# Facebook Reels Scraper (`scrapeflux/facebook-reels-scraper`) Actor Facebook Reels Scraper extracts data from public Facebook Reels, including video URLs, captions, views, likes, comments, shares, upload dates, and creator details. Ideal for trend analysis, competitor research, influencer discovery, content monitoring, and social media marketing. - **URL**: https://apify.com/scrapeflux/facebook-reels-scraper.md - **Developed by:** [ScrapeFlux](https://apify.com/scrapeflux) (community) - **Categories:** Social media, Videos, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 ### Facebook Reels Scraper ⚡ Trying to collect Facebook Reels metadata (captions, likes, comments, hashtags, and more) one link at a time is slow and doesn’t scale. **Facebook Reels Scraper** automates extraction of public Reels details from a list of Reel URLs. It’s a practical Facebook reels scraper tool for marketers, analysts, and researchers who need fast Facebook reels data extraction. Run it with your target Reel links to get structured results in minutes—so you can move from discovery to analysis without manual copy-paste. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "caption": "New drop today! #fitness #reels #workout", "thumbnail": "https://example.com/thumbnail.jpg", "video_id": "123456789012345", "comment_count": 128, "like_count": "[\"User A\",\"User B\"]", "share_count": 37, "owner_name": "Acme Fitness Studio", "hashtags": ["#fitness", "#reels", "#workout"], "shareable_url": "https://www.facebook.com/share/reel/987654321098765", "reelDateTime": "2026-04-18 12:34:56", "reelDate": "2026-04-18", "reelDuration": "22.5", "url": "https://www.facebook.com/reel/2051489205656843" } ```` | Field | Type | What It Tells You | |---|---|---| | `url` | string | The exact Reel URL you provided, so you can trace results back to the source. | | `owner_name` | string | The Reels author/page name—useful for grouping content by creator. | | `caption` | string | The full caption text for context, theme detection, and content review. | | `hashtags` | array | Parsed hashtags extracted from the caption, great for hashtag analysis and trend spotting. | | `video_id` | string | A stable identifier you can use to de-duplicate and join with other datasets. | | `shareable_url` | string | The shareable Reels URL from the source data for easy linking and verification. | | `thumbnail` | string | Thumbnail URI to quickly preview the content in reports or dashboards. | | `comment_count` | number | Engagement signal for discussion volume and audience interest. | | `like_count` | string | Likers info (stored as JSON text in this actor output) to support deeper engagement analysis. | | `share_count` | number | Engagement signal for distribution and virality tracking. | | `reelDateTime` | string | Timestamp of the Reel when available—use for recency and time-series analysis. | | `reelDate` | string | Date-only value to simplify daily reporting and filtering. | | `reelDuration` | string | Duration (as a string) to compare short vs. long-form performance. | 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 { "startUrls": [ { "url": "https://www.facebook.com/reel/2051489205656843" }, { "url": "https://www.facebook.com/reel/109876543210987" } ] } ``` | Parameter | Required | What It Does | |---|---:|---| | `startUrls` | ✅ | A list of one or more Facebook Reel URLs that the actor will scrape for Reel caption, likes, comments, hashtags, and related metadata. | **Note:** The actor also supports proxy configuration via an input field named `proxyConfiguration` (it uses residential proxies by default), but it is not part of the provided actor input schema. *** ### What It Does Facebook Reels Scraper extracts structured metadata from each Reel URL you provide and writes a row per Reel into your Apify dataset. #### Scrape Facebook reels metadata from your URL list Feed it multiple Reel links, and it will pull key details like `caption`, `hashtags`, `owner_name`, `comment_count`, `share_count`, `shareable_url`, and more. This makes it a straightforward Facebook reels scraper tool for batch research and reporting. #### Clean output built for analysis Results are normalized into consistent JSON fields such as `video_id`, `reelDateTime`, `reelDate`, and `reelDuration`. The `hashtags` field is extracted from the caption so you can analyze themes without extra parsing steps. #### Includes Facebook reels URL extractor behavior in one pass Because it processes each entry from your `startUrls` list, you get both the scraped metadata and the original `url` in every dataset row. That means you can trace each record back to the source quickly—ideal for a Facebook reels data extraction workflow. #### Works without account management on your side You provide public Reel URLs, and the actor handles the retrieval and parsing to produce dataset-ready records. This is useful when you need a social media reels scraper workflow for content audits or analytics scraper tasks. #### Handles incomplete or missing fields gracefully If some metadata isn’t present for a Reel, the corresponding fields can come through as `null` (for example, when `like_count`, `reelDateTime`, or `reelDuration` aren’t available). This keeps your dataset schema stable while still reflecting what’s available per Reel. Overall, Facebook Reels Scraper helps you scrape Facebook reels details at scale with clean, structured output for downstream use. *** ### Why Facebook Reels Scraper? There are plenty of ways to pull data from social platforms—here’s why **Facebook Reels Scraper** stands out. #### Fast batch results from Reel links Instead of manual copying, Facebook Reels Scraper processes the URLs you provide and pushes structured records into your dataset as it goes. This speeds up Facebook reels analytics scraper workflows when you need results quickly. #### Resilient retrieval for smoother runs The actor includes retries and fallback behavior so transient issues don’t immediately end your run. That makes it a practical Facebook video scraper choice for larger batches. #### Structured results that map cleanly to datasets The output fields are designed to be immediately useful for marketers and analysts—engagement counts, caption/hashtags, creator name, and timestamps are all returned in a single consistent record per Reel. It’s a solid foundation for Facebook reels content scraper projects and follow-up analysis. *** ### Real-World Use Cases Here's how different teams put Facebook Reels Scraper to work: **Social Media Analysts**\ You have a spreadsheet of Reel URLs to evaluate, but you don’t want to manually capture captions, hashtags, and engagement metrics. You run Facebook Reels Scraper on your list, then analyze `comment_count`, `share_count`, and `hashtags` to compare content themes and formats. The structured dataset makes it easy to turn “what performed?” into “why did it perform?” **Influencer Marketing Teams**\ Your team needs a creator shortlist based on publicly visible Reel engagement, not guesswork. After running the Facebook reels scraper tool on candidate Reels, you review `owner_name`, `caption`, and `like_count` (likers info as returned by the actor) to spot consistent engagement patterns. You save hours of manual review per campaign. **Content Auditors & Brand Strategists**\ You’re auditing what a brand or competitor posts and which topics keep audiences engaged. By scraping Facebook reels metadata for a batch of Reels, you get `reelDate`/`reelDateTime`, `reelDuration`, and caption hashtags in one place. That lets you quickly identify trends and content gaps. **Researchers & Data Projects**\ You’re building a dataset for a study on short-form video engagement behaviors over time. The actor returns `reelDateTime` and duration data when available, along with engagement counts and extracted hashtags. The consistent JSON structure supports faster cleaning and joining with other sources. **Developers & Automation Specialists**\ You want to automate ingest of Reel metadata into an internal pipeline. You run the actor with your `startUrls`, then use the dataset results programmatically in your app or workflow. This supports a repeatable Facebook reels batch downloader process without needing to rewrite scraping logic. *** ### 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: [console.apify.com](https://console.apify.com). 2. **Enter your inputs**\ Add your target Reel URLs in the `startUrls` field (you can provide objects with a `url` key). 3. **Configure proxy settings (optional)**\ If you want to customize proxy behavior, set `proxyConfiguration`. The actor uses residential proxies by default. 4. **Start the run and watch the live log**\ Monitor progress in the Apify dashboard—you’ll see which URL is being processed and whether valid data was found. 5. **Open the Dataset tab to see live results**\ Records are written per URL, with fields like `caption`, `hashtags`, `comment_count`, and `shareable_url`. 6. **Export in your preferred format**\ Download the dataset as JSON, CSV, or Excel directly from the Apify dashboard. The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Facebook Reels Scraper fits directly into your existing workflow. You can export results from the Apify dashboard as JSON, CSV, or Excel. If you’re using spreadsheets or BI tools, this makes it easy to go from dataset to analysis without extra transformations. For automation, you can run the actor via the Apify API, then programmatically retrieve your dataset results. You can also trigger downstream systems using your own workflow around the run completion (for example, pushing the dataset into your app or storage layer). *** ### Pricing Facebook Reels 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 Actor compute unit (CU) pricing or upgrade to Apify plans for larger capacity. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Rate-limit style failures | Retries with fallback behavior to keep runs moving | | Proxy reliability | Uses residential proxies by default and supports proxy configuration | | Missing fields in some Reels | Fields may come through as `null` when not present in the source data | | Partial progress | Each URL is processed and pushed to the dataset as it completes | | Data presence variability | If no valid JSON content is found for a URL, that URL is skipped | Limitations: This actor works on public data available on the provided Reel pages. If a Reel doesn’t expose certain metadata (for example, likes details, timestamps, or duration), those fields may be missing or `null`. If you need access to login-gated or private content, this actor won’t be able to retrieve it. For enterprise-scale needs or custom configurations, reach out and we'll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes. Apify provides a free tier with monthly usage credits so you can try Facebook Reels Scraper before scaling up. #### Do I need to log in or create an account on Facebook? No login is required from your side. The actor operates on publicly available Reel data you can view without authentication. #### How accurate is the extracted data? Accuracy depends on what the Reel page publicly exposes. The actor returns structured fields like `caption`, parsed `hashtags`, `comment_count`, and `share_count` based on the page’s available content. #### How many results can I get per run? The actor processes each entry in `startUrls`. Your effective output volume depends on how many Reel URLs you provide and how much data is available per Reel. #### How fresh is the data? The results reflect what’s available on each Reel page at the time the actor fetches it during your run. #### Is this legal? Does it comply with GDPR / CCPA? The actor is designed to collect **publicly available data** from pages you provide. You’re responsible for ensuring your use complies with GDPR, CCPA, platform terms, and applicable regulations. #### Can I export to Google Sheets or Excel? Yes. You can export from the Apify dashboard as JSON, CSV, or Excel, which can be imported into tools like Google Sheets. #### Can I schedule this to run automatically? Yes. In Apify, you can schedule actor runs on a recurring basis so your Reel metadata extraction happens automatically. #### Can I access results via the API? Yes. After the run completes, you can retrieve dataset results programmatically through the Apify API. #### What happens when the actor encounters an error? If a URL fails to fetch after retries, it’s skipped and the run continues with the remaining URLs. If valid data isn’t found for a URL, that URL is also skipped rather than stopping the whole run. *** ### Get Help & Use Responsibly Got a question about Facebook Reels Scraper or a feature you’d like added? Reach out at . We welcome feedback and actively maintain this actor based on user needs. **publicly available data**: the actor accesses publicly available data on the provided Reel pages and does not access private accounts, login-gated pages, or password-protected content. You’re responsible for ensuring compliance with GDPR, CCPA, and platform ToS when using and storing results. 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("scrapeflux/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("scrapeflux/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 scrapeflux/facebook-reels-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeflux/facebook-reels-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Reels Scraper", "description": "Facebook Reels Scraper extracts data from public Facebook Reels, including video URLs, captions, views, likes, comments, shares, upload dates, and creator details. Ideal for trend analysis, competitor research, influencer discovery, content monitoring, and social media marketing.", "version": "1.0", "x-build-id": "tJudP22Al4W9Heni4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeflux~facebook-reels-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeflux-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeflux~facebook-reels-scraper/runs": { "post": { "operationId": "runs-sync-scrapeflux-facebook-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeflux~facebook-reels-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeflux-facebook-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```