# Instagram User Posts Scraper (No Login Required) (`iron-crawler/instagram-posts`) Actor Bulk extract Instagram post data at lightning speed. Zero-config API pipeline exports engagement metrics in JSON/CSV for instant social media performance analysis. - **URL**: https://apify.com/iron-crawler/instagram-posts.md - **Developed by:** [Iron Crawler](https://apify.com/iron-crawler) (community) - **Categories:** Social media, Automation, Developer tools - **Stats:** 10 total users, 0 monthly users, 100.0% runs succeeded, 0 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 Instagram User Posts Scraper (No Login Required) do? This tool extracts comprehensive post data from any public Instagram profile without requiring authentication or login credentials. Built with a cookieless architecture, it bypasses the need for Instagram accounts, session management, or API tokens, making it accessible to anyone who needs Instagram data quickly and reliably. **Key Features:** - Scrape all posts from any public Instagram profile - Extract engagement metrics (likes, comments) for performance analysis - Download media URLs for content archiving - Export data in JSON, CSV, or Excel formats - Filter posts by date range or media type - No login required: completely cookieless operation - Batch process multiple profiles simultaneously ### Why scrape Instagram user posts? Social media influencers need to extract their own Instagram posts' engagement metrics and content history to analyze their performance and optimize future content strategies. Understanding what resonates with your audience is critical for growth, sponsorship opportunities, and maintaining competitive advantage in the creator economy. **Primary Use Cases:** - **Content Performance Analysis:** Track which posts generate the most engagement, identify optimal posting times, and understand audience preferences to refine your content strategy. - **Competitive Intelligence:** Monitor competitor profiles to benchmark your performance, discover trending content formats, and identify gaps in your niche. - **Portfolio Building:** Create comprehensive archives of your content history for media kits, brand partnerships, or personal records before posts are deleted or accounts change. ### How to scrape Instagram using this tool? **Step 1: Find the Profile** Locate the Instagram username, user ID, or profile URL you want to scrape. For example, if you want to scrape Cristiano Ronaldo's posts, you would use the username `cristiano` or the full URL `https://www.instagram.com/cristiano/`. **Step 2: Configure Input Parameters** Enter the username in the input field. You can optionally set limits on the number of posts to scrape (**1 page ≈ 50 items**) or filter by date range to focus on recent content only. **Step 3: Run and Download** Start the scraper and wait for it to complete. Once finished, download your data in your preferred format (JSON, CSV, or Excel) for analysis in spreadsheets, databases, or analytics tools. ### What are the input parameters? | Field | Type | Description | |-------|------|-------------| | `username` | String | The Instagram username (e.g., `cristiano`) or full profile URL. This is the primary identifier for the profile you want to scrape. | | `maxPosts` | Integer (Optional) | Maximum number of posts to extract. Leave empty to scrape all available posts from the profile. | | `dateFrom` | Date (Optional) | Only scrape posts published after this date (format: YYYY-MM-DD). | | `dateTo` | Date (Optional) | Only scrape posts published before this date (format: YYYY-MM-DD). | ### What data can you extract? You can download the following data in JSON, CSV, or Excel formats: ```json { ""post_id"": ""3179846502341567"", ""user_id"": ""8275649301"", ""caption"": ""Summer vibes 🌊 #beachday #sunset"", ""media_type"": ""carousel"", ""like_count"": 1247, ""comment_count"": 83, ""media_url"": ""https://scontent.cdninstagram.com/v/t51.2885-15/e35/123456789_1.jpg"", ""timestamp"": ""2025-12-18T14:30:22+0000"" } ```` **Available Data Fields:** - `post_id`: Unique identifier for each Instagram post - `user_id`: The profile's unique Instagram user ID - `caption`: Full text caption including hashtags and mentions - `media_type`: Content format (photo, video, carousel, reel) - `like_count`: Total number of likes on the post - `comment_count`: Total number of comments - `media_url`: Direct URL to the image or video file - `timestamp`: Publication date and time in ISO 8601 format *** Whether you need an Instagram scraper for lead generation, a social media scraping tool for market research, or an Instagram data extractor to export Instagram posts for performance tracking, this Instagram user posts scraper delivers reliable results without authentication barriers." # 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("iron-crawler/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("iron-crawler/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 iron-crawler/instagram-posts --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iron-crawler/instagram-posts", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram User Posts Scraper (No Login Required)", "description": "Bulk extract Instagram post data at lightning speed. Zero-config API pipeline exports engagement metrics in JSON/CSV for instant social media performance analysis.", "version": "1.0", "x-build-id": "Ludd61MephMnHzM9z" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iron-crawler~instagram-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iron-crawler-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/iron-crawler~instagram-posts/runs": { "post": { "operationId": "runs-sync-iron-crawler-instagram-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/iron-crawler~instagram-posts/run-sync": { "post": { "operationId": "run-sync-iron-crawler-instagram-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```