Zep

Overview

Zep is an end-to-end context engineering platform designed specifically for AI agents. It solves the agent context problem by assembling comprehensive, relationship-aware context from multiple data sources with sub-200ms latency.

Architecture

Zep uses Graphiti, a bi-temporal graph engine that stores both event time and ingestion time. This temporal knowledge graph tracks how facts change over time and maintains historical relationships between entities.

Core Component: Graphiti

Graphiti is a temporally-aware knowledge graph engine that:

• Dynamically synthesizes unstructured conversational data and structured business data
• Maintains historical relationships across all data sources
• Tracks when facts were true, not just what facts exist
• Provides relationship-aware context blocks optimized for LLMs

Key Features

• Temporal Knowledge Graph: Bi-temporal model tracking event time and ingestion time
• Multi-Source Integration: Combines chat history, business data, documents, and app events
• Hybrid Search: Combines graph-based memory with vector search
• Sub-200ms Latency: Fast context retrieval for real-time applications
• Automatic Relationship Extraction: Dynamically identifies and maintains entity relationships
• Historical Context: Understands how context evolves over time

Performance

In the Deep Memory Retrieval (DMR) benchmark established by the MemGPT team, Zep achieves 94.8% accuracy compared to MemGPT's 93.4%, demonstrating superior performance in agent memory tasks.

Integration

Zep integrates with:

• LangChain
• LlamaIndex
• Custom AI agent frameworks
• Various LLM providers (OpenAI, Anthropic, etc.)

Use Cases

• Long-term memory for AI agents
• Customer support chatbots with conversation history
• Enterprise AI systems requiring relationship modeling
• Temporal reasoning applications
• Context-aware recommendation systems

Pricing

Zep offers both cloud-hosted and self-hosted options with tiered pricing based on usage.