Zep AI Memory

Long-term memory layer for AI agents and LLM apps

4.8 (4)

Évalué par Daniel Nikulshyn·Mis à jour mai 2026

Knowledge Graph Semantic Search SDK LLM Infrastructure API Memory Developer Tools

Aperçu

Zep AI Memory is a developer-focused memory service that gives AI agents persistent, structured recall across conversations and sessions. It captures chat history, extracts key facts, and organizes them into a knowledge graph so agents can retrieve relevant context on demand instead of stuffing entire histories into prompts. The platform handles summarization, entity extraction, and semantic search behind a simple API, letting teams add stateful memory to chatbots, copilots, and autonomous agents without building custom retrieval infrastructure. It is designed to scale with production workloads while keeping prompt sizes and token costs predictable. Zep integrates with common LLM frameworks like LangChain and LlamaIndex and provides SDKs for popular languages, making it straightforward to drop into existing agent stacks.

Fonctionnalités clés

Long-term conversational memory
Automatic fact and entity extraction
Knowledge graph storage
Semantic and hybrid search
LangChain and LlamaIndex integrations
Multi-language SDKs

Cas d’usage

Persistent memory for customer support chatbots

Give support bots recall of past tickets, preferences, and entities across sessions so users don't need to repeat context, improving resolution quality and continuity.

Stateful copilots with reduced token costs

Replace full chat-history prompt stuffing with targeted semantic retrieval from Zep, keeping prompts small and predictable while preserving relevant long-term context.

Autonomous agents with structured recall

Power multi-step agents using Zep's knowledge graph to remember facts, entities, and relationships gathered across runs, enabling more coherent long-horizon task execution.

LangChain or LlamaIndex memory backend

Drop Zep into existing LLM framework pipelines as the memory layer, adding fact extraction and hybrid search without building custom retrieval infrastructure.

Pour & contre

Pour

Persistent memory across sessions
Reduces prompt size and token costs
Knowledge graph for structured recall
Works with major LLM frameworks
Developer-friendly SDKs and API

Contre

Requires engineering integration work
Geared toward developers, not end users
Adds another service to the stack

Avis

4.8

Moyenne sur 4 avis.

Connecte-toi pour laisser un avis.

Kwame Mensah

Does the job

Pretty happy overall. Automatic fact and entity extraction just works and persistent memory across sessions. Geared toward developers, not end users can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Esther Adeyemi

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on knowledge graph storage, and reduces prompt size and token costs caught me off guard. still, I'd recommend giving it a real trial.

Ingrid Bauer

Compared a few options

Evaluated this against two competitors. Where it wins: langChain and LlamaIndex integrations and persistent memory across sessions. On balance the feature set — especially multi-language SDKs — justifies the 5 stars for our use case.

Marcus Bell

Does the job

Pretty happy overall. LangChain and LlamaIndex integrations just works and knowledge graph for structured recall. Requires engineering integration work can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.