Transforms code repositories and development documentation into a queryable Neo4j knowledge graph, enabling AI assistants to perform intelligent code analysis, dependency mapping, impact assessment, and automated documentation generation across 15+ programming languages.
Provides a graph-based code analysis to coding agents, enabling tools to trace call dependencies, assess change impact, and identify hotspots using tree-sitter and Neo4j.
A static-analysis knowledge base for TypeScript/NestJS codebases, served to AI agents over MCP for understanding code without hallucination and excessive token usage.
Enables natural language queries about JavaScript/TypeScript codebases using graph and vector search. Allows finding functions by semantic description, callers, callees, and function details.
Enables semantic code search across multiple repositories using AST-aware chunking and relationship tracking. Supports local LLM embeddings, real-time indexing, and cross-codebase dependency analysis through vector and graph databases.
