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How to migrate to LangGraph memory

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.

  • Users that rely on RunnableWithMessageHistory or BaseChatMessageHistory do not need to make any changes, but are encouraged to consider using LangGraph for more complex use cases.
  • Users that rely on deprecated memory abstractions from LangChain 0.0.x should follow this guide to upgrade to the new LangGraph persistence feature in LangChain 0.3.x.

Why use LangGraph for memory?​

The main advantages of persistence in LangGraph are:

  • Built-in support for multiple users and conversations, which is a typical requirement for real-world conversational AI applications.
  • Ability to save and resume complex conversations at any point. This helps with:
    • Error recovery
    • Allowing human intervention in AI workflows
    • Exploring different conversation paths ("time travel")
  • Full compatibility with both traditional language models and modern chat models. Early memory implementations in LangChain weren't designed for newer chat model APIs, causing issues with features like tool-calling. LangGraph memory can persist any custom state.
  • Highly customizable, allowing you to fully control how memory works and use different storage backends.

Evolution of memory in LangChain​

The concept of memory has evolved significantly in LangChain since its initial release.

LangChain 0.0.x memory​

Broadly speaking, LangChain 0.0.x memory was used to handle three main use cases:

Use CaseExample
Managing conversation historyKeep only the last n turns of the conversation between the user and the AI.
Extraction of structured informationExtract structured information from the conversation history, such as a list of facts learned about the user.
Composite memory implementationsCombine 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 BaseChatMessageHistory​

note

Please see How to use BaseChatMessageHistory with LangGraph, if you would like to use BaseChatMessageHistory (with or without RunnableWithMessageHistory) in LangGraph.

As 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:

  • Orchestration: In LangGraph, users define graphs that specify the flow of the application. This allows users to keep using LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.
  • Persistence: Users can rely on LangGraph's persistence to store and retrieve data. LangGraph persistence is extremely flexible and can support a much wider range of use cases than the 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.

Migrations​

Prerequisites

These guides assume some familiarity with the following concepts:

1. Managing conversation history​

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 TypeHow to MigrateDescription
ConversationBufferMemoryLink to Migration GuideA basic memory implementation that simply stores the conversation history.
ConversationStringBufferMemoryLink to Migration GuideA special case of ConversationBufferMemory designed for LLMs and no longer relevant.
ConversationBufferWindowMemoryLink to Migration GuideKeeps the last n turns of the conversation. Drops the oldest turn when the buffer is full.
ConversationTokenBufferMemoryLink to Migration GuideKeeps 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.
ConversationSummaryMemoryLink to Migration GuideContinually summarizes the conversation history. The summary is updated after each conversation turn. The abstraction returns the summary of the conversation history.
ConversationSummaryBufferMemoryLink to Migration GuideProvides 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.
VectorStoreRetrieverMemorySee related long-term memory agent tutorialStores the conversation history in a vector store and retrieves the most relevant parts of past conversation based on the input.

2. Extraction of structured information from the conversation history​

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 TypeDescription
BaseEntityStoreAn 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.
ConversationEntityMemoryCombines the ability to summarize the conversation while extracting structured information from the conversation history.

And specific backend implementations of abstractions:

Memory TypeDescription
InMemoryEntityStoreAn implementation of BaseEntityStore that stores the information in the literal computer memory (RAM).
RedisEntityStoreA specific implementation of BaseEntityStore that uses Redis as the backend.
SQLiteEntityStoreA specific implementation of BaseEntityStore that uses SQLite as the backend.
UpstashRedisEntityStoreA specific implementation of BaseEntityStore that uses Upstash as the backend.

These abstractions have received limited development since their initial release. The 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:

  1. Please review this Long-term memory agent tutorial which should provide a good starting point for how to extract structured information from the conversation history.
  2. If you're still struggling, please open an issue on the LangChain GitHub repository, explain your use case, and we'll try to provide more guidance on how to migrate these abstractions.

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 implementations​

Memory classes that fall into this category include:

Memory TypeDescription
CombinedMemoryThis abstraction accepted a list of BaseMemory and fetched relevant memory information from each of them based on the input.
SimpleMemoryUsed to add read-only hard-coded context. Users can simply write this information into the prompt.
ReadOnlySharedMemoryProvided 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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