Low Level Conceptual Guide¶

Graphs¶

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

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

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.

MessagesAnnotation¶

MessagesAnnotation is one of the few opinionated components in LangGraph. MessagesAnnotation is a special state annotation designed to make it easy to use an array of messages as a key in your state. Specifically, importing and using the prebuilt MessagesAnnotation like this:

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);

Checkpointer¶

LangGraph has a built-in persistence layer, implemented through checkpointers. When you use a checkpointer with a graph, you can interact with the state of that graph. When you use a checkpointer with a graph, you can interact with and manage the graph's state. The checkpointer saves a checkpoint of the graph state at every super-step, enabling several powerful capabilities:

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

Second, it allows for "memory" between interactions. You can use checkpointers to create threads and save the state of a thread after a graph executes. In the case of repeated human interactions (like conversations) any follow up messages can be sent to that checkpoint, which will retain its memory of previous ones.

See this guide for how to add a checkpointer to your graph.

Threads¶

Threads enable the checkpointing of multiple different runs, making them essential for multi-tenant chat applications and other scenarios where maintaining separate states is necessary. A thread is a unique ID assigned to a series of checkpoints saved by a checkpointer. When using a checkpointer, you must specify a thread_id when running the graph.

thread_id is simply the ID of a thread. This is always required

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

const config = { configurable: { thread_id: "a" }};
await graph.invoke(inputs, config);

See this guide for how to use threads.

Checkpointer state¶

When interacting with the checkpointer state, you must specify a thread identifier. Each checkpoint saved by the checkpointer has two properties:

values: This is the value of the state at this point in time.
next: This is a tuple of the nodes to execute next in the graph.

Get state¶

You can get the state of a checkpointer by calling await graph.getState(config). The config should contain thread_id, and the state will be fetched for that thread.

Get state history¶

You can also call await graph.getStateHistory(config) to get a list of the history of the graph. The config should contain thread_id, and the state history will be fetched for that thread.

Update state¶

You can also interact with the state directly and update it. This takes three different components:

config
values
asNode

config

The config should contain thread_id specifying which thread to update.

values

These are the values that will be used to update the state. Note that this update is treated exactly as any update from a node is treated. This means that these values will be passed to the reducer functions that are part of the state. So this does NOT automatically overwrite the state. Let's walk through an example.

Let's assume you have defined the state of your graph as:

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

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

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

If you update the state as below:

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

Then the new state of the graph will be:

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

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

asNode

The final thing you specify when calling updateState is asNode. This update will be applied as if it came from node asNode. If asNode is not provided, it will be set to the last node that updated the state, if not ambiguous.

The reason this matters is that the next steps in the graph to execute depend on the last node to have given an update, so this can be used to control which node executes next.

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 checkpoiner when using breakpoints. This is because your graph needs to be able to resume execution.

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.

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.