> ## 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.

# Streaming

> Stream real-time updates from deep agent runs and subagent execution

<Info>
  **In preview:** Try event streaming typed Deep Agents projections over coordinator messages, delegated subagents, tool calls, and final output. Start with [Deep Agents Event Streaming](/oss/javascript/deepagents/event-streaming), or explore runnable examples in the [streaming cookbook](https://github.com/langchain-ai/streaming-cookbook).
</Info>

Deep Agents build on LangGraph's streaming infrastructure with first-class support for subagent streams. When a deep agent delegates work to subagents, you can stream updates from each subagent independently—tracking progress, LLM tokens, and tool calls in real time.

What's possible with deep agent streaming:

* <Icon icon="diagram-subtask" size={16} /> [**Stream subagent progress**](#subagent-progress)—track each subagent's execution as it runs in parallel.
* <Icon icon="square-binary" size={16} /> [**Stream LLM tokens**](#llm-tokens)—stream tokens from the main agent and each subagent.
* <Icon icon="screwdriver-wrench" size={16} /> [**Stream tool calls**](#tool-calls)—see tool calls and results from within subagent execution.
* <Icon icon="table" size={16} /> [**Stream custom updates**](#custom-updates)—emit user-defined signals from inside subagent nodes.

## Enable subgraph streaming

Deep Agents use LangGraph's subgraph streaming to surface events from subagent execution. To receive subagent events, enable `stream_subgraphs` when streaming.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  systemPrompt: "You are a helpful research assistant",
  subagents: [
    {
      name: "researcher",
      description: "Researches a topic in depth",
      systemPrompt: "You are a thorough researcher.",
    },
  ],
});

for await (const [namespace, chunk] of await agent.stream(
  { messages: [{ role: "user", content: "Research quantum computing advances" }] },
  {
    streamMode: "updates",
    subgraphs: true,  // [!code highlight]
  }
)) {
  if (namespace.length > 0) {
    // Subagent event - namespace identifies the source
    console.log(`[subagent: ${namespace.join("|")}]`);
  } else {
    // Main agent event
    console.log("[main agent]");
  }
  console.log(chunk);
}
```

## Namespaces

When `subgraphs` is enabled, each streaming event includes a **namespace** that identifies which agent produced it. The namespace is a path of node names and task IDs that represents the agent hierarchy.

| Namespace                                  | Source                                                           |
| ------------------------------------------ | ---------------------------------------------------------------- |
| `()` (empty)                               | Main agent                                                       |
| `("tools:abc123",)`                        | A subagent spawned by the main agent's `task` tool call `abc123` |
| `("tools:abc123", "model_request:def456")` | The model request node inside a subagent                         |

Use namespaces to route events to the correct UI component:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
for await (const [namespace, chunk] of await agent.stream(
  { messages: [{ role: "user", content: "Plan my vacation" }] },
  { streamMode: "updates", subgraphs: true }
)) {
  // Check if this event came from a subagent
  const isSubagent = namespace.some(
    (segment: string) => segment.startsWith("tools:")
  );

  if (isSubagent) {
    // Extract the tool call ID from the namespace
    const toolCallId = namespace
      .find((s: string) => s.startsWith("tools:"))
      ?.split(":")[1];
    console.log(`Subagent ${toolCallId}:`, chunk);
  } else {
    console.log("Main agent:", chunk);
  }
}
```

## Subagent progress

Use `stream_mode="updates"` to track subagent progress as each step completes. This is useful for showing which subagents are active and what work they've completed.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  systemPrompt:
    "You are a project coordinator. Always delegate research tasks " +
    "to your researcher subagent using the task tool. Keep your final response to one sentence.",
  subagents: [
    {
      name: "researcher",
      description: "Researches topics thoroughly",
      systemPrompt:
        "You are a thorough researcher. Research the given topic " +
        "and provide a concise summary in 2-3 sentences.",
    },
  ],
});

for await (const [namespace, chunk] of await agent.stream(
  {
    messages: [
      { role: "user", content: "Write a short summary about AI safety" },
    ],
  },
  { streamMode: "updates", subgraphs: true },
)) {
  // Main agent updates (empty namespace)
  if (namespace.length === 0) {
    for (const [nodeName, data] of Object.entries(chunk)) {
      if (nodeName === "tools") {
        // Subagent results returned to main agent
        for (const msg of (data as any).messages ?? []) {
          if (msg.type === "tool") {
            console.log(`\nSubagent complete: ${msg.name}`);
            console.log(`  Result: ${String(msg.content).slice(0, 200)}...`);
          }
        }
      } else {
        console.log(`[main agent] step: ${nodeName}`);
      }
    }
  }
  // Subagent updates (non-empty namespace)
  else {
    for (const [nodeName] of Object.entries(chunk)) {
      console.log(`  [${namespace[0]}] step: ${nodeName}`);
    }
  }
}
```

```shell title="Output" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
Main agent step: model_request
  [tools:call_abc123] step: model_request
  [tools:call_abc123] step: tools
  [tools:call_abc123] step: model_request
Subagent complete: task
Result: ## AI Safety Report...
Main agent step: model_request
  [tools:call_def456] step: model_request
  [tools:call_def456] step: model_request
Subagent complete: task
Result: # Comprehensive Report on AI Safety...
Main agent step: model_request
```

## LLM tokens

Use `stream_mode="messages"` to stream individual tokens from both the main agent and subagents. Each message event includes metadata that identifies the source agent.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
let currentSource = "";

for await (const [namespace, chunk] of await agent.stream(
  {
    messages: [
      {
        role: "user",
        content: "Research quantum computing advances",
      },
    ],
  },
  { streamMode: "messages", subgraphs: true },
)) {
  const [message] = chunk;

  // Check if this event came from a subagent (namespace contains "tools:")
  const isSubagent = namespace.some((s: string) => s.startsWith("tools:"));

  if (isSubagent) {
    // Token from a subagent
    const subagentNs = namespace.find((s: string) => s.startsWith("tools:"))!;
    if (subagentNs !== currentSource) {
      process.stdout.write(`\n\n--- [subagent: ${subagentNs}] ---\n`);
      currentSource = subagentNs;
    }
    if (message.text) {
      process.stdout.write(message.text);
    }
  } else {
    // Token from the main agent
    if ("main" !== currentSource) {
      process.stdout.write(`\n\n--- [main agent] ---\n`);
      currentSource = "main";
    }
    if (message.text) {
      process.stdout.write(message.text);
    }
  }
}

process.stdout.write("\n");
```

## Tool calls

When subagents use tools, you can stream tool call events to display what each subagent is doing. Tool call chunks appear in the `messages` stream mode.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { AIMessageChunk, ToolMessage } from "langchain";

for await (const [namespace, chunk] of await agent.stream(
  {
    messages: [
      {
        role: "user",
        content: "Research recent quantum computing advances",
      },
    ],
  },
  { streamMode: "messages", subgraphs: true },
)) {
  const [message] = chunk;

  // Identify source: "main" or the subagent namespace segment
  const isSubagent = namespace.some((s: string) => s.startsWith("tools:"));
  const source = isSubagent
    ? namespace.find((s: string) => s.startsWith("tools:"))!
    : "main";

  // Tool call chunks (streaming tool invocations)
  if (AIMessageChunk.isInstance(message) && message.tool_call_chunks?.length) {
    for (const tc of message.tool_call_chunks) {
      if (tc.name) {
        console.log(`\n[${source}] Tool call: ${tc.name}`);
      }
      // Args stream in chunks - write them incrementally
      if (tc.args) {
        process.stdout.write(tc.args);
      }
    }
  }

  // Tool results
  if (ToolMessage.isInstance(message)) {
    console.log(
      `\n[${source}] Tool result [${message.name}]: ${message.text?.slice(0, 150)}`,
    );
  }

  // Regular AI content (skip tool call messages)
  if (
    AIMessageChunk.isInstance(message) &&
    message.text &&
    !message.tool_call_chunks?.length
  ) {
    process.stdout.write(message.text);
  }
}

process.stdout.write("\n");
```

## Custom updates

Use `config.writer` inside your subagent tools to emit custom progress events:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createDeepAgent } from "deepagents";
import { tool, type ToolRuntime } from "langchain";
import { z } from "zod";

/**
 * A tool that emits custom progress events via config.writer.
 * The writer sends data to the "custom" stream mode.
 */
const analyzeData = tool(
  async ({ topic }: { topic: string }, config: ToolRuntime) => {
    const writer = config.writer;

    writer?.({ status: "starting", topic, progress: 0 });
    await new Promise((r) => setTimeout(r, 500));

    writer?.({ status: "analyzing", progress: 50 });
    await new Promise((r) => setTimeout(r, 500));

    writer?.({ status: "complete", progress: 100 });
    return `Analysis of "${topic}": Customer sentiment is 85% positive, driven by product quality and support response times.`;
  },
  {
    name: "analyze_data",
    description:
      "Run a data analysis on a given topic. " +
      "This tool performs the actual analysis and emits progress updates. " +
      "You MUST call this tool for any analysis request.",
    schema: z.object({
      topic: z.string().describe("The topic or subject to analyze"),
    }),
  },
);

const agent = createDeepAgent({
  systemPrompt:
    "You are a coordinator. For any analysis request, you MUST delegate " +
    "to the analyst subagent using the task tool. Never try to answer directly. " +
    "After receiving the result, summarize it in one sentence.",
  subagents: [
    {
      name: "analyst",
      description: "Performs data analysis with real-time progress tracking",
      systemPrompt:
        "You are a data analyst. You MUST call the analyze_data tool " +
        "for every analysis request. Do not use any other tools. " +
        "After the analysis completes, report the result.",
      tools: [analyzeData],
    },
  ],
});

for await (const [namespace, chunk] of await agent.stream(
  {
    messages: [
      {
        role: "user",
        content: "Analyze customer satisfaction trends",
      },
    ],
  },
  { streamMode: "custom", subgraphs: true },
)) {
  const isSubagent = namespace.some((s: string) => s.startsWith("tools:"));
  if (isSubagent) {
    const subagentNs = namespace.find((s: string) => s.startsWith("tools:"))!;
    console.log(`[${subagentNs}]`, chunk);
  } else {
    console.log("[main]", chunk);
  }
}
```

```shell title="Output" theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[tools:call_abc123] { status: 'fetching', progress: 0 }
[tools:call_abc123] { status: 'analyzing', progress: 50 }
[tools:call_abc123] { status: 'complete', progress: 100 }
```

## Stream multiple modes

Combine multiple stream modes to get a complete picture of agent execution:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// Skip internal middleware steps - only show meaningful node names
const INTERESTING_NODES = new Set(["model_request", "tools"]);

let lastSource = "";
let midLine = false; // true when we've written tokens without a trailing newline

for await (const [namespace, mode, data] of await agent.stream(
  {
    messages: [
      {
        role: "user",
        content: "Analyze the impact of remote work on team productivity",
      },
    ],
  },
  { streamMode: ["updates", "messages", "custom"], subgraphs: true },
)) {
  const isSubagent = namespace.some((s: string) => s.startsWith("tools:"));
  const source = isSubagent ? "subagent" : "main";

  if (mode === "updates") {
    for (const nodeName of Object.keys(data)) {
      if (!INTERESTING_NODES.has(nodeName)) continue;
      if (midLine) {
        process.stdout.write("\n");
        midLine = false;
      }
      console.log(`[${source}] step: ${nodeName}`);
    }
  } else if (mode === "messages") {
    const [message] = data;
    if (message.text) {
      // Print a header when the source changes
      if (source !== lastSource) {
        if (midLine) {
          process.stdout.write("\n");
          midLine = false;
        }
        process.stdout.write(`\n[${source}] `);
        lastSource = source;
      }
      process.stdout.write(message.text);
      midLine = true;
    }
  } else if (mode === "custom") {
    if (midLine) {
      process.stdout.write("\n");
      midLine = false;
    }
    console.log(`[${source}] custom event:`, data);
  }
}

process.stdout.write("\n");
```

## Common patterns

### Track subagent lifecycle

Monitor when subagents start, run, and complete:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
for await (const [namespace, chunk] of await agent.stream(
  {
    messages: [
      { role: "user", content: "Research the latest AI safety developments" },
    ],
  },
  { streamMode: "updates", subgraphs: true },
)) {
  for (const [nodeName, data] of Object.entries(chunk)) {
    // ─── Phase 1: Detect subagent starting ────────────────────────
    // When the main agent's model_request contains task tool calls,
    // a subagent has been spawned.
    if (namespace.length === 0 && nodeName === "model_request") {
      for (const msg of (data as any).messages ?? []) {
        for (const tc of msg.tool_calls ?? []) {
          if (tc.name === "task") {
            activeSubagents.set(tc.id, {
              type: tc.args?.subagent_type,
              description: tc.args?.description?.slice(0, 80),
              status: "pending",
            });
            console.log(
              `[lifecycle] PENDING  → subagent "${tc.args?.subagent_type}" (${tc.id})`,
            );
          }
        }
      }
    }

    // ─── Phase 2: Detect subagent running ─────────────────────────
    // When we receive events from a tools:UUID namespace, that
    // subagent is actively executing.
    if (namespace.length > 0 && namespace[0].startsWith("tools:")) {
      const pregelId = namespace[0].split(":")[1];
      // Check if any pending subagent needs to be marked running.
      // Note: the pregel task ID differs from the tool_call_id,
      // so we mark any pending subagent as running on first subagent event.
      for (const [id, sub] of activeSubagents) {
        if (sub.status === "pending") {
          sub.status = "running";
          console.log(
            `[lifecycle] RUNNING  → subagent "${sub.type}" (pregel: ${pregelId})`,
          );
          break;
        }
      }
    }

    // ─── Phase 3: Detect subagent completing ──────────────────────
    // When the main agent's tools node returns a tool message,
    // the subagent has completed and returned its result.
    if (namespace.length === 0 && nodeName === "tools") {
      for (const msg of (data as any).messages ?? []) {
        if (msg.type === "tool") {
          const subagent = activeSubagents.get(msg.tool_call_id);
          if (subagent) {
            subagent.status = "complete";
            console.log(
              `[lifecycle] COMPLETE → subagent "${subagent.type}" (${msg.tool_call_id})`,
            );
            console.log(
              `  Result preview: ${String(msg.content).slice(0, 120)}...`,
            );
          }
        }
      }
    }
  }
}

// Print final state
console.log("\n--- Final subagent states ---");
for (const [id, sub] of activeSubagents) {
  console.log(`  ${sub.type}: ${sub.status}`);
}
```

## Related

* [Subagents](/oss/javascript/deepagents/subagents)—Configure and use subagents with Deep Agents
* [Frontend streaming](/oss/javascript/deepagents/streaming/frontend)—Build React UIs with `useStream` for Deep Agents
* [LangChain streaming overview](/oss/javascript/langchain/streaming/overview)—General streaming concepts with LangChain agents

***

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