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Alpha Notice: These docs cover the v1-alpha release. Content is incomplete and subject to change.For the latest stable version, see the v0 LangChain Python or LangChain JavaScript docs.

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Overview

LangChain agents use LangGraph persistence to enable long-term memory. This is a more advanced topic and requires knowledge of LangGraph to use.

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Memory storage

LangGraph stores long-term memories as JSON documents in a store. Each memory is organized under a custom namespace (similar to a folder) and a distinct key (like a file name). Namespaces often include user or org IDs or other labels that makes it easier to organize information. This structure enables hierarchical organization of memories. Cross-namespace searching is then supported through content filters. 
from langgraph.store.memory import InMemoryStore   def embed(texts: list[str]) -> list[list[float]]:  # Replace with an actual embedding function or LangChain embeddings object  return [[1.0, 2.0] * len(texts)]   # InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production use. store = InMemoryStore(index={"embed": embed, "dims": 2})  user_id = "my-user" application_context = "chitchat" namespace = (user_id, application_context)  store.put(   namespace,  "a-memory",  {  "rules": [  "User likes short, direct language",  "User only speaks English & python",  ],  "my-key": "my-value",  }, ) # get the "memory" by ID item = store.get(namespace, "a-memory")  # search for "memories" within this namespace, filtering on content equivalence, sorted by vector similarity items = store.search(   namespace, filter={"my-key": "my-value"}, query="language preferences" )
For more information about the memory store, see the Persistence guide.

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Read long-term memory in tools

A tool the agent can use to look up user information
from dataclasses import dataclass  from langchain_core.runnables import RunnableConfig from langchain.agents import create_agent from langgraph.config import get_store from langgraph.runtime import get_runtime from langgraph.store.memory import InMemoryStore  @dataclass class Context:  user_id: str  # InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production. store = InMemoryStore()   # Write sample data to the store using the put method store.put(   ("users",), # Namespace to group related data together (users namespace for user data)  "user_123", # Key within the namespace (user ID as key)  {  "name": "John Smith",  "language": "English",  } # Data to store for the given user )  def get_user_info() -> str:  """Look up user info."""  # Access the store - same as that provided to `create_agent`  store = get_runtime().store   user_id = get_runtime(Context).context.user_id  # Retrieve data from store - returns StoreValue object with value and metadata  user_info = store.get(("users",), user_id)   return str(user_info.value) if user_info else "Unknown user"  agent = create_agent(  model="anthropic:claude-3-7-sonnet-latest",  tools=[get_user_info],  # Pass store to agent - enables agent to access store when running tools  store=store,   context_schema=Context )  # Run the agent agent.invoke(  {"messages": [{"role": "user", "content": "look up user information"}]},  context=Context(user_id="user_123")  )

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Write long-term memory from tools

Example of a tool that updates user information
from typing_extensions import TypedDict  from langchain_core.runnables import RunnableConfig from langchain.agents import create_agent from langgraph.config import get_store from langgraph.store.memory import InMemoryStore  # InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production. store = InMemoryStore()   # TypedDict defines the structure of user information for the LLM class UserInfo(TypedDict):  name: str  # Tool that allows agent to update user information (useful for chat applications) def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str:  """Save user info."""  # Access the store - same as that provided to `create_agent`  store = get_store()   user_id = config["configurable"].get("user_id")  # Store data in the store (namespace, key, data)  store.put(("users",), user_id, user_info)   return "Successfully saved user info."  agent = create_agent(  model="anthropic:claude-3-7-sonnet-latest",  tools=[save_user_info],  store=store  )  # Run the agent agent.invoke(  {"messages": [{"role": "user", "content": "My name is John Smith"}]},  # user_id passed in config to identify whose information is being updated  config={"configurable": {"user_id": "user_123"}}  )  # You can access the store directly to get the value store.get(("users",), "user_123").value
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