📚Complete Guide

Zep Tutorial: Get Started in 5 Minutes [2026]

Master Zep with our step-by-step tutorial, detailed feature walkthrough, and expert tips.

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Getting Started with Zep

Sign up for free Zep account at app.getzep.com and create your first project Install the SDK for your preferred language (Python, TypeScript, or Go) and configure API credentials Define your data model with custom entity types and relationships for your business domain Start ingesting conversation data and business information using the simple three

line integration Test context retrieval and optimize configuration parameters for your accuracy and latency requirements

💡 Quick Start: Follow these 2 steps in order to get up and running with Zep quickly.

🔍 Zep Features Deep Dive

Explore the key features that make Zep powerful for ai memory & search workflows.

Temporal Knowledge Graph

What it does:

Builds evolving knowledge graphs from conversations and business data, tracking how entities and relationships change over time. Automatically invalidates outdated facts while preserving provenance, ensuring agents access current, accurate information.

Use case:

Customer support agent understands that a user's payment method was updated last week, invalidating previous 'expired card' status while maintaining history of the resolution process.

Unified Context Assembly

What it does:

Automatically ingests and correlates data from chat history, CRM systems, JSON business data, and documents into a single context graph. Retrieves and formats relevant information for LLM consumption in one API call.

Use case:

Sales agent accessing prospect's conversation history, CRM data, and product interaction logs to provide personalized recommendations based on complete customer journey.

Real-Time Context Retrieval

What it does:

Delivers assembled context with <200ms P95 latency using optimized graph traversal and caching. Multiple configuration options balance accuracy, speed, and token efficiency for different use cases.

Use case:

Voice agent providing immediate, personalized responses during live customer calls without noticeable delays, accessing complete customer context in real-time.

Graph RAG Integration

What it does:

Combines relationship-aware retrieval with traditional RAG, understanding connections between entities to surface relevant context. Supports custom entity types and relationship models for domain-specific knowledge.

Use case:

Healthcare agent understanding patient's medication history, doctor relationships, and treatment outcomes to provide contextually appropriate health guidance.

Custom Context Templates

What it does:

Pre-formatted context blocks optimized for different LLM prompting strategies. Allows fine-tuned control over how entities, relationships, and facts are presented to agents.

Use case:

E-commerce agent receiving customer context formatted with purchase history, browsing patterns, and preference summaries tailored for product recommendation workflows.

Enterprise Security & Compliance

What it does:

SOC2 Type 2 certified with HIPAA BAA support, multiple deployment models including BYOK, BYOM, and BYOC. Audit logs, guaranteed SLAs, and data residency controls for regulated industries.

Use case:

Healthcare organization deploying AI patient assistants with full HIPAA compliance, encrypted data processing, and audit trails for regulatory requirements.

❓ Frequently Asked Questions

How does Zep's context engineering differ from traditional RAG systems?

Traditional RAG retrieves static documents based on similarity. Zep builds temporal knowledge graphs that understand entity relationships and track how facts change over time. This enables queries like 'how has the customer's preference evolved?' that static RAG cannot handle. Zep also assembles context from multiple sources (chat, CRM, business data) in one API call.

What makes Zep faster than other agent memory systems?

Zep achieves <200ms P95 retrieval latency through optimized graph traversal, intelligent caching, and single-shot context assembly. Unlike systems that require multiple tool calls or agentic loops, Zep delivers complete assembled context in one API request, eliminating the round-trip delays that slow down other approaches.

How does Zep handle fact conflicts and outdated information?

Zep's temporal knowledge graph automatically invalidates outdated facts when new information conflicts with existing data. It maintains provenance to source messages and timestamps, allowing agents to reason about when facts were true and how they've changed. This prevents agents from acting on stale information.

Can Zep integrate with existing agent frameworks like LangChain?

Yes. Zep is framework-agnostic with native SDKs for Python, TypeScript, and Go. It integrates with LangChain, LlamaIndex, AutoGen, CrewAI, and custom frameworks through simple API calls. The three-line integration works with any system that can make HTTP requests.

What deployment options does Zep offer for enterprise customers?

Enterprise customers can choose from Managed (fully hosted), BYOK (bring your own encryption keys), BYOM (bring your own model provider), or BYOC (bring your own cloud/VPC). All enterprise plans include SOC2 Type 2 certification, HIPAA BAA support, guaranteed SLAs, and dedicated account management.

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Follow our tutorial and master this powerful ai memory & search tool in minutes.

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Tutorial updated March 2026

🔍 Zep Features Deep Dive

Explore the key features that make Zep powerful for ai memory & search workflows.

Temporal Knowledge Graph

What it does:

Use case:

Customer support agent understands that a user's payment method was updated last week, invalidating previous 'expired card' status while maintaining history of the resolution process.

Unified Context Assembly

What it does:

Use case:

Sales agent accessing prospect's conversation history, CRM data, and product interaction logs to provide personalized recommendations based on complete customer journey.

Real-Time Context Retrieval

What it does:

Delivers assembled context with <200ms P95 latency using optimized graph traversal and caching. Multiple configuration options balance accuracy, speed, and token efficiency for different use cases.

Use case:

Voice agent providing immediate, personalized responses during live customer calls without noticeable delays, accessing complete customer context in real-time.

Graph RAG Integration

What it does:

Use case:

Healthcare agent understanding patient's medication history, doctor relationships, and treatment outcomes to provide contextually appropriate health guidance.

Custom Context Templates

What it does:

Pre-formatted context blocks optimized for different LLM prompting strategies. Allows fine-tuned control over how entities, relationships, and facts are presented to agents.

Use case:

E-commerce agent receiving customer context formatted with purchase history, browsing patterns, and preference summaries tailored for product recommendation workflows.

Enterprise Security & Compliance

What it does:

SOC2 Type 2 certified with HIPAA BAA support, multiple deployment models including BYOK, BYOM, and BYOC. Audit logs, guaranteed SLAs, and data residency controls for regulated industries.

Use case:

Healthcare organization deploying AI patient assistants with full HIPAA compliance, encrypted data processing, and audit trails for regulatory requirements.