# Export public post comments and engagement

**Use case:** 

Turn a public post URL into structured text, author, media, engagement, and the first publicly available comments.

## Input

```json
{
  "postUrls": [
    "https://www.linkedin.com/posts/openai_a-note-to-it-admins-activity-7487581078262681600-vAk2"
  ]
}
```

## Output

```json
{
  "post_id": {
    "label": "Post id",
    "format": "string"
  },
  "post_urn": {
    "label": "Post urn",
    "format": "string"
  },
  "attributed_urn": {
    "label": "Attributed urn",
    "format": "string"
  },
  "post_url": {
    "label": "Post url",
    "format": "string"
  },
  "author": {
    "label": "Author",
    "format": "object"
  },
  "headline": {
    "label": "Headline",
    "format": "string"
  },
  "text": {
    "label": "Text",
    "format": "string"
  },
  "published_at": {
    "label": "Published at",
    "format": "string"
  },
  "content_type": {
    "label": "Content type",
    "format": "string"
  },
  "is_repost": {
    "label": "Is repost",
    "format": "boolean"
  },
  "original_author": {
    "label": "Original author",
    "format": "object"
  },
  "media": {
    "label": "Media",
    "format": "array"
  },
  "article": {
    "label": "Article",
    "format": "object"
  },
  "hashtags": {
    "label": "Hashtags",
    "format": "array"
  },
  "mentions": {
    "label": "Mentions",
    "format": "array"
  },
  "external_urls": {
    "label": "External urls",
    "format": "array"
  },
  "reaction_count": {
    "label": "Reaction count",
    "format": "integer"
  },
  "comment_count": {
    "label": "Comment count",
    "format": "integer"
  },
  "repost_count": {
    "label": "Repost count",
    "format": "integer"
  },
  "comments_returned": {
    "label": "Comments returned",
    "format": "integer"
  },
  "comments_truncated": {
    "label": "Comments truncated",
    "format": "boolean"
  },
  "comments": {
    "label": "Comments",
    "format": "array"
  },
  "scraped_at": {
    "label": "Scraped at",
    "format": "string"
  },
  "source": {
    "label": "Source",
    "format": "string"
  }
}
```

## About this Actor

This example demonstrates how to use [LinkedIn Post Scraper (No Cookies or Login Required)](https://apify.com/curly/linkedin-post-scraper) with a specific input configuration. Visit the [Actor detail page](https://apify.com/curly/linkedin-post-scraper) to learn more, explore other use cases, and run it yourself.


## 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.
This Task's input is already configured above — use it as-is rather than inventing a new one.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/curly/linkedin-post-scraper.md

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).