Building a Neo4j Memory and Graph Agent for IBM watsonx Orchestrate

This guide outlines the architecture and implementation steps for building a Neo4j-backed agent on IBM watsonx Orchestrate. It covers two capabilities. First, a native Orchestrate agent that answers questions about companies, people, and investments by querying Neo4j through the Model Context Protocol (MCP), augmented with a custom Python tool. Second, a LangGraph agent imported into Orchestrate that adds long-term memory across sessions, using the Neo4j Agent Memory Service (NAMS), so Neo4j serves as both the knowledge layer and the memory layer. You can find the full code in the Neo4j Agent Integrations repository. Platform-native agents: The primary agent is a declarative Orchestrate agent. Its behavior is defined by a model, instructions, and a toolset, with no application code to host. Zero-infrastructure MCP: The official Neo4j MCP server runs as a local (stdio) toolkit that Orchestrate installs and executes inside its own runtime. There is no container to deploy and no public…