# Facebook Reels Posts Scraper - Low-costπŸ’²πŸ”₯πŸ“˜πŸŽ¬ (`delectable_incubator/facebook-reels-posts-scraper-low-cost`) Actor Scrape Facebook Reels by keyword πŸ”πŸŽ¬ with a powerful short-form video scraper. Extract reel captions, views, durations, upload dates, reel URLs, and more from Facebook. Ideal for trend analysis, content research, video datasets, AI pipelines, and social media intelligence πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/facebook-reels-posts-scraper-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** Lead generation, Social media, Videos - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 - PrimeScrape

--- ## Facebook Reels Scraper πŸŽ¬πŸ“˜πŸš€ The Facebook Reels Scraper is a powerful and scalable Apify Actor designed to extract structured Facebook Reels data directly from Facebook keyword searches. It enables short-form content discovery, competitor monitoring, trend analysis, engagement tracking, AI dataset creation, marketing intelligence, and structured Reel dataset generation from Facebook public Reels. --- ### 🎯 What This Scraper Does Simply provide one or more keywords and the scraper handles everything automatically. βœ… Extracts structured Facebook Reels data βœ… Supports bulk keyword scraping βœ… Automatically processes Facebook Reels search results βœ… Handles pagination automatically βœ… Applies MAX_ITEMS limits per keyword βœ… Extracts Reel metadata and engagement information βœ… Collects views, likes, captions, thumbnails, and Reel URLs βœ… Generates clean and structured datasets βœ… Ready for analytics and research workflows βœ… Export-ready output format --- ### πŸ“Š Data Extracted #### 🎬 Reel Information | Field | Description | | -------------- | ------------------------------- | | πŸ†” reelId | Unique Facebook Reel identifier | | πŸ“„ title | Reel title or caption | | πŸ”— reelUrl | Direct Reel URL | | πŸ‘οΈ views | Number of views | | ❀️ likes | Number of likes | | ⏱️ duration | Reel duration | | πŸ•’ uploadDate | Upload date | | πŸ“ description | Reel description or caption | | πŸ“‘ snippet | Preview snippet text | | 🌐 platform | Platform source | | πŸ–ΌοΈ thumbnail | Reel thumbnail image URL | --- ### πŸ›  How to Use #### 1️⃣ Configure Input Provide one or multiple Reel keywords: ```` { "KEYWORDS": \[ "fitness", "cooking" ], "TIME\_FILTER": "any", "MAX\_ITEMS": 20 } ``` #### 2️⃣ Run the Actor β€’ Loads Facebook Reels search results β€’ Processes Reel listings automatically β€’ Extracts structured Reel information β€’ Collects engagement and metadata β€’ Applies MAX_ITEMS limits β€’ Stops automatically when limits are reached #### 3️⃣ Export the Dataset Download your results in multiple formats: βœ… JSON βœ… CSV βœ… Excel βœ… XML βœ… HTML --- ### βš™οΈ Input Configuration #### πŸ“₯ Input Example ``` { "KEYWORDS": \[ "fitness", "cooking" ], "TIME\_FILTER": "any", "MAX\_ITEMS": 20 } ``` #### πŸ“₯ Input Schema ``` { "title": "Facebook Reels Scraper", "type": "object", "schemaVersion": 1, "properties": { "KEYWORDS": { "title": "Keywords", "type": "array", "description": "List of keywords to search for Reels. One keyword per entry.", "editor": "stringList", "prefill": \[ "fitness", "cooking" ] }, "TIME\_FILTER": { "title": "Time Filter", "type": "string", "description": "Filter Reels by upload time", "default": "any", "prefill": "any", "enum": \[ "any", "month", "year" ], "enumTitles": \[ "Any time", "Last month", "Last year" ], "editor": "select" }, "MAX\_ITEMS": { "title": "Max Items", "type": "integer", "description": "Maximum number of Reels to extract PER KEYWORD", "default": 10, "prefill": 10, "editor": "number" } }, "required": \[ "KEYWORDS", "TIME\_FILTER", "MAX\_ITEMS" ] } ``` #### Input Fields | Field | Type | Description | | ----------- | ------- | ---------------------------------------------- | | KEYWORDS | array | List of keywords used to search Facebook Reels | | TIME_FILTER | string | Filter Reels by upload time | | MAX_ITEMS | integer | Maximum number of Reels to collect per keyword | --- ### πŸ“€ Output Example ``` { "reelId": "983472938472938", "title": "Quick 20s Fat-Burning Move πŸ”₯", "reelUrl": "https://www.facebook.com/reel/983472938472938", "views": "12K", "likes": "1.2K", "duration": "00:20", "uploadDate": "March 18, 2024", "description": "Try this quick fat-burning move! No equipment needed πŸ’ͺπŸ”₯", "snippet": "20s Workout β€’ 12K views β€” More Reels like this: HIIT fast burn | Core blast | Cardio quick tips...", "platform": "Facebook Reels", "thumbnail": "https://scontent.xx.fbcdn.net/v/t39.30808-6/reel\_thumbnail\_example.jpg" } ```` --- ### πŸ“Š Output Explanation | Use Case | Description | | ------------------------ | ------------------------------------------------ | | 🎬 Reels Research | Discover trending short-form Facebook videos | | πŸ“ˆ Marketing Analysis | Analyze engagement and Reel performance | | πŸ€– AI Dataset Creation | Build structured Reel datasets for ML/AI | | πŸ”Ž Competitor Monitoring | Track creators, niches, and viral content | | πŸ“Š Content Intelligence | Study captions, formats, and engagement patterns | --- ### 🌍 Why Use This Scraper? 🎬 Discover Facebook Reels at scale πŸ“ˆ Analyze trending short-form content ⚑ Fast and automated extraction πŸ€– Automation-ready output πŸ“¦ Bulk keyword scraping support 🧠 Ideal for marketers, researchers, creators, and analysts πŸš€ Scalable for both small and enterprise workloads --- ### ❓ FAQ #### How does this scraper work? The scraper loads Facebook Reel search results and extracts structured Reel information including captions, views, likes, thumbnails, and Reel URLs. #### Can I scrape multiple keywords in one run? Yes. You can provide multiple keywords inside the KEYWORDS array. #### What happens if fewer Reels are returned? It means Facebook has fewer public Reels available for the selected keyword and time filter. #### Can I monitor trends over time? Yes. You can schedule recurring Apify runs to track new or trending Reels. #### Is the data collected live? Yes. Data is extracted directly from Facebook during every run. #### What export formats are supported? JSON, CSV, Excel, XML, and HTML. #### Can I use the extracted data commercially? Yes. The extracted data can be used for analytics, automation, research, monitoring, and commercial applications. #### How long does a run take? Most extractions complete within minutes depending on keyword volume and requested results. --- ### πŸš€ How to Use 1️⃣ Sign up β€” Create a free Apify account 2️⃣ Find the tool β€” Search for "Facebook Reels Scraper" in the Apify Store 3️⃣ Configure keywords β€” Add one or multiple Reel keywords 4️⃣ Run it β€” Start the Actor and wait for extraction 5️⃣ Export data β€” Download results in JSON, CSV, Excel, XML, or HTML --- ### ⚠️ Disclaimer This tool is an independent solution and is not affiliated with, endorsed by, or sponsored by Facebook or Meta Platforms. --- ### πŸ’Έ Pricing This scraper runs on a **pay-per-result pricing model**. You only pay for successfully extracted records. πŸ’³ **Price:** $4.97 / 1,000 results --- ### Related Actors If you're interested in other Social Media, Marketing, Analytics, Content Research, Lead Generation, or Automation scraping solutions, explore more tools: (Coming soon) --- ### πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a 5-star rating if you like this tool --- ### 🌍 PrimeScrape Built for scalable web data extraction & automation. Contact us for custom scraping solutions or enterprise requests via Apify or by email. # Actor input Schema ## `KEYWORDS` (type: `array`): List of keywords to search for Reels. One keyword per entry. ## `TIME_FILTER` (type: `string`): Filter Reels by upload time ## `MAX_ITEMS` (type: `integer`): Maximum number of Reels to extract PER KEYWORD ## Actor input object example ```json { "KEYWORDS": [ "fitness", "cooking" ], "TIME_FILTER": "any", "MAX_ITEMS": 10 } ```` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "KEYWORDS": [ "fitness", "cooking" ], "TIME_FILTER": "any", "MAX_ITEMS": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/facebook-reels-posts-scraper-low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "KEYWORDS": [ "fitness", "cooking", ], "TIME_FILTER": "any", "MAX_ITEMS": 10, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/facebook-reels-posts-scraper-low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "KEYWORDS": [ "fitness", "cooking" ], "TIME_FILTER": "any", "MAX_ITEMS": 10 }' | apify call delectable_incubator/facebook-reels-posts-scraper-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/facebook-reels-posts-scraper-low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Reels Posts Scraper - Low-costπŸ’²πŸ”₯πŸ“˜πŸŽ¬", "description": "Scrape Facebook Reels by keyword πŸ”πŸŽ¬ with a powerful short-form video scraper. Extract reel captions, views, durations, upload dates, reel URLs, and more from Facebook. Ideal for trend analysis, content research, video datasets, AI pipelines, and social media intelligence πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "H2toDTb1bTmMeodF9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~facebook-reels-posts-scraper-low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-facebook-reels-posts-scraper-low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~facebook-reels-posts-scraper-low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-facebook-reels-posts-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~facebook-reels-posts-scraper-low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-facebook-reels-posts-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "KEYWORDS", "TIME_FILTER", "MAX_ITEMS" ], "properties": { "KEYWORDS": { "title": "Keywords", "type": "array", "description": "List of keywords to search for Reels. One keyword per entry.", "items": { "type": "string" } }, "TIME_FILTER": { "title": "Time Filter", "enum": [ "any", "month", "year" ], "type": "string", "description": "Filter Reels by upload time", "default": "any" }, "MAX_ITEMS": { "title": "Max Items", "type": "integer", "description": "Maximum number of Reels to extract PER KEYWORD", "default": 10 } } }, "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 } } } } } } } } } } ```