> ## Documentation Index
> Fetch the complete documentation index at: https://cndoc-langchain.site/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，您可以使用 [LangGraph](/oss/python/langgraph/overview) 定义自己的定制执行流程。您完全控制图结构——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:#F6FFDB,stroke:#6E8900,stroke-width:2px,color:#2E3900
classDef process fill:#E5F4FF,stroke:#006DDD,stroke-width:2px,color:#030710
classDef decision fill:#FDF3FF,stroke:#7E65AE,stroke-width:2px,color:#504B5F
class A,G trigger
class C,D process
class B decision
```
## 关键特性
* 完全控制图结构
* 将确定性逻辑与智能体行为相结合
* 支持顺序步骤、条件分支、循环和并行执行
* 将其他模式作为节点嵌入到您的工作流中
## 何时使用
当标准模式（子智能体、技能等）不符合您的需求，您需要将确定性逻辑与智能体行为相结合，或者您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、一次 LLM 调用，或一个带有[工具](/oss/python/langchain/tools)的完整[智能体](/oss/python/langchain/agents)。您还可以在自定义工作流中组合其他架构——例如，将一个多智能体系统嵌入为单个节点。
有关自定义工作流的完整示例，请参阅下面的教程。
[路由模式](/oss/python/langchain/multi-agent/router)是自定义工作流的一个示例。本教程将指导您构建一个路由器，该路由器并行查询 GitHub、Notion 和 Slack，然后综合结果。
>
## 基本实现
核心思想是，您可以在任何 LangGraph 节点内直接调用 LangChain 智能体，将自定义工作流的灵活性与预构建智能体的便利性相结合：
```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-5.4", 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)与智能体相结合。本示例构建了一个 WNBA 统计助手，它可以从知识库中检索信息，并能获取实时新闻。
该工作流演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)重写用户查询以获得更好的检索效果。
* **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，并可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:#F6FFDB,stroke:#6E8900,stroke-width:2px,color:#2E3900
classDef process fill:#E5F4FF,stroke:#006DDD,stroke-width:2px,color:#030710
class A,E trigger
class B,C,D process
```
您可以使用 LangGraph 状态在工作流步骤之间传递信息。这允许您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。
```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
# WNBA 知识库，包含阵容、比赛结果和球员统计数据
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.",
# 球员统计数据
"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。"""
# 您的新闻 API 在这里
return "Latest: The WNBA announced expanded playoff format for 2025..."
agent = create_agent(
model="openai:gpt-5.4",
tools=[get_latest_news],
)
model = ChatOpenAI(model="gpt-5.4")
class RewrittenQuery(BaseModel):
query: str
def rewrite_query(state: State) -> dict:
"""重写用户查询以获得更好的检索效果。"""
system_prompt = """Rewrite this query to retrieve relevant WNBA information.
The knowledge base contains: team rosters, game results with scores, and player statistics (PPG, RPG, APG).
Focus on specific player names, team names, or stat categories mentioned."""
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"])
```
***
[将这些文档连接](/use-these-docs)到 Claude、VSCode 等，通过 MCP 获取实时答案。
[在 GitHub 上编辑此页面](https://github.com/langchain-ai/docs/edit/main/src/oss/langchain/multi-agent/custom-workflow.mdx) 或 [提交问题](https://github.com/langchain-ai/docs/issues/new/choose)。