codebase-memory

Trace function call chains and dependencies across a large codebase by querying a pre-built knowledge graph. This structural analysis approach uses graph tools to find callers and callees precisely, replacing manual text searches with instant relationship mapping.

Identify dead code in a software project by analyzing a pre-built codebase knowledge graph. Instead of scanning raw source files manually, structural querying instantly isolates unreferenced symbols and unused dependencies across the entire indexed project.

Change impact analysis works before refactoring core utilities by mapping git diffs to affected code symbols within a knowledge graph. This assesses risk by instantly tracing all downstream callers, preventing unexpected breaks in unrelated execution flows.

Yes, you need MCP knowledge graph tools to perform structural code querying. The analysis requires access to specific graph tools including search_graph, trace_path, and detect_changes to query code relationships and symbol metadata without scanning raw source files.

Yes, you can perform dependency auditing without scanning raw source files. By querying a pre-built codebase knowledge graph, the analysis directly retrieves symbol metadata and code relationships, bypassing the need for traditional grep or manual text search.

Use a knowledge graph for codebase exploration instead of grep to achieve fast, precise structural analysis. Grep relies on slow, error-prone manual text matching, while graph querying instantly retrieves exact callers, callees, and dependencies across any indexed project.