# X(Twitter) User Highlights Scraper (`powerai/x-twitter-user-highlights-scraper`) Actor Export a user’s X Highlights—one structured row per post with text, media, author, and engagement for reporting and analysis. - **URL**: https://apify.com/powerai/x-twitter-user-highlights-scraper.md - **Developed by:** [PowerAI](https://apify.com/powerai) (community) - **Categories:** Social media, Videos, Integrations - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 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. 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 ## Twitter User Highlights Scraper Collect **Highlight posts** for a given **X (Twitter) account**—the same posts users see on the Highlights tab—into dataset rows. Each row is one post with nested details for text, media, author, and engagement. ### Who it’s for - **Social & comms** teams auditing what a brand showcases in Highlights. - **Researchers** who need structured exports instead of manual scrolling. - **Data & analytics** workflows that consume rich JSON per post. ### What you can do with it - **Gather multiple highlight posts** until you reach the limit you set or there is nothing more to load. - **Match rows to your own user list** using **`requestedUserId`** on each row (the account id you entered). ### How it works (in plain terms) You enter the **numeric account id** (not the `@handle`). The tool loads highlights in successive batches until your **maximum** is reached or the list ends—you do not need to manage paging yourself. ### Input | Field | Required | What it means | |-------|----------|----------------| | **User ID** (`user`) | Yes | The account’s numeric id, not `@handle`. | | **Maximum results** (`maxResults`) | No | Upper limit on how many highlight posts to collect (default **50**). | ### Output - **One row per highlight post** (until your cap). - **Post identity & stats**: identifiers such as **`rest_id`**, posting **`source`**, **view** counts where available, edit-related **`edit_control`**, and other top-level flags when present. - **Author**: nested **`core.user_results`** with profile fields (**`screen_name`**, **`name`**, follower counts, images, pinned posts, etc.). - **Content & engagement**: **`legacy`** blocks with **`full_text`**, **`created_at`**, **`entities`** (hashtags, links, media), and counters like favorites, reposts, replies, and quotes. - **Export metadata**: **`requestedUserId`** repeats the account you asked for; **`scrapedAt`** is when that row was saved. Rows can be large, and optional fields vary by post. Details may change as X updates its product. #### Sample output (one dataset row, heavily trimmed) ```json { "__typename": "Tweet", "rest_id": "2033543815437451270", "core": { "user_results": { "result": { "__typename": "User", "rest_id": "877807935493033984", "legacy": { "screen_name": "binance", "name": "Binance", "followers_count": 15841109 } } } }, "legacy": { "full_text": "The future of TradFi trading is already on #Binance\n\nMarkets aren't 9–5 anymore. They're 24/7.\n\nRead more 👉 https://t.co/KSNKN1knty https://t.co/7iu9ZXIvMK", "created_at": "Mon Mar 16 14:00:01 +0000 2026", "id_str": "2033543815437451270", "conversation_id_str": "2033543815437451270", "favorite_count": 1191, "retweet_count": 500, "reply_count": 361, "quote_count": 21, "entities": { "hashtags": [{ "indices": [43, 51], "text": "Binance" }], "media": [ { "type": "photo", "display_url": "pic.x.com/7iu9ZXIvMK", "expanded_url": "https://x.com/binance/status/2033543815437451270/photo/1" } ] } }, "views": { "count": "1135420", "state": "EnabledWithCount" }, "requestedUserId": "877807935493033984", "scrapedAt": "2026-03-25T08:33:59.135Z" } ```` ### Good to know - The **account id** must be valid; wrong ids or restricted profiles may yield no rows. - Respect X/Twitter’s rules and applicable laws when using exported data. # Actor input Schema ## `user` (type: `string`): The account’s numeric id on X (not @handle). ## `maxResults` (type: `integer`): Maximum highlight posts to save to your dataset. ## Actor input object example ```json { "user": "877807935493033984", "maxResults": 20 } ``` # 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 = { "user": "877807935493033984", "maxResults": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("powerai/x-twitter-user-highlights-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 = { "user": "877807935493033984", "maxResults": 20, } # Run the Actor and wait for it to finish run = client.actor("powerai/x-twitter-user-highlights-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 '{ "user": "877807935493033984", "maxResults": 20 }' | apify call powerai/x-twitter-user-highlights-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=powerai/x-twitter-user-highlights-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X(Twitter) User Highlights Scraper", "description": "Export a user’s X Highlights—one structured row per post with text, media, author, and engagement for reporting and analysis.", "version": "0.0", "x-build-id": "I82BWbaeQH2DlN0pm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/powerai~x-twitter-user-highlights-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-powerai-x-twitter-user-highlights-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/powerai~x-twitter-user-highlights-scraper/runs": { "post": { "operationId": "runs-sync-powerai-x-twitter-user-highlights-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/powerai~x-twitter-user-highlights-scraper/run-sync": { "post": { "operationId": "run-sync-powerai-x-twitter-user-highlights-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "user" ], "properties": { "user": { "title": "User ID", "type": "string", "description": "The account’s numeric id on X (not @handle).", "default": "877807935493033984" }, "maxResults": { "title": "Maximum results", "minimum": 20, "maximum": 10000, "type": "integer", "description": "Maximum highlight posts to save to your dataset.", "default": 20 } } }, "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 } } } } } } } } } } ```