Zep is a context engineering and memory platform for AI agents that automatically selects the right context for each request. It pulls signals from chat history, business systems, and user behavior, then unifies them into a graph-based knowledge model.
Millisecond context for agents
Zep uses a unified knowledge graph and Graph RAG to generate precise, personalized context for LLM agents. This helps agents produce more relevant and consistent answers, even in complex workflows and at larger data volumes.
One API for integration
Zep is designed to work with any framework and can be added with just a few lines of code. A single pipeline and API cover key tasks end to end:
- Connecting data sources
- Storing and managing agent memory
- Assembling context for each query
- Returning context quickly (around 200 ms latency)
Built for developers and enterprise teams
Zep targets teams building advanced assistants, chatbots, and internal AI tools. It fits both startups and enterprise organizations that need scalable knowledge management and reliable agent memory without maintaining complex in-house infrastructure.
