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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

Performance Score

4.8/5

Content/Output Quality

High, consistent, and context-aware

Interface

Clean and developer-focused

AI Technology
LLMNLPContext Graph
Purpose of Tool

To provide persistent, governed, and scalable memory for AI agents using temporal context graphs.

Compatibility

Works with any agent framework via Python, TypeScript, and Go SDKs; supports cloud, BYOK, and BYOC deployments.

Pricing

Freemium with paid tiers

Features

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

Who Is It For?

AI Engineering Teams

Enterprise Architects

Customer Support Teams

Product Teams

Data Scientists

Startups

Compliance Officers

DevOps Teams

Sales Teams

Research Labs

Pricing

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

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Pros & Cons

Pros

  • Enterprise-grade governance with access control, retention, and audit built into the substrate.
  • Consistently fast retrieval under 200ms even at 100M graph nodes.

Cons

  • 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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