Enables AI agents to retrieve precise structural and semantic context from codebases using hybrid search across 166+ languages. Supports offline/local operation and automatic fallback for resilient, token-efficient code understanding.
Adds semantic code search to AI coding agents, enabling natural language queries across entire codebases to retrieve relevant code chunks, saving tokens and providing deep context.
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.
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.