Beyond Vector Search: Why Agents Need Real Memory Systems

Vector databases provide useful information retrieval but lack true memory—they ignore temporal change, can't self-edit, and accumulate noise over time. Eidentic introduces a temporal knowledge graph with self-editing memory blocks and consolidation that lets agents genuinely learn and improve, not just search increasingly large haystacks. This approach is critical for deploying agents in real-world scenarios where memory must remain accurate and actionable across months of operation.