GoalOS - Smart AI Goal Intelligence System

Built for the Algolia Agent Studio Challenge

GoalOS is a smart AI goal intelligence system that leverages Algolia Agent Studio to provide data-driven, evidence-based goal planning and adaptive guidance. Unlike typical goal apps, GoalOS learns from historical success data to give users practical plans that actually work.

🎯 Core Features

1. AI-Powered Goal Creation: Agent Studio generates personalized weekly plans based on similar successful goals
2. Adaptive Recommendations: When users struggle, AI provides proven strategies from the knowledge base
3. Community Intelligence: Learn from public goal templates with high success rates
4. Risk Assessment: AI identifies common failure points before they happen
5. Weekly Check-ins: Track progress and get dynamic plan adjustments

🏗️ Architecture

React Frontend ←→ Node.js API ←→ Algolia Agent Studio
                        ↓
                  Algolia Indices
              (Knowledge Base)

• Frontend: React + Vite + Tailwind CSS
• Backend: Node.js + Express
• AI Platform: Algolia Agent Studio (non-conversational prompts)
• Data Storage: Algolia indices (goals_knowledge, strategies_db)

🚀 Quick Start

1. Install Dependencies

npm run setup

2. Configure Algolia Agent Studio

Follow the detailed guide: docs/algolia-setup-guide.md

Quick checklist:

• ✅ Create Algolia account and application
• ✅ Set up Agent Studio with LLM provider
• ✅ Create indices: goals_knowledge, strategies_db
• ✅ Configure Agent Studio prompts
• ✅ Get API keys and Agent ID

3. Set Environment Variables

Backend (backend/.env):

ALGOLIA_APP_ID=your_app_id
ALGOLIA_AGENT_STUDIO_API_KEY=your_agent_studio_api_key
ALGOLIA_SEARCH_API_KEY=your_search_api_key
AGENT_STUDIO_AGENT_ID=your_agent_id
AGENT_STUDIO_BASE_URL=https://agent-studio-api.algolia.com
PORT=3001
NODE_ENV=development

Frontend (frontend/.env):

VITE_API_BASE_URL=http://localhost:3001
VITE_ALGOLIA_APP_ID=your_app_id
VITE_ALGOLIA_SEARCH_API_KEY=your_search_api_key

4. Test Connection & Load Sample Data

npm run test:connection
npm run load:data

5. Start Development

npm run dev

Access:

• Frontend: http://localhost:5173
• Backend API: http://localhost:3001
• Health Check: http://localhost:3001/health

📊 Project Structure

goalos/
├── backend/              # Node.js + Express API
│   ├── src/
│   │   ├── services/     # Agent Studio & Algolia integration
│   │   ├── routes/       # API endpoints
│   │   └── app.js        # Express configuration
│   └── package.json
├── frontend/             # React application
│   ├── src/
│   │   ├── components/   # React components
│   │   ├── pages/        # Page components
│   │   ├── contexts/     # State management
│   │   └── services/     # API client
│   └── package.json
├── sample-data/          # Sample goals and strategies
├── scripts/              # Data loading scripts
├── docs/                 # Documentation
│   ├── algolia-setup-guide.md
│   └── demo-script.md
└── SETUP.md             # Quick setup guide

🎪 Demo Flow

1. Create Goal: Enter "Learn React in 30 days" → Agent Studio generates personalized plan
2. View Plan: See week-by-week breakdown with tasks, milestones, and risk points
3. Weekly Check-in: Report "stuck" status → Get adaptive recommendations
4. Browse Templates: Explore successful goal templates from the community

See docs/demo-script.md for complete demo presentation.

🔑 Key Technical Highlights

Algolia Agent Studio Integration

• Non-conversational prompts for plan generation and adaptation
• Retrieval-augmented generation using knowledge base
• Custom prompts for different use cases (planning, adaptation, discovery)

Data Architecture

• goals_knowledge index: Stores goal data, plans, success rates
• strategies_db index: Proven strategies and failure patterns
• Faceted search: Filter by difficulty, duration, category, success rate
• Custom ranking: Prioritize high-success-rate goals

Adaptive Intelligence

• Real-time retrieval from knowledge base
• Evidence-based recommendations (not generic AI advice)
• Learns from collective success patterns

📚 Documentation

• Setup Guide: Complete Algolia & Agent Studio setup
• Demo Script: Hackathon presentation guide
• Quick Setup: Fast-track setup instructions

🛠️ Available Scripts

npm run setup           # Install all dependencies
npm run test:connection # Test Algolia connection
npm run load:data      # Load sample data into Algolia
npm run dev            # Start both frontend and backend
npm run dev:backend    # Start backend only
npm run dev:frontend   # Start frontend only
npm run build          # Build frontend for production

🎯 Use Cases

• Personal Goal Achievement: Learning, fitness, habits
• Professional Development: Career goals, skill acquisition
• Business Planning: Startup MVPs, project milestones
• Educational Platforms: Course completion, learning paths
• Coaching Applications: Client goal tracking and guidance

🏆 Hackathon Submission

Challenge: Algolia Agent Studio Challenge
Category: Non-conversational prompt-based agent
Innovation: Retrieval-based AI for goal achievement using historical success data

Why GoalOS Stands Out

1. Real Data, Real Results: AI recommendations based on actual success patterns
2. Adaptive Intelligence: Dynamic plan adjustments based on user progress
3. Collective Learning: Every successful goal improves future recommendations
4. Practical Application: Solves a real problem with measurable impact

🤝 Contributing

This is a hackathon project built for the Algolia Agent Studio Challenge. Feel free to fork and adapt for your own use cases!

📝 License

MIT License - Built for educational and hackathon purposes.

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Built with ❤️ using Algolia Agent Studio

Algolia Agent Studio Configuration

ALGOLIA_APP_ID=VTT523SVNQ ALGOLIA_AGENT_STUDIO_API_KEY=d877d3d1befeb59870b4daa2c97de5ce ALGOLIA_SEARCH_API_KEY=692849989cfcc49b879b525fe88bc930 ALGOLIA_ADMIN_API_KEY=a926258edf7ca8fb766e7e1900b72439 AGENT_STUDIO_AGENT_ID=fee32fca-e0d4-4b2b-8f5b-1f29c3d31c10 AGENT_STUDIO_BASE_URL=https://vtt523svnq.algolia.net/agent-studio/1/agents/fee32fca-e0d4-4b2b-8f5b-1f29c3d31c10/completions?compatibilityMode=ai-sdk-5

Application Configuration

PORT=3001 NODE_ENV=development

Optional: LLM Provider Configuration (if using custom provider)

LLM_PROVIDER=openai LLM_API_KEY=your_llm_api_key_here