Enables coding agents to search a locally indexed repository and get back a token-budgeted deck of ranked code and doc snippets instead of a full-tree grep dump, with lexical, semantic, and prose-answering modes plus index status reporting. Runs fully offline for lexical queries and optionally uses an OpenAI-compatible provider for semantic or ask modes.
Semantic code search for AI agents — hybrid vector + BM25 with cross-encoder reranking, AST-aware chunking for 14 languages, conversation memory, code annotations, and search analytics. Zero config, zero API keys. Just bunx
Enables semantic and keyword search over a local codebase by chunking source files along function and class boundaries, embedding them offline, and fusing vector and BM25 results. Runs entirely on your machine in a single SQLite file, so AI coding agents can find relevant code with no API keys, telemetry, or cloud calls.