# 🚀 X.com | Twitter Full Thread Scrape | Tweet ID Unlimited API (`yellowchimp/twitterx`) Actor Retrieve detailed Tweet metadata (text, likes, replies, views, author, etc.) without API keys—just use Tweet IDs or URLs. Perfect for data analytics, ML, sentiment analysis, and trend monitoring. ✅ Batch processing ✅ Async & fast ✅ Structured JSON output ✅ Error handling. Start scraping from 0.39€/k - **URL**: https://apify.com/yellowchimp/twitterx.md - **Developed by:** [INFO-YC Studios](https://apify.com/yellowchimp) (community) - **Categories:** AI, Automation, Social media - **Stats:** 115 total users, 0 monthly users, 100.0% runs succeeded, 5 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### X.com (Twitter.com) Scraper A robust, async-ready Python Apify Actor for retrieving detailed Tweet metadata from X (Twitter) using only public Tweet IDs or URLs—no API key or user authentication required. --- ### What does the X.com Scraper do? The **X.com Scraper** extracts full Tweet metadata for any public Tweet, given either its tweet ID or a standard X/Twitter URL. This Actor is purpose-built for machine learning, data analytics, sentiment analysis, trend monitoring, digital marketing, and social media research, allowing you to automate collection of Tweet data at scale without relying on limited or expensive official APIs. --- ### Why use the X.com Scraper? - **No API key required:** Works with public Tweet IDs or URLs, bypassing API rate limits and credential requirements. - **Scalable Automation:** Designed for batch scraping—input hundreds or thousands of Tweets per run. - **Structured Output:** Returns rich, normalized JSON for each Tweet, ideal for pipelines, analytics, and ML ingestion. - **Handles Multiple Input Types:** Accepts both direct Tweet IDs and full Tweet URLs in the same run. - **Async Performance:** Fully async, enabling high concurrency for faster data gathering. - **Transparent Errors:** Each result includes errors (if encountered) for robust downstream handling. - **Easy Integration:** Standard Apify storage and dataset outputs. --- ### Features - **Accepts both Tweet IDs and URLs:** Mix and match in your input. - **Automatic ID extraction:** Recognizes and parses IDs from any regular X/Twitter status URL. - **Async HTTP Requests:** Efficiently fetches many Tweets in parallel. - **Rich Metadata:** Retrieves all structured Tweet fields, including: - Text, user info, hashtags, mentions, media, timestamps, engagement counts, and more. - **Error Handling:** Detailed info even for missing, deleted, or protected tweets. - **Apify SDK Integration:** Leverages the Apify Python SDK for local or cloud runs, storage, and logging. - **Machine Learning Ready:** Output is optimized for ML, data science, social analytics, and market research. - **Ideal for Web Analytics:** Use for tracking brands, campaigns, topics, or market sentiment in real time. - **Open for Automation:** Integrate easily with your own workflows, CRON jobs, or cloud automations. --- ### Use Cases - **Machine Learning:** Ingest Tweet data for language modeling, sentiment analysis, or fine-tuning. - **Web & Social Analytics:** Track hashtag campaigns, trending topics, or competitor mentions. - **Sales & Brand Monitoring:** Monitor customer feedback, complaints, and product buzz at scale. - **Digital Marketing:** Aggregate results for engagement analysis, campaign reporting, or influencer research. - **Academic & Journalistic Research:** Collect primary source social posts for large-scale, ethical studies. --- ### How It Works - Add your logic inside the `async with Actor:` block (see [`src/main.py`](src/main.py)). - Input: Submit a list of Tweet IDs and/or direct Tweet URLs. - The Actor: - Extracts valid Tweet IDs from each input. - Makes async HTTP requests to Twitter/X's public syndication endpoint. - Collects and normalizes JSON responses. - Stores everything in the Apify dataset for easy download or further processing. --- ## Example ```json {"tweet_ids": ["719484841172054016", "https://twitter.com/user/status/1517493440075018240"]} ```` ## Actor input object example ```json {} ``` # 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("yellowchimp/twitterx").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("yellowchimp/twitterx").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 yellowchimp/twitterx --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=yellowchimp/twitterx", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🚀 X.com | Twitter Full Thread Scrape | Tweet ID Unlimited API", "description": "Retrieve detailed Tweet metadata (text, likes, replies, views, author, etc.) without API keys—just use Tweet IDs or URLs. Perfect for data analytics, ML, sentiment analysis, and trend monitoring. ✅ Batch processing ✅ Async & fast ✅ Structured JSON output ✅ Error handling. Start scraping from 0.39€/k", "version": "0.0", "x-build-id": "B2noswCDhf5IGrhTu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/yellowchimp~twitterx/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-yellowchimp-twitterx", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/yellowchimp~twitterx/runs": { "post": { "operationId": "runs-sync-yellowchimp-twitterx", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/yellowchimp~twitterx/run-sync": { "post": { "operationId": "run-sync-yellowchimp-twitterx", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": {} }, "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 } } } } } } } } } } ```