Persistence

LangGraph has a built-in persistence layer that saves graph state as checkpoints. When you compile a graph with a checkpointer, a snapshot of the graph state is saved at every step of execution, organized into threads. This enables human-in-the-loop workflows, conversational memory, time travel debugging, and fault-tolerant execution.

Agent Server handles checkpointing automatically When using the Agent Server, you don’t need to implement or configure checkpointers manually. The server handles all persistence infrastructure for you behind the scenes.

Why use persistence

Persistence is required for the following features:

Human-in-the-loop: Checkpointers facilitate human-in-the-loop workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the person has to be able to view the state of a graph at any point in time, and the graph has to be able to resume execution after the person has made any updates to the state. See Interrupts for examples.
Memory: Checkpointers allow for “memory” between interactions. In the case of repeated human interactions (like conversations) any follow up messages can be sent to that thread, which will retain its memory of previous ones. See Add memory for information on how to add and manage conversation memory using checkpointers.
Time travel: Checkpointers allow for “time travel”, allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
Fault-tolerance: Checkpointing provides fault-tolerance and error recovery: if one or more nodes fail at a given superstep, you can restart your graph from the last successful step.
Pending writes: When a graph node fails mid-execution at a given super-step, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that super-step. When you resume graph execution from that super-step you don’t re-run the successful nodes.

Core concepts

Threads

A thread is a unique ID or thread identifier assigned to each checkpoint saved by a checkpointer. It contains the accumulated state of a sequence of runs. When a run is executed, the state of the underlying graph of the assistant will be persisted to the thread. When invoking a graph with a checkpointer, you must specify a thread_id as part of the configurable portion of the config:

{"configurable": {"thread_id": "1"}}

A thread’s current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run. The LangSmith API provides several endpoints for creating and managing threads and thread state. See the API reference for more details. The checkpointer uses thread_id as the primary key for storing and retrieving checkpoints. Without it, the checkpointer cannot save state or resume execution after an interrupt, since the checkpointer uses thread_id to load the saved state.

Checkpoints

The state of a thread at a particular point in time is called a checkpoint. A checkpoint is a snapshot of the graph state saved at each super-step and is represented by a StateSnapshot object (see StateSnapshot fields for the full field reference).

Super-steps

LangGraph created a checkpoint at each super-step boundary. A super-step is a single “tick” of the graph where all nodes scheduled for that step execute (potentially in parallel). For a sequential graph like START -> A -> B -> END, there are separate super-steps for the input, node A, and node B — producing a checkpoint after each one. Understanding super-step boundaries is important for time travel, because you can only resume execution from a checkpoint (i.e., a super-step boundary). Checkpoints are persisted and can be used to restore the state of a thread at a later time. Let’s see what checkpoints are saved when a simple graph is invoked as follows:

from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import InMemorySaver
from langchain_core.runnables import RunnableConfig
from typing import Annotated
from typing_extensions import TypedDict
from operator import add

class State(TypedDict):
    foo: str
    bar: Annotated[list[str], add]

def node_a(state: State):
    return {"foo": "a", "bar": ["a"]}

def node_b(state: State):
    return {"foo": "b", "bar": ["b"]}


workflow = StateGraph(State)
workflow.add_node(node_a)
workflow.add_node(node_b)
workflow.add_edge(START, "node_a")
workflow.add_edge("node_a", "node_b")
workflow.add_edge("node_b", END)

checkpointer = InMemorySaver()
graph = workflow.compile(checkpointer=checkpointer)

config: RunnableConfig = {"configurable": {"thread_id": "1"}}
graph.invoke({"foo": "", "bar":[]}, config)

After we run the graph, we expect to see exactly 4 checkpoints:

Empty checkpoint with START as the next node to be executed
Checkpoint with the user input {'foo': '', 'bar': []} and node_a as the next node to be executed
Checkpoint with the outputs of node_a {'foo': 'a', 'bar': ['a']} and node_b as the next node to be executed
Checkpoint with the outputs of node_b {'foo': 'b', 'bar': ['a', 'b']} and no next nodes to be executed

Note that we bar channel values contain outputs from both nodes as we have a reducer for bar channel.

Checkpoint namespace

Each checkpoint has a checkpoint_ns (checkpoint namespace) field that identifies which graph or subgraph it belongs to:

"" (empty string): The checkpoint belongs to the parent (root) graph.
"node_name:uuid": The checkpoint belongs to a subgraph invoked as the given node. For nested subgraphs, namespaces are joined with | separators (e.g., "outer_node:uuid|inner_node:uuid").

You can access the checkpoint namespace from within a node via the config:

from langchain_core.runnables import RunnableConfig

def my_node(state: State, config: RunnableConfig):
    checkpoint_ns = config["configurable"]["checkpoint_ns"]
    # "" for the parent graph, "node_name:uuid" for a subgraph

See Subgraphs for more details on working with subgraph state and checkpoints.

Get and update state

Get state

When interacting with the saved graph state, you must specify a thread identifier. You can view the latest state of the graph by calling graph.get_state(config). This will return a StateSnapshot object that corresponds to the latest checkpoint associated with the thread ID provided in the config or a checkpoint associated with a checkpoint ID for the thread, if provided.

# get the latest state snapshot
config = {"configurable": {"thread_id": "1"}}
graph.get_state(config)

# get a state snapshot for a specific checkpoint_id
config = {"configurable": {"thread_id": "1", "checkpoint_id": "1ef663ba-28fe-6528-8002-5a559208592c"}}
graph.get_state(config)

In our example, the output of get_state will look like this:

StateSnapshot(
    values={'foo': 'b', 'bar': ['a', 'b']},
    next=(),
    config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28fe-6528-8002-5a559208592c'}},
    metadata={'source': 'loop', 'writes': {'node_b': {'foo': 'b', 'bar': ['b']}}, 'step': 2},
    created_at='2024-08-29T19:19:38.821749+00:00',
    parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f9-6ec4-8001-31981c2c39f8'}}, tasks=()
)

StateSnapshot fields

Field	Type	Description
`values`	`dict`	State channel values at this checkpoint.
`next`	`tuple[str, ...]`	Node names to execute next. Empty `()` means the graph is complete.
`config`	`dict`	Contains `thread_id`, `checkpoint_ns`, and `checkpoint_id`.
`metadata`	`dict`	Execution metadata. Contains `source` (`"input"`, `"loop"`, or `"update"`), `writes` (node outputs), and `step` (super-step counter).
`created_at`	`str`	ISO 8601 timestamp of when this checkpoint was created.
`parent_config`	`dict \| None`	Config of the previous checkpoint. `None` for the first checkpoint.
`tasks`	`tuple[PregelTask, ...]`	Tasks to execute at this step. Each task has `id`, `name`, `error`, `interrupts`, and optionally `state` (subgraph snapshot, when using `subgraphs=True`).

Get state history

You can get the full history of the graph execution for a given thread by calling graph.get_state_history(config). This will return a list of StateSnapshot objects associated with the thread ID provided in the config. Importantly, the checkpoints will be ordered chronologically with the most recent checkpoint / StateSnapshot being the first in the list.

config = {"configurable": {"thread_id": "1"}}
list(graph.get_state_history(config))

In our example, the output of get_state_history will look like this:

[
    StateSnapshot(
        values={'foo': 'b', 'bar': ['a', 'b']},
        next=(),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28fe-6528-8002-5a559208592c'}},
        metadata={'source': 'loop', 'writes': {'node_b': {'foo': 'b', 'bar': ['b']}}, 'step': 2},
        created_at='2024-08-29T19:19:38.821749+00:00',
        parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f9-6ec4-8001-31981c2c39f8'}},
        tasks=(),
    ),
    StateSnapshot(
        values={'foo': 'a', 'bar': ['a']},
        next=('node_b',),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f9-6ec4-8001-31981c2c39f8'}},
        metadata={'source': 'loop', 'writes': {'node_a': {'foo': 'a', 'bar': ['a']}}, 'step': 1},
        created_at='2024-08-29T19:19:38.819946+00:00',
        parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f4-6b4a-8000-ca575a13d36a'}},
        tasks=(PregelTask(id='6fb7314f-f114-5413-a1f3-d37dfe98ff44', name='node_b', error=None, interrupts=()),),
    ),
    StateSnapshot(
        values={'foo': '', 'bar': []},
        next=('node_a',),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f4-6b4a-8000-ca575a13d36a'}},
        metadata={'source': 'loop', 'writes': None, 'step': 0},
        created_at='2024-08-29T19:19:38.817813+00:00',
        parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f0-6c66-bfff-6723431e8481'}},
        tasks=(PregelTask(id='f1b14528-5ee5-579c-949b-23ef9bfbed58', name='node_a', error=None, interrupts=()),),
    ),
    StateSnapshot(
        values={'bar': []},
        next=('__start__',),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef663ba-28f0-6c66-bfff-6723431e8481'}},
        metadata={'source': 'input', 'writes': {'foo': ''}, 'step': -1},
        created_at='2024-08-29T19:19:38.816205+00:00',
        parent_config=None,
        tasks=(PregelTask(id='6d27aa2e-d72b-5504-a36f-8620e54a76dd', name='__start__', error=None, interrupts=()),),
    )
]

Find a specific checkpoint

You can filter the state history to find checkpoints matching specific criteria:

history = list(graph.get_state_history(config))

# Find the checkpoint before a specific node executed
before_node_b = next(s for s in history if s.next == ("node_b",))

# Find a checkpoint by step number
step_2 = next(s for s in history if s.metadata["step"] == 2)

# Find checkpoints created by update_state
forks = [s for s in history if s.metadata["source"] == "update"]

# Find the checkpoint where an interrupt occurred
interrupted = next(
    s for s in history
    if s.tasks and any(t.interrupts for t in s.tasks)
)

Replay

Replay re-executes steps from a prior checkpoint. Invoke the graph with a prior checkpoint_id to re-run nodes after that checkpoint. Nodes before the checkpoint are skipped (their results are already saved). Nodes after the checkpoint re-execute, including any LLM calls, API requests, or interrupts — which are always re-triggered during replay. See Time travel for full details and code examples on replaying past executions.

Update state

You can edit the graph state using update_state. This creates a new checkpoint with the updated values — it does not modify the original checkpoint. The update is treated the same as a node update: values are passed through reducer functions when defined, so channels with reducers accumulate values rather than overwrite them. You can optionally specify as_node to control which node the update is treated as coming from, which affects which node executes next. See Time travel: as_node for details.

Memory store

A state schema specifies a set of keys that are populated as a graph is executed. As discussed above, state can be written by a checkpointer to a thread at each graph step, enabling state persistence. What if we want to retain some information across threads? Consider the case of a chatbot where we want to retain specific information about the user across all chat conversations (e.g., threads) with that user! With checkpointers alone, we cannot share information across threads. This motivates the need for the Store interface. As an illustration, we can define an InMemoryStore to store information about a user across threads. We simply compile our graph with a checkpointer, as before, and pass the store.

LangGraph API handles stores automatically When using the LangGraph API, you don’t need to implement or configure stores manually. The API handles all storage infrastructure for you behind the scenes.

InMemoryStore is suitable for development and testing. For production, use a persistent store like PostgresStore or RedisStore. All implementations extend BaseStore, which is the type annotation to use in node function signatures.

Basic usage

First, let’s showcase this in isolation without using LangGraph.

from langgraph.store.memory import InMemoryStore
store = InMemoryStore()

Memories are namespaced by a tuple, which in this specific example will be (<user_id>, "memories"). The namespace can be any length and represent anything, does not have to be user specific.

user_id = "1"
namespace_for_memory = (user_id, "memories")

We use the store.put method to save memories to our namespace in the store. When we do this, we specify the namespace, as defined above, and a key-value pair for the memory: the key is simply a unique identifier for the memory (memory_id) and the value (a dictionary) is the memory itself.

memory_id = str(uuid.uuid4())
memory = {"food_preference" : "I like pizza"}
store.put(namespace_for_memory, memory_id, memory)

We can read out memories in our namespace using the store.search method, which will return all memories for a given user as a list. The most recent memory is the last in the list.

memories = store.search(namespace_for_memory)
memories[-1].dict()
{'value': {'food_preference': 'I like pizza'},
 'key': '07e0caf4-1631-47b7-b15f-65515d4c1843',
 'namespace': ['1', 'memories'],
 'created_at': '2024-10-02T17:22:31.590602+00:00',
 'updated_at': '2024-10-02T17:22:31.590605+00:00'}

Each memory type is a Python class (Item) with certain attributes. We can access it as a dictionary by converting via .dict as above. The attributes it has are:

value: The value (itself a dictionary) of this memory
key: A unique key for this memory in this namespace
namespace: A tuple of strings, the namespace of this memory type
While the type is tuple[str, ...], it may be serialized as a list when converted to JSON (for example, ['1', 'memories']).
created_at: Timestamp for when this memory was created
updated_at: Timestamp for when this memory was updated

Semantic search

Beyond simple retrieval, the store also supports semantic search, allowing you to find memories based on meaning rather than exact matches. To enable this, configure the store with an embedding model:

from langchain.embeddings import init_embeddings

store = InMemoryStore(
    index={
        "embed": init_embeddings("openai:text-embedding-3-small"),  # Embedding provider
        "dims": 1536,                              # Embedding dimensions
        "fields": ["food_preference", "$"]              # Fields to embed
    }
)

Now when searching, you can use natural language queries to find relevant memories:

# Find memories about food preferences
# (This can be done after putting memories into the store)
memories = store.search(
    namespace_for_memory,
    query="What does the user like to eat?",
    limit=3  # Return top 3 matches
)

You can control which parts of your memories get embedded by configuring the fields parameter or by specifying the index parameter when storing memories:

# Store with specific fields to embed
store.put(
    namespace_for_memory,
    str(uuid.uuid4()),
    {
        "food_preference": "I love Italian cuisine",
        "context": "Discussing dinner plans"
    },
    index=["food_preference"]  # Only embed "food_preferences" field
)

# Store without embedding (still retrievable, but not searchable)
store.put(
    namespace_for_memory,
    str(uuid.uuid4()),
    {"system_info": "Last updated: 2024-01-01"},
    index=False
)

Using in LangGraph

With this all in place, we use the store in LangGraph. The store works hand-in-hand with the checkpointer: the checkpointer saves state to threads, as discussed above, and the store allows us to store arbitrary information for access across threads. We compile the graph with both the checkpointer and the store as follows.

from dataclasses import dataclass
from langgraph.checkpoint.memory import InMemorySaver

@dataclass
class Context:
    user_id: str

# We need this because we want to enable threads (conversations)
checkpointer = InMemorySaver()

# ... Define the graph ...

# Compile the graph with the checkpointer and store
builder = StateGraph(MessagesState, context_schema=Context)
# ... add nodes and edges ...
graph = builder.compile(checkpointer=checkpointer, store=store)

We invoke the graph with a thread_id, as before, and also with a user_id, which we’ll use to namespace our memories to this particular user as we showed above.

# Invoke the graph
config = {"configurable": {"thread_id": "1"}}

# First let's just say hi to the AI
for update in graph.stream(
    {"messages": [{"role": "user", "content": "hi"}]},
    config,
    stream_mode="updates",
    context=Context(user_id="1"),
):
    print(update)

You can access the store and the user_id in any node by using the Runtime object. The Runtime is automatically injected by LangGraph when you add it as a parameter to your node function. Here’s how you might use it to save memories:

from langgraph.runtime import Runtime
from dataclasses import dataclass

@dataclass
class Context:
    user_id: str

async def update_memory(state: MessagesState, runtime: Runtime[Context]):

    # Get the user id from the runtime context
    user_id = runtime.context.user_id

    # Namespace the memory
    namespace = (user_id, "memories")

    # ... Analyze conversation and create a new memory

    # Create a new memory ID
    memory_id = str(uuid.uuid4())

    # We create a new memory
    await runtime.store.aput(namespace, memory_id, {"memory": memory})

As we showed above, we can also access the store in any node and use the store.search method to get memories. Recall the memories are returned as a list of objects that can be converted to a dictionary.

memories[-1].dict()
{'value': {'food_preference': 'I like pizza'},
 'key': '07e0caf4-1631-47b7-b15f-65515d4c1843',
 'namespace': ['1', 'memories'],
 'created_at': '2024-10-02T17:22:31.590602+00:00',
 'updated_at': '2024-10-02T17:22:31.590605+00:00'}

We can access the memories and use them in our model call.

from dataclasses import dataclass
from langgraph.runtime import Runtime

@dataclass
class Context:
    user_id: str

async def call_model(state: MessagesState, runtime: Runtime[Context]):
    # Get the user id from the runtime context
    user_id = runtime.context.user_id

    # Namespace the memory
    namespace = (user_id, "memories")

    # Search based on the most recent message
    memories = await runtime.store.asearch(
        namespace,
        query=state["messages"][-1].content,
        limit=3
    )
    info = "\n".join([d.value["memory"] for d in memories])

    # ... Use memories in the model call

If we create a new thread, we can still access the same memories so long as the user_id is the same.

# Invoke the graph on a new thread
config = {"configurable": {"thread_id": "2"}}

# Let's say hi again
for update in graph.stream(
    {"messages": [{"role": "user", "content": "hi, tell me about my memories"}]},
    config,
    stream_mode="updates",
    context=Context(user_id="1"),
):
    print(update)

When we use the LangSmith, either locally (e.g., in Studio) or hosted with LangSmith, the base store is available to use by default and does not need to be specified during graph compilation. To enable semantic search, however, you do need to configure the indexing settings in your langgraph.json file. For example:

{
    ...
    "store": {
        "index": {
            "embed": "openai:text-embeddings-3-small",
            "dims": 1536,
            "fields": ["$"]
        }
    }
}

See the deployment guide for more details and configuration options.

Checkpointer libraries

Under the hood, checkpointing is powered by checkpointer objects that conform to BaseCheckpointSaver interface. LangGraph provides several checkpointer implementations, all implemented via standalone, installable libraries:

langgraph-checkpoint: The base interface for checkpointer savers (BaseCheckpointSaver) and serialization/deserialization interface (SerializerProtocol). Includes in-memory checkpointer implementation (InMemorySaver) for experimentation. LangGraph comes with langgraph-checkpoint included.
langgraph-checkpoint-sqlite: An implementation of LangGraph checkpointer that uses SQLite database (SqliteSaver / AsyncSqliteSaver). Ideal for experimentation and local workflows. Needs to be installed separately.
langgraph-checkpoint-postgres: An advanced checkpointer that uses Postgres database (PostgresSaver / AsyncPostgresSaver), used in LangSmith. Ideal for using in production. Needs to be installed separately.
langgraph-checkpoint-cosmosdb: An implementation of LangGraph checkpointer that uses Azure Cosmos DB (@[CosmosDBSaver] / @[AsyncCosmosDBSaver]). Ideal for using in production with Azure. Supports both sync and async operations. Needs to be installed separately.

Checkpointer interface

Each checkpointer conforms to BaseCheckpointSaver interface and implements the following methods:

.put - Store a checkpoint with its configuration and metadata.
.put_writes - Store intermediate writes linked to a checkpoint (i.e. pending writes).
.get_tuple - Fetch a checkpoint tuple using for a given configuration (thread_id and checkpoint_id). This is used to populate StateSnapshot in graph.get_state().
.list - List checkpoints that match a given configuration and filter criteria. This is used to populate state history in graph.get_state_history()

If the checkpointer is used with asynchronous graph execution (i.e. executing the graph via .ainvoke, .astream, .abatch), asynchronous versions of the above methods will be used (.aput, .aput_writes, .aget_tuple, .alist).

For running your graph asynchronously, you can use InMemorySaver, or async versions of Sqlite/Postgres checkpointers — AsyncSqliteSaver / AsyncPostgresSaver checkpointers.

Serializer

When checkpointers save the graph state, they need to serialize the channel values in the state. This is done using serializer objects. langgraph_checkpoint defines protocol for implementing serializers provides a default implementation (JsonPlusSerializer) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.

Serialization with `pickle`

The default serializer, JsonPlusSerializer, uses ormsgpack and JSON under the hood, which is not suitable for all types of objects. If you want to fallback to pickle for objects not currently supported by our msgpack encoder (such as Pandas dataframes), you can use the pickle_fallback argument of the JsonPlusSerializer:

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer

# ... Define the graph ...
graph.compile(
    checkpointer=InMemorySaver(serde=JsonPlusSerializer(pickle_fallback=True))
)

Encryption

Checkpointers can optionally encrypt all persisted state. To enable this, pass an instance of EncryptedSerializer to the serde argument of any BaseCheckpointSaver implementation. The easiest way to create an encrypted serializer is via from_pycryptodome_aes, which reads the AES key from the LANGGRAPH_AES_KEY environment variable (or accepts a key argument):

import sqlite3

from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
from langgraph.checkpoint.sqlite import SqliteSaver

serde = EncryptedSerializer.from_pycryptodome_aes()  # reads LANGGRAPH_AES_KEY
checkpointer = SqliteSaver(sqlite3.connect("checkpoint.db"), serde=serde)

from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
from langgraph.checkpoint.postgres import PostgresSaver

serde = EncryptedSerializer.from_pycryptodome_aes()
checkpointer = PostgresSaver.from_conn_string("postgresql://...", serde=serde)
checkpointer.setup()

When running on LangSmith, encryption is automatically enabled whenever LANGGRAPH_AES_KEY is present, so you only need to provide the environment variable. Other encryption schemes can be used by implementing CipherProtocol and supplying it to EncryptedSerializer.

Edit this page on GitHub or file an issue.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Get started

Capabilities

Production

Frontend

LangGraph APIs

Why use persistence

Core concepts

Threads

Checkpoints

Super-steps

Checkpoint namespace

Get and update state

Get state

StateSnapshot fields

Get state history

Find a specific checkpoint

Replay

Update state

Memory store

Basic usage

Semantic search

Using in LangGraph

Checkpointer libraries

Checkpointer interface

Serializer

Serialization with `pickle`

Encryption

Get started

Capabilities

Production

Frontend

LangGraph APIs

​Why use persistence

​Core concepts

​Threads

​Checkpoints

​Super-steps

​Checkpoint namespace

​Get and update state

​Get state

​StateSnapshot fields

​Get state history

​Find a specific checkpoint

​Replay

​Update state

​Memory store

​Basic usage

​Semantic search

​Using in LangGraph

​Checkpointer libraries

​Checkpointer interface

​Serializer

​Serialization with pickle

​Encryption

Why use persistence

Core concepts

Threads

Checkpoints

Super-steps

Checkpoint namespace

Get and update state

Get state

StateSnapshot fields

Get state history

Find a specific checkpoint

Replay

Update state

Memory store

Basic usage

Semantic search

Using in LangGraph

Checkpointer libraries

Checkpointer interface

Serializer

Serialization with `pickle`

Encryption