# 💼 LinkedIn Profile Scraper (No Cookies) ✅ (`ayeeyee/linkedin-profile-no-cookie-premium`) Actor LinkedIn profiles without login cookies. Skills, seniority, company, and MCP-ready metadata. Desktop+mobile fallback chain. Optional Clearbit/Apollo. 3 modes. - **URL**: https://apify.com/ayeeyee/linkedin-profile-no-cookie-premium.md - **Developed by:** [Virtual Footprint LLC](https://apify.com/ayeeyee) (community) - **Categories:** Social media, Business - **Stats:** 2 total users, 1 monthly users, 33.3% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.21 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. 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). # README ## LinkedIn Profile Scraper (No Cookie Premium) ![Apify](https://img.shields.io/badge/Apify-Actor-ff6b37?logo=apify) ![Version](https://img.shields.io/badge/version-3.0-blue) ![Pricing](https://img.shields.io/badge/pricing-PAY_PER_EVENT-success) ![Platform](https://img.shields.io/badge/platform-LinkedIn-0A66C2?logo=linkedin) ![Modes](https://img.shields.io/badge/modes-fast_lookup%7Cenrich%7Cbatch-orange) ![MCP-ready](https://img.shields.io/badge/MCP-ready-brightgreen) > 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. --- ### 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. --- ### Architecture ```mermaid 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 ```` *** ### 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+) | *** ### 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 ```json { "mode": "enrich", "queries": ["https://www.linkedin.com/in/johndoe/", "https://www.linkedin.com/in/janedoe/"], "maxResults": 50, "webhookUrl": "https://your-app.com/webhook" } ``` *** ### 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 ```json { "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" } ``` *** ### 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. *** ### 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 *** ### Integration Examples #### Python (Apify SDK) ```python 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 ```bash 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) ```bash 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. *** ### 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. *** ### 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. *** ### 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). *** ### 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`) *** ### 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. *** ### Links - **Apify Store:** https://apify.com/ayeeyee/linkedin-profile-no-cookie-premium - **Actor ID:** `QbJphsbOFZ9phAMjE` # Actor input Schema ## `mode` (type: `string`): fast\_lookup: cache-first. enrich: multi-provider enrichment. batch: queue-based. ## `queries` (type: `array`): Search queries, profile URLs, or usernames ## `maxResults` (type: `integer`): Maximum results per query (1-1000) ## `webhookUrl` (type: `string`): Optional webhook URL for completion notification ## Actor input object example ```json { "mode": "enrich", "queries": [ "https://linkedin.com/in/satyanadella" ], "maxResults": 25, "webhookUrl": "" } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ayeeyee/linkedin-profile-no-cookie-premium").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("ayeeyee/linkedin-profile-no-cookie-premium").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call ayeeyee/linkedin-profile-no-cookie-premium --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ayeeyee/linkedin-profile-no-cookie-premium", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "💼 LinkedIn Profile Scraper (No Cookies) ✅", "description": "LinkedIn profiles without login cookies. Skills, seniority, company, and MCP-ready metadata. Desktop+mobile fallback chain. Optional Clearbit/Apollo. 3 modes.", "version": "3.0", "x-build-id": "GdhhNwWjOBy6uBZam" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ayeeyee~linkedin-profile-no-cookie-premium/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ayeeyee-linkedin-profile-no-cookie-premium", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ayeeyee~linkedin-profile-no-cookie-premium/runs": { "post": { "operationId": "runs-sync-ayeeyee-linkedin-profile-no-cookie-premium", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ayeeyee~linkedin-profile-no-cookie-premium/run-sync": { "post": { "operationId": "run-sync-ayeeyee-linkedin-profile-no-cookie-premium", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "queries" ], "properties": { "mode": { "title": "Execution Mode", "enum": [ "fast_lookup", "enrich", "batch" ], "type": "string", "description": "fast_lookup: cache-first. enrich: multi-provider enrichment. batch: queue-based.", "default": "enrich" }, "queries": { "title": "Search Queries", "type": "array", "description": "Search queries, profile URLs, or usernames", "default": [ "https://linkedin.com/in/satyanadella" ] }, "maxResults": { "title": "Max Results per Query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum results per query (1-1000)", "default": 25 }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional webhook URL for completion notification", "default": "" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```