Zep Memory

Zep is an AI agent memory platform that provides end-to-end context engineering for building intelligent agents. It addresses the common problem of AI agents failing due to a lack of relevant context by assembling information from chat history, business data, and user behavior into a unified, temporal knowledge graph. This allows agents to have a holistic and continuously updated understanding of users and their interactions. The platform is designed for developers and organizations building AI agents, including conversational AI, voice agents, and live support systems. It integrates with any agent framework and offers low-latency retrieval, making it suitable for real-time applications. Zep aims to improve agent accuracy, reduce inference costs, and lower response latency by providing agents with rich, personalized, and evolving context, moving beyond simple chat memory or static RAG approaches. Zep's core functionality involves ingesting diverse data sources, automatically extracting entities and relationships to construct a dynamic knowledge graph, and then assembling relevant, token-efficient context for Large Language Models (LLMs). It also supports custom graph ontologies to tailor memory to specific business domains, ensuring high precision and relevance.