# Universal RAG Web Scraper (`express_kingfisher/rag-web-scraper`) Actor Turn any website into clean, LLM-ready Markdown. Automatically strips ads, navigation, and noise using Mozilla Readability. Perfect for feeding data to ChatGPT, Claude, or Vector Databases (RAG). - **URL**: https://apify.com/express\_kingfisher/rag-web-scraper.md - **Developed by:** [Prince Raj](https://apify.com/express_kingfisher) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 4 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 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 ## 🕷️ Universal RAG Web Scraper **Turn any website into LLM-ready Markdown.** This Actor is designed specifically for AI engineers and developers building RAG (Retrieval-Augmented Generation) pipelines. It scrapes websites and converts them into clean, structured Markdown, stripping away noise like navigation bars, ads, and footers. ### 🚀 Why use this Actor? - **LLM-Optimized Output**: Uses Mozilla's Readability engine + Turndown to produce noise-free Markdown. - **Metadata Rich**: Extracts title, byline, published time, and site name for better vector embedding. - **Smart Chunking Ready**: The output format is perfect for semantic chunking strategies. - **Deep & Focused**: Supports glob patterns to scrape complete documentation sections (e.g., `**/docs/**`). ### 🛠️ Usage #### Inputs | Field | Type | Description | Default | |-------|------|-------------|---------| | `startUrls` | Array | List of URLs to start crawling. | (Required) | | `maxDepth` | Integer | How deep to crawl. `0` = single page, `1` = links on page. | `1` | | `includeGlobs` | Array | Only visit URLs matching these patterns (e.g., `**/blog/**`). | `[]` | | `excludeGlobs` | Array | Skip URLs matching these patterns. | `[]` | | `cleanMarkdown` | Boolean | Remove images and links for text-only output. | `false` | #### Output Example ```json { "url": "https://example.com/blog/article", "title": "How to Build a Scraper", "description": "A comprehensive guide to scraping.", "markdown": "# How to Build a Scraper\n\nScraping is fun...", "metadata": { "siteName": "Example Blog", "publishedTime": "2023-10-01" }, "crawledAt": "2023-10-25T10:00:00.000Z" } ```` ### 📦 Integration Easily integrate with LangChain, Haystack, or your custom Python/JS scripts using the Apify API. ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("antigravity/rag-web-scraper").call(run_input={ "startUrls": [{"url": "https://react.dev/reference/react"}], "maxDepth": 2, "includeGlobs": ["**/reference/**"] }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["markdown"]) ``` ### 🏆 Goals Built to solve the "Context Retrieval" problem for modern AI applications. # Actor input Schema ## `startUrls` (type: `array`): URLs to start scraping from. ## `maxDepth` (type: `integer`): How deep to crawl. 0 for single page. ## `includeGlobs` (type: `array`): Glob patterns to include. E.g. '**/docs/**' ## `excludeGlobs` (type: `array`): Glob patterns to exclude. ## `cleanMarkdown` (type: `boolean`): Remove links and images for cleaner text. ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" } ], "maxDepth": 1, "includeGlobs": [], "excludeGlobs": [], "cleanMarkdown": 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 = { "startUrls": [ { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("express_kingfisher/rag-web-scraper").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/academy/web-scraping-for-beginners" }] } # Run the Actor and wait for it to finish run = client.actor("express_kingfisher/rag-web-scraper").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/academy/web-scraping-for-beginners" } ] }' | apify call express_kingfisher/rag-web-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=express_kingfisher/rag-web-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Universal RAG Web Scraper", "description": "Turn any website into clean, LLM-ready Markdown. Automatically strips ads, navigation, and noise using Mozilla Readability. Perfect for feeding data to ChatGPT, Claude, or Vector Databases (RAG).", "version": "1.0", "x-build-id": "Mqj7qGYtebwIwAz7x" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/express_kingfisher~rag-web-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-express_kingfisher-rag-web-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/express_kingfisher~rag-web-scraper/runs": { "post": { "operationId": "runs-sync-express_kingfisher-rag-web-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/express_kingfisher~rag-web-scraper/run-sync": { "post": { "operationId": "run-sync-express_kingfisher-rag-web-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Start URLs", "type": "array", "description": "URLs to start scraping from.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxDepth": { "title": "Max Crawl Depth", "minimum": 0, "type": "integer", "description": "How deep to crawl. 0 for single page.", "default": 1 }, "includeGlobs": { "title": "Include Globs", "type": "array", "description": "Glob patterns to include. E.g. '**/docs/**'", "default": [], "items": { "type": "string" } }, "excludeGlobs": { "title": "Exclude Globs", "type": "array", "description": "Glob patterns to exclude.", "default": [], "items": { "type": "string" } }, "cleanMarkdown": { "title": "Clean Markdown", "type": "boolean", "description": "Remove links and images for cleaner text.", "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 } } } } } } } } } } ```