Codebase Intelligence for Agents: Building a Tool for Monoliths

The author shares their experience in developing a custom codebase intelligence tool designed for large-scale projects. After finding existing solutions inefficient for a Ruby monolith exceeding 3.5 million lines of code, the developer opted to build a local, privacy-focused alternative. The primary goal was to enable AI agents to maintain deep context of the codebase without relying on cloud-based indexing or risking data exposure. The tool leverages semantic search using Ollama and Qdrant, ensuring that all code, embeddings, and call graphs remain local. The article details the journey from architectural selection to implementing a system capable of handling enterprise-level projects. The author emphasizes the necessity of local processing for security and performance, providing a robust alternative to cloud services that often struggle with the scale and complexity of real-world monolithic architectures.