Add memory with the Functional API

Add memory to a Functional API entrypoint with a checkpointer and, optionally, a store. Checkpoints persist state across invocations of the same thread_id. A store persists facts across thread IDs. For concepts, see the Functional API overview. For Graph API memory recipes, see Memory.

Save state on a thread

Pass a checkpointer when you create the entrypoint. Identify each run with thread_id. Add a keyword-only previous parameter. LangGraph sets it to the last value saved on this thread. On the first run it is None.

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint

checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def accumulate(n: int, *, previous: int | None = None) -> int:
    previous = previous or 0
    return previous + n


config = {"configurable": {"thread_id": "1"}}
print(accumulate.invoke(1, config))  # 1
print(accumulate.invoke(2, config))  # 3
print(accumulate.invoke(3, config))  # 6

Return a different saved value

Use entrypoint.final when the caller should receive one value and the next run should read another. value is returned to the caller. save is what the next previous read sees. For example, return a status or summary, and save the full internal state.

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint

checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def accumulate(n: int, *, previous: int | None = None) -> entrypoint.final[int, int]:
    previous = previous or 0
    total = previous + n
    # Return the *previous* value to the caller but save the *new* total.
    return entrypoint.final(value=previous, save=total)


config = {"configurable": {"thread_id": "my-thread"}}

print(accumulate.invoke(1, config=config))  # 0
print(accumulate.invoke(2, config=config))  # 1
print(accumulate.invoke(3, config=config))  # 3

Inspect checkpoints

View or delete checkpoints on the accumulate workflow from Return a different saved value. values is the saved checkpoint (save), not the value returned to the caller.

View thread state

config = {
    "configurable": {
        "thread_id": "my-thread",
        # optionally provide an ID for a specific checkpoint,
        # otherwise the latest checkpoint is shown
        # "checkpoint_id": "1f1c1826-2179-626c-8004-96dc43069e17"  
    }
}
print(accumulate.get_state(config))

StateSnapshot(
    values=3,
    next=(),
    config={'configurable': {'thread_id': 'my-thread', 'checkpoint_ns': '', 'checkpoint_id': '1f1c1826-2179-626c-8004-96dc43069e17'}},
    metadata={'source': 'loop', 'step': 4, 'parents': {}},
    created_at='2026-10-06T12:35:16.081724+00:00',
    parent_config={'configurable': {'thread_id': 'my-thread', 'checkpoint_ns': '', 'checkpoint_id': '1f1c1826-2178-6682-8003-feda9f2ec8db'}},
    tasks=(),
    interrupts=()
)

View thread history

config = {"configurable": {"thread_id": "my-thread"}}
history = list(accumulate.get_state_history(config))
print(history[0])

StateSnapshot(
    values=3,
    next=(),
    config={'configurable': {'thread_id': 'my-thread', 'checkpoint_ns': '', 'checkpoint_id': '1f1c1826-2179-626c-8004-96dc43069e17'}},
    metadata={'source': 'loop', 'step': 4, 'parents': {}},
    created_at='2026-10-06T12:35:16.081724+00:00',
    parent_config={'configurable': {'thread_id': 'my-thread', 'checkpoint_ns': '', 'checkpoint_id': '1f1c1826-2178-6682-8003-feda9f2ec8db'}},
    tasks=(),
    interrupts=()
)
# ... earlier snapshots, including values=1, values=0, and values=None

Delete checkpoints for a thread

checkpointer.delete_thread("my-thread")
print(accumulate.get_state(config))
# StateSnapshot(values={}, next=(), config={'configurable': {'thread_id': 'my-thread'}}, ...)

Build a chatbot with checkpoints

Keep the conversation on the thread. Return the model message to the caller, and save the full list for the next turn with entrypoint.final.

from langchain_core.messages import BaseMessage
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.graph import add_messages

model = ChatAnthropic(model="google_genai:gemini-3.6-flash")


@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage] | None = None):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))


config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
print(workflow.invoke([input_message], config))
# -> AIMessage with a greeting that uses the name Bob

input_message = {"role": "user", "content": "what's my name?"}
print(workflow.invoke([input_message], config))
# -> AIMessage that answers "Bob"

from langchain_core.messages import BaseMessage
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.graph import add_messages

model = ChatAnthropic(model="openai:gpt-5.5")


@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage] | None = None):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))


config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
print(workflow.invoke([input_message], config))
# -> AIMessage with a greeting that uses the name Bob

input_message = {"role": "user", "content": "what's my name?"}
print(workflow.invoke([input_message], config))
# -> AIMessage that answers "Bob"

from langchain_core.messages import BaseMessage
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.graph import add_messages

model = ChatAnthropic(model="anthropic:claude-sonnet-4-6")


@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage] | None = None):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))


config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
print(workflow.invoke([input_message], config))
# -> AIMessage with a greeting that uses the name Bob

input_message = {"role": "user", "content": "what's my name?"}
print(workflow.invoke([input_message], config))
# -> AIMessage that answers "Bob"

from langchain_core.messages import BaseMessage
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.graph import add_messages

model = ChatAnthropic(model="openrouter:z-ai/glm-5.2")


@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage] | None = None):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))


config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
print(workflow.invoke([input_message], config))
# -> AIMessage with a greeting that uses the name Bob

input_message = {"role": "user", "content": "what's my name?"}
print(workflow.invoke([input_message], config))
# -> AIMessage that answers "Bob"

from langchain_core.messages import BaseMessage
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.graph import add_messages

model = ChatAnthropic(model="fireworks:accounts/fireworks/models/glm-5p2")


@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage] | None = None):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))


config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
print(workflow.invoke([input_message], config))
# -> AIMessage with a greeting that uses the name Bob

input_message = {"role": "user", "content": "what's my name?"}
print(workflow.invoke([input_message], config))
# -> AIMessage that answers "Bob"

from langchain_core.messages import BaseMessage
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.graph import add_messages

model = ChatAnthropic(model="baseten:zai-org/GLM-5.2")


@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage] | None = None):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))


config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
print(workflow.invoke([input_message], config))
# -> AIMessage with a greeting that uses the name Bob

input_message = {"role": "user", "content": "what's my name?"}
print(workflow.invoke([input_message], config))
# -> AIMessage that answers "Bob"

from langchain_core.messages import BaseMessage
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.graph import add_messages

model = ChatAnthropic(model="ollama:north-mini-code-1.0")


@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage] | None = None):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))


config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
print(workflow.invoke([input_message], config))
# -> AIMessage with a greeting that uses the name Bob

input_message = {"role": "user", "content": "what's my name?"}
print(workflow.invoke([input_message], config))
# -> AIMessage that answers "Bob"

Save facts across threads

Long-term memory lives in a store, not in the checkpointer. Save a fact on one thread_id and read it on another. Pass an InMemoryStore when you decorate the entrypoint. Read it from runtime.store.

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint
from langgraph.runtime import Runtime
from langgraph.store.memory import InMemoryStore

checkpointer = InMemorySaver()
store = InMemoryStore()


@entrypoint(checkpointer=checkpointer, store=store)
def remember(inputs: dict, *, runtime: Runtime):
    memory_store = runtime.store
    namespace = (inputs["user_id"], "memories")
    fact = inputs.get("fact")
    if fact:
        memory_store.put(namespace, "profile", {"fact": fact})
    items = memory_store.search(namespace)
    return [item.value for item in items]


config_a = {"configurable": {"thread_id": "thread-a"}}
config_b = {"configurable": {"thread_id": "thread-b"}}

remember.invoke(
    {"user_id": "user-1", "fact": "Prefers dark mode"},
    config_a,
)
print(remember.invoke({"user_id": "user-1"}, config_b))
# -> [{'fact': 'Prefers dark mode'}]

Get started

Capabilities

Patterns

Build with the Graph API

Deployment

Frontend

Advanced

Add memory with the Functional API

Save state on a thread

Return a different saved value

Inspect checkpoints

View thread state

View thread history

Delete checkpoints for a thread

Build a chatbot with checkpoints

Save facts across threads

See also

​Save state on a thread

​Return a different saved value

​Inspect checkpoints

​View thread state

​View thread history

​Delete checkpoints for a thread

​Build a chatbot with checkpoints

​Save facts across threads

​See also

Save state on a thread

Return a different saved value

Inspect checkpoints

View thread state

View thread history

Delete checkpoints for a thread

Build a chatbot with checkpoints

Save facts across threads

See also