What is a context graph?

A context graph serves as a persistent memory system for AI agents, integrating long-term enterprise knowledge, short-term conversation history, and reasoning memory to ensure consistent and informed decision-making. By preserving and traversing the connections between memories, it enables agents to gather full context, produce accurate answers, and explain their decisions while leaving an audit trail for workflows. Unlike knowledge graphs, which provide semantic structure and business meaning, context graphs connect domain knowledge to conversation states and decision traces, allowing AI agents to access relevant context easily. Neo4j's Agent Memory facilitates the building of context graphs by connecting conversations, facts, tool usage, and reasoning traces into a queryable structure, enhancing agents' ability to maintain and utilize memory across sessions and tasks. This approach supports multi-agent systems by providing a shared memory space, improving explainability, reducing error likelihood, and optimizing token use, ultimately making AI agents more reliable and effective in long-running workflows.

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