As of the v0.3 release of LangChain, we recommend that LangChain users take advantage of LangGraph persistence to incorporate memory into their LangChain application.
RunnableWithMessageHistory or BaseChatMessageHistory do not need to make any changes, but are encouraged to consider using LangGraph for more complex use cases.The main advantages of persistence in LangGraph are:
The concept of memory has evolved significantly in LangChain since its initial release.
LangChain 0.0.x memoryBroadly speaking, LangChain 0.0.x memory was used to handle three main use cases:
Use Case Example Managing conversation history Keep only the lastn turns of the conversation between the user and the AI. Extraction of structured information Extract structured information from the conversation history, such as a list of facts learned about the user. Composite memory implementations Combine multiple memory sources, e.g., a list of known facts about the user along with facts learned during a given conversation.
While the LangChain 0.0.x memory abstractions were useful, they were limited in their capabilities and not well suited for real-world conversational AI applications. These memory abstractions lacked built-in support for multi-user, multi-conversation scenarios, which are essential for practical conversational AI systems.
Most of these implementations have been officially deprecated in LangChain 0.3.x in favor of LangGraph persistence.
RunnableWithMessageHistory and BaseChatMessageHistoryAs of LangChain v0.1, we started recommending that users rely primarily on BaseChatMessageHistory. BaseChatMessageHistory serves as a simple persistence for storing and retrieving messages in a conversation.
At that time, the only option for orchestrating LangChain chains was via LCEL. To incorporate memory with LCEL, users had to use the RunnableWithMessageHistory interface. While sufficient for basic chat applications, many users found the API unintuitive and challenging to use.
As of LangChain v0.3, we recommend that new code takes advantage of LangGraph for both orchestration and persistence:
LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.RunnableWithMessageHistory interface.important
If you have been using RunnableWithMessageHistory or BaseChatMessageHistory, you do not need to make any changes. We do not plan on deprecating either functionality in the near future. This functionality is sufficient for simple chat applications and any code that uses RunnableWithMessageHistory will continue to work as expected.
The goal of managing conversation history is to store and retrieve the history in a way that is optimal for a chat model to use.
Often this involves trimming and / or summarizing the conversation history to keep the most relevant parts of the conversation while having the conversation fit inside the context window of the chat model.
Memory classes that fall into this category include:
Memory Type How to Migrate DescriptionConversationBufferMemory Link to Migration Guide A basic memory implementation that simply stores the conversation history. ConversationStringBufferMemory Link to Migration Guide A special case of ConversationBufferMemory designed for LLMs and no longer relevant. ConversationBufferWindowMemory Link to Migration Guide Keeps the last n turns of the conversation. Drops the oldest turn when the buffer is full. ConversationTokenBufferMemory Link to Migration Guide Keeps only the most recent messages in the conversation under the constraint that the total number of tokens in the conversation does not exceed a certain limit. ConversationSummaryMemory Link to Migration Guide Continually summarizes the conversation history. The summary is updated after each conversation turn. The abstraction returns the summary of the conversation history. ConversationSummaryBufferMemory Link to Migration Guide Provides a running summary of the conversation together with the most recent messages in the conversation under the constraint that the total number of tokens in the conversation does not exceed a certain limit. VectorStoreRetrieverMemory See related long-term memory agent tutorial Stores the conversation history in a vector store and retrieves the most relevant parts of past conversation based on the input.
Please see long-term memory agent tutorial implements an agent that can extract structured information from the conversation history.
Memory classes that fall into this category include:
Memory Type DescriptionBaseEntityStore An abstract interface that resembles a key-value store. It was used for storing structured information learned during the conversation. The information had to be represented as a dictionary of key-value pairs. ConversationEntityMemory Combines the ability to summarize the conversation while extracting structured information from the conversation history.
And specific backend implementations of abstractions:
Memory Type DescriptionInMemoryEntityStore An implementation of BaseEntityStore that stores the information in the literal computer memory (RAM). RedisEntityStore A specific implementation of BaseEntityStore that uses Redis as the backend. SQLiteEntityStore A specific implementation of BaseEntityStore that uses SQLite as the backend. UpstashRedisEntityStore A specific implementation of BaseEntityStore that uses Upstash as the backend.
These abstractions have received limited development since their initial release. This is because they generally require significant customization for a specific application to be effective, making them less widely used than the conversation history management abstractions.
For this reason, there are no migration guides for these abstractions. If you're struggling to migrate an application that relies on these abstractions, please:
The general strategy for extracting structured information from the conversation history is to use a chat model with tool calling capabilities to extract structured information from the conversation history. The extracted information can then be saved into an appropriate data structure (e.g., a dictionary), and information from it can be retrieved and added into the prompt as needed.
3. Implementations that provide composite logic on top of one or more memory implementationsMemory classes that fall into this category include:
Memory Type DescriptionCombinedMemory This abstraction accepted a list of BaseMemory and fetched relevant memory information from each of them based on the input. SimpleMemory Used to add read-only hard-coded context. Users can simply write this information into the prompt. ReadOnlySharedMemory Provided a read-only view of an existing BaseMemory implementation.
These implementations did not seem to be used widely or provide significant value. Users should be able to re-implement these without too much difficulty in custom code.
Explore persistence with LangGraph:
Add persistence with simple LCEL (favor langgraph for more complex use cases):
Working with message history:
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