Vector Memory Forgets. Graphs Don't.
Store, connect, and query three types of long-term memory, semantic, episodic, and procedural as a unified graph that any AI system can reason over in real time.
Vector memory degrades fast.
LLMs are stateless. Vector-based memory retrieves what sounds similar, not what's structurally relevant given the full history of interactions, preferences, and evolving state.
Three types of memory. One graph.
Semantic Memory
What the system knows.
Facts, preferences, entity knowledge. As a graph: entities with typed relationships - traversal, not similarity matching.
SHOW SCHEMA INFO returns the graph ontology in constant time.
Episodic Memory
What the system experienced.
Past interactions with temporal context. As a graph: interaction nodes encoding sequence, causation, and consequence.
In-memory writes for live conversation. Retrieval via Agentic GraphRAG.
Procedural Memory
What the system knows how to do.
Workflows, escalation paths, decision trees. As a graph: steps as nodes, transitions as edges.
Graph of Skills models competencies, dependencies, and decay rates.
Memory types interconnect.
Semantic fact → episodic event → procedural response. That interconnection is a graph problem - multi-hop relationships between memory types that vector databases cannot represent.
User Query
Schedule a follow-up with the client like last time.
Semantic
Client info, timezone
Episodic
Last meeting: 30min Tuesday
Procedural
Book calendar, send invite
AI Response
Done ✅ 30min Tuesday slot booked, invite sent.
Why Memgraph.
Sub-millisecond traversals. Native vector search. Real-time writes. Built for AI pipelines that can't afford to wait.
Sub-millisecond reads, real-time writes
Conversation is live. Write latency matters. Memgraph's in-memory architecture handles both at the speed memory systems require.
Schema as navigational structure
SHOW SCHEMA INFO returns the graph ontology in constant time. An LLM cannot retrieve facts unless it understands what entity types exist and how they connect.
Agentic GraphRAG for memory retrieval
The same retrieval strategies that power GraphRAG - Text2Cypher, pivot search, query-focused summarisation - serve as the query layer across all three memory types.
Enterprise governance
Fine-grained access control filters what each user or agent is permitted to see, ensuring memory respects compliance constraints.
Two paths to AI memory.
Integrate
Use Mem0 or Cognee with Memgraph as the graph backend. These tools handle entity extraction, memory management, and retrieval patterns. Memgraph stores the graph and serves queries.
Integrations: Mem0 · Cognee
Build custom
Enterprise memory requirements are specific to each organisation's data, workflows, and compliance constraints. Memgraph provides the engine for building custom memory systems on your own infrastructure.
Tools: Cypher · MAGE · MCP Server
Explore other AI workloads.
[**GraphRAG**
Execute your entire retrieval pipeline as a single atomic database operation.
Explore now](https://memgraph.com/graphrag) [**Agentic AI**
Memgraph acts as the agent's working memory and execution graph for rapid, concurrent state management.
Explore now](https://memgraph.com/agentic-ai)