# VividSeats.com Scraper (`lexis-solutions/vividseats-com-scraper`) Actor VividSeats.com events and ticket scraper for concerts and sports, extracting event details, venues, ticket prices, sections, rows, quantities and checkout URLs for ticketing and secondary marketplace analytics. - **URL**: https://apify.com/lexis-solutions/vividseats-com-scraper.md - **Developed by:** [Lexis Solutions](https://apify.com/lexis-solutions) (community) - **Categories:** AI, Agents, Lead generation - **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.90 / 1,000 events 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 ![VividSeats Scraper](https://i.postimg.cc/1z4x2bW2/image.png) ### What does VividSeats Scraper do? VividSeats Scraper extracts event and ticket listing data from **vividseats.com**. It collects event-level metadata (production title, date, venue), listing counts, and per-listing snapshots (section, row, price, quantity) where available. The actor is designed to run from search pages or direct event detail pages and outputs structured event objects suitable for aggregation and analysis. ### What data can I extract from VividSeats.com with this scraper? With this actor you can extract: - Event name and production title - Event date and time (when available) - Venue name, address, state, and country - Listing counts and summarized ticket availability - Individual ticket snapshots: section, row, quantity, price, notes, badges, perks, and checkout URL - Ticket identifiers useful for linking or checkout ### How to use VividSeats Scraper? This actor is ready to run on the Apify platform or locally. Provide start URLs (search pages or event detail pages) and optional `maxItems` to limit results. Steps: 1. Provide start URLs (search or detail pages) in the input or use the `startUrls` field in the console. 2. Set `maxItems` to limit how many events to collect per run. 3. Run the actor and download the dataset when complete. ### Input The actor accepts the following input parameters: - `startUrls` (array of objects) - **Required**. URLs to start with. Can be search result URLs or individual event detail pages. - `maxItems` (integer) - The maximum number of events/items to scrape. Example: 5 - `proxyConfiguration` (object) - Proxy configuration settings. #### Supported URL Types (examples) You may provide either search page URLs or direct event detail pages: **Search URL example:** https://www.vividseats.com/search?searchTerm=imagine **Detail URL example:** https://www.vividseats.com/candlelight---coldplay-and-imagine-dragons-tickets-washington-st-francis-hall-3-12-2026--concerts-classical/production/6748600 Example input: ```json { "startUrls": [ { "url": "https://www.vividseats.com/search?searchTerm=imagine" }, { "url": "https://www.vividseats.com/candlelight---coldplay-and-imagine-dragons-tickets-washington-st-francis-hall-3-12-2026--concerts-classical/production/6748600" } ], "maxItems": 5, "proxyConfiguration": { "useApifyProxy": false } } ```` #### Note: The `startUrls` field is required ### Output The scraped data is saved to the default dataset. Each item represents an event with the following top-level fields (example in the dataset): `url`, `productionName`, `ticketCount`, `venueDetails`, `tickets` (array). Each `ticket` includes a `checkoutUrl` when available. #### Notes and Limitations - The actor relies on the current VividSeats API/HTML structure; if the website changes, updates may be required. - Respect site Terms of Service and robots.txt; use rate-limiting and proper concurrency settings. - For large-scale scraping, use Apify's proxy services to reduce blocking risk. Vivid Seats organizes inventory by event type and location. Event pages typically expose listing counts, price ranges, and seat map contexts. A scraper can gather event catalogs by city/date and capture listing-level snapshots (section/row/price) where accessible. #### πŸ” Looking to Scrape more Events Websites? In addition to this actor, you can explore our suite of dedicated scrapers tailored for other popular events websites. Each scraper is optimized for its target site to ensure accurate, efficient, and high-performance data extraction. | Scraper | Country | Description | | ---------------------------------------------------------------------------------- | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [Dice.fm Scraper](https://apify.com/lexis-solutions/dice-fm) | Global | Scrape event data from Dice.fm - including event names, dates, venues, artists, and ticket info. Ideal for event aggregation, trend analysis, and market research. Fast, structured, and customizable extraction from a leading live music ticketing platform. | | [Bandsintown Scraper](https://apify.com/lexis-solutions/bandsintown-scraper) | Global | Scrape event and artist data from Bandsintown.comβ€”including event names, artists, dates, venues, and ticket links. Ideal for event aggregation, trend analysis, and marketing insights. Fast, structured, and customizable extraction from a leading live music platform. | | [Billetto.co.uk Scraper](https://apify.com/lexis-solutions/billetto-co-uk-scraper) | United Kingdom | Scrape event listings from Billetto.co.uk - including event names, dates, venues, ticket prices, and organizer info. Ideal for event aggregation, market research, and trend analysis. Fast, structured, and customizable extraction from the UK’s leading event platform. | Explore these solutions to expand your data collection capabilities across events data extraction websites. *** πŸ‘€ p.s. Got feedback or need an extension? Lexis Solutions is a [certified Apify Partner](https://apify.com/partners). We can help you with custom solutions or data extraction projects. Contact us over [Email](mailto:info@lexis.solutions) or [LinkedIn](https://www.linkedin.com/company/lexis-solutions) Image Credit: https://vividseats.com/ # Actor input Schema ## `startUrls` (type: `array`): URLs to start with. ## `maxItems` (type: `integer`): Maximum number of events that will be extracted. ## `proxyConfiguration` (type: `object`): Select proxies to be used by your crawler. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.vividseats.com/search?searchTerm=imagine" } ], "maxItems": 5, "proxyConfiguration": { "useApifyProxy": false } } ``` # 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 = { "startUrls": [ { "url": "https://www.vividseats.com/search?searchTerm=imagine" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("lexis-solutions/vividseats-com-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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://www.vividseats.com/search?searchTerm=imagine" }] } # Run the Actor and wait for it to finish run = client.actor("lexis-solutions/vividseats-com-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://www.vividseats.com/search?searchTerm=imagine" } ] }' | apify call lexis-solutions/vividseats-com-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lexis-solutions/vividseats-com-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "VividSeats.com Scraper", "description": "VividSeats.com events and ticket scraper for concerts and sports, extracting event details, venues, ticket prices, sections, rows, quantities and checkout URLs for ticketing and secondary marketplace analytics.", "version": "1.0", "x-build-id": "5Xvy8Fobflzus0IAK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lexis-solutions~vividseats-com-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lexis-solutions-vividseats-com-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lexis-solutions~vividseats-com-scraper/runs": { "post": { "operationId": "runs-sync-lexis-solutions-vividseats-com-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lexis-solutions~vividseats-com-scraper/run-sync": { "post": { "operationId": "run-sync-lexis-solutions-vividseats-com-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 with.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxItems": { "title": "Maximum number of items", "type": "integer", "description": "Maximum number of events that will be extracted.", "default": 5 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies to be used by your crawler.", "default": { "useApifyProxy": 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 } } } } } } } } } } ```