> For the complete documentation index, see [llms.txt](https://aisuko.gitbook.io/wiki/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aisuko.gitbook.io/wiki/ai-techniques/chain/langchain/memory.md). # Memory > By default, Chains and Agents are stateless, meaning that they treat each incoming query independently (as are the underlying LLMs and chat models). In some applications (chatbots being a GREAT example) it is highly important to remember previous interactions, both at a short term but also at a long term level. The Memory does exactly that. > LangChain provides memory components in two forms. First, LangChain provides helper utilities for managing and manipulating previous chat messages. These are designed to be modular and useful regardless of how they are used. Secondly, LangChain provides easy ways to incorporate these utilities into chains. ## ChatMessageHistory ChatMessageHistory is a light wight memory wrapper which exposes convenience methods for saving Human messages, AI messages, and then fetching them all. ## ConversationBufferMemory ConversationBufferMemory is just a wrapper around ChatMessageHistory that extracts the messages in a variable. ## ConversationBufferWindowMemory ConversationBufferWindowMemory keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large. ## ConversationKGMemory This type of memory uses a knowledge graph to recreate memory. ## ConversationSummaryMemory This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time. ## ConversationSummaryBufferMemory ConversationSummaryBufferMemory combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. Unlike the previous implementation though, it uses token length rather than number of interactions to determine when to flush interactions. ## ConversationTokenBufferMemory ConversationTokenBufferMemory keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions. ## VectorStore-Backed Memory VectorStoreRetrieverMemory stores memories in a VectorDB and queries the top-K most “salient” docs every time it is called. ## Store memory in db Memories can be stored in different type of database include SQLite, PostgresSQL, redis , mongodb and so on --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://aisuko.gitbook.io/wiki/ai-techniques/chain/langchain/memory.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.