# Facebook Reels Scraper (`solid-scraper/facebook-reels-scraper`) Actor 📹 Facebook Reels Scraper extracts trending Reels data fast for marketing, research & content planning. Automatically collect links, captions, hashtags & more—save hours and boost insights with AI-driven efficiency. 🚀 - **URL**: https://apify.com/solid-scraper/facebook-reels-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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 🎥 **Facebook Reels Scraper** is a purpose-built tool for scraping public Facebook Reels data such as caption, likes, comments, author/owner, hashtags, and more. If you’re looking for a *Facebook Reels scraper*, a *scrape Facebook reels* workflow, or a *Facebook reels video scraper tool* for automated Facebook reels scraping, this actor helps you collect structured Reel metadata at scale—saving you hours of manual work. Whether you’re a marketer, researcher, or data analyst, this Facebook Reels metadata scraper supports your project needs by turning Reel pages into clean, exportable results you can use right away. --- ### ✅ Why choose Facebook Reels Scraper? | Feature | Benefit | |---|---| | ✅ **Reels metadata extraction** | Pulls caption, hashtags, owner name, video ID, shareable URL, thumbnail, and engagement counts in one run | | ✅ **Built-in proxy support** | Improves scraping reliability for public web data using residential proxies by default | | ✅ **Resilient execution** | Includes retry logic (up to 3 attempts per URL) for better chances of successful collection | | ✅ **Structured dataset output** | Saves results with consistent fields designed for easy export and analysis | | ✅ **Batch-friendly URL processing** | Scrape one or many Facebook Reel URLs without changing code | | ✅ **Clear result mapping** | Dataset columns align with the extracted Reel fields for straightforward downstream use | --- ### 🔑 Key features - 📊 **Caption + hashtag extraction**: Captures Reel caption text and extracts hashtags from the caption - 🌐 **Facebook Reel URL support**: Works directly from `startUrls` containing one or more Facebook Reel URLs - 💾 **Structured output for analysis**: Pushes cleaned fields (URL, owner, caption, engagement, timing, thumbnail, etc.) into a dataset table - 🛡️ **Residential proxy support by default**: Uses residential proxies as the default scraping configuration - 🔄 **Retries for resilience**: Attempts each URL up to 3 times to reduce transient failures - 🧾 **Engagement metrics included**: Extracts comment count, like-related info, and share count - 🎬 **Reel timing + duration fields**: Returns `reelDateTime`, `reelDate`, and `reelDuration` when present - 🖼️ **Thumbnail capture**: Stores the Reel thumbnail image URI when available --- ### 📝 Input Provide input via an `input.json` file. Example structure: ```json { "startUrls": [ { "url": "https://www.facebook.com/reel/2051489205656843" } ] } ```` #### Input Fields | Key | Required | Description | |---|---:|---| | `startUrls` | ✅ | List one or more Facebook Reel URLs to scrape. You can provide the URLs as objects with a `url` property. | **Notes on `startUrls`:** - The actor also supports `startUrls` as a list of strings (plain URL strings). - If no URLs are provided, the actor logs a warning and exits. *** ### 📦 Output The actor saves each scraped Reel’s data in JSON format to the default dataset table **“Scraped Reels”**. Example output object: ```json { "caption": "Sample caption text with #hashtags", "thumbnail": "https://example.com/thumbnail.jpg", "video_id": "1234567890", "comment_count": 42, "like_count": "[{\"name\":\"User A\"}]", "share_count": 7, "owner_name": "Page or Creator Name", "hashtags": ["#hashtags"], "shareable_url": "https://www.facebook.com/reel/2051489205656843", "reelDateTime": "2025-01-15 12:34:56", "reelDate": "2025-01-15", "reelDuration": "12.5", "url": "https://www.facebook.com/reel/2051489205656843" } ``` #### Output Fields | Field | Type | Description | |---|---|---| | `url` | text | The source URL that was processed | | `owner_name` | text | The Reel owner name (video owner) | | `caption` | text | Reel caption text | | `hashtags` | array | Hashtags extracted from the caption (e.g., `#example`) | | `video_id` | text | The extracted video ID | | `shareable_url` | link | The extracted shareable URL | | `thumbnail` | image (URI/text) | The thumbnail image URI (when available) | | `comment_count` | number | Total comment count | | `like_count` | text | JSON-stringified “likers” info when available | | `share_count` | number | Share count | | `reelDateTime` | text | Reel date and time when available | | `reelDuration` | text | Reel duration as a string when available | **Export tip:** After a run, open the dataset and export the table to **JSON or CSV** from Apify’s UI. *** ### 🚀 How to use Facebook Reels Scraper (via Apify Console) 1. **Open Apify Console**\ Go to [console.apify.com](https://console.apify.com) and sign in. 2. **Find the actor**\ Search for **Facebook Reels Scraper** and open its details page. 3. **Go to the INPUT panel**\ In the actor’s **INPUT** section, paste your input JSON. 4. **Add your Reel URLs**\ Provide `startUrls` as an array of objects like `{ "url": "https://www.facebook.com/reel/..." }` (or as a list of plain URL strings).\ This is the input you’ll see reflected in SEO-friendly “Facebook reels URL scraper” style workflows. 5. **Review proxy behavior (default: residential)**\ The actor uses residential proxies by default for reliable scraping of public web data. 6. **Click Run and watch logs**\ During the run, check the log output for processing status per URL. Each URL can be retried up to **3** times if needed. 7. **Open the OUTPUT dataset**\ After completion, open the dataset named **“Scraped Reels”** to view results in a table. 8. **Export your data**\ Export the dataset as **JSON or CSV** for analysis, CRM loading, or reporting. No coding required—get accurate results in minutes with this Facebook reels data extraction tool. ✅ *** ### ⚙️ Advanced features & SEO optimization - 🎯 **Engineered for Facebook Reels scraping and extraction**: Built specifically to excel at “Facebook Reels analytics scraper” and “Facebook reels data extraction” use cases, focusing on Reel metadata fields like caption, hashtags, and engagement counts. - 🔄 **Retry + fallback resilience**: Includes retries and resilient handling for better results when the platform intermittently blocks requests. - 🧠 **Input flexibility for batches**: Supports `startUrls` as URL objects or as plain URL strings, making it easy to run “automated Facebook reels scraping” jobs. - 🕒 **Timing and duration capture**: Returns `reelDateTime`, `reelDate`, and `reelDuration` when present to support trend and recency analysis. - 🧾 **Consistent schema for downstream tools**: Dataset fields are designed to be straightforward for marketers and analysts to consume for reporting and dashboards. *** ### 🧠 Best use cases - 📈 **Marketing teams building Reel insights**: Compile engagement and content signals across multiple Facebook Reels for campaign analysis and creative reporting. - 🔍 **Researchers studying content trends**: Collect caption text, hashtags, share and comment counts, and Reel timing to analyze audience response patterns. - 🗂️ **Social media analysts comparing performance**: Use structured fields like `comment_count`, `like_count`, and `share_count` to compare Reels consistently. - 🏢 **B2B lead generation workflows**: Extract owner names and Reel context to enrich prospect research pipelines (useful for “Facebook Reels analytics scraper” style research). - 💻 **Data engineering pipelines**: Feed scraped Reel metadata into analytics or ETL systems thanks to the consistent dataset schema. - 🧪 **Content auditing and moderation research**: Gather caption/hashtag content and engagement metrics for auditing studies using publicly available data. - 🔁 **Batch data collection for reporting**: Run “Facebook reels batch downloader” style jobs by passing many `startUrls` at once. *** ### 🔧 Technical specifications - **Supported Input Formats** - ✅ `startUrls` as an array of objects with a `url` field - ✅ `startUrls` as an array of plain URL strings - **Proxy Support** - ✅ Residential proxies supported (residential proxies by default as described in the actor input description) - **Retry Mechanism** - ✅ Retries included (up to **3** attempts per URL) - **Dataset Structure** - ✅ Default dataset: **“Scraped Reels”** - ✅ Table fields include: `url`, `owner_name`, `caption`, `hashtags`, `video_id`, `shareable_url`, `thumbnail`, `comment_count`, `like_count`, `share_count`, `reelDateTime`, `reelDuration` - **Rate Limits & Performance** - ✅ Designed for reliability with timeouts during HTTP fetching and resilient execution (exact throughput varies) - **Limitations** - ❌ Only extracts data available from public web sources for the provided Reel URLs - ❌ If the Reel page doesn’t contain the expected structured data, some fields may be missing (the actor will still push whatever it can extract) *** ### ❓ FAQ #### ✅ What does Facebook Reels Scraper extract from each Reel? It extracts fields including `caption`, `hashtags`, `owner_name`, `video_id`, `shareable_url`, `thumbnail`, `comment_count`, `like_count`, `share_count`, `reelDateTime`, `reelDate`, and `reelDuration`, and it also stores the processed source `url`. #### ✅ How do I provide Facebook Reel links to the actor? You pass them in the `startUrls` input. You can provide each entry as an object with a `url` property or pass a list of plain URL strings—both are supported. #### ✅ Does it support scraping multiple reels in one run? Yes. `startUrls` is an array, so you can submit one or many Facebook Reel URLs in a single execution. #### ✅ Where can I find the scraped results after the run? The actor saves results to the default dataset **“Scraped Reels”**, with a table view of the extracted Reel fields. #### ❌ Does Facebook Reels Scraper require any login? This actor is designed to scrape **publicly available sources** from the provided Reel URLs; it doesn’t require you to provide account credentials in the input schema. #### 💻 Can I use the output in Excel or Google Sheets? Yes. After the run, export the dataset from Apify as JSON or CSV, then import it into your preferred spreadsheet tool. #### ⚖️ Is this tool legal and compliant? It collects information only from **publicly available sources**. You are responsible for ensuring your use complies with applicable laws (including GDPR/CCPA where relevant) and the platform’s terms and policies. #### 🔁 Why might some fields be empty for certain reels? Some reels may not contain all fields in the underlying publicly available structured data the actor looks for, so values like `thumbnail`, `reelDateTime`, or `reelDuration` may be missing depending on what’s available. *** ### 🤝 Support & feature requests Have feedback on **Facebook Reels Scraper** or want improvements to your “Facebook reels scraper” workflow? We’d love to hear from you. - 💡 **Feature Requests**: If you want enhancements such as additional fields, alternative export formats, or improved batch handling, share what you need. - 📧 **Contact**: Email us at . Your input helps shape future updates to Facebook reels video scraper tool capabilities. *** ### *Get started today with the most comprehensive Facebook Reels Scraper—and export clean metadata in minutes.* *** ### Disclaimer **This tool only accesses publicly accessible sources** based on the Facebook Reel URLs you provide. It does not access private profiles, authenticated content, or password-protected pages. You are responsible for complying with applicable laws (including GDPR/CCPA where relevant), spam regulations, and platform terms of service. For data removal requests, contact . Please use Facebook Reels Scraper responsibly, ethically, and for legitimate purposes only. # 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("solid-scraper/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("solid-scraper/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 solid-scraper/facebook-reels-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/facebook-reels-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Reels Scraper", "description": "📹 Facebook Reels Scraper extracts trending Reels data fast for marketing, research & content planning. Automatically collect links, captions, hashtags & more—save hours and boost insights with AI-driven efficiency. 🚀", "version": "1.0", "x-build-id": "ctefHVZ9abkCWEkGm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~facebook-reels-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~facebook-reels-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-facebook-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~facebook-reels-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-facebook-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```