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文档索引

在以下地址获取完整的文档索引：https://docs.langchain.org.cn/llms.txt

在进一步探索之前，请使用此文件发现所有可用页面。

Deep Agents 基于 LangGraph 的流式传输基础设施，并提供对子代理流的一等支持。当深度代理将工作委派给子代理时，您可以独立流式传输每个子代理的更新——实时追踪进度、LLM Token 和工具调用。 深度代理流式传输可以实现：

​
启用子图流式传输

Deep Agents 使用 LangGraph 的子图流式传输来展示子代理执行过程中的事件。要接收子代理事件，请在流式传输时启用 stream_subgraphs。 
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,
  }
)) {
  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);
}

​
命名空间

当启用 subgraphs 时，每个流式传输事件都会包含一个命名空间，用于标识是哪个代理产生了该事件。命名空间是一条由节点名称和任务 ID 组成的路径，代表了代理的层级结构。
命名空间来源
() (空)主智能体
("tools:abc123",)由主代理的 task 工具调用 abc123 所生成的子代理
("tools:abc123", "model_request:def456")子代理内部的模型请求节点
使用命名空间将事件路由到正确的 UI 组件 
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);
  }
}

​
子代理进度

使用 stream_mode="updates" 在每一步完成时追踪子代理进度。这对于显示哪些子代理处于活跃状态以及它们已完成的工作非常有用。 
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}`);
    }
  }
}
输出
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

使用 stream_mode="messages" 从主代理和子代理流式传输单个 Token。每个消息事件都包含标识来源代理的元数据。 
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");

​
工具调用

当子代理使用工具时，您可以流式传输工具调用事件，以展示每个子代理正在执行的操作。工具调用块会出现在 messages 流模式中。 
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");

​
自定义更新

在子代理工具内部使用 config.writer 来发出自定义进度事件 
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);
  }
}
输出
[tools:call_abc123] { status: 'fetching', progress: 0 }
[tools:call_abc123] { status: 'analyzing', progress: 50 }
[tools:call_abc123] { status: 'complete', progress: 100 }

​
流式传输多种模式

结合多种流模式以获得代理执行的完整视图 
// 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");

​
常见模式

​
追踪子代理生命周期

监控子代理的启动、运行和完成时间 
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}`);
}
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