Enables natural-language Q&A over codebases via MCP, using AST-aware chunking, hybrid retrieval, reranking, and call-graph expansion to answer with file:line citations and impact analysis.
A semantic code retrieval engine for AI agents that enables hybrid search, graph expansion, and token-aware context packing, integrating with MCP to provide precise code context to LLMs.
Enables AI agents to locally search, query, and understand codebases with token-efficient context, dependency graphs, history, architecture diagrams, and metrics through MCP.
Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.