How does LangChain handle memory in conversational AI applications, and what are the best practices for maintaining context across long conversations?

[kashyap797]

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LangChain provides different memory modules to help maintain context in conversations. The ConversationBufferMemory stores past interactions as a running log, while ConversationSummaryMemory summarizes conversations to save space. For longer conversations, ConversationKGMemory creates a knowledge graph, and VectorStoreRetrieverMemory stores embeddings for efficient retrieval.

To ensure smooth performance, it's best to limit stored messages, use summarization for long interactions, and integrate external storage when needed.