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Build frontends that visualize deep agent workflows in real time. These patterns show how to render subagent progress, task planning, and streaming content from agents created with createDeepAgent.

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Architecture

Deep agents use a coordinator-worker architecture. The main agent plans tasks and delegates to specialized subagents, each running in isolation. On the frontend, useStream surfaces both the coordinator’s messages and each subagent’s streaming state. 
from deepagents import create_deep_agent

agent = create_deep_agent(
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
    subagents=[
        {
            "name": "researcher",
            "description": "Research assistant",
        }
    ],
)
On the frontend, connect with useStream the same way as with createAgent. Deep agent patterns use additional useStream features like stream.subagents, stream.values.todos, and filterSubagentMessages to render subagent-specific UIs. 
import { useStream } from "@langchain/react";

function App() {
  const stream = useStream<typeof agent>({
    apiUrl: "http://localhost:2024",
    assistantId: "agent",
  });

  // Deep agent state beyond messages
  const todos = stream.values?.todos;
  const subagents = stream.subagents;
}

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Patterns

Subagent streaming

Display specialist subagents with streaming content, progress tracking, and collapsible cards.

Todo list

Track agent progress with a real-time todo list synced from agent state.
The LangChain frontend patterns — markdown messages, tool calling, human-in-the-loop, and more — all work with deep agents too. Deep agents are built on the same LangGraph runtime, so useStream provides the same core API.
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