Persistence¶

LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile graph with a checkpointer, the checkpointer saves a checkpoint of the graph state at every super-step. Those checkpoints are saved to a thread, which can be accessed after graph execution. Because threads allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. See this how-to guide for an end-to-end example on how to add and use checkpointers with your graph. Below, we'll discuss each of these concepts in more detail.

Checkpoints

Threads¶

A thread is a unique ID or thread identifier assigned to each checkpoint saved by a checkpointer. When invoking graph with a checkpointer, you must specify a thread_id as part of the configurable portion of the config:

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

Checkpoints¶

Checkpoint is a snapshot of the graph state saved at each super-step and is represented by StateSnapshot object with the following key properties:

config: Config associated with this checkpoint.
metadata: Metadata associated with this checkpoint.
values: Values of the state channels at this point in time.
next A tuple of the node names to execute next in the graph.
tasks: A tuple of PregelTask objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted dynamically from within a node, tasks will contain additional data associated with interrupts.

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 MemorySaver
from typing import TypedDict, Annotated
from operator import add

class State(TypedDict):
    foo: int
    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 = MemorySaver()
graph = workflow.compile(checkpointer=checkpointer)

config = {"configurable": {"thread_id": "1"}}
graph.invoke({"foo": ""}, 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.

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=()
)

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=()),),
    )
]

State

Replay¶

It's also possible to play-back a prior graph execution. If we invoking a graph with a thread_id and a checkpoint_id, then we will re-play the graph from a checkpoint that corresponds to the checkpoint_id.

thread_id is simply the ID of a thread. This is always required.
checkpoint_id This identifier refers to a specific checkpoint within a thread.

You must pass these when invoking the graph as part of the configurable portion of the config:

# {"configurable": {"thread_id": "1"}}  # valid config
# {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config

config = {"configurable": {"thread_id": "1"}}
graph.invoke(inputs, config=config)

Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply re-plays that particular step in the graph and does not re-execute the step. See this how to guide on time-travel to learn more about replaying.

Replay

Update state¶

In addition to re-playing the graph from specific checkpoints, we can also edit the graph state. We do this using graph.update_state(). This method three different arguments:

`config`¶

The config should contain thread_id specifying which thread to update. When only the thread_id is passed, we update (or fork) the current state. Optionally, if we include checkpoint_id field, then we fork that selected checkpoint.

`values`¶

These are the values that will be used to update the state. Note that this update is treated exactly as any update from a node is treated. This means that these values will be passed to the reducer functions, if they are defined for some of the channels in the graph state. This means that update_state does NOT automatically overwrite the channel values for every channel, but only for the channels without reducers. Let's walk through an example.

Let's assume you have defined the state of your graph with the following schema (see full example above):

from typing import TypedDict, Annotated
from operator import add

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

Let's now assume the current state of the graph is

{"foo": 1, "bar": ["a"]}

If you update the state as below:

graph.update_state(config, {"foo": 2, "bar": ["b"]})

Then the new state of the graph will be:

{"foo": 2, "bar": ["a", "b"]}

The foo key (channel) is completely changed (because there is no reducer specified for that channel, so update_state overwrites it). However, there is a reducer specified for the bar key, and so it appends "b" to the state of bar.

`as_node`¶

The final thing you can optionally specify when calling update_state is as_node. If you provided it, the update will be applied as if it came from node as_node. If as_node is not provided, it will be set to the last node that updated the state, if not ambiguous. The reason this matters is that the next steps to execute depend on the last node to have given an update, so this can be used to control which node executes next. See this how to guide on time-travel to learn more about forking state.

Update

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 (MemorySaver) 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 LangGraph Cloud. Ideal for using in production. 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).

Note

For running your graph asynchronously, you can use MemorySaver, 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.

Capabilities¶

Human-in-the-loop¶

First, checkpointers facilitate human-in-the-loop workflows workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See these how-to guides for concrete examples.

Memory¶

Second, 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 this how-to guide for an end-to-end example on how to add and manage conversation memory using checkpointers.

Time Travel¶

Third, 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¶

Lastly, checkpointing also 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. Additionally, when a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.

Pending writes¶

Additionally, when a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.