Vercel AI SDK

Documentation Index

Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt

Use this file to discover all available pages before exploring further.

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Prerequisites

• Obtain your LANGWATCH_API_KEY from the LangWatch dashboard.

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Installation

npm install langwatch

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Configuration

LANGWATCH_API_KEY='your_api_key_here'

import { LangWatch } from 'langwatch';

const langwatch = new LangWatch({
  apiKey: 'your_api_key_here',
});

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Basic Concepts

• Each message triggering your LLM pipeline as a whole is captured with a Trace.
• A Trace contains multiple Spans, which are the steps inside your pipeline.
  • A span can be an LLM call, a database query for a RAG retrieval, or a simple function transformation.
  • Different types of Spans capture different parameters.
  • Spans can be nested to capture the pipeline structure.
• Traces can be grouped together on LangWatch Dashboard by having the same thread_id in their metadata, making the individual messages become part of a conversation.
  • It is also recommended to provide the user_id metadata to track user analytics.

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Installation

npm i langwatch ai @ai-sdk/openai

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Usage

import { setupObservability } from "langwatch/observability/node";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

setupObservability({ serviceName: "<project_name>" });

async function main(message: string): Promise<string> {
  const response = await generateText({
    model: openai("gpt-5-mini"),
    prompt: message,
    experimental_telemetry: { isEnabled: true },
  });
  return response.text;
}

console.log(await main("Hey, tell me a joke"));

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Related

• Capturing RAG - Learn how to capture RAG data from retrievers and tools
• Capturing Metadata and Attributes - Add custom metadata and attributes to your traces and spans
• Capturing Evaluations & Guardrails - Log evaluations and implement guardrails in your Vercel AI SDK applications