BeFit is an intelligent AI-powered workout assistant that combines Agentic AI, RAG-enhanced knowledge, and MediaPipe pose detection to create personalized workout routines and provide real-time form correction — designed for fitness enthusiasts at every level.
Live App | Demo Video (YouTube) | Demo Video (Google Drive
- Intelligent Chat Assistant: Natural language conversation for fitness advice and workout creation
- Tool-Based Execution: Uses specialized tools for workout generation, exercise config creation, and data persistence
- Context-Aware Responses: Maintains conversation flow with multi-step reasoning
- User Session Management: Personalized experience with login/logout functionality
- Personalized Routines: AI creates workouts based on goals, equipment, experience level, and duration
- Unlimited Exercise Support: No longer limited to predefined exercises - AI generates tracking configs for any exercise
- Automatic Exercise Config Generation: MediaPipe pose tracking configurations created dynamically using LLM analysis
- Smart Caching: Efficient reuse of generated configurations for performance
- Database Integration: Saves workouts with linked exercise configurations for future use
- Vector Database: Qdrant-powered embedding storage of exercise science research
- Relevance Filtering: Only uses high-relevance content (70%+ similarity) for responses
- Evidence-Based Recommendations: Leverages latest research in biomechanics, anatomy, and exercise science
- Source Citation: References specific research sources in responses
- OpenRouter Integration: Cloud-based LLM and embedding generation via OpenRouter API
- MediaPipe Integration: Real-time pose landmark detection via webcam
- Multi-Joint Tracking: Composite angle analysis from multiple body joints
- Adaptive Peak Detection: Intelligent rep counting with trend analysis
- Target Angle Guidance: ROM (Range of Motion) optimization with personalized targets
- Bilateral Tracking: Left and right side angle measurements for balanced analysis
- AI-Powered Analysis: LLM-based feedback generation considering form, tempo, and ROM
- Configurable Performance Modes:
- Fast Mode: Quick text feedback for immediate responsiveness
- Enhanced Reference: RAG-enhanced feedback with research backing
- Voice Feedback: Spoken guidance in Bengali for hands-free operation
- Combined Mode: Both enhanced reference and voice for comprehensive feedback
- Scoring System: 0-100 performance scores with "good/okay/bad" classifications
- Progressive ROM Targets: Adjustable range of motion goals (Low/Standard/High/Maximum)
- Modern SvelteKit Frontend: Built with shadcn/ui components for a clean, responsive design
- Dashboard Overview: Centralized view of workouts, progress, and AI interactions
- Exercise Config Management: Visual interface for viewing and testing generated configurations
- Form Analysis Page: Real-time pose detection with visual feedback
- Workout Library: Save, organize, and access personalized workout routines
- Mobile-Responsive: Works seamlessly across desktop and mobile devices
BeFit implements a sophisticated agentic AI system that orchestrates multiple tools and knowledge sources to provide comprehensive fitness assistance:
- SvelteKit: Modern web framework with server-side rendering
- shadcn/ui: Beautiful, accessible component library built on Radix UI
- Tailwind CSS: Utility-first CSS framework for rapid styling
- TypeScript: Type-safe development with enhanced developer experience
- Vercel AI SDK: Agentic framework for tool-based AI interactions with streaming support
- OpenRouter: Unified API for accessing multiple LLM providers (OpenAI, Anthropic, Qwen, etc.)
- MediaPipe: Google's pose detection and landmark tracking for real-time form analysis
- OpenAI-Compatible API: Flexible model integration
- Opik (Comet): LLM observability platform for tracing, monitoring, and debugging AI calls
- Purpose: Track all LLM interactions (chat, feedback, exercise config generation)
- Features: Monitor token usage, latency, tool calls, and conversation flows
- Integration: Automatic tracing via OpenTelemetry with OpikExporter
- Benefits: Debug AI responses, optimize costs, and evaluate model performance
- PostgreSQL: Primary database via Prisma ORM for workouts, exercises, and user data
- Qdrant: Vector database for RAG embeddings and semantic search
- Prisma: Type-safe database client and migration tool
- Better Auth: Secure authentication and session management
- SvelteKit API Routes: Backend API endpoints for chat, feedback, and exercise configs
- Docker: Containerized Qdrant deployment
- OpenTelemetry: Distributed tracing for LLM observability
- User Input → Express fitness goals, available equipment, and experience level
- AI Analysis → Agent analyzes requirements and queries RAG system for evidence-based recommendations
- Workout Generation → AI crafts personalized plan with exercises, sets, reps, and rest periods
- Exercise Config Creation → Automatically generates MediaPipe tracking configurations for each exercise
- Database Storage → Saves complete workout with linked exercise configurations
- Real-Time Tracking → Use saved workouts for live pose detection and form feedback
- Comet ML (Opik): For powerful LLM observability and monitoring capabilities
- MediaPipe Team: For excellent pose detection technology
- Fitness Community: Feedback and testing from real users and trainers
Built with ❤️ for the fitness community. Transform your workouts with AI-powered intelligence.