# Accout reels scraper (`scrapers-hub/accout-reels-scraper`) Actor Powerful Facebook Reels Scraper to extract captions, thumbnails, video IDs, likes, comments, shares, and timestamps from public reels. Bulk scrape multiple URLs and get clean JSON output for analytics, competitor research, influencer tracking, and social media automation. - **URL**: https://apify.com/scrapers-hub/accout-reels-scraper.md - **Developed by:** [Scrapers Hub](https://apify.com/scrapers-hub) (community) - **Categories:** Social media, Videos, Automation - **Stats:** 15 total users, 4 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $9.99/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ### ๐ŸŽฌ Facebook Reels Scraper: The Ultimate Enterprise Solution for Deep Video Intelligence and Mass Engagement Harvesting In the hyper-dynamic digital ecosystem of the modern era, short-form video content has become the definitive pulse of consumer behavior and brand authority. The Facebook Reels Scraper is a professional-grade, industrial-strength data extraction engine meticulously engineered to decode the underlying structure of public social identities and high-fidelity visual assets. By deploying the Facebook Reels Scraper, global organizations, marketing agencies, and forensic data scientists can move beyond surface-level observation and establish an autonomous pipeline for creative archiving, trend mapping, and large-scale asset management with surgical precision. ๐Ÿ“ˆ ๐Ÿ“ฝ๏ธ The Facebook Reels Scraper serves as a robust, non-invasive bridge between the vast, unstructured environment of public media and the refined, high-fidelity databases required by modern AI training models and Business Intelligence (BI) tools. This documentation offers an exhaustive deep-dive into the technical framework, operational methodologies, and strategic utilities of the Facebook Reels Scraper. ๐ŸŒ ๐Ÿง  #### ๐Ÿ—๏ธ 1. Technical Architecture of the Facebook Reels Scraper The core engineering of the Facebook Reels Scraper is built upon a resilient, multi-layered browser-simulation infrastructure specifically optimized for the heavy media environment of social platforms. Unlike standard tools that rely on fragile official APIs with restrictive quotas, the Facebook Reels Scraper utilizes a proprietary parsing engine that interacts directly with the platform's public-facing metadata layers. This ensures that the Facebook Reels Scraper can extract data even from the most high-traffic viral content without interruption. ๐Ÿ› ๏ธ โš™๏ธ #### โš™๏ธ Input Configuration Simply provide the list of Facebook Reels URLs you wish to scrape. The tool intelligently handles the technical extraction behind the scenes. Input Example: ```json { "urls": [ "https://www.facebook.com/reel/2051489205656843", "https://www.facebook.com/reel/1875178263081112", "https://www.facebook.com/reel/1372521511118228" ] } ```` #### ๐Ÿ“ค Output Structure The scraper delivers a clean, structured JSON output containing the Reelโ€™s metadata and its community interactions. Sample Output Data: ```json { "facebookUrl": "https://web.facebook.com/humansofnewyork/posts/pfbid0BAXGdCY4Uhww4Mn8uCCLCW36hSUNMDw2CJRWyB395SFmDeu1JasBeDYwVGafamGjl?comment_id=191072573008645", "commentUrl": "https://web.facebook.com/humansofnewyork/posts/pfbid0BAXGdCY4Uhww4Mn8uCCLCW36hSUNMDw2CJRWyB395SFmDeu1JasBeDYwVGafamGjl?comment_id=191072573008645", "id": "pfbid0UEcyhMKjEaZaYfWUXGg9wLTNT3CmB3hdCbwNA7VG4LPHnFvmvFzzLtob918dgZWUl", "feedbackId": "pfbid0UEcyhMKjEaZaYfWUXGg9wLTNT3CmB3hdCbwNA7VG4LPHnFvmvFzzLtob918dgZWUl", "date": "2021-08-16 02:03:04", "text": "My son's charter school has a garden and the school has there own chef that makes the students healthy foods,my son said when he grows up he wants to be a Gardner i guess a garden teacher,anyone know here how can I help him what education does he need?", "profileId": "pfbid0UEcyhMKjEaZaYfWUXGg9wLTNT3CmB3hdCbwNA7VG4LPHnFvmvFzzLtob918dgZWUl", "profileName": "Elvia Cabrera", "likesCount": "406", "commentsCount": 82, "comments": [], "threadingDepth": 0, "facebookId": "pfbid0UEcyhMKjEaZaYfWUXGg9wLTNT3CmB3hdCbwNA7VG4LPHnFvmvFzzLtob918dgZWUl" ........ }, ``` #### โš™๏ธ Systems Optimization & Logic Asynchronous Interaction Parsing: The Facebook Reels Scraper identifies and catalogs video metadata, high-res media URIs, and user interactions simultaneously, ensuring maximum speed during extraction cycles. โšก ๐ŸŽ๏ธ DOM Synchronization & Virtual Scrolling: To maintain 100% data fidelity, the Facebook Reels Scraper implements an intelligent infinite-scroll simulation that captures dynamically loaded content without missing a single entry. ๐Ÿ›ก๏ธ โœ… Multi-Stream Separation: The Facebook Reels Scraper features an advanced layer that isolates MP4 video files from metadata, providing maximum flexibility for content repurposing. ๐Ÿท๏ธ ๐Ÿ” Resource Prioritization: The Facebook Reels Scraper selectively targets high-value JSON fragments, significantly reducing computational overhead while maximizing download quality. ๐Ÿ’พ ๐Ÿ“‰ #### ๐ŸŒŸ 2. Strategic Features of the Facebook Reels Scraper The Facebook Reels Scraper is distinguished by its ability to extract "silent" engagement data points that are often invisible to the average observer. Below are the functional pillars that define the power of the Facebook Reels Scraper: ๐Ÿ’Ž ๐Ÿ”“ ๐Ÿ”’ Zero-Authentication Discovery: The Facebook Reels Scraper functions entirely as an independent observer. It never requires an official account login or session cookie, protecting your professional identity from platform detection. ๐Ÿ•ต๏ธโ€โ™‚๏ธ ๐Ÿ›ก๏ธ ๐Ÿ“„ Complete Narrative Harvesting: Captures not just the video URL but also high-resolution thumbnails, full descriptions, and hashtags through the Facebook Reels Scraper's advanced text-parsing layer. ๐Ÿท๏ธ ๐Ÿ“ก ๐Ÿข Performance Metrics Auditing: Automatically identifies play counts, likes, and share statistics via the Facebook Reels Scraper, revealing the hidden layers of content virality. ๐Ÿ“Š ๐Ÿ“ˆ ๐Ÿ–ผ๏ธ Engagement Stream Mapping: The Facebook Reels Scraper finds the full comment stream associated with each Reel, including author profiles and reply counts, perfect for community sentiment analysis. ๐ŸŽจ ๐Ÿ“ธ โšก Extreme Concurrency Support: Engineered to handle massive batch requests, the Facebook Reels Scraper can be distributed across global clusters to process thousands of links per hour. ๐Ÿš€ ๐ŸŒŒ ๐Ÿ“Š Unified Data Schema: Every result from the Facebook Reels Scraper is normalized into a clean, flattened structure ready for immediate database ingestion. ๐Ÿ“ ๐Ÿ’พ ๐Ÿ›ก๏ธ 3. The Stealth-Audit Security Protocol of the Facebook Reels Scraper In the high-security environment of professional web architecture, platforms employ highly sensitive AI-driven bot-detection. The Facebook Reels Scraper stays ahead with a multi-layered stealth defense system that ensures uninterrupted operation and consistent data flow. ๐Ÿคบ ๐Ÿ”’ ๐ŸŽญ Pseudo-User Fingerprinting: Every session of the Facebook Reels Scraper is assigned a unique browser signatureโ€”spoofing RAM, CPU cores, and GPU signaturesโ€”to mimic a legitimate organic visitor. ๐ŸŽญ ๐Ÿ’ป ๐Ÿšซ Rate-Limit Resilience: The Facebook Reels Scraper features adaptive sleep timers that expand and contract based on the server's response headers to prevent IP flagging. โณ ๐Ÿšฆ ๐Ÿ›ก๏ธ Residential Proxy Synergy: By funneling requests through a global network of home-based IP addresses, the Facebook Reels Scraper makes its traffic indistinguishable from standard human traffic. ๐Ÿ  ๐ŸŒŽ ๐ŸŒ Localized Content Discovery: Use the Facebook Reels Scraper to see specific trending content or localized descriptions restricted to certain geographic regions. ๐Ÿ“ ๐Ÿ—บ๏ธ #### ๐Ÿ“Š 4. Schema Mapping: Comprehensive Scraped Data Points The output generated by the Facebook Reels Scraper is designed for mathematical precision and ease of use in high-end Business Intelligence (BI) tools. ๐Ÿ“‹ ๐Ÿงช | **Category** | **Fields Extracted by Facebook Reels Scraper** | **Analytical Value ๐Ÿ’ก** | | -------------------------- | ---------------------------------------------- | ---------------------------------------------------- | | **Institutional Identity** | profileName, profileId, facebookId | Source classification and brand auditing ๐Ÿ†” | | **Temporal Data** | date, timestamp, upload\_time | Trend timing and frequency optimization โฑ๏ธ | | **Engagement** | likesCount, commentsCount, play\_count | Viral coefficient and audience resonance auditing ๐Ÿ“ˆ | | **Content** | text, hashtags, description | Semantic analysis and keyword extraction โœ๏ธ | | **Media Assets** | facebookUrl, thumbnail\_url, video\_url | Visual strategy and creative auditing ๐Ÿ“ธ | | **Interaction** | comments (Array), threadingDepth | Community depth and sentiment mapping โค๏ธ | #### ๐Ÿ“ˆ 5. Enterprise Use Cases for the Facebook Reels Scraper Elite marketing firms, PR agencies, and tech startups leverage the Facebook Reels Scraper to build a 360-degree view of their competitive landscape and asset library. ๐Ÿ† ๐Ÿข ๐ŸŽฏ Creative Content Auditing Utilize the Facebook Reels Scraper to download and analyze the visual aesthetic of the top 100 competitors in your niche. The Facebook Reels Scraper allows you to see which visual styles and hooks are driving the highest engagement density. ๐Ÿ•ต๏ธโ€โ™€๏ธ ๐ŸŽจ ๐Ÿ“Š Competitive Sentiment Research Before launching a new campaign, use the Facebook Reels Scraper to audit the feedback on high-performance Reels from competing brands. The Facebook Reels Scraper provides the raw comment data needed to identify recurring customer pain points. ๐Ÿ“‰ ๐Ÿ” ๐Ÿš€ Lead Generation & Referral Monitoring Automatically scan thousands of public interactions using the Facebook Reels Scraper to find users tagging specific brands or services. The Facebook Reels Scraper helps you identify high-intent prospects who are actively seeking solutions your business provides. ๐Ÿค ๐Ÿ’ธ ๐Ÿ“‰ Crisis Detection & Narrative Monitoring Monitor the spread of specific viral videos tagging your brand using the Facebook Reels Scraper. By extracting engagement metrics at scale, the Facebook Reels Scraper acts as an early-warning system for emerging public relations risks. ๐Ÿšจ ๐Ÿ›ก๏ธ ๐Ÿข AI Training & NLP Dataset Construction Feed the clean, structured text results from the Facebook Reels Scraper directly into your custom Large Language Models (LLMs). The Facebook Reels Scraper provides the massive, niche-specific datasets required for predictive behavior modeling. ๐Ÿค– ๐Ÿง  #### ๐Ÿง  6. Advanced 2026 Predictive Analytics Features Beyond basic data collection, the Facebook Reels Scraper allows for "Forward-Looking" strategies that give your business an unfair advantage: ๐Ÿ”ฎ โšก ๐Ÿค– AI-Ready Markdown Architecture The Facebook Reels Scraper now automatically converts Reel captions and transcripts into clean Markdown or LLM-optimized JSON. This allows users to feed data directly into RAG (Retrieval-Augmented Generation) systems and AI agents without manual cleaning. ๐Ÿ“„ ๐Ÿค– ๐ŸŽ™๏ธ Automated Speech-to-Text (Transcripts) Uses on-device AI to generate high-accuracy transcripts of the video audio. The Facebook Reels Scraper enables full searchability for "Social Search" optimization, allowing you to find videos based on what was said, not just what was written. ๐Ÿ—ฃ๏ธ ๐Ÿ” ๐Ÿ“Š Viral Velocity Logic A specialized data field within the Facebook Reels Scraper that calculates the Engagement Growth Rate. This tells you how fast likes and shares are increasing per hour, rather than just showing a static total. ๐Ÿš€ โฑ๏ธ ๐Ÿ›๏ธ Social Commerce Extraction The Facebook Reels Scraper specifically identifies and extracts product tags, prices, and "Buy" links embedded within the Reels. This is essential for monitoring the growth of social commerce across the platform. ๐Ÿ’ธ ๐Ÿท๏ธ ๐Ÿ–ผ๏ธ OCR Metadata Harvesting Uses Optical Character Recognition to read on-screen text, burnt-in captions, and brand logos within the video frames. The Facebook Reels Scraper ensures no detail of the visual strategy is missed. ๐Ÿ“‘ ๐Ÿ” #### ๐Ÿ›ก๏ธ 7. Scalability Benchmarks & Anti-Detection The Facebook Reels Scraper is engineered for high-volume enterprise operations where millions of entries need to be audited across global markets. ๐ŸŒ ๐Ÿš€ ๐ŸŽญ Hardware Footprint Rotation: The Facebook Reels Scraper constantly changes its virtual hardware profileโ€”spoofing RAM, CPU cores, and GPU signaturesโ€”making it indistinguishable from organic devices. ๐Ÿ’ป ๐Ÿ“ฑ ๐ŸŒ Proxy Pool Diversification: Supports massive rotating residential proxy pools, ensuring the Facebook Reels Scraper achieves a 99.9% success rate regardless of volume. ๐Ÿ  ๐ŸŒŽ โณ Smart Throttling Logic: To protect the integrity of the data source, the Facebook Reels Scraper automatically adjusts its request velocity based on server response time. โš–๏ธ โฑ๏ธ #### โ“ 8. Frequently Asked Questions (FAQ) ๐Ÿข Can the Facebook Reels Scraper see private account content? No. The Facebook Reels Scraper is strictly for public transparency. If the content is not publicly accessible on the web without a login, the Facebook Reels Scraper will not extract it. ๐Ÿ”’ ๐Ÿ”ข ๐Ÿ“… How accurate are the engagement counts? The Facebook Reels Scraper retrieves real-time data directly from the live video UI, ensuring you get the most accurate snapshot of performance at that specific moment. โœ… ๐Ÿท๏ธ โ™พ๏ธ Is there a limit on the number of URLs? The Facebook Reels Scraper shatters limitations. Whether you need data for 10 videos or 1,000,000, the tool processes the collection with surgical precision. ๐Ÿš€ ๐ŸŒŒ ๐Ÿ“‚ What output formats are available? The Facebook Reels Scraper delivers results in JSON, CSV, and Excel (XLSX). ๐Ÿ“ ๐Ÿ’พ #### ๐Ÿ›ก๏ธ 9. Compliance & Ethical Framework Using the Facebook Reels Scraper comes with a commitment to professional data ethics and responsible usage. โš–๏ธ ๐Ÿค ๐Ÿ›๏ธ Public Data Protocol: The Facebook Reels Scraper only extracts content that users and brands have explicitly shared for public viewing. ๐Ÿ“– ๐Ÿ”“ ๐Ÿšฆ Rate Limit Adherence: The Facebook Reels Scraper is designed to be a "considerate visitor," maintaining optimal traffic patterns to protect server integrity. ๐Ÿšฆ ๐Ÿ›ก๏ธ โš–๏ธ Data Privacy Alignment: The Facebook Reels Scraper focuses on engagement metrics and public content; it does not harvest non-public personal information or private messages. ๐Ÿ”’ ๐Ÿ›ก๏ธ ๐Ÿค Strategic Integrity: We advocate for using the Facebook Reels Scraper for competitive transparency and academic research within legal boundaries (GDPR/CCPA). ๐Ÿ›๏ธ โš–๏ธ #### ๐Ÿ“Š 10. Data Enrichment API Ecosystem Integrate the Facebook Reels Scraper with your existing tech stack to unlock even deeper insights: ๐Ÿ”— ๐Ÿงช | **Enrichment Feature** | **Tool ๐Ÿ› ๏ธ** | **Purpose with Facebook Reels Scraper** | | ---------------------- | ------------- | ------------------------------------------------------------------------------------------------------- | | **Sentiment Analysis** | Azure AI | Scores the verbal and emotional tone of user comments to measure audience sentiment and risk signals ๐Ÿง  | | **Object Recognition** | Google Vision | Identifies products, logos, and visual objects inside Reels media for creative and brand analysis ๐Ÿ‘“ | | **Influencer Mapping** | BuzzSumo | Maps account IDs to global influence metrics to identify high-impact creators ๐Ÿ“ˆ | | **Automated Alerts** | Slack | Sends real-time notifications when competitor videos reach predefined engagement milestones ๐Ÿค– | #### ๐Ÿ 11. Conclusion: Master the Professional Identity Economy In the data-driven landscape, those who can observe and catalog their professional environment most accurately win. The Facebook Reels Scraper is the ultimate bridge between unstructured social noise and clear, strategic business intelligence. It eliminates the manual work of searching and replaces it with a streamlined, automated, and intelligent data pipeline. ๐ŸŒŸ ๐Ÿ† Whether you are a startup looking for your first viral hook, an agency building a regional lead list, or a researcher tracking the history of digital identity shifts, the Facebook Reels Scraper is your most reliable partner. By choosing the Facebook Reels Scraper, you are investing in a scalable intelligence strategy that will serve your organization for years to come. ๐Ÿš€๐Ÿ“ˆ #### ๐Ÿš€ Get Started with the Facebook Reels Scraper Today! Are you ready to revolutionize your social lead generation and market intelligence? The Facebook Reels Scraper is waiting for your first target URL. If you need a custom configuration for the Facebook Reels Scraper or help setting up a cloud-based schedule, our technical team is here to support you. ๐Ÿค โœจ # 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("scrapers-hub/accout-reels-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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("scrapers-hub/accout-reels-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://www.facebook.com/reel/2051489205656843" } ] }' | apify call scrapers-hub/accout-reels-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapers-hub/accout-reels-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Accout reels scraper", "description": "Powerful Facebook Reels Scraper to extract captions, thumbnails, video IDs, likes, comments, shares, and timestamps from public reels. Bulk scrape multiple URLs and get clean JSON output for analytics, competitor research, influencer tracking, and social media automation.", "version": "1.0", "x-build-id": "Co2Bzqo3YW1d0o513" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapers-hub~accout-reels-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapers-hub-accout-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapers-hub~accout-reels-scraper/runs": { "post": { "operationId": "runs-sync-scrapers-hub-accout-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapers-hub~accout-reels-scraper/run-sync": { "post": { "operationId": "run-sync-scrapers-hub-accout-reels-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```