LinkedIn Post Engagers Scraper

Extract the people engaging with public LinkedIn posts. Paste one or more LinkedIn post URLs and get one clean dataset row per engager: name, profile URL, their comment, and the post's reaction and comment stats. Turn on enrichment and every engager row also carries their current company, company URL, location, follower count, and an about preview — lead-list-ready without any LinkedIn login or cookies.

Designed for B2B outreach teams, recruiters, and account-based marketers who need prospecting lists from people who visibly interact with competitor, industry, or thought-leadership posts. Commenters are the highest-intent engagers on LinkedIn: they wrote something, which makes them warmer leads than passive likers. Pair it with LinkedIn Email Finder to turn the engagerProfileUrl values into verified outreach contacts.

When to use this Actor

• Build prospect lists from people commenting on competitor or industry posts
• Find decision-makers engaging with your target accounts' announcements
• Recruit candidates who participate in professional conversations
• Feed CRM or AI outreach workflows with engagement context — the comment text and post preview power personalization

Best fit & sibling routing

• For the posts themselves (full text, media, author stats), start with LinkedIn Posts Scraper, then pass its post URLs to this Actor for the people layer.
• For full comment threads with replies as the primary entity, use LinkedIn Post Comments & Engagements Scraper — this Actor is optimized for people-rows, while that one goes deep on thread structure.
• For reaction count summaries per post, use LinkedIn Post Reactions Scraper when post-level reaction metrics are the goal.
• Reactor (liker) identity lists sit outside this Actor's scope: public no-login data exposes commenters with identity and reactions as counts, so this Actor ships an honest commenters contract built entirely on public data.

Input

Field	Type	Default	Description
postUrls	array (required)	1 example URL	Up to 100 LinkedIn post/activity/pulse URLs per run
maxEngagersPerPost	integer	100	Cap engager rows saved per post (output + cost cap)
enrichProfiles	boolean	false	Add company, location, followers, about to each engager ($0.012/row extra)
providerOrder	enum	scrapecreators-first	Data provider priority; fallback fires automatically
maxConcurrency	integer	2	Posts processed in parallel (1–5)

Example input

{
    "postUrls": [
        "https://www.linkedin.com/feed/update/urn:li:activity:7468770468041502720/"
    ],
    "maxEngagersPerPost": 100,
    "enrichProfiles": true
}

Output

One dataset row per engager. Fields:

Field	Always present	Description
engagerName	✅	Public name of the engager
engagerProfileUrl	when exposed	Public LinkedIn profile URL
engagementType	✅	commenter
commentText	when exposed	Full comment text
commentPosition	✅	Position within the visible comment list
postUrl, postId	✅	Source post attribution
postAuthorName, postAuthorUrl, postPublishedAt	when exposed	Post author context
postReactionCount, postCommentCount	when exposed	Post-level engagement stats
postTextPreview	when exposed	First 280 chars of the post (outreach context)
enriched	✅	Whether profile enrichment succeeded
currentCompanyName, currentCompanyUrl, engagerLocation, followerCount, aboutPreview	enriched rows	Public profile enrichment fields
provider, scrapedAt	✅	Provenance and freshness

Example output row (real field shape from a live run)

{
    "engagerName": "Bhaskar Gangipamula",
    "engagerProfileUrl": "https://www.linkedin.com/in/bhaskargangi",
    "engagementType": "commenter",
    "commentText": "Looking at AI adoption county by county is where the real story is...",
    "commentPosition": 1,
    "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7468770468041502720/",
    "postId": "7468770468041502720",
    "postAuthorName": "Microsoft",
    "postReactionCount": 379,
    "postCommentCount": 25,
    "postTextPreview": "Our newest AI Diffusion Report is out...",
    "enriched": true,
    "currentCompanyName": "Quadrant Technologies",
    "currentCompanyUrl": "https://www.linkedin.com/company/quadranttechnologies-1",
    "engagerLocation": "Greater Seattle Area",
    "followerCount": 14662,
    "aboutPreview": "Currently building Quadrant Technologies, a globally recognized IT services company...",
    "provider": "scrapecreators",
    "enrichmentProvider": "scrapecreators",
    "scrapedAt": "2026-08-17T00:00:00.000Z"
}

Every terminal run also writes OUTPUT and RUN_SUMMARY records with outcome (COMPLETE / PARTIAL / VALID_EMPTY / INVALID_INPUT / UPSTREAM_FAILED / CONFIG_ERROR), per-post results, provider diagnostics, and billing counters.

Workflow story: from a competitor's post to a prospect list

A growth lead at a BI vendor starts with a competitor's launch post URL. She pastes it into this Actor with enrichProfiles: true. The run starts, each post URL is fetched through the provider chain, and one row per commenter lands in the dataset. She then filters rows by currentCompanyName against her 50-account target list — eight commenters work at three target accounts. Next she exports the dataset to Sheets, then passes the warmest engagerProfileUrl values to LinkedIn Email Finder to verify contact details, and feeds the final list into her sequencer. Total elapsed time: under five minutes, total event cost under $0.20.

Pricing — Pay per event + platform usage

Event	Price	Charged when
Actor start	$0.00005	Once per run (scaled by memory)
engager-found	$0.006	One engager row saved to the dataset
engager-enriched	$0.012	Additional, only when a row was successfully enriched

That is $6 per 1,000 engager rows, or $18 per 1,000 rows when enriched. A typical single post with ~9 visible commenters costs about $0.054 (unenriched) or $0.16 (enriched); a 10-post enriched batch tops out near $1.60 for 100 engager rows. Your run log prints the exact cost cap before any charge fires, and only saved rows are billed. Platform usage (compute/proxy) is billed on top at Apify's rates — the live Pricing tab is the current source of truth for effective prices on your plan.

API example

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~linkedin-post-engagers-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "postUrls": ["https://www.linkedin.com/feed/update/urn:li:activity:7468770468041502720/"],
    "maxEngagersPerPost": 100,
    "enrichProfiles": true
  }'

Results land in the run's default dataset: https://api.apify.com/v2/datasets/{datasetId}/items?clean=true. Paginate large batches with offset/limit on that endpoint.

AI-agent / MCP usage

This Actor is MCP-ready: one tool call with postUrls returns structured engager rows.

"Given these LinkedIn post URLs, list everyone who commented, with their profile URL, current company, and location, so I can build an outreach list."

Agents should expect: dataset rows via get-actor-output with clean pagination; outcome/RUN_SUMMARY readback for scope and provenance (provider, scrapedAt on every row); unavailable or deleted posts reported per-URL in diagnostics; empty comment sections as a clean VALID_EMPTY outcome with zero fabricated rows; and a printed cost cap before any charge fires.

Best results guidance

• Pick posts with 20–200 comments: enough signal, still fully visible to public data.
• For outreach lists, run with enrichProfiles: true and filter rows by currentCompanyName against your target-account list.
• Deduplicate across runs by engagerProfileUrl; a person engaging with several of your posts is your warmest lead.
• Batch up to 100 post URLs per run; keep maxConcurrency at 2 unless you raise it after watching provider pacing in the log.

Scope, data boundary & recovery

• Commenters are the engager type with identity in public data. Reactions are returned as post-level counts; reactor identities sit behind LinkedIn login and stay outside this Actor's public-data scope.
• Top visible comments per post. The data provider returns roughly the top 10 visible commenters per post; busier posts are capped at what public data exposes (flagged per post in the run summary).
• Public-page enrichment only. Company, location, followers, and about come from public profile pages; emails and private network data stay outside the contract.
• Per-URL resilience. Provider hiccups are retried with backoff, then routed to the second provider; a run with at least one working URL finishes as PARTIAL with diagnostics, a run where every URL is unavailable ends UPSTREAM_FAILED, and only saved rows are billed.
• Input validation. Missing or non-post URLs exit fast as INVALID_INPUT with an actionable message.

Evidence & freshness

• Field shape verified against live provider payloads for real posts during the August 2026 build: commenter rows with full identity, post reaction counts, and enrichment fields (current company, location, follower count, about) all confirmed on real profiles.
• Competitor positioning checked against the live Apify Store API during my 2026 niche research: the engagers niche is one of the highest-demand LinkedIn categories on the Store, dominated by raw unenriched row dumps — this Actor differentiates on enriched, high-intent commenter leads and an honest public-data-only scope.
• Every row carries provider and scrapedAt; release acceptance evidence for the shipped build is recorded in the actor's private build audit before publication.

Builder's note

I built this after probing both ScrapeCreators and SociaVault live against real posts: I found both expose commenter identities and reaction counts, while reactor identities stay behind login — so I shipped an honest commenters contract with dual-provider fallback. In my testing, raw commenter rows (name + URL) answered "who", but outreach teams kept asking "where do they work" — that is why enrichment (company, location, followers) is a first-class option rather than an afterthought.

Integrations

Rows are plain JSON — pipe the dataset into Google Sheets, Airtable, a CRM, or an email-finder step. Natural chain: this Actor, then pass engagerProfileUrl values to LinkedIn Email Finder (profile URL → verified email) to complete the contact step, and monitor new engagement by scheduling re-runs of the same post URLs weekly.

Legal

This Actor retrieves publicly available data. You are responsible for complying with LinkedIn's Terms of Service, applicable data-protection laws (GDPR/CCPA), and Apify's Exclusion Policy when using the results.