AI-Powered Strava Analysis Workflow for Multi-Sport Coaching

Transform your Strava activities into actionable coaching insights with this AI-powered workflow that delivers sport-specific analysis, historical performance tracking, and automated report generation across multiple platforms.

What this workflow does

This comprehensive n8n automation captures Strava activities and processes them through an intelligent analysis pipeline. The workflow triggers on new activities, normalizes sport-specific data, and routes each activity to dedicated AI sub-workflows optimized for running, cycling, swimming, snowboarding, and cross-training. Using OpenAI GPT-4o-mini with sport-specific prompts, it generates detailed HTML coaching reports while creating compact RAG cards stored in PGVector for semantic search and historical context.

The system maintains 14-day training load context, performs duplicate checking, and automatically exports coaching reports to your preferred notes application including Trilium, Obsidian, Notion, or any notes API. Each analysis costs approximately $0.01-0.03 per activity, making it highly cost-effective for regular training analysis.

Key use cases

• Multi-sport coaching: Automated analysis across running, cycling, swimming, snowboarding, and cross-training activities
• Training load monitoring: Continuous 14-day context analysis for overtraining prevention and performance optimization
• Historical performance insights: RAG-powered semantic search through your complete training history
• Coaching report automation: Export detailed analyses to Trilium, Obsidian, Notion, or custom notes systems

Technical details

Built with essential n8n nodes including if, set, code, limit, strava, and switch components, this workflow integrates seamlessly with Strava webhooks, OpenAI APIs, PostgreSQL with PGVector extension, and various notes applications. The sport-specific AI prompts significantly reduce hallucinations while improving analysis accuracy, and the PGVector memory system enables powerful semantic search across your training history.

Requirements: Strava API access, OpenAI account, PostgreSQL database with PGVector, and your preferred notes API integration.