OpenLLMetry (Standalone Instrumentors)

# Standalone usage — no traceloop-sdk required
# Requires only: opentelemetry-sdk + opentelemetry-exporter-otlp + individual instrumentors

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

# 1. Set up TracerProvider and exporter (any OTLP backend)
tracer_provider = TracerProvider()
exporter = OTLPSpanExporter(
    endpoint='http://localhost:4318/v1/traces',  # Jaeger, Grafana, Phoenix, Datadog, etc.
)
tracer_provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(tracer_provider)

# 2. Instrument providers — must call before importing/using the library
from opentelemetry.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.instrumentation.langchain import LangChainInstrumentor

OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)

# 3. Use your libraries normally — spans are captured automatically
import openai
client = openai.OpenAI(api_key=os.environ['OPENAI_API_KEY'])
response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'Hello!'}]
)

# To suppress prompt/completion content from traces (PII/privacy):
os.environ['TRACELOOP_TRACE_CONTENT'] = 'false'
# Must be set before .instrument() is called

# Using with traceloop-sdk (alternative — SDK manages the provider):
# from traceloop.sdk import Traceloop
# Traceloop.init(app_name='my-app')  # handles all of the above automatically