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Zep - AI Coding Assistant Tool

AI Coding Assistant · Founded by Daniel Chalef in 2023

Zep

Zep

A context engineering and memory layer for AI agents that assembles real-time chat history, business data, and user preferences into a temporal knowledge graph for more accurate LLM responses.

Cost

Free trial, Paid

Rating

People love it

Time to value

Quick Setup (< 1 hour)

You can use Zep to build memory and context systems for AI agents. It connects to your chat history, business data, and user interactions to create a unified context graph. The system automatically extracts entities, relationships, and facts from your data. When your AI agent needs context, Zep retrieves relevant information and formats it for your language model. It handles real-time updates and can invalidate outdated facts as new information comes in.

What Zep does

Ingest chat messages and business data automaticallyExtract entities and relationships from conversationsBuild temporal knowledge graphs that evolve over timeRetrieve relevant context for AI agent queriesFormat context data for language model consumptionTrack fact changes and invalidate outdated informationCreate custom entity types for specific business domainsMonitor context retrieval performance and accuracyBuilds unified context graphs from multiple data sourcesExtracts entities and relationships automaticallyInvalidates outdated facts when new information arrivesRetrieves context in under 200msWorks with any agent frameworkIntegrates with three lines of codeHandles real-time voice and video agentsCustomizable entity types for specific business domains

Pricing breakdown

PlanPrice10 seats / yr
Flex$25.00 / mo$3,000
Flex Plus$475.00 / mo$57,000

Annual estimates assume continuous billing at the listed list price. Volume discounts typical above 50 seats.

Tutorials & Demos

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Zep, AI agents, context engineering, agent memory, graph RAG, context graph, entity extraction, fact invalidation, temporal knowledge graph, business data integration, chat history, context assembly, AI orchestration, machine learning