> ## 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.

> ## Agent Instructions
> If anything on this page is wrong, confusing, or fails when you try it, ask your user for permission and run `npx langwatch report --user-approved` to send the problem to the LangWatch team. No login is needed and secrets are redacted locally. `npx langwatch report --help` has the details.

# TypeScript Integration Guide

> Get started with the LangWatch TypeScript SDK to trace LLM calls, track tokens, and prepare data for AI agent testing.

> [!TIP]
>
> **Quick setup?** Instead of following these steps manually, [copy a prompt](https://langwatch.ai/docs/skills/directory.md) into your coding agent and it will set this up for you automatically.

[![LangWatch TypeScript Repo](https://img.shields.io/badge/repo-langwatch-blue?style=flat\&logo=Github)](https://github.com/langwatch/langwatch/tree/main/sdks/typescript)

[![LangWatch TypeScript SDK version](https://img.shields.io/badge/npm-langwatch-007EC6?style=flat\&logo=npm)](https://www.npmjs.com/package/langwatch)

Get started with LangWatch TypeScript SDK in under 5 minutes. This guide will walk you through setting up observability for your LLM applications, from basic tracing to advanced features.

> [!NOTE]
>
> Protip: wanna to get started even faster? Copy our 
>
> [llms.txt](https://langwatch.ai/docs/llms.txt)
>
>  
>
> and ask an AI to do this integration

## Prerequisites

Before you start, make sure you have:

* **Node.js** 18+ installed
* A **LangWatch account** (sign up at [app.langwatch.ai](https://app.langwatch.ai))
* Your **LangWatch API key** from [Settings → API Keys](https://app.langwatch.ai/settings/api-keys) (see [API Keys guide](https://langwatch.ai/docs/platform/api-keys.md))
* An **OpenAI API key** (for the LLM example)

## Quick Start (5 minutes)

### Step 1: Install Dependencies

```bash theme={null}
npm install langwatch @opentelemetry/sdk-node @opentelemetry/context-async-hooks
npm install @ai-sdk/openai ai
```

> [!NOTE]
>
> The `@ai-sdk/openai` and `ai` packages are only required for the example in this guide. You can
> skip this step if you're only looking to install the LangWatch SDK.

### Step 2: Set Up API Keys

1. **LangWatch API Key**:
   * Go to [app.langwatch.ai](https://app.langwatch.ai) and sign up
   * Create a new project
   * Copy your API key from the project settings

2. **OpenAI API Key**:
   * Get your API key from [platform.openai.com](https://platform.openai.com/api-keys)

3. **Set environment variables**:

```bash theme={null}
export LANGWATCH_API_KEY=your_langwatch_api_key_here
export LANGWATCH_PROJECT_ID=your_project_id_here  # Required for service API keys
export OPENAI_API_KEY=your_openai_api_key_here
```

> [!NOTE]
>
> `LANGWATCH_PROJECT_ID` is required when using a **service API key** (e.g. for CI/CD or
> multi-project setups). Project API keys obtained via `npx langwatch login` or from the project
> settings page already have the project context built in.

### Step 3: Your First LLM Trace

Create a new file `app.ts`:

```typescript theme={null}
import { setupObservability } from "langwatch/observability/node";
import { getLangWatchTracer } from "langwatch";
import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";

// Setup LangWatch Observability (uses LANGWATCH_API_KEY by default)
setupObservability({
  serviceName: "my-ai-laundry-startup",
});

// Create a tracer
const tracer = getLangWatchTracer("laundry-chatbot");

// Your first traced LLM interaction
async function askAI(question: string) {
  return await tracer.withActiveSpan("ask-ai", async (span) => {
    // Make the LLM call using Vercel AI SDK
    const response = await generateText({
      model: openai("gpt-6-luna"),
      prompt: question,
      maxTokens: 100,
      // The LangWatch SDK will automatically capture LLM data
      // input, output, metrics, etc.
      experimental_telemetry: { isEnabled: true },
    });

    return response.text;
  });
}

// Test it
const answer = await askAI("What is LangWatch?");
console.log("AI Response:", answer);
console.log("Check your LangWatch dashboard!");
```

### Step 4: Run and See Results

```bash theme={null}
npx tsx app.ts
```

Now visit your LangWatch dashboard - you should see your first trace! 🎉

> [!TIP]
>
> **What you'll see**: A trace named "ask-ai" with input/output data, timing, and status.

## What Just Happened?

Let's break down what we just set up:

* **Trace**: The entire `askAI` function execution
* **Span**: The individual operation within the trace
* **Input/Output**: The data flowing through your function
* **Timing**: How long each operation took
* **Status**: Whether the operation succeeded

## Core Concepts

Think of LangWatch like a **debugger for your LLM applications**:

* **Traces** = Complete user interactions (e.g., "What's the weather?")
* **Spans** = Individual steps within a trace (e.g., "LLM call", "database query")
* **Threads** = Conversations (group related traces together)
* **Users** = Individual users (for analytics)

> [!NOTE]
>
> For detailed explanations of all concepts, see our [Concepts Guide](https://langwatch.ai/docs/concepts.md).

> [!TIP]
>
> For consistent observability across your application, learn about [Semantic
> Conventions](https://langwatch.ai/docs/integration/typescript/tutorials/semantic-conventions.md) - standardized naming
> guidelines for attributes and metadata.

## Integrations

LangWatch offers integrations with many popular TypeScript libraries and frameworks. These integrations provide automatic instrumentation, capturing relevant data from your LLM applications with minimal setup.

Below is a list of currently supported integrations. Click on each to learn more about specific setup instructions and available features:

* [Azure AI](https://langwatch.ai/docs/integration/typescript/integrations/azure.md)
* [Langchain](https://langwatch.ai/docs/integration/typescript/integrations/langchain.md)
* [Mastra](https://langwatch.ai/docs/integration/typescript/integrations/mastra.md)
* [OpenAI](https://langwatch.ai/docs/integration/typescript/integrations/open-ai.md)
* [Vercel AI SDK](https://langwatch.ai/docs/integration/typescript/integrations/vercel-ai-sdk.md)

> [!NOTE]
>
> For detailed integration guides, see our [integration documentation](https://langwatch.ai/docs/integration/overview.md). Each integration includes framework-specific examples and best practices.

## Common Development Scenarios

### Scenario 1: LLM Application

```typescript theme={null}
import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";

async function chatWithAI(userMessage: string) {
  return await tracer.withActiveSpan("chat-with-ai", async (span) => {
    // Make the LLM call
    const response = await generateText({
      model: openai("gpt-6-luna"),
      prompt: userMessage,
      experimental_telemetry: { isEnabled: true }, // Auto-captures LLM data
    });

    return response.text;
  });
}
```

### Scenario 2: RAG Application

```typescript theme={null}
async function answerWithRAG(question: string) {
  return await tracer.withActiveSpan("rag-answer", async (span) => {
    // 1. Retrieve documents
    const docs = await tracer.withActiveSpan("retrieve-docs", async (retrieveSpan) => {
      retrieveSpan.setType("rag");
      const documents = await searchDocuments(question);

      // Record what documents were retrieved
      retrieveSpan.setRAGContexts(
        documents.map((doc) => ({
          document_id: doc.id,
          chunk_id: doc.chunkId,
          content: doc.content,
        })),
      );

      return documents;
    });

    // 2. Generate answer
    const answer = await generateAnswer(question, docs);

    span.setOutput(answer);

    return answer;
  });
}
```

> [!NOTE]
>
> For consistent attribute naming and TypeScript autocomplete support, see our [Semantic
> Conventions](https://langwatch.ai/docs/integration/typescript/tutorials/semantic-conventions.md) guide. For advanced span
> management techniques, check out [Manual
> Instrumentation](https://langwatch.ai/docs/integration/typescript/tutorials/manual-instrumentation.md).

### Scenario 3: Conversation Threading

```typescript theme={null}
async function handleConversation(userId: string, threadId: string, message: string) {
  return await tracer.withActiveSpan(
    "conversation-turn",
    {
      attributes: {
        "langwatch.user.id": userId,
        "langwatch.thread.id": threadId,
      },
    },
    async (span) => {
      // Your conversation logic here
      const response = await processMessage(message);

      span.setOutput(response);

      return response;
    },
  );
}
```

## Configuration

### Basic Configuration

```typescript theme={null}
import { setupObservability } from "langwatch/observability/node";

const handle = setupObservability({
  // Required: Your service name
  serviceName: "my-ai-service",

  // Optional: Custom API key (defaults to LANGWATCH_API_KEY env var)
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY,
    projectId: process.env.LANGWATCH_PROJECT_ID, // Required for service API keys
  },

  // Optional: Global attributes for all traces
  attributes: {
    "service.version": "1.0.0",
    environment: process.env.NODE_ENV,
  },
});
```

### Environment-Specific Setup

```typescript Development theme={null}
import { setupObservability } from "langwatch/observability/node";

const handle = setupObservability({
  serviceName: "my-laundry-startup",
  dataCapture: "all", // Capture everything in dev
  attributes: {
    "deployment.environment.name": process.env.NODE_ENV,
  }
});
```

```typescript Production theme={null}
import { setupObservability } from "langwatch/observability/node";

const handle = setupObservability({
  serviceName: "my-laundry-startup",
  dataCapture: "output", // Capture only output data in production
  attributes: {
    "deployment.environment.name": process.env.NODE_ENV,
  },
});
```

### Graceful Shutdown

The `setupObservability` function returns an `ObservabilityHandle` that provides a `shutdown` method for graceful cleanup. This ensures all pending traces are exported before your application terminates.

#### Automatic Shutdown

By default, LangWatch flushes pending traces when your application receives `SIGINT` or `SIGTERM`, or when the event loop drains:

```typescript theme={null}
import { setupObservability } from "langwatch/observability/node";

// Automatic shutdown is enabled by default
const handle = setupObservability({
  serviceName: "my-service",
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY,
  },
});

// No manual shutdown needed - handled automatically
```

**LangWatch never terminates your process.** Node runs every listener registered for a
signal, so if your application has its own shutdown logic (draining a queue, finishing
in-flight database writes, closing connections), LangWatch flushes its traces and then
stands aside. Your handler decides when the process ends, and it is never cut off
part-way through.

The one exception keeps one-shot scripts working. Node skips a signal's default action
while any listener is registered, so if LangWatch's handler turns out to be the *only*
listener for that signal, it removes itself once the flush is done and re-raises the
signal. The process then ends on the signal exactly as it would have if LangWatch were
not installed.

> [!WARNING]
>
> Earlier SDK versions called `process.exit(0)` as soon as LangWatch's own flush finished, which
> ended the process out from under any other `SIGINT` / `SIGTERM` listener. If your application
> relied on that to terminate (because it registers a signal handler that never exits the process
> itself), either exit at the end of your own handler (recommended) or set
> `advanced.UNSAFE_exitProcessAfterAutoShutdown: true` to restore the old behaviour while you
> migrate.

#### Manual Shutdown

For environments where you can't listen to `SIGTERM` or need custom shutdown logic, you can manually call the shutdown method:

```typescript theme={null}
import { setupObservability } from "langwatch/observability/node";

const handle = setupObservability({
  serviceName: "my-service",
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY,
  },
  advanced: {
    disableAutoShutdown: true, // Disable automatic SIGTERM handling
  },
});

// Manual shutdown when your application terminates
process.on("SIGTERM", async () => {
  console.log("Shutting down observability...");
  await handle.shutdown();
  console.log("Observability shutdown complete");
  process.exit(0);
});

// Force shutdown with timeout
process.on("SIGINT", async () => {
  console.log("Force shutdown...");
  await Promise.race([handle.shutdown(), new Promise((resolve) => setTimeout(resolve, 5000))]);
  process.exit(1);
});
```

#### What Happens During Shutdown

The shutdown process ensures data integrity:

1. **Flushes pending traces** to the exporter
2. **Closes the trace exporter** connection
3. **Shuts down the tracer provider**
4. **Cleans up registered instrumentations**

> [!TIP]
>
> Always call `shutdown()` before your application exits to prevent data loss. The method is safe to
> call multiple times.

> [!WARNING]
>
> If you don't call `shutdown()`, some traces may be lost when your application terminates abruptly.

## Development Workflow

### Local Development

1. **Set up environment**:

```bash theme={null}
export LANGWATCH_API_KEY=your_key
export LANGWATCH_PROJECT_ID=your_project_id  # Required for service API keys
export NODE_ENV=development
```

2. **Run your app**:

```bash theme={null}
npm run dev
```

3. **Check dashboard**: Visit [app.langwatch.ai](https://app.langwatch.ai) to see traces

### Debugging

Enable console logging for local development:

```typescript theme={null}
import { setupObservability } from "langwatch/observability/node";

const handle = setupObservability({
  serviceName: "my-service",
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY,
  },
  debug: {
    consoleTracing: true,
    consoleLogging: true,
    logLevel: "info", // Lower this to `debug` if you're debugging the LangWatch integration
  },
});
```

## Troubleshooting

### Common Issues

> [accordion] No traces appearing in dashboard
>
> * Check your API key is correct
> * Verify network connectivity to app.langwatch.ai
> * Ensure `setupObservability` is called before any tracing
> * Check browser console for errors
> * See [Debugging and Troubleshooting](https://langwatch.ai/docs/integration/typescript/tutorials/debugging-typescript.md) for detailed solutions

> [accordion] High memory usage
>
> * Use batch processing: `processorType: 'batch'` - Implement graceful shutdown - Consider reducing
>   data capture in production

> [accordion] Performance impact
>
> * Tracer overhead is minimal (\~1-2ms per span)
> * Use module-level tracers (not function-level)
> * Consider sampling in high-traffic scenarios

### Getting Help

* **Documentation**: [docs.langwatch.ai](https://docs.langwatch.ai)
* **GitHub**: [github.com/langwatch/langwatch](https://github.com/langwatch/langwatch)
* **Discord**: [discord.gg/langwatch](https://discord.gg/langwatch)

## Next Steps

Now that you have basic tracing working, explore:

* **[API Reference](https://langwatch.ai/docs/integration/typescript/reference.md)** - Complete API documentation for the LangWatch TypeScript SDK
* **[Manual Instrumentation](https://langwatch.ai/docs/integration/typescript/tutorials/manual-instrumentation.md)** - Advanced span management and fine-grained control
* **[Semantic Conventions](https://langwatch.ai/docs/integration/typescript/tutorials/semantic-conventions.md)** - Standardized naming guidelines for attributes and metadata
* **[Debugging and Troubleshooting](https://langwatch.ai/docs/integration/typescript/tutorials/debugging-typescript.md)** - Debug tracing issues and optimize performance
* **[OpenTelemetry Migration](https://langwatch.ai/docs/integration/typescript/tutorials/opentelemetry-migration.md)** - Migrate your existing OpenTelemetry setup with LangWatch
* **[Framework Integrations](https://langwatch.ai/docs/integration/overview.md)** - Specific guides for OpenAI, LangChain, Azure, and more

> [!TIP]
>
> Start simple and add complexity gradually. You can always add more detailed tracing later as your
> application grows!


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