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AI coding agents lose critical structural understanding of codebases when context compaction occurs. Code graphs provide persistent external memory—representing functions, classes, and dependencies as queryable relationships—so agents can recover context without re-reading files from scratch.
How Supermodel built Uncompact—a tool that maintains a persistent code graph across Claude Code's context compaction events—and the key lessons learned shipping it to production: simplicity over detail, invisibility enables adoption, and layered verification over blind trust.
A look inside Supermodel's real-time code analysis API: the five-stage processing pipeline, multi-language abstraction via a unified node schema, incremental graph updates, and the sub-100ms response time requirement that shaped every design decision.