Beyond Vibe Coding: AI Agents for Technical Debt Detection with Neo4j

In the fast-paced world of software development, “”vibe coding””—where intuition and momentum drive decisions—can lead to rapid progress but often accrues unseen technical debt. This session unveils a suite of AI agents I’ve developed to detect and analyze the subtle signs of such debt using Neo4j’s graph capabilities.

I’ve built an extensible framework using Dagger and Neo4j that:

– Parses diverse codebases (Python, JavaScript, TypeScript, etc.) using specialized parsers
– Constructs a comprehensive code graph in Neo4j including:
– File nodes with language and path metadata
– Symbol nodes (Functions, Classes, Interfaces) with scope, signature, and line positioning
– Relationship edges: DEFINED_IN, IMPORTS, CALLS, CONTAINS, and re-export patterns
– Executes Cypher queries through a dedicated cypher-shell client container
– Live Demonstration

Speaker: Kambui Nurse

Resources:
Get Started with Aura – https://bit.ly/3LOLrjh
Deployment Center – https://bit.ly/4jOelM3
Ground AI Systems and Agents with Neo4j – https://bit.ly/4oVsnyb

#nodes2025 #neo4j #graphdatabase #graphrag #knowledgegraph