# Browseract Search Parser Spider (`getdataforme/browseract-search-parser-spider`) Actor This Apify Actor automates scraping and parsing of Browseract templates, extracting key details like titles, summaries, pricing, and metadata.... - **URL**: https://apify.com/getdataforme/browseract-search-parser-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** E-commerce, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 This Apify Actor automates scraping and parsing of Browseract templates, extracting key details like titles, summaries, pricing, and metadata.... --- ## Browseract Search Parser Spider ### Introduction The Browseract Search Parser Spider is a powerful Apify Actor designed to scrape and parse template data from the Browseract platform. It automates the extraction of detailed information about available templates, including titles, summaries, pricing, and metadata, enabling users to efficiently gather insights for research, analysis, or integration purposes. This tool streamlines data collection from Browseract's search results, providing structured JSON outputs that can be easily exported to CSV, Excel, or other formats for further processing. ### Features - **Automated Scraping**: Seamlessly extracts template details from Browseract search pages without manual intervention. - **Comprehensive Data Extraction**: Captures key fields such as IDs, titles, slugs, URLs, images, summaries, authors, pricing, tags, and timestamps. - **High Reliability**: Built on Apify's robust infrastructure, ensuring consistent performance and minimal downtime. - **Flexible Output**: Generates clean JSON arrays, ideal for integration with data pipelines or analysis tools. - **No Input Required**: Operates with default settings, making it easy to run out-of-the-box for quick data pulls. - **Scalable Performance**: Handles multiple templates efficiently, with built-in error handling for resilient scraping. - **Metadata Insights**: Includes category IDs, official status, and update timestamps for in-depth analysis. ### Input Parameters The Actor does not require any input parameters. It runs with default configurations to scrape the latest template data from Browseract. | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | N/A | N/A | No | No input parameters are needed. The Actor uses default settings. | N/A | ### Example Usage To run the Actor, simply start it without any inputs. Below is an example of the output JSON structure. **Example Input JSON** (No inputs required): ```json {} ```` **Example Output JSON**: ```json [ { "id": "56425105605007222", "title": "Extract Video Info From YouTube", "slug": "extract-video-info-from-youtube", "urls": { "template": "https://www.browseract.com/template/extract-video-info-from-youtube" }, "images": { "cover": "https://browseract-prod.browseract.com/workflow/template/cover-1759916777403.png", "logo": "https://browseract-prod.browseract.com/workflow/template/recommend-logo-1759979982414.png" }, "summary": "Extract YouTube video data automatically — title, views, likes, comments, and channel info in CSV or Sheets", "author": "Rowan", "pricing": { "min": "15", "max": "30" }, "tags": [ { "name": "Social Analytics", "slug": "social-analytics", "url": "https://www.browseract.com/template?tag=social-analytics" } ], "meta": { "categoryId": "3", "official": false, "top": false }, "timestamps": { "created": 1759916787000, "updated": 1766394070000 }, "actor_id": "98qNYxGhXt2LLjSSn", "run_id": "kfFiXJWd7EV7gLbNc" }, { "id": "56196278832335478", "title": "Extract Videos From A YouTube Channel", "slug": "extract-videos-from-a-youtube-channel", "urls": { "template": "https://www.browseract.com/template/extract-videos-from-a-youtube-channel" }, "images": { "cover": "https://browseract-prod.browseract.com/workflow/template/cover-1759807662699.png", "logo": "https://browseract-prod.browseract.com/workflow/template/recommend-logo-1759979917823.png" }, "summary": "Automatically extract YouTube channel video data including title, views, duration, thumbnail, and link. This workflow collects all videos for reporting or competitor analysis.", "author": "Rowan", "pricing": { "min": "20", "max": "40" }, "tags": [ { "name": "Data Processing", "slug": "data-processing", "url": "https://www.browseract.com/template?tag=data-processing" } ], "meta": { "categoryId": "3", "official": false, "top": false }, "timestamps": { "created": 1759807673000, "updated": 1766393323000 }, "actor_id": "98qNYxGhXt2LLjSSn", "run_id": "kfFiXJWd7EV7gLbNc" }, { "id": "54950145518732406", "title": "YouTube Channel Shorts Scraper", "slug": "youtube-channel-shorts-scraper", "urls": { "template": "https://www.browseract.com/template/youtube-channel-shorts-scraper" }, "images": { "cover": "https://browseract-prod.browseract.com/workflow/template/cover-1759213462943.png", "logo": "https://browseract-prod.browseract.com/workflow/template/recommend-logo-1759979939426.png" }, "summary": "Automatically scrape YouTube playlist data including titles, video counts, and thumbnails. Export results to CSV for competitor monitoring, influencer insights, and content strategy research.", "author": "Rowan", "pricing": { "min": "20", "max": "40" }, "tags": [ { "name": "Data Processing", "slug": "data-processing", "url": "https://www.browseract.com/template?tag=data-processing" } ], "meta": { "categoryId": "3", "official": false, "top": false }, "timestamps": { "created": 1759213471000, "updated": 1766393291000 }, "actor_id": "98qNYxGhXt2LLjSSn", "run_id": "kfFiXJWd7EV7gLbNc" } ] ``` ### Use Cases - **Market Research**: Analyze trending Browseract templates to identify popular tools and pricing trends. - **Competitive Intelligence**: Monitor competitors' template offerings and strategies based on extracted data. - **Content Aggregation**: Collect summaries and metadata for building databases of automation templates. - **Academic Research**: Study patterns in template creation, authorship, and categorization for data science projects. - **Business Automation**: Integrate template data into workflows for automated reporting or decision-making. - **Developer Insights**: Explore available templates for inspiration or integration into custom applications. ### Installation and Usage 1. Search for "Browseract Search Parser Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters (none required). 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 a JSON array of objects, each representing a Browseract template. Key fields include: - `id`: Unique identifier for the template. - `title`: Human-readable name of the template. - `slug`: URL-friendly version of the title. - `urls`: Object containing the template URL. - `images`: Object with cover and logo image URLs. - `summary`: Brief description of the template's functionality. - `author`: Creator's name. - `pricing`: Object with minimum and maximum pricing values. - `tags`: Array of tag objects with name, slug, and URL. - `meta`: Metadata including category ID, official status, and top ranking. - `timestamps`: Creation and update timestamps in Unix format. - `actor_id` and `run_id`: Associated Apify identifiers. This structure ensures easy parsing and integration. ### 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! *** *** ### Latest PR Information | Field | Value | |-------|-------| | **PR Number** | [#45](https://github.com/getdataforme/satya_webscraping_team/pull/45) | | **PR Title** | Add empty line for code readability | | **Author** | roshankdk | | **Merged At** | 2026-02-12T09:15:52Z | | **Trello Ticket** | [N/A](N/A) | # Actor input Schema ## Actor input object example ```json {} ``` # 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/browseract-search-parser-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/browseract-search-parser-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/browseract-search-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/browseract-search-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Browseract Search Parser Spider", "description": "This Apify Actor automates scraping and parsing of Browseract templates, extracting key details like titles, summaries, pricing, and metadata....", "version": "0.0", "x-build-id": "VRODxZGyLU5qXxXnE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~browseract-search-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-browseract-search-parser-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~browseract-search-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-browseract-search-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~browseract-search-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-browseract-search-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 } } } } } } } } } } ```