# Instagram Reels Scraper - Captions & Metrics (`xtech/instagram-reels-scraper-pro`) Actor 𝗦𝗰𝗿𝗮𝗽𝗲 𝗜𝗻𝘀𝘁𝗮𝗴𝗿𝗮𝗺 𝗥𝗲𝗲𝗹𝘀 by URL and export captions, creator handles, likes, comments, thumbnails, and publish times. Build ready-to-use Reel data for influencer research, campaign reporting, competitor analysis, social media monitoring, and creative planning. - **URL**: https://apify.com/xtech/instagram-reels-scraper-pro.md - **Developed by:** [Xtech](https://apify.com/xtech) (community) - **Categories:** Social media, Videos - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.90 / 1,000 reel results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 Reels Scraper turns public Reel URLs into a clean dataset of captions, creator handles, visible engagement, thumbnails, and publish times. Use it to collect individual Reels in bulk without copying details into a spreadsheet by hand. Independent tool. Not affiliated with, endorsed by, or sponsored by Instagram. ### Scrape Instagram Reels by URL Add up to 200 public Instagram Reel URLs in one run. URLs can be pasted individually or in bulk, and duplicate Reels are processed once. The optional concurrency setting controls how many URLs are collected at the same time; the default works well for most runs. ```json { "reelUrls": [ "https://www.instagram.com/reel/ABC123example/" ], "maxConcurrency": 3 } ```` ### Instagram Reel data you can export Each successful result is one Reel row containing the Reel shortcode and URL, title and caption when available, creator username, visible like and comment counts, thumbnail URL, publication time, and collection time. Download the dataset as JSON, CSV, or Excel, or connect it to your reporting workflow. ### Popular use cases - Build a reference library for short-form creative research. - Compare captions and visible engagement across selected Reels. - Review creator or brand campaign content. - Collect competitor examples for reporting and planning. - Create structured inputs for analysis, dashboards, or internal tools. ### Pricing Pricing is based on successful Reel rows saved to the dataset when result-based pricing applies. Invalid, unavailable, or duplicate inputs do not create paid Reel results. Check the Pricing tab for the current rate and set a maximum run spend when you need a firm budget. ### Limitations This Actor accepts direct public Reel URLs only. It does not discover Reels by username, profile, hashtag, or keyword, and it does not download video or audio files. Instagram may omit some metadata, so captions, engagement counts, thumbnails, or publication times can be empty. Private, removed, restricted, or unavailable Reels may not produce a result. ### Frequently asked questions #### Can I process multiple Reel URLs at once? Yes. Add up to 200 URLs per run. Duplicate Reel URLs are collected once. #### Does it download Instagram Reel videos? No. The output contains metadata and a thumbnail URL when available, not downloaded media. #### What happens when a Reel is unavailable? Other valid URLs continue processing. An unavailable Reel does not create a successful Reel row. #### Can I schedule recurring runs? Yes. Save the input as a task and use an Apify schedule to refresh the selected Reel metadata at your preferred interval. # Actor input Schema ## `reelUrls` (type: `array`): Required. Add up to 200 public Instagram Reel URLs. Duplicate Reels are processed once. ## `maxConcurrency` (type: `integer`): How many Reels to collect at the same time. ## Actor input object example ```json { "reelUrls": [ "https://www.instagram.com/reel/ABC123example/" ], "maxConcurrency": 3 } ``` # Actor output Schema ## `reels` (type: `string`): One clean metadata row per collected Instagram Reel. ## `sourceResults` (type: `string`): Inputs that could not produce a Reel row. ## `runSummary` (type: `string`): Counts for this run. # 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 = { "reelUrls": [ "https://www.instagram.com/reel/ABC123example/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("xtech/instagram-reels-scraper-pro").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 = { "reelUrls": ["https://www.instagram.com/reel/ABC123example/"] } # Run the Actor and wait for it to finish run = client.actor("xtech/instagram-reels-scraper-pro").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 '{ "reelUrls": [ "https://www.instagram.com/reel/ABC123example/" ] }' | apify call xtech/instagram-reels-scraper-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=xtech/instagram-reels-scraper-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Reels Scraper - Captions & Metrics", "description": "𝗦𝗰𝗿𝗮𝗽𝗲 𝗜𝗻𝘀𝘁𝗮𝗴𝗿𝗮𝗺 𝗥𝗲𝗲𝗹𝘀 by URL and export captions, creator handles, likes, comments, thumbnails, and publish times. Build ready-to-use Reel data for influencer research, campaign reporting, competitor analysis, social media monitoring, and creative planning.", "version": "0.1", "x-build-id": "txy3N7bIxvG0ROyxU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/xtech~instagram-reels-scraper-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-xtech-instagram-reels-scraper-pro", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/xtech~instagram-reels-scraper-pro/runs": { "post": { "operationId": "runs-sync-xtech-instagram-reels-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/xtech~instagram-reels-scraper-pro/run-sync": { "post": { "operationId": "run-sync-xtech-instagram-reels-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "reelUrls" ], "properties": { "reelUrls": { "title": "Reel URLs", "minItems": 1, "maxItems": 200, "uniqueItems": true, "type": "array", "description": "Required. Add up to 200 public Instagram Reel URLs. Duplicate Reels are processed once.", "items": { "type": "string" } }, "maxConcurrency": { "title": "Maximum concurrent Reels", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many Reels to collect at the same time.", "default": 3 } } }, "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 } } } } } } } } } } ```