Pricing

$19.99/month + usage

LinkedIn Post, Comments and Engagements Scraper

Extract detailed engagement data from LinkedIn posts using the LinkedIn Post, Comments and Engagements Scraper. Collect post content, likes, comments, reactions, timestamps, and author details automatically. Ideal for social media research and content performance analysis.

Pricing

$19.99/month + usage

Rating

0.0

(0)

Developer

API Empire

Actor stats

Bookmarked

Total users

Monthly active users

3 days ago

Last modified

Categories

Social media

Automation

Jobs

You can access the LinkedIn Post, Comments and Engagements Scraper programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

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        "required": [
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        "properties": {
          "startUrls": {
            "title": "🔗 Post URLs or IDs (required)",
            "type": "array",
            "description": "📎 Enter one or more LinkedIn post URLs or numeric activity IDs. You can paste full URLs (e.g. https://www.linkedin.com/feed/update/urn:li:activity:123456789/) or just the activity ID (e.g. 123456789). Add multiple entries for bulk scraping — the actor processes each post and saves comments to the dataset. One URL or ID per line. 🚀",
            "items": {
              "type": "string"
            }
          },
          "liAt": {
            "title": "🍪 LinkedIn session cookie (li_at)",
            "type": "string",
            "description": "🔐 Optional. If you want to scrape as a logged-in user, paste your li_at cookie from LinkedIn (get it from your browser dev tools while logged in). Leave this empty to run without login — the actor will still attempt to fetch public comment data. Using a cookie can help with rate limits or access on some posts."
          },
          "pageNumber": {
            "title": "📄 Page Number",
            "minimum": 1,
            "type": "integer",
            "description": "📃 The page number for comment pagination (1-based). Use this if you want to start from a specific page of results. Default is 1 (first page).",
            "default": 1
          },
          "sortOrder": {
            "title": "🔀 Sort order",
            "enum": [
              "REVERSE_CHRONOLOGICAL",
              "RELEVANCE"
            ],
            "type": "string",
            "description": "📊 Choose how comments are sorted for each post. Most recent (default) returns newest comments first (REVERSE_CHRONOLOGICAL). Most relevant returns LinkedIn’s relevance order (RELEVANCE).",
            "default": "REVERSE_CHRONOLOGICAL"
          },
          "resultLimitPerPost": {
            "title": "🎯 Result limit per post",
            "minimum": 1,
            "maximum": 500,
            "type": "integer",
            "description": "📈 Maximum number of comments to fetch per post (1–500). Default is 100. Increase this to get more comments per post; lower it for faster runs or when you only need a sample.",
            "default": 100
          },
          "proxyConfiguration": {
            "title": "🌐 Proxy Configuration",
            "type": "object",
            "description": "🔒 Optional. By default the actor sends requests directly to LinkedIn (no proxy). If LinkedIn blocks or rate-limits, the actor automatically falls back to Apify datacenter proxy, then to residential proxy with up to 3 retries, and sticks with residential for the rest of the run. You can also configure a proxy here to use from the start (e.g. Apify proxy with a specific group). All proxy changes are logged in the run log. 🛡️"
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                "format": "date-time",
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                "type": "string",
                "example": "READY"
              },
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                "type": "object",
                "properties": {
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                    "type": "string",
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                  },
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                    "type": "string"
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                    "type": "integer",
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                    "type": "integer",
                    "example": 0
                  },
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                    "type": "integer",
                    "example": 0
                  },
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                  },
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}

LinkedIn Post, Comments and Engagements Scraper OpenAPI definition

OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.

OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.

By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.

You can download the OpenAPI definitions for LinkedIn Post, Comments and Engagements Scraper from the options below:

OpenAPI.json

If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.

You can also check out our other API clients:

LinkedIn Post, Comments and Engagements Scraper API in Python

LinkedIn Post, Comments and Engagements Scraper API in JavaScript

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