Enables AI agents to interact with TigerGraph databases through the Model Context Protocol, supporting graph operations, schema queries, and GSQL execution via natural language.
Enables AI assistants to interact with Neo4j graph databases through natural language, supporting Cypher queries, schema management, data manipulation, and graph algorithms.
Enables AI assistants to interact with Gremlin-compatible graph databases through natural language, supporting schema discovery, complex graph queries, relationship analysis, and data import/export operations.
Enables AI agents to store, retrieve, and connect information in a Neo4j graph database as persistent memory, with semantic relationships, natural language search, and temporal tracking across conversations.
Enables AI agents to build, populate, and search knowledge graphs by providing tools for entity extraction, relationship mapping, and graph traversal. It manages the underlying database infrastructure so users can create searchable knowledge bases from text through natural language commands.
Serves as a universal interface between AI Agents and AnalyticDB PostgreSQL databases, enabling metadata retrieval and SQL execution, with additional capabilities for knowledge graph and LLM memory management.