Gives coding agents a local, SQLite-backed memory of a codebase, enabling them to query symbol impact, change history, and task scope through MCP while recording what actually changed after edits.
Enables AI agents to locally search, query, and understand codebases with token-efficient context, dependency graphs, history, architecture diagrams, and metrics through MCP.
Provides intelligent code indexing with 15 MCP tools for symbol extraction, hybrid search (FTS5+vector), call graphs, and incremental indexing of local folders and remote repos, enabling token-efficient code retrieval for AI agents.
Enables AI agents to search code by meaning, explore codebase structure, store and query knowledge with temporal facts, and read source code through a set of MCP tools.
Provides a dynamic SQLite-backed code knowledge graph for AI assistants, with MCP tools to inspect symbols, find dependency paths, and analyze code structure across multiple languages.