Live Traces (OTLP)

# pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http
import json, uuid
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

provider = TracerProvider(resource=Resource.create({
    "service.name": "support-agent",
    "deployment.environment.name": "production",
}))
provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(
    endpoint="https://api.getusertrace.com/v1/traces",
    headers={"x-usertrace-api-key": "ut_live_YOUR_KEY_HERE"},
)))
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("support-agent")

conversation_id = f"conv_{uuid.uuid4().hex[:8]}"
history = []

def record_turn(user_text, assistant_text, usage):
    """One turn = one span. Start it before the LLM call, end it after."""
    history.append({"role": "user", "parts": [{"type": "text", "content": user_text}]})
    with tracer.start_as_current_span("chat gpt-4o") as span:
        span.set_attribute("gen_ai.conversation.id", conversation_id)
        span.set_attribute("gen_ai.operation.name", "chat")
        span.set_attribute("gen_ai.request.model", "gpt-4o")
        span.set_attribute("gen_ai.response.model", usage["model"])
        span.set_attribute("gen_ai.usage.input_tokens", usage["input_tokens"])
        span.set_attribute("gen_ai.usage.output_tokens", usage["output_tokens"])
        span.set_attribute("gen_ai.input.messages", json.dumps(history))

        answer = {"role": "assistant",
                  "parts": [{"type": "text", "content": assistant_text}],
                  "finish_reason": "stop"}
        span.set_attribute("gen_ai.output.messages", json.dumps([answer]))
        history.append(answer)

// npm i @opentelemetry/sdk-node @opentelemetry/exporter-trace-otlp-http \
//       @opentelemetry/api @opentelemetry/resources
import { trace } from '@opentelemetry/api';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { resourceFromAttributes } from '@opentelemetry/resources';
import { NodeSDK } from '@opentelemetry/sdk-node';

const sdk = new NodeSDK({
  resource: resourceFromAttributes({
    'service.name': 'support-agent',
    'deployment.environment.name': 'production',
  }),
  traceExporter: new OTLPTraceExporter({
    url: 'https://api.getusertrace.com/v1/traces',
    headers: { 'x-usertrace-api-key': 'ut_live_YOUR_KEY_HERE' },
  }),
});
sdk.start();

const tracer = trace.getTracer('support-agent');
const conversationId = `conv_${crypto.randomUUID().slice(0, 8)}`;
const history: unknown[] = [];

// One turn = one span. Start it before the LLM call, end it after.
export async function recordTurn(userText: string, call: () => Promise<{
  text: string; model: string; inputTokens: number; outputTokens: number;
}>) {
  history.push({ role: 'user', parts: [{ type: 'text', content: userText }] });

  return tracer.startActiveSpan('chat gpt-4o', async (span) => {
    span.setAttributes({
      'gen_ai.conversation.id': conversationId,
      'gen_ai.operation.name': 'chat',
      'gen_ai.request.model': 'gpt-4o',
      'gen_ai.input.messages': JSON.stringify(history),
    });

    const res = await call();
    const answer = {
      role: 'assistant',
      parts: [{ type: 'text', content: res.text }],
      finish_reason: 'stop',
    };
    history.push(answer);

    span.setAttributes({
      'gen_ai.response.model': res.model,
      'gen_ai.usage.input_tokens': res.inputTokens,
      'gen_ai.usage.output_tokens': res.outputTokens,
      'gen_ai.output.messages': JSON.stringify([answer]),
    });
    span.end();
    return res;
  });
}

curl -X POST "https://api.getusertrace.com/v1/traces" \
  -H "x-usertrace-api-key: ut_live_YOUR_KEY_HERE" \
  -H "Content-Type: application/json" \
  -d '{
    "resourceSpans": [{
      "resource": {"attributes": [
        {"key": "service.name", "value": {"stringValue": "support-agent"}}
      ]},
      "scopeSpans": [{
        "spans": [{
          "traceId": "4b1e9a7c3d2f48619ae05c7b1f83d240",
          "spanId": "a1b2c3d4e5f60718",
          "name": "chat gpt-4o",
          "startTimeUnixNano": "1789034400000000000",
          "endTimeUnixNano": "1789034402150000000",
          "attributes": [
            {"key": "gen_ai.conversation.id", "value": {"stringValue": "conv_8f14c2"}},
            {"key": "gen_ai.operation.name", "value": {"stringValue": "chat"}},
            {"key": "gen_ai.input.messages", "value": {"stringValue":
              "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"Where is my order A-4471?\"}]}]"}},
            {"key": "gen_ai.output.messages", "value": {"stringValue":
              "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"It shipped on 8 Sep.\"}],\"finish_reason\":\"stop\"}]"}}
          ]
        }]
      }]
    }]
  }'