# TikTok Top-Level Comments Scraper Lite (`itayekk1/tiktok-comments-scraper-lite`) Actor Extract bounded public top-level comments from known TikTok video URLs. - **URL**: https://apify.com/itayekk1/tiktok-comments-scraper-lite.md - **Developed by:** [Itay Elgazar](https://apify.com/itayekk1) (community) - **Categories:** Social media, Videos - **Stats:** 2 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 ## TikTok Top-Level Comments Scraper Lite Extract public top-level comments from known TikTok video URLs. Use it when you already have TikTok video links and want a small structured sample of public comments. ### What you get Each comment row can include: - TikTok video URL - video ID - comment ID - comment text - public author fields - like count - reply count - creation time - pagination fields - structured error details when comments are unavailable ### Quick start ```json { "postURLs": [ "https://www.tiktok.com/@apifyoffice/video/7200360993149553925" ], "maxCommentsPerPost": 10, "includeReplies": false } ```` ### Input fields - `postURLs` — direct TikTok video URLs. - `maxCommentsPerPost` — maximum top-level comments per video. - `cursor` — optional cursor from a previous run. - `includeReplies` — currently kept off for this lite Actor. - `maxRepliesPerComment` — currently not used when replies are off. ### Output The Actor saves one dataset row per top-level comment, plus error rows for videos that cannot be processed or have no available public comments. ### Limits This Actor collects top-level public comments from direct video URLs only. It does not collect replies in this version, scrape profiles, search TikTok, scrape hashtag feeds, log in, use user cookies, bypass CAPTCHA, or access private data. # Actor input Schema ## `postURLs` (type: `array`): One or more public TikTok video URLs whose top-level public comments should be scraped. ## `maxCommentsPerPost` (type: `integer`): Maximum top-level public comments to return per video. Hard-capped at 50. ## `cursor` (type: `string`): Optional TikTok comments cursor to start from. Leave as 0 for the first page. Cursor values are returned in output rows as cursorAfter. ## `includeReplies` (type: `boolean`): Replies are not supported in this lite Actor. Leave off. ## `maxRepliesPerComment` (type: `integer`): Replies are not supported in this lite Actor. ## Actor input object example ```json { "postURLs": [ "https://www.tiktok.com/@apifyoffice/video/7200360993149553925" ], "maxCommentsPerPost": 10, "cursor": "0", "includeReplies": false, "maxRepliesPerComment": 0 } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `runSummary` (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 = { "postURLs": [ "https://www.tiktok.com/@apifyoffice/video/7200360993149553925" ] }; // Run the Actor and wait for it to finish const run = await client.actor("itayekk1/tiktok-comments-scraper-lite").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 = { "postURLs": ["https://www.tiktok.com/@apifyoffice/video/7200360993149553925"] } # Run the Actor and wait for it to finish run = client.actor("itayekk1/tiktok-comments-scraper-lite").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 '{ "postURLs": [ "https://www.tiktok.com/@apifyoffice/video/7200360993149553925" ] }' | apify call itayekk1/tiktok-comments-scraper-lite --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=itayekk1/tiktok-comments-scraper-lite", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TikTok Top-Level Comments Scraper Lite", "description": "Extract bounded public top-level comments from known TikTok video URLs.", "version": "0.1", "x-build-id": "8UnTquBh6FIyU9QjZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/itayekk1~tiktok-comments-scraper-lite/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-itayekk1-tiktok-comments-scraper-lite", "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/itayekk1~tiktok-comments-scraper-lite/runs": { "post": { "operationId": "runs-sync-itayekk1-tiktok-comments-scraper-lite", "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/itayekk1~tiktok-comments-scraper-lite/run-sync": { "post": { "operationId": "run-sync-itayekk1-tiktok-comments-scraper-lite", "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": [ "postURLs" ], "properties": { "postURLs": { "title": "Direct TikTok video URLs", "type": "array", "description": "One or more public TikTok video URLs whose top-level public comments should be scraped.", "default": [ "https://www.tiktok.com/@apifyoffice/video/7200360993149553925" ], "items": { "type": "string" } }, "maxCommentsPerPost": { "title": "Maximum comments per post", "minimum": 0, "maximum": 50, "type": "integer", "description": "Maximum top-level public comments to return per video. Hard-capped at 50.", "default": 10 }, "cursor": { "title": "Start cursor", "type": "string", "description": "Optional TikTok comments cursor to start from. Leave as 0 for the first page. Cursor values are returned in output rows as cursorAfter.", "default": "0" }, "includeReplies": { "title": "Include replies", "type": "boolean", "description": "Replies are not supported in this lite Actor. Leave off.", "default": false }, "maxRepliesPerComment": { "title": "Maximum replies per comment", "minimum": 0, "maximum": 0, "type": "integer", "description": "Replies are not supported in this lite Actor.", "default": 0 } } }, "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 } } } } } } } } } } ```