# AI / RAG Web Crawler (`groupoject/ai-rag-web-crawler`) Actor Crawl any website and extract clean, LLM-ready Markdown chunks to feed AI agents, chatbots, and RAG pipelines. One row per embeddable chunk. - **URL**: https://apify.com/groupoject/ai-rag-web-crawler.md - **Developed by:** [Group Oject](https://apify.com/groupoject) (community) - **Categories:** AI, Automation, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 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 / RAG Web Crawler **Crawl any website and get clean, LLM-ready Markdown chunks — ready to feed AI agents, chatbots, and RAG pipelines.** Point it at a docs site, knowledge base, or blog. It crawls the pages, strips the navigation/ads/boilerplate, converts the main content to clean Markdown, and (optionally) splits it into overlapping chunks. **One dataset row per chunk** — pipe it straight into a vector database. > ⚡ Fast HTTP crawler (no headless browser). No API key required. --- ### What it does 1. **Crawls** from your start URLs, following links up to a depth/page limit you set (same-domain by default). 2. **Extracts** the main content — removes nav, header, footer, sidebars, scripts, ads. 3. **Converts** it to clean Markdown (headings, lists, links, code preserved). 4. **Chunks** it into overlapping, embeddings-sized pieces for RAG. Output is **one row per chunk**, each tagged with its source URL, title, and chunk position — exactly the shape you want for an embeddings/vector pipeline. --- ### Who it's for - **AI/RAG builders** — turn a docs site or knowledge base into a clean corpus for retrieval. - **Chatbot makers** — feed your support docs into a customer-facing assistant. - **Agent developers** — give an agent a fresh, structured snapshot of a site. - **Data teams** — bulk-convert web content to Markdown without writing a parser. --- ### Popular use cases - **Docs to RAG dataset** - crawl product documentation into LLM-ready Markdown chunks for embeddings. - **Help center chatbot data** - turn support articles, FAQs, and knowledge bases into clean chatbot context. - **Website to Markdown export** - convert public pages into structured Markdown for analysis or archiving. - **AI agent knowledge refresh** - schedule repeat crawls so agents work from current website content. - **Competitor docs monitoring** - snapshot competitor documentation, pricing pages, or changelogs. - **Blog corpus builder** - collect editorial content into chunked rows for semantic search and content analysis. --- ### Input | Field | Type | Default | Description | |---|---|---|---| | `startUrls` | array | — | URLs to crawl (plain strings or `{ "url": "..." }`) | | `maxCrawlPages` | integer | `50` | Total page cap | | `maxCrawlDepth` | integer | `1` | Link-hops from start URLs (0 = start URLs only) | | `sameDomainOnly` | boolean | `true` | Only follow links on the start domain(s) | | `includeUrlGlobs` | array | — | Only crawl URLs matching these globs (e.g. `https://site.com/docs/*`) | | `excludeUrlGlobs` | array | — | Skip URLs matching these globs (e.g. `*.pdf`) | | `chunkContent` | boolean | `true` | Split pages into RAG chunks (one row each) | | `chunkSize` | integer | `1000` | Target characters per chunk | | `chunkOverlap` | integer | `100` | Overlap chars between chunks | | `minChunkChars` | integer | `50` | Drop chunks smaller than this | | `saveHtml` | boolean | `false` | Also include cleaned HTML | | `maxConcurrency` | integer | `10` | Pages crawled in parallel | | `proxyConfiguration` | object | — | Optional Apify Proxy | #### Example input ```json { "startUrls": [{ "url": "https://docs.apify.com/" }], "maxCrawlPages": 30, "maxCrawlDepth": 2, "includeUrlGlobs": ["https://docs.apify.com/*"], "chunkContent": true, "chunkSize": 1000, "chunkOverlap": 100 } ```` More in [`examples/`](examples/). *** ### Output One dataset row per chunk: ```json { "url": "https://docs.apify.com/platform/actors", "title": "Actors | Apify Docs", "description": "Learn how Apify Actors work.", "chunkIndex": 0, "chunkCount": 4, "content": "# Actors\n\nActors are serverless programs...", "contentChars": 980, "depth": 1, "crawledAt": "2026-06-15T12:00:00.000Z" } ``` To build a vector index: embed the `content` field, store `url` + `title` + `chunkIndex` as metadata. Done. #### Key-value store outputs - `SUMMARY` — pages crawled/failed, total chunks, average chunk size, settings *** ### Tips for clean RAG data - **Use `includeUrlGlobs`** to stay inside the section you care about (e.g. `.../docs/*`) and skip marketing pages. - **`chunkSize` 800–1200 chars** suits most embedding models; bump `chunkOverlap` to 150–200 for prose-heavy sites. - **Turn off `chunkContent`** if you want whole pages (one row each) and prefer to chunk in your own pipeline. - **Exclude noise** with `excludeUrlGlobs` (`*.pdf`, `*/tag/*`, `*/author/*`). *** ### Limitations & compliance - HTTP crawler — it reads server-rendered HTML. Pages that render content purely client-side (heavy SPA) may yield little; those need a browser-based crawler. - Main-content extraction is heuristic (prefers `
`/`
`, strips common boilerplate). Unusual layouts may include or drop some content. - **You choose the targets.** Crawl only sites you're permitted to, respect each site's terms and robots policy, and don't collect private or paywalled data. This Actor accesses publicly reachable pages only. *** ### Changelog See [CHANGELOG.md](CHANGELOG.md). # Actor input Schema ## `startUrls` (type: `array`): URLs to start crawling from. Accepts plain URLs or { "url": "..." } objects. ## `maxCrawlPages` (type: `integer`): Maximum number of pages to crawl in total. ## `maxCrawlDepth` (type: `integer`): How many link-hops to follow from the start URLs. 0 = only the start URLs. ## `sameDomainOnly` (type: `boolean`): Only follow links on the same domain(s) as the start URLs. ## `includeUrlGlobs` (type: `array`): Only crawl URLs matching these glob patterns (e.g. https://site.com/docs/\*). Empty = no include filter. ## `excludeUrlGlobs` (type: `array`): Skip URLs matching these glob patterns (e.g. *.pdf, https://site.com/blog/*). ## `chunkContent` (type: `boolean`): Split each page into overlapping chunks (one dataset row per chunk) — ready for embeddings. Off = one row per page. ## `chunkSize` (type: `integer`): Target characters per chunk. ## `chunkOverlap` (type: `integer`): Characters of context carried between consecutive chunks. ## `minChunkChars` (type: `integer`): Drop chunks shorter than this many characters. ## `saveHtml` (type: `boolean`): Also include the cleaned HTML of the main content in each row. ## `maxConcurrency` (type: `integer`): How many pages to crawl in parallel. ## `proxyConfiguration` (type: `object`): Optional. Use Apify Proxy to crawl sites that block datacenter IPs. ## `debugMode` (type: `boolean`): Verbose logging. ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.apify.com/" } ], "maxCrawlPages": 50, "maxCrawlDepth": 1, "sameDomainOnly": true, "chunkContent": true, "chunkSize": 1000, "chunkOverlap": 100, "minChunkChars": 50, "saveHtml": false, "maxConcurrency": 10, "proxyConfiguration": { "useApifyProxy": true }, "debugMode": false } ``` # Actor output Schema ## `chunks` (type: `string`): One row per chunk: url, title, chunk index, and LLM-ready Markdown content. ## `summary` (type: `string`): Pages crawled/failed, total chunks, average chunk size, settings. # 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/" } ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("groupoject/ai-rag-web-crawler").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/" }], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("groupoject/ai-rag-web-crawler").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/" } ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call groupoject/ai-rag-web-crawler --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=groupoject/ai-rag-web-crawler", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI / RAG Web Crawler", "description": "Crawl any website and extract clean, LLM-ready Markdown chunks to feed AI agents, chatbots, and RAG pipelines. One row per embeddable chunk.", "version": "1.0", "x-build-id": "crjNSgogH8a7BfQ7M" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/groupoject~ai-rag-web-crawler/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-groupoject-ai-rag-web-crawler", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/groupoject~ai-rag-web-crawler/runs": { "post": { "operationId": "runs-sync-groupoject-ai-rag-web-crawler", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/groupoject~ai-rag-web-crawler/run-sync": { "post": { "operationId": "run-sync-groupoject-ai-rag-web-crawler", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 crawling from. Accepts plain URLs or { \"url\": \"...\" } objects.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxCrawlPages": { "title": "Max pages", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of pages to crawl in total.", "default": 50 }, "maxCrawlDepth": { "title": "Max crawl depth", "minimum": 0, "maximum": 20, "type": "integer", "description": "How many link-hops to follow from the start URLs. 0 = only the start URLs.", "default": 1 }, "sameDomainOnly": { "title": "Stay on same domain", "type": "boolean", "description": "Only follow links on the same domain(s) as the start URLs.", "default": true }, "includeUrlGlobs": { "title": "Include URL globs", "type": "array", "description": "Only crawl URLs matching these glob patterns (e.g. https://site.com/docs/*). Empty = no include filter.", "items": { "type": "string" } }, "excludeUrlGlobs": { "title": "Exclude URL globs", "type": "array", "description": "Skip URLs matching these glob patterns (e.g. *.pdf, https://site.com/blog/*).", "items": { "type": "string" } }, "chunkContent": { "title": "Chunk content for RAG", "type": "boolean", "description": "Split each page into overlapping chunks (one dataset row per chunk) — ready for embeddings. Off = one row per page.", "default": true }, "chunkSize": { "title": "Chunk size (characters)", "minimum": 200, "maximum": 20000, "type": "integer", "description": "Target characters per chunk.", "default": 1000 }, "chunkOverlap": { "title": "Chunk overlap (characters)", "minimum": 0, "maximum": 2000, "type": "integer", "description": "Characters of context carried between consecutive chunks.", "default": 100 }, "minChunkChars": { "title": "Minimum chunk size", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Drop chunks shorter than this many characters.", "default": 50 }, "saveHtml": { "title": "Include cleaned HTML", "type": "boolean", "description": "Also include the cleaned HTML of the main content in each row.", "default": false }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 50, "type": "integer", "description": "How many pages to crawl in parallel.", "default": 10 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional. Use Apify Proxy to crawl sites that block datacenter IPs." }, "debugMode": { "title": "Debug mode", "type": "boolean", "description": "Verbose logging.", "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 } } } } } } } } } } ```