Streaming

Tip

For new applications, we recommend event streaming—the typed-projection API introduced in Deep Agents v0.6. Event streaming gives you separate iterators per projection (subagents, messages, tool calls, values) so you can consume them independently instead of branching on stream_mode chunks.

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:

Stream subagent progress—track each subagent's execution as it runs in parallel.
Stream LLM tokens—stream tokens from the main agent and each subagent.
Stream tool calls—see tool calls and results from within subagent execution.
Stream 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.

Google

OpenAI

Anthropic

OpenRouter

Fireworks

Baseten

Ollama

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  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);
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "openai:gpt-5.5",
  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);
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  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);
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  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);
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  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);
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  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);
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  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:

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.

Google

OpenAI

Anthropic

OpenRouter

Fireworks

Baseten

Ollama

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "google:gemini-3.6-flash",
  systemPrompt:
    "You are a project coordinator with no research knowledge. " +
    "For every user request, you must call the task() tool with " +
    "subagent_type set to researcher. Never answer research questions yourself. " +
    "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}`);
    }
  }
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "openai:gpt-5.5",
  systemPrompt:
    "You are a project coordinator with no research knowledge. " +
    "For every user request, you must call the task() tool with " +
    "subagent_type set to researcher. Never answer research questions yourself. " +
    "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}`);
    }
  }
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "anthropic:claude-sonnet-5",
  systemPrompt:
    "You are a project coordinator with no research knowledge. " +
    "For every user request, you must call the task() tool with " +
    "subagent_type set to researcher. Never answer research questions yourself. " +
    "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}`);
    }
  }
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "openrouter:z-ai/glm-5.2",
  systemPrompt:
    "You are a project coordinator with no research knowledge. " +
    "For every user request, you must call the task() tool with " +
    "subagent_type set to researcher. Never answer research questions yourself. " +
    "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}`);
    }
  }
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  systemPrompt:
    "You are a project coordinator with no research knowledge. " +
    "For every user request, you must call the task() tool with " +
    "subagent_type set to researcher. Never answer research questions yourself. " +
    "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}`);
    }
  }
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "baseten:zai-org/GLM-5.2",
  systemPrompt:
    "You are a project coordinator with no research knowledge. " +
    "For every user request, you must call the task() tool with " +
    "subagent_type set to researcher. Never answer research questions yourself. " +
    "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}`);
    }
  }
}

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: "ollama:north-mini-code-1.0",
  systemPrompt:
    "You are a project coordinator with no research knowledge. " +
    "For every user request, you must call the task() tool with " +
    "subagent_type set to researcher. Never answer research questions yourself. " +
    "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}`);
    }
  }
}

Output

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.

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.

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:

Google

OpenAI

Anthropic

OpenRouter

Fireworks

Baseten

Ollama

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({
  model: "google:gemini-3.6-flash",
  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);
  }
}

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({
  model: "openai:gpt-5.5",
  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);
  }
}

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({
  model: "anthropic:claude-sonnet-5",
  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);
  }
}

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({
  model: "openrouter:z-ai/glm-5.2",
  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);
  }
}

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({
  model: "fireworks:accounts/fireworks/models/glm-5p2",
  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);
  }
}

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({
  model: "baseten:zai-org/GLM-5.2",
  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);
  }
}

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({
  model: "ollama:north-mini-code-1.0",
  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);
  }
}

Output

[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:

// Skip internal middleware steps - only show meaningful node names
const INTERESTING_NODES = new Set(["model", "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:

function getToolCalls(message: unknown): Array<{
  id?: string;
  name?: string;
  args?: Record<string, unknown>;
}> {
  if (!message || typeof message !== "object") {
    return [];
  }
  const record = message as Record<string, unknown>;
  const toolCalls = record.tool_calls ?? record.toolCalls;
  return Array.isArray(toolCalls)
    ? (toolCalls as Array<{
        id?: string;
        name?: string;
        args?: Record<string, unknown>;
      }>)
    : [];
}

const activeSubagents = new Map<
  string,
  { type?: string; description?: string; status: string }
>();

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 emits a task tool call, a subagent has been spawned.
    if (namespace.length === 0) {
      for (const msg of (data as { messages?: unknown[] }).messages ?? []) {
        for (const tc of getToolCalls(msg)) {
          if (tc.name === "task" && tc.id) {
            activeSubagents.set(tc.id, {
              type: tc.args?.subagent_type as string | undefined,
              description: String(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.
      let markedRunning = false;
      for (const [, sub] of activeSubagents) {
        if (sub.status === "pending") {
          sub.status = "running";
          markedRunning = true;
          console.log(
            `[lifecycle] RUNNING  → subagent "${sub.type}" (pregel: ${pregelId})`,
          );
          break;
        }
      }
      if (!markedRunning && activeSubagents.size === 0) {
        activeSubagents.set(pregelId, {
          type: "researcher",
          status: "running",
        });
        console.log(
          `[lifecycle] RUNNING  → subagent "researcher" (pregel: ${pregelId})`,
        );
      }
    }

    // ─── 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 { messages?: Array<Record<string, unknown>> })
        .messages ?? []) {
        if (msg.type === "tool") {
          const toolCallId = String(msg.tool_call_id ?? msg.toolCallId ?? "");
          const subagent = activeSubagents.get(toolCallId);
          if (subagent) {
            subagent.status = "complete";
            console.log(
              `[lifecycle] COMPLETE → subagent "${subagent.type}" (${toolCallId})`,
            );
            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}`);
}

Handle human-in-the-loop interrupts

When interrupt_on is configured, the updates stream may contain __interrupt__ entries that pause execution for human approval. Detect them by checking for the __interrupt__ key in update chunks. Resume with the same flat {"decisions": [...]} payload you use with .invoke():

import { v7 as uuid7 } from "uuid";

const config = { configurable: { thread_id: uuid7() } };

for await (const [mode, data] of await agent.stream(
  { messages: [{ role: "user", content: "Delete temp.txt" }] },
  { streamMode: ["messages", "updates"], ...config }
)) {
  if (mode === "updates" && data?.__interrupt__) {  // [!code highlight]
    for (const interruptObj of data.__interrupt__) {
      console.log("Approval needed:", interruptObj.value);
    }
  }
}

See Human-in-the-loop: Handle interrupts with streaming for the complete stream-resume loop pattern.

Stream chunk shapes

With subgraphs=True, chunk shape depends on whether you pass one stream mode or several:

`stream_mode`	Chunk shape
Single mode (for example `"updates"`)	`(namespace, data)`
Multiple modes (for example `["messages", "updates"]`)	`(namespace, mode, data)`

Without subgraphs, a single mode yields data directly, and multiple modes yield (mode, data).

# Single mode + subgraphs
for namespace, data in agent.stream(
    {"messages": [{"role": "user", "content": "Research quantum computing"}]},
    stream_mode="updates",
    subgraphs=True,
):
    print(namespace)  # () for main agent, ("tools:<id>",) for subagent
    print(data)

# Multiple modes + subgraphs
for namespace, mode, data in agent.stream(
    {"messages": [{"role": "user", "content": "Research quantum computing"}]},
    stream_mode=["updates", "messages", "custom"],
    subgraphs=True,
):
    print(mode)       # "updates", "messages", or "custom"
    print(namespace)  # () for main agent, ("tools:<id>",) for subagent
    print(data)

Human-in-the-loop—Configure approval workflows for sensitive tool operations
Subagents—Configure and use subagents with Deep Agents
Frontend streaming—Build React UIs with useStream for Deep Agents
LangChain Event Streaming—General streaming concepts with LangChain agents

Enable subgraph streaming

Namespaces

Subagent progress

LLM tokens

Tool calls

Custom updates

Stream multiple modes

Common patterns

Track subagent lifecycle

Handle human-in-the-loop interrupts

Stream chunk shapes

Related