What It Does

Zep is an enterprise-grade agent memory infrastructure that provides persistent, contextual memory for AI agents at scale. Built on a proprietary Context Graph Engine, Zep creates temporal context graphs that track facts, entities, and relationships across time, ingesting data from every source an AI agent interacts with. The platform enables agents to remember users, business context, and previous work while maintaining sub-200ms retrieval performance regardless of graph size.

Who It's For

Zep is designed for enterprise AI teams building production-grade agentic systems that require sophisticated memory capabilities. It's ideal for organizations deploying multiple AI agents across customer service, sales, analytics, and operational workflows who need governed, auditable, and scalable memory infrastructure.

Key Features and Capabilities

• Context Lake: Manage millions of context graphs as a unified system with data-lake patterns applied to agent context
• Temporal Context Graphs: Track how facts and relationships change over time, automatically invalidating outdated information
• Multi-Source Ingestion: Create memories from chat history, business data, user interactions, and any structured or unstructured source
• Automated Context Assembly: Retrieve relevant, token-efficient context with sub-200ms latency at any scale (10K to 100M+ graphs)
• Observations: Surface patterns, recurrences, and co-occurrences across memory to provide agents with global perspective
• Enterprise Governance: Attribute-based access control, retention policies, legal hold, and comprehensive audit logs built into the substrate
• Provenance Tracking: Trace every fact back to its source episode for complete auditability

Deployment and Integration

Zep offers flexible deployment models including managed cloud (SOC 2 Type II, HIPAA BAA compliant), Bring Your Own Keys (BYOK) for customer-controlled encryption, and Bring Your Own Cloud (BYOC) for VPC deployment. The platform integrates with any agent framework through simple Python, TypeScript, and Go SDKs, requiring just three lines of code to add memory capabilities. Developers can add messages, ingest business data, and retrieve context through REST API, with built-in observability for latency, error rates, and retrieval activity.

Performance and Benchmarks

Zep leads industry benchmarks with 94.7% accuracy on LoCoMo and 90.2% on LongMemEval while maintaining 155ms average retrieval latency and reducing context size to 5,760 tokens—delivering superior accuracy, speed, and token efficiency simultaneously.

What Differentiates It

Unlike static document retrieval or simple conversation history systems, Zep's temporal context graphs understand when information changes and automatically maintain the latest state while preserving historical context. The Context Lake architecture governs millions of graphs as a single system with policy enforcement at the substrate level, making it uniquely suited for enterprise-scale deployments where compliance, auditability, and performance are critical.