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.
Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.
Enables AI assistants to interact with Neo4j graph databases through natural language, supporting Cypher queries, schema management, data manipulation, and graph algorithms.
Enables storage and retrieval of knowledge in a graph database format, allowing users to create, update, search, and delete entities and relationships in a Neo4j-powered knowledge graph through natural language.
Enables AI coding agents to navigate massive codebases through fast code property graph queries, sandboxed recursive language model execution, durable semantic memory, and swarm concurrency coordination.
