LLM Agent Memory Systems: Vector vs Graph vs Event Logs

Reliable multi‑agent systems hinge on a robust memory design. When agents call tools, collaborate, and execute extended workflows, the system must know what to store, how to retrieve it, and how to react when information is missing or corrupted. The article introduces six memory‑system patterns that populate modern agent stacks, grouped into three families: vector embeddings, graph structures, and event logs.

Vector memories store high‑dimensional embeddings that capture semantic similarity, making them ideal for recall‑heavy tasks such as natural‑language question answering and context retrieval. Graph memories represent knowledge as nodes and edges, enabling reasoning over relationships and supporting complex queries like “find all agents that collaborated on project X.” Event‑log memories record chronological sequences of actions, providing a transparent audit trail and facilitating rollback or replay of past interactions. The comparison also covers hybrid approaches that combine vector search with graph traversal or event‑based provenance, illustrating how these patterns can be layered to achieve both speed and explainability.

Choosing the right pattern depends on workload characteristics. If latency and approximate similarity are paramount, vector memory with approximate nearest‑neighbor search excels. For systems that require explicit rules, constraints, or multi‑step reasoning, a graph store offers the richest representation. When traceability, auditability, or debugging are critical—especially in regulated environments—event logs become indispensable. The article concludes with best‑practice guidelines: start simple, monitor retrieval quality, and iterate by adding complementary patterns as complexity grows. By aligning the memory design with specific agent responsibilities, developers can build more reliable, maintainable, and performant LLM‑driven multi‑agent architectures.

Key takeaway: A well‑chosen memory pattern—vector, graph, or event‑log—tailored to the agent’s workload is essential for reliable, traceable, and efficient multi‑agent LLM systems.