Graph Definitions

StateGraph

Bases: Graph

A graph whose nodes communicate by reading and writing to a shared state. The signature of each node is State -> Partial.

Each state key can optionally be annotated with a reducer function that will be used to aggregate the values of that key received from multiple nodes. The signature of a reducer function is (Value, Value) -> Value.

Parameters:

Name	Type	Description	Default
state_schema	Optional[type[Any]]	The schema class that defines the state.	None
config_schema	Optional[type[Any]]	The schema class that defines the configuration. Use this to expose configurable parameters in your API.	None

Example

from langchain_core.runnables import RunnableConfig
from typing_extensions import Annotated, TypedDict
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph

def reducer(a: list, b: int | None) -> list:
    if b is not None:
        return a + [b]
    return a

class State(TypedDict):
    x: Annotated[list, reducer]

class ConfigSchema(TypedDict):
    r: float

graph = StateGraph(State, config_schema=ConfigSchema)

def node(state: State, config: RunnableConfig) -> dict:
    r = config["configurable"].get("r", 1.0)
    x = state["x"][-1]
    next_value = x * r * (1 - x)
    return {"x": next_value}

graph.add_node("A", node)
graph.set_entry_point("A")
graph.set_finish_point("A")
compiled = graph.compile()

print(compiled.config_specs)
# [ConfigurableFieldSpec(id='r', annotation=<class 'float'>, name=None, description=None, default=None, is_shared=False, dependencies=None)]

step1 = compiled.invoke({"x": 0.5}, {"configurable": {"r": 3.0}})
# {'x': [0.5, 0.75]}

Methods:

Name	Description
add_node	Add a new node to the state graph.
add_edge	Add a directed edge from the start node (or list of start nodes) to the end node.
add_conditional_edges	Add a conditional edge from the starting node to any number of destination nodes.
add_sequence	Add a sequence of nodes that will be executed in the provided order.
compile	Compiles the state graph into a CompiledStateGraph object.

add_node

add_node(
    node: Union[str, RunnableLike],
    action: Optional[RunnableLike] = None,
    *,
    metadata: Optional[dict[str, Any]] = None,
    input: Optional[type[Any]] = None,
    retry: Optional[
        Union[RetryPolicy, Sequence[RetryPolicy]]
    ] = None,
    destinations: Optional[
        Union[dict[str, str], tuple[str, ...]]
    ] = None
) -> Self

Add a new node to the state graph.

Parameters:

Name	Type	Description	Default
node	Union[str, RunnableLike]	The function or runnable this node will run. If a string is provided, it will be used as the node name, and action will be used as the function or runnable.	required
action	Optional[RunnableLike]	The action associated with the node. (default: None) Will be used as the node function or runnable if node is a string (node name).	None
metadata	Optional[dict[str, Any]]	The metadata associated with the node. (default: None)	None
input	Optional[type[Any]]	The input schema for the node. (default: the graph's input schema)	None
retry	Optional[Union[RetryPolicy, Sequence[RetryPolicy]]]	The policy for retrying the node. (default: None) If a sequence is provided, the first matching policy will be applied.	None
destinations	Optional[Union[dict[str, str], tuple[str, ...]]]	Destinations that indicate where a node can route to. This is useful for edgeless graphs with nodes that return Command objects. If a dict is provided, the keys will be used as the target node names and the values will be used as the labels for the edges. If a tuple is provided, the values will be used as the target node names. NOTE: this is only used for graph rendering and doesn't have any effect on the graph execution.	None

Raises: ValueError: If the key is already being used as a state key.

Example

from langgraph.graph import START, StateGraph

def my_node(state, config):
    return {"x": state["x"] + 1}

builder = StateGraph(dict)
builder.add_node(my_node)  # node name will be 'my_node'
builder.add_edge(START, "my_node")
graph = builder.compile()
graph.invoke({"x": 1})
# {'x': 2}

Customize the name:

builder = StateGraph(dict)
builder.add_node("my_fair_node", my_node)
builder.add_edge(START, "my_fair_node")
graph = builder.compile()
graph.invoke({"x": 1})
# {'x': 2}

Returns:

Name	Type	Description
Self	Self	The instance of the state graph, allowing for method chaining.

add_edge

add_edge(
    start_key: Union[str, list[str]], end_key: str
) -> Self

Add a directed edge from the start node (or list of start nodes) to the end node.

When a single start node is provided, the graph will wait for that node to complete before executing the end node. When multiple start nodes are provided, the graph will wait for ALL of the start nodes to complete before executing the end node.

Parameters:

Name	Type	Description	Default
start_key	Union[str, list[str]]	The key(s) of the start node(s) of the edge.	required
end_key	str	The key of the end node of the edge.	required

Raises:

Type	Description
ValueError	If the start key is 'END' or if the start key or end key is not present in the graph.

Returns:

Name	Type	Description
Self	Self	The instance of the state graph, allowing for method chaining.

add_conditional_edges

add_conditional_edges(
    source: str,
    path: Union[
        Callable[..., Union[Hashable, list[Hashable]]],
        Callable[
            ..., Awaitable[Union[Hashable, list[Hashable]]]
        ],
        Runnable[Any, Union[Hashable, list[Hashable]]],
    ],
    path_map: Optional[
        Union[dict[Hashable, str], list[str]]
    ] = None,
    then: Optional[str] = None,
) -> Self

Add a conditional edge from the starting node to any number of destination nodes.

Parameters:

Name	Type	Description	Default
source	str	The starting node. This conditional edge will run when exiting this node.	required
path	Union[Callable[..., Union[Hashable, list[Hashable]]], Callable[..., Awaitable[Union[Hashable, list[Hashable]]]], Runnable[Any, Union[Hashable, list[Hashable]]]]	The callable that determines the next node or nodes. If not specifying path_map it should return one or more nodes. If it returns END, the graph will stop execution.	required
path_map	Optional[Union[dict[Hashable, str], list[str]]]	Optional mapping of paths to node names. If omitted the paths returned by path should be node names.	None
then	Optional[str]	The name of a node to execute after the nodes selected by path.	None

Returns:

Name	Type	Description
Self	Self	The instance of the graph, allowing for method chaining.

Without typehints on the path function's return value (e.g., -> Literal["foo", "__end__"]:)

or a path_map, the graph visualization assumes the edge could transition to any node in the graph.

add_sequence

add_sequence(
    nodes: Sequence[
        Union[RunnableLike, tuple[str, RunnableLike]]
    ],
) -> Self

Add a sequence of nodes that will be executed in the provided order.

Parameters:

Name	Type	Description	Default
nodes	Sequence[Union[RunnableLike, tuple[str, RunnableLike]]]	A sequence of RunnableLike objects (e.g. a LangChain Runnable or a callable) or (name, RunnableLike) tuples. If no names are provided, the name will be inferred from the node object (e.g. a runnable or a callable name). Each node will be executed in the order provided.	required

Raises:

Type	Description
ValueError	if the sequence is empty.
ValueError	if the sequence contains duplicate node names.

Returns:

Name	Type	Description
Self	Self	The instance of the state graph, allowing for method chaining.

compile

compile(
    checkpointer: Checkpointer = None,
    *,
    store: Optional[BaseStore] = None,
    interrupt_before: Optional[
        Union[All, list[str]]
    ] = None,
    interrupt_after: Optional[Union[All, list[str]]] = None,
    debug: bool = False,
    name: Optional[str] = None
) -> CompiledStateGraph

Compiles the state graph into a CompiledStateGraph object.

The compiled graph implements the Runnable interface and can be invoked, streamed, batched, and run asynchronously.

Parameters:

Name	Type	Description	Default
checkpointer	Checkpointer	A checkpoint saver object or flag. If provided, this Checkpointer serves as a fully versioned "short-term memory" for the graph, allowing it to be paused, resumed, and replayed from any point. If None, it may inherit the parent graph's checkpointer when used as a subgraph. If False, it will not use or inherit any checkpointer.	None
interrupt_before	Optional[Union[All, list[str]]]	An optional list of node names to interrupt before.	None
interrupt_after	Optional[Union[All, list[str]]]	An optional list of node names to interrupt after.	None
debug	bool	A flag indicating whether to enable debug mode.	False
name	Optional[str]	The name to use for the compiled graph.	None

Returns:

Name	Type	Description
CompiledStateGraph	CompiledStateGraph	The compiled state graph.

CompiledStateGraph

Bases: CompiledGraph

Methods:

Name	Description
stream	Stream graph steps for a single input.
astream	Asynchronously stream graph steps for a single input.
invoke	Run the graph with a single input and config.
ainvoke	Asynchronously invoke the graph on a single input.
get_state	Get the current state of the graph.
aget_state	Get the current state of the graph.
get_state_history	Get the history of the state of the graph.
aget_state_history	Asynchronously get the history of the state of the graph.
update_state	Update the state of the graph with the given values, as if they came from
aupdate_state	Asynchronously update the state of the graph with the given values, as if they came from
bulk_update_state	Apply updates to the graph state in bulk. Requires a checkpointer to be set.
abulk_update_state	Asynchronously apply updates to the graph state in bulk. Requires a checkpointer to be set.
get_graph	Return a drawable representation of the computation graph.
aget_graph	Return a drawable representation of the computation graph.
get_subgraphs	Get the subgraphs of the graph.
aget_subgraphs	Get the subgraphs of the graph.
with_config	Create a copy of the Pregel object with an updated config.

stream

stream(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: (
        StreamMode | list[StreamMode] | None
    ) = None,
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    subgraphs: bool = False
) -> Iterator[dict[str, Any] | Any]

Stream graph steps for a single input.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input to the graph.	required
config	RunnableConfig | None	The configuration to use for the run.	None
stream_mode	StreamMode | list[StreamMode] | None	The mode to stream output, defaults to self.stream_mode. Options are: "values": Emit all values in the state after each step, including interrupts. When used with functional API, values are emitted once at the end of the workflow. "updates": Emit only the node or task names and updates returned by the nodes or tasks after each step. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately. "custom": Emit custom data from inside nodes or tasks using StreamWriter. "messages": Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks. "debug": Emit debug events with as much information as possible for each step.	None
output_keys	str | Sequence[str] | None	The keys to stream, defaults to all non-context channels.	None
interrupt_before	All | Sequence[str] | None	Nodes to interrupt before, defaults to all nodes in the graph.	None
interrupt_after	All | Sequence[str] | None	Nodes to interrupt after, defaults to all nodes in the graph.	None
checkpoint_during	bool | None	Whether to checkpoint intermediate steps, defaults to True. If False, only the final checkpoint is saved.	None
debug	bool | None	Whether to print debug information during execution, defaults to False.	None
subgraphs	bool	Whether to stream subgraphs, defaults to False.	False

Yields:

Type	Description
dict[str, Any] | Any	The output of each step in the graph. The output shape depends on the stream_mode.

Using stream_mode="values":

import operator
from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START

class State(TypedDict):
    alist: Annotated[list, operator.add]
    another_list: Annotated[list, operator.add]

builder = StateGraph(State)
builder.add_node("a", lambda _state: {"another_list": ["hi"]})
builder.add_node("b", lambda _state: {"alist": ["there"]})
builder.add_edge("a", "b")
builder.add_edge(START, "a")
graph = builder.compile()

for event in graph.stream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
    print(event)

# {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
# {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
# {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}

Using stream_mode="updates":

for event in graph.stream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
    print(event)

# {'a': {'another_list': ['hi']}}
# {'b': {'alist': ['there']}}

Using stream_mode="debug":

for event in graph.stream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
    print(event)

# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}

Using stream_mode="custom":

from langgraph.types import StreamWriter

def node_a(state: State, writer: StreamWriter):
    writer({"custom_data": "foo"})
    return {"alist": ["hi"]}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

for event in graph.stream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
    print(event)

# {'custom_data': 'foo'}

Using stream_mode="messages":

from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

class State(TypedDict):
    question: str
    answer: str

def node_a(state: State):
    response = llm.invoke(state["question"])
    return {"answer": response.content}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

for event in graph.stream({"question": "What is the capital of France?"}, stream_mode="messages"):
    print(event)

# (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
# (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
# (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})

astream async

astream(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: (
        StreamMode | list[StreamMode] | None
    ) = None,
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    subgraphs: bool = False
) -> AsyncIterator[dict[str, Any] | Any]

Asynchronously stream graph steps for a single input.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input to the graph.	required
config	RunnableConfig | None	The configuration to use for the run.	None
stream_mode	StreamMode | list[StreamMode] | None	The mode to stream output, defaults to self.stream_mode. Options are: "values": Emit all values in the state after each step, including interrupts. When used with functional API, values are emitted once at the end of the workflow. "updates": Emit only the node or task names and updates returned by the nodes or tasks after each step. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately. "custom": Emit custom data from inside nodes or tasks using StreamWriter. "messages": Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks. "debug": Emit debug events with as much information as possible for each step.	None
output_keys	str | Sequence[str] | None	The keys to stream, defaults to all non-context channels.	None
interrupt_before	All | Sequence[str] | None	Nodes to interrupt before, defaults to all nodes in the graph.	None
interrupt_after	All | Sequence[str] | None	Nodes to interrupt after, defaults to all nodes in the graph.	None
checkpoint_during	bool | None	Whether to checkpoint intermediate steps, defaults to True. If False, only the final checkpoint is saved.	None
debug	bool | None	Whether to print debug information during execution, defaults to False.	None
subgraphs	bool	Whether to stream subgraphs, defaults to False.	False

Yields:

Type	Description
AsyncIterator[dict[str, Any] | Any]	The output of each step in the graph. The output shape depends on the stream_mode.

Using stream_mode="values":

import operator
from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START

class State(TypedDict):
    alist: Annotated[list, operator.add]
    another_list: Annotated[list, operator.add]

builder = StateGraph(State)
builder.add_node("a", lambda _state: {"another_list": ["hi"]})
builder.add_node("b", lambda _state: {"alist": ["there"]})
builder.add_edge("a", "b")
builder.add_edge(START, "a")
graph = builder.compile()

async for event in graph.astream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
    print(event)

# {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
# {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
# {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}

Using stream_mode="updates":

async for event in graph.astream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
    print(event)

# {'a': {'another_list': ['hi']}}
# {'b': {'alist': ['there']}}

Using stream_mode="debug":

async for event in graph.astream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
    print(event)

# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}

Using stream_mode="custom":

from langgraph.types import StreamWriter

async def node_a(state: State, writer: StreamWriter):
    writer({"custom_data": "foo"})
    return {"alist": ["hi"]}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

async for event in graph.astream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
    print(event)

# {'custom_data': 'foo'}

Using stream_mode="messages":

from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

class State(TypedDict):
    question: str
    answer: str

async def node_a(state: State):
    response = await llm.ainvoke(state["question"])
    return {"answer": response.content}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

async for event in graph.astream({"question": "What is the capital of France?"}, stream_mode="messages"):
    print(event)

# (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
# (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
# (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})

invoke

invoke(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: StreamMode = "values",
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    **kwargs: Any
) -> dict[str, Any] | Any

Run the graph with a single input and config.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input data for the graph. It can be a dictionary or any other type.	required
config	RunnableConfig | None	Optional. The configuration for the graph run.	None
stream_mode	StreamMode	Optional[str]. The stream mode for the graph run. Default is "values".	'values'
output_keys	str | Sequence[str] | None	Optional. The output keys to retrieve from the graph run.	None
interrupt_before	All | Sequence[str] | None	Optional. The nodes to interrupt the graph run before.	None
interrupt_after	All | Sequence[str] | None	Optional. The nodes to interrupt the graph run after.	None
debug	bool | None	Optional. Enable debug mode for the graph run.	None
**kwargs	Any	Additional keyword arguments to pass to the graph run.	{}

Returns:

Type	Description
dict[str, Any] | Any	The output of the graph run. If stream_mode is "values", it returns the latest output.
dict[str, Any] | Any	If stream_mode is not "values", it returns a list of output chunks.

ainvoke async

ainvoke(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: StreamMode = "values",
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    **kwargs: Any
) -> dict[str, Any] | Any

Asynchronously invoke the graph on a single input.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input data for the computation. It can be a dictionary or any other type.	required
config	RunnableConfig | None	Optional. The configuration for the computation.	None
stream_mode	StreamMode	Optional. The stream mode for the computation. Default is "values".	'values'
output_keys	str | Sequence[str] | None	Optional. The output keys to include in the result. Default is None.	None
interrupt_before	All | Sequence[str] | None	Optional. The nodes to interrupt before. Default is None.	None
interrupt_after	All | Sequence[str] | None	Optional. The nodes to interrupt after. Default is None.	None
debug	bool | None	Optional. Whether to enable debug mode. Default is None.	None
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
dict[str, Any] | Any	The result of the computation. If stream_mode is "values", it returns the latest value.
dict[str, Any] | Any	If stream_mode is "chunks", it returns a list of chunks.

get_state

get_state(
    config: RunnableConfig, *, subgraphs: bool = False
) -> StateSnapshot

Get the current state of the graph.

aget_state async

aget_state(
    config: RunnableConfig, *, subgraphs: bool = False
) -> StateSnapshot

Get the current state of the graph.

get_state_history

get_state_history(
    config: RunnableConfig,
    *,
    filter: dict[str, Any] | None = None,
    before: RunnableConfig | None = None,
    limit: int | None = None
) -> Iterator[StateSnapshot]

Get the history of the state of the graph.

aget_state_history async

aget_state_history(
    config: RunnableConfig,
    *,
    filter: dict[str, Any] | None = None,
    before: RunnableConfig | None = None,
    limit: int | None = None
) -> AsyncIterator[StateSnapshot]

Asynchronously get the history of the state of the graph.

update_state

update_state(
    config: RunnableConfig,
    values: dict[str, Any] | Any | None,
    as_node: str | None = None,
) -> RunnableConfig

Update the state of the graph with the given values, as if they 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.

aupdate_state async

aupdate_state(
    config: RunnableConfig,
    values: dict[str, Any] | Any,
    as_node: str | None = None,
) -> RunnableConfig

Asynchronously update the state of the graph with the given values, as if they 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.

bulk_update_state

bulk_update_state(
    config: RunnableConfig,
    supersteps: Sequence[Sequence[StateUpdate]],
) -> RunnableConfig

Apply updates to the graph state in bulk. Requires a checkpointer to be set.

Parameters:

Name	Type	Description	Default
config	RunnableConfig	The config to apply the updates to.	required
supersteps	Sequence[Sequence[StateUpdate]]	A list of supersteps, each including a list of updates to apply sequentially to a graph state. Each update is a tuple of the form (values, as_node).	required

Raises:

Type	Description
ValueError	If no checkpointer is set or no updates are provided.
InvalidUpdateError	If an invalid update is provided.

Returns:

Name	Type	Description
RunnableConfig	RunnableConfig	The updated config.

abulk_update_state async

abulk_update_state(
    config: RunnableConfig,
    supersteps: Sequence[Sequence[StateUpdate]],
) -> RunnableConfig

Asynchronously apply updates to the graph state in bulk. Requires a checkpointer to be set.

Parameters:

Name	Type	Description	Default
config	RunnableConfig	The config to apply the updates to.	required
supersteps	Sequence[Sequence[StateUpdate]]	A list of supersteps, each including a list of updates to apply sequentially to a graph state. Each update is a tuple of the form (values, as_node).	required

Raises:

Type	Description
ValueError	If no checkpointer is set or no updates are provided.
InvalidUpdateError	If an invalid update is provided.

Returns:

Name	Type	Description
RunnableConfig	RunnableConfig	The updated config.

get_graph

get_graph(
    config: RunnableConfig | None = None,
    *,
    xray: int | bool = False
) -> Graph

Return a drawable representation of the computation graph.

aget_graph async

aget_graph(
    config: RunnableConfig | None = None,
    *,
    xray: int | bool = False
) -> Graph

Return a drawable representation of the computation graph.

get_subgraphs

get_subgraphs(
    *, namespace: str | None = None, recurse: bool = False
) -> Iterator[tuple[str, PregelProtocol]]

Get the subgraphs of the graph.

Parameters:

Name	Type	Description	Default
namespace	str | None	The namespace to filter the subgraphs by.	None
recurse	bool	Whether to recurse into the subgraphs. If False, only the immediate subgraphs will be returned.	False

Returns:

Type	Description
Iterator[tuple[str, PregelProtocol]]	Iterator[tuple[str, PregelProtocol]]: An iterator of the (namespace, subgraph) pairs.

aget_subgraphs async

aget_subgraphs(
    *, namespace: str | None = None, recurse: bool = False
) -> AsyncIterator[tuple[str, PregelProtocol]]

Get the subgraphs of the graph.

Parameters:

Name	Type	Description	Default
namespace	str | None	The namespace to filter the subgraphs by.	None
recurse	bool	Whether to recurse into the subgraphs. If False, only the immediate subgraphs will be returned.	False

Returns:

Type	Description
AsyncIterator[tuple[str, PregelProtocol]]	AsyncIterator[tuple[str, PregelProtocol]]: An iterator of the (namespace, subgraph) pairs.

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Self

Create a copy of the Pregel object with an updated config.

Graph

Methods:

Name	Description
add_node	Add a new node to the graph.
add_edge	Add a directed edge from the start node to the end node.
add_conditional_edges	Add a conditional edge from the starting node to any number of destination nodes.
compile	Compiles the graph into a CompiledGraph object.

add_node

add_node(
    node: Union[str, RunnableLike],
    action: Optional[RunnableLike] = None,
    *,
    metadata: Optional[dict[str, Any]] = None
) -> Self

Add a new node to the graph.

Parameters:

Name	Type	Description	Default
node	Union[str, RunnableLike]	The function or runnable this node will run. If a string is provided, it will be used as the node name, and action will be used as the function or runnable.	required
action	Optional[RunnableLike]	The action associated with the node. (default: None) Will be used as the node function or runnable if node is a string (node name).	None
metadata	Optional[dict[str, Any]]	The metadata associated with the node. (default: None)	None

add_edge

add_edge(start_key: str, end_key: str) -> Self

Add a directed edge from the start node to the end node.

Parameters:

Name	Type	Description	Default
start_key	str	The key of the start node of the edge.	required
end_key	str	The key of the end node of the edge.	required

add_conditional_edges

add_conditional_edges(
    source: str,
    path: Union[
        Callable[..., Union[Hashable, list[Hashable]]],
        Callable[
            ..., Awaitable[Union[Hashable, list[Hashable]]]
        ],
        Runnable[Any, Union[Hashable, list[Hashable]]],
    ],
    path_map: Optional[
        Union[dict[Hashable, str], list[str]]
    ] = None,
    then: Optional[str] = None,
) -> Self

Add a conditional edge from the starting node to any number of destination nodes.

Parameters:

Name	Type	Description	Default
source	str	The starting node. This conditional edge will run when exiting this node.	required
path	Union[Callable[..., Union[Hashable, list[Hashable]]], Callable[..., Awaitable[Union[Hashable, list[Hashable]]]], Runnable[Any, Union[Hashable, list[Hashable]]]]	The callable that determines the next node or nodes. If not specifying path_map it should return one or more nodes. If it returns END, the graph will stop execution.	required
path_map	Optional[Union[dict[Hashable, str], list[str]]]	Optional mapping of paths to node names. If omitted the paths returned by path should be node names.	None
then	Optional[str]	The name of a node to execute after the nodes selected by path.	None

Returns:

Name	Type	Description
Self	Self	The instance of the graph, allowing for method chaining.

Without typehints on the path function's return value (e.g., -> Literal["foo", "__end__"]:)

or a path_map, the graph visualization assumes the edge could transition to any node in the graph.

compile

compile(
    checkpointer: Checkpointer = None,
    interrupt_before: Optional[
        Union[All, list[str]]
    ] = None,
    interrupt_after: Optional[Union[All, list[str]]] = None,
    debug: bool = False,
    name: Optional[str] = None,
) -> CompiledGraph

Compiles the graph into a CompiledGraph object.

The compiled graph implements the Runnable interface and can be invoked, streamed, batched, and run asynchronously.

Parameters:

Name	Type	Description	Default
checkpointer	Checkpointer	A checkpoint saver object or flag. If provided, this Checkpointer serves as a fully versioned "short-term memory" for the graph, allowing it to be paused, resumed, and replayed from any point. If None, it may inherit the parent graph's checkpointer when used as a subgraph. If False, it will not use or inherit any checkpointer.	None
interrupt_before	Optional[Union[All, list[str]]]	An optional list of node names to interrupt before.	None
interrupt_after	Optional[Union[All, list[str]]]	An optional list of node names to interrupt after.	None
debug	bool	A flag indicating whether to enable debug mode.	False
name	Optional[str]	The name to use for the compiled graph.	None

Returns:

Name	Type	Description
CompiledGraph	CompiledGraph	The compiled graph.

CompiledGraph

Bases: Pregel

Methods:

Name	Description
stream	Stream graph steps for a single input.
astream	Asynchronously stream graph steps for a single input.
invoke	Run the graph with a single input and config.
ainvoke	Asynchronously invoke the graph on a single input.
get_state	Get the current state of the graph.
aget_state	Get the current state of the graph.
get_state_history	Get the history of the state of the graph.
aget_state_history	Asynchronously get the history of the state of the graph.
update_state	Update the state of the graph with the given values, as if they came from
aupdate_state	Asynchronously update the state of the graph with the given values, as if they came from
bulk_update_state	Apply updates to the graph state in bulk. Requires a checkpointer to be set.
abulk_update_state	Asynchronously apply updates to the graph state in bulk. Requires a checkpointer to be set.
get_graph	Return a drawable representation of the computation graph.
aget_graph	Return a drawable representation of the computation graph.
get_subgraphs	Get the subgraphs of the graph.
aget_subgraphs	Get the subgraphs of the graph.
with_config	Create a copy of the Pregel object with an updated config.

stream

stream(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: (
        StreamMode | list[StreamMode] | None
    ) = None,
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    subgraphs: bool = False
) -> Iterator[dict[str, Any] | Any]

Stream graph steps for a single input.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input to the graph.	required
config	RunnableConfig | None	The configuration to use for the run.	None
stream_mode	StreamMode | list[StreamMode] | None	The mode to stream output, defaults to self.stream_mode. Options are: "values": Emit all values in the state after each step, including interrupts. When used with functional API, values are emitted once at the end of the workflow. "updates": Emit only the node or task names and updates returned by the nodes or tasks after each step. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately. "custom": Emit custom data from inside nodes or tasks using StreamWriter. "messages": Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks. "debug": Emit debug events with as much information as possible for each step.	None
output_keys	str | Sequence[str] | None	The keys to stream, defaults to all non-context channels.	None
interrupt_before	All | Sequence[str] | None	Nodes to interrupt before, defaults to all nodes in the graph.	None
interrupt_after	All | Sequence[str] | None	Nodes to interrupt after, defaults to all nodes in the graph.	None
checkpoint_during	bool | None	Whether to checkpoint intermediate steps, defaults to True. If False, only the final checkpoint is saved.	None
debug	bool | None	Whether to print debug information during execution, defaults to False.	None
subgraphs	bool	Whether to stream subgraphs, defaults to False.	False

Yields:

Type	Description
dict[str, Any] | Any	The output of each step in the graph. The output shape depends on the stream_mode.

Using stream_mode="values":

import operator
from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START

class State(TypedDict):
    alist: Annotated[list, operator.add]
    another_list: Annotated[list, operator.add]

builder = StateGraph(State)
builder.add_node("a", lambda _state: {"another_list": ["hi"]})
builder.add_node("b", lambda _state: {"alist": ["there"]})
builder.add_edge("a", "b")
builder.add_edge(START, "a")
graph = builder.compile()

for event in graph.stream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
    print(event)

# {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
# {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
# {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}

Using stream_mode="updates":

for event in graph.stream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
    print(event)

# {'a': {'another_list': ['hi']}}
# {'b': {'alist': ['there']}}

Using stream_mode="debug":

for event in graph.stream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
    print(event)

# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}

Using stream_mode="custom":

from langgraph.types import StreamWriter

def node_a(state: State, writer: StreamWriter):
    writer({"custom_data": "foo"})
    return {"alist": ["hi"]}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

for event in graph.stream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
    print(event)

# {'custom_data': 'foo'}

Using stream_mode="messages":

from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

class State(TypedDict):
    question: str
    answer: str

def node_a(state: State):
    response = llm.invoke(state["question"])
    return {"answer": response.content}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

for event in graph.stream({"question": "What is the capital of France?"}, stream_mode="messages"):
    print(event)

# (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
# (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
# (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})

astream async

astream(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: (
        StreamMode | list[StreamMode] | None
    ) = None,
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    subgraphs: bool = False
) -> AsyncIterator[dict[str, Any] | Any]

Asynchronously stream graph steps for a single input.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input to the graph.	required
config	RunnableConfig | None	The configuration to use for the run.	None
stream_mode	StreamMode | list[StreamMode] | None	The mode to stream output, defaults to self.stream_mode. Options are: "values": Emit all values in the state after each step, including interrupts. When used with functional API, values are emitted once at the end of the workflow. "updates": Emit only the node or task names and updates returned by the nodes or tasks after each step. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately. "custom": Emit custom data from inside nodes or tasks using StreamWriter. "messages": Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks. "debug": Emit debug events with as much information as possible for each step.	None
output_keys	str | Sequence[str] | None	The keys to stream, defaults to all non-context channels.	None
interrupt_before	All | Sequence[str] | None	Nodes to interrupt before, defaults to all nodes in the graph.	None
interrupt_after	All | Sequence[str] | None	Nodes to interrupt after, defaults to all nodes in the graph.	None
checkpoint_during	bool | None	Whether to checkpoint intermediate steps, defaults to True. If False, only the final checkpoint is saved.	None
debug	bool | None	Whether to print debug information during execution, defaults to False.	None
subgraphs	bool	Whether to stream subgraphs, defaults to False.	False

Yields:

Type	Description
AsyncIterator[dict[str, Any] | Any]	The output of each step in the graph. The output shape depends on the stream_mode.

Using stream_mode="values":

import operator
from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START

class State(TypedDict):
    alist: Annotated[list, operator.add]
    another_list: Annotated[list, operator.add]

builder = StateGraph(State)
builder.add_node("a", lambda _state: {"another_list": ["hi"]})
builder.add_node("b", lambda _state: {"alist": ["there"]})
builder.add_edge("a", "b")
builder.add_edge(START, "a")
graph = builder.compile()

async for event in graph.astream({"alist": ['Ex for stream_mode="values"']}, stream_mode="values"):
    print(event)

# {'alist': ['Ex for stream_mode="values"'], 'another_list': []}
# {'alist': ['Ex for stream_mode="values"'], 'another_list': ['hi']}
# {'alist': ['Ex for stream_mode="values"', 'there'], 'another_list': ['hi']}

Using stream_mode="updates":

async for event in graph.astream({"alist": ['Ex for stream_mode="updates"']}, stream_mode="updates"):
    print(event)

# {'a': {'another_list': ['hi']}}
# {'b': {'alist': ['there']}}

Using stream_mode="debug":

async for event in graph.astream({"alist": ['Ex for stream_mode="debug"']}, stream_mode="debug"):
    print(event)

# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': []}, 'triggers': ['start:a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 1, 'payload': {'id': '...', 'name': 'a', 'result': [('another_list', ['hi'])]}}
# {'type': 'task', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'input': {'alist': ['Ex for stream_mode="debug"'], 'another_list': ['hi']}, 'triggers': ['a']}}
# {'type': 'task_result', 'timestamp': '2024-06-23T...+00:00', 'step': 2, 'payload': {'id': '...', 'name': 'b', 'result': [('alist', ['there'])]}}

Using stream_mode="custom":

from langgraph.types import StreamWriter

async def node_a(state: State, writer: StreamWriter):
    writer({"custom_data": "foo"})
    return {"alist": ["hi"]}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

async for event in graph.astream({"alist": ['Ex for stream_mode="custom"']}, stream_mode="custom"):
    print(event)

# {'custom_data': 'foo'}

Using stream_mode="messages":

from typing_extensions import Annotated, TypedDict
from langgraph.graph import StateGraph, START
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

class State(TypedDict):
    question: str
    answer: str

async def node_a(state: State):
    response = await llm.ainvoke(state["question"])
    return {"answer": response.content}

builder = StateGraph(State)
builder.add_node("a", node_a)
builder.add_edge(START, "a")
graph = builder.compile()

async for event in graph.astream({"question": "What is the capital of France?"}, stream_mode="messages"):
    print(event)

# (AIMessageChunk(content='The', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], 'langgraph_path': ('__pregel_pull', 'a'), 'langgraph_checkpoint_ns': '...', 'checkpoint_ns': '...', 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o-mini', 'ls_model_type': 'chat', 'ls_temperature': 0.7})
# (AIMessageChunk(content=' capital', additional_kwargs={}, response_metadata={}, id='...'), {'langgraph_step': 1, 'langgraph_node': 'a', 'langgraph_triggers': ['start:a'], ...})
# (AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' France', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' is', additional_kwargs={}, response_metadata={}, id='...'), {...})
# (AIMessageChunk(content=' Paris', additional_kwargs={}, response_metadata={}, id='...'), {...})

invoke

invoke(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: StreamMode = "values",
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    **kwargs: Any
) -> dict[str, Any] | Any

Run the graph with a single input and config.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input data for the graph. It can be a dictionary or any other type.	required
config	RunnableConfig | None	Optional. The configuration for the graph run.	None
stream_mode	StreamMode	Optional[str]. The stream mode for the graph run. Default is "values".	'values'
output_keys	str | Sequence[str] | None	Optional. The output keys to retrieve from the graph run.	None
interrupt_before	All | Sequence[str] | None	Optional. The nodes to interrupt the graph run before.	None
interrupt_after	All | Sequence[str] | None	Optional. The nodes to interrupt the graph run after.	None
debug	bool | None	Optional. Enable debug mode for the graph run.	None
**kwargs	Any	Additional keyword arguments to pass to the graph run.	{}

Returns:

Type	Description
dict[str, Any] | Any	The output of the graph run. If stream_mode is "values", it returns the latest output.
dict[str, Any] | Any	If stream_mode is not "values", it returns a list of output chunks.

ainvoke async

ainvoke(
    input: dict[str, Any] | Any,
    config: RunnableConfig | None = None,
    *,
    stream_mode: StreamMode = "values",
    output_keys: str | Sequence[str] | None = None,
    interrupt_before: All | Sequence[str] | None = None,
    interrupt_after: All | Sequence[str] | None = None,
    checkpoint_during: bool | None = None,
    debug: bool | None = None,
    **kwargs: Any
) -> dict[str, Any] | Any

Asynchronously invoke the graph on a single input.

Parameters:

Name	Type	Description	Default
input	dict[str, Any] | Any	The input data for the computation. It can be a dictionary or any other type.	required
config	RunnableConfig | None	Optional. The configuration for the computation.	None
stream_mode	StreamMode	Optional. The stream mode for the computation. Default is "values".	'values'
output_keys	str | Sequence[str] | None	Optional. The output keys to include in the result. Default is None.	None
interrupt_before	All | Sequence[str] | None	Optional. The nodes to interrupt before. Default is None.	None
interrupt_after	All | Sequence[str] | None	Optional. The nodes to interrupt after. Default is None.	None
debug	bool | None	Optional. Whether to enable debug mode. Default is None.	None
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Type	Description
dict[str, Any] | Any	The result of the computation. If stream_mode is "values", it returns the latest value.
dict[str, Any] | Any	If stream_mode is "chunks", it returns a list of chunks.

get_state

get_state(
    config: RunnableConfig, *, subgraphs: bool = False
) -> StateSnapshot

Get the current state of the graph.

aget_state async

aget_state(
    config: RunnableConfig, *, subgraphs: bool = False
) -> StateSnapshot

Get the current state of the graph.

get_state_history

get_state_history(
    config: RunnableConfig,
    *,
    filter: dict[str, Any] | None = None,
    before: RunnableConfig | None = None,
    limit: int | None = None
) -> Iterator[StateSnapshot]

Get the history of the state of the graph.

aget_state_history async

aget_state_history(
    config: RunnableConfig,
    *,
    filter: dict[str, Any] | None = None,
    before: RunnableConfig | None = None,
    limit: int | None = None
) -> AsyncIterator[StateSnapshot]

Asynchronously get the history of the state of the graph.

update_state

update_state(
    config: RunnableConfig,
    values: dict[str, Any] | Any | None,
    as_node: str | None = None,
) -> RunnableConfig

Update the state of the graph with the given values, as if they 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.

aupdate_state async

aupdate_state(
    config: RunnableConfig,
    values: dict[str, Any] | Any,
    as_node: str | None = None,
) -> RunnableConfig

Asynchronously update the state of the graph with the given values, as if they 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.

bulk_update_state

bulk_update_state(
    config: RunnableConfig,
    supersteps: Sequence[Sequence[StateUpdate]],
) -> RunnableConfig

Apply updates to the graph state in bulk. Requires a checkpointer to be set.

Parameters:

Name	Type	Description	Default
config	RunnableConfig	The config to apply the updates to.	required
supersteps	Sequence[Sequence[StateUpdate]]	A list of supersteps, each including a list of updates to apply sequentially to a graph state. Each update is a tuple of the form (values, as_node).	required

Raises:

Type	Description
ValueError	If no checkpointer is set or no updates are provided.
InvalidUpdateError	If an invalid update is provided.

Returns:

Name	Type	Description
RunnableConfig	RunnableConfig	The updated config.

abulk_update_state async

abulk_update_state(
    config: RunnableConfig,
    supersteps: Sequence[Sequence[StateUpdate]],
) -> RunnableConfig

Asynchronously apply updates to the graph state in bulk. Requires a checkpointer to be set.

Parameters:

Name	Type	Description	Default
config	RunnableConfig	The config to apply the updates to.	required
supersteps	Sequence[Sequence[StateUpdate]]	A list of supersteps, each including a list of updates to apply sequentially to a graph state. Each update is a tuple of the form (values, as_node).	required

Raises:

Type	Description
ValueError	If no checkpointer is set or no updates are provided.
InvalidUpdateError	If an invalid update is provided.

Returns:

Name	Type	Description
RunnableConfig	RunnableConfig	The updated config.

get_graph

get_graph(
    config: RunnableConfig | None = None,
    *,
    xray: int | bool = False
) -> Graph

Return a drawable representation of the computation graph.

aget_graph async

aget_graph(
    config: RunnableConfig | None = None,
    *,
    xray: int | bool = False
) -> Graph

Return a drawable representation of the computation graph.

get_subgraphs

get_subgraphs(
    *, namespace: str | None = None, recurse: bool = False
) -> Iterator[tuple[str, PregelProtocol]]

Get the subgraphs of the graph.

Parameters:

Name	Type	Description	Default
namespace	str | None	The namespace to filter the subgraphs by.	None
recurse	bool	Whether to recurse into the subgraphs. If False, only the immediate subgraphs will be returned.	False

Returns:

Type	Description
Iterator[tuple[str, PregelProtocol]]	Iterator[tuple[str, PregelProtocol]]: An iterator of the (namespace, subgraph) pairs.

aget_subgraphs async

aget_subgraphs(
    *, namespace: str | None = None, recurse: bool = False
) -> AsyncIterator[tuple[str, PregelProtocol]]

Get the subgraphs of the graph.

Parameters:

Name	Type	Description	Default
namespace	str | None	The namespace to filter the subgraphs by.	None
recurse	bool	Whether to recurse into the subgraphs. If False, only the immediate subgraphs will be returned.	False

Returns:

Type	Description
AsyncIterator[tuple[str, PregelProtocol]]	AsyncIterator[tuple[str, PregelProtocol]]: An iterator of the (namespace, subgraph) pairs.

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Self

Create a copy of the Pregel object with an updated config.

Functions:

Name	Description
add_messages	Merges two lists of messages, updating existing messages by ID.

add_messages

add_messages(
    left: Messages,
    right: Messages,
    *,
    format: Optional[Literal["langchain-openai"]] = None
) -> Messages

Merges two lists of messages, updating existing messages by ID.

By default, this ensures the state is "append-only", unless the new message has the same ID as an existing message.

Parameters:

Name	Type	Description	Default
left	Messages	The base list of messages.	required
right	Messages	The list of messages (or single message) to merge into the base list.	required
format	Optional[Literal['langchain-openai']]	The format to return messages in. If None then messages will be returned as is. If 'langchain-openai' then messages will be returned as BaseMessage objects with their contents formatted to match OpenAI message format, meaning contents can be string, 'text' blocks, or 'image_url' blocks and tool responses are returned as their own ToolMessages. Requirement Must have langchain-core>=0.3.11 installed to use this feature.	None

Returns:

Type	Description
Messages	A new list of messages with the messages from right merged into left.
Messages	If a message in right has the same ID as a message in left, the
Messages	message from right will replace the message from left.

Example

Basic usage

from langchain_core.messages import AIMessage, HumanMessage
msgs1 = [HumanMessage(content="Hello", id="1")]
msgs2 = [AIMessage(content="Hi there!", id="2")]
add_messages(msgs1, msgs2)
# [HumanMessage(content='Hello', id='1'), AIMessage(content='Hi there!', id='2')]

Overwrite existing message

msgs1 = [HumanMessage(content="Hello", id="1")]
msgs2 = [HumanMessage(content="Hello again", id="1")]
add_messages(msgs1, msgs2)
# [HumanMessage(content='Hello again', id='1')]

Use in a StateGraph

from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph

class State(TypedDict):
    messages: Annotated[list, add_messages]

builder = StateGraph(State)
builder.add_node("chatbot", lambda state: {"messages": [("assistant", "Hello")]})
builder.set_entry_point("chatbot")
builder.set_finish_point("chatbot")
graph = builder.compile()
graph.invoke({})
# {'messages': [AIMessage(content='Hello', id=...)]}

Use OpenAI message format

from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, add_messages

class State(TypedDict):
    messages: Annotated[list, add_messages(format='langchain-openai')]

def chatbot_node(state: State) -> list:
    return {"messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Here's an image:",
                    "cache_control": {"type": "ephemeral"},
                },
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/jpeg",
                        "data": "1234",
                    },
                },
            ]
        },
    ]}

builder = StateGraph(State)
builder.add_node("chatbot", chatbot_node)
builder.set_entry_point("chatbot")
builder.set_finish_point("chatbot")
graph = builder.compile()
graph.invoke({"messages": []})
# {
#     'messages': [
#         HumanMessage(
#             content=[
#                 {"type": "text", "text": "Here's an image:"},
#                 {
#                     "type": "image_url",
#                     "image_url": {"url": "data:image/jpeg;base64,1234"},
#                 },
#             ],
#         ),
#     ]
# }

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