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Agent Memory|Graph RAG|Context Assembly

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What is Context Engineering?

Context Engineering assembles relevant information around an LLM for reliable task completion. Unlike basic prompting, it dynamically integrates user preferences, conversation history, and business data.

Agents often fail due to missing personalized context. Zep solves this by automatically creating temporal knowledge graphs to organize and retrieve context for each interaction.

Learn about Context Engineering

APPLICATION & INTERFACE

AGENT FRAMEWORK

CONTEXT ENGINEERING

ZEP CAPABILITIES

Context Orchestration

Agent Memory

Graph RAG

Search Tools

Prompt Construction

Tool Integration

LLM

tobi lutke@tobi·Jun 18, 2025

I really like the term "context engineering" over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.

Andrej Karpathy@karpathy·Jun 25, 2025

+1 for "context engineering" over "prompt engineering". People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window

How Zep Works

Zep transforms conversations, business data, & user interactions into a living knowledge graph that evolves with every interaction. Relevant and accurate context is automatically assembled into a context block when you need it.

Graph Construction

Automatic extraction of entities, relationships, and facts from conversations and business data, reconciling new information with existing data to maintain accuracy.

Relevant Retrieval

When your agent needs context, Zep searches the knowledge graph and returns the most relevant information.

Context Assembly

Zep delivers structured, LLM-ready context combining user traits, interactions, and business data while being token efficient.

How Zep Works - Data flow diagram showing the transformation from raw data sources through knowledge graph construction to delivered context

Product Features

Robbie

2024-09-07 14:27

I only wear Adidas shoes. I love them!

Facts

Robbie only wears Adidas shoes.
Robbie strongly favors Adidas shoes.

Robbie

2024-10-14 09:12

My shoes fell apart and I need to return them. I'm super angry! I'll be wearing Nike going forward!

Facts

Robbie only wears Adidas shoes.
Robbie strongly favors Adidas shoes.
Robbie's Adidas shoes fell apart.
Robbie needs to return their shoes.
Robbie is angry about their Adidas shoes falling apart.
Robbie intends to wear Nike shoes in the future.

Agent Memory

Deploy Agents with Perfect Memory

Your agents remember user preferences, past conversations, and business context across all interactions. No more starting from scratch or losing important details.

Explore Agent Memory

Graph RAG

Super Fast Graph RAG for Dynamic Data

Connect agents to your business data with Graph RAG that understands relationships and context, and automatically handles dynamic data. Your agents access the right information at the right time, in milliseconds.

Discover Graph RAG

FACTS and ENTITIES provide relevant context for the current conversation.

# Key facts with their date ranges
# format: FACT (Date range: from - to)

  - Emily Painter cannot log in. (2024-11-14 02:13:19+00:00 - present)
  - Account Emily0e62 is suspended due to payment failure. (2024-11-14 02:03:58+00:00 - present)
  - Payment failed using card ending in 1234. (2024-09-15 00:00:00+00:00 - present)
  - Failure reason: Card expired. (2024-09-15 00:00:00+00:00 - present)


# Key entities and their descriptions
# ENTITY_NAME: entity summary

  - Emily0e62: User account owned by Emily Painter, currently suspended due to payment failure and login issues.
  - Card expired: Reason for the failed payment transaction.

Context Assembly

Automated Context Creation

Stop manually crafting prompts. Zep automatically assembles relevant user memory and business context into optimized prompts for reliable agent performance.

See Agent Context

Trusted by Industry Leaders

See how teams are transforming their AI applications with Zep

“Zep is one of the most exciting things I've seen for real-world agent use cases in a long time. Their innovative approach is truly game-changing.”

Ken Collins

VP of Product, Torq and GenAI Expert

“Zep just introduced a game-changing way for AI agents to remember and learn. Unlike other systems that only retrieve static documents, Zep uses a temporal knowledge graph to combine conversations and structured business data, keeping track of how things change over time.”

Lior Sinclair

Founder/CEO, AlphaSignal

“Zep AI was instrumental in enabling the Sidekick's personalized experience through dynamic memory retrieval. Their innovative tech stack is powering groundbreaking projects like ArtPrize 2024, taking personalized AI experiences to the next level.”

Mark Losey

CTO at Flockx

“Zep AI empowers AI systems to think and remember like humans. By organizing memories into structured episodes and extracting key insights, it builds smarter, more intuitive AI agents that revolutionize how businesses harness intelligence.”

Vijay Morampudi

Senior Director - AI CoE, Axtria

“Zep is one of the most exciting things I've seen for real-world agent use cases in a long time. Their innovative approach is truly game-changing.”

Ken Collins

VP of Product, Torq and GenAI Expert

“Zep just introduced a game-changing way for AI agents to remember and learn. Unlike other systems that only retrieve static documents, Zep uses a temporal knowledge graph to combine conversations and structured business data, keeping track of how things change over time.”

Lior Sinclair

Founder/CEO, AlphaSignal

“Zep AI was instrumental in enabling the Sidekick's personalized experience through dynamic memory retrieval. Their innovative tech stack is powering groundbreaking projects like ArtPrize 2024, taking personalized AI experiences to the next level.”

Mark Losey

CTO at Flockx

“Zep AI empowers AI systems to think and remember like humans. By organizing memories into structured episodes and extracting key insights, it builds smarter, more intuitive AI agents that revolutionize how businesses harness intelligence.”

Vijay Morampudi

Senior Director - AI CoE, Axtria

Built for Teams, Proven at Scale

Deploy personalized agents in days, not months. Enterprise-grade compliance meets developer-friendly APIs.

For Developers

Skip building complex infrastructure. Three lines of code to production.

example.py

# Add conversation to memory
zep.memory.add(session_id, messages)

# Get relevant context
memory = zep.memory.get(session_id)
context = memory.context

User memory with three lines of code

Ingest business data as JSON, text, or messages

Works with LangChain, LangGraph, and popular frameworks

For Developers Start Building

For Engineering Leaders

Measurable performance improvements with enterprise compliance.

Enterprise compliance with SOC2 Type 2 and HIPAA

Days vs. months implementation without hiring scarce AI talent

100%+ accuracy improvements through personalized context

For Engineering Leaders

Customized for Your Domain

Zep adapts to your business through custom entity types and relationship models. These models enable precision recall of exactly the required context, so your agents understand your business domain, not just generic conversations.

Sales and Marketing

Store lead preferences, product interests, campaign interactions, and buying signals.

Your sales agents understand prospect history, pricing discussions, and engagement patterns to personalize outreach and close deals faster.

sales_and_marketing_entities.py

class Lead(EntityModel):
    """Represents a sales lead or prospect."""
    company_size = Field(
        description="startup, SMB, mid-market, enterprise"
    )
    budget_range = Field(
        description="Budget discussed or indicated"
    )
    decision_timeline = Field(
        description="Expected decision timeframe"
    )

Zep is the State of the Art in Agent Memory

Zep is the current state-of-the-art in agent memory, excelling in the LongMemEval benchmark, a challenging evaluation that closely models enterprise use cases.

Read the Paper

100%+

Accuracy Improvements

Agents perform better when provided with the right context at the right time.

90%

Latency Reduction

Optimized context retrieval delivers the right information without overwhelming LLMs with irrelevant data.

98%

Token Efficiency

Smart context assembly reduces token usage while maintaining comprehensive understanding.

Graphiti Graph Framework

Powered by Open-Source

Zep's open-source temporal knowledge graph library, Graphiti, serves as the core of our capability to swiftly integrate new data streams and provide comprehensive historical context regarding user states.

View on GitHub

Graphiti GitHub repository - Build Real-Time Knowledge Graphs for AI Agents

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