# Cyclingnews Races & News Scraper (`jungle_synthesizer/cyclingnews-races-news-scraper`) Actor Scrapes pro-cycling news articles and race reports from Cyclingnews.com. Extracts headline, author, dates, body text, summary, and LATAM-cycling relevance flags (riders and races). For sports-analytics, LLM training, and cycling intelligence dashboards. - **URL**: https://apify.com/jungle\_synthesizer/cyclingnews-races-news-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Sports, News - **Stats:** 2 total users, 1 monthly users, 91.7% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## Cyclingnews Races & News Scraper Scrapes pro-cycling news articles and race reports from [Cyclingnews.com](https://www.cyclingnews.com) — the largest English-language cycling news outlet, owned by Future plc. Returns structured article data including headline, author, publish date, full body text, and a curated LATAM-cycling relevance layer. The site is server-rendered with rich JSON-LD structured data on every article. No browser required. The scraper pulls from the Google News sitemap and the live /news/ listing page, so each run returns the freshest content without you managing pagination or archives. ### What It Returns Every record is one article. The dataset includes: | Field | Type | Description | |---|---|---| | `article_id` | String | URL-slug identifier derived from the canonical URL | | `article_url` | String | Canonical URL of the article | | `article_title` | String | Headline (HTML entities decoded) | | `article_author` | String | Primary author name | | `article_published_at` | String | ISO-8601 publish timestamp | | `article_modified_at` | String | ISO-8601 last-modified timestamp | | `article_body_text` | String | Plain-text article body, up to 50,000 characters | | `article_summary` | String | Sub-headline or deck | | `article_section` | String | Section label (e.g. Racing, Women's Cycling, Teams & Riders) | | `article_tags` | Array | Open Graph article:tag values | | `latam_relevant` | Boolean | True if the article mentions a curated LATAM rider or race | | `latam_riders` | Array | LATAM riders mentioned (Quintana, Bernal, Carapaz, Higuita, etc.) | | `latam_races` | Array | LATAM races mentioned (Tour Colombia, Vuelta San Juan, etc.) | | `source_url` | String | Always `https://www.cyclingnews.com` | | `scraped_at` | String | ISO-8601 scrape timestamp | ### LATAM Enrichment The `latam_relevant` flag and companion arrays are the value-add. The scraper checks every article against a curated list of ~30 Colombian, Ecuadorian, and other Latin American riders — Nairo Quintana, Egan Bernal, Richard Carapaz, Sergio Higuita, Santiago Buitrago, and others — plus ~25 LATAM races including Tour Colombia, Vuelta a Colombia, Vuelta San Juan, and Ruta de los Conquistadores. Downstream models and dashboards can filter on `latam_relevant: true` without re-reading the body text. ### Input Parameters | Parameter | Type | Default | Description | |---|---|---|---| | `maxItems` | Integer | 10 | Maximum articles to scrape. The Google News sitemap refreshes every few hours with ~27 recent articles. | ### How It Works Each run: 1. Fetches `sitemap-news.xml` (Google News sitemap — always publicly accessible) and collects article URLs for the past 48–72 hours. 2. Also scrapes the live `/news/` listing page for any articles not yet indexed in the sitemap. 3. Deduplicates and caps to `maxItems`, then fetches each article. 4. Parses JSON-LD NewsArticle schema for structured metadata, `#article-body` for body text. The scraper uses [impit](https://github.com/apify/impit) — a Chrome TLS fingerprint HTTP client — which passes Fastly CDN edge checks without a browser. No proxy required. ### Use Cases - **Sports-analytics pipelines**: feed article bodies into NLP models to extract race results, rider performance signals, and team news. - **LLM training corpora**: Cyclingnews is the canonical English-language source for pro-cycling narrative. The body text is editorial-quality, structured, and tagged. - **LATAM cycling intelligence dashboards**: the `latam_riders` and `latam_races` arrays make it simple to track Colombian Grand Tour coverage, contract news, and race reports without keyword scanning. - **Journalism aggregators**: combine with a scheduling trigger to catch every article within hours of publication. ### Coverage Cyclingnews publishes 50–80 articles per week across racing, women's cycling, teams & riders, tech/gear, and features. The Google News sitemap covers the rolling 48-hour window — run on a daily or twice-daily schedule to maintain a complete archive. A single run with `maxItems: 0` captures all available articles (~27 from the news sitemap plus the listing page). ### Limitations The Google News sitemap covers recent articles only (~48–72 hours). Historical article archives are not accessible without pagination, which Future plc gates with 403 on non-recent listing pages. For historical ingestion, supply a list of known article URLs via a custom pipeline. --- *Data sourced from Cyclingnews.com (Future plc). Use in accordance with applicable terms of service.* # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of articles to scrape. 0 means no limit (scrapes all available from the news sitemap). ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 } ```` # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/cyclingnews-races-news-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/cyclingnews-races-news-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }' | apify call jungle_synthesizer/cyclingnews-races-news-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/cyclingnews-races-news-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Cyclingnews Races & News Scraper", "description": "Scrapes pro-cycling news articles and race reports from Cyclingnews.com. Extracts headline, author, dates, body text, summary, and LATAM-cycling relevance flags (riders and races). For sports-analytics, LLM training, and cycling intelligence dashboards.", "version": "0.1", "x-build-id": "rcfWqRk2S7IDDqdqb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~cyclingnews-races-news-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-cyclingnews-races-news-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/jungle_synthesizer~cyclingnews-races-news-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-cyclingnews-races-news-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/jungle_synthesizer~cyclingnews-races-news-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-cyclingnews-races-news-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": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of articles to scrape. 0 means no limit (scrapes all available from the news sitemap).", "default": 10 } } }, "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 } } } } } } } } } } ```