This PR adds automatic instrumentation for LangChain chat clients in Node SDK, we cover most used providers mentioned in https://python.langchain.com/docs/integrations/chat/.

What's added?

TLDR; a LangChain Callback Handler that:

• Creates a stateful callback handler that tracks LangChain lifecycle events
• Handles LLM/Chat Model events (start, end, error, streaming)
• Handles Chain events (start, end, error)
• Handles Tool events (start, end, error)
• Extracts and normalizes request/respo

How it works?

1. Module Patching: When a LangChain provider package is loaded (e.g., @langchain/anthropic), the instrumentation:

   • Finds the chat model class (e.g., ChatAnthropic)
   • Wraps the invoke, stream, and batch methods on the prototype
   • Uses a Proxy to intercept method calls

2. Callback Injection: When a LangChain method is called:

   • The wrapper intercepts the call
   • Augments the options.callbacks array with Sentry's callback handler
   • Calls the original method with the augmented callbacks

The integration is enabled by default when you initialize Sentry in Node.js:

import * as Sentry from '@sentry/node';
import { ChatAnthropic } from '@langchain/anthropic';
Sentry.init({
 dsn: 'your-dsn',
 tracesSampleRate: 1.0,
 sendDefaultPii: true, // Enable to record inputs/outputs
});
// LangChain calls are automatically instrumented
const model = new ChatAnthropic({
 model: 'claude-3-5-sonnet-20241022',
});
await model.invoke('What is the capital of France?');

You can configure what data is recorded:

Sentry.init({
 integrations: [
 Sentry.langChainIntegration({
 recordInputs: true, // Record prompts/messages
 recordOutputs: true, // Record responses
 })
 ],
});

Note: We need to disable integrations for AI providers that LangChain use to avoid duplicate spans, this will be handled in a follow up PR.