Zep is a memory and context management infrastructure built specifically for AI agents, chatbots, and LLM-based applications. It allows developers to store, retrieve, and manage conversational memory to support long-term context, personalization, and improved reasoning. Zep is designed to be integrated seamlessly with LLM apps, assistants, RAG pipelines, and autonomous agents, enabling them to function beyond one-off prompt interactions.

Ideal for developers and product teams building LLM-powered apps, Zep provides tools to handle session memory, embeddings, vector search, and metadata, making it easier to maintain context, recall relevant past events, and deliver more intelligent user experiences.

What are the main features of Zep?

Long-term memory for LLM applications

Zep enables persistent memory for agents and apps:

• Store complete or summarized conversation history tied to user sessions.

• Retrieve past context to inform future model responses.

• Helps agents remain consistent, contextual, and relevant across long interactions.

Built-in vector storage and semantic search

Zep includes native vector database capabilities:

• Supports embedding storage for semantic memory and retrieval.

• Allows similarity search over previous messages, documents, or events.

• Enhances contextual accuracy in RAG workflows and agent reasoning.

Session management and metadata support

Developers can manage session-based memory structures:

• Group messages, events, and context by user or session.

• Attach and query custom metadata (e.g., user intent, timestamps, sentiment).

• Enables fine-grained memory access and filtering.

Flexible integration with LLM ecosystems

Zep is model-agnostic and works with any LLM or orchestration layer:

• Integrates with tools like LangChain, OpenAI, Anthropic, LlamaIndex, and more.

• Simple REST API for memory write/read operations.

• Works with both real-time assistants and background agents.

Lightweight, developer-friendly infrastructure

Zep is designed to be easy to deploy and scale:

• Available as a self-hosted or managed service.

• Minimal setup and well-documented API.

• Designed for production use cases requiring reliable memory at scale.

Why choose Zep?

• Purpose-built for AI memory: Designed specifically to manage context for LLM apps.

• Improves agent intelligence: Enables reasoning across time and past events.

• Easy to integrate: Works with any stack, model, or framework.

• Scalable and production-ready: Suitable for real-world assistant and agent deployments.

• Enhances personalization: Supports user-aware, context-rich interactions.