> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# 自定义工作流

在**自定义工作流**架构中，你可以使用 [LangGraph](/oss/python/langgraph/overview) 定义自己的定制化执行流程。你可以完全控制图结构——包括顺序步骤、条件分支、循环和并行执行。

```mermaid theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
graph LR
    A([输入]) --> B{{条件判断}}
    B -->|路径_a| C[确定性步骤]
    B -->|路径_b| D((智能体步骤))
    C --> G([输出])
    D --> G([输出])

    classDef trigger fill:#DCFCE7,stroke:#16A34A,stroke-width:2px,color:#14532D
    classDef process fill:#DBEAFE,stroke:#2563EB,stroke-width:2px,color:#1E3A8A
    classDef decision fill:#FEF3C7,stroke:#F59E0B,stroke-width:2px,color:#78350F

    class A,G trigger
    class C,D process
    class B decision
```

## 主要特点

* 完全控制图结构
* 混合确定性逻辑与智能体行为
* 支持顺序步骤、条件分支、循环和并行执行
* 可将其他模式作为节点嵌入工作流中

## 使用场景

当标准模式（子智能体、技能等）不符合你的需求时，当你需要混合确定性逻辑与智能体行为时，或者你的用例需要复杂路由或多阶段处理时，请使用自定义工作流。

工作流中的每个节点可以是一个简单函数、一个LLM调用，或是一个完整的带有[工具](/oss/python/langchain/tools)的[智能体](/oss/python/langchain/agents)。你也可以在自定义工作流中组合其他架构——例如，将多智能体系统作为单个节点嵌入。

有关自定义工作流的完整示例，请参阅下面的教程。

<Card title="教程：构建具有路由功能的多源知识库" icon="book" href="/oss/python/langchain/multi-agent/router-knowledge-base" arrow cta="了解更多">
  [路由模式](/oss/python/langchain/multi-agent/router)是自定义工作流的一个示例。本教程将逐步指导你构建一个并行查询GitHub、Notion和Slack，然后综合结果的路由器。
</Card>

## 基础实现

核心思路是，你可以在任何LangGraph节点中直接调用LangChain智能体，从而将自定义工作流的灵活性与预构建智能体的便利性结合起来：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain.agents import create_agent
from langgraph.graph import StateGraph, START, END

agent = create_agent(model="openai:gpt-4.1", tools=[...])

def agent_node(state: State) -> dict:
    """一个调用LangChain智能体的LangGraph节点。"""
    result = agent.invoke({
        "messages": [{"role": "user", "content": state["query"]}]
    })
    return {"answer": result["messages"][-1].content}

# 构建一个简单的工作流
workflow = (
    StateGraph(State)
    .add_node("agent", agent_node)
    .add_edge(START, "agent")
    .add_edge("agent", END)
    .compile()
)
```

## 示例：RAG管道

一个常见的用例是将[检索](/oss/python/langchain/retrieval)与智能体结合。这个示例构建了一个WNBA数据助手，它可以从知识库中检索信息，并能获取实时新闻。

<Accordion title="自定义RAG工作流">
  该工作流展示了三种类型的节点：

  * **模型节点**（重写）：使用[结构化输出](/oss/python/langchain/structured-output)重写用户查询以优化检索。
  * **确定性节点**（检索）：执行向量相似性搜索——不涉及LLM。
  * **智能体节点**（智能体）：基于检索到的上下文进行推理，并能通过工具获取额外信息。

  ```mermaid theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  graph LR
      A([查询]) --> B{{重写}}
      B --> C[(检索)]
      C --> D((智能体))
      D --> E([响应])

      classDef trigger fill:#DCFCE7,stroke:#16A34A,stroke-width:2px,color:#14532D
      classDef process fill:#DBEAFE,stroke:#2563EB,stroke-width:2px,color:#1E3A8A

      class A,E trigger
      class B,C,D process
  ```

  <Tip>
    你可以使用LangGraph状态在工作流步骤之间传递信息。这允许工作流的每个部分读取和更新结构化字段，从而轻松地在节点之间共享数据和上下文。
  </Tip>

  ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  from typing import TypedDict
  from pydantic import BaseModel
  from langgraph.graph import StateGraph, START, END
  from langchain.agents import create_agent
  from langchain.tools import tool
  from langchain_openai import ChatOpenAI, OpenAIEmbeddings
  from langchain_core.vectorstores import InMemoryVectorStore

  class State(TypedDict):
      question: str
      rewritten_query: str
      documents: list[str]
      answer: str

  # 包含阵容、比赛结果和球员数据的WNBA知识库
  embeddings = OpenAIEmbeddings()
  vector_store = InMemoryVectorStore(embeddings)
  vector_store.add_texts([
      # 阵容
      "New York Liberty 2024 roster: Breanna Stewart, Sabrina Ionescu, Jonquel Jones, Courtney Vandersloot.",
      "Las Vegas Aces 2024 roster: A'ja Wilson, Kelsey Plum, Jackie Young, Chelsea Gray.",
      "Indiana Fever 2024 roster: Caitlin Clark, Aliyah Boston, Kelsey Mitchell, NaLyssa Smith.",
      # 比赛结果
      "2024 WNBA Finals: New York Liberty defeated Minnesota Lynx 3-2 to win the championship.",
      "June 15, 2024: Indiana Fever 85, Chicago Sky 79. Caitlin Clark had 23 points and 8 assists.",
      "August 20, 2024: Las Vegas Aces 92, Phoenix Mercury 84. A'ja Wilson scored 35 points.",
      # 球员数据
      "A'ja Wilson 2024 season stats: 26.9 PPG, 11.9 RPG, 2.6 BPG. Won MVP award.",
      "Caitlin Clark 2024 rookie stats: 19.2 PPG, 8.4 APG, 5.7 RPG. Won Rookie of the Year.",
      "Breanna Stewart 2024 stats: 20.4 PPG, 8.5 RPG, 3.5 APG.",
  ])
  retriever = vector_store.as_retriever(search_kwargs={"k": 5})

  @tool
  def get_latest_news(query: str) -> str:
      """获取最新的WNBA新闻和更新。"""
      # 你的新闻API在这里
      return "Latest: The WNBA announced expanded playoff format for 2025..."

  agent = create_agent(
      model="openai:gpt-4.1",
      tools=[get_latest_news],
  )

  model = ChatOpenAI(model="gpt-4.1")

  class RewrittenQuery(BaseModel):
      query: str

  def rewrite_query(state: State) -> dict:
      """重写用户查询以优化检索。"""
      system_prompt = """重写此查询以检索相关的WNBA信息。
  知识库包含：球队阵容、带比分的比赛结果和球员统计数据（PPG、RPG、APG）。
  重点关注提到的具体球员姓名、球队名称或统计类别。"""
      response = model.with_structured_output(RewrittenQuery).invoke([
          {"role": "system", "content": system_prompt},
          {"role": "user", "content": state["question"]}
      ])
      return {"rewritten_query": response.query}

  def retrieve(state: State) -> dict:
      """基于重写后的查询检索文档。"""
      docs = retriever.invoke(state["rewritten_query"])
      return {"documents": [doc.page_content for doc in docs]}

  def call_agent(state: State) -> dict:
      """使用检索到的上下文生成答案。"""
      context = "\n\n".join(state["documents"])
      prompt = f"Context:\n{context}\n\nQuestion: {state['question']}"
      response = agent.invoke({"messages": [{"role": "user", "content": prompt}]})
      return {"answer": response["messages"][-1].content_blocks}

  workflow = (
      StateGraph(State)
      .add_node("rewrite", rewrite_query)
      .add_node("retrieve", retrieve)
      .add_node("agent", call_agent)
      .add_edge(START, "rewrite")
      .add_edge("rewrite", "retrieve")
      .add_edge("retrieve", "agent")
      .add_edge("agent", END)
      .compile()
  )

  result = workflow.invoke({"question": "Who won the 2024 WNBA Championship?"})
  print(result["answer"])
  ```
</Accordion>

***

<div className="source-links">
  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/i18n\zh-CN\oss\langchain\multi-agent\custom-workflow.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>

  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>
</div>