# Scrape Instagram User Posts by Username - No Login - Cookieless (`patient_discovery/instagram-posts`) Actor Extract Instagram posts from any public profile by username with this cookieless scraper. Get likes, comments, captions, hashtags, tagged users, media URLs, and engagement metrics in JSON or CSV. Ideal for competitor analysis, influencer research, and Instagram content performance tracking. - **URL**: https://apify.com/patient\_discovery/instagram-posts.md - **Developed by:** [Surge Street](https://apify.com/patient_discovery) (community) - **Categories:** Social media, Other - **Stats:** 114 total users, 17 monthly users, 100.0% runs succeeded, 1 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 User Posts Scraper - No Login Required** ===================================================== **What does this scraper do?** ------------------------------ This actor extracts Instagram posts from any public user profile. Enter a username, run the actor, and get structured post data including engagement metrics, captions, media URLs, and tagged users. No Instagram login, no cookies, no session handling required. Cookieless design ensures easy, risk-free, and scalable automation. **Why scrape Instagram user posts?** ------------------------------------ Instagram posts contain valuable engagement and content signals. This helps creators, agencies, and analysts to: * Track post performance over time * Analyze likes, comments, and shares * Study caption strategies, hashtags, and mentions * Benchmark competitors * Discover collaborators and tagged users * Identify high-performing content formats * Build datasets for content intelligence dashboards Its cookieless design fits scalable data pipelines without needing Instagram accounts. **How much will scraping cost?** -------------------------------- The pricing for this actor is **$2.50 per 1,000 scraped results**. Refer to the pricing page. Because this actor does not require login or session management, it reduces operational complexity and lowers the risk associated with account-based scraping. This predictable architecture keeps your data pipelines highly stable. **How to use the scraper** -------------------------- Here is a **step-by-step guide**: **Step 1: Open the actor:** Go to your Apify Console and open the scraper. **Step 2: Enter your input parameters:** In the input field, enter the Instagram username, user ID, or profile URL (e.g., "cristiano", "whoop"). **Step 3: Start the run:** Click Start to begin scraping. The actor will automatically fetch posts from the specified public profile. **Step 4: Export or integrate:** Once complete, download the dataset in JSON, CSV, or connect it via API to your analytics or automation system. You can sort posts by metrics like `like_count` or `share_count` to identify high-performing content. **Input parameters** -------------------- Below are the configuration options you can use to control the scraper. **Input example** ```json { "username": "whoop" } ```` | Field | Type | Description | |-------|------|-------------| | username | String | Instagram username, user ID, or profile URL of the target public account | ## **What data does this scraper extract?** **Formats**: JSON, CSV, Excel **Key Fields Extracted**: - `id` - Unique post identifier - `code` - Instagram shortcode for the post - `taken_at` - Unix timestamp when the post was created - `taken_at_date` - ISO 8601 formatted date - `like_count` - Number of likes - `comment_count` - Number of comments - `share_count` - Number of shares - `caption.text` - Full caption text - `caption.hashtags` - Array of hashtags used - `caption.mentions` - Array of mentioned users - `user.username` - Profile username - `user.full_name` - Profile display name - `user.is_verified` - Verification status - `carousel_media` - Array of images or videos in carousel posts - `image_versions.items` - URLs for post images at different resolutions - `tagged_users` - Array of users tagged in the post - `coauthor_producers` - Collaborators on the post - `is_video` - Boolean indicating video content - `thumbnail_url` - Post thumbnail URL All data is returned as structured JSON with null-safe fields for reliable downstream processing. ### Sample Output ```json { "id": "3786151211327352732", "code": "DSLHHeBFO-c", "taken_at": 1765564421, "taken_at_date": "2025-12-12T18:33:41+00:00", "like_count": 502341, "comment_count": 6294, "share_count": 3928, "caption": { "text": "Wherever you play, these bands keep you ready for every moment. Are you home or away? 👀👇", "hashtags": [], "mentions": [] }, "user": { "username": "whoop", "full_name": "WHOOP", "id": "1431248158", "is_verified": true, "is_private": false }, "carousel_media_count": 2, "is_video": false, "media_type": 8, "tagged_users": [ { "user": { "username": "cristiano", "full_name": "Cristiano Ronaldo", "is_verified": true } } ], "thumbnail_url": "https://scontent-vie1-1.cdninstagram.com/..." } ``` All data is delivered in structured JSON format suitable for content analysis, influencer research, and Instagram post performance tracking. ## **Key Features:** - 📈 Capture engagement metrics including likes, comments, and shares - 📊 Extract captions with hashtags and mentions - ⚡ Structured JSON output ready for analytics and automation - 📈 Identify tagged users and collaborators - 📊 Retrieve carousel media and image URLs - ⚡ Access post thumbnails and video indicators - 🔒 Fully cookieless architecture with no login required ## **FAQs** **Does this scraper require Instagram login?** No. It is fully cookieless and does not require login credentials. **Can it scrape private accounts?** No. Only publicly accessible profiles can be scraped. **Can I scrape multiple profiles?** Yes. Run separate executions for each username. **Other Instagram scrapers that you may find useful:** [Instagram Followers Scraper](https://apify.com/patient_discovery/instagram-followers-scraper---no-login) [Instagram User Info Scraper](https://apify.com/patient_discovery/instagram-user-info-scraper-cookieless) [Instagram Comments Scraper](https://apify.com/patient_discovery/instagram-comments-scraper-no-login-required) [Instagram likes scraper](https://apify.com/patient_discovery/instagram-likes) [Instagram search hashtags](https://apify.com/patient_discovery/instagram-hashtags-scraper-no-login-required) [Instagram location posts](https://apify.com/patient_discovery/instagram-location-posts) [Instagram following](https://apify.com/patient_discovery/instagram-following) [Instagram user posts](https://apify.com/patient_discovery/instagram-posts) [Instagram search users](https://apify.com/patient_discovery/instagram-search-users) [Instagram search reels](https://apify.com/patient_discovery/instagram-search-reels) # Actor input Schema ## `username` (type: `string`): Instagram username, user ID, or profile URL ## `maxPages` (type: `integer`): Maximum number of pages to fetch (pagination handled automatically) ## Actor input object example ```json { "username": "cristiano", "maxPages": 1 } ``` # 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("patient_discovery/instagram-posts").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("patient_discovery/instagram-posts").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 patient_discovery/instagram-posts --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=patient_discovery/instagram-posts", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Scrape Instagram User Posts by Username - No Login - Cookieless", "description": "Extract Instagram posts from any public profile by username with this cookieless scraper. Get likes, comments, captions, hashtags, tagged users, media URLs, and engagement metrics in JSON or CSV. Ideal for competitor analysis, influencer research, and Instagram content performance tracking.", "version": "1.0", "x-build-id": "yGBplgUIYRrmSkHvs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/patient_discovery~instagram-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-patient_discovery-instagram-posts", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/patient_discovery~instagram-posts/runs": { "post": { "operationId": "runs-sync-patient_discovery-instagram-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/patient_discovery~instagram-posts/run-sync": { "post": { "operationId": "run-sync-patient_discovery-instagram-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Username", "type": "string", "description": "Instagram username, user ID, or profile URL", "default": "cristiano" }, "maxPages": { "title": "Maximum Pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of pages to fetch (pagination handled automatically)", "default": 1 } } }, "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 } } } } } } } } } } ```