Conversational Memory in LangChain
Explore three conversational memory strategies in LangChain that enable large language models to maintain context across interactions. Understand how ConversationBufferMemory preserves full chat history for short sessions, ConversationSummaryMemory compresses discussions for longer dialogs within token limits, and VectorStoreRetrieverMemory enables cross-session recall using vector databases. Discover how to select and combine these memory types to design effective, context-aware LLM applications.
Every chain covered so far in this course, whether it is an LLMChain, a SequentialChain, or a RouterChain, shares a fundamental limitation. Each invocation starts with a completely blank slate. The chain has zero awareness of anything that happened in previous turns. Consider a customer-support chatbot built with a stateless chain. The user says, “My order number is 12345.” The bot acknowledges it. On the very next turn, the user asks, “Can you check the status of my order?” and the bot has no idea what order the user is talking about. It has already been forgotten. The user is forced to repeat themselves, and the experience breaks down immediately.
Conversational memory solves this by injecting prior exchanges into the prompt context so the LLM can reference earlier turns as if it “remembers” them. This lesson covers three memory strategies available in LangChain, each designed for a different set of constraints.
ConversationBufferMemory retains the full, verbatim chat history and injects it into every prompt.
ConversationSummaryMemory uses a secondary LLM call to compress the running conversation into a concise summary, keeping token usage roughly constant.
VectorStoreRetrieverMemory embeds each turn into a vector database and ...