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getzep/zep

4,564PythonAudience · developerComplexity · 3/5Setup · moderate

TLDR

A platform that gives AI agents persistent memory by building a temporal knowledge graph from your conversations and business data, then retrieving the right context in under 200ms when the agent needs it to respond accurately.

Mindmap

mindmap
  root((Zep))
    What it does
      AI agent memory
      Temporal knowledge graph
      Fast context retrieval
    Tech Stack
      Python
      TypeScript
      Go
      Graphiti
    Use Cases
      Persistent chatbots
      Business data memory
      Multi-agent context
    Audience
      AI developers
      Agent builders
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Things people build with this

USE CASE 1

Give your LangChain or LlamaIndex AI agent persistent memory across sessions without building your own storage layer.

USE CASE 2

Build a customer service bot that remembers past conversations and updates its knowledge when facts change over time.

USE CASE 3

Connect an AutoGen agent to a temporal knowledge graph so it can reason about whether information is current or outdated.

Tech stack

PythonTypeScriptGoLangChainLlamaIndex

Getting it running

Difficulty · moderate Time to first run · 30min

The core Zep server is a managed cloud service, the original self-hosted Community Edition has been deprecated and moved to a legacy folder.

In plain English

Zep is a platform that helps AI agents remember and use context across conversations. The core problem it addresses is that AI agents often lack the information they need to give accurate, relevant responses, especially when that information comes from previous conversations, business data, or documents that change over time. Zep collects that information, builds a structured map of the relationships within it, and then retrieves the right pieces at the right moment for the agent to use. The system works in three steps. You feed it data as it arrives: chat messages, business records, events from your application. Zep processes that data automatically, building what it calls a temporal knowledge graph that tracks not just facts but also how those facts have changed over time. When the agent needs context for a new request, Zep assembles a relevant summary quickly, aiming for response times under 200 milliseconds. The technical foundation is a separate open-source project called Graphiti, which is the knowledge graph framework that Zep is built on top of. Graphiti handles the storage of facts with timestamps indicating when each fact was valid, so the system can reason about current versus outdated information. The repository itself is primarily a collection of examples and integration packages rather than the core Zep server, which is a managed cloud service. The examples show how to connect Zep to popular AI frameworks like LangChain, LlamaIndex, and AutoGen. SDKs are available for Python, TypeScript, and Go. The original self-hosted open-source version of Zep, known as Community Edition, has been deprecated and moved to a legacy folder. The project now focuses on the cloud service and the example integrations in this repository.

Copy-paste prompts

Prompt 1
I'm building a LangChain chatbot and want to use Zep for memory. Show me how to connect Zep and store conversation history so context persists between sessions.
Prompt 2
Using the Zep Python SDK, how do I feed business records into Zep's knowledge graph and retrieve relevant context for an AI agent's next response?
Prompt 3
Show me how to integrate Zep with an AutoGen agent so it has access to a knowledge graph with timestamped facts that can go stale.
Prompt 4
I want to use Zep for my LlamaIndex RAG pipeline. Walk me through the integration using the example code in the Zep repo.
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