如何设置 JavaScript 应用程序 - 以编程方式使用文档

应用程序必须使用配置文件才能部署到 LangSmith（或自托管）。本操作指南讨论了使用 package.json 指定项目依赖项的基本步骤。

本演示基于此仓库，您可以通过它来了解更多关于如何设置应用程序以进行部署的信息。

最终的仓库结构大致如下：

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 your 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

每一步都提供了示例文件目录，以展示如何组织代码。

指定依赖项

可以在 package.json中指定依赖项。如果未创建这些文件，则稍后可在配置文件.

示例 package.json file:

{
  "name": "langgraphjs-studio-starter",
  "packageManager": "yarn@1.22.22",
  "dependencies": {
    "@langchain/core": "^0.2.31",
    "@langchain/langgraph": "^0.2.0",
    "@langchain/openai": "^0.2.8",
    "@langchain/tavily": "^0.1.5"
  }
}

部署应用时，依赖项将使用您选择的包管理器安装，前提是它们符合以下列出的兼容版本范围：

"@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中指定（例如 .env）。请参阅环境变量参考以配置部署的其他变量。

示例 .env file:

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

实现您的图。图可以在单个文件或多个文件中定义。请记下每个要包含在应用程序中的已编译图的变量名称。变量名称将在后续创建时使用配置文件.

以下是示例 agent.ts:

const tools = [new TavilySearch({ 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-5.5",
  }).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://langchain-ai.github.io/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.

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 your 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API配置

创建一个配置文件名为 langgraph.json。请参阅配置文件参考以获取配置文件JSON对象中每个键的详细说明。

示例 langgraph.json file:

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

请注意，以下内容的变量名称 CompiledGraph 出现在顶层 graphs 键（即 :<variable_name>).

下一步

在您设置项目并将其放置在GitHub仓库中后，就该部署您的应用.