Use LinkedIn Post Comment & Reply Thread Extractor with Python

Pull top-level comments from LinkedIn posts, with optional nested reply threads, author identity, reaction counts, and linked profiles found in the comment text.

1. Choose the data you need

Review the published README for supported collection modes, examples, and limits. The recipe below uses the current published input definition.

2. Prepare a source-based input

This JSON contains published prefills and required-field defaults. Review every value for your task before running it; it is not a promise of successful collection.

The published schema does not supply an example for these required fields: cookie. Supply them using the reference documentation before execution.

{
  "excludeAuthorTypes": [],
  "includeReplies": false,
  "language": "en",
  "maxCommentsPerPost": 50,
  "minReactions": 0,
  "postUrls": [
    "https://www.linkedin.com/feed/update/urn:li:activity:7329761432933134337/"
  ],
  "replyDepth": 1,
  "sortBy": "RELEVANCE"
}

Download input.example.json and save your reviewed copy as input.json.

Field	Type	Required	Description
`postUrls`	array	required	LinkedIn post URLs to scrape comments from. Accepts formats like: https://www.linkedin.com/feed/update/urn:li:activity:7329761432933134337/ or https://www.linkedin.com/posts/username_some-slug-7329761432933134337/. Use a public, high-engagement post URL for best results. The prefill example uses a Bill Gates post from early 2025.
`cookie`	string	required	Your LinkedIn session cookie. Accepts either: (1) the li_at value from browser DevTools → Application → Cookies, or (2) full cookies JSON array exported from an extension like EditThisCookie.
`maxCommentsPerPost`	integer	optional	Maximum number of top-level comments to scrape per post.
`includeReplies`	boolean	optional	Whether to fetch replies to top-level comments. Each reply is output as a separate dataset row with isReply=true and parentCommentId linking to the parent.
`replyDepth`	integer	optional	How many levels deep to recurse when fetching replies. Only honored when Include Replies is enabled. 1 = direct replies only, 2 = replies of replies, 3 = three levels.
`sortBy`	string	optional	Sort order for comments. RELEVANCE uses LinkedIn's default 'Most relevant'. RECENT shows newest first.
`minReactions`	integer	optional	Skip comments with fewer than this many reactions (likes + other reaction types). 0 disables the filter and emits every comment.
`language`	string	optional	Preferred LinkedIn UI language. Sets the Accept-Language header and x-li-lang for Voyager API calls; influences which translations LinkedIn returns for comment text.
`excludeAuthorTypes`	array	optional	Drop comments authored by the selected actor types. PERSON = individual member profiles, COMPANY = company / organization pages.
`proxyConfiguration`	object	optional	Optional Apify proxy configuration. Residential proxy recommended for best results.

3. Start an Actor run

Set APIFY_TOKEN in your environment. Python 3.10 or newer is sufficient; this recipe uses no third-party packages.

Execution may incur charges. Review current pricing on Apify. The request asks for a 120-second timeout and a charge limit; these settings are subject to Apify’s pricing and do not guarantee an all-inclusive spending cap.

import json
import os
import urllib.request

# Save the schema-derived example as input.json; review and edit it first.
with open("input.json", encoding="utf-8") as stream:
    actor_input = json.load(stream)

token = os.environ["APIFY_TOKEN"]
request = urllib.request.Request(
    "https://api.apify.com/v2/acts/9IrDMYd0zWDgF1HtA/runs?timeout=120&maxTotalChargeUsd=0.50",
    data=json.dumps(actor_input).encode("utf-8"),
    headers={"Authorization": "Bearer " + token, "Content-Type": "application/json"},
    method="POST",
)
# This starts a potentially billable run. Do not automatically retry this POST.
with urllib.request.urlopen(request, timeout=40) as response:
    run = json.load(response)["data"]
print("Run ID:", run["id"])
print("Status:", run["status"])
# Inspect and download results in Apify Console once the run succeeds.

4. Inspect the actual results

Open the run in Apify Console and wait for a successful completion. Use the published output schema to locate results and the dataset schema to understand declared views. Missing or optional record fields must not be treated as guaranteed values.

LinkedIn post comments and reply threads with author identity, reaction counts, and linked profiles.

Questions before you run

Which inputs are required?

The published required fields are postUrls, cookie

What does this guide verify?

The fields, prefills, and schema links are rendered from the published build. This is a usage recipe, not a paid live extraction test. Source availability and collection results can vary.

What if the run request times out?

Check Apify Console before retrying. The first request may already have created a run. Do not automatically repeat a run-creation POST.

Open on Apify Full Actor reference

Generated from source build CBKbM1dBIO5OKW2ZN and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.