# TikTok Comments Scraper (`seemuapps/tiktok-comments-scraper`) Actor Scrape comments and nested replies from any public TikTok video and export them to a dataset. - **URL**: https://apify.com/seemuapps/tiktok-comments-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 comment scrapeds 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 ## TikTok Comments Scraper Extract comments and nested replies from any public TikTok video — no login required. ### What you get - Comment text, author username, display name, and TikTok sec UID - Like count and reply count per comment - Full nested replies for each comment, included in the same record - Timestamps in both ISO 8601 and Unix formats - Export to JSON, CSV, Excel, or Google Sheets directly from the Apify console ### Limits TikTok's API caps the total number of retrievable comments per video at **1,000**, regardless of how many are shown in the app. This is a platform-level restriction — setting `maxComments` above 1,000 will not return more results. For most use cases (sentiment analysis, research, moderation) 1,000 comments is more than sufficient. ### Use cases - Sentiment analysis and brand monitoring on TikTok content - Competitive research — see how audiences respond to competitor videos - Influencer vetting — assess comment quality and engagement authenticity - Content research — find recurring questions or pain points in comment sections - Academic research on social media behaviour ### How to use 1. Paste the full TikTok video URL (e.g. `https://www.tiktok.com/@username/video/1234567890`) 2. Set **Max Comments** (default 1,000 — see limits above) 3. Toggle **Include Replies** on or off (default: on) 4. Optionally set **Max Replies Per Comment** to cap reply depth (default: 100) 5. Run the actor — results appear in the **Dataset** tab 6. Export to your preferred format ### Output format Each dataset record represents one top-level comment with its replies nested inside: ```json { "videoId": "7613574458594331911", "commentId": "7613713773107692296", "text": "this is incredible 🔥", "authorUsername": "johndoe", "authorNickname": "John Doe", "authorSecUid": "MS4wLjABAAAA...", "likeCount": 142, "replyCount": 3, "createdAt": "2025-07-01T14:23:00.000Z", "createdAtTimestamp": 1751379780, "replies": [ { "commentId": "7613720000000000000", "text": "agreed!!", "authorUsername": "janedoe", "authorNickname": "Jane Doe", "authorSecUid": "MS4wLjABAAAA...", "likeCount": 12, "createdAt": "2025-07-01T14:45:00.000Z", "createdAtTimestamp": 1751381100 } ] } ```` ### Pricing $0.002 per comment scraped (~$2.00 per 1,000 comments). Replies are included. # Actor input Schema ## `videoUrl` (type: `string`): Full TikTok video URL, e.g. https://www.tiktok.com/@username/video/1234567890123456789 ## `maxComments` (type: `integer`): Maximum number of top-level comments to scrape. ## `includeReplies` (type: `boolean`): Fetch replies for each comment and nest them in the output record. ## `maxRepliesPerComment` (type: `integer`): Maximum replies to fetch per comment. Only used when Include Replies is enabled. ## Actor input object example ```json { "videoUrl": "https://www.tiktok.com/@australia/video/7632203705881824520", "maxComments": 1000, "includeReplies": true, "maxRepliesPerComment": 100 } ``` # Actor output Schema ## `results` (type: `string`): Each record contains commentId, text, authorUsername, authorNickname, authorSecUid, likeCount, replyCount, createdAt, createdAtTimestamp, videoId, and a replies array with the same fields (minus replyCount). # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "videoUrl": "https://www.tiktok.com/@australia/video/7632203705881824520" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/tiktok-comments-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "videoUrl": "https://www.tiktok.com/@australia/video/7632203705881824520" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/tiktok-comments-scraper").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 '{ "videoUrl": "https://www.tiktok.com/@australia/video/7632203705881824520" }' | apify call seemuapps/tiktok-comments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/tiktok-comments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TikTok Comments Scraper", "description": "Scrape comments and nested replies from any public TikTok video and export them to a dataset.", "version": "1.0", "x-build-id": "45pymILEOteHtjbQq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~tiktok-comments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-tiktok-comments-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~tiktok-comments-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-tiktok-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~tiktok-comments-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-tiktok-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "videoUrl" ], "properties": { "videoUrl": { "title": "Video URL", "type": "string", "description": "Full TikTok video URL, e.g. https://www.tiktok.com/@username/video/1234567890123456789" }, "maxComments": { "title": "Max Comments", "minimum": 1, "type": "integer", "description": "Maximum number of top-level comments to scrape.", "default": 1000 }, "includeReplies": { "title": "Include Replies", "type": "boolean", "description": "Fetch replies for each comment and nest them in the output record.", "default": true }, "maxRepliesPerComment": { "title": "Max Replies Per Comment", "minimum": 1, "type": "integer", "description": "Maximum replies to fetch per comment. Only used when Include Replies is enabled.", "default": 100 } } }, "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 } } } } } } } } } } ```