# Meta Threads Profile Scraper (`scrapecraze/meta-threads-profile-scraper`) Actor 🧠 Meta Threads Profile Scraper pulls public Threads profile data quickly & accurately perfect for B2B leads, market research, and recruitment. 🔎 Save time, streamline outreach, and boost insights with meta-threads-profile-scraper. 🚀 - **URL**: https://apify.com/scrapecraze/meta-threads-profile-scraper.md - **Developed by:** [ScrapeCraze](https://apify.com/scrapecraze) (community) - **Categories:** Lead generation, Jobs, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.99 / 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 ### Meta Threads Profile Scraper ⚡ Scraping Threads profiles one by one for analytics and lead research wastes hours—and you end up with incomplete data. **Meta Threads Profile Scraper** extracts comprehensive profile metadata and the latest posts for a list of Threads usernames in one run. This Threads profile scraper tool is ideal for Threads account scraper workflows, from outreach prep to creator research and profile list scraping. Built for marketers, data analysts, and researchers who need public profile data at scale, it helps you go from a list of handles to structured results in minutes. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "result": [ { "url": "https://www.threads.net/@guinnessworldrecords", "is_private": false, "profile_pic_url": "https://example.com/guinnessworldrecords.jpg", "friendship_status": "NOT_FOLLOWING", "has_onboarded_to_text_post_app": true, "pk": "1234567890", "text_post_app_is_private": false, "username": "guinnessworldrecords", "text_post_app_remove_mention_entrypoint": null, "text_app_custom_feeds": null, "gating": null, "follower_count": 1234567, "profile_context_facepile_users": [], "hd_profile_pic_versions": [], "text_post_app_public_views": 987654321, "is_verified": true, "biography": "Official Guinness World Records account.", "text_app_biography": "Official Guinness World Records account.", "full_name": "Guinness World Records", "bio_links": [ { "url": "https://guinnessworldrecords.com" } ], "profile_tags": [ "Records", "News" ], "transparency_label": null, "show_text_post_app_badge": true, "platform_podcast_info": null, "platform_podcast_episode_info": null, "id": "abcdef123456", "latestPosts": [ { "id": "post1", "logging_info_token": "token1", "pk": "pk1", "user": { "username": "guinnessworldrecords" }, "text_post_app_info": { "timezone_offset": 0 }, "is_paid_partnership": false, "audio": null, "caption": "New record update 🎉", "caption_is_edited": false, "transcription_data": null, "carousel_media": [], "code": null, "image_versions2": null, "original_height": 1080, "original_width": 1080, "accessibility_caption": null, "usertags": [], "video_versions": null, "has_audio": false, "media_type": 1, "caption_add_on": null, "like_count": 1234, "giphy_media_info": null, "prototyping_only_glimmer_post_info": null, "media_overlay_info": null, "metaPlace": null, "meta_place": null, "gen_ai_detection_method": null, "taken_at": 1710000000, "organic_tracking_token": null, "__token": null, "canonical_url": "https://www.threads.net/@guinnessworldrecords/post/post1", "like_and_view_counts_disabled": false } ] } ] } ```` | Field | Type | What It Tells You | |---|---|---| | `url` | string | The normalized Threads profile URL for the scraped user. | | `is_private` | boolean | Whether the profile is private—useful when building expectations for Threads profile analytics scraper projects. | | `profile_pic_url` | string | Quick access to the profile image URL for reporting or UI previews. | | `friendship_status` | (any) | Relationship state as returned by the profile page data. | | `pk` | (any) | A profile identifier you can use to join records across datasets. | | `username` | string | The Threads username used for scraping (also helpful for deduping). | | `follower_count` | number | Audience size for ranking accounts in influencer research workflows. | | `is_verified` | boolean | Verification status for vetting creators and brands. | | `biography` | string | Main bio text—often key for categorization and messaging. | | `full_name` | string | Display name for reporting and contact list personalization. | | `bio_links` | array | Link(s) shown in the profile bio, helpful for company/creator discovery. | | `profile_tags` | array | Tags attached to the profile for quicker segmentation. | | `latestPosts` | array | A structured list of the most recent post data (useful for engagement and content analysis). | | `error` | string | Only appears on failures; tells you what went wrong for the specific username. | | `username` (in error) | string | The username tied to the error so you can retry or audit outcomes. | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "usernames": [ "guinnessworldrecords", "some_other_threads_username" ], "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---|---| | `usernames` | ✅ | Provide a list of Threads usernames to scrape in a single run. | | `usernames` (items) | ✅ | Each item should be a Threads handle (without the `@`) whose profile metadata and latest posts you want. | | `proxyConfiguration` | ⬜ | Optional proxy settings for the run (configured via Apify Proxy settings). | | `proxyConfiguration` ↳ `proxy support` | ⬜ | Set this to `true` to route requests through Apify Proxy for improved reliability. | *** ### What It Does Meta Threads Profile Scraper pulls structured profile metadata and latest post data for each Threads username you provide, returning a clean JSON object per profile. #### Extract comprehensive Threads profile metadata For each username, the actor returns profile-level fields like `is_private`, `profile_pic_url`, `follower_count`, `is_verified`, `biography`, and `full_name`. This makes it useful for Threads profile analytics scraper projects and creator/influencer shortlisting. #### Retrieve latest posts in a structured `latestPosts` list Alongside the profile data, it also returns `latestPosts`, formatted into a consistent list of post objects with fields like `id`, `pk`, `caption`, and media-related attributes. If you’re building a Threads engagement scraper or content-performance dataset, this structure saves you time. #### Built-in proxy support for reliable scraping The actor supports running with proxy settings to help keep data collection stable across larger batches. This is especially helpful when you’re scraping Threads profiles as part of a profile list scraper workflow. #### Produces integration-ready output (one dataset item per run) All results are pushed together as a single item under the `result` key, containing an array of profile objects. This makes it straightforward to export and load into analytics tools, dashboards, or downstream pipelines. #### Handles failures per username If a profile can’t be scraped, the actor returns an `error` object that includes the `username` that failed. That lets you spot gaps quickly and rerun only the missing usernames. In short, Meta Threads Profile Scraper turns a simple list of Threads handles into structured profile + latest post data in one run. *** ### Why Meta Threads Profile Scraper? There are plenty of ways to pull data from Threads—here’s why Meta Threads Profile Scraper stands out. #### Built for list-based workflows Instead of manually scraping one profile at a time, Meta Threads Profile Scraper is made to process a list of `usernames` and return consistent results. That’s exactly what you want for Threads account scraper use cases like lead research and influencer discovery. #### Clean, schema-aligned post formatting The actor formats latest posts into a predictable object structure inside `latestPosts`. This reduces the work needed for Threads user profile extraction and makes the dataset easier to analyze. #### Resilient run behavior with clear per-profile errors When something goes wrong, you get an `error` response tied to a specific `username`. This keeps Threads profile scraping software workflows practical—especially when you’re running batch jobs and need accountability. *** ### Real-World Use Cases Here's how different teams put Meta Threads Profile Scraper to work: **Sales Teams**\ A sales team has a spreadsheet of Threads usernames from outbound research. They run Meta Threads Profile Scraper to collect follower counts, verification status, bios, and `latestPosts`, then prioritize accounts whose public content aligns with their offering. The structured output helps them move faster from prospecting to outreach personalization. **Marketing Agencies**\ An agency managing multiple creator partnerships needs quick profile context for campaign reporting. They scrape Threads profiles for a client roster, then use the returned metadata and latest post data to summarize positioning, audience signals, and content themes. The result is a ready-to-export dataset for campaign decks and performance notes. **Freelance Researchers**\ A freelance researcher is building a Threads profile analytics scraper dataset for a report on public-facing creator behavior. They submit a batch of usernames, export the dataset, and analyze the latest posts alongside profile metadata. This avoids hours of manual copy-paste while keeping the output consistent across profiles. **Automation & Data Engineering**\ A developer integrates Threads profile extraction into an ETL pipeline. They trigger Meta Threads Profile Scraper via the Apify API, then ingest the single `result` payload into a database for further processing. The consistent JSON schema supports repeatable updates for ongoing Threads profile search scraping projects. **Community & Creator Management**\ A community manager wants to track which creators are active and how their bios present themselves publicly. By collecting `biography`, `profile_tags`, and `latestPosts`, they can spot changes over time and maintain a living creator list. This makes Threads influencer scraper workflows far more manageable. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor on Apify**\ Go to the actor page on Apify at and find **Meta Threads Profile Scraper**. 2. **Enter your inputs**\ In the `usernames` field, add the Threads usernames you want to scrape (as an array of strings). Refer to the `usernames` schema from the setup section. 3. **Configure proxy settings (optional but recommended for scale)**\ If you’re running larger batches, enable proxy settings using `proxyConfiguration` ↳ `proxy support`. 4. **Start the run and watch the live log**\ Launch the run and monitor progress in the Apify interface. Each username is processed with a short pacing delay to help keep runs stable. 5. **Open the Dataset tab to see live results**\ Results are pushed under the `result` key, containing an array of per-profile objects. 6. **Export in your preferred format**\ Download from the Apify dataset tab as JSON, CSV, or Excel. The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Meta Threads Profile Scraper fits directly into your existing workflow. You can export your dataset in the Apify dashboard as JSON, CSV, or Excel from the Dataset tab. This is useful for marketing operations, reporting, and quick analysis. For deeper automation, you can connect via the Apify API to fetch results programmatically, or use Apify’s no-code automation options like Zapier/Make and scheduled runs to refresh your Threads account lists automatically. *** ### Pricing Meta Threads Profile Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. Free tier access includes $5 platform credits on sign-up, enough for several real test runs. For heavier workloads, you can scale using Apify’s pay-as-you-go billing per Actor compute unit (CU) without monthly fee lock-in. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Scraping public profile pages | Uses profile page responses to locate profile data and latest post data. | | Proxy-based stability | Supports proxy configuration to improve reliability for bulk runs. | | Per-username failures | Returns an `error` field tied to the failing `username`. | | Output consistency | Formats latest post fields into a structured `latestPosts` list per profile. | | Batch pacing | Adds a small delay between usernames to reduce rate-limit pressure. | Limitations: The actor targets publicly accessible Threads profile pages. If a profile’s data isn’t available in the returned page content or the actor can’t locate the expected data blob, the output will include an `error`. This actor does not provide access to login-gated or private account content. For enterprise-scale needs or custom configurations, reach out and we'll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes. Apify offers a free tier with monthly usage credits, so smaller runs of Meta Threads Profile Scraper typically fit within the free allowance. #### Do I need to log in or create an account on Threads? No. This actor works with publicly available profile pages and doesn’t require you to log in to Threads. #### How accurate is the extracted data? Accuracy depends on what’s publicly visible on each Threads profile. Meta Threads Profile Scraper extracts the profile metadata and latest post data present in the page content and formats it into the returned JSON fields. #### How many results can I get per run? You can pass as many entries as you want in the `usernames` array, and the actor returns one profile object per username inside the final `result` array. For very large lists, consider using proxy settings to keep runs stable. #### How fresh is the data? The `latestPosts` content reflects what’s currently available when the actor fetches each profile page during the run. For fresher snapshots, run it again on a schedule. #### Is this legal? Does it comply with GDPR / CCPA? Meta Threads Profile Scraper accesses **publicly available data**. It’s your responsibility to ensure your usage complies with GDPR, CCPA, platform Terms of Service, and any applicable local regulations. #### Can I export to Google Sheets or Excel? Yes. Export the dataset from the Apify dashboard as JSON, CSV, or Excel, then import it into Google Sheets or any spreadsheet tool you use. #### Can I schedule this to run automatically? Yes. You can set up scheduled runs in Apify so your Threads profile lists get refreshed automatically without manual rework. #### Can I access results via the API? Yes. You can trigger runs and retrieve results programmatically using the Apify API, which is ideal for building repeatable Threads profile analytics pipelines. #### What happens when the actor encounters an error? If a specific username fails, the actor returns an `error` entry that includes the failing `username`. Successful profiles still appear in the final `result` array. *** ### Get Help & Use Responsibly Got a question about Meta Threads Profile Scraper or a feature you'd like added? Reach out at and we’ll help—support requests like “add a new output field for latest post analysis” or “help debugging an unexpected missing field” are welcome. The actor is actively maintained based on user feedback. **publicly available data** only: it does not access private accounts, login-gated pages, or password-protected content. Please ensure your use complies with GDPR, CCPA, and the platform’s Terms of Service. For data-removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `usernames` (type: `array`): List of Threads usernames to scrape. ## Actor input object example ```json { "usernames": [ "guinnessworldrecords" ] } ``` # 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 = { "usernames": [ "guinnessworldrecords" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapecraze/meta-threads-profile-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 = { "usernames": ["guinnessworldrecords"] } # Run the Actor and wait for it to finish run = client.actor("scrapecraze/meta-threads-profile-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 '{ "usernames": [ "guinnessworldrecords" ] }' | apify call scrapecraze/meta-threads-profile-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapecraze/meta-threads-profile-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Threads Profile Scraper", "description": "🧠 Meta Threads Profile Scraper pulls public Threads profile data quickly & accurately perfect for B2B leads, market research, and recruitment. 🔎 Save time, streamline outreach, and boost insights with meta-threads-profile-scraper. 🚀", "version": "0.1", "x-build-id": "oufOuBTWi8uUisIFa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapecraze~meta-threads-profile-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapecraze-meta-threads-profile-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapecraze~meta-threads-profile-scraper/runs": { "post": { "operationId": "runs-sync-scrapecraze-meta-threads-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapecraze~meta-threads-profile-scraper/run-sync": { "post": { "operationId": "run-sync-scrapecraze-meta-threads-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "usernames" ], "properties": { "usernames": { "title": "Usernames", "type": "array", "description": "List of Threads usernames to scrape.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```