Provides semantic code search and retrieval capabilities for AI agents, enabling them to query codebases using natural language with automatic learning, hybrid search, and intelligent chunking of functions and classes.
Gives coding agents a memory of codebases by searching repositories using semantic similarity and structural call/import graphs, enabling reuse of proven patterns and reducing token usage.
Enables AI agents to semantically search and navigate code repositories using natural language, with support for multiple repos, incremental indexing, and no local install needed.
Enables AI coding agents to retrieve only the specific code sections that answer their questions, drastically reducing context usage while running fully locally.
Enables semantic code search for AI assistants by indexing codebases with embeddings and Tree-sitter, returning relevant snippets via natural language queries.