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

November 14, 2025
Length: 28 min

Kambui Nurse Presenter Subscribe

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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