LangGraph runtime ↗

Original Documentation

Documentation Index#
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt Use this file to discover all available pages before exploring further.

Pregel implements LangGraph’s runtime, managing the execution of LangGraph applications.

Compiling a StateGraph or creating an entrypoint produces a Pregel instance that can be invoked with input.

This guide explains the runtime at a high level and provides instructions for directly implementing applications with Pregel.

Note: The Pregel runtime is named after Google’s Pregel algorithm, which describes an efficient method for large-scale parallel computation using graphs.

Overview#

In LangGraph, 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:

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 or to accumulate values over the course of multiple steps.
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; e.g.,total = BinaryOperatorAggregate(int, operator.add)

Examples#

While most users will interact with Pregel through the StateGraph API or the entrypoint decorator, it is possible to interact with Pregel directly.

Below are a few different examples to give you a sense of the Pregel API.

    import { EphemeralValue } from "@langchain/langgraph/channels";
    import { Pregel, NodeBuilder } from "@langchain/langgraph/pregel";

    const node1 = new NodeBuilder()
      .subscribeOnly("a")
      .do((x: string) => x + x)
      .writeTo("b");

    const app = new Pregel({
      nodes: { node1 },
      channels: {
        a: new EphemeralValue<string>(),
        b: new EphemeralValue<string>(),
      },
      inputChannels: ["a"],
      outputChannels: ["b"],
    });

    await app.invoke({ a: "foo" });
    ```

```console
    { b: 'foofoo' }
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Multiple nodes"></span>
```typescript
    import { LastValue, EphemeralValue } from "@langchain/langgraph/channels";
    import { Pregel, NodeBuilder } from "@langchain/langgraph/pregel";

    const node1 = new NodeBuilder()
      .subscribeOnly("a")
      .do((x: string) => x + x)
      .writeTo("b");

    const node2 = new NodeBuilder()
      .subscribeOnly("b")
      .do((x: string) => x + x)
      .writeTo("c");

    const app = new Pregel({
      nodes: { node1, node2 },
      channels: {
        a: new EphemeralValue<string>(),
        b: new LastValue<string>(),
        c: new EphemeralValue<string>(),
      },
      inputChannels: ["a"],
      outputChannels: ["b", "c"],
    });

    await app.invoke({ a: "foo" });
    ```

```console
    { b: 'foofoo', c: 'foofoofoofoo' }
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Topic"></span>
```typescript
    import { EphemeralValue, Topic } from "@langchain/langgraph/channels";
    import { Pregel, NodeBuilder } from "@langchain/langgraph/pregel";

    const node1 = new NodeBuilder()
      .subscribeOnly("a")
      .do((x: string) => x + x)
      .writeTo("b", "c");

    const node2 = new NodeBuilder()
      .subscribeTo("b")
      .do((x: { b: string }) => x.b + x.b)
      .writeTo("c");

    const app = new Pregel({
      nodes: { node1, node2 },
      channels: {
        a: new EphemeralValue<string>(),
        b: new EphemeralValue<string>(),
        c: new Topic<string>({ accumulate: true }),
      },
      inputChannels: ["a"],
      outputChannels: ["c"],
    });

    await app.invoke({ a: "foo" });
    ```

```console
    { c: ['foofoo', 'foofoofoofoo'] }
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="BinaryOperatorAggregate"></span>
This example demonstrates how to use the [`BinaryOperatorAggregate`](https://reference.langchain.com/javascript/langchain-langgraph/channels/BinaryOperatorAggregate) channel to implement a reducer.

```typescript
    import { EphemeralValue, BinaryOperatorAggregate } from "@langchain/langgraph/channels";
    import { Pregel, NodeBuilder } from "@langchain/langgraph/pregel";

    const node1 = new NodeBuilder()
      .subscribeOnly("a")
      .do((x: string) => x + x)
      .writeTo("b", "c");

    const node2 = new NodeBuilder()
      .subscribeOnly("b")
      .do((x: string) => x + x)
      .writeTo("c");

    const reducer = (current: string, update: string) => {
      if (current) {
        return current + " | " + update;
      } else {
        return update;
      }
    };

    const app = new Pregel({
      nodes: { node1, node2 },
      channels: {
        a: new EphemeralValue<string>(),
        b: new EphemeralValue<string>(),
        c: new BinaryOperatorAggregate<string>({ operator: reducer }),
      },
      inputChannels: ["a"],
      outputChannels: ["c"],
    });

    await app.invoke({ a: "foo" });
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Cycle"></span>
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 `null` value is written to the channel.

```typescript
    import { EphemeralValue } from "@langchain/langgraph/channels";
    import { Pregel, NodeBuilder, ChannelWriteEntry } from "@langchain/langgraph/pregel";

    const exampleNode = new NodeBuilder()
      .subscribeOnly("value")
      .do((x: string) => x.length < 10 ? x + x : null)
      .writeTo(new ChannelWriteEntry("value", { skipNone: true }));

    const app = new Pregel({
      nodes: { exampleNode },
      channels: {
        value: new EphemeralValue<string>(),
      },
      inputChannels: ["value"],
      outputChannels: ["value"],
    });

    await app.invoke({ value: "a" });
    ```

```console
    { value: 'aaaaaaaaaaaaaaaa' }
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## High-level API

LangGraph provides two high-level APIs for creating a Pregel application: the [StateGraph (Graph API)](/oss/javascript/langgraph/graph-api) and the [Functional API](/oss/javascript/langgraph/functional-api).

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="StateGraph (Graph API)"></span>
The [StateGraph (Graph API)](https://reference.langchain.com/javascript/langchain-langgraph/index/StateGraph) is a higher-level abstraction that simplifies the creation of Pregel applications. It allows you to define a graph of nodes and edges. When you compile the graph, the StateGraph API automatically creates the Pregel application for you.

```typescript
    import { START, StateGraph } from "@langchain/langgraph";

    interface Essay {
      topic: string;
      content?: string;
      score?: number;
    }

    const writeEssay = (essay: Essay) => {
      return {
        content: `Essay about ${essay.topic}`,
      };
    };

    const scoreEssay = (essay: Essay) => {
      return {
        score: 10
      };
    };

    const builder = new StateGraph<Essay>({
      channels: {
        topic: null,
        content: null,
        score: null,
      }
    })
      .addNode("writeEssay", writeEssay)
      .addNode("scoreEssay", scoreEssay)
      .addEdge(START, "writeEssay")
      .addEdge("writeEssay", "scoreEssay");

    // Compile the graph.
    // This will return a Pregel instance.
    const graph = builder.compile();
    ```

The compiled Pregel instance will be associated with a list of nodes and channels. You can inspect the nodes and channels by printing them.

```typescript
    console.log(graph.nodes);
    ```

You will see something like this:

```console
    {
      __start__: PregelNode { ... },
      writeEssay: PregelNode { ... },
      scoreEssay: PregelNode { ... }
    }
    ```

```typescript
    console.log(graph.channels);
    ```

You should see something like this

```console
    {
      topic: LastValue { ... },
      content: LastValue { ... },
      score: LastValue { ... },
      __start__: EphemeralValue { ... },
      writeEssay: EphemeralValue { ... },
      scoreEssay: EphemeralValue { ... },
      'branch:__start__:__self__:writeEssay': EphemeralValue { ... },
      'branch:__start__:__self__:scoreEssay': EphemeralValue { ... },
      'branch:writeEssay:__self__:writeEssay': EphemeralValue { ... },
      'branch:writeEssay:__self__:scoreEssay': EphemeralValue { ... },
      'branch:scoreEssay:__self__:writeEssay': EphemeralValue { ... },
      'branch:scoreEssay:__self__:scoreEssay': EphemeralValue { ... },
      'start:writeEssay': EphemeralValue { ... }
    }
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Functional API"></span>
In the [Functional API](/oss/javascript/langgraph/functional-api), you can use an [`entrypoint`](https://reference.langchain.com/javascript/langchain-langgraph/index/entrypoint) to create a Pregel application. The `entrypoint` decorator allows you to define a function that takes input and returns output.

```typescript
    import { MemorySaver } from "@langchain/langgraph";
    import { entrypoint } from "@langchain/langgraph/func";

    interface Essay {
      topic: string;
      content?: string;
      score?: number;
    }

    const checkpointer = new MemorySaver();

    const writeEssay = entrypoint(
      { checkpointer, name: "writeEssay" },
      async (essay: Essay) => {
        return {
          content: `Essay about ${essay.topic}`,
        };
      }
    );

    console.log("Nodes: ");
    console.log(writeEssay.nodes);
    console.log("Channels: ");
    console.log(writeEssay.channels);
    ```

```console
    Nodes:
    { writeEssay: PregelNode { ... } }
    Channels:
    {
      __start__: EphemeralValue { ... },
      __end__: LastValue { ... },
      __previous__: LastValue { ... }
    }
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

***


  <span class="callout-start" data-callout-type="note"></span>
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langgraph/pregel.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  <span class="callout-end"></span>

  <span class="callout-start" data-callout-type="note"></span>
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  <span class="callout-end"></span>

Link last verified June 7, 2026. View original ↗

Source: LangChain Docs

Link last verified: 2026-03-04