LangGraph Glossary

Graphs

At its core, LangGraph models agent workflows as graphs. You define the behavior of your agents using three key components:

1. State: A shared data structure that represents the current snapshot of your application. It is represented by an Annotation object.

2. Nodes: JavaScript/TypeScript functions that encode the logic of your agents. They receive the current State as input, perform some computation or side-effect, and return an updated State.

3. Edges: JavaScript/TypeScript functions that determine which Node to execute next based on the current State. They can be conditional branches or fixed transitions.

By composing Nodes and Edges, you can create complex, looping workflows that evolve the State over time. The real power, though, comes from how LangGraph manages that State. To emphasize: Nodes and Edges are nothing more than JavaScript/TypeScript functions - they can contain an LLM or just good ol' JavaScript/TypeScript code.

In short: nodes do the work. edges tell what to do next.

LangGraph's underlying graph algorithm uses message passing to define a general program. When a Node completes its operation, it sends messages along one or more edges to other node(s). These recipient nodes then execute their functions, pass the resulting messages to the next set of nodes, and the process continues. Inspired by Google's Pregel system, the program proceeds in discrete "super-steps."

A super-step can be considered a single iteration over the graph nodes. Nodes that run in parallel are part of the same super-step, while nodes that run sequentially belong to separate super-steps. At the start of graph execution, all nodes begin in an inactive state. A node becomes active when it receives a new message (state) on any of its incoming edges (or "channels"). The active node then runs its function and responds with updates. At the end of each super-step, nodes with no incoming messages vote to halt by marking themselves as inactive. The graph execution terminates when all nodes are inactive and no messages are in transit.

StateGraph

The StateGraph class is the main graph class to use. This is parameterized by a user defined State object. (defined using the Annotation object and passed as the first argument)

MessageGraph (legacy)

The MessageGraph class is a special type of graph. The State of a MessageGraph is ONLY an array of messages. This class is rarely used except for chatbots, as most applications require the State to be more complex than an array of messages.

Compiling your graph

To build your graph, you first define the state, you then add nodes and edges, and then you compile it. What exactly is compiling your graph and why is it needed?

Compiling is a pretty simple step. It provides a few basic checks on the structure of your graph (no orphaned nodes, etc). It is also where you can specify runtime args like checkpointers and breakpoints. You compile your graph by just calling the .compile method:

const graph = graphBuilder.compile(...);

You MUST compile your graph before you can use it.

State

The first thing you do when you define a graph is define the State of the graph. The State includes information on the structure of the graph, as well as reducer functions which specify how to apply updates to the state. The schema of the State will be the input schema to all Nodes and Edges in the graph, and should be defined using an Annotation object. All Nodes will emit updates to the State which are then applied using the specified reducer function.

Annotation

The way to specify the schema of a graph is by defining a root Annotation object, where each key is an item in the state.

Multiple schemas

Typically, all graph nodes communicate with a single state annotation. This means that they will read and write to the same state channels. But, there are cases where we want more control over this:

• Internal nodes can pass information that is not required in the graph's input / output.
• We may also want to use different input / output schemas for the graph. The output might, for example, only contain a single relevant output key.

It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private annotation, PrivateState. See this notebook for more detail.

It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains all keys relevant to graph operations. But, we also define input and output schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See this guide for more detail.

Let's look at an example:

import { Annotation, StateGraph } from "@langchain/langgraph";

const InputStateAnnotation = Annotation.Root({
  user_input: Annotation<string>,
});

const OutputStateAnnotation = Annotation.Root({
  graph_output: Annotation<string>,
});

const OverallStateAnnotation = Annotation.Root({
  foo: Annotation<string>,
  bar: Annotation<string>,
  user_input: Annotation<string>,
  graph_output: Annotation<string>,
});

const node1 = async (state: typeof InputStateAnnotation.State) => {
  // Write to OverallStateAnnotation
  return { foo: state.user_input + " name" };
};

const node2 = async (state: typeof OverallStateAnnotation.State) => {
  // Read from OverallStateAnnotation, write to OverallStateAnnotation
  return { bar: state.foo + " is" };
};

const node3 = async (state: typeof OverallStateAnnotation.State) => {
  // Read from OverallStateAnnotation, write to OutputStateAnnotation
  return { graph_output: state.bar + " Lance" };
};

const graph = new StateGraph({
  input: InputStateAnnotation,
  output: OutputStateAnnotation,
  stateSchema: OverallStateAnnotation,
})
  .addNode("node1", node1)
  .addNode("node2", node2)
  .addNode("node3", node3)
  .addEdge("__start__", "node1")
  .addEdge("node1", "node2")
  .addEdge("node2", "node3")
  .compile();

await graph.invoke({ user_input: "My" });

{ graph_output: "My name is Lance" }

Note that we pass state: typeof InputStateAnnotation.State as the input schema to node1. But, we write out to foo, a channel in OverallStateAnnotation. How can we write out to a state channel that is not included in the input schema? This is because a node can write to any state channel in the graph state. The graph state is the union of of the state channels defined at initialization, which includes OverallStateAnnotation and the filters InputStateAnnotation and OutputStateAnnotation.

Reducers

Reducers are key to understanding how updates from nodes are applied to the State. Each key in the State has its own independent reducer function. If no reducer function is explicitly specified then it is assumed that all updates to that key should override it. Let's take a look at a few examples to understand them better.

Example A:

import { StateGraph, Annotation } from "@langchain/langgraph";

const State = Annotation.Root({
  foo: Annotation<number>,
  bar: Annotation<string[]>,
});

const graphBuilder = new StateGraph(State);

In this example, no reducer functions are specified for any key. Let's assume the input to the graph is { foo: 1, bar: ["hi"] }. Let's then assume the first Node returns { foo: 2 }. This is treated as an update to the state. Notice that the Node does not need to return the whole State schema - just an update. After applying this update, the State would then be { foo: 2, bar: ["hi"] }. If the second node returns { bar: ["bye"] } then the State would then be { foo: 2, bar: ["bye"] }

Example B:

import { StateGraph, Annotation } from "@langchain/langgraph";

const State = Annotation.Root({
  foo: Annotation<number>,
  bar: Annotation<string[]>({
    reducer: (state: string[], update: string[]) => state.concat(update),
    default: () => [],
  }),
});

const graphBuilder = new StateGraph(State);

In this example, we've updated our bar field to be an object containing a reducer function. This function will always accept two positional arguments: state and update, with state representing the current state value, and update representing the update returned from a Node. Note that the first key remains unchanged. Let's assume the input to the graph is { foo: 1, bar: ["hi"] }. Let's then assume the first Node returns { foo: 2 }. This is treated as an update to the state. Notice that the Node does not need to return the whole State schema - just an update. After applying this update, the State would then be { foo: 2, bar: ["hi"] }. If the second node returns{ bar: ["bye"] } then the State would then be { foo: 2, bar: ["hi", "bye"] }. Notice here that the bar key is updated by concatenating the two arrays together.

Working with Messages in Graph State

Why use messages?

Most modern LLM providers have a chat model interface that accepts a list of messages as input. LangChain's ChatModel in particular accepts a list of Message objects as inputs. These messages come in a variety of forms such as HumanMessage (user input) or AIMessage (LLM response). To read more about what message objects are, please refer to this conceptual guide.

Using Messages in your Graph

In many cases, it is helpful to store prior conversation history as a list of messages in your graph state. To do so, we can add a key (channel) to the graph state that stores a list of Message objects and annotate it with a reducer function (see messages key in the example below). The reducer function is vital to telling the graph how to update the list of Message objects in the state with each state update (for example, when a node sends an update). If you don't specify a reducer, every state update will overwrite the list of messages with the most recently provided value.

However, you might also want to manually update messages in your graph state (e.g. human-in-the-loop). If you were to use something like (a, b) => a.concat(b) as a reducer, the manual state updates you send to the graph would be appended to the existing list of messages, instead of updating existing messages. To avoid that, you need a reducer that can keep track of message IDs and overwrite existing messages, if updated. To achieve this, you can use the prebuilt messagesStateReducer function. For brand new messages, it will simply append to existing list, but it will also handle the updates for existing messages correctly.

Serialization

In addition to keeping track of message IDs, the messagesStateReducer function will also try to deserialize messages into LangChain Message objects whenever a state update is received on the messages channel. This allows sending graph inputs / state updates in the following format:

// this is supported
{
  messages: [new HumanMessage({ content: "message" })];
}

// and this is also supported
{
  messages: [{ role: "user", content: "message" }];
}

Below is an example of a graph state annotation that uses messagesStateReducer as it's reducer function.

import type { BaseMessage } from "@langchain/core/messages";
import { Annotation, type Messages } from "@langchain/langgraph";

const StateAnnotation = Annotation.Root({
  messages: Annotation<BaseMessage[], Messages>({
    reducer: messagesStateReducer,
  }),
});

MessagesAnnotation

Since having a list of messages in your state is so common, there exists a prebuilt annotation called MessagesAnnotation which makes it easy to use messages as graph state. MessagesAnnotation is defined with a single messages key which is a list of BaseMessage objects and uses the messagesStateReducer reducer.

import { MessagesAnnotation, StateGraph } from "@langchain/langgraph";

const graph = new StateGraph(MessagesAnnotation)
  .addNode(...)
  ...

Is equivalent to initializing your state manually like this:

import { BaseMessage } from "@langchain/core/messages";
import { Annotation, StateGraph, messagesStateReducer } from "@langchain/langgraph";

export const StateAnnotation = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: messagesStateReducer,
    default: () => [],
  }),
});

const graph = new StateGraph(StateAnnotation)
  .addNode(...)
  ...

The state of a MessagesAnnotation has a single key called messages. This is an array of BaseMessages, with messagesStateReducer as a reducer. messagesStateReducer basically adds messages to the existing list (it also does some nice extra things, like convert from OpenAI message format to the standard LangChain message format, handle updates based on message IDs, etc).

We often see an array of messages being a key component of state, so this prebuilt state is intended to make it easy to use messages. Typically, there is more state to track than just messages, so we see people extend this state and add more fields, like:

import { Annotation, MessagesAnnotation } from "@langchain/langgraph";

const StateWithDocuments = Annotation.Root({
  ...MessagesAnnotation.spec, // Spread in the messages state
  documents: Annotation<string[]>,
});

Nodes

In LangGraph, nodes are typically JavaScript/TypeScript functions (sync or async) where the first positional argument is the state, and (optionally), the second positional argument is a "config", containing optional configurable parameters (such as a thread_id).

Similar to NetworkX, you add these nodes to a graph using the addNode method:

import { RunnableConfig } from "@langchain/core/runnables";
import { StateGraph, Annotation } from "@langchain/langgraph";

const GraphAnnotation = Annotation.Root({
  input: Annotation<string>,
  results: Annotation<string>,
})

// The state type can be extracted using `typeof <annotation variable name>.State`
const myNode = (state: typeof GraphAnnotation.State, config?: RunnableConfig) => {
  console.log("In node: ", config.configurable?.user_id);
  return {
    results: `Hello, ${state.input}!`
  }
}

// The second argument is optional
const myOtherNode = (state: typeof GraphAnnotation.State) => {
  return state
}

const builder = new StateGraph(GraphAnnotation)
  .addNode("myNode", myNode)
  .addNode("myOtherNode", myOtherNode)
  ...

Behind the scenes, functions are converted to RunnableLambda's, which adds batch and streaming support to your function, along with native tracing and debugging.

START Node

The START Node is a special node that represents the node sends user input to the graph. The main purpose for referencing this node is to determine which nodes should be called first.

import { START } from "@langchain/langgraph";

graph.addEdge(START, "nodeA");

END Node

The END Node is a special node that represents a terminal node. This node is referenced when you want to denote which edges have no actions after they are done.

import { END } from "@langchain/langgraph";

graph.addEdge("nodeA", END);

Edges

Edges define how the logic is routed and how the graph decides to stop. This is a big part of how your agents work and how different nodes communicate with each other. There are a few key types of edges:

• Normal Edges: Go directly from one node to the next.
• Conditional Edges: Call a function to determine which node(s) to go to next.
• Entry Point: Which node to call first when user input arrives.
• Conditional Entry Point: Call a function to determine which node(s) to call first when user input arrives.

A node can have MULTIPLE outgoing edges. If a node has multiple out-going edges, all of those destination nodes will be executed in parallel as a part of the next superstep.

Normal Edges

If you always want to go from node A to node B, you can use the addEdge method directly.

graph.addEdge("nodeA", "nodeB");

Conditional Edges

If you want to optionally route to 1 or more edges (or optionally terminate), you can use the addConditionalEdges method. This method accepts the name of a node and a "routing function" to call after that node is executed:

graph.addConditionalEdges("nodeA", routingFunction);

Similar to nodes, the routingFunction accept the current state of the graph and return a value.

By default, the return value routingFunction is used as the name of the node (or an array of nodes) to send the state to next. All those nodes will be run in parallel as a part of the next superstep.

You can optionally provide an object that maps the routingFunction's output to the name of the next node.

graph.addConditionalEdges("nodeA", routingFunction, {
  true: "nodeB",
  false: "nodeC",
});

Entry Point

The entry point is the first node(s) that are run when the graph starts. You can use the addEdge method from the virtual START node to the first node to execute to specify where to enter the graph.

import { START } from "@langchain/langgraph";

graph.addEdge(START, "nodeA");

Conditional Entry Point

A conditional entry point lets you start at different nodes depending on custom logic. You can use addConditionalEdges from the virtual START node to accomplish this.

import { START } from "@langchain/langgraph";

graph.addConditionalEdges(START, routingFunction);

You can optionally provide an object that maps the routingFunction's output to the name of the next node.

graph.addConditionalEdges(START, routingFunction, {
  true: "nodeB",
  false: "nodeC",
});

Send

By default, Nodes and Edges are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of State to exist at the same time. A common of example of this is with map-reduce design patterns. In this design pattern, a first node may generate an array of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input State to the downstream Node should be different (one for each generated object).

To support this design pattern, LangGraph supports returning Send objects from conditional edges. Send takes two arguments: first is the name of the node, and second is the state to pass to that node.

const continueToJokes = (state: { subjects: string[] }) => {
  return state.subjects.map(
    (subject) => new Send("generate_joke", { subject })
  );
};

graph.addConditionalEdges("nodeA", continueToJokes);

Persistence

LangGraph provides built-in persistence for your agent's state using checkpointers. Checkpointers save snapshots of the graph state at every superstep, allowing resumption at any time. This enables features like human-in-the-loop interactions, memory management, and fault-tolerance. You can even directly manipulate a graph's state after its execution using the appropriate get and update methods. For more details, see the conceptual guide for more information.

Threads

Threads in LangGraph represent individual sessions or conversations between your graph and a user. When using checkpointing, turns in a single conversation (and even steps within a single graph execution) are organized by a unique thread ID.

Storage

LangGraph provides built-in document storage through the BaseStore interface. Unlike checkpointers, which save state by thread ID, stores use custom namespaces for organizing data. This enables cross-thread persistence, allowing agents to maintain long-term memories, learn from past interactions, and accumulate knowledge over time. Common use cases include storing user profiles, building knowledge bases, and managing global preferences across all threads.

Graph Migrations

LangGraph can easily handle migrations of graph definitions (nodes, edges, and state) even when using a checkpointer to track state.

• For threads at the end of the graph (i.e. not interrupted) you can change the entire topology of the graph (i.e. all nodes and edges, remove, add, rename, etc)
• For threads currently interrupted, we support all topology changes other than renaming / removing nodes (as that thread could now be about to enter a node that no longer exists) -- if this is a blocker please reach out and we can prioritize a solution.
• For modifying state, we have full backwards and forwards compatibility for adding and removing keys
• State keys that are renamed lose their saved state in existing threads
• State keys whose types change in incompatible ways could currently cause issues in threads with state from before the change -- if this is a blocker please reach out and we can prioritize a solution.

Configuration

When creating a graph, you can also mark that certain parts of the graph are configurable. This is commonly done to enable easily switching between models or system prompts. This allows you to create a single "cognitive architecture" (the graph) but have multiple different instance of it.

You can then pass this configuration into the graph using the configurable config field.

const config = { configurable: { llm: "anthropic" } };

await graph.invoke(inputs, config);

You can then access and use this configuration inside a node:

const nodeA = (state, config) => {
  const llmType = config?.configurable?.llm;
  let llm: BaseChatModel;
  if (llmType) {
    const llm = getLlm(llmType);
  }
  ...
};

See this guide for a full breakdown on configuration

Breakpoints

It can often be useful to set breakpoints before or after certain nodes execute. This can be used to wait for human approval before continuing. These can be set when you "compile" a graph, or thrown dynamically using a special error called a NodeInterrupt. You can set breakpoints either before a node executes (using interruptBefore) or after a node executes (using interruptAfter).

You MUST use a checkpointer when using breakpoints. This is because your graph needs to be able to resume execution after interrupting.

In order to resume execution, you can just invoke your graph with null as the input and the same thread_id.

const config = { configurable: { thread_id: "foo" } };

// Initial run of graph
await graph.invoke(inputs, config);

// Let's assume it hit a breakpoint somewhere, you can then resume by passing in None
await graph.invoke(null, config);

See this guide for a full walkthrough of how to add breakpoints.

Dynamic Breakpoints

It may be helpful to dynamically interrupt the graph from inside a given node based on some condition. In LangGraph you can do so by using NodeInterrupt -- a special error that can be raised from inside a node.

function myNode(
  state: typeof GraphAnnotation.State
): typeof GraphAnnotation.State {
  if (state.input.length > 5) {
    throw new NodeInterrupt(
      `Received input that is longer than 5 characters: ${state.input}`
    );
  }

  return state;
}

Subgraphs

A subgraph is a graph that is used as a node in another graph. This is nothing more than the age-old concept of encapsulation, applied to LangGraph. Some reasons for using subgraphs are:

• building multi-agent systems
• when you want to reuse a set of nodes in multiple graphs, which maybe share some state, you can define them once in a subgraph and then use them in multiple parent graphs
• when you want different teams to work on different parts of the graph independently, you can define each part as a subgraph, and as long as the subgraph interface (the input and output schemas) is respected, the parent graph can be built without knowing any details of the subgraph

There are two ways to add subgraphs to a parent graph:

• add a node with the compiled subgraph: this is useful when the parent graph and the subgraph share state keys and you don't need to transform state on the way in or out

.addNode("subgraph", subgraphBuilder.compile());

• add a node with a function that invokes the subgraph: this is useful when the parent graph and the subgraph have different state schemas and you need to transform state before or after calling the subgraph

const subgraph = subgraphBuilder.compile();

const callSubgraph = async (state: typeof StateAnnotation.State) => {
  return subgraph.invoke({ subgraph_key: state.parent_key });
};

builder.addNode("subgraph", callSubgraph);

Let's take a look at examples for each.

As a compiled graph

The simplest way to create subgraph nodes is by using a compiled subgraph directly. When doing so, it is important that the parent graph and the subgraph state schemas share at least one key which they can use to communicate. If your graph and subgraph do not share any keys, you should use write a function invoking the subgraph instead.

Note

If you pass extra keys to the subgraph node (i.e., in addition to the shared keys), they will be ignored by the subgraph node. Similarly, if you return extra keys from the subgraph, they will be ignored by the parent graph.

import { StateGraph, Annotation } from "@langchain/langgraph";

const StateAnnotation = Annotation.Root({
  foo: Annotation<string>,
});

const SubgraphStateAnnotation = Annotation.Root({
  foo: Annotation<string>, // note that this key is shared with the parent graph state
  bar: Annotation<string>,
});

// Define subgraph
const subgraphNode = async (state: typeof SubgraphStateAnnotation.State) => {
  // note that this subgraph node can communicate with
  // the parent graph via the shared "foo" key
  return { foo: state.foo + "bar" };
};

const subgraph = new StateGraph(SubgraphStateAnnotation)
  .addNode("subgraph", subgraphNode);
  ...
  .compile();

// Define parent graph
const parentGraph = new StateGraph(StateAnnotation)
  .addNode("subgraph", subgraph)
  ...
  .compile();

As a function

You might want to define a subgraph with a completely different schema. In this case, you can create a node function that invokes the subgraph. This function will need to transform the input (parent) state to the subgraph state before invoking the subgraph, and transform the results back to the parent state before returning the state update from the node.

import { StateGraph, Annotation } from "@langchain/langgraph";

const StateAnnotation = Annotation.Root({
  foo: Annotation<string>,
});

const SubgraphStateAnnotation = Annotation.Root({
  // note that none of these keys are shared with the parent graph state
  bar: Annotation<string>,
  baz: Annotation<string>,
});

// Define subgraph
const subgraphNode = async (state: typeof SubgraphStateAnnotation.State) => {
  return { bar: state.bar + "baz" };
};

const subgraph = new StateGraph(SubgraphStateAnnotation)
  .addNode("subgraph", subgraphNode);
  ...
  .compile();

// Define parent graph
const subgraphWrapperNode = async (state: typeof StateAnnotation.State) => {
  // transform the state to the subgraph state
  const response = await subgraph.invoke({
    bar: state.foo,
  });
  // transform response back to the parent state
  return {
    foo: response.bar,
  };
}

const parentGraph = new StateGraph(StateAnnotation)
  .addNode("subgraph", subgraphWrapperNode)
  ...
  .compile();

Visualization

It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with a nice built-in way to render a graph as a Mermaid diagram. You can use the getGraph() method like this:

const representation = graph.getGraph();
const image = await representation.drawMermaidPng();
const arrayBuffer = await image.arrayBuffer();
const buffer = new Uint8Array(arrayBuffer);

You can also check out LangGraph Studio for a bespoke IDE that includes powerful visualization and debugging features.

Streaming

LangGraph is built with first class support for streaming. There are several different streaming modes that LangGraph supports:

• "values": This streams the full value of the state after each step of the graph.
• "updates: This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.

In addition, you can use the streamEvents method to stream back events that happen inside nodes. This is useful for streaming tokens of LLM calls.

LangGraph is built with first class support for streaming, including streaming updates from graph nodes during execution, streaming tokens from LLM calls and more. See this conceptual guide for more information.