LinkedIn Profile Scraper (No Cookie Premium)

LinkedIn profile intelligence without login cookies: skills normalization, seniority classification, company extraction, and MCP-ready providerHealth metadata. Desktop+mobile fallback chain. Optional Clearbit/Apollo enrichment.

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Why This Actor Is Better

Competitor comparison

Feature	This Actor	Apify LI Scraper (top)	PhantomBuster	Lusha/Apollo
No login cookies required	✅	❌	❌	n/a
Desktop+mobile fallback	✅	❌	❌	n/a
Skills taxonomy normalization	✅ 8 categories	❌	❌	partial
Seniority classification	✅ c_level/vp/director/manager/senior/entry	❌	❌	partial
Company extraction from headline	✅	partial	partial	n/a
Email extraction from profile	✅	❌	❌	✅ paid
Optional Clearbit firmographics	✅ user key	❌	❌	✅ paid
Optional Apollo contact enrichment	✅ user key	❌	❌	✅ paid
Confidence score (0-1)	✅	❌	❌	❌
MCP-ready metadata	✅ providerHealth	❌	❌	❌
Price / 1K profiles	$2.21	~$3.00	~$5-10	~$10-30

Key Features

• 🛡️ Multi-API fallback chain — LinkedIn desktop (Playwright) primary with automatic mobile HTML fallback.
• 🎯 Skills normalization — maps free-text headlines to 8 taxonomy categories (engineering, data, design, product, marketing, sales, finance, leadership).
• 📈 Seniority classification — c_level / vp / director / manager / senior / entry / individual.
• 🏢 Company extraction — regex-based company parsing from headline ("Senior Engineer at Acme").
• ✉️ Email extraction — from profile page text (no paid API required).
• 🎯 Confidence scoring — 0.0–1.0 reliability score.
• 🔗 Source attribution — know which providers contributed each field.
• ⚡ Cache-first mode — fast_lookup hits KVS cache (2h TTL — profiles change slowly).
• 🤖 MCP-ready — providerHealth{} on every result.
• 🔌 Optional paid enrichment — drop in CLEARBIT_API_KEY / APOLLO_API_KEY for firmographics and verified contacts. Disabled by default.
• 💰 Transparent PPE pricing — pay only for successful profiles.

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Architecture

flowchart TD
    A[Input: profile URLs + mode] --> B{Cache hit?}
    B -- yes --> C[Return cached base profile]
    B -- no --> D[Primary: LinkedIn desktop Playwright]
    D -- fails --> E[Fallback: LinkedIn mobile HTML httpx]
    D --> F[Normalize: name/headline/company/location]
    E --> F
    F --> G[Enrichment layer]
    G --> G1[Skills normalization 8 categories]
    G --> G2[Seniority classification]
    G --> G3[Company extraction from headline]
    G --> G4[Email extraction]
    G --> G5[Optional: Clearbit company firmographics]
    G --> G6[Optional: Apollo contact enrichment]
    G1 --> H[Confidence scoring + source attribution]
    G2 --> H
    G3 --> H
    G4 --> H
    G5 --> H
    G6 --> H
    H --> I[Progressive dataset push]
    I --> J[Webhook + MCP-ready metadata]
    C --> J

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Modes

Mode	Description	Target latency	Use case
fast_lookup	Cache-first, base profile only	<800ms cached	Quick lookups, dedup
enrich	Skills + seniority + company + optional paid enrichment	~3-6s/profile	Sales intelligence, recruiting
batch	Queue-based, full enrichment, per-item isolation	varies	Large URL lists (100+)

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Input

Parameter	Type	Required	Default	Description
mode	string	—	enrich	fast_lookup | enrich | batch
queries	array	✅	["https://linkedin.com/in/johndoe"]	LinkedIn profile URLs
maxResults	integer	—	25	Max profiles per query (1–1000)
webhookUrl	string	—	—	Webhook for completion notification

Example input

{
  "mode": "enrich",
  "queries": ["https://www.linkedin.com/in/johndoe/", "https://www.linkedin.com/in/janedoe/"],
  "maxResults": 50,
  "webhookUrl": "https://your-app.com/webhook"
}

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Output

Field	Type	Description
query	string	Input query
url	string	Profile URL
name	string	Full name
headline	string	Profile headline
profilePic	string	Profile picture URL
location	string	Location
company	string	Current company (extracted from headline)
seniority	string	c_level | vp | director | manager | senior | entry | individual
skills	array	Normalized skill categories (engineering/data/design/product/marketing/sales/finance/leadership)
emails	array	Emails found on profile
companyInfo	object | null	Clearbit firmographics (if key provided)
contactInfo	object | null	Apollo contact data (if key provided)
confidenceScore	number	0.0–1.0 reliability
sources	array	Provider attribution
providerHealth	object	Per-provider status/latency
cacheStatus	string	hit | miss | degraded
mode	string	Execution mode
extractedAt	string	ISO timestamp

Example output

{
  "query": "https://www.linkedin.com/in/johndoe/",
  "url": "https://www.linkedin.com/in/johndoe/",
  "name": "John Doe",
  "headline": "Senior Software Engineer at Acme Corp",
  "profilePic": "https://...",
  "location": "San Francisco, CA",
  "company": "Acme Corp",
  "seniority": "senior",
  "skills": ["engineering"],
  "emails": ["john@acme.com"],
  "confidenceScore": 0.85,
  "sources": ["linkedin", "skills_normalizer", "seniority_classifier"],
  "providerHealth": {
    "linkedin_desktop": {"status": "ok", "latency_ms": 5200, "error": null},
    "skills_normalizer": {"status": "ok", "latency_ms": 0, "error": null},
    "seniority_classifier": {"status": "ok", "latency_ms": 0, "error": null}
  },
  "cacheStatus": "miss",
  "mode": "enrich",
  "extractedAt": "2026-06-29T00:05:00.000Z"
}

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Pricing

Plan	Price per 1K profiles	Savings vs. top competitor
Leading competitors	~$3.00/1K	—
This actor (≤10K/mo)	$2.21/1K	26% cheaper
This actor (10K–100K/mo)	$1.85/1K	38% cheaper
This actor (100K+/mo)	$1.50/1K	50% cheaper

Optional event: contact_found at $1.00/1K profiles with extracted email.

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Use Cases

• B2B sales intelligence — build prospect lists with seniority, skills, and company
• Recruiting — find candidates by seniority level and skill category
• Account-based marketing — enrich target accounts with key decision-maker profiles
• Lead scoring — use seniority + skills + company to prioritize outreach
• CRM enrichment — append LinkedIn data to existing contact records
• MCP agent workflows — providerHealth lets agents route around failures
• Market mapping — track talent distribution by company and seniority
• Competitive intelligence — monitor competitor team composition and hiring

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Integration Examples

Python (Apify SDK)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("ayeeyee/linkedin-profile-no-cookie-premium").call(run_input={
    "mode": "enrich",
    "queries": ["https://www.linkedin.com/in/johndoe/"],
    "maxResults": 50,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} — {item['seniority']} @ {item['company']} | skills: {item['skills']}")

cURL

curl -X POST "https://api.apify.com/v2/acts/ayeeyee~linkedin-profile-no-cookie-premium/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"mode":"enrich","queries":["https://www.linkedin.com/in/johndoe/"],"maxResults":25}'

MCP (Model Context Protocol)

$npx -y @apify/actors-mcp-server --tools actors,ayeeyee/linkedin-profile-no-cookie-premium

Agents can call call-actor and use providerHealth + seniority + skills + confidenceScore to route and filter prospects.

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FAQ

Q: Do I need LinkedIn login cookies? No. Public profiles are scraped via meta tags, JSON-LD, and DOM selectors — no login or session cookies required. Private profiles return base data only.

Q: How does the desktop+mobile fallback work? If Playwright fails (blocked, login wall, timeout), the actor falls back to a lightweight mobile HTML fetch (degraded — meta tags only). providerHealth shows which provider succeeded.

Q: How are skills categorized? Open-source keyword matching against 8 taxonomy categories: engineering, data, design, product, marketing, sales, finance, leadership. A headline mentioning "Python" and "engineer" gets categorized as engineering.

Q: How is seniority classified? Regex matching against title keywords: CEO/CTO/CFO → c_level, VP → vp, Director → director, Manager/Lead → manager, Senior → senior, Junior/Intern → entry. Default: individual.

Q: Where do emails come from? Emails found in the public profile page text. No paid email-finder API is required. For higher match rates, provide an Apollo API key.

Q: Can I call this from an LLM agent? Yes. MCP-ready with providerHealth{}, seniority, skills, and confidenceScore for routing and filtering decisions.

Q: What is the cache TTL? 2 hours — LinkedIn profiles change slowly. fast_lookup returns in <800ms on cache hit.

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Legal & Compliance

Scrapes publicly available LinkedIn profile data. Does not access private data, bypass authentication, or store credentials. Users are responsible for complying with GDPR/CCPA and LinkedIn's ToS.

This actor is intended for legitimate research, recruiting, and B2B sales use cases. It must not be used for spam, harassment, or unlawful activity.

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AI-DLC / Data Lifecycle

• Collection — Public data only; respects robots.txt and rate limits.
• Processing — In-memory normalization; no PII logging.
• Storage — Results in user's Apify dataset, not retained by actor.
• Usage — Sales intelligence, recruiting, legitimate B2B outreach.
• Disposal — No long-term caching (2h TTL for base results only).

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Enhancement Roadmap (API / MCP Integrations)

• Clearbit company firmographics MCP — industry, employees, revenue (optional, user key)
• Apollo contact enrichment MCP — verified emails/phones (optional, user key)
• Humantic personality insights MCP — personality-based sales positioning
• Crunchbase funding MCP — company funding history for account scoring
• LangGraph workflow — LI profile → company → news → AI lead scoring (see multi-api-orchestration-spec.md)

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Changelog

• v3.0 — Multi-API orchestration edition: desktop+mobile fallback, skills/seniority classification, MCP-ready providerHealth, optional Clearbit/Apollo, expanded FAQ, integration examples, volume pricing.
• v2.0 — Premium README, AI-DLC docs, confidence scoring, source attribution.
• v1.0 — Initial release with Playwright scraping and structured output.

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Links

• Apify Store: https://apify.com/ayeeyee/linkedin-profile-no-cookie-premium
• Actor ID: QbJphsbOFZ9phAMjE