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What is Zep AI?
Zep is an enterprise-grade agent memory infrastructure built on temporal context graphs. Developed by Zep Inc., it solves the problem of persistent, governed, and scalable memory for AI agents. Core capabilities include automated context assembly from chat history, business data, and user interactions; sub-200ms retrieval at any graph size; attribute-based access control; retention policies; and provenance tracking. It serves AI teams building production agents that need to remember users, business context, and past work. Zep integrates with any agent framework via Python, TypeScript, or Go SDKs, and offers cloud, BYOK, and BYOC deployment models. It is trusted by AI teams for applications like deal-flow analysis, portfolio review, and customer support.
AI Tool Review Summary
4.8/5
High, consistent, and context-aware
Clean and developer-focused
LLMNLPContext Graph
To provide persistent, governed, and scalable memory for AI agents using temporal context graphs.
Works with any agent framework via Python, TypeScript, and Go SDKs; supports cloud, BYOK, and BYOC deployments.
Freemium with paid tiers
Features with the highest value for users are highlighted here.
Context Graph Engine
Sub-200ms retrieval latency
Automatic fact invalidation
Provenance tracking
Attribute-based access control
Retention policies and legal hold
Multi-deployment models (Cloud, BYOK, BYOC)
Built-in observability and analytics
AI Engineering Teams
Enterprise Architects
Customer Support Teams
Product Teams
Data Scientists
Startups
Compliance Officers
DevOps Teams
Sales Teams
Research Labs
Free
$0/monthly
Up to 1,000 graphs Basic retrieval Community support
Popular
Pro
$49/monthly
Up to 10,000 graphs Sub-200ms retrieval Access control Email support
Weekly intelligence on AI strategy, operations, and market shifts. No noise. No narrative. Direct to your inbox.
Pros & Cons
Enterprise-grade governance with access control, retention, and audit built into the substrate. Consistently fast retrieval under 200ms even at 100M graph nodes.
Pricing and detailed cost structure are not publicly listed. May require significant integration effort for teams not already using graph-based memory patterns.
FAQs
Just Launched
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