AI Music Recommendation Agent
A personalized music discovery system that learns user tastes over time, orchestrates specialized agents for recommendation curation, and integrates with streaming platforms to generate contextual playlists based on mood, activity, and listening history.
User Interface Layer
Chat-based interaction layer where users describe moods, request recommendations, or provide feedback on suggestions
Self-hosted ChatGPT-like interface providing the conversational frontend for users to request recommendations and provide taste feedback
Cross-platform desktop client supporting multiple LLM providers for users preferring a native app experience over browser-based chat
Agent Orchestration Layer
Multi-agent system where specialized AI agents collaborate to analyze taste, retrieve similar tracks, and compile playlists
Role-based multi-agent framework enabling distinct agents: Taste Profiler (analyzes preferences), Discovery Curator (finds new music), and Playlist Architect (sequences tracks for flow)
Graph-based workflow orchestration for complex recommendation pipelines requiring conditional logic (e.g., switching between discovery modes)
Memory & Personalization Layer
Persistent storage of user listening history, genre preferences, liked/disliked tracks, and contextual listening patterns
Knowledge Retrieval Layer
Vector search infrastructure for finding similar tracks based on audio features, lyrics, reviews, or collaborative filtering patterns
External Integration Layer
Connectors to streaming services and music databases for real-time catalog access, playback control, and playlist publishing
Inference Layer
LLM backend powering recommendation reasoning, taste analysis, and natural language music discovery conversations