Mu transforms large codebases into a rich semantic graph, enabling AI assistants to achieve deep understanding without consuming vast context windows. By parsing code into nodes, edges, importance scores, and summaries, it allows AI to precisely retrieve and analyze relevant code snippets, bypassing boilerplate and focusing on semantic relationships. This intelligent approach dramatically improves LLM performance and accuracy when interacting with complex software projects, facilitating advanced search, navigation, review, and impact analysis capabilities through specialized tools.
