# X Twitter Replies Scraper - \[No Cookies] (`khadinakbar/x-twitter-replies-scraper`) Actor Scrape public X/Twitter replies from tweet URLs or IDs with author profiles, metrics, media, language, and cursors. MCP optimized. $8.00/1K replies. - **URL**: https://apify.com/khadinakbar/x-twitter-replies-scraper.md - **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community) - **Categories:** Social media, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $8.00 / 1,000 x/twitter reply scrapeds 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 Twitter Replies Scraper Extract public replies from X/Twitter tweet URLs or numeric tweet IDs. The actor is provider-backed, requires no cookies, and returns clean reply records for sentiment analysis, conversation monitoring, social listening, creator research, support triage, and AI-agent workflows. ### What it does This actor scrapes the public reply timeline for specific X/Twitter posts. It accepts full tweet URLs or tweet IDs, paginates through reply results, dedupes repeated replies, and saves one normalized dataset row per reply. Use it for: - Brand and campaign reply monitoring - Sentiment analysis on public tweet conversations - Customer support and escalation discovery - Creator, influencer, and community research - Lead and intent discovery from public replies - MCP optimized AI-agent research pipelines Do not use it for keyword search, profile timelines, quote tweets, retweeters, private accounts, deleted tweets, DMs, or login-only data. ### Input Important fields: - `tweetUrls` - one or more X/Twitter tweet URLs or numeric tweet IDs - `rankingMode` - `Relevance`, `Recency`, or `Likes` - `maxReplies` - total reply rows to save across all tweets - `maxPagesPerTweet` - pagination depth per tweet - `startCursor` - optional continuation cursor from `RUN_SUMMARY.nextCursors` - `includeRaw` - include raw provider payloads for debugging - `dedupeReplies` - skip duplicate reply tweets across pages Example: ```json { "tweetUrls": ["https://x.com/OpenAI/status/2065225362544726371"], "rankingMode": "Relevance", "maxReplies": 100, "maxPagesPerTweet": 10, "dedupeReplies": true } ```` ### Output Each dataset item is a flat, AI-agent-friendly reply record: - `parentTweetId`, `parentTweetUrl`, `rankingMode` - `replyId`, `replyUrl`, `text`, `createdAt`, `language` - `conversationId`, `inReplyToTweetId` - `authorUsername`, `authorName`, `authorId`, `authorUrl` - `authorVerified`, `authorFollowers`, `authorFollowing`, `authorBio`, `authorProfileImageUrl` - `replyCount`, `retweetCount`, `quoteCount`, `likeCount`, `viewCount`, `bookmarkCount` - `hashtags`, `mentions`, `urls`, `mediaUrls`, `source` - `provider`, `providerPage`, `scrapedAt` The actor also writes: - `OUTPUT` - concise machine-readable run summary - `RUN_SUMMARY` - detailed telemetry, warnings, provider status codes, and next cursors ### Public data only This actor collects public reply data visible through the provider-backed X/Twitter reply endpoint. It does not unlock private accounts, hidden replies that are not exposed upstream, deleted posts, DMs, private follower graphs, or login-only fields. ### Pricing The actor uses pay-per-event pricing: - Actor start: `$0.00005` - X/Twitter reply saved: `$0.008` ### Reliability The actor uses provider retries, cursor pagination, status-code telemetry, duplicate detection, cost-cap status messages, clean empty-result handling, and graceful terminal summaries. Missing provider credentials or upstream failures are written to `OUTPUT` and `RUN_SUMMARY` with actionable status messages. ### Tips - Use `Relevance` for the default conversation view. - Use `Recency` for fresh monitoring. - Use `Likes` when you want high-engagement replies first. - Use `startCursor` only when continuing the same tweet and ranking mode from a prior run. - Keep `includeRaw` disabled unless debugging field drift. # Actor input Schema ## `tweetUrls` (type: `array`): Use this when you need public replies from specific X/Twitter posts. Enter full URLs like https://x.com/openai/status/1930000000000000000 or numeric tweet IDs. Defaults to one working public tweet example. This is not for keyword search or profile timelines. ## `rankingMode` (type: `string`): Use this when choosing how X should order replies for each tweet. Relevance returns the default conversation ranking, Recency returns newer replies first, and Likes prioritizes liked replies. Defaults to Relevance. This does not scrape quote tweets or retweeters. ## `maxReplies` (type: `integer`): Use this to cap the total reply rows saved across all input tweets. The actor stops before charging beyond this limit. Defaults to 100 and supports up to 5000. This is a hard billing and dataset cap, not a per-tweet target. ## `maxPagesPerTweet` (type: `integer`): Use this as a pagination safety valve for each tweet conversation. Higher values collect deeper reply pages when the provider returns a next cursor. Defaults to 10 and supports up to 100. This does not override maxReplies. ## `startCursor` (type: `string`): Use this when continuing one tweet from a previous RUN\_SUMMARY nextCursors value. Paste the cursor string exactly as returned by the actor. Leave blank for a fresh run. This is not a tweet URL or tweet ID field. ## `includeRaw` (type: `boolean`): Use this for debugging provider field drift or building custom parsers. When enabled, each dataset row includes the raw SociaVault reply object. Defaults to false for smaller datasets. This is not needed for normal analytics exports. ## `dedupeReplies` (type: `boolean`): Use this to skip duplicate reply tweets across pages and input tweets. The actor dedupes by reply ID first, then reply URL or text fallback. Defaults to true. Disable only when you need to inspect provider pagination overlap. ## Actor input object example ```json { "tweetUrls": [ "https://x.com/OpenAI/status/2065225362544726371" ], "rankingMode": "Relevance", "maxReplies": 100, "maxPagesPerTweet": 10, "includeRaw": false, "dedupeReplies": true } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description ## `runSummary` (type: `string`): No description # 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/OpenAI/status/2065225362544726371" ] }; // Run the Actor and wait for it to finish const run = await client.actor("khadinakbar/x-twitter-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/OpenAI/status/2065225362544726371"] } # Run the Actor and wait for it to finish run = client.actor("khadinakbar/x-twitter-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/OpenAI/status/2065225362544726371" ] }' | apify call khadinakbar/x-twitter-replies-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=khadinakbar/x-twitter-replies-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X Twitter Replies Scraper - [No Cookies]", "description": "Scrape public X/Twitter replies from tweet URLs or IDs with author profiles, metrics, media, language, and cursors. MCP optimized. $8.00/1K replies.", "version": "1.0", "x-build-id": "W2PaA8Fi1DKa8hGHL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/khadinakbar~x-twitter-replies-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-khadinakbar-x-twitter-replies-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/khadinakbar~x-twitter-replies-scraper/runs": { "post": { "operationId": "runs-sync-khadinakbar-x-twitter-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/khadinakbar~x-twitter-replies-scraper/run-sync": { "post": { "operationId": "run-sync-khadinakbar-x-twitter-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", "minItems": 1, "maxItems": 50, "type": "array", "description": "Use this when you need public replies from specific X/Twitter posts. Enter full URLs like https://x.com/openai/status/1930000000000000000 or numeric tweet IDs. Defaults to one working public tweet example. This is not for keyword search or profile timelines.", "items": { "type": "string" } }, "rankingMode": { "title": "Reply ranking mode", "enum": [ "Relevance", "Recency", "Likes" ], "type": "string", "description": "Use this when choosing how X should order replies for each tweet. Relevance returns the default conversation ranking, Recency returns newer replies first, and Likes prioritizes liked replies. Defaults to Relevance. This does not scrape quote tweets or retweeters.", "default": "Relevance" }, "maxReplies": { "title": "Maximum replies", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Use this to cap the total reply rows saved across all input tweets. The actor stops before charging beyond this limit. Defaults to 100 and supports up to 5000. This is a hard billing and dataset cap, not a per-tweet target.", "default": 100 }, "maxPagesPerTweet": { "title": "Maximum pages per tweet", "minimum": 1, "maximum": 100, "type": "integer", "description": "Use this as a pagination safety valve for each tweet conversation. Higher values collect deeper reply pages when the provider returns a next cursor. Defaults to 10 and supports up to 100. This does not override maxReplies.", "default": 10 }, "startCursor": { "title": "Start cursor", "maxLength": 500, "type": "string", "description": "Use this when continuing one tweet from a previous RUN_SUMMARY nextCursors value. Paste the cursor string exactly as returned by the actor. Leave blank for a fresh run. This is not a tweet URL or tweet ID field." }, "includeRaw": { "title": "Include raw provider payload", "type": "boolean", "description": "Use this for debugging provider field drift or building custom parsers. When enabled, each dataset row includes the raw SociaVault reply object. Defaults to false for smaller datasets. This is not needed for normal analytics exports.", "default": false }, "dedupeReplies": { "title": "Dedupe replies", "type": "boolean", "description": "Use this to skip duplicate reply tweets across pages and input tweets. The actor dedupes by reply ID first, then reply URL or text fallback. Defaults to true. Disable only when you need to inspect provider pagination overlap.", "default": true } } }, "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 } } } } } } } } } } ```