Building a Neo4j Memory and Graph Agent for IBM watsonx Orchestrate

Neo4j’s integration with IBM watsonx Orchestrate demonstrates two approaches to building graph-enabled AI agents: a declarative native agent that queries a Neo4j companies knowledge graph through the Model Context Protocol and a code-based LangGraph agent that adds persistent cross-session memory through Neo4j Agent Memory Service. The native agent uses Orchestrate-managed connections to inject Neo4j credentials into a locally executed MCP server, avoiding separately hosted infrastructure, while also supporting curated Python tools for predefined queries such as finding a company’s investors. Its behavior is configured through YAML instructions that guide tool selection, schema inspection, and read-only Cypher queries, although the described ADK release has a command-line limitation for importing native agents with toolkits. The LangGraph implementation supplements graph querying with a memory loop that recalls relevant stored facts, generates responses using graph tools when needed, and persists new user information to NAMS for later extraction and retrieval. This allows the agent to personalize answers across distinct conversations, though newly submitted facts may not be immediately searchable because entity extraction occurs asynchronously.

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