# Instagram Likes Scraper (no login needed) (`iron-crawler/instagram-likes`) Actor Scrape Instagram likes at lightning speed. Zero-config API pipeline exports bulk engagement data in JSON/CSV for influencer analytics and brand strategy optimization. - **URL**: https://apify.com/iron-crawler/instagram-likes.md - **Developed by:** [Iron Crawler](https://apify.com/iron-crawler) (community) - **Categories:** Social media, Developer tools, Automation - **Stats:** 130 total users, 4 monthly users, 97.7% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Instagram Likes Scraper (Cookieless) ### What does Instagram Likes Scraper (Cookieless) do? Instagram Likes Scraper (Cookieless) is a powerful tool that extracts detailed engagement data from Instagram posts without requiring login credentials or cookies. This no-login architecture means you can collect Instagram engagement data instantly without worrying about account restrictions, rate limits, or authentication headaches. Simply provide a post URL, and the scraper retrieves comprehensive information about every user who liked that post. **Key Features:** - **Cookieless extraction**: No Instagram login required - **Bulk data export**: Download likes data in JSON, CSV, or Excel formats - **User profile details**: Get usernames, user IDs, verification status, and profile pictures - **Timestamp tracking**: Capture when each like occurred for temporal analysis - **Fast processing**: Extract thousands of likes in minutes - **API integration**: Connect directly to your analytics pipeline ### Why scrape Instagram likes data? Social media influencers need to scrape Instagram likes from top-performing posts to analyze engagement metrics and refine their content strategy for brand partnerships. Understanding who engages with your content helps you identify your true audience, measure campaign effectiveness, and demonstrate ROI to potential sponsors. This Instagram data extraction capability transforms raw engagement into actionable insights. **Common Use Cases:** - **Influencer marketing analysis**: Identify which audience segments engage most with sponsored content and optimize partnership strategies - **Competitor benchmarking**: Analyze engagement patterns on competitor posts to understand what resonates with shared audiences - **Audience research**: Build detailed profiles of engaged users to refine targeting and content creation strategies ### How to scrape Instagram likes using this tool? **Step 1: Find the Instagram post URL** Navigate to the Instagram post you want to analyze and copy its URL from your browser (e.g., `https://www.instagram.com/metaindia/p/DP4EgyaD0GG/`). **Step 2: Configure the input parameters** Paste the post URL into the `postCode` field. The scraper will automatically extract all available likes data. Note: **1 post can contain thousands of likes**, and the tool will paginate through all results automatically. **Step 3: Run and download** Click "Start" to begin extraction. Once complete, download your data in JSON, CSV, or Excel format for immediate analysis. ### What are the input parameters? | Field | Type | Description | |-------|------|-------------| | `postCode` | String | Instagram post URL, post code, or media ID (e.g., `https://www.instagram.com/metaindia/p/DP4EgyaD0GG/` or just `DP4EgyaD0GG`) | ### What data can you extract? You can download the following data in JSON, CSV, or Excel formats: ```json { "like_id": "17851234567891234", "user_id": "4815162342", "username": "travel_enthusiast", "media_id": "2891234567891234", "like_timestamp": "1703174283", "is_verified": false, "profile_pic_url": "https://instagram.fcdg2-1.fna.fbcdn.net/v/t51.2885-19/123456789_123.jpg" } ```` **Extracted Fields:** - `like_id`: Unique identifier for the like interaction - `user_id`: Instagram user ID of the person who liked the post - `username`: Instagram handle of the user - `media_id`: Unique identifier of the Instagram post - `like_timestamp`: Unix timestamp indicating when the like occurred - `is_verified`: Boolean indicating if the user has a verified badge - `profile_pic_url`: Direct URL to the user's profile picture *** This Instagram scraper serves as a comprehensive Instagram likes scraper that lets you export Instagram likes data at scale. Whether you need an Instagram analytics API alternative or a reliable social media scraping tool, this cookieless solution helps you collect Instagram engagement data without technical barriers, making Instagram data extraction accessible for marketers, researchers, and analysts alike. # Actor input Schema ## `postCode` (type: `string`): Instagram post code, ID, or URL ## Actor input object example ```json { "postCode": "DVEGQskiMcy" } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("iron-crawler/instagram-likes").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 = {} # Run the Actor and wait for it to finish run = client.actor("iron-crawler/instagram-likes").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 '{}' | apify call iron-crawler/instagram-likes --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iron-crawler/instagram-likes", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Likes Scraper (no login needed)", "description": "Scrape Instagram likes at lightning speed. Zero-config API pipeline exports bulk engagement data in JSON/CSV for influencer analytics and brand strategy optimization.", "version": "1.0", "x-build-id": "scigbLAwd710s0xyu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iron-crawler~instagram-likes/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iron-crawler-instagram-likes", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/iron-crawler~instagram-likes/runs": { "post": { "operationId": "runs-sync-iron-crawler-instagram-likes", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/iron-crawler~instagram-likes/run-sync": { "post": { "operationId": "run-sync-iron-crawler-instagram-likes", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "postCode" ], "properties": { "postCode": { "title": "Post Code", "type": "string", "description": "Instagram post code, ID, or URL", "default": "DVEGQskiMcy" } } }, "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 } } } } } } } } } } ```