LangChain

pip install "langsmith[otel]" langchain \
            opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

npm install langsmith @opentelemetry/api @opentelemetry/context-async-hooks \
            @opentelemetry/sdk-trace-base @opentelemetry/exporter-trace-otlp-proto

import os
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://otel.agnost.ai/v1/traces",
            headers={"X-Agnost-Org-ID": os.environ["AGNOST_ORG_ID"]},
        )
    )
)
trace.set_tracer_provider(provider)

os.environ["LANGSMITH_OTEL_ENABLED"] = "true"
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_OTEL_ONLY"] = "true"

# Import LangChain AFTER the provider is registered.
from langchain_openai import ChatOpenAI

import { trace, SpanStatusCode } from '@opentelemetry/api';

process.env.LANGSMITH_TRACING = 'true';
process.env.LANGCHAIN_TRACING_V2 = 'true';
process.env.LANGSMITH_TRACING_MODE = 'otel';

const { initializeOTEL } = await import('langsmith/experimental/otel/setup');

initializeOTEL({
  exporterConfig: {
    url: 'https://otel.agnost.ai/v1/traces',
    headers: { 'X-Agnost-Org-ID': process.env.AGNOST_ORG_ID! },
  },
});

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello"}]},
    config={
        "metadata": {
            "session_id": "conv-abc123",
            "user_id": "user-42",
        },
    },
)

const tracer = trace.getTracer('langchain-ts');
const userMessage = 'Hello';

const result = await tracer.startActiveSpan('langchain.invoke', {
  attributes: {
    'langsmith.traceable': 'true',
    'langsmith.metadata.session_id': 'conv-abc123',
    'langsmith.metadata.user_id': 'user-42',
    'gen_ai.operation.name': 'chat',
    'gen_ai.system': 'langchain',
    'gen_ai.input.messages': JSON.stringify([{ role: 'user', content: userMessage }]),
  },
}, async (span) => {
  try {
    const result = await chain.invoke(
      { messages: [{ role: 'user', content: userMessage }] },
      {
        metadata: {
          session_id: 'conv-abc123',
          user_id: 'user-42',
        },
        tags: ['agnost', 'langchain-ts'],
      },
    );
    const answer = typeof result.content === 'string' ? result.content : JSON.stringify(result);
    span.setAttribute('gen_ai.output.messages', JSON.stringify([{ role: 'assistant', content: answer }]));
    span.setStatus({ code: SpanStatusCode.OK });
    return result;
  } catch (error) {
    span.recordException(error as Error);
    span.setStatus({ code: SpanStatusCode.ERROR, message: error instanceof Error ? error.message : String(error) });
    throw error;
  } finally {
    span.end();
  }
});