Miró: Synthetic Audience Analysis using LLM Agents and Graph Database

In this demo, I will do a code deep-dive into “Miró”, an engine I built to forecast the social and critical reception of upcoming books using synthetic readers. Instead of a product pitch, I will focus entirely on the technical architecture and the integration layer between LLMs and Graph Database.

I’ll walk through the code live, showing:

Agent Generation Pipeline: How I parse static PDFs containing psychological profiles and translate them into “Synthetic Reader” nodes with their respective master prompts.
The Predictive Engine: The orchestration code that drives how agents “read” the book, interact with each other, and how these interactions continuously update the graph database state.
Graph-based Memory Management: A look into how I solved the challenge of persistent agent memory by dynamically creating complex relationships (such as CHATTED_ABOUT) and appending conversation histories as extendable edge properties using RAG.
Analysis Dashboard: A quick look at how the Python backend consumes this dynamic graph network to feed an interactive react frontend, rendering resonance, friction, and abandonment connections.