text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryCassandra Chat Message HistoryDynamodb Chat Message HistoryEntity Memory with SQLite storageMomento Chat Message HistoryMongodb Chat Message HistoryMotÃ¶rhead MemoryMotÃ¶rhead Memory (Managed)Postgres Chat Message HistoryRedis Chat Message HistoryZep MemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsMemoryZep MemoryOn this pageZep MemoryREACT Agent Chat Message History with Zep - A long-term memory store for LLM applications.â€‹This notebook demonstrates how to use the Zep Long-term Memory Store as memory for your chatbot.We'll demonstrate:Adding conversation history to the Zep memory store.Running an agent and having message automatically added to the store.Viewing the enriched messages.Vector search over the conversation history.More on Zep:â€‹Zep stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.Key Features:Fast! Zepâ€™s async extractors operate independently of the your chat loop, ensuring a snappy user experience.Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.Hybrid search over memories and metadata, with messages automatically embedded on creation.Entity Extractor that automatically extracts named entities from messages and stores them in the message metadata.Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.Python and JavaScript SDKs.Zep project: https://github.com/getzep/zep