features - **Markdown Optimized:** Automatically converts HTML to clean Markdown while preserving headers, code blocks, and tables. - **Noise Removal:** Smartly identifies and strips out navbars, footers, sidebars, and cookie banners to focus only on the content. - **Modern Web Support:** Powered by **Playwright**, it easily handles JavaScript-heavy documentation sites (React, Docusaurus, GitBook, Next.js). - **Recursive Crawling:** Provide a homepage, and the scraper will automatically follow internal links to map out the entire documentation set. - **AI-Agent Ready:** Output is structured perfectly for Vector Databases (Pinecone, Weaviate) or direct upload to ChatGPT/Claude. --- ### 🚀 How to Use 1. **Input URLs:** Enter the starting URL of the documentation you want to scrape (e.g., `https://docs.apify.com/`). 2. **Set Page Limit:** Define how many pages you want to crawl to stay within your budget. 3. **Run & Download:** Start the Actor and download your results in JSON, CSV, or Excel. --- ### 🛠️ Input Configuration | Field | Type | Description | | :--- | :--- | :--- | | **Start URLs** | Array | The entry points for the crawl. Supports multiple URLs. | | **Max Pages** | Integer | The maximum number of pages to crawl (default: 50). | | **Proxy** | Object | Uses Apify Proxy to ensure high success rates and avoid rate limits. | --- ### 📊 Sample Output ```json { "url": "[https://crawlee.dev/docs/quick-start](https://crawlee.dev/docs/quick-start)", "title": "Quick Start | Crawlee", "markdown": "## Quick Start\n\nInstall Crawlee using npm...\n\n```bash\nnpm install crawlee playwright\n```", "scrapedAt": "2026-05-07T12:00:00Z" } # Actor input Schema ## `startUrls` (type: `array`): Enter the homepages of the technical documentation you want to scrape. ## `maxPages` (type: `integer`): Limit the number of pages to scrape to control your costs. ## `proxyConfiguration` (type: `object`): Recommended: Use 'Automatic' proxies to avoid being blocked by anti-bot systems. ## `outputFormat` (type: `string`): Select your preferred format for the extracted content. ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.apify.com/" } ], "maxPages": 50, "proxyConfiguration": { "useApifyProxy": true }, "outputFormat": "Markdown" } ```` # 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": "https://docs.apify.com/" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("marbled_jury/my-actor").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": "https://docs.apify.com/" }] } # Run the Actor and wait for it to finish run = client.actor("marbled_jury/my-actor").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": "https://docs.apify.com/" } ] }' | apify call marbled_jury/my-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=marbled_jury/my-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "rag-docs-scraper", "description": "Extract clean, RAG-optimized Markdown from any technical documentation. Built for LLMs and AI agents. No noise, just high-fidelity data.", "version": "0.0", "x-build-id": "oS5RweiRqyquWb90X" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/marbled_jury~my-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-marbled_jury-my-actor", "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/marbled_jury~my-actor/runs": { "post": { "operationId": "runs-sync-marbled_jury-my-actor", "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/marbled_jury~my-actor/run-sync": { "post": { "operationId": "run-sync-marbled_jury-my-actor", "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": [ "startUrls" ], "properties": { "startUrls": { "title": "Documentation URLs", "type": "array", "description": "Enter the homepages of the technical documentation you want to scrape.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxPages": { "title": "Maximum Pages", "minimum": 1, "type": "integer", "description": "Limit the number of pages to scrape to control your costs.", "default": 50 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Recommended: Use 'Automatic' proxies to avoid being blocked by anti-bot systems.", "default": { "useApifyProxy": true } }, "outputFormat": { "title": "Output Format", "enum": [ "Markdown", "HTML", "Plain Text" ], "type": "string", "description": "Select your preferred format for the extracted content.", "default": "Markdown" } } }, "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 } } } } } } } } } } ```