# Youtube Influencer Finder (`quaint_wallpaper/youtube-influencer-finder`) Actor Discover high-performing YouTube influencers in any niche in seconds. Get ranked channels by subscribers, views, engagement, and growth trends. Perfect for marketers, agencies, and brands looking to scale campaigns fast. - **URL**: https://apify.com/quaint\_wallpaper/youtube-influencer-finder.md - **Developed by:** [micah munang](https://apify.com/quaint_wallpaper) (community) - **Categories:** Videos, Social media, SEO tools - **Stats:** 5 total users, 1 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 Influencer Finder Find YouTube influencers in any niche instantly. Enter a keyword and get a ranked list of channels with subscriber counts, view stats, and tier classification — exported as clean structured data. ### What it does - Searches YouTube for videos matching your keyword - Groups results by channel to find unique influencers - Ranks channels by subscriber count - Classifies each channel into tiers (Mega, Macro, Mid, Micro) - Returns structured data ready for CSV export or API integration ### Use cases - **Marketing agencies** — find influencers for brand partnerships - **Businesses** — discover creators in your product niche - **Researchers** — analyze the YouTube creator landscape - **Content creators** — find competitors and collaborators ### Input | Field | Type | Description | |---|---|---| | `keyword` | String | Niche or topic (e.g. "fitness", "cooking", "personal finance") | | `maxResults` | Integer | Max videos to scan (10–200, default 50) | ### Output Each result contains: | Field | Description | |---|---| | `rank` | Position in results (1 = most subscribers) | | `channelName` | YouTube channel name | | `channelUrl` | Link to the channel | | `subscribers` | Subscriber count | | `tier` | Mega (1M+), Macro (100K+), Mid (10K+), or Micro | | `totalViews` | Total views across scraped videos | | `totalLikes` | Total likes across scraped videos | | `videoCount` | Number of videos found in search | | `topVideo` | Title of their top video in results | | `topVideoUrl` | Link to their top video | | `keyword` | The keyword used for this search | ### Pricing This Actor uses **pay-per-result** pricing. You are charged per influencer result returned. ### Example output ```json { "rank": 1, "channelName": "Jeff Nippard", "channelUrl": "https://www.youtube.com/@JeffNippard", "subscribers": 8340000, "tier": "Mega (1M+)", "totalViews": 1250000, "totalLikes": 45000, "videoCount": 3, "topVideo": "The Best Science-Based Diet", "keyword": "fitness" } ```` # Actor input Schema ## `keyword` (type: `string`): The niche or topic to search for (e.g. fitness, cooking, personal finance, gaming) ## `maxResults` (type: `integer`): How many YouTube videos to scan. More videos = more channels found but costs more credits. ## Actor input object example ```json { "keyword": "fitness", "maxResults": 50 } ``` # Actor output Schema ## `results` (type: `string`): Full dataset of ranked YouTube influencers # 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("quaint_wallpaper/youtube-influencer-finder").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("quaint_wallpaper/youtube-influencer-finder").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 quaint_wallpaper/youtube-influencer-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=quaint_wallpaper/youtube-influencer-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Youtube Influencer Finder", "description": "Discover high-performing YouTube influencers in any niche in seconds. Get ranked channels by subscribers, views, engagement, and growth trends. Perfect for marketers, agencies, and brands looking to scale campaigns fast.", "version": "0.0", "x-build-id": "B2oC6GBqvNSvlA15f" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/quaint_wallpaper~youtube-influencer-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-quaint_wallpaper-youtube-influencer-finder", "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/quaint_wallpaper~youtube-influencer-finder/runs": { "post": { "operationId": "runs-sync-quaint_wallpaper-youtube-influencer-finder", "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/quaint_wallpaper~youtube-influencer-finder/run-sync": { "post": { "operationId": "run-sync-quaint_wallpaper-youtube-influencer-finder", "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": [ "keyword" ], "properties": { "keyword": { "title": "Niche / Keyword", "type": "string", "description": "The niche or topic to search for (e.g. fitness, cooking, personal finance, gaming)", "default": "fitness" }, "maxResults": { "title": "Max videos to scan", "minimum": 10, "maximum": 200, "type": "integer", "description": "How many YouTube videos to scan. More videos = more channels found but costs more credits.", "default": 50 } } }, "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 } } } } } } } } } } ```