BeatDebate — Technical Deep Dive
Introduction
Purpose
BeatDebate is a proof-of-concept web app that shows how a large-language-model (LLM) planner can orchestrate specialised agents to deliver transparent, long-tail music recommendations in under seven seconds.
Check out the project GitHub repository for the full code and detailed documentation. Here is the web application. Check out the AgentX course.
Problem Statement
Standard collaborative-filtering pipelines optimise clicks but amplify popularity bias and tell listeners nothing about why a song appears. BeatDebate flips the workflow: first an LLM writes an explicit machine-readable plan, then lightweight agents execute it, and finally a Judge agent converts plan weights into human-readable explanations.
What you’ll learn
- Designing an LLM-planned recommender — externalising reasoning as JSON so downstream agents become cheap and debuggable.
- Using LangGraph for agent orchestration — a typed DAG with retries, time-outs, and state-passing.
- Balancing novelty and relevance with dual advocate agents (Genre-Mood vs. Discovery).
- Generating explanations by design rather than post-hoc.
- Running at interactive speed on commodity hardware for <$0.04 per query.