From Observable to Understandable: Building Agent-Native Code Knowledge Graphs with UModel

Short summary

Code-understanding for AI agents splits between agentic search (real-time, privacy-first, but expensive at scale) and vector indexing (pre-computed embeddings, fast retrieval, but domain-limited). UModel addresses this gap with agent-native code knowledge graphs using deterministic AST parsing and cross-domain associations to model architecture-level dependencies. This bridges both paradigms by adding structural context that vector similarity search and grep-based search alone cannot provide.

• •Agentic search (Claude Code) trades indexing cost for real-time freshness and privacy; vector indexing (Cursor/Copilot) enables semantic search but remains domain-limited
• •Five paradigms explored: agentic search, vector indexing, hybrid CodeIndex, abstract model, and UModel knowledge graphs
• •UModel adds architecture-level dependency modeling to unify code and operational domain data, answering structural questions that search alone cannot

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