> ## 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.
# Quick Start
**Quick setup?** Instead of following these steps manually, [copy a prompt](/skills/code-prompts#instrument-my-code) into your coding agent and it will set this up for you automatically.
LangWatch helps you understand every user interaction (**Thread**), each individual AI task (**Trace**), and all the underlying steps (**Span**) involved. We've made getting started super smooth.
Let's get cracking.
First step: if you haven't already, grab your LangWatch account. Head over to [langwatch.ai](https://app.langwatch.ai), sign up, and get your API key ready.
Open a terminal and run the following command to sign in with LangWatch:
```bash theme={null}
npx langwatch login
```
This will add the `LANGWATCH_API_KEY` to your local `.env` file.
Install the [LangWatch MCP Server](/integration/mcp) and ask your coding assistant (Cursor, Claude Code, Codex, etc.) to instrument your codebase with LangWatch, OR keep following the steps below to instrument your codebase manually.
Add the LangWatch MCP to your editor:
```json theme={null}
{
"mcpServers": {
"langwatch": {
"command": "npx",
"args": ["-y", "@langwatch/mcp-server"]
}
}
}
```
Then ask your coding assistant to instrument your codebase with LangWatch:
```plaintext theme={null}
"Instrument my codebase with LangWatch"
```
We have official SDKs for Python and Node.js ready to go. If you're using another language, our [OpenTelemetry Integration Guide](/integration/opentelemetry/guide) provides the details you need.
```bash Python theme={null}
pip install langwatch
# or
uv add langwatch
```
```bash JavaScript theme={null}
npm install langwatch @vercel/otel @opentelemetry/api-logs @opentelemetry/instrumentation @opentelemetry/sdk-logs
```
Time to connect LangWatch. Initialize the SDK within your project. Here's how you can set it up:
```python Python theme={null}
import langwatch
import os
from langwatch.instrumentors import OpenAIInstrumentor
langwatch.setup(
api_key=os.getenv("LANGWATCH_API_KEY"), # Your LangWatch API key
instrumentors=[OpenAIInstrumentor()] # Add the instrumentor for your LLM
)
```
```javascript JavaScript theme={null}
// ./next.config.js - Enable the Next.js instrumentation hook
/** @type {import('next').NextConfig} */
const nextConfig = {
experimental: {
instrumentationHook: true,
},
};
module.exports = nextConfig;
// ./src/instrumentation.ts - Configure LangWatch export
import { registerOTel } from '@vercel/otel';
import { LangWatchExporter } from 'langwatch';
export function register() {
registerOTel({
serviceName: 'your-app-name', // Give your service a clear name
traceExporter: new LangWatchExporter({
apiKey: process.env.LANGWATCH_API_KEY, // Your LangWatch API key
})
});
}
// ./src/index.ts - Enable telemetry where needed
const result = await generateText({
model: openai('gpt-5'),
prompt: 'How many calories do I burn jumping to conclusions?',
experimental_telemetry: {
isEnabled: true, // Ensure telemetry is active for relevant operations
},
});
```
You're all set! Jump into your LangWatch dashboard to see your data flowing in. You'll find **Traces** (individual AI tasks) and their detailed **Spans** (the steps within), all organized into **Threads** (complete user sessions). Start exploring and use **User IDs** or custom **Labels** to dive deeper!
