# X (Twitter) Post Replies Scraper (`scrapeflow/twitter-x-reply-scraper`) Actor - **URL**: https://apify.com/scrapeflow/twitter-x-reply-scraper.md - **Developed by:** [ScrapeFlow](https://apify.com/scrapeflow) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.99 / 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 ## Twitter / X Reply Scraper (Apify Actor) Scrapes **Latest** search results via X GraphQL `SearchTimeline`, then loads direct replies per tweet with `TweetDetail` (same flow as the standalone scraper: dynamic query IDs from `main.*.js`, `x-client-transaction-id` from XClientTransaction, `controller_data` from the first timeline page). ### Input | Field | Description | |--------|-------------| | `startUrls` | Start URLs — X **search** URLs (`q=` is parsed as the query). Legacy `urls` still accepted. | | `maxSearchResults` | **Maximum Search Results** — max tweets per start URL (default `5`). | | `maxReplies` | **Maximum Replies** — max direct replies stored per tweet (default `5`). | | `authToken` / `ct0` | Session cookies, or env `AUTH_TOKEN` / `CT0`. | | `proxyConfiguration` | `useApifyProxy: false` → direct first, then DC → residential on blocks. `true` → Apify proxy from the start. | ### Output (dataset) Each item: `tweetLink`, `tweetContent`, `tweetDate`, `handle`, counts, `repliesData` (nested reply objects). ### Run locally ```bash cd Twitter-X-Reply-Scraper pip install -r requirements.txt ## Set AUTH_TOKEN and CT0, then: apify run ```` Data is only from public X content; you are responsible for compliance with X’s terms and applicable law. # Actor input Schema ## `startUrls` (type: `array`): 📋 Add one or more X search page links (the same URLs you get when you search on x.com). Each link defines what topic or keywords you want to follow. You can add as many as you need — great for tracking several topics in one run! 🌍✅ ## `maxSearchResults` (type: `integer`): 🎯 Choose how many posts you want from each search link — higher numbers give you a broader snapshot; lower numbers finish faster. Pick what fits your project! ⚡📈 ## `maxReplies` (type: `integer`): 💡 For each post found, decide how many replies to keep in your results. Ideal when you want a taste of the conversation or a deeper thread — you’re in control! 🙌🧵 ## `authToken` (type: `string`): 🔑 Your X account session token (or set the AUTH\_TOKEN environment variable in Apify). This lets the actor use your logged-in session. Keep it private — never share it publicly! 🤫🔒 ## `ct0` (type: `string`): ✨ The companion security value for your X session (or use the CT0 env var). It works together with your auth token so everything stays in sync — like a key and a lock! 🔐💙 ## `proxyConfiguration` (type: `object`): 🌍 Optional **Apify Proxy** settings for smoother runs in the cloud. Turn it on if you prefer routing traffic through Apify’s network — handy for stable jobs and privacy-friendly setups. Off by default; enable when it suits you! ☁️✅ ## Actor input object example ```json { "startUrls": [ "https://x.com/search?q=%23journorequest+min_replies%3A10&src=typed_query&f=live" ], "maxSearchResults": 10, "maxReplies": 5, "authToken": "", "ct0": "", "proxyConfiguration": { "useApifyProxy": false } } ``` # 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 = { "startUrls": [ "https://x.com/search?q=%23journorequest+min_replies%3A10&src=typed_query&f=live" ], "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeflow/twitter-x-reply-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 = { "startUrls": ["https://x.com/search?q=%23journorequest+min_replies%3A10&src=typed_query&f=live"], "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("scrapeflow/twitter-x-reply-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 '{ "startUrls": [ "https://x.com/search?q=%23journorequest+min_replies%3A10&src=typed_query&f=live" ], "proxyConfiguration": { "useApifyProxy": false } }' | apify call scrapeflow/twitter-x-reply-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeflow/twitter-x-reply-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X (Twitter) Post Replies Scraper", "version": "0.1", "x-build-id": "Z6uAF9NmpfEJIY5Vv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeflow~twitter-x-reply-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeflow-twitter-x-reply-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeflow~twitter-x-reply-scraper/runs": { "post": { "operationId": "runs-sync-scrapeflow-twitter-x-reply-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeflow~twitter-x-reply-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeflow-twitter-x-reply-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "🔗 Start URLs — where to look on X", "type": "array", "description": "📋 Add one or more X search page links (the same URLs you get when you search on x.com). Each link defines what topic or keywords you want to follow. You can add as many as you need — great for tracking several topics in one run! 🌍✅", "items": { "type": "string" } }, "maxSearchResults": { "title": "📊 Maximum posts to collect", "minimum": 1, "maximum": 5000, "type": "integer", "description": "🎯 Choose how many posts you want from each search link — higher numbers give you a broader snapshot; lower numbers finish faster. Pick what fits your project! ⚡📈", "default": 10 }, "maxReplies": { "title": "💬 Maximum replies per post", "minimum": 1, "maximum": 500, "type": "integer", "description": "💡 For each post found, decide how many replies to keep in your results. Ideal when you want a taste of the conversation or a deeper thread — you’re in control! 🙌🧵", "default": 5 }, "authToken": { "title": "🔐 Session — auth token", "type": "string", "description": "🔑 Your X account session token (or set the AUTH_TOKEN environment variable in Apify). This lets the actor use your logged-in session. Keep it private — never share it publicly! 🤫🔒", "default": "" }, "ct0": { "title": "🛡️ Session — security (ct0)", "type": "string", "description": "✨ The companion security value for your X session (or use the CT0 env var). It works together with your auth token so everything stays in sync — like a key and a lock! 🔐💙", "default": "" }, "proxyConfiguration": { "title": "🌐 Connection & privacy (proxy)", "type": "object", "description": "🌍 Optional **Apify Proxy** settings for smoother runs in the cloud. Turn it on if you prefer routing traffic through Apify’s network — handy for stable jobs and privacy-friendly setups. Off by default; enable when it suits you! ☁️✅" } } }, "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 } } } } } } } } } } ```