# TikTok Ultra Product Ranklist (`datamagnet/tiktok-ultra-product-ranklist`) Actor Collect TikTok product rankings by date, region, and category to spot top-selling and fast-rising items. - **URL**: https://apify.com/datamagnet/tiktok-ultra-product-ranklist.md - **Developed by:** [Datamagnet](https://apify.com/datamagnet) (community) - **Categories:** E-commerce, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $7.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Ultra Product Ranklist Find the products that are moving up or dominating in TikTok shopping rankings. This data helps you quickly spot what shoppers are buying, which categories are gaining attention, and how product performance changes over time. ### What you get - Product ranking position for the selected date and region - Ranking cycle selection: daily, weekly, or monthly - Ranking field based on sales count or interest count - Page-by-page results for browsing large lists - Category filters for narrowing results by product category - Simple status information showing whether your request was successful - A request reference number for tracking your run ### How to use 1. Sign up and open the TikTok Ultra Product Ranklist tool. 2. Configure your search by choosing the ranking cycle, ranking field, region, date, and any category filters. 3. Run the tool to generate the product rank list for your selected settings. 4. Review the results to find the products you care about most. 5. Download or save the output for reporting, research, or comparison later. ### Inputs | Field | Required | Plain-English description | |---|---:|---| | rank_type | Yes | Choose the ranking cycle: 1 for daily, 2 for weekly, or 3 for monthly. | | product_rank_field | Yes | Choose how products are ranked: 1 for total sales count, 2 for total interest count. | | page_size | Yes | Set how many results to show on each page. | | maxResults | No | Limit the total number of results you want returned. | | region | Yes | Select the region you want to review, such as US. | | date | Yes | Pick the ranking date in yyyy-MM-dd format. | | category_l3_id | No | Narrow results to a third-level category when needed. | | category_id | No | Narrow results to a category when needed. | | category_l2_id | No | Narrow results to a second-level category when needed. | | maxRetries | No | Set how many times the run should try again if needed. | #### Date limits - Daily ranking can only look back 30 days - Weekly ranking can only look back 12 weeks - Monthly ranking can only look back 12 months ### Output The results include the following fields in plain English: - code: A status number that shows whether the request worked. A value of 0 means success. - message: A short text message about the result, such as success. - data: The list of ranked product results. If nothing is returned, this may be empty. - requestId: A unique reference number for your request, useful for support or tracking. - id: A unique internal record ID for the completed run. - name: The saved name of the run, if one was given. - accessed_at: The time the run was last opened or viewed. - created_at: The time the run was first created. - modified_at: The time the run was last updated. - item_count: The number of items included in the saved run. ### Use cases - Track which TikTok products are rising in popularity over time - Compare daily, weekly, or monthly product performance across regions - Discover high-performing items for merchandising or sourcing ideas - Monitor category trends to support market research and content planning # Actor input Schema ## `rank_type` (type: `string`): Choose how the ranking is grouped: daily, weekly, or monthly. ## `product_rank_field` (type: `string`): Choose what the ranking should be based on. ## `maxResults` (type: `integer`): Maximum number of results to return (default: 20) ## `region` (type: `string`): Enter the region code you want to search, such as US. ## `date` (type: `string`): Choose the date for the ranking list in yyyy-MM-dd format. ## `category_l3_id` (type: `string`): Optional third-level category ID. ## `category_id` (type: `string`): Optional category ID. ## `category_l2_id` (type: `string`): Optional second-level category ID. ## Actor input object example ```json { "rank_type": "1", "product_rank_field": "1", "maxResults": 20, "region": "US", "date": "2026-06-01" } ```` # Actor output Schema ## `results` (type: `string`): Formatted table view with labeled columns — ideal for reviewing data in Apify Console ## `rawData` (type: `string`): Unformatted JSON items exactly as collected from the API — use this for programmatic processing # 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 = { "rank_type": "1", "product_rank_field": "1", "maxResults": 20, "region": "US", "date": "2026-06-01" }; // Run the Actor and wait for it to finish const run = await client.actor("datamagnet/tiktok-ultra-product-ranklist").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 = { "rank_type": "1", "product_rank_field": "1", "maxResults": 20, "region": "US", "date": "2026-06-01", } # Run the Actor and wait for it to finish run = client.actor("datamagnet/tiktok-ultra-product-ranklist").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 '{ "rank_type": "1", "product_rank_field": "1", "maxResults": 20, "region": "US", "date": "2026-06-01" }' | apify call datamagnet/tiktok-ultra-product-ranklist --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamagnet/tiktok-ultra-product-ranklist", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TikTok Ultra Product Ranklist", "description": "Collect TikTok product rankings by date, region, and category to spot top-selling and fast-rising items.", "version": "1.0", "x-build-id": "8LO7n8yJBQbZfQDGX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamagnet~tiktok-ultra-product-ranklist/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamagnet-tiktok-ultra-product-ranklist", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamagnet~tiktok-ultra-product-ranklist/runs": { "post": { "operationId": "runs-sync-datamagnet-tiktok-ultra-product-ranklist", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamagnet~tiktok-ultra-product-ranklist/run-sync": { "post": { "operationId": "run-sync-datamagnet-tiktok-ultra-product-ranklist", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "date", "region", "product_rank_field", "rank_type" ], "properties": { "rank_type": { "title": "Ranking cycle", "enum": [ "1", "2", "3" ], "type": "string", "description": "Choose how the ranking is grouped: daily, weekly, or monthly.", "default": "1" }, "product_rank_field": { "title": "Ranking by", "enum": [ "1", "2" ], "type": "string", "description": "Choose what the ranking should be based on.", "default": "1" }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of results to return (default: 20)", "default": 20 }, "region": { "title": "Region code", "type": "string", "description": "Enter the region code you want to search, such as US.", "default": "US" }, "date": { "title": "List date", "type": "string", "description": "Choose the date for the ranking list in yyyy-MM-dd format.", "default": "2026-06-01" }, "category_l3_id": { "title": "Category L3 ID", "type": "string", "description": "Optional third-level category ID." }, "category_id": { "title": "Category ID", "type": "string", "description": "Optional category ID." }, "category_l2_id": { "title": "Category L2 ID", "type": "string", "description": "Optional second-level category ID." } } }, "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 } } } } } } } } } } ```