Pregel¶

NodeBuilder ¶

Methods:

Name	Description
`subscribe_only`	Subscribe to a single channel.
`subscribe_to`	Add channels to subscribe to. Node will be invoked when any of these
`read_from`	Adds the specified channels to read from, without subscribing to them.
`do`	Adds the specified node.
`write_to`	Add channel writes.
`meta`	Add tags or metadata to the node.
`retry`	Adds retry policies to the node.
`cache`	Adds cache policies to the node.
`build`	Builds the node.

subscribe_only ¶

subscribe_only(channel: str) -> Self

Subscribe to a single channel.

subscribe_to ¶

subscribe_to(*channels: str, read: bool = True) -> Self

Add channels to subscribe to. Node will be invoked when any of these channels are updated, with a dict of the channel values as input.

Parameters:

Name	Type	Description	Default
`channels`	`str`	Channel name(s) to subscribe to	`()`
`read`	`bool`	If True, the channels will be included in the input to the node. Otherwise, they will trigger the node without being sent in input.	`True`

Returns:

Type	Description
`Self`	Self for chaining

read_from ¶

read_from(*channels: str) -> Self

Adds the specified channels to read from, without subscribing to them.

do ¶

do(node: RunnableLike) -> Self

Adds the specified node.

write_to ¶

write_to(
    *channels: str | ChannelWriteEntry, **kwargs: WriteValue
) -> Self

Add channel writes.

Parameters:

Name	Type	Description	Default
`*channels`	`str \| ChannelWriteEntry`	Channel names to write to	`()`
`**kwargs`	`WriteValue`	Channel name and value mappings	`{}`

Returns:

Type	Description
`Self`	Self for chaining

meta ¶

meta(*tags: str, **metadata: Any) -> Self

Add tags or metadata to the node.

retry ¶

retry(*policies: RetryPolicy) -> Self

Adds retry policies to the node.

cache ¶

cache(policy: CachePolicy) -> Self

Adds cache policies to the node.

build ¶

build() -> PregelNode

Builds the node.

Pregel ¶

Bases: PregelProtocol

Pregel manages the runtime behavior for LangGraph applications.

Overview¶

Pregel combines actors and channels into a single application. Actors read data from channels and write data to channels. Pregel organizes the execution of the application into multiple steps, following the Pregel Algorithm/Bulk Synchronous Parallel model.

Each step consists of three phases:

Plan: Determine which actors to execute in this step. For example, in the first step, select the actors that subscribe to the special input channels; in subsequent steps, select the actors that subscribe to channels updated in the previous step.
Execution: Execute all selected actors in parallel, until all complete, or one fails, or a timeout is reached. During this phase, channel updates are invisible to actors until the next step.
Update: Update the channels with the values written by the actors in this step.

Repeat until no actors are selected for execution, or a maximum number of steps is reached.

Actors¶

An actor is a PregelNode. It subscribes to channels, reads data from them, and writes data to them. It can be thought of as an actor in the Pregel algorithm. PregelNodes implement LangChain's Runnable interface.

Channels¶

Channels are used to communicate between actors (PregelNodes). Each channel has a value type, an update type, and an update function – which takes a sequence of updates and modifies the stored value. Channels can be used to send data from one chain to another, or to send data from a chain to itself in a future step. LangGraph provides a number of built-in channels:

Basic channels: LastValue and Topic¶

LastValue: The default channel, stores the last value sent to the channel, useful for input and output values, or for sending data from one step to the next
Topic: A configurable PubSub Topic, useful for sending multiple values between actors, or for accumulating output. Can be configured to deduplicate values, and/or to accumulate values over the course of multiple steps.

Advanced channels: Context and BinaryOperatorAggregate¶

Context: exposes the value of a context manager, managing its lifecycle. Useful for accessing external resources that require setup and/or teardown. eg. client = Context(httpx.Client)
BinaryOperatorAggregate: stores a persistent value, updated by applying a binary operator to the current value and each update sent to the channel, useful for computing aggregates over multiple steps. eg. total = BinaryOperatorAggregate(int, operator.add)

Examples¶

Most users will interact with Pregel via a StateGraph (Graph API) or via an entrypoint (Functional API).

However, for advanced use cases, Pregel can be used directly. If you're not sure whether you need to use Pregel directly, then the answer is probably no – you should use the Graph API or Functional API instead. These are higher-level interfaces that will compile down to Pregel under the hood.

Here are some examples to give you a sense of how it works:

Single node application

from langgraph.channels import EphemeralValue
from langgraph.pregel import Pregel, NodeBuilder

node1 = (
    NodeBuilder().subscribe_only("a")
    .do(lambda x: x + x)
    .write_to("b")
)

app = Pregel(
    nodes={"node1": node1},
    channels={
        "a": EphemeralValue(str),
        "b": EphemeralValue(str),
    },
    input_channels=["a"],
    output_channels=["b"],
)

app.invoke({"a": "foo"})

{'b': 'foofoo'}

Using multiple nodes and multiple output channels

from langgraph.channels import LastValue, EphemeralValue
from langgraph.pregel import Pregel, NodeBuilder

node1 = (
    NodeBuilder().subscribe_only("a")
    .do(lambda x: x + x)
    .write_to("b")
)

node2 = (
    NodeBuilder().subscribe_to("b")
    .do(lambda x: x["b"] + x["b"])
    .write_to("c")
)


app = Pregel(
    nodes={"node1": node1, "node2": node2},
    channels={
        "a": EphemeralValue(str),
        "b": LastValue(str),
        "c": EphemeralValue(str),
    },
    input_channels=["a"],
    output_channels=["b", "c"],
)

app.invoke({"a": "foo"})

{'b': 'foofoo', 'c': 'foofoofoofoo'}

Using a Topic channel

from langgraph.channels import LastValue, EphemeralValue, Topic
from langgraph.pregel import Pregel, NodeBuilder

node1 = (
    NodeBuilder().subscribe_only("a")
    .do(lambda x: x + x)
    .write_to("b", "c")
)

node2 = (
    NodeBuilder().subscribe_only("b")
    .do(lambda x: x + x)
    .write_to("c")
)


app = Pregel(
    nodes={"node1": node1, "node2": node2},
    channels={
        "a": EphemeralValue(str),
        "b": EphemeralValue(str),
        "c": Topic(str, accumulate=True),
    },
    input_channels=["a"],
    output_channels=["c"],
)

app.invoke({"a": "foo"})

{'c': ['foofoo', 'foofoofoofoo']}

Using a BinaryOperatorAggregate channel

from langgraph.channels import EphemeralValue, BinaryOperatorAggregate
from langgraph.pregel import Pregel, NodeBuilder


node1 = (
    NodeBuilder().subscribe_only("a")
    .do(lambda x: x + x)
    .write_to("b", "c")
)

node2 = (
    NodeBuilder().subscribe_only("b")
    .do(lambda x: x + x)
    .write_to("c")
)


def reducer(current, update):
    if current:
        return current + " | " + update
    else:
        return update

app = Pregel(
    nodes={"node1": node1, "node2": node2},
    channels={
        "a": EphemeralValue(str),
        "b": EphemeralValue(str),
        "c": BinaryOperatorAggregate(str, operator=reducer),
    },
    input_channels=["a"],
    output_channels=["c"]
)

app.invoke({"a": "foo"})

{'c': 'foofoo | foofoofoofoo'}

Introducing a cycle

This example demonstrates how to introduce a cycle in the graph, by having a chain write to a channel it subscribes to. Execution will continue until a None value is written to the channel.

from langgraph.channels import EphemeralValue
from langgraph.pregel import Pregel, NodeBuilder, ChannelWriteEntry

example_node = (
    NodeBuilder().subscribe_only("value")
    .do(lambda x: x + x if len(x) < 10 else None)
    .write_to(ChannelWriteEntry(channel="value", skip_none=True))
)

app = Pregel(
    nodes={"example_node": example_node},
    channels={
        "value": EphemeralValue(str),
    },
    input_channels=["value"],
    output_channels=["value"]
)

app.invoke({"value": "a"})

{'value': 'aaaaaaaaaaaaaaaa'}

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. Will be emitted as 2-tuples `(LLM token, metadata)`. `"debug"`: Emit debug events with as much information as possible for each step. You can pass a list as the `stream_mode` parameter to stream multiple modes at once. The streamed outputs will be tuples of `(mode, data)`. See LangGraph streaming guide for more details.	`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 False. 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 events from inside subgraphs, defaults to False. If True, the events will be emitted as tuples `(namespace, data)`, or `(namespace, mode, data)` if `stream_mode` is a list, where `namespace` is a tuple with the path to the node where a subgraph is invoked, e.g. `("parent_node:<task_id>", "child_node:<task_id>")`. See LangGraph streaming guide for more details.	`False`

Yields:

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

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. Will be emitted as 2-tuples `(LLM token, metadata)`. `"debug"`: Emit debug events with as much information as possible for each step. You can pass a list as the `stream_mode` parameter to stream multiple modes at once. The streamed outputs will be tuples of `(mode, data)`. See LangGraph streaming guide for more details.	`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 False. 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 events from inside subgraphs, defaults to False. If True, the events will be emitted as tuples `(namespace, data)`, or `(namespace, mode, data)` if `stream_mode` is a list, where `namespace` is a tuple with the path to the node where a subgraph is invoked, e.g. `("parent_node:<task_id>", "child_node:<task_id>")`. See LangGraph streaming guide for more details.	`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.

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,
    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,
    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.

Pregel¶

NodeBuilder ¶

subscribe_only ¶

subscribe_to ¶

read_from ¶

do ¶

write_to ¶

meta ¶

retry ¶

cache ¶

build ¶

Pregel ¶

Overview¶

Actors¶

Channels¶

Basic channels: LastValue and Topic¶

Advanced channels: Context and BinaryOperatorAggregate¶

Examples¶

stream ¶

astream async ¶

invoke ¶

ainvoke async ¶

get_state ¶

aget_state async ¶

get_state_history ¶

aget_state_history async ¶

update_state ¶

aupdate_state async ¶

bulk_update_state ¶

abulk_update_state async ¶

get_graph ¶

aget_graph async ¶

get_subgraphs ¶

aget_subgraphs async ¶

with_config ¶

astream `async` ¶

ainvoke `async` ¶

aget_state `async` ¶

aget_state_history `async` ¶

aupdate_state `async` ¶

abulk_update_state `async` ¶

aget_graph `async` ¶

aget_subgraphs `async` ¶