# Threads Scraper — Posts & Replies from Meta Threads \[Fast] (`knotless_cadence/threads-scraper`) Actor Extract posts, replies, and profiles from Meta Threads. Get text, media, engagement metrics, and timestamps. Bulk export to JSON/CSV. Ideal for social listening, trend analysis, and content research. No login required. Custom pipeline: $100 pilot, spinov001@gmail.com - **URL**: https://apify.com/knotless\_cadence/threads-scraper.md - **Developed by:** [Alex](https://apify.com/knotless_cadence) (community) - **Categories:** Social media - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Threads Scraper Scrape posts and profiles from Meta's Threads — extract user profiles, post text, engagement data, and follower counts. Monitor influencer activity, track brand mentions, and analyze content trends on the fastest-growing social platform. ### Features - **Profile extraction** — username, display name, biography, avatar, follower count, and following count - **Post scraping** — extract post text from user profiles with embedded JSON parsing - **Search by keyword** — find posts across Threads matching your search terms - **Multiple profiles** — scrape several user profiles in a single run - **Smart data extraction** — parses React/Next.js server-rendered data for maximum field coverage - **Meta tag fallback** — extracts Open Graph data when embedded JSON is not available - **No login required** — scrapes publicly accessible Threads pages ### Output Example ```json { "_type": "PROFILE", "username": "zuck", "displayName": "Mark Zuckerberg", "biography": "Building the future of social connection", "followers": 12500000, "following": 450, "url": "https://www.threads.net/@zuck", "scrapedAt": "2026-03-18T12:00:00.000Z" } ```` ```json { "_type": "POST", "author": "zuck", "text": "Threads just crossed 200 million monthly active users!", "source": "profile:zuck", "scrapedAt": "2026-03-18T12:00:00.000Z" } ``` ### Use Cases - **Social media monitoring** — track brand mentions and competitor activity on Threads - **Influencer analysis** — compare follower counts, posting frequency, and content themes across influencers - **Content research** — discover trending topics and popular post formats on the platform - **Market research** — analyze public opinion and consumer sentiment on Threads discussions - **PR monitoring** — track how your brand or executives are being discussed on the platform ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `usernames` | Array | `[]` | Threads usernames to scrape (e.g., `["zuck", "mosseri"]`) | | `searchQueries` | Array | `[]` | Keywords to search for across Threads | | `maxPostsPerSource` | Number | `50` | Maximum posts to extract per profile/query | ### Cost Estimation - \~$0.25 per 10 profiles scraped - \~$0.50 per 50 posts extracted - Free tier: up to 10 profiles with Apify free plan ### FAQ **Q: Can I scrape private Threads accounts?** A: No. Only public profiles and posts visible without logging in are accessible. Private accounts require authentication which is not supported. **Q: Does it extract engagement metrics (likes, replies) for each post?** A: The scraper extracts post text and author information. Engagement metrics availability depends on what Threads exposes in its public page data, which may vary. **Q: How do I format usernames?** A: Enter usernames without the @ symbol. For example, use `"zuck"` instead of `"@zuck"`. The scraper automatically cleans the input format. # Actor input Schema ## `usernames` (type: `array`): Threads usernames to scrape (e.g., 'zuck', 'instagram') ## `searchQueries` (type: `array`): Search Threads for posts containing these keywords ## `maxPostsPerSource` (type: `integer`): Maximum number of posts to extract per username or search query ## Actor input object example ```json { "usernames": [], "searchQueries": [], "maxPostsPerSource": 50 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("knotless_cadence/threads-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("knotless_cadence/threads-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 '{}' | apify call knotless_cadence/threads-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=knotless_cadence/threads-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Threads Scraper — Posts & Replies from Meta Threads [Fast]", "description": "Extract posts, replies, and profiles from Meta Threads. Get text, media, engagement metrics, and timestamps. Bulk export to JSON/CSV. Ideal for social listening, trend analysis, and content research. No login required. Custom pipeline: $100 pilot, spinov001@gmail.com", "version": "1.0", "x-build-id": "AXTEJkO7uF4xQ9ohq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/knotless_cadence~threads-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-knotless_cadence-threads-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/knotless_cadence~threads-scraper/runs": { "post": { "operationId": "runs-sync-knotless_cadence-threads-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/knotless_cadence~threads-scraper/run-sync": { "post": { "operationId": "run-sync-knotless_cadence-threads-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", "properties": { "usernames": { "title": "Usernames", "type": "array", "description": "Threads usernames to scrape (e.g., 'zuck', 'instagram')", "default": [], "items": { "type": "string" } }, "searchQueries": { "title": "Search Queries", "type": "array", "description": "Search Threads for posts containing these keywords", "default": [], "items": { "type": "string" } }, "maxPostsPerSource": { "title": "Max Posts Per Source", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of posts to extract per username or search query", "default": 50 } } }, "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 } } } } } } } } } } ```