Core Functionality - Zep aims to build human-like memory for agents, addressing real-time knowledge updates and fast data retrieval challenges in agentic and RAG systems [1] - Zep organizes agent memories into episodes, extracts entities and relationships, and stores them in a knowledge graph [1] - The system features an Episode Subgraph for capturing raw, timestamped data, a Semantic Entity Subgraph for extracting and versioning entities and facts, and a Community Subgraph for grouping related entities [1][2] Performance Metrics - Zep delivers up to 1850% (18.5 times) higher accuracy with 90% lower latency compared to tools like MemGPT [2] Open Source Nature - Zep is fully open-source [2]

Technology & Efficiency - RAG (Retrieval-Augmented Generation) system achieves query speeds of under 0.03 seconds across 36 million+ vectors [1] - A technique enhances RAG system memory efficiency by approximately 32x [1] Industry Adoption - Perplexity incorporates the technique in its search index [1] - Azure utilizes the technique in its search pipeline [1] - HubSpot employs the technique in its AI assistant [1]