# Article Extractor — Clean Web Content to Markdown/Text (`omao/article-extractor`) Actor Extract the main article from any web page into clean Markdown or text, with title, author, date and description. Strips nav, ads and boilerplate. Fast, no setup. - **URL**: https://apify.com/omao/article-extractor.md - **Developed by:** [Marouane Oulabass](https://apify.com/omao) (community) - **Categories:** Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.16 / 1,000 article extracteds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Article Extractor — Clean Web Content to Markdown / Text **Extract the main article from any web page — as clean Markdown or plain text.** This actor strips away nav bars, ads, cookie banners, sidebars and boilerplate, and returns just the content that matters, plus metadata: title, author, publish date, description, site name and language. Perfect for feeding clean text to LLMs, building datasets, or archiving content. > AI-agent ready: this Actor is callable and payable by AI agents via [x402](https://docs.apify.com/platform/integrations/x402) (USDC on Base), no Apify account required. Give it a URL, get back a clean article. No readability tuning, no HTML soup, no setup. ### Who uses this - 🤖 **AI / LLM & RAG pipelines** — get clean Markdown to embed, summarize or fine-tune on. - 📰 **Content & news monitoring** — archive articles as readable text with metadata. - 🔎 **Researchers & analysts** — build clean corpora from the open web. - ✍️ **Marketers & writers** — pull competitor content and references without the clutter. - 🧑‍💻 **Developers** — a reliable readability/boilerplate-removal endpoint by URL. ### What you get — one clean row per URL | Field | Description | |---|---| | `content` | **The main article** as clean Markdown or plain text | | `title` | Article title | | `author` | Author / byline | | `date` | Publish date | | `description` | Meta description / summary | | `sitename` | Publisher / site name | | `language` | Detected language | | `wordCount` | Word count of the extracted body | | `bodyFormat` | `markdown` or `txt` | | `url`, `scrapedAt` | Source & provenance | ### Example **Input** ```json { "urls": ["https://en.wikipedia.org/wiki/Web_scraping"], "outputFormat": "markdown" } ```` **Output (one item)** ```json { "url": "https://en.wikipedia.org/wiki/Web_scraping", "title": "Web scraping", "author": null, "date": "2026-05-01", "sitename": "Wikipedia", "language": "en", "bodyFormat": "markdown", "content": "# Web scraping\n\nWeb scraping is the process of automatically extracting data...", "wordCount": 3421 } ``` ### Why this extractor - ✅ **Boilerplate gone** — nav, ads, cookie banners, sidebars and footers removed. - 📝 **Markdown or plain text** — pick the format your pipeline needs. - 🏷️ **Rich metadata** — title, author, date, description, site name and language. - 🤖 **LLM-ready** — clean Markdown is ideal for RAG, summarization and fine-tuning. - ⚡ **Fast & affordable** — pay only per article extracted. ### Input options - `urls` *(required)* — one or more page URLs (`https://` added if missing). - `outputFormat` — `markdown` (default) or `txt`. ### FAQ **Markdown or plain text?** Both — set `outputFormat` to `markdown` or `txt`. **Does it remove ads and navigation?** Yes — it isolates the main article and strips nav, ads, cookie banners and boilerplate. **Can I process many URLs in one run?** Yes — pass a list; each row carries its own `url` and metadata. **Is it good for LLM / RAG use?** Yes — clean Markdown with metadata is exactly what embedding and summarization pipelines need. **How fresh is the data?** Live — it fetches each page in real time on every run. **Can I export to CSV/Excel/Google Sheets?** Yes — the dataset exports to JSON, CSV, Excel and HTML, or pull it via the API. **What does it cost?** Pay-per-article — you're only charged for articles actually extracted. *** *Tip: pair this with the RSS Feed Reader — discover URLs from feeds, then extract full clean article text for your dataset.* # Actor input Schema ## `urls` (type: `array`): Web page URLs to extract the main article from (https:// added if missing). ## `outputFormat` (type: `string`): Format of the extracted article body. ## `healthCheckMode` (type: `boolean`): Internal monitoring: extract a canary article and FAIL if broken (no billing). ## Actor input object example ```json { "urls": [ "https://example.com/blog/post", "https://news.site/article" ], "outputFormat": "markdown", "healthCheckMode": false } ``` # Actor output Schema ## `articles` (type: `string`): All extracted articles (title, author, date, clean Markdown/text content). # 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://en.wikipedia.org/wiki/Web_scraping" ] }; // Run the Actor and wait for it to finish const run = await client.actor("omao/article-extractor").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://en.wikipedia.org/wiki/Web_scraping"] } # Run the Actor and wait for it to finish run = client.actor("omao/article-extractor").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://en.wikipedia.org/wiki/Web_scraping" ] }' | apify call omao/article-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=omao/article-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Article Extractor — Clean Web Content to Markdown/Text", "description": "Extract the main article from any web page into clean Markdown or text, with title, author, date and description. Strips nav, ads and boilerplate. Fast, no setup.", "version": "0.1", "x-build-id": "QEMLVtHwrlIOfdFjh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/omao~article-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-omao-article-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/omao~article-extractor/runs": { "post": { "operationId": "runs-sync-omao-article-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/omao~article-extractor/run-sync": { "post": { "operationId": "run-sync-omao-article-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Web page URLs to extract the main article from (https:// added if missing).", "items": { "type": "string" } }, "outputFormat": { "title": "Output format", "enum": [ "markdown", "txt" ], "type": "string", "description": "Format of the extracted article body.", "default": "markdown" }, "healthCheckMode": { "title": "Health-check mode", "type": "boolean", "description": "Internal monitoring: extract a canary article and FAIL if broken (no billing).", "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 } } } } } } } } } } ```