# Instagram Reel Scraper (`scraperoka/instagram-reel-scraper`) Actor 🔍 Instagram Reel Scraper helps you extract Reel data fast—captions, hashtags, links & more. ⚡ Save hours on research, competitor analysis & content insights. 📈 Perfect for marketers, creators & data-driven teams. 🚀 Get started! - **URL**: https://apify.com/scraperoka/instagram-reel-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Social media, Automation, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $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 ### Instagram Reel Scraper 🚀 Manually visiting profile pages to collect Reels and post metadata wastes hours you don’t have. **Instagram Reel Scraper** automatically pulls Reels and Posts data (including comments, likes, and more) from a target Instagram username. This Instagram reel scraper is ideal for marketers, recruiters, and growth teams who need structured Instagram reels data scraping—at scale—down to thousands of records in minutes. --- ### What You Get: Sample Output Here’s a sample record from a single run: ```json { "id": "12345678901234567", "type": "Video", "shortCode": "AbCdEfGhIjK", "caption": "New drop 🎬 #reels #marketing @brandpartner", "hashtags": ["reels", "marketing"], "mentions": ["brandpartner"], "url": "https://www.instagram.com/p/AbCdEfGhIjK/", "commentsCount": 42, "dimensionsHeight": 1920, "dimensionsWidth": 1080, "images": [ "https://example.com/image1.jpg", "https://example.com/image2.jpg" ], "videoUrl": "https://example.com/video.mp4", "likesCount": 987, "timestamp": "2026-05-18T12:34:56.000Z", "ownerFullName": "Jane Doe", "ownerUsername": "janedoe_creator", "ownerId": "555666777", "isPinned": false, "productType": null, "videoDuration": 31.2, "inputUrl": "https://www.instagram.com/janedoe_creator", "firstComment": "Loved this!", "latestComments": [ { "id": "99887766554433", "text": "Loved this!", "ownerUsername": "commenter_user", "ownerProfilePicUrl": "https://example.com/pic.jpg", "timestamp": "2026-05-18T12:40:00.000Z", "repliesCount": 1, "replies": [], "likesCount": 6, "owner": { "id": "1122334455", "is_verified": false, "profile_pic_url": "https://example.com/pic.jpg", "username": "commenter_user" } } ], "displayUrl": "https://example.com/image1.jpg", "audioUrl": null, "alt": "Alt text for accessibility", "videoViewCount": 12345, "videoPlayCount": 13000, "childPosts": [], "taggedUsers": [ { "full_name": "Brand Partner", "id": "333444555", "is_verified": false, "profile_pic_url": "https://example.com/partner.jpg", "username": "brandpartner" } ], "musicInfo": { "artist_name": "Original Audio Artist", "song_name": "Original audio title", "uses_original_audio": true, "should_mute_audio": false, "should_mute_audio_reason": "", "audio_id": "777888999" }, "isCommentsDisabled": false } ```` **Output Fields** | Field | Type | What It Tells You | |---|---|---| | `id` | string | Unique ID for the scraped media item | | `type` | string | Whether it’s a `Video`, `Image`, or `Carousel` | | `shortCode` | string | The media shortcode you can use to reconstruct the post URL | | `caption` | string | The post caption text as published | | `hashtags` | array | Hashtags extracted directly from the caption | | `mentions` | array | Mentions extracted directly from the caption | | `url` | string | Direct Instagram URL to the post/Reel | | `commentsCount` | number | Total comments count for quick engagement scoring | | `likesCount` | number | Total likes count for quick performance comparisons | | `timestamp` | string | UTC timestamp for when the media was taken (formatted as ISO 8601) | | `ownerFullName` | string | Display name of the account that posted the Reel | | `ownerUsername` | string | Instagram username of the account that posted the Reel | | `videoUrl` | string | null | Video source URL when available (otherwise null) | | `latestComments` | array | A small set of the most recent comment objects (each includes text, author, timestamp, and likes count) | | `isCommentsDisabled` | boolean | Indicates whether comments are disabled on the media | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Instagram Reel Scraper? There are a lot of ways to pull data from Instagram — here’s what sets Instagram Reel Scraper (and other instagram reel scraper workflows like it) apart. #### Built for complete Reel and post metadata Instagram Reel Scraper focuses on extracting rich media details, including likes, comments, captions, hashtags, mentions, dimensions, and timestamps—so your analytics scraper or Instagram reels analytics scraper pipeline starts with structured data. #### Resilient scraping flow The actor includes retries and fallbacks for resilience when requests fail, so you can keep running long batches with fewer interruptions. #### Output designed for analysis and automation Results are written to the dataset record-by-record as the actor processes items, making it easy to transform into reports, dashboards, or downstream automations (think: instagram reels content scraper, batch downloader workflows, and more). #### Works from a single username input You only provide `username` and a `max_posts` limit—ideal for “one account at a time” research tasks like public instagram reel scraper studies, content audits, and competitor monitoring. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "username": "cristiano", "max_posts": 10 } ``` | Parameter | Required | What It Does | |---|---|---| | `username` | ✅ | The Instagram username to scrape without the `@` symbol (e.g., `cristiano`) | | `max_posts` | ⬜ | Maximum number of reels to scrape (default is `10`, minimum is `1`) | *** ### Core Capabilities #### Scrape Instagram Reels and Posts by username Instagram Reel Scraper pulls post URLs for the provided `username` and then scrapes each media page. This makes it straightforward to build an instagram reel URL scraper workflow without manually collecting links. #### Extract caption-derived insights (hashtags + mentions) From each caption, the actor extracts hashtags and mentions. This is handy for marketing research and for tagging your dataset for later segmentation in instagram reels data scraping. #### Capture comments and engagement signals Each output includes engagement counts such as `likesCount` and `commentsCount`, plus comment details in `latestComments` when available. It’s a practical foundation for engagement benchmarking in an instagram reel viewer for download or creator performance review. #### Includes music and tagged users details When music information and tagged users are present, the actor structures them into `musicInfo` and `taggedUsers`. That’s useful when you’re building instagram reels analytics scraper datasets that connect content to collaborators and audio. #### Saves results as it goes As each post is successfully scraped, the actor writes the record to your Apify dataset immediately. This reduces the “all-or-nothing” risk when you’re running larger instagram reels batch downloader jobs. *** ### Who Gets the Most Out of This Here’s how different teams put Instagram Reel Scraper to work: **Influencer marketing managers** — Use it to pull Reels and post metadata for a creator shortlist, then compare likes, comments, and caption themes to pick candidates faster. **Sales and partnerships teams** — Use it to build creator and brand-content intelligence (engagement + tagged accounts) so outreach lists are informed, not guessed. **Freelance researchers** — Pull public instagram reel scraper datasets for a specific username, then export structured outputs for content analysis, topic tagging, and trend comparisons. **Data analysts and BI teams** — Ingest the dataset into your pipeline to build analytics around `likesCount`, `commentsCount`, timestamps, hashtags, mentions, and media type for repeatable reporting. **Automation developers** — Trigger runs through the Apify platform, then push results to your own systems to power workflows that look like an instagram reel downloader or instagram reel scraping tool—but with structured JSON. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Instagram Reel Scraper from start to finish: 1. **Open the actor on Apify** — go to the actor page at [console.apify.com](https://console.apify.com). 2. **Enter your inputs** — set `username` (required) and optionally `max_posts`. 3. **Configure proxy settings** — choose the proxy preference as needed in the run settings (the actor supports proxy configuration). 4. **Hit Run and watch the live log** — track progress as it collects post URLs and scrapes each media page. 5. **View results in the dataset tab** — each scraped media item is appended as a result record. 6. **Export as JSON, CSV, or Excel** — download from the Apify dataset export options. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, Instagram Reel Scraper plugs directly into your existing workflow. Export formats include JSON, CSV, and Excel directly from the Apify dataset tab, so you can move quickly into spreadsheets, analysis tools, or reporting dashboards. You can also use Apify API access to retrieve results programmatically, and connect to automation workflows using tools like Zapier/Make or webhooks to run downstream steps when a scrape finishes. For implementation details, refer to the Apify documentation at [apify.com/docs/api](https://apify.com/docs/api). *** ### Pricing & Free Trial Instagram Reel Scraper runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You can use the free tier for initial testing runs and then scale based on your workload. For current pricing, credit amounts, and compute unit billing, check the Apify pricing page (prices can change over time). Start for free at [apify.com](https://apify.com) and scale when you’re ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Rate-limit style disruptions | Includes delays between requests and resilient request handling | | Temporary request failures | Retries and fallbacks when scraping individual items | | Proxy support | Uses proxy configuration for more reliable scraping | | Partial progress | Saves results per successfully scraped item so you keep what’s done | | Batch size management | Uses a `max_posts` cap so you control how many reels/posts are processed | **Limitations:** The actor is designed for publicly accessible pages; private or login-gated content may not be available. Some fields may be missing depending on what each media item contains. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial? Yes, Apify offers a **free tier** you can use to run Instagram Reel Scraper for testing before scaling. #### Do I need to log in to Instagram to use this? No. Instagram Reel Scraper works with publicly available data and does not require you to provide login credentials. #### How accurate is the data? The actor extracts the fields it finds on the media pages, including caption-derived hashtags and mentions, engagement counts, and structured details like `musicInfo` and `taggedUsers` when available. #### How many results can I get per run? You control the size using `max_posts`, which sets the maximum number of reels/posts the actor will scrape. #### How often is the data updated / how fresh is it? The data reflects what’s available at the time you run the actor. If you need fresh updates, schedule or re-run the actor on your desired cadence. #### Is this legal? Does it comply with GDPR / CCPA? Instagram Reel Scraper accesses **publicly available data**, and compliance depends on how you use and store the results. You’re responsible for ensuring your collection and processing follow GDPR/CCPA requirements and applicable platform terms. #### Can I export results to Google Sheets or Excel? Yes. You can export from the Apify dataset tab (JSON, CSV, or Excel), and then load the data into your preferred tools like spreadsheets. #### Can I run this on a schedule automatically? Yes. You can schedule runs on the Apify platform (use Apify scheduling features available in the console and via API). #### Can I access this via API? Yes. Apify supports API-based access to run results—see [apify.com/docs/api](https://apify.com/docs/api) for details. #### What happens if the actor hits an error? If scraping a specific item fails, the actor logs the error and continues processing other items. The dataset will include records only for successfully scraped media items. *** ### Need Help or Have a Request? Got a question about Instagram Reel Scraper or want a new feature added? Reach out at . We actively maintain this actor based on user feedback and can help with workflow ideas like batch CSV upload support and webhook notifications on completion. *** ### Disclaimer & Responsible Use *Instagram Reel Scraper is the fastest, most reliable way to collect structured Instagram Reels and post metadata—start your free run today.* The actor uses **publicly available data** and does not access private accounts, login-gated content, or password-protected pages. You are responsible for complying with GDPR, CCPA, platform Terms of Service, and any applicable laws. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `username` (type: `string`): The Instagram username to scrape without the @ symbol (e.g., cristiano) ## `max_posts` (type: `integer`): Maximum number of reels to scrape ## Actor input object example ```json { "username": "cristiano", "max_posts": 10 } ``` # 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 = { "username": "cristiano" }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/instagram-reel-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 = { "username": "cristiano" } # Run the Actor and wait for it to finish run = client.actor("scraperoka/instagram-reel-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 '{ "username": "cristiano" }' | apify call scraperoka/instagram-reel-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/instagram-reel-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Reel Scraper", "description": "🔍 Instagram Reel Scraper helps you extract Reel data fast—captions, hashtags, links & more. ⚡ Save hours on research, competitor analysis & content insights. 📈 Perfect for marketers, creators & data-driven teams. 🚀 Get started!", "version": "1.0", "x-build-id": "hsvedUJZbvaJX4wOk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~instagram-reel-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-instagram-reel-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~instagram-reel-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-instagram-reel-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~instagram-reel-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-instagram-reel-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "username" ], "properties": { "username": { "title": "Instagram Username", "type": "string", "description": "The Instagram username to scrape without the @ symbol (e.g., cristiano)" }, "max_posts": { "title": "Maximum reels", "minimum": 1, "type": "integer", "description": "Maximum number of reels to scrape", "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 } } } } } } } } } } ```