PeopleHub solves a common problem: finding and researching professionals is either slow (manual LinkedIn searching) or expensive (premium tools charging $5+ per profile).

Instead of spending hours manually searching LinkedIn and taking notes, just type what you're looking for in plain English:

• "10 AI engineers in Israel"
• "Product managers in San Francisco with startup experience"
• "Elon Musk"

PeopleHub combines Google Gemini 2.0 for intelligent query parsing, Bright Data APIs for LinkedIn scraping, and LangGraph for automated research workflows.

Search for professionals using plain English. No complex filters or Boolean operators needed.

Example queries:

• "5 AI Engineers in Israel"
• "Software engineers at Google"
• "Product managers in San Francisco with startup experience"

• 70-90% cost reduction through intelligent caching
• Redis hot cache for popular searches (sub-millisecond lookups)
• PostgreSQL persistent cache with 180-day freshness tracking
• Batch optimization for multiple profiles

Automated due diligence with LangGraph workflows:

• LinkedIn profile analysis
• Web scraping for recent projects and achievements
• AI-powered summarization of technical expertise
• Industry reputation analysis
• Comprehensive reports with sources

• PostgreSQL for long-term storage
• Redis for hot cache (optional)
• Intelligent freshness checks
• Popularity-based cache prioritization

Country-specific search results with automatic geo-targeting

• Glassmorphism design with aurora background effects
• 3D magnifying glass animations
• Expandable profile cards
• Auto-refreshing previous searches page

• Node.js 18+
• Supabase account (free tier works)
• Bright Data account with API token
• Google AI Studio API key (Gemini)

1. Clone the repository

git clone https://github.com/magicdevstar/pepole-hub
cd peoplehub

2. Install dependencies

npm install

3. Set up environment variables

cp .env.example .env

Edit .env and add your credentials:

# Database (Supabase PostgreSQL)
DATABASE_URL="your-supabase-connection-pooling-url"
DIRECT_URL="your-supabase-direct-url"

# Supabase (optional for future features)
NEXT_PUBLIC_SUPABASE_URL="your-supabase-project-url"
NEXT_PUBLIC_SUPABASE_ANON_KEY="your-supabase-anon-key"

# Bright Data API
BRIGHTDATA_API_TOKEN="your-brightdata-api-token"

# Google AI (Gemini 2.0)
GOOGLE_GENERATIVE_AI_API_KEY="your-google-ai-api-key"

# Redis (optional, for hot cache)
REDIS_URL="your-redis-url"

4. Set up database

npx prisma generate
npx prisma db push

5. Run the development server

npm run dev

Open http://localhost:3000 to see the app.

User Query → AI Parser (Gemini 2.0) → Cache Check → LinkedIn Scraper → Database → Results
                                              ↓
                                         Research Engine (LangGraph)
                                              ↓
                                     Web Scraping + Summarization
                                              ↓
                                         Research Report

• Framework: Next.js 15.5.4 with App Router (API Routes)
• Runtime: Node.js 18+
• Language: TypeScript 5 (strict mode)
• ORM: Prisma 6.5.0
• Database: PostgreSQL (Supabase)
• Cache: Redis with ioredis 5.8.2 (optional, hot cache)

• Query Parsing: Google Gemini 2.0 Flash (gemini-2.0-flash-exp)
• AI SDK: Vercel AI SDK 5.0.60 (@ai-sdk/google 2.0.17)
• Research Workflows: LangChain + LangGraph 1.0.1
• Schema Validation: Zod 3.25.76

• Bright Data: Google Search API, LinkedIn Scraper API, Web Scraper
• Custom MCP Client: Model Context Protocol SDK 1.19.1 for advanced tool access

• UI: React 19.1.0 with Next.js
• State: Zustand 5.0.2 + TanStack Query 5.62.18
• Styling: Tailwind CSS 4 with custom animation utilities
• 3D Graphics: React Three Fiber, @react-three/drei

User queries are parsed using Google Gemini 2.0 Flash with structured output via Zod schemas:

Input: "5 AI Engineers in Israel"

AI Parsed Output:

{
  "count": 5,
  "role": "AI Engineer",
  "location": "Israel",
  "countryCode": "IL",
  "keywords": [],
  "googleQuery": "site:linkedin.com/in \"AI Engineer\" \"Israel\""
}

1. Cache Check: Query Redis and PostgreSQL for existing results
2. Google Search: Use Bright Data's Google Search API to find LinkedIn URLs
3. Profile Scraping: Batch scrape LinkedIn profiles via Bright Data's LinkedIn Scraper API
4. Data Storage: Save profiles to PostgreSQL with metadata tracking
5. Results: Return comprehensive profile data with experience, education, and more

Tier 1: Redis (Hot Cache)

• Sub-millisecond lookups for popular searches
• 30-minute TTL for search results
• Reduces database load by 70-90%

Tier 2: PostgreSQL (Persistent Cache)

• 180-day freshness tracking
• Popularity-based prioritization (searchCount field)
• Batch optimization for multiple profiles

Performance Impact:

• First search: ~120 seconds (LinkedIn scraping bottleneck)
• Cached search: ~2.5 seconds (database lookup)
• Batch lookup: 10-50ms for 100 profiles
• Cost reduction: 70-90% with 90% cache hit rate

Automated due diligence reports using LangChain's LangGraph framework:

Features:

• Stateful multi-step workflows
• Parallel web scraping (fan-out/fan-in pattern)
• LinkedIn profile analysis
• Google search for recent projects
• AI-powered content summarization
• Comprehensive report generation with sources

Example Research Report Sections:

• Professional Background
• Recent Projects and Achievements
• Technical Expertise
• Industry Reputation
• Sources with URLs

peoplehub/
├── prisma/
│   └── schema.prisma              # Database schema (Person, Search, Research models)
├── src/
│   ├── app/
│   │   ├── api/
│   │   │   ├── search/            # Main search endpoint
│   │   │   ├── research/          # Research engine endpoint
│   │   │   ├── profiles/          # Recent profiles API
│   │   │   └── proxy-image/       # Image proxy for LinkedIn avatars
│   │   ├── search/                # Search results page
│   │   ├── previous/              # Previous searches page
│   │   ├── research/[id]/         # Research report page
│   │   └── page.tsx               # Homepage with aurora background
│   ├── components/
│   │   ├── ui/                    # shadcn/ui components
│   │   ├── Navigation.tsx         # Glassmorphism navbar
│   │   ├── PersonCard.tsx         # Expandable profile card
│   │   ├── SearchBar.tsx          # Search input component
│   │   ├── FloatingOrbs.tsx       # 3D magnifying glasses
│   │   └── LoadingState.tsx       # Skeleton loader
│   ├── lib/
│   │   ├── brightdata/            # Bright Data API integration
│   │   │   ├── search.ts          # Google Search API
│   │   │   ├── linkedin.ts        # LinkedIn Scraper API
│   │   │   └── client.ts          # MCP client for research
│   │   ├── cache/                 # Caching layer (DB + Redis)
│   │   ├── redis/                 # Redis hot cache
│   │   ├── search/                # AI query parsing (Gemini)
│   │   ├── research/              # LangGraph research engine
│   │   │   ├── graph.ts           # Research workflow graph
│   │   │   ├── nodes.ts           # Individual workflow nodes
│   │   │   └── runner.ts          # Graph execution
│   │   └── prisma.ts              # Prisma client singleton
│   └── types/
│       └── linkedin.ts            # LinkedIn profile types
├── tests/                         # Test scripts
└── package.json

• npm run dev - Start development server
• npm run build - Build for production
• npm run start - Start production server
• npm run lint - Run ESLint

• npx prisma generate - Generate Prisma Client
• npx prisma db push - Push schema changes to database
• npx prisma studio - Open Prisma Studio (database GUI)

• npx tsx src/tests/test-parser.ts - Test AI query parsing
• npx tsx src/tests/test-search-flow.ts - Test search → Google → LinkedIn flow
• npx tsx src/tests/test-cache.ts - Test caching layer
• npx tsx src/tests/test-recent-api.ts - Test /api/profiles/recent endpoint

• Recruiting & Talent Acquisition: Find candidates with specific skills and experience
• Due Diligence: Research executives, entrepreneurs, and business partners
• Competitive Intelligence: Analyze professional networks and industry trends
• Academic Research: Study professional networks and career patterns
• Sales Prospecting: Identify decision-makers and build targeted lists

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure your PR:

• Includes tests for new features
• Updates documentation as needed

MIT License - see LICENSE file for details.

• Bright Data for the BEST web data
• Google AI for Gemini 2.0 Flash
• Vercel for the AI SDK
• LangChain for LangGraph framework
• Supabase for PostgreSQL hosting

Built by Meir Kadosh

Questions or feedback? Open an issue or reach out!