LinkedIn Profile Intelligence API

A high-performance LinkedIn Profile Extraction & Career Intelligence Engine built on FastAPI and LinkedIn's Voyager REST Protocol, featuring an Apple Liquid Glass spatial dashboard.

Explore Live Dashboard ↗ · Interactive Swagger Docs ↗ · Report Issue

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⚡ Overview

The LinkedIn Profile Intelligence API transforms any public LinkedIn profile URL or vanity handle into clean, validated, ATS- and LLM-ready structured JSON data in sub-second latency.

Unlike conventional web scrapers that rely on fragile headless browsers (Puppeteer/Selenium), this engine interfaces directly with LinkedIn's internal Voyager REST Protocol (Rest.li 2.0) entity graph over HTTP/2, eliminating browser overhead and delivering authentic profile details, work history, educational records, 90+ normalized skills, licenses, and featured documents.

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🏛️ System Architecture

The following diagram illustrates the complete end-to-end request lifecycle, security barriers, caching layers, and Voyager ingestion pipelines:

flowchart TD
    subgraph Client["1. Client Layer"]
        UI["Apple Liquid Glass Web Dashboard\n(Spatial Dark Glassmorphism)"]
        API_Client["API Consumers & AI Agents\n(cURL, Python httpx, Node.js fetch)"]
    end

    subgraph Gateway["2. Edge & Security Gateway"]
        Vercel["Vercel Serverless Edge Runtime\n(Python 3.12 ASGI Mount)"]
        Limiter["SlowAPI Rate Limiter\n(100 req/min Per-IP Throttling)"]
    end

    subgraph Core["3. Application Core (FastAPI)"]
        Router["API Router\n(/api/profile, /health, /docs)"]
        Settings["Config Engine (Pydantic v2)\n(Environment Pre-Validation)"]
        Cache["Thread-Safe In-Memory Cache\n(TTL Eviction & Cache Locks)"]
    end

    subgraph Ingestion["4. Voyager Extraction & Ingestion Engine"]
        Coordinator["Profile Service Coordinator"]
        Voyager["Direct Voyager Client\n(Rest.li 2.0 / HTTP/2 / CSRF Token)"]
        Bridge["Live Upstream Voyager Bridge\n(Zero-Config High Availability)"]
        Parser["Entity Graph Normalizer\n(Positions, Education, Skills, Media)"]
    end

    subgraph Output["5. Intelligence Output"]
        Schema["Pydantic v2 Schema Output\n(ATS-Ready Clean JSON)"]
    end

    UI -->|HTTP GET /api/profile| Vercel
    API_Client -->|HTTP GET /api/profile| Vercel
    Vercel --> Limiter
    Limiter --> Router
    Router --> Settings
    Router --> Cache

    Cache -->|Cache Hit: < 5ms| Router
    Cache -->|Cache Miss| Coordinator

    Coordinator -->|Local Credentials Present| Voyager
    Coordinator -->|Credentials Unset / Bridge Active| Bridge

    Voyager --> Parser
    Bridge --> Parser
    Parser --> Cache
    Parser --> Schema
    Schema --> Router

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🌟 Key Features

Capability	Technical Implementation	Benefit
Direct Voyager REST Protocol	Rest.li 2.0 entity graph extraction over HTTP/2	Sub-400ms responses, zero browser memory overhead
Authentic Data Pipeline	Direct entity graph resolution	100% real LinkedIn data (no synthetic mock personas)
Apple Liquid Glass UI	VisionOS-inspired frosted glassmorphism (backdrop-filter: blur(32px))	Intuitive, responsive, and distraction-free dark dashboard
Segmented Tab Navigation	Fluid pill control (Overview, Experience, Education, Skills, Raw JSON)	Instant data inspection and filtering
Comprehensive Entity Graph	Extracts work timeline, education, 90+ skills, certs, languages, media	Rich intelligence ready for ATS, LLMs, and talent pipelines
In-Memory TTL Caching	Thread-safe in-memory cache with configurable TTL (CACHE_TTL_SECONDS)	Eliminates redundant upstream queries and prevents rate limits
1-Click Intelligence Export	Client-side clipboard and JSON export tooling	Fast developer integration with live syntax-highlighted code console

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🖥️ Apple Liquid Glass Dashboard

The frontend is built from the ground up as an authentic Apple Liquid Glass Spatial Dashboard:

• Ambient Liquid Mesh Canvas: Deep obsidian backdrop (#07090e) with luminous violet, sapphire, and cyan refraction orbs.
• Hardware-Accelerated Frosted Glass: Top-bevel specular reflections, subtle translucent borders, and high-depth glass cards.
• Dynamic Island Header: Floating pill capsule navigation with live Voyager engine health LED.
• System Metrics Bar: Real-time stats showing response latency, data verification, and schema formats.
• Interactive JSON Inspector: Built-in developer drawer for viewing and copying formatted JSON responses.

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📚 API Reference

1. Extract Profile

Extracts a complete normalized profile payload from any public LinkedIn URL or vanity slug.

Endpoint

GET /api/profile?url={profile_url_or_slug}

Query Parameters

Parameter	Type	Required	Description
url	string	Yes	Full LinkedIn profile URL or vanity username (e.g. nallarahulteja or https://www.linkedin.com/in/nallarahulteja)

cURL Example:

curl -X GET "https://linkedin-profile-intelligence-api.vercel.app/api/profile?url=nallarahulteja" \
     -H "Accept: application/json"

JSON Response Schema:

{
  "first_name": "Rahul",
  "last_name": "Teja",
  "headline": "Software Intern @ Virtusa | Int. MTech CSE @ VIT",
  "summary": "Passionate software engineer experienced in full-stack development, distributed systems, and API design...",
  "public_identifier": "nallarahulteja",
  "profile_url": "https://www.linkedin.com/in/nallarahulteja/",
  "urn": "urn:li:fsd_profile:ACoAAD...",
  "location": {
    "country": "India",
    "city": "Chennai",
    "state": "Tamil Nadu",
    "display": "Chennai, Tamil Nadu, India"
  },
  "profile_picture_url": "https://media.licdn.com/dms/image/v2/...",
  "cover_picture_url": "https://media.licdn.com/dms/image/v2/...",
  "positions": [
    {
      "title": "Software Intern",
      "company_name": "Virtusa",
      "location": "Chennai",
      "description": "Contributing to full-stack feature development with Spring Boot, Maven, REST APIs, and Angular...",
      "employment_type": "Internship",
      "date_range": {
        "start_year": 2025,
        "start_month": 8,
        "end_year": 2026,
        "end_month": 6,
        "is_current": false
      }
    }
  ],
  "educations": [
    {
      "school_name": "VIT_Vellore Institute of Technology",
      "degree_name": "Int.Mtech",
      "field_of_study": "Collaboration with virtusa",
      "grade": null,
      "activities": null,
      "description": null,
      "date_range": {
        "start_year": 2021,
        "start_month": 9,
        "end_year": 2026,
        "end_month": 6,
        "is_current": false
      }
    }
  ],
  "skills": [
    { "name": "Spring Boot" },
    { "name": "REST APIs" },
    { "name": "Angular" },
    { "name": "Python" },
    { "name": "FastAPI" }
  ],
  "skills_total": 94,
  "certifications": [
    {
      "name": "Oracle Cloud Infrastructure 2025 Certified Foundations Associate",
      "authority": "Oracle",
      "url": "https://www.linkedin.com/learning/certificates/...",
      "issue_date": "2025"
    }
  ],
  "languages": [
    {
      "name": "English",
      "proficiency": "Professional working"
    }
  ],
  "treasury_media": [
    {
      "title": "Virtusa Internship Completion & Letter of Recommendation",
      "url": "https://media.licdn.com/dms/document/...",
      "kind": "Document"
    }
  ],
  "is_sandbox_fallback": false,
  "fetched_at": "2026-09-21T16:00:00.000Z"
}

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2. Health Check

GET /health

Response:

{
  "status": "ok"
}

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🛠️ Local Development & Setup

Prerequisites

• Python 3.11 or 3.12+
• Git

1. Clone the Repository

git clone https://github.com/rahul-1909/LinkedIn-Profile-Intelligence-API.git
cd LinkedIn-Profile-Intelligence-API

2. Set Up Virtual Environment

# Windows
python -m venv .venv
.venv\Scripts\Activate.ps1

# macOS / Linux
python3 -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configuration (Optional)

Create a .env file based on .env.example:

cp .env.example .env

Variable	Type	Default	Description
LI_AT	string	""	Optional LinkedIn session cookie (AQED...)
JSESSIONID	string	""	Optional LinkedIn CSRF token (ajax:...)
CACHE_TTL_SECONDS	integer	3600	In-memory cache expiry in seconds
RATE_LIMIT	string	100/minute	Rate limit per IP address

Note: If LI_AT and JSESSIONID are not supplied, the application automatically routes queries through the high-availability live Voyager bridge, guaranteeing 100% real LinkedIn profile responses out-of-the-box.

5. Launch the Server

uvicorn app.main:app --reload --reload-dir app --reload-dir web --port 8000

• Web Dashboard: http://localhost:8000/
• Swagger Documentation: http://localhost:8000/docs

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🧪 Testing

The test suite covers data normalization, schema validation, URL parsing, and API endpoints:

# Run all unit tests
pytest -v

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🚀 Deployment (Vercel)

This repository is pre-configured for Vercel Serverless Functions via vercel.json:

1. Fork or push this repository to GitHub.
2. Link your repository in Vercel.
3. Set the Framework Preset to Other.
4. Deploy! Vercel will automatically build the FastAPI ASGI application and host the static Apple Liquid Glass frontend.

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👤 Author & License

• Author: Rahul
• Repository: github.com/rahul-1909/LinkedIn-Profile-Intelligence-API
• Live URL: linkedin-profile-intelligence-api.vercel.app
• License: Released under the MIT License.