Enables fast code analysis and navigation through hybrid semantic search, graph-based relationship tracking, and structure exploration across multiple programming languages with optimized indexing for large codebases.
Enables semantic code search across projects using AI embeddings to find code by meaning rather than just text matching. Provides fast intelligent search, symbol analysis, and code similarity detection with multi-language support.
Enables semantic code search across codebases with automatic incremental indexing. Searches return relevant code snippets with file paths and line numbers based on natural language queries.
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.
Enables querying and analyzing code relationships by building a lightweight graph of TypeScript and Python symbols. Supports symbol lookup, reference tracking, impact analysis from diffs, and code snippet retrieval through natural language.
