# Budtrainer Category Scraper (`getdataforme/budtrainer-category-scraper`) Actor Extract cannabis product data from budtrainer.com with precision. Search by category or keyword, set custom limits, and receive structured JSON output for market research, price monitoring, and automation. Reliable, fast, and no-code required for seamless insights. - **URL**: https://apify.com/getdataforme/budtrainer-category-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **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 ## Budtrainer Category Scraper ### Introduction The Budtrainer Category Scraper is a powerful Apify Actor designed to extract product data from budtrainer.com, a leading resource for cannabis education and product information. It enables users to search for specific categories or products and retrieve detailed listings efficiently, making it ideal for data-driven insights in the cannabis industry. With robust scraping capabilities, this Actor delivers high-quality, structured data to support research, monitoring, and automation needs. ### Features - **Targeted Scraping**: Precisely queries budtrainer.com based on user-defined search terms to fetch relevant product categories and details. - **Customizable Limits**: Allows setting maximum items to scrape, ensuring control over data volume and processing time. - **High Reliability**: Built with error-handling mechanisms to manage site changes and network issues, minimizing failed runs. - **Structured Output**: Produces clean, JSON-formatted data for easy integration with analytics tools, databases, or spreadsheets. - **Fast Performance**: Optimized for speed, handling large queries efficiently without overwhelming the target site. - **No Coding Required**: User-friendly interface in the Apify Store, perfect for non-technical users. - **Scalable**: Supports batch processing and can be integrated into automated workflows for continuous data collection. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|------------------| | searchQuery | string | Yes | The search term to query on budtrainer.com, such as a product category or keyword. | "cbd oil" | | maxItems | integer | Yes | The maximum number of products to scrape from the search results. | 50 | ### Example Usage To run the Actor, provide the following input JSON: ```json { "searchQuery": "cbd oil", "maxItems": 50 } ```` Example output JSON (structured data from scraped products): ```json [ { "productName": "Premium CBD Oil 500mg", "category": "CBD Products", "price": "$29.99", "description": "High-quality CBD oil extracted from hemp.", "url": "https://budtrainer.com/products/premium-cbd-oil", "imageUrl": "https://budtrainer.com/images/cbd-oil.jpg", "rating": 4.5, "reviewsCount": 120 }, { "productName": "Full Spectrum CBD Tincture", "category": "CBD Products", "price": "$39.99", "description": "Full-spectrum tincture for wellness.", "url": "https://budtrainer.com/products/full-spectrum-tincture", "imageUrl": "https://budtrainer.com/images/tincture.jpg", "rating": 4.7, "reviewsCount": 85 } ] ``` ### Use Cases - **Market Research**: Analyze trends in cannabis products by scraping category data for competitive analysis. - **Price Monitoring**: Track pricing changes across products to inform pricing strategies. - **Content Aggregation**: Collect product details for blogs, e-commerce sites, or educational content. - **Competitive Intelligence**: Gather insights on competitors' offerings in the cannabis space. - **Academic Research**: Support studies on cannabis products by accessing structured data. - **Business Automation**: Integrate into workflows for automated reporting or inventory management. ### Installation and Usage 1. Search for "Budtrainer Category Scraper" 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 Actor outputs data in JSON format as an array of objects, each representing a scraped product. Key fields include: - `productName`: The name of the product. - `category`: The category it belongs to. - `price`: Current price as a string. - `description`: Brief product description. - `url`: Direct link to the product page. - `imageUrl`: URL of the product image. - `rating`: Average user rating (numeric). - `reviewsCount`: Number of reviews. This structure ensures easy parsing and use in downstream applications. ### 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 ## `searchQuery` (type: `string`): Search term to query on budtrainer.com. ## `maxItems` (type: `integer`): Maximum number of products to scrape. ## Actor input object example ```json { "searchQuery": "bud", "maxItems": 100 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/budtrainer-category-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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/budtrainer-category-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 '{}' | apify call getdataforme/budtrainer-category-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/budtrainer-category-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Budtrainer Category Scraper", "description": "Extract cannabis product data from budtrainer.com with precision. Search by category or keyword, set custom limits, and receive structured JSON output for market research, price monitoring, and automation. Reliable, fast, and no-code required for seamless insights.", "version": "0.0", "x-build-id": "cAWB6Y7zXqeJeJuzZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~budtrainer-category-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-budtrainer-category-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~budtrainer-category-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-budtrainer-category-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~budtrainer-category-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-budtrainer-category-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQuery", "maxItems" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Search term to query on budtrainer.com.", "default": "bud" }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of products to scrape.", "default": 100 } } }, "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 } } } } } } } } } } ```