# X/Twitter Tweet Replies Scraper (`scrapesmith/x-twitter-tweet-replies-scraper`) Actor Extract all replies and conversations from any X (Twitter) tweet. Get full reply threads with author details, engagement stats, timestamps and media. Perfect for sentiment analysis, customer feedback tracking and archiving viral threads. - **URL**: https://apify.com/scrapesmith/x-twitter-tweet-replies-scraper.md - **Developed by:** [Scrape Smith](https://apify.com/scrapesmith) (community) - **Categories:** Social media, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.15 / 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 ## X (Twitter) Tweet Replies Scraper Extract every reply and conversation from any tweet on X (Twitter). Just paste a tweet URL or ID and get back the full conversation thread — the original tweet plus every reply, with complete author details and engagement metrics. ### What It Does This scraper follows a tweet's entire reply thread, capturing every comment, nested reply, and quote reply in the conversation. Whether you're tracking brand sentiment, moderating discussions, or archiving a viral thread before it disappears, this actor gives you structured, ready-to-use data in seconds. Simply provide one or more tweet links (or bare tweet IDs), and the scraper returns the original tweet along with all of its replies — no login, no browser setup, and no manual scrolling required. ### Key Features - **Full conversation threads** — captures the entire reply chain, not just top-level comments - **Nested replies included** — replies-to-replies are captured, preserving the real conversation structure - **Complete author profiles** — username, display name, follower count, verification status, bio, and profile picture for every participant - **Rich engagement metrics** — likes, retweets, quote counts, bookmarks, and view counts on every tweet and reply - **Media extraction** — photos, videos, and GIFs attached to any tweet in the thread - **Bulk processing** — submit multiple tweet URLs in a single run and get all their conversations back together - **Fast turnaround** — results typically ready in seconds to a couple of minutes depending on thread size ### Input Parameters | Parameter | Description | |---|---| | **Tweet URLs or IDs** | A list of tweet links (e.g. `https://x.com/user/status/123456789`) or bare tweet IDs. Add as many as you like — each one is processed in turn. | | **Max replies per tweet** | The maximum number of replies to collect for each tweet. Leave at the default for a solid sample, or raise it to pull deeper into long threads. | | **Max total items** | An overall cap on how many items (across all tweets in the run) get saved. Useful for controlling large batch jobs. | ### Output Fields Each result in your dataset is a tweet (either the original post or one of its replies) with the following fields: **Tweet content** - `id` — unique tweet ID - `url` — direct link to the tweet - `text` — full tweet text - `lang` — detected language - `createdAt` — publish timestamp - `media` — attached photos/videos, if any **Conversation context** - `isReply` — whether this item is a reply or the original tweet - `replyToUser` — the username being replied to - `conversationId` — groups every item in the same thread together - `isQuote` / `quotedTweet` — quote-tweet details, if applicable **Engagement metrics** - `likeCount`, `retweetCount`, `replyCount`, `quoteCount`, `bookmarkCount`, `viewCount` **Author details** - `author.userName`, `author.name`, `author.followers`, `author.verified`, `author.description`, `author.profilePicture`, and more ### Example Use Cases - **Sentiment analysis** — pull every reply to a brand announcement or product launch and run it through your sentiment model - **Customer feedback monitoring** — track what people are saying in response to your company's tweets or support threads - **Viral thread archiving** — save a fast-moving viral thread in full before replies get deleted or buried - **Community & moderation research** — analyze how a conversation evolves, who's engaging, and what themes emerge - **Influencer & competitor monitoring** — see how audiences are responding to a competitor's or influencer's posts - **Academic & market research** — collect real conversation data for studies on public discourse and engagement patterns ### Pricing This actor uses a **pay-per-result** pricing model — you're only charged for each reply/tweet actually delivered to your dataset. There are no charges for failed lookups or empty threads. A limited number of free results are included on the free plan so you can test the actor before committing. ### Getting Started 1. Paste in one or more tweet URLs (or IDs) 2. Set how many replies you want per tweet 3. Run the actor and export your results as JSON, CSV, or Excel No X/Twitter account, API keys, or technical setup required on your end. # Actor input Schema ## `tweetUrls` (type: `array`): One or more tweet links or bare tweet IDs to pull replies from. Accepts full URLs (e.g. https://x.com/user/status/123456789) or just the numeric ID (e.g. 123456789). Add multiple entries to process several tweets in one run. ## `maxReplies` (type: `integer`): The maximum number of replies to collect for each tweet (the original tweet itself doesn't count against this limit). Increase this to pull deeper into long conversation threads. ## `maxItems` (type: `integer`): An overall cap on the total number of items saved across all tweets in this run. Leave blank to use your plan's default limit. ## Actor input object example ```json { "tweetUrls": [ "https://x.com/elonmusk/status/2072034905299563005", "1885000000000000001" ], "maxReplies": 100 } ```` # 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 = { "tweetUrls": [ "https://x.com/elonmusk/status/2072034905299563005" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapesmith/x-twitter-tweet-replies-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 = { "tweetUrls": ["https://x.com/elonmusk/status/2072034905299563005"] } # Run the Actor and wait for it to finish run = client.actor("scrapesmith/x-twitter-tweet-replies-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 '{ "tweetUrls": [ "https://x.com/elonmusk/status/2072034905299563005" ] }' | apify call scrapesmith/x-twitter-tweet-replies-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapesmith/x-twitter-tweet-replies-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X/Twitter Tweet Replies Scraper", "description": "Extract all replies and conversations from any X (Twitter) tweet. Get full reply threads with author details, engagement stats, timestamps and media. Perfect for sentiment analysis, customer feedback tracking and archiving viral threads.", "version": "0.0", "x-build-id": "XMGpyPhTVsYgLx8Oy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapesmith~x-twitter-tweet-replies-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapesmith-x-twitter-tweet-replies-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapesmith~x-twitter-tweet-replies-scraper/runs": { "post": { "operationId": "runs-sync-scrapesmith-x-twitter-tweet-replies-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapesmith~x-twitter-tweet-replies-scraper/run-sync": { "post": { "operationId": "run-sync-scrapesmith-x-twitter-tweet-replies-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "tweetUrls" ], "properties": { "tweetUrls": { "title": "Tweet URLs or IDs", "type": "array", "description": "One or more tweet links or bare tweet IDs to pull replies from. Accepts full URLs (e.g. https://x.com/user/status/123456789) or just the numeric ID (e.g. 123456789). Add multiple entries to process several tweets in one run.", "items": { "type": "string" } }, "maxReplies": { "title": "Max replies per tweet", "minimum": 0, "maximum": 50000, "type": "integer", "description": "The maximum number of replies to collect for each tweet (the original tweet itself doesn't count against this limit). Increase this to pull deeper into long conversation threads.", "default": 10 }, "maxItems": { "title": "Max total items", "minimum": 1, "maximum": 50000, "type": "integer", "description": "An overall cap on the total number of items saved across all tweets in this run. Leave blank to use your plan's default limit." } } }, "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 } } } } } } } } } } ```