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AI applications need memory to share context across multiple interactions. In LangGraph, you can add two types of memory:
- Add short-term memory as a part of your agent's state to enable multi-turn conversations.
- Add long-term memory to store user-specific or application-level data across sessions.
Add short-term memory
Short-term memory (thread-level persistence) enables agents to track multi-turn conversations. To add short-term memory:
from langgraph.checkpoint.memory import InMemorySaver # [!code highlight]
from langgraph.graph import StateGraph
checkpointer = InMemorySaver() # [!code highlight]
builder = StateGraph(...)
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
graph.invoke(
{"messages": [{"role": "user", "content": "hi! i am Bob"}]},
{"configurable": {"thread_id": "1"}}, # [!code highlight]
)Use in production
In production, use a checkpointer backed by a database:
from langgraph.checkpoint.postgres import PostgresSaver
DB_URI = "postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer: # [!code highlight]
builder = StateGraph(...)
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]Example: using Postgres checkpointer
pip install -U "psycopg[binary,pool]" langgraph langgraph-checkpoint-postgres
Tip
You need to call checkpointer.setup() the first time you're using Postgres checkpointer
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres import PostgresSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
DB_URI = "postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer: # [!code highlight]
# checkpointer.setup()
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
DB_URI = "postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable"
async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer: # [!code highlight]
# await checkpointer.setup()
async def call_model(state: MessagesState):
response = await model.ainvoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)Example: using MongoDB checkpointer
pip install -U pymongo langgraph langgraph-checkpoint-mongodb
Tip
Setup To use the MongoDB checkpointer, you will need a MongoDB cluster. Follow this guide to create a cluster if you don't already have one. For an agent-focused walkthrough, see short-term memory with MongoDB Atlas.
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.mongodb import MongoDBSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
MONGODB_URI = "localhost:27017"
with MongoDBSaver.from_conn_string(MONGODB_URI) as checkpointer: # [!code highlight]
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.mongodb.aio import AsyncMongoDBSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
MONGODB_URI = "localhost:27017"
async with AsyncMongoDBSaver.from_conn_string(MONGODB_URI) as checkpointer: # [!code highlight]
async def call_model(state: MessagesState):
response = await model.ainvoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)Example: using Redis checkpointer
pip install -U langgraph langgraph-checkpoint-redis
Tip
You need to call checkpointer.setup() the first time you're using Redis checkpointer.
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis import RedisSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
DB_URI = "redis://localhost:6379"
with RedisSaver.from_conn_string(DB_URI) as checkpointer: # [!code highlight]
# checkpointer.setup()
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis.aio import AsyncRedisSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
DB_URI = "redis://localhost:6379"
async with AsyncRedisSaver.from_conn_string(DB_URI) as checkpointer: # [!code highlight]
# await checkpointer.asetup()
async def call_model(state: MessagesState):
response = await model.ainvoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)Example: using Oracle checkpointer
pip install -U langgraph langgraph-oracledb
Note
Setup
To use the Oracle checkpointer, you will need an Oracle AI Database instance. A local container (for example gvenzl/oracle-free:23-slim) or an Oracle Autonomous Database in OCI both work.
Tip
You need to call checkpointer.setup() the first time you're using the Oracle checkpointer.
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph_oracledb.checkpoint.oracle import OracleSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
DB_URI = "user/password@localhost:1521/FREEPDB1"
with OracleSaver.from_conn_string(DB_URI) as checkpointer: # [!code highlight]
# checkpointer.setup()
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
print(snapshot)
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph_oracledb.checkpoint.oracle import AsyncOracleSaver # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
DB_URI = "user/password@localhost:1521/FREEPDB1"
async with AsyncOracleSaver.from_conn_string(DB_URI) as checkpointer: # [!code highlight]
# await checkpointer.setup()
async def call_model(state: MessagesState):
response = await model.ainvoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config, # [!code highlight]
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
Use in subgraphs
If your graph contains subgraphs, you only need to provide the checkpointer when compiling the parent graph. LangGraph will automatically propagate the checkpointer to the child subgraphs.
from langgraph.graph import START, StateGraph
from langgraph.checkpoint.memory import InMemorySaver
from typing import TypedDict
class State(TypedDict):
foo: str
# Subgraph
def subgraph_node_1(state: State):
return {"foo": state["foo"] + "bar"}
subgraph_builder = StateGraph(State)
subgraph_builder.add_node(subgraph_node_1)
subgraph_builder.add_edge(START, "subgraph_node_1")
subgraph = subgraph_builder.compile() # [!code highlight]
# Parent graph
builder = StateGraph(State)
builder.add_node("node_1", subgraph) # [!code highlight]
builder.add_edge(START, "node_1")
checkpointer = InMemorySaver()
graph = builder.compile(checkpointer=checkpointer) # [!code highlight]You can configure subgraph-specific checkpointing behavior. See subgraph persistence for details on persistence levels including interrupt support and stateful continuations.
subgraph_builder = StateGraph(...)
subgraph = subgraph_builder.compile(checkpointer=True) # [!code highlight]Add long-term memory
Use long-term memory to store user-specific or application-specific data across conversations.
from langgraph.store.memory import InMemoryStore # [!code highlight]
from langgraph.graph import StateGraph
store = InMemoryStore() # [!code highlight]
builder = StateGraph(...)
graph = builder.compile(store=store) # [!code highlight]
Access the store inside nodes
Once you compile a graph with a store, LangGraph automatically injects the store into your node functions. The recommended way to access the store is through the Runtime object.
from dataclasses import dataclass
from langgraph.runtime import Runtime
from langgraph.graph import StateGraph, MessagesState, START
from langchain_core.utils.uuid import uuid7
@dataclass
class Context:
user_id: str
async def call_model(state: MessagesState, runtime: Runtime[Context]): # [!code highlight]
user_id = runtime.context.user_id # [!code highlight]
namespace = (user_id, "memories")
# Search for relevant memories
memories = await runtime.store.asearch( # [!code highlight]
namespace, query=state["messages"][-1].content, limit=3
)
info = "\n".join([d.value["data"] for d in memories])
# ... Use memories in model call
# Store a new memory
await runtime.store.aput( # [!code highlight]
namespace, str(uuid7()), {"data": "User prefers dark mode"}
)
builder = StateGraph(MessagesState, context_schema=Context) # [!code highlight]
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(store=store)
# Pass context at invocation time
graph.invoke(
{"messages": [{"role": "user", "content": "hi"}]},
{"configurable": {"thread_id": "1"}},
context=Context(user_id="1"), # [!code highlight]
)
Use in production
In production, use a store backed by a database:
from langgraph.store.postgres import PostgresStore
DB_URI = "postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable"
with PostgresStore.from_conn_string(DB_URI) as store: # [!code highlight]
builder = StateGraph(...)
graph = builder.compile(store=store) # [!code highlight]
Example: using Postgres store
pip install -U "psycopg[binary,pool]" langgraph langgraph-checkpoint-postgres
Tip
You need to call store.setup() the first time you're using Postgres store
from dataclasses import dataclass
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
from langgraph.store.postgres.aio import AsyncPostgresStore # [!code highlight]
from langgraph.runtime import Runtime # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
@dataclass
class Context:
user_id: str
async def call_model( # [!code highlight]
state: MessagesState,
runtime: Runtime[Context], # [!code highlight]
):
user_id = runtime.context.user_id # [!code highlight]
namespace = ("memories", user_id)
memories = await runtime.store.asearch(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
await runtime.store.aput(namespace, str(uuid7()), {"data": memory}) # [!code highlight]
response = await model.ainvoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
DB_URI = "postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable"
async with (
AsyncPostgresStore.from_conn_string(DB_URI) as store, # [!code highlight]
AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer,
):
# await store.setup()
# await checkpointer.setup()
builder = StateGraph(MessagesState, context_schema=Context) # [!code highlight]
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
config = {"configurable": {"thread_id": "1"}}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
config = {"configurable": {"thread_id": "2"}}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
from dataclasses import dataclass
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.store.postgres import PostgresStore # [!code highlight]
from langgraph.runtime import Runtime # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
@dataclass
class Context:
user_id: str
def call_model( # [!code highlight]
state: MessagesState,
runtime: Runtime[Context], # [!code highlight]
):
user_id = runtime.context.user_id # [!code highlight]
namespace = ("memories", user_id)
memories = runtime.store.search(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
runtime.store.put(namespace, str(uuid7()), {"data": memory}) # [!code highlight]
response = model.invoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
DB_URI = "postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable"
with (
PostgresStore.from_conn_string(DB_URI) as store, # [!code highlight]
PostgresSaver.from_conn_string(DB_URI) as checkpointer,
):
# store.setup()
# checkpointer.setup()
builder = StateGraph(MessagesState, context_schema=Context) # [!code highlight]
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
config = {"configurable": {"thread_id": "1"}}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
for snapshot in stream.values:
print(snapshot)
config = {"configurable": {"thread_id": "2"}}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
for snapshot in stream.values:
print(snapshot)
Example: using MongoDB store
pip install -U langgraph-store-mongodb langgraph-checkpoint-mongodb
Tip
Semantic search with query needs MongoDB Vector Search ($vectorSearch), which runs on MongoDB Atlas or on self-managed MongoDB with Search and Vector Search (mongot). A plain mongod without mongot is not enough. Pass index_config from create_vector_index_config when you open the store. Index creation can exceed the default 15 second wait, so set auto_index_timeout higher on first run. For Atlas cluster setup, see this guide. For vector index setup and an agent-focused walkthrough, see long-term memory with MongoDB Atlas.
import os
from collections.abc import Sequence
from dataclasses import dataclass
from uuid import uuid4
from langchain.chat_models import init_chat_model
from langgraph.checkpoint.mongodb import MongoDBSaver # [!code highlight]
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.runtime import Runtime # [!code highlight]
from langgraph.store.mongodb import MongoDBStore, create_vector_index_config # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
def embed(texts: Sequence[str]) -> list[list[float]]:
# Replace with an embeddings model or LangChain Embeddings instance
return [[1.0, 2.0] for _ in texts]
@dataclass
class Context:
user_id: str
async def call_model( # [!code highlight]
state: MessagesState,
runtime: Runtime[Context], # [!code highlight]
):
user_id = runtime.context.user_id # [!code highlight]
namespace = ("memories", user_id)
memories = await runtime.store.asearch(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
await runtime.store.aput(namespace, str(uuid4()), {"data": memory}) # [!code highlight]
response = await model.ainvoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
MONGODB_ATLAS_URI = os.environ["MONGODB_ATLAS_URI"]
index_config = create_vector_index_config(embed=embed, dims=2, fields=["data"]) # [!code highlight]
with (
MongoDBStore.from_conn_string( # [!code highlight]
MONGODB_ATLAS_URI,
index_config=index_config, # [!code highlight]
auto_index_timeout=120,
) as store,
MongoDBSaver.from_conn_string(MONGODB_ATLAS_URI) as checkpointer,
):
builder = StateGraph(MessagesState, context_schema=Context) # [!code highlight]
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
import asyncio
async def run():
config = {"configurable": {"thread_id": "1"}}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
async for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
config = {"configurable": {"thread_id": "2"}}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
async for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
asyncio.run(run())
import os
from collections.abc import Sequence
from dataclasses import dataclass
from uuid import uuid4
from langchain.chat_models import init_chat_model
from langgraph.checkpoint.mongodb import MongoDBSaver # [!code highlight]
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.runtime import Runtime # [!code highlight]
from langgraph.store.mongodb import MongoDBStore, create_vector_index_config # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
def embed(texts: Sequence[str]) -> list[list[float]]:
# Replace with an embeddings model or LangChain Embeddings instance
return [[1.0, 2.0] for _ in texts]
@dataclass
class Context:
user_id: str
def call_model( # [!code highlight]
state: MessagesState,
runtime: Runtime[Context], # [!code highlight]
):
user_id = runtime.context.user_id # [!code highlight]
namespace = ("memories", user_id)
memories = runtime.store.search(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
runtime.store.put(namespace, str(uuid4()), {"data": memory}) # [!code highlight]
response = model.invoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
MONGODB_ATLAS_URI = os.environ["MONGODB_ATLAS_URI"]
index_config = create_vector_index_config(embed=embed, dims=2, fields=["data"]) # [!code highlight]
with (
MongoDBStore.from_conn_string( # [!code highlight]
MONGODB_ATLAS_URI,
index_config=index_config, # [!code highlight]
auto_index_timeout=120,
) as store,
MongoDBSaver.from_conn_string(MONGODB_ATLAS_URI) as checkpointer,
):
builder = StateGraph(MessagesState, context_schema=Context) # [!code highlight]
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
config = {"configurable": {"thread_id": "1"}}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
config = {"configurable": {"thread_id": "2"}}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
Example: using Redis store
pip install -U langgraph langgraph-checkpoint-redis
Tip
You need to call store.setup() the first time you're using Redis store.
from dataclasses import dataclass
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis.aio import AsyncRedisSaver
from langgraph.store.redis.aio import AsyncRedisStore # [!code highlight]
from langgraph.runtime import Runtime # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
@dataclass
class Context:
user_id: str
async def call_model( # [!code highlight]
state: MessagesState,
runtime: Runtime[Context], # [!code highlight]
):
user_id = runtime.context.user_id # [!code highlight]
namespace = ("memories", user_id)
memories = await runtime.store.asearch(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
await runtime.store.aput(namespace, str(uuid7()), {"data": memory}) # [!code highlight]
response = await model.ainvoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
DB_URI = "redis://localhost:6379"
async with (
AsyncRedisStore.from_conn_string(DB_URI) as store, # [!code highlight]
AsyncRedisSaver.from_conn_string(DB_URI) as checkpointer,
):
# await store.setup()
# await checkpointer.asetup()
builder = StateGraph(MessagesState, context_schema=Context) # [!code highlight]
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
config = {"configurable": {"thread_id": "1"}}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
async for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
config = {"configurable": {"thread_id": "2"}}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
async for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
from dataclasses import dataclass
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis import RedisSaver
from langgraph.store.redis import RedisStore # [!code highlight]
from langgraph.runtime import Runtime # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
@dataclass
class Context:
user_id: str
def call_model( # [!code highlight]
state: MessagesState,
runtime: Runtime[Context], # [!code highlight]
):
user_id = runtime.context.user_id # [!code highlight]
namespace = ("memories", user_id)
memories = runtime.store.search(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
runtime.store.put(namespace, str(uuid7()), {"data": memory}) # [!code highlight]
response = model.invoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
DB_URI = "redis://localhost:6379"
with (
RedisStore.from_conn_string(DB_URI) as store, # [!code highlight]
RedisSaver.from_conn_string(DB_URI) as checkpointer,
):
store.setup()
checkpointer.setup()
builder = StateGraph(MessagesState, context_schema=Context) # [!code highlight]
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
config = {"configurable": {"thread_id": "1"}}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
config = {"configurable": {"thread_id": "2"}}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config,
version="v3",
context=Context(user_id="1"), # [!code highlight]
)
for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
Example: using Oracle store
pip install -U langgraph langgraph-oracledb langchain-openai
Note
Setup
To use the Oracle store, you will need an Oracle AI Database instance — the vector index used for semantic search requires Oracle AI Vector Search.
Tip
You need to call store.setup() and checkpointer.setup() the first time you're using the Oracle store and checkpointer.
from langchain.chat_models import init_chat_model
from langchain.embeddings import init_embeddings
from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.store.base import BaseStore
from langgraph_oracledb.checkpoint.oracle import OracleSaver
from langgraph_oracledb.store.oracle import OracleStore # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
embeddings = init_embeddings("openai:text-embedding-3-small")
DB_URI = "user/password@localhost:1521/FREEPDB1"
with (
OracleStore.from_conn_string( # [!code highlight]
DB_URI,
index={"embed": embeddings, "dims": 1536}, # [!code highlight]
) as store,
OracleSaver.from_conn_string(DB_URI) as checkpointer,
):
store.setup()
checkpointer.setup()
def call_model(
state: MessagesState,
config: RunnableConfig,
*,
store: BaseStore, # [!code highlight]
):
user_id = config["configurable"]["user_id"]
namespace = ("memories", user_id)
memories = store.search(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
store.put(namespace, str(uuid7()), {"data": memory}) # [!code highlight]
response = model.invoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
config = {
"configurable": {
"thread_id": "1", # [!code highlight]
"user_id": "1", # [!code highlight]
}
}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
config = {
"configurable": {
"thread_id": "2", # [!code highlight]
"user_id": "1",
}
}
stream = graph.stream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config, # [!code highlight]
version="v3",
)
for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
from langchain.chat_models import init_chat_model
from langchain.embeddings import init_embeddings
from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.store.base import BaseStore
from langgraph_oracledb.checkpoint.oracle import AsyncOracleSaver
from langgraph_oracledb.store.oracle import AsyncOracleStore # [!code highlight]
model = init_chat_model(model="claude-haiku-4-5-20251001")
embeddings = init_embeddings("openai:text-embedding-3-small")
DB_URI = "user/password@localhost:1521/FREEPDB1"
async with (
AsyncOracleStore.from_conn_string( # [!code highlight]
DB_URI,
index={"embed": embeddings, "dims": 1536}, # [!code highlight]
) as store,
AsyncOracleSaver.from_conn_string(DB_URI) as checkpointer,
):
await store.setup()
await checkpointer.setup()
async def call_model(
state: MessagesState,
config: RunnableConfig,
*,
store: BaseStore, # [!code highlight]
):
user_id = config["configurable"]["user_id"]
namespace = ("memories", user_id)
memories = await store.asearch(namespace, query=str(state["messages"][-1].content)) # [!code highlight]
info = "\n".join([d.value["data"] for d in memories])
system_msg = f"You are a helpful assistant talking to the user. User info: {info}"
# Store new memories if the user asks the model to remember
last_message = state["messages"][-1]
if "remember" in last_message.content.lower():
memory = "User name is Bob"
await store.aput(namespace, str(uuid7()), {"data": memory}) # [!code highlight]
response = await model.ainvoke(
[{"role": "system", "content": system_msg}] + state["messages"]
)
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(
checkpointer=checkpointer,
store=store, # [!code highlight]
)
config = {
"configurable": {
"thread_id": "1", # [!code highlight]
"user_id": "1", # [!code highlight]
}
}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
config, # [!code highlight]
version="v3",
)
async for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
config = {
"configurable": {
"thread_id": "2", # [!code highlight]
"user_id": "1",
}
}
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "what is my name?"}]},
config, # [!code highlight]
version="v3",
)
async for snapshot in stream.values:
snapshot["messages"][-1].pretty_print()
Use semantic search
Enable semantic search in your graph's memory store to let graph agents search for items in the store by semantic similarity.
from langchain.embeddings import init_embeddings
from langgraph.store.memory import InMemoryStore
# Create store with semantic search enabled
embeddings = init_embeddings("openai:text-embedding-3-small")
store = InMemoryStore(
index={
"embed": embeddings,
"dims": 1536,
}
)
store.put(("user_123", "memories"), "1", {"text": "I love pizza"})
store.put(("user_123", "memories"), "2", {"text": "I am a plumber"})
items = store.search(
("user_123", "memories"), query="I'm hungry", limit=1
)
Long-term memory with semantic search
from langchain.embeddings import init_embeddings
from langchain.chat_models import init_chat_model
from langgraph.store.memory import InMemoryStore
from langgraph.graph import START, MessagesState, StateGraph
from langgraph.runtime import Runtime # [!code highlight]
model = init_chat_model("gpt-5.4-mini")
# Create store with semantic search enabled
embeddings = init_embeddings("openai:text-embedding-3-small")
store = InMemoryStore(
index={
"embed": embeddings,
"dims": 1536,
}
)
store.put(("user_123", "memories"), "1", {"text": "I love pizza"})
store.put(("user_123", "memories"), "2", {"text": "I am a plumber"})
async def chat(state: MessagesState, runtime: Runtime): # [!code highlight]
# Search based on user's last message
items = await runtime.store.asearch( # [!code highlight]
("user_123", "memories"), query=state["messages"][-1].content, limit=2
)
memories = "\n".join(item.value["text"] for item in items)
memories = f"## Memories of user\n{memories}" if memories else ""
response = await model.ainvoke(
[
{"role": "system", "content": f"You are a helpful assistant.\n{memories}"},
*state["messages"],
]
)
return {"messages": [response]}
builder = StateGraph(MessagesState)
builder.add_node(chat)
builder.add_edge(START, "chat")
graph = builder.compile(store=store)
stream = await graph.astream_events(
{"messages": [{"role": "user", "content": "I'm hungry"}]},
version="v3",
)
async for message in stream.messages:
async for token in message.text:
print(token, end="", flush=True)
Manage short-term memory
With short-term memory enabled, a checkpointer persists graph state across turns within the same thread. The node that calls the model controls which data from state to include in the model input. Persisting state does not automatically send all of it to the model. Passing a long message history to the model can exceed its context window. Common solutions are:
- Trim messages: Remove first or last N messages (before calling LLM)
- Delete messages from LangGraph state permanently
- Summarize messages: Summarize earlier messages in the history and replace them with a summary
- Manage checkpoints to store and retrieve message history
- Custom strategies (e.g., message filtering, etc.)
This allows the agent to keep track of the conversation without exceeding the LLM's context window.
Trim messages
Most LLMs have a maximum supported context window (denominated in tokens). One way to decide when to truncate messages is to count the tokens in the message history and truncate whenever it approaches that limit. If you're using LangChain, you can use the trim messages utility and specify the number of tokens to keep from the list, as well as the strategy (e.g., keep the last max_tokens) to use for handling the boundary.
In the following example, the node passes a trimmed message list to the model without removing messages from state.
To trim message history, use the trim_messages function:
from langchain_core.messages.utils import ( # [!code highlight]
trim_messages, # [!code highlight]
count_tokens_approximately # [!code highlight]
) # [!code highlight]
def call_model(state: MessagesState):
messages = trim_messages( # [!code highlight]
state["messages"],
strategy="last",
token_counter=count_tokens_approximately,
max_tokens=128,
start_on="human",
end_on=("human", "tool"),
)
response = model.invoke(messages)
return {"messages": [response]}
builder = StateGraph(MessagesState)
builder.add_node(call_model)
...
Full example: trim messages
from langchain_core.messages.utils import (
trim_messages, # [!code highlight]
count_tokens_approximately # [!code highlight]
)
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, START, MessagesState
model = init_chat_model("claude-sonnet-4-6")
summarization_model = model.bind(max_tokens=128)
def call_model(state: MessagesState):
messages = trim_messages( # [!code highlight]
state["messages"],
strategy="last",
token_counter=count_tokens_approximately,
max_tokens=128,
start_on="human",
end_on=("human", "tool"),
)
response = model.invoke(messages)
return {"messages": [response]}
checkpointer = InMemorySaver()
builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
graph.invoke({"messages": "hi, my name is bob"}, config)
graph.invoke({"messages": "write a short poem about cats"}, config)
graph.invoke({"messages": "now do the same but for dogs"}, config)
final_response = graph.invoke({"messages": "what's my name?"}, config)
final_response["messages"][-1].pretty_print()
================================== Ai Message ==================================
Your name is Bob, as you mentioned when you first introduced yourself.
Delete messages
You can delete messages from the graph state to manage the message history. This is useful when you want to remove specific messages or clear the entire message history.
To delete messages from the graph state, you can use the RemoveMessage. For RemoveMessage to work, you need to use a state key with add_messages reducer, like MessagesState.
To remove specific messages:
from langchain.messages import RemoveMessage # [!code highlight]
def delete_messages(state):
messages = state["messages"]
if len(messages) > 2:
# remove the earliest two messages
return {"messages": [RemoveMessage(id=m.id) for m in messages[:2]]} # [!code highlight]
To remove all messages:
from langgraph.graph.message import REMOVE_ALL_MESSAGES # [!code highlight]
def delete_messages(state):
return {"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)]} # [!code highlight]
Warning
When deleting messages, make sure that the resulting message history is valid. Check the limitations of the LLM provider you're using. For example:
- Some providers expect message history to start with a
usermessage - Most providers require
assistantmessages with tool calls to be followed by correspondingtoolresult messages.
Full example: delete messages
from langchain.messages import RemoveMessage # [!code highlight]
def delete_messages(state):
messages = state["messages"]
if len(messages) > 2:
# remove the earliest two messages
return {"messages": [RemoveMessage(id=m.id) for m in messages[:2]]} # [!code highlight]
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
builder = StateGraph(MessagesState)
builder.add_sequence([call_model, delete_messages])
builder.add_edge(START, "call_model")
checkpointer = InMemorySaver()
app = builder.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
stream = app.stream_events(
{"messages": [{"role": "user", "content": "hi! I'm bob"}]},
config,
version="v3"
)
for snapshot in stream.values:
print([(message.type, message.content) for message in snapshot["messages"]])
stream = app.stream_events(
{"messages": [{"role": "user", "content": "what's my name?"}]},
config,
version="v3"
)
for snapshot in stream.values:
print([(message.type, message.content) for message in snapshot["messages"]])
[('human', "hi! I'm bob")]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?')]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?'), ('human', "what's my name?")]
[('human', "hi! I'm bob"), ('ai', 'Hi Bob! How are you doing today? Is there anything I can help you with?'), ('human', "what's my name?"), ('ai', 'Your name is Bob.')]
[('human', "what's my name?"), ('ai', 'Your name is Bob.')]
Summarize messages
The problem with trimming or removing messages, as shown above, is that you may lose information from culling of the message queue. Because of this, some applications benefit from a more sophisticated approach of summarizing the message history using a chat model.
Prompting and orchestration logic can be used to summarize the message history. For example, in LangGraph you can extend the MessagesState to include a summary key:
from langgraph.graph import MessagesState
class State(MessagesState):
summary: strThen, you can generate a summary of the chat history, using any existing summary as context for the next summary. This summarize_conversation node can be called after some number of messages have accumulated in the messages state key.
def summarize_conversation(state: State):
# First, we get any existing summary
summary = state.get("summary", "")
# Create our summarization prompt
if summary:
# A summary already exists
summary_message = (
f"This is a summary of the conversation to date: {summary}\n\n"
"Extend the summary by taking into account the new messages above:"
)
else:
summary_message = "Create a summary of the conversation above:"
# Add prompt to our history
messages = state["messages"] + [HumanMessage(content=summary_message)]
response = model.invoke(messages)
# Delete all but the 2 most recent messages
delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
return {"summary": response.content, "messages": delete_messages}
Full example: summarize messages
from typing import Any, TypedDict
from langchain.chat_models import init_chat_model
from langchain.messages import AnyMessage
from langchain_core.messages.utils import count_tokens_approximately
from langgraph.graph import StateGraph, START, MessagesState
from langgraph.checkpoint.memory import InMemorySaver
from langmem.short_term import SummarizationNode, RunningSummary # [!code highlight]
model = init_chat_model("claude-sonnet-4-6")
summarization_model = model.bind(max_tokens=128)
class State(MessagesState):
context: dict[str, RunningSummary] # [!code highlight]
class LLMInputState(TypedDict): # [!code highlight]
summarized_messages: list[AnyMessage]
context: dict[str, RunningSummary]
summarization_node = SummarizationNode( # [!code highlight]
token_counter=count_tokens_approximately,
model=summarization_model,
max_tokens=256,
max_tokens_before_summary=256,
max_summary_tokens=128,
)
def call_model(state: LLMInputState): # [!code highlight]
response = model.invoke(state["summarized_messages"])
return {"messages": [response]}
checkpointer = InMemorySaver()
builder = StateGraph(State)
builder.add_node(call_model)
builder.add_node("summarize", summarization_node) # [!code highlight]
builder.add_edge(START, "summarize")
builder.add_edge("summarize", "call_model")
graph = builder.compile(checkpointer=checkpointer)
# Invoke the graph
config = {"configurable": {"thread_id": "1"}}
graph.invoke({"messages": "hi, my name is bob"}, config)
graph.invoke({"messages": "write a short poem about cats"}, config)
graph.invoke({"messages": "now do the same but for dogs"}, config)
final_response = graph.invoke({"messages": "what's my name?"}, config)
final_response["messages"][-1].pretty_print()
print("\nSummary:", final_response["context"]["running_summary"].summary)
- We will keep track of our running summary in the
contextfield
(expected by the SummarizationNode).
- Define private state that will be used only for filtering
the inputs to call_model node.
- We're passing a private input state here to isolate the messages returned by the summarization node
================================== Ai Message ==================================
From our conversation, I can see that you introduced yourself as Bob. That's the name you shared with me when we began talking.
Summary: In this conversation, I was introduced to Bob, who then asked me to write a poem about cats. I composed a poem titled "The Mystery of Cats" that captured cats' graceful movements, independent nature, and their special relationship with humans. Bob then requested a similar poem about dogs, so I wrote "The Joy of Dogs," which highlighted dogs' loyalty, enthusiasm, and loving companionship. Both poems were written in a similar style but emphasized the distinct characteristics that make each pet special.
Manage checkpoints
You can view and delete the information stored by the checkpointer.
View thread state
config = {
"configurable": {
"thread_id": "1", # [!code highlight]
# optionally provide an ID for a specific checkpoint,
# otherwise the latest checkpoint is shown
# "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a" # [!code highlight]
}
}
graph.get_state(config) # [!code highlight]
StateSnapshot(
values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today?), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]}, next=(),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
metadata={
'source': 'loop',
'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}},
'step': 4,
'parents': {},
'thread_id': '1'
},
created_at='2025-05-05T16:01:24.680462+00:00',
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
tasks=(),
interrupts=()
)
config = {
"configurable": {
"thread_id": "1", # [!code highlight]
# optionally provide an ID for a specific checkpoint,
# otherwise the latest checkpoint is shown
# "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a" # [!code highlight]
}
}
checkpointer.get_tuple(config) # [!code highlight]
CheckpointTuple(
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
checkpoint={
'v': 3,
'ts': '2025-05-05T16:01:24.680462+00:00',
'id': '1f029ca3-1f5b-6704-8004-820c16b69a5a',
'channel_versions': {'__start__': '00000000000000000000000000000005.0.5290678567601859', 'messages': '00000000000000000000000000000006.0.3205149138784782', 'branch:to:call_model': '00000000000000000000000000000006.0.14611156755133758'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000004.0.5736472536395331'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000005.0.1410174088651449'}},
'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today?), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},
},
metadata={
'source': 'loop',
'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}},
'step': 4,
'parents': {},
'thread_id': '1'
},
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
pending_writes=[]
)
View the history of the thread
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
list(graph.get_state_history(config)) # [!code highlight]
[
StateSnapshot(
values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},
next=(),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}}, 'step': 4, 'parents': {}, 'thread_id': '1'},
created_at='2025-05-05T16:01:24.680462+00:00',
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
tasks=(),
interrupts=()
),
StateSnapshot(
values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?")]},
next=('call_model',),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
metadata={'source': 'loop', 'writes': None, 'step': 3, 'parents': {}, 'thread_id': '1'},
created_at='2025-05-05T16:01:23.863421+00:00',
parent_config={...}
tasks=(PregelTask(id='8ab4155e-6b15-b885-9ce5-bed69a2c305c', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Your name is Bob.')}),),
interrupts=()
),
StateSnapshot(
values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},
next=('__start__',),
config={...},
metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}}, 'step': 2, 'parents': {}, 'thread_id': '1'},
created_at='2025-05-05T16:01:23.863173+00:00',
parent_config={...}
tasks=(PregelTask(id='24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "what's my name?"}]}),),
interrupts=()
),
StateSnapshot(
values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},
next=(),
config={...},
metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}}, 'step': 1, 'parents': {}, 'thread_id': '1'},
created_at='2025-05-05T16:01:23.862295+00:00',
parent_config={...}
tasks=(),
interrupts=()
),
StateSnapshot(
values={'messages': [HumanMessage(content="hi! I'm bob")]},
next=('call_model',),
config={...},
metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}, 'thread_id': '1'},
created_at='2025-05-05T16:01:22.278960+00:00',
parent_config={...}
tasks=(PregelTask(id='8cbd75e0-3720-b056-04f7-71ac805140a0', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}),),
interrupts=()
),
StateSnapshot(
values={'messages': []},
next=('__start__',),
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565'}},
metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}, 'step': -1, 'parents': {}, 'thread_id': '1'},
created_at='2025-05-05T16:01:22.277497+00:00',
parent_config=None,
tasks=(PregelTask(id='d458367b-8265-812c-18e2-33001d199ce6', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}),),
interrupts=()
)
]
config = {
"configurable": {
"thread_id": "1" # [!code highlight]
}
}
list(checkpointer.list(config)) # [!code highlight]
[
CheckpointTuple(
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
checkpoint={
'v': 3,
'ts': '2025-05-05T16:01:24.680462+00:00',
'id': '1f029ca3-1f5b-6704-8004-820c16b69a5a',
'channel_versions': {'__start__': '00000000000000000000000000000005.0.5290678567601859', 'messages': '00000000000000000000000000000006.0.3205149138784782', 'branch:to:call_model': '00000000000000000000000000000006.0.14611156755133758'},
'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000004.0.5736472536395331'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000005.0.1410174088651449'}},
'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},
},
metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}}, 'step': 4, 'parents': {}, 'thread_id': '1'},
parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
pending_writes=[]
),
CheckpointTuple(
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
checkpoint={
'v': 3,
'ts': '2025-05-05T16:01:23.863421+00:00',
'id': '1f029ca3-1790-6b0a-8003-baf965b6a38f',
'channel_versions': {'__start__': '00000000000000000000000000000005.0.5290678567601859', 'messages': '00000000000000000000000000000006.0.3205149138784782', 'branch:to:call_model': '00000000000000000000000000000006.0.14611156755133758'},
'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000004.0.5736472536395331'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000005.0.1410174088651449'}},
'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?")], 'branch:to:call_model': None}
},
metadata={'source': 'loop', 'writes': None, 'step': 3, 'parents': {}, 'thread_id': '1'},
parent_config={...},
pending_writes=[('8ab4155e-6b15-b885-9ce5-bed69a2c305c', 'messages', AIMessage(content='Your name is Bob.'))]
),
CheckpointTuple(
config={...},
checkpoint={
'v': 3,
'ts': '2025-05-05T16:01:23.863173+00:00',
'id': '1f029ca3-1790-616e-8002-9e021694a0cd',
'channel_versions': {'__start__': '00000000000000000000000000000004.0.5736472536395331', 'messages': '00000000000000000000000000000003.0.7056767754077798', 'branch:to:call_model': '00000000000000000000000000000003.0.22059023329132854'},
'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0.7040775356287469'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000002.0.9300422176788571'}},
'channel_values': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}, 'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]}
},
metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}}, 'step': 2, 'parents': {}, 'thread_id': '1'},
parent_config={...},
pending_writes=[('24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', 'messages', [{'role': 'user', 'content': "what's my name?"}]), ('24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', 'branch:to:call_model', None)]
),
CheckpointTuple(
config={...},
checkpoint={
'v': 3,
'ts': '2025-05-05T16:01:23.862295+00:00',
'id': '1f029ca3-178d-6f54-8001-d7b180db0c89',
'channel_versions': {'__start__': '00000000000000000000000000000002.0.18673090920108737', 'messages': '00000000000000000000000000000003.0.7056767754077798', 'branch:to:call_model': '00000000000000000000000000000003.0.22059023329132854'},
'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0.7040775356287469'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000002.0.9300422176788571'}},
'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]}
},
metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}}, 'step': 1, 'parents': {}, 'thread_id': '1'},
parent_config={...},
pending_writes=[]
),
CheckpointTuple(
config={...},
checkpoint={
'v': 3,
'ts': '2025-05-05T16:01:22.278960+00:00',
'id': '1f029ca3-0874-6612-8000-339f2abc83b1',
'channel_versions': {'__start__': '00000000000000000000000000000002.0.18673090920108737', 'messages': '00000000000000000000000000000002.0.30296526818059655', 'branch:to:call_model': '00000000000000000000000000000002.0.9300422176788571'},
'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0.7040775356287469'}},
'channel_values': {'messages': [HumanMessage(content="hi! I'm bob")], 'branch:to:call_model': None}
},
metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}, 'thread_id': '1'},
parent_config={...},
pending_writes=[('8cbd75e0-3720-b056-04f7-71ac805140a0', 'messages', AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'))]
),
CheckpointTuple(
config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565'}},
checkpoint={
'v': 3,
'ts': '2025-05-05T16:01:22.277497+00:00',
'id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565',
'channel_versions': {'__start__': '00000000000000000000000000000001.0.7040775356287469'},
'versions_seen': {'__input__': {}},
'channel_values': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}
},
metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}, 'step': -1, 'parents': {}, 'thread_id': '1'},
parent_config=None,
pending_writes=[('d458367b-8265-812c-18e2-33001d199ce6', 'messages', [{'role': 'user', 'content': "hi! I'm bob"}]), ('d458367b-8265-812c-18e2-33001d199ce6', 'branch:to:call_model', None)]
)
]
Delete all checkpoints for a thread
thread_id = "1"
checkpointer.delete_thread(thread_id)Database management
If you are using any database-backed persistence implementation (such as Postgres, Redis, or Oracle) to store short and/or long-term memory, you will need to run migrations to set up the required schema before you can use it with your database.
By convention, most database-specific libraries define a setup() method on the checkpointer or store instance that runs the required migrations. However, you should check with your specific implementation of BaseCheckpointSaver or BaseStore to confirm the exact method name and usage.
We recommend running migrations as a dedicated deployment step, or you can ensure they're run as part of server startup.
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