# Loom Comments Scraper (`neatrat/loom-comments-scraper`) Actor Extract comments and replies from Loom videos using just a URL or video ID. - **URL**: https://apify.com/neatrat/loom-comments-scraper.md - **Developed by:** [Neatrat](https://apify.com/neatrat) (community) - **Categories:** Videos, Developer tools, Automation - **Stats:** 8 total users, 0 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing $0.50 / 1,000 comments 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 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 ## Loom Comment Scraper ### Introduction Need to extract comments from Loom videos without manual copying? The Loom Comment Scraper is your solution for automatically retrieving and processing comments from any Loom video. Whether you need to analyze feedback, track discussions, or archive important information from video comments, this tool extracts complete comment threads with just a URL or video ID. ### Features - **Simple Input Options**: Extract comments using either a Loom video URL or the video ID - **Multiple Output Formats**: Choose from two different comment formats: - Default: Well-structured comments with user details and replies - Raw: Complete JSON response with all metadata - **Automatic URL Parsing**: Handles various Loom URL formats, including share and embed links - **Clean Data Structure**: Well-organized output with video ID, URL, and comment content - **Error Handling**: Clear error messages for invalid inputs or unavailable comments - **Apify Integration**: Seamlessly runs on the Apify platform with structured outputs ### Use Cases - **Feedback Collection**: Extract user comments and feedback from Loom videos - **Discussion Analysis**: Analyze conversations happening in comment threads - **Content Moderation**: Monitor comments for inappropriate content - **Community Engagement**: Track user interactions and engagement - **Data Processing**: Feed video comments into NLP pipelines for sentiment analysis - **Archiving**: Preserve comment discussions from important video communications - **Batch Processing**: Extract comments from multiple videos in an automated workflow ### How to Use Getting started with Loom Comment Scraper is simple: 1. **Set Your Parameters**: - **URL** or **Video ID**: Provide either the full Loom video URL (like https://www.loom.com/share/473fad25ebd24b5ea8091503253dfecf) or just the video ID (473fad25ebd24b5ea8091503253dfecf) - **Return Type**: Choose how you want the comments formatted: - Default: Structured comment data with user information and replies (default) - Raw: Complete JSON response with all metadata 2. **Run the Scraper**: Launch the actor and let it extract the comment data 3. **Access Results**: View and download the structured comment data from the Apify dataset ### Input Format You can configure the scraper with the following input parameters: ```json { "url": "https://www.loom.com/share/473fad25ebd24b5ea8091503253dfecf", "videoId": "", "returnType": "default" } ```` Or alternatively: ```json { "url": "", "videoId": "473fad25ebd24b5ea8091503253dfecf", "returnType": "raw" } ``` #### Input Parameters | Parameter | Type | Description | |-----------|------|-------------| | url | String | The complete URL of the Loom video (supports share and embed URLs) | | videoId | String | The Loom video ID (alternative to providing the URL) | | returnType | String | Format of the returned comments: "default" or "raw" | **Note**: You must provide either a URL or videoId. If both are provided, videoId takes precedence. ### Output Format The scraper generates structured data with the following information: ```json { "videoId": "473fad25ebd24b5ea8091503253dfecf", "url": "https://www.loom.com/share/473fad25ebd24b5ea8091503253dfecf", "comments": [ { "user": "John Doe", "userId": "user_1234", "content": "Great video! I have a question about...", "date": "2023-08-15T14:25:33Z", "childComments": [ { "user": "Jane Smith", "userId": "user_5678", "content": "I can answer that - you'll want to...", "date": "2023-08-15T14:30:15Z" } ] }, { "user": "Alex Johnson", "userId": "user_9012", "content": "Thanks for sharing this information!", "date": "2023-08-15T15:10:22Z", "childComments": [] } ] } ``` When using the "raw" return type, the comments field will contain the complete comment data objects as returned by the Loom API. ### Limitations - Private Loom videos may not be accessible - Videos with no comments will return an empty array ### Troubleshooting If you encounter any issues: - Verify the Loom video URL or ID is correct - Check that the video is publicly accessible - Ensure the video has comments enabled # Actor input Schema ## `url` (type: `string`): The URL of the loom video you want to get the comments from. ## `videoId` (type: `string`): The ID of the loom video you want to get the comments from. ## `returnType` (type: `string`): Format of the returned comments data ## Actor input object example ```json { "url": "https://www.loom.com/share/57e37a60bce44aa0a7e974e37d2172f1", "videoId": "57e37a60bce44aa0a7e974e37d2172f1", "returnType": "default" } ``` # 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 = { "url": "https://www.loom.com/share/57e37a60bce44aa0a7e974e37d2172f1", "videoId": "57e37a60bce44aa0a7e974e37d2172f1" }; // Run the Actor and wait for it to finish const run = await client.actor("neatrat/loom-comments-scraper").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 = { "url": "https://www.loom.com/share/57e37a60bce44aa0a7e974e37d2172f1", "videoId": "57e37a60bce44aa0a7e974e37d2172f1", } # Run the Actor and wait for it to finish run = client.actor("neatrat/loom-comments-scraper").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 '{ "url": "https://www.loom.com/share/57e37a60bce44aa0a7e974e37d2172f1", "videoId": "57e37a60bce44aa0a7e974e37d2172f1" }' | apify call neatrat/loom-comments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=neatrat/loom-comments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Loom Comments Scraper", "description": "Extract comments and replies from Loom videos using just a URL or video ID.", "version": "0.0", "x-build-id": "RspTSrQd73hJAT5or" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/neatrat~loom-comments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-neatrat-loom-comments-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/neatrat~loom-comments-scraper/runs": { "post": { "operationId": "runs-sync-neatrat-loom-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/neatrat~loom-comments-scraper/run-sync": { "post": { "operationId": "run-sync-neatrat-loom-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "url": { "title": "URL of the page", "type": "string", "description": "The URL of the loom video you want to get the comments from." }, "videoId": { "title": "Video ID", "type": "string", "description": "The ID of the loom video you want to get the comments from." }, "returnType": { "title": "Return Type", "enum": [ "default", "raw" ], "type": "string", "description": "Format of the returned comments data", "default": "default" } } }, "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 } } } } } } } } } } ```