Supermodel's engineering team built a real-time code analysis API designed to handle millions of lines across multiple programming languages. The core requirement was speed—the system needed to respond quickly enough that AI agents could query it during conversations without noticeable delays.
The Processing Pipeline
The system operates through five sequential stages:
- File ingestion — Monitoring and processing only new or modified files
- Language-specific parsing — AST parsers extract structural elements from supported languages
- Graph construction — Parsed elements become nodes and edges in a directed graph
- Storage and indexing — Graph storage enables fast traversal queries
- API serving — RESTful endpoints deliver sub-100ms response times
Technical Approach
Multi-Language Abstraction: Rather than building separate systems per language, the team created a unified node schema capturing essential code properties—name, kind (function, class, module), location, and relationships—regardless of syntax differences. This lets the rest of the pipeline treat all languages identically once parsing is complete.
Incremental Updates: When files change, the system invalidates only affected nodes, re-parses modified files, and merges updates back into the graph while preserving cross-file relationships. This keeps the graph current without the latency of full rebuilds.
Future Direction
The roadmap includes semantic analysis extending beyond structural relationships to understand data flow, shared invariants, and code patterns between elements—moving from where code lives to what it does.