AI Agents persistence with LangGraph and TypeDB
This guide shows you how to use the TypeDB plugin for LangGraph to persist the state of an AI agent and store long-term memory that can be shared across agents and conversations.
Get started
Install the checkpointer and store packages:
pip install langgraph-checkpoint-typedb langgraph-store-typedbUsing the checkpointer
The checkpointer lets you persist the state of a LangGraph agent, so that conversations can be resumed at a later time even if the agent process is stopped. For background, see LangGraph’s persistence and checkpointing guide.
from langgraph_checkpoint_typedb import TypeDBSaver
from langgraph.graph import StateGraph
from langchain_core.messages import HumanMessage
from typedb.driver import Credentials, DriverOptions, TypeDB
driver = TypeDB.driver(
"localhost:1729",
Credentials("admin", "password"),
DriverOptions(is_tls_enabled=False),
)
checkpointer = TypeDBSaver(driver, database="langgraph_checkpoints")
checkpointer.ensure_database()
checkpointer.ensure_schema()
graph = state_graph.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
graph.invoke({"messages": [HumanMessage(content="hello")]}, config=config)Using the long-term store
The long-term store holds memory that can be accessed by multiple LangGraph agents, such as facts the agent has learned about the user. This is particularly useful when you have multiple agents, or when you have some information that needs to be remembered across conversations.
import uuid
from langgraph_store_typedb import TypeDBStore
from langchain.tools import tool
store = TypeDBStore(driver, database="langgraph_memory")
store.ensure_database()
store.ensure_schema()
NAMESPACE = ("users", "default-user", "notes")
@tool
def remember(note: str) -> str:
"""Save a note to the user's long-term memory."""
key = uuid.uuid4().hex
store.put(NAMESPACE, key, {"note": note})
return f"saved note {key}"
@tool
def recall() -> list[str]:
"""List every note the user has saved."""
items = store.search(NAMESPACE, limit=100)
return [it.value["note"] for it in items]
graph = state_graph.compile(checkpointer=checkpointer, store=store)