# Instagram Comments API: (`gabruck97/instagram-comments-api`) Actor Fast & anonymous Instagram Comments API. Extract comments with metadata from Posts & Reels without login. Export to JSON/CSV instantly! - **URL**: https://apify.com/gabruck97/instagram-comments-api.md - **Developed by:** [Frajola Inc](https://apify.com/gabruck97) (community) - **Categories:** Social media, Agents, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.80 / 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. 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/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 Comments Scraper (Apify Actor) — Fast & Anonymous GraphQL Scraper [![Apify Actor](https://img.shields.io/badge/Apify-Actor-orange?style=flat-square&logo=apify)](https://apify.com) [![Python 3.11](https://img.shields.io/badge/Python-3.11-blue?style=flat-square&logo=python)](https://python.org) [![Playwright](https://img.shields.io/badge/Playwright-Async-green?style=flat-square&logo=playwright)](https://playwright.dev) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT) The **Instagram Comments Scraper** is a high-speed, asynchronous **Apify Actor** designed to extract comments with metadata (user metadata, like counts...), from public Instagram **Posts** and **Reels** without requiring login credentials. It leverages headless **Playwright** for session token generation (`X-CSRFToken`, `LSD`, and `Cookies`) and **HTTPX Async** for rapid GraphQL pagination. --- ### 🔥 Key Features & SEO Benefits * **No Instagram Login Required:** Safely scrape Instagram comments without login, avoiding account bans or credential exposure. * **Asynchronous & Fast Data Extraction:** Powered by `asyncio` and `httpx` for high-throughput GraphQL comment extraction. * **Single-URL Scraping Precision:** Target specific Instagram Posts or Reels URLs effortlessly. * **Customizable Scraping Limits:** Set a comment limit (`maxComments`) or scrape **all** comments from any Instagram post. * **Complete Metadata Retrieval:** Extract user IDs, profile pictures, verification status, comment timestamps, like counts, and parent/child reply threads. * **Export Options:** Instantly export data to **CSV, JSON, XLSX, Excel, XML, or HTML** via the Apify Dataset interface. --- ### 💡 Use Cases * **Sentiment Analysis:** Analyze user sentiment, brand perception, and audience feedback on Instagram posts and Reels. * **Influencer Marketing & Campaign Tracking:** Track user engagement, comment density, and top fans on sponsored content. * **Social Listening & Market Research:** Monitor competitor posts, product launches, and trending topics across social media. * **Bot & Spam Detection:** Identify automated comment spam or network patterns using user IDs and creation timestamps. --- ### 📥 Input Schema (`input_schema.json`) Configure your scraping job using the parameters below: | Parameter | Type | Required | Default | Description | | :--- | :--- | :--- | :--- | :--- | | `url` | `String` | **Yes** | — | Direct public link to an Instagram Post or Reel. | | `maxComments` | `Integer` | No | `0` | Max number of comments to extract (`0` = scrape **all** comments). | #### Input Example (JSON) ```json { "url": "https://www.instagram.com/p/DadkE2wKA12/", "maxComments": 100 } ```` 📊 Output Schema (output\_schema.json) The dataset output is defined according to the Apify Dataset Schema specifications. Below is the full JSON schema definition used by the Actor to structure and display scraped comment data in the Apify Console. ````json { "title": "Instagram Comments Scraper Dataset", "type": "object", "schemaVersion": 1, "properties": { "Link_post": { "title": "Post URL", "type": "string", "description": "URL of the scraped Instagram Post or Reel." }, "Usuario": { "title": "Username", "type": "string", "description": "Username of the commenter." }, "ID_Usuario": { "title": "User ID", "type": "string", "description": "Permanent numeric ID of the user." }, "Foto_Perfil": { "title": "Profile Picture", "type": "string", "description": "URL to the user's profile picture." }, "Is_Verified": { "title": "Verified Badge", "type": "boolean", "description": "Indicates if the user has a verified badge." }, "ID_Comentario": { "title": "Comment ID", "type": "string", "description": "Unique numeric ID of the comment." }, "Data": { "title": "Date & Time", "type": "string", "description": "Formatted creation date (DD/MM/YYYY HH:MM:SS)." }, "Timestamp_Unix": { "title": "Unix Timestamp", "type": "integer", "description": "Creation time in raw Unix seconds." }, "Likes": { "title": "Likes Count", "type": "integer", "description": "Number of likes received by the comment." }, "Comentario": { "title": "Comment Text", "type": "string", "description": "Full text content of the comment." } } } # Actor input Schema ## `url` (type: `string`): URL do post ou reel público do Instagram para processar. ## `maxComments` (type: `integer`): Quantidade máxima de comentários a coletar. Use 0 para coletar todos os comentários disponíveis. ## Actor input object example ```json { "maxComments": 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("gabruck97/instagram-comments-api").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("gabruck97/instagram-comments-api").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 gabruck97/instagram-comments-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gabruck97/instagram-comments-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Comments API:", "description": "Fast & anonymous Instagram Comments API. Extract comments with metadata from Posts & Reels without login. Export to JSON/CSV instantly!", "version": "0.0", "x-build-id": "TWbtTzuQQDSJkyD6a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gabruck97~instagram-comments-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gabruck97-instagram-comments-api", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/gabruck97~instagram-comments-api/runs": { "post": { "operationId": "runs-sync-gabruck97-instagram-comments-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/gabruck97~instagram-comments-api/run-sync": { "post": { "operationId": "run-sync-gabruck97-instagram-comments-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "url" ], "properties": { "url": { "title": "Link do post/reel", "type": "string", "description": "URL do post ou reel público do Instagram para processar." }, "maxComments": { "title": "Máximo de comentários", "minimum": 0, "type": "integer", "description": "Quantidade máxima de comentários a coletar. Use 0 para coletar todos os comentários disponíveis.", "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 } } } } } } } } } } ```