Zep
Open-source memory layer for AI agents that persists conversation history, extracts user facts, and enables semantic search across past interactions.
Zep is an open-source memory store for LLM applications and AI agents that solves the context window limitation by persisting conversation history, extracting user facts, and enabling semantic retrieval of past interactions. Unlike stuffing raw conversation history into context, Zep automatically extracts and maintains a structured user knowledge graph so agents can recall preferences, facts, and prior decisions across sessions. The platform supports integration with LangChain, LlamaIndex, and any LLM framework via a REST API. Zep offers both a self-hosted open-source version and a managed cloud service for teams who need a turnkey solution. As AI agents increasingly require persistent long-running memory, Zep provides production infrastructure used by enterprise teams building personalized AI assistants.
Key Features
- Persistent conversation memory - stores and retrieves full session history across multiple user interactions
- Automatic user fact extraction - builds a structured knowledge graph of user attributes from conversation content
- Semantic memory search - retrieve relevant past interactions by meaning using vector similarity, not just keywords
- REST API and Python/TypeScript SDKs for integration with any LLM framework or custom agent architecture
- Native LangChain and LlamaIndex integrations for embedding memory into existing agent workflows
- Self-hosted open-source deployment or managed cloud for teams needing a turnkey production solution
Use Cases
- AI agent developers building personalized assistants that need to remember user preferences across multiple sessions
- Enterprise teams creating customer-facing AI that maintains consistent context over long-running support relationships
- LangChain and LlamaIndex developers adding persistent memory to existing chatbot and agent applications
- Developers building autonomous agents that need to recall prior task outputs and decisions across workflow runs
Pros
- Self-hosted open-source option gives teams full control over sensitive user memory data without vendor dependency
- Structured fact extraction builds a queryable user knowledge graph automatically - not just raw conversation storage
- Deep LangChain and LlamaIndex integrations make adoption low-friction for teams already using those frameworks
Cons
- Self-hosting requires DevOps resources and ongoing maintenance to run at production scale reliably
- Cloud pricing is higher than comparable memory solutions for teams processing very large conversation volumes
- Knowledge graph extraction accuracy can degrade on complex or ambiguous conversations, producing incorrect user facts
Zep Alternatives
Explore similar tools and alternatives
Looking for alternatives to Zep? Here are some similar tools you might like:
Mem0
Open-source memory layer for AI agents and chatbots that enables persistent, context-aware conversations across sessions, with 25k+ GitHub stars.
LangChain
The most widely adopted framework for building LLM-powered applications - 100K+ GitHub stars, supports every major LLM and vector store.
LlamaIndex
Leading open-source data framework for building LLM applications with RAG and agents - 40,000+ GitHub stars and a managed LlamaCloud service.
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