AI Memory on Memgraph: Persistent Context Across Session, Team and Organizationh

Memgraph Context Graph is presented as a persistent AI memory architecture that preserves useful context beyond individual agent sessions, tools, and model harnesses, allowing work begun in one environment, such as Claude Code, to continue in another without manually transferring reasoning, discoveries, and decisions. It records raw agent activity in Memgraph and then performs post-session reconciliation to extract structured memories, entities, and relationships while retaining links to the complete underlying history. This graph-based approach enables agents to retrieve concise relevant facts, trace them to their sources, and explore connected context such as system ownership, prior fixes, and related actions. The system uses locally run GLiNER for entity extraction rather than an additional external LLM call, while model providers used by agent harnesses may still receive context. For shared organizational deployments, Memgraph Enterprise adds access controls, multi-tenancy, and auditing to ensure retrieval reflects both relevance and user permissions. Context Graph is part of the Memgraph AI Toolkit, can be customized through its ontology, connectors, and pipeline components, stores data queryable with Cypher, and is positioned as an adaptable implementation of a broader AI memory workload.

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