# Google YouTube Data (`seemuapps/google-youtube-data`) Actor Search YouTube via the Data API v3 and return enriched video metadata — title, channel, stats, duration, and thumbnail — for every result. - **URL**: https://apify.com/seemuapps/google-youtube-data.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, AI, Developer tools - **Stats:** 1 total users, 0 monthly users, 37.5% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 search query executeds 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 ## Google YouTube Data Turn any list of YouTube search queries into a clean dataset of video metadata — title, channel, view/like/comment counts, duration, publish date, and thumbnail — pulled live from the official YouTube Data API v3. ### What you get - **One dataset row per (query, video)** — perfect for pivoting by keyword - **Full statistics** — viewCount, likeCount, commentCount as parsed integers - **Parsed duration** — both raw ISO 8601 (`PT4M13S`) and seconds (`253`) - **Best-available thumbnail URL** (high → medium → default) - **Channel attribution** — channelId and channelTitle on every row - **Sort and filter** by relevance, date, rating, view count, or title - **Time-bounded queries** — optional `publishedAfter` ISO 8601 cutoff - Error-tolerant — failed queries emit a single error row, the run continues ### Use cases - **Channel competitive analysis** — pull every top-ranking video for a keyword set and compare which channels dominate - **Content discovery** — surface the newest high-engagement videos in any niche, refreshed on a schedule - **Trend tracking** — feed the dataset into a dashboard to chart view-count growth across topics over time - **Influencer research** — identify creators ranking for target keywords, ranked by subscribers and engagement - **Video SEO datasets** — build training data of titles, descriptions, durations, and view counts to model what ranks - **Ad targeting** — assemble lists of high-traffic videos by topic for YouTube placement campaigns ### How to use 1. Paste your **Search Queries** — one per entry 2. Set **Max Results Per Query** (1–50, default 10) 3. Pick an **Order** — `relevance` (default), `date`, `rating`, `viewCount`, or `title` 4. Optionally set **Published After** to a datetime to filter out older videos 5. Run the actor — results appear in the **Dataset** tab ### Output format Each dataset record: ```json { "query": "apify scraping", "videoId": "dQw4w9WgXcQ", "status": "success", "title": "How to scrape any website with Apify", "channelId": "UCxxxxxxxxxxxxxxxxxxxx", "channelTitle": "Apify", "publishedAt": "2024-03-12T14:00:00Z", "description": "In this video we show…", "thumbnailUrl": "https://i.ytimg.com/vi/dQw4w9WgXcQ/hqdefault.jpg", "viewCount": 12453, "likeCount": 412, "commentCount": 38, "durationIso": "PT8M21S", "durationSeconds": 501, "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "error": null } ```` If a query fails, one row is emitted with `status: "error"`, `videoId: ""`, and `error` containing the reason — the run continues with the remaining queries. ### Order options | Order | Best for | |---|---| | `relevance` | Default YouTube ranking — what a typical user would see | | `date` | Newest videos first — trend monitoring, news topics | | `rating` | Highest-rated videos — quality discovery | | `viewCount` | Most-watched videos — competitive benchmarking | | `title` | Alphabetical by title — deterministic ordering for diffs | ### Notes - Powered by the official YouTube Data API v3 — every result is current, no scraping fragility - Each query costs 100 API units (search) + 1 unit per 50 videos (videos.list) - A video that matches multiple input queries is emitted once **per query** so you can analyse keyword overlap # Actor input Schema ## `searchQueries` (type: `array`): List of YouTube search queries. The actor runs one /search call per query and enriches every returned video with full statistics. ## `maxResultsPerQuery` (type: `integer`): Number of videos to return per query (1–50). ## `order` (type: `string`): Sort order applied to YouTube search results. ## `publishedAfter` (type: `string`): Optional ISO 8601 datetime — only return videos published after this moment. ## Actor input object example ```json { "searchQueries": [ "apify scraping", "web scraping tutorial" ], "maxResultsPerQuery": 10, "order": "relevance" } ``` # Actor output Schema ## `results` (type: `string`): One record per (query, video): query, videoId, status (success|error), title, channelId, channelTitle, publishedAt, description, thumbnailUrl, viewCount, likeCount, commentCount, durationIso, durationSeconds, url, error. # 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 = { "searchQueries": [ "apify scraping", "web scraping tutorial" ] }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/google-youtube-data").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 = { "searchQueries": [ "apify scraping", "web scraping tutorial", ] } # Run the Actor and wait for it to finish run = client.actor("seemuapps/google-youtube-data").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 '{ "searchQueries": [ "apify scraping", "web scraping tutorial" ] }' | apify call seemuapps/google-youtube-data --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/google-youtube-data", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google YouTube Data", "description": "Search YouTube via the Data API v3 and return enriched video metadata — title, channel, stats, duration, and thumbnail — for every result.", "version": "1.0", "x-build-id": "lmKQyHPetTVl4I5g8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~google-youtube-data/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-google-youtube-data", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~google-youtube-data/runs": { "post": { "operationId": "runs-sync-seemuapps-google-youtube-data", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~google-youtube-data/run-sync": { "post": { "operationId": "run-sync-seemuapps-google-youtube-data", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQueries" ], "properties": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "List of YouTube search queries. The actor runs one /search call per query and enriches every returned video with full statistics.", "items": { "type": "string" } }, "maxResultsPerQuery": { "title": "Max Results Per Query", "minimum": 1, "maximum": 50, "type": "integer", "description": "Number of videos to return per query (1–50).", "default": 10 }, "order": { "title": "Order", "enum": [ "relevance", "date", "rating", "viewCount", "title" ], "type": "string", "description": "Sort order applied to YouTube search results.", "default": "relevance" }, "publishedAfter": { "title": "Published After", "type": "string", "description": "Optional ISO 8601 datetime — only return videos published after this moment." } } }, "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 } } } } } } } } } } ```