How to implement generative user interfaces with LangGraph

Prerequisites

• LangSmith
• Agent Server
• useStream() React Hook

Generative user interfaces (Generative UI) allows agents to go beyond text and generate rich user interfaces. This enables creating more interactive and context-aware applications where the UI adapts based on the conversation flow and AI responses.

LangSmith supports colocating your React components with your graph code. This allows you to focus on building specific UI components for your graph while easily plugging into existing chat interfaces such as Agent Chat and loading the code only when actually needed.

Tutorial

1. Define and configure UI components

First, create your first UI component. For each component you need to provide an unique identifier that will be used to reference the component in your graph code.

src/agent/ui.tsx

const WeatherComponent = (props: { city: string }) => {
  return <div>Weather for {props.city}</div>;
};

export default {
  weather: WeatherComponent,
};

Next, define your UI components in your langgraph.json configuration:

{
  "node_version": "20",
  "graphs": {
    "agent": "./src/agent/index.ts:graph"
  },
  "ui": {
    "agent": "./src/agent/ui.tsx"
  }
}

The ui section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see Customise the namespace of UI components for more details.

LangSmith will automatically bundle your UI components code and styles and serve them as external assets that can be loaded by the LoadExternalComponent component. Some dependencies such as react and react-dom will be automatically excluded from the bundle.

CSS and Tailwind 4.x is also supported out of the box, so you can freely use Tailwind classes as well as shadcn/ui in your UI components.

src/agent/ui.tsx

import "./styles.css";

const WeatherComponent = (props: { city: string }) => {
  return <div className="bg-red-500">Weather for {props.city}</div>;
};

export default {
  weather: WeatherComponent,
};

src/agent/styles.css

@import "tailwindcss";

2. Send the UI components in your graph

Python

src/agent.py

import uuid
from typing import Annotated, Sequence, TypedDict

from langchain.messages import AIMessage
from langchain_core.messages import BaseMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.graph.ui import AnyUIMessage, ui_message_reducer, push_ui_message


class AgentState(TypedDict):  # noqa: D101
    messages: Annotated[Sequence[BaseMessage], add_messages]
    ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]


async def weather(state: AgentState):
    class WeatherOutput(TypedDict):
        city: str

    weather: WeatherOutput = (
        await ChatOpenAI(model="gpt-5.4-mini")
        .with_structured_output(WeatherOutput)
        .with_config({"tags": ["nostream"]})
        .ainvoke(state["messages"])
    )

    message = AIMessage(
        id=str(uuid.uuid4()),
        content=f"Here's the weather for {weather['city']}",
    )

    # Emit UI elements associated with the message
    push_ui_message("weather", weather, message=message)
    return {"messages": [message]}


workflow = StateGraph(AgentState)
workflow.add_node(weather)
workflow.add_edge("__start__", "weather")
graph = workflow.compile()

JS

Use the typedUi utility to emit UI elements from your agent nodes:

src/agent/index.ts

import {
  typedUi,
  uiMessageReducer,
} from "@langchain/langgraph-sdk/react-ui/server";

import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";

import type ComponentMap from "./ui.js";

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

const AgentState = Annotation.Root({
  ...MessagesAnnotation.spec,
  ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
});

export const graph = new StateGraph(AgentState)
  .addNode("weather", async (state, config) => {
    // Provide the type of the component map to ensure
    // type safety of `ui.push()` calls as well as
    // pushing the messages to the `ui` and sending a custom event as well.
    const ui = typedUi<typeof ComponentMap>(config);

    const weather = await new ChatOpenAI({ model: "gpt-5.4-mini" })
      .withStructuredOutput(z.object({ city: z.string() }))
      .withConfig({ tags: ["nostream"] })
      .invoke(state.messages);

    const response = {
      id: crypto.randomUUID(),
      type: "ai",
      content: `Here's the weather for ${weather.city}`,
    };

    // Emit UI elements associated with the AI message
    ui.push({ name: "weather", props: weather }, { message: response });

    return { messages: [response] };
  })
  .addEdge("__start__", "weather")
  .compile();

3. Handle UI elements in your React application

On the client side, you can use useStream() and LoadExternalComponent to display the UI elements.

src/app/page.tsx

"use client";

import { useStream } from "@langchain/langgraph-sdk/react";
import { LoadExternalComponent } from "@langchain/langgraph-sdk/react-ui";

export default function Page() {
  const { thread, values } = useStream({
    apiUrl: "http://localhost:2024",
    assistantId: "agent",
  });

  return (
    <div>
      {thread.messages.map((message) => (
        <div key={message.id}>
          {message.content}
          {values.ui
            ?.filter((ui) => ui.metadata?.message_id === message.id)
            .map((ui) => (
              <LoadExternalComponent key={ui.id} stream={thread} message={ui} />
            ))}
        </div>
      ))}
    </div>
  );
}

Behind the scenes, LoadExternalComponent will fetch the JS and CSS for the UI components from LangSmith and render them in a shadow DOM, thus ensuring style isolation from the rest of your application.

How-to guides

Provide custom components on the client side

If you already have the components loaded in your client application, you can provide a map of such components to be rendered directly without fetching the UI code from LangSmith.

const clientComponents = {
  weather: WeatherComponent,
};

<LoadExternalComponent
  stream={thread}
  message={ui}
  components={clientComponents}
/>;

Show loading UI when components are loading

You can provide a fallback UI to be rendered when the components are loading.

<LoadExternalComponent
  stream={thread}
  message={ui}
  fallback={<div>Loading...</div>}
/>

Customise the namespace of UI components.

By default LoadExternalComponent will use the assistantId from useStream() hook to fetch the code for UI components. You can customise this by providing a namespace prop to the LoadExternalComponent component.

src/app/page.tsx

<LoadExternalComponent
  stream={thread}
  message={ui}
  namespace="custom-namespace"
/>

langgraph.json

{
  "ui": {
    "custom-namespace": "./src/agent/ui.tsx"
  }
}

Access and interact with the thread state from the UI component

You can access the thread state inside the UI component by using the useStreamContext hook.

import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";

const WeatherComponent = (props: { city: string }) => {
  const { thread, submit } = useStreamContext();
  return (
    <>
      <div>Weather for {props.city}</div>

      <button
        onClick={() => {
          const newMessage = {
            type: "human",
            content: `What's the weather in ${props.city}?`,
          };

          submit({ messages: [newMessage] });
        }}
      >
        Retry
      </button>
    </>
  );
};

Pass additional context to the client components

You can pass additional context to the client components by providing a meta prop to the LoadExternalComponent component.

<LoadExternalComponent stream={thread} message={ui} meta={{ userId: "123" }} />

Then, you can access the meta prop in the UI component by using the useStreamContext hook.

import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";

const WeatherComponent = (props: { city: string }) => {
  const { meta } = useStreamContext<
    { city: string },
    { MetaType: { userId?: string } }
  >();

  return (
    <div>
      Weather for {props.city} (user: {meta?.userId})
    </div>
  );
};

Streaming UI messages from the server

You can stream UI messages before the node execution is finished by using the onCustomEvent callback of the useStream() hook. This is especially useful when updating the UI component as the LLM is generating the response.

import { uiMessageReducer } from "@langchain/langgraph-sdk/react-ui";

const { thread, submit } = useStream({
  apiUrl: "http://localhost:2024",
  assistantId: "agent",
  onCustomEvent: (event, options) => {
    options.mutate((prev) => {
      const ui = uiMessageReducer(prev.ui ?? [], event);
      return { ...prev, ui };
    });
  },
});

Then you can push updates to the UI component by calling ui.push() / push_ui_message() with the same ID as the UI message you wish to update.

Python

from typing import Annotated, Sequence, TypedDict

from langchain_anthropic import ChatAnthropic
from langchain.messages import AIMessage, AIMessageChunk, BaseMessage
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.graph.ui import AnyUIMessage, push_ui_message, ui_message_reducer


class AgentState(TypedDict):  # noqa: D101
    messages: Annotated[Sequence[BaseMessage], add_messages]
    ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]


class CreateTextDocument(TypedDict):
    """Prepare a document heading for the user."""

    title: str


async def writer_node(state: AgentState):
    model = ChatAnthropic(model="claude-sonnet-4-6")
    message: AIMessage = await model.bind_tools(
        tools=[CreateTextDocument],
        tool_choice={"type": "tool", "name": "CreateTextDocument"},
    ).ainvoke(state["messages"])

    tool_call = next(
        (x["args"] for x in message.tool_calls if x["name"] == "CreateTextDocument"),
        None,
    )

    if tool_call:
        ui_message = push_ui_message("writer", tool_call, message=message)
        ui_message_id = ui_message["id"]

        # We're already streaming the LLM response to the client through UI messages
        # so we don't need to stream it again to the `messages` stream mode.
        content_stream = model.with_config({"tags": ["nostream"]}).astream(
            f"Create a document with the title: {tool_call['title']}"
        )

        content: AIMessageChunk | None = None
        async for chunk in content_stream:
            content = content + chunk if content else chunk

            push_ui_message(
                "writer",
                {"content": content.text()},
                id=ui_message_id,
                message=message,
                # Use `merge=True` to merge props with the existing UI message
                merge=True,
            )

    return {"messages": [message]}

JS

import {
  Annotation,
  MessagesAnnotation,
  type LangGraphRunnableConfig,
} from "@langchain/langgraph";
import { z } from "zod";
import { ChatAnthropic } from "@langchain/anthropic";
import {
  typedUi,
  uiMessageReducer,
} from "@langchain/langgraph-sdk/react-ui/server";
import type { AIMessageChunk } from "@langchain/core/messages";

import type ComponentMap from "./ui";

const AgentState = Annotation.Root({
  ...MessagesAnnotation.spec,
  ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
});

async function writerNode(
  state: typeof AgentState.State,
  config: LangGraphRunnableConfig
): Promise<typeof AgentState.Update> {
  const ui = typedUi<typeof ComponentMap>(config);

  const model = new ChatAnthropic({ model: "claude-sonnet-4-6" });
  const message = await model
    .bindTools(
      [
        {
          name: "create_text_document",
          description: "Prepare a document heading for the user.",
          schema: z.object({ title: z.string() }),
        },
      ],
      { tool_choice: { type: "tool", name: "create_text_document" } }
    )
    .invoke(state.messages);

  type ToolCall = { name: "create_text_document"; args: { title: string } };
  const toolCall = message.tool_calls?.find(
    (tool): tool is ToolCall => tool.name === "create_text_document"
  );

  if (toolCall) {
    const { id, name } = ui.push(
      { name: "writer", props: { title: toolCall.args.title } },
      { message }
    );

    const contentStream = await model
      // We're already streaming the LLM response to the client through UI messages
      // so we don't need to stream it again to the `messages` stream mode.
      .withConfig({ tags: ["nostream"] })
      .stream(`Create a short poem with the topic: ${message.text}`);

    let content: AIMessageChunk | undefined;
    for await (const chunk of contentStream) {
      content = content?.concat(chunk) ?? chunk;

      ui.push(
        { id, name, props: { content: content?.text } },
        // Use `merge: true` to merge props with the existing UI message
        { message, merge: true }
      );
    }
  }

  return { messages: [message] };
}

ui.tsx

function WriterComponent(props: { title: string; content?: string }) {
  return (
    <article>
      <h2>{props.title}</h2>
      <p style={{ whiteSpace: "pre-wrap" }}>{props.content}</p>
    </article>
  );
}

export default {
  weather: WriterComponent,
};

Remove UI messages from state

Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling remove_ui_message / ui.delete with the ID of the UI message.

Python

from langgraph.graph.ui import push_ui_message, delete_ui_message

# push message
message = push_ui_message("weather", {"city": "London"})

# remove said message
delete_ui_message(message["id"])

JS

// push message
const message = ui.push({ name: "weather", props: { city: "London" } });

// remove said message
ui.delete(message.id);

Learn more

• JS/TS SDK Reference

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