如何设置用于部署的 LangGraph.js 应用¶

一个 LangGraph.js 应用必须配置一个 LangGraph API 配置文件才能部署到 LangGraph Cloud（或进行自托管）。本操作指南讨论了使用 package.json 指定项目依赖来设置用于部署的 LangGraph.js 应用的基本步骤。

本演练基于此仓库，你可以使用它来了解如何设置用于部署的 LangGraph 应用。

最终的仓库结构将如下所示

my-app/
├── src # all project code lies within here
│   ├── utils # optional utilities for your graph
│   │   ├── tools.ts # tools for your graph
│   │   ├── nodes.ts # node functions for you graph
│   │   └── state.ts # state definition of your graph
│   └── agent.ts # code for constructing your graph
├── package.json # package dependencies
├── .env # environment variables
└── langgraph.json # configuration file for LangGraph

每个步骤后，都提供了一个示例文件目录来演示代码如何组织。

指定依赖¶

依赖项可以在 package.json 中指定。如果未创建这些文件，则可以在后续的 LangGraph API 配置文件中指定依赖项。

示例 package.json 文件

{
  "name": "langgraphjs-studio-starter",
  "packageManager": "yarn@1.22.22",
  "dependencies": {
    "@langchain/community": "^0.2.31",
    "@langchain/core": "^0.2.31",
    "@langchain/langgraph": "^0.2.0",
    "@langchain/openai": "^0.2.8"
  }
}

部署应用时，将使用你选择的包管理器安装依赖项，前提是它们符合以下兼容版本范围

"@langchain/core": "^0.3.42",
"@langchain/langgraph": "^0.2.57",
"@langchain/langgraph-checkpoint": "~0.0.16",

示例文件目录

my-app/
└── package.json # package dependencies

指定环境变量¶

环境变量可以选择在一个文件（例如 .env）中指定。请参阅环境变量参考以配置部署的其他变量。

示例 .env 文件

MY_ENV_VAR_1=foo
MY_ENV_VAR_2=bar
OPENAI_API_KEY=key
TAVILY_API_KEY=key_2

示例文件目录

my-app/
├── package.json
└── .env # environment variables

定义图¶

实现你的图！图可以定义在单个文件或多个文件中。记下要包含在 LangGraph 应用中的每个已编译图的变量名。这些变量名稍后将在创建 LangGraph API 配置文件时使用。

这是一个示例 agent.ts

import type { AIMessage } from "@langchain/core/messages";
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
import { ChatOpenAI } from "@langchain/openai";

import { MessagesAnnotation, StateGraph } from "@langchain/langgraph";
import { ToolNode } from "@langchain/langgraph/prebuilt";

const tools = [new TavilySearchResults({ maxResults: 3 })];

// Define the function that calls the model
async function callModel(state: typeof MessagesAnnotation.State) {
  /**
   * Call the LLM powering our agent.
   * Feel free to customize the prompt, model, and other logic!
   */
  const model = new ChatOpenAI({
    model: "gpt-4o",
  }).bindTools(tools);

  const response = await model.invoke([
    {
      role: "system",
      content: `You are a helpful assistant. The current date is ${new Date().getTime()}.`,
    },
    ...state.messages,
  ]);

  // MessagesAnnotation supports returning a single message or array of messages
  return { messages: response };
}

// Define the function that determines whether to continue or not
function routeModelOutput(state: typeof MessagesAnnotation.State) {
  const messages = state.messages;
  const lastMessage: AIMessage = messages[messages.length - 1];
  // If the LLM is invoking tools, route there.
  if ((lastMessage?.tool_calls?.length ?? 0) > 0) {
    return "tools";
  }
  // Otherwise end the graph.
  return "__end__";
}

// Define a new graph.
// See https://github.langchain.ac.cn/langgraphjs/how-tos/define-state/#getting-started for
// more on defining custom graph states.
const workflow = new StateGraph(MessagesAnnotation)
  // Define the two nodes we will cycle between
  .addNode("callModel", callModel)
  .addNode("tools", new ToolNode(tools))
  // Set the entrypoint as `callModel`
  // This means that this node is the first one called
  .addEdge("__start__", "callModel")
  .addConditionalEdges(
    // First, we define the edges' source node. We use `callModel`.
    // This means these are the edges taken after the `callModel` node is called.
    "callModel",
    // Next, we pass in the function that will determine the sink node(s), which
    // will be called after the source node is called.
    routeModelOutput,
    // List of the possible destinations the conditional edge can route to.
    // Required for conditional edges to properly render the graph in Studio
    ["tools", "__end__"]
  )
  // This means that after `tools` is called, `callModel` node is called next.
  .addEdge("tools", "callModel");

// Finally, we compile it!
// This compiles it into a graph you can invoke and deploy.
export const graph = workflow.compile();

将 CompiledGraph 赋给变量

LangGraph Cloud 的构建过程要求将 CompiledGraph 对象赋给 JavaScript 模块顶层的变量（或者，你可以提供一个创建图的函数）。

示例文件目录

my-app/
├── src # all project code lies within here
│   ├── utils # optional utilities for your graph
│   │   ├── tools.ts # tools for your graph
│   │   ├── nodes.ts # node functions for you graph
│   │   └── state.ts # state definition of your graph
│   └── agent.ts # code for constructing your graph
├── package.json # package dependencies
├── .env # environment variables
└── langgraph.json # configuration file for LangGraph

创建 LangGraph API 配置¶

创建名为 langgraph.json 的 LangGraph API 配置文件。有关配置文件 JSON 对象中每个键的详细解释，请参阅 LangGraph CLI 参考。

示例 langgraph.json 文件

{
  "node_version": "20",
  "dockerfile_lines": [],
  "dependencies": ["."],
  "graphs": {
    "agent": "./src/agent.ts:graph"
  },
  "env": ".env"
}

请注意，CompiledGraph 的变量名出现在顶层 graphs 键下每个子键值的末尾（即 :）。

配置位置

LangGraph API 配置文件必须放在一个目录中，该目录与包含已编译图和相关依赖的 TypeScript 文件位于同一级别或更高级别。

下一页¶

设置好项目并将其放入 github 仓库后，就可以部署你的应用了。