无需设置环境变量即可追踪 - 以编程方式使用文档

以下环境变量允许您配置追踪启用、API端点、API密钥和追踪项目：

- LANGSMITH_TRACING
- LANGSMITH_API_KEY
- LANGSMITH_ENDPOINT
- LANGSMITH_PROJECT

如果您需要使用自定义配置追踪运行，在不支持典型环境变量的环境中工作（如Cloudflare Workers），或不愿依赖环境变量，LangSmith允许您以编程方式配置追踪。

- Python：在Python中实现这一目标的推荐方式是使用 tracing_context 上下文管理器。这适用于使用 traceable 装饰器注释的代码以及 trace 上下文管理器中的代码。
- TypeScript：您可以同时传递客户端和 tracingEnabled 标志到 traceable decorator.

from langsmith import Client, tracing_context, traceable
from langsmith.wrappers import wrap_openai

langsmith_client = Client(
  api_key="YOUR_LANGSMITH_API_KEY",  # This can be retrieved from a secrets manager
  api_url="https://api.smith.langchain.com",  # Update appropriately for self-hosted installations or regional SaaS
  workspace_id="YOUR_WORKSPACE_ID", # Must be specified for API keys scoped to multiple workspaces
)

client = wrap_openai(openai.Client())

@traceable(run_type="tool", name="Retrieve Context")
def my_tool(question: str) -> str:
  return "During this morning's meeting, we solved all world conflict."

@traceable
def chat_pipeline(question: str):
  context = my_tool(question)
  messages = [
      { "role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
      { "role": "user", "content": f"Question: {question}\nContext: {context}"}
  ]
  chat_completion = client.chat.completions.create(
      model="gpt-5.4-mini", messages=messages
  )
  return chat_completion.choices[0].message.content

# Can set to False to disable tracing here without changing code structure
with tracing_context(enabled=True):
  # Use langsmith_extra to pass in a custom client
  chat_pipeline("Can you summarize this morning's meetings?", langsmith_extra={"client": langsmith_client})

const client = new Client({
    apiKey: "YOUR_API_KEY",  // This can be retrieved from a secrets manager
    apiUrl: "https://api.smith.langchain.com",  // Update appropriately for self-hosted installations or regional SaaS
});

const openai = wrapOpenAI(new OpenAI());

const tool = traceable((question: string) => {
    return "During this morning's meeting, we solved all world conflict.";
}, { name: "Retrieve Context", runType: "tool" });

const pipeline = traceable(
    async (question: string) => {
        const context = await tool(question);

        const completion = await openai.chat.completions.create({
            model: "gpt-5.4-mini",
            messages: [
                { role: "system" as const, content: "You are a helpful assistant. Please respond to the user's request only based on the given context." },
                { role: "user" as const, content: `Question: ${question}\nContext: ${context}`}
            ]
        });

        return completion.choices[0].message.content;
    },
    { name: "Chat", client, tracingEnabled: true }
);

await pipeline("Can you summarize this morning's meetings?");

如果您偏好视频教程，请查看追踪替代方法视频来自LangSmith入门课程。

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