# Opensea Categories Spider (`getdataforme/opensea-categories-spider`) Actor Extract comprehensive NFT data from OpenSea collections, including metadata, attributes, rarity, and pricing. Ideal for traders, researchers, and developers seeking reliable insights for analysis and automation. Features robust scraping, JSON output, and scalable performance. - **URL**: https://apify.com/getdataforme/opensea-categories-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, E-commerce, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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 ### Description Extract comprehensive NFT data from OpenSea collections, including metadata, attributes, rarity, and pricing. Ideal for traders, researchers, and developers seeking reliable insights for analysis and automation. Features robust scraping, JSON output, and scalable performance. --- ## Opensea Categories Spider The Opensea Categories Spider is a powerful Apify Actor designed to extract detailed NFT data from OpenSea collections. It efficiently scrapes asset information, including metadata, attributes, rarity, and pricing details, enabling users to gather comprehensive insights into digital collectibles. This tool is ideal for researchers, traders, and developers seeking reliable data for analysis and automation. ### Features - **Comprehensive Data Extraction**: Retrieves key NFT details such as token ID, name, image URL, contract address, chain, owner, rarity rank, creation date, and best offers in USD. - **Attribute Parsing**: Captures detailed trait attributes for each NFT, including trait types and values, essential for rarity analysis. - **High Reliability**: Built with robust error handling to ensure consistent data scraping from OpenSea, minimizing failures. - **Scalable Performance**: Handles multiple URLs efficiently, supporting batch processing for large collections. - **JSON Output**: Delivers structured, machine-readable data perfect for integration with databases or analytics tools. - **Real-Time Insights**: Extracts current best offers and ownership data for up-to-date market intelligence. - **Easy Configuration**: Simple input setup with URL lists, requiring no advanced technical skills. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | Urls | array | No | A list of OpenSea collection URLs to scrape. Each URL must be a valid HTTP/HTTPS link pointing to a collection page. At least one URL is required for the actor to run. | ["https://opensea.io/collection/hypurr-hyperevm"] | ### Example Usage To run the actor, provide the input parameters in JSON format. Here's an example input: ```json { "Urls": [ "https://opensea.io/collection/hypurr-hyperevm" ] } ```` The actor will output a JSON array of NFT objects. Example output: ```json [ { "id": "11f2c56ef72a39a2bb7a77c9668c81bb", "token_id": "3759", "name": "Hypurr #3759", "image": "https://i2c.seadn.io/hyperevm/0x9125e2d6827a00b0f8330d6ef7bef07730bac685/196a1b093eade55dad2ac84f02b14a/78196a1b093eade55dad2ac84f02b14a.png", "contract": "0x9125e2d6827a00b0f8330d6ef7bef07730bac685", "chain": "hyperevm", "owner": "0x62f8a1e42843edce1907af86e7624a6361b66d9f", "rarity_rank": 4127, "rarity_category": "NONE", "created_at": "2025-09-28T14:34:00.000Z", "best_offer_usd": 14628.124, "attributes": [ { "traitType": "Accessories", "value": "Teddy Bear Purr", "__typename": "StaticItemAttributeType" } ], "actor_id": "TEoHcgFkjdeACZPme", "run_id": "C5ibpjfMaFSXQgMGd" } ] ``` ### Use Cases - **Market Research**: Analyze trends in NFT collections by extracting rarity and pricing data for investment decisions. - **Competitive Intelligence**: Monitor rival collections' assets and attributes to benchmark performance. - **Price Monitoring**: Track best offers and ownership changes for real-time valuation updates. - **Content Aggregation**: Build databases of NFT metadata for galleries, blogs, or marketplaces. - **Academic Research**: Study NFT ecosystems, including trait distributions and market dynamics. - **Business Automation**: Automate data feeds for dashboards or alerts on new listings. ### Installation and Usage 1. Search for "Opensea Categories Spider" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 4. Click "Start" to begin extraction 5. Monitor progress in the log 6. Export results in your preferred format (JSON, CSV, Excel) ### Output Format The output is a JSON array of objects, each representing an NFT from the specified collections. Key fields include: - `id`: Unique identifier for the NFT. - `token_id`: The token's ID within the collection. - `name`: NFT title. - `image`: URL to the NFT image. - `contract`: Smart contract address. - `chain`: Blockchain network (e.g., "hyperevm"). - `owner`: Current owner's wallet address. - `rarity_rank`: Numerical rarity score. - `rarity_category`: Rarity classification (e.g., "NONE"). - `created_at`: ISO timestamp of creation. - `best_offer_usd`: Best offer price in USD. - `attributes`: Array of trait objects with `traitType`, `value`, and `__typename`. - `actor_id` and `run_id`: Metadata from the Apify run. Data is extracted directly from OpenSea pages, ensuring accuracy but subject to site changes. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `Urls` (type: `array`): The urls for the spider. ## Actor input object example ```json { "Urls": [ "https://opensea.io/collection/hypurr-hyperevm" ] } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/opensea-categories-spider").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/opensea-categories-spider").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 '{}' | apify call getdataforme/opensea-categories-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/opensea-categories-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Opensea Categories Spider", "description": "Extract comprehensive NFT data from OpenSea collections, including metadata, attributes, rarity, and pricing. Ideal for traders, researchers, and developers seeking reliable insights for analysis and automation. Features robust scraping, JSON output, and scalable performance.", "version": "0.0", "x-build-id": "dYfgfNvzm5G1N8t9D" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~opensea-categories-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-opensea-categories-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~opensea-categories-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-opensea-categories-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~opensea-categories-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-opensea-categories-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "Urls": { "title": "Urls", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://opensea.io/collection/hypurr-hyperevm" ], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```