# Youtube Scraping Tool (`httpsrequests/youtube-scraping-tool`) Actor YouTube Scraping Tool lets you search and extract YouTube videos quickly and efficiently. Collect metadata for both regular videos and Shorts, with automated pagination handling and full statistics. - **URL**: https://apify.com/httpsrequests/youtube-scraping-tool.md - **Developed by:** [Alexander](https://apify.com/httpsrequests) (community) - **Categories:** Videos - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## YouTube Scraping Tool **URL:** [https://requests/youtube-scraping-tool](https://requests/youtube-scraping-tool) ### Overview **YouTube Scraping Tool** allows you to **search and collect YouTube videos** efficiently. Supports both **regular videos and Shorts**, automatically handles pagination, and provides detailed metadata and statistics for each video. ### Key Features - Search by one or multiple keywords (`search_queries`). - Limit the number of videos per query (`max_videos`). - Detects video type: **regular** or **shorts**. - Collects video metadata: - Title, URL, and Channel name - Views and view text - Publication date - Duration (formatted and in seconds) - Thumbnail - Video type (regular / short) - Automatic handling of pagination with continuation tokens. - Saves results to **Dataset** and full JSON (`OUTPUT.json`). - Generates statistics including: - Total videos collected - Total views - Unique channels - Shorts vs regular videos - Oldest and newest video dates ### Input Parameters | Parameter | Type | Description | Example | |-----------------|------------------|-------------------------------------------------|---------------------------------| | `search_queries` | `string` or `array[string]` | Search keywords (comma-separated string or array) | `"Python tutorial, Django"` | | `max_videos` | `integer` | Maximum number of videos per query | `10` | ### Output - **Dataset**: Each video object is saved individually. - **OUTPUT.json**: Complete JSON including results, metadata, and statistics. #### Example Video Object { "videoId": "abc123", "title": "Python Tutorial for Beginners", "url": "https://www.youtube.com/watch?v=abc123", "channelName": "Tech Channel", "views": 12345, "viewsText": "12,345 views", "publishedAt": "2024-03-15", "duration": "10:35", "durationSeconds": 635, "thumbnail": "https://i.ytimg.com/vi/abc123/hqdefault.jpg", "query": "Python tutorial", "video_type": "regular", "timestamp": "2026-04-03T12:00:00Z" } ### Usage Examples #### Single query { "search_queries": "Python tutorial", "max_videos": 5 } #### Multiple queries { "search_queries": ["Python tutorial", "Django framework"], "max_videos": 10 } ### Notes - Automatically handles pagination to retrieve multiple pages of results. - Differentiates between Shorts and regular videos based on YouTube data. - Includes logging of progress and top videos in Actor logs. This actor is perfect for content research, competitor analysis, or building YouTube datasets. # Actor input Schema ## `search_queries` (type: `string`): Enter keywords to search (comma separated) ## `max_videos` (type: `integer`): Maximum videos to collect per query ## Actor input object example ```json { "search_queries": "Python programming, Data science", "max_videos": 10 } ```` # 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("httpsrequests/youtube-scraping-tool").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("httpsrequests/youtube-scraping-tool").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 httpsrequests/youtube-scraping-tool --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=httpsrequests/youtube-scraping-tool", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Youtube Scraping Tool", "description": "YouTube Scraping Tool lets you search and extract YouTube videos quickly and efficiently. Collect metadata for both regular videos and Shorts, with automated pagination handling and full statistics.", "version": "1.0", "x-build-id": "1y4Yx7suPCQscEFiV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/httpsrequests~youtube-scraping-tool/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-httpsrequests-youtube-scraping-tool", "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/httpsrequests~youtube-scraping-tool/runs": { "post": { "operationId": "runs-sync-httpsrequests-youtube-scraping-tool", "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/httpsrequests~youtube-scraping-tool/run-sync": { "post": { "operationId": "run-sync-httpsrequests-youtube-scraping-tool", "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": [ "search_queries" ], "properties": { "search_queries": { "title": "Search Queries", "type": "string", "description": "Enter keywords to search (comma separated)", "default": "Python programming, Data science" }, "max_videos": { "title": "Max Videos per Query", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum videos to collect per query", "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 } } } } } } } } } } ```