# Facebook Reels Scraper (`scraperoka/facebook-reels-scraper`) Actor 📹 Facebook Reels Scraper automates extracting Reels data with speed and accuracy—captions, hashtags, engagement, and more. Perfect for content research, marketing insights, and competitor analysis. 🚀 Save time, boost strategy! - **URL**: https://apify.com/scraperoka/facebook-reels-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Social media, Automation, Lead generation - **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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ### Facebook Reels Scraper 🎯 — Extract Reel Metadata at Scale Manually visiting Facebook Reels pages to copy captions, likes, comments, and other details wastes hours you don’t have. **Facebook Reels Scraper** solves that by extracting structured Facebook Reels data from the URLs you provide. Facebook Reels Scraper (and related Facebook Reels scraper tool / metadata scraper workflows) is ideal for marketers, researchers, and growth teams who need thousands of records in minutes. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json { "caption": "New drop tonight! 🎬 #Reels #Creators #BehindTheScenes", "thumbnail": "https://example.com/thumbs/reel_123.jpg", "video_id": "123456789012345", "comment_count": 42, "like_count": "[{\"name\":\"Alex\"},{\"name\":\"Sam\"}]", "share_count": 7, "owner_name": "Studio Nova", "hashtags": ["#Reels", "#Creators", "#BehindTheScenes"], "shareable_url": "https://www.facebook.com/share/r/abc123/", "reelDateTime": "2025-05-28 19:34:12", "reelDate": "2025-05-28", "reelDuration": "28.0", "url": "https://www.facebook.com/reel/2051489205656843" } ```` **Output Fields** | Field | Type | What It Tells You | |---|---|---| | `url` | string | The exact Reel URL you provided, so you can trace every record back to its source | | `owner_name` | string | The author/owner name associated with the Reel for easy creator attribution | | `caption` | string | The Reel’s caption text—useful for content analysis and thematic clustering | | `hashtags` | array | Extracted hashtags from the caption so you can group performance by topic | | `video_id` | string | The Reel’s video identifier for deduping or cross-referencing | | `shareable_url` | string | A shareable link to reference the Reel consistently across systems | | `thumbnail` | string | Thumbnail URI to preview content quality in reports | | `comment_count` | number | Engagement signal for audience discussion volume | | `like_count` | text | Likers information (serialized) when available—handy for deeper reaction insights | | `share_count` | number | Engagement signal for reshares and distribution strength | | `reelDateTime` | string | Full timestamp captured from the source data for time-series analysis | | `reelDuration` | string | Reel duration (as returned) to compare performance vs length | | `reelDate` | string | Date portion of `reelDateTime` for day-level trend reporting | | `status / error` | (not present) | Note: this actor pushes only scraped fields; failures are logged rather than returned as a structured `status` field | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Facebook Reels Scraper? There are a lot of ways to pull data from Facebook Reels — here’s what sets Facebook Reels Scraper (and Facebook Reels video scraper workflows) apart. #### URL-based scraping for quick batch runs You provide a list of Facebook Reel URLs, and the actor processes each one and writes results to your dataset as it goes. This makes it practical for Facebook Reels data extraction tasks like content auditing and campaign research. #### Clean, integration-ready JSON output Results are returned as structured fields (caption, hashtags, likes, comments, author, and more), designed to drop directly into analytics or spreadsheets. If you’re building a Facebook Reels metadata scraper pipeline, you get consistent keys across records. #### Built-in resilience for real-world access issues The actor includes retries and fallback handling to improve the chances of completing runs across multiple URLs. That means fewer “all-or-nothing” failures when you’re scraping Facebook Reels in bulk. #### Dataset-first workflow for analysis at scale Each Reel is pushed into the dataset immediately after extraction, so you can export or analyze partial results without re-running everything. This supports bulk download Facebook Reels-style workflows where speed and continuity matter. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "startUrls": [ { "url": "https://www.facebook.com/reel/2051489205656843" }, { "url": "https://www.facebook.com/reel/123456789012345" } ] } ``` **Input Parameters** | Parameter | Required | What It Does | |---|---:|---| | `startUrls` | ✅ | A list of one or more Facebook Reel URLs to scrape. Each item can be an object with a `url` field (or you can pass a list of URL strings). | | ↳ `startUrls[].url` | ✅ (inside the list) | The actual Reel URL. The actor extracts the `url` value from each entry. | *** ### Core Capabilities #### Facebook Reels metadata extraction Facebook Reels Scraper pulls key Reel details including `caption`, `hashtags`, `owner_name`, engagement counts, `shareable_url`, and `thumbnail`. It’s built for researchers who need reliable Facebook Reels post scraper data without manual copying. #### Structured caption and hashtag parsing Hashtags are extracted from the Reel caption, giving you a ready-to-analyze `hashtags` array. This helps when you’re doing Facebook Reels analytics scraping by topic or running content clustering. #### Engagement and identity fields included You get engagement metrics like `comment_count` and `share_count`, plus `like_count` (likers info serialized when available). The output also includes `video_id` and `owner_name` so you can deduplicate and attribute results. #### Date/time and duration for trend analysis The actor returns `reelDateTime`, `reelDate`, and `reelDuration` (as returned), enabling time-based analysis and comparison across content length. This is useful for Facebook Reels video scraper work where timing and duration often correlate with performance. #### Automated dataset writing for scalability For each provided Reel URL, the actor pushes the extracted record into the Apify dataset. That means your Facebook Reels URL extractor pipeline can feed directly into dashboards, reporting, or downstream enrichment. *** ### Who Gets the Most Out of This Here's how different teams put Facebook Reels Scraper to work: **Social Media Marketers** — Use Facebook Reels scraper tool output to analyze which captions and hashtags correlate with higher comments and shares, then build a short-list of topics to test next. You save time that would otherwise go to manual copying and spreadsheet cleanup. **Influencer & Creator Recruiters** — When you need to evaluate creators at speed, the actor provides owner names, engagement signals, and shareable links for each Reel. That helps you compare candidates consistently across your research batch. **Content Researchers & Analysts** — If your workflow is focused on themes, timings, and formats, you can use caption text, extracted hashtags, and `reelDateTime` / `reelDuration` for structured analysis. This turns Facebook Reels metadata scraper tasks into dataset-ready inputs. **Growth Teams & Community Managers** — Use scraped engagement metrics to benchmark content performance, identify repeatable patterns, and track what resonates over time. The structured output makes reporting fast and repeatable. **Developers & Automation Specialists** — If you’re integrating Facebook Reels content scraping into an internal pipeline, the actor’s dataset-first output provides consistent JSON fields you can store, transform, and join with other sources. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Facebook Reels Scraper from start to finish: 1. **Open the actor on Apify** — visit [console.apify.com](https://console.apify.com) and search for Facebook Reels Scraper. 2. **Enter your inputs** — add your Reel URLs under `startUrls` (either as `{ "url": "..." }` objects or as URL strings). 3. **Configure proxy settings (if prompted)** — keep the default behavior (residential proxies are used by default) to improve reliability for public web access. 4. **Hit Run and watch the live log** — monitor progress as the actor processes each URL. 5. **View results in the dataset tab** — each successful Reel gets pushed with the structured fields shown above. 6. **Export as JSON, CSV, or Excel** — download from the Apify dashboard when your dataset looks right. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, Facebook Reels Scraper plugs directly into your existing workflow. You can export your dataset in **JSON**, **CSV**, or **Excel** from the Apify dataset tab. If you want automation, you can use Apify integrations with tools like **Zapier** or **Make**, and also connect via **API** to pull results programmatically. For more event-driven workflows, you can trigger downstream steps with **webhooks** when a run completes—great for scheduled Facebook Reels data extraction jobs. For deeper details on API usage and integrations, refer to the Apify documentation at https://apify.com/docs/api. *** ### Pricing & Free Trial Facebook Reels Scraper runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You’ll typically start with a small amount of platform credits for test runs, then scale as needed. Apify uses a pay-as-you-go model based on Actor compute, so you can run small Facebook Reels metadata scraper tests or larger bulk extraction jobs without changing your workflow. Start for free at [apify.com](https://apify.com) and scale when you’re ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Rate limits and access variability | Improves success by using the configured proxy approach (residential by default) | | Unstable requests | Includes retries to reduce the chance of missing data for a URL | | Partial progress | Results are pushed per URL as they are scraped, so you keep work even if later URLs fail | | Data structure consistency | Outputs a consistent set of fields for each Reel record in the dataset | | Batch usage | Designed for running across multiple `startUrls` in one job | **Limitations:** This actor works with data available from public Reel pages. If a Reel doesn’t expose the expected details, some fields may be missing or empty in the output. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial? Yes—Apify provides a **free tier** with platform credits so you can test Facebook Reels Scraper before scaling up. Exact availability depends on your Apify account and current plan. #### Do I need to log in to Facebook to use this? No. Facebook Reels Scraper is designed to scrape publicly accessible Reel pages based on the URLs you provide. #### How accurate is the data? The actor returns the metadata fields it can find on the target pages, including `caption`, `hashtags`, engagement counts, and Reel timing fields when available. Accuracy depends on what the Reel author has publicly shared. #### How many results can I get per run? You can provide one or more URLs in `startUrls`, and the actor processes each URL and writes results into your dataset. The practical limit depends on your Apify run resources and input size. #### How often is the data updated / how fresh is it? Freshness depends on when you run the actor. When you re-run Facebook Reels scraper tool jobs with updated URLs, you’ll get the latest fields available at that time. #### Is this legal? Does it comply with GDPR / CCPA? Data from publicly available sources can still require careful handling under privacy laws and platform terms. You’re responsible for complying with GDPR, CCPA, platform Terms of Service, and any applicable regulations in your use case. #### Can I export results to Google Sheets or Excel? Yes. You can export your Apify dataset as **JSON, CSV, or Excel** directly from the dashboard. From there, you can import into tools like Google Sheets or other systems that accept CSV/Excel. #### Can I run this on a schedule automatically? Yes. Because it runs as an Apify actor, you can schedule runs using Apify’s scheduling options for recurring Facebook Reels data extraction. #### Can I access this via API? Yes. You can trigger runs and retrieve results programmatically using the Apify API. See https://apify.com/docs/api for details. #### What happens if the actor hits an error? If a URL can’t be fetched or yields no usable data, the actor logs the issue and continues processing the remaining URLs. Successful URLs are still pushed to the dataset as they complete. *** ### Need Help or Have a Request? Got a question about Facebook Reels Scraper or want a new feature added? Reach out at . We’re actively maintaining this actor and respond to user requests. If you’d like, tell us what you need for your Facebook Reels video scraper workflow—examples include webhook notifications on completion or additional export-friendly formatting. *** ### Disclaimer & Responsible Use *Facebook Reels Scraper is the fastest, most reliable way to turn public Reel URLs into structured metadata datasets — start your free run today.* This actor uses **publicly available data** from Facebook Reels pages. It does not access private accounts, login-gated content, or password-protected pages. You’re responsible for using the results in compliance with GDPR, CCPA, platform Terms of Service, and applicable regulations. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `startUrls` (type: `array`): List one or more Facebook Reel URLs. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.facebook.com/reel/2051489205656843" } ] } ``` # 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": [ { "url": "https://www.facebook.com/reel/2051489205656843" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/facebook-reels-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": [{ "url": "https://www.facebook.com/reel/2051489205656843" }] } # Run the Actor and wait for it to finish run = client.actor("scraperoka/facebook-reels-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": [ { "url": "https://www.facebook.com/reel/2051489205656843" } ] }' | apify call scraperoka/facebook-reels-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/facebook-reels-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Reels Scraper", "description": "📹 Facebook Reels Scraper automates extracting Reels data with speed and accuracy—captions, hashtags, engagement, and more. Perfect for content research, marketing insights, and competitor analysis. 🚀 Save time, boost strategy!", "version": "1.0", "x-build-id": "8sf1qd3WVf7kz6nzD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~facebook-reels-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-facebook-reels-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~facebook-reels-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-facebook-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~facebook-reels-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-facebook-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Facebook Reel URLs", "type": "array", "description": "List one or more Facebook Reel URLs.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } } } }, "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 } } } } } } } } } } ```