Enables semantic code search across multiple repositories using natural language queries. Provides intelligent code discovery, symbol lookups, and cross-repo dependency analysis for AI coding agents.
Provides efficient code navigation and graph-based analysis for AI agents, enabling symbol resolution, callers, implementations, and type schemas with minimal token usage.
Enables LLM agents to efficiently understand and navigate a codebase by providing semantic search over symbols and a reference graph, replacing expensive grep/glob calls with structured tools like definition lookup, caller/callee queries, and change-impact analysis.
Provides AI coding assistants with deep, semantic understanding of local codebases via AST-aware chunking, cross-repo symbol graphs, and architectural memory, enabling context-aware code search and dependency tracing.
Exposes type-aware code navigation and fast file search to AI agents via language servers, enabling definitions, references, symbols, and file lookup without reading entire codebases.
Provides a semantic understanding of your codebase by parsing with tree-sitter and building a graph of symbols and dependencies. Enables AI assistants to navigate code, analyze changes, and discover architecture using 18 tools with minimal context overhead.