Observability for AG2 with Opik

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## First we will configure the OpenTelemetry
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from opentelemetry import trace
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.resources import Resource
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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from opentelemetry.instrumentation.openai import OpenAIInstrumentor
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from opentelemetry.instrumentation.threading import ThreadingInstrumentor
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def setup_telemetry():
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    """Configure OpenTelemetry with HTTP exporter"""
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    # Create a resource with service name and other metadata
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    resource = Resource.create(
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        {
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            "service.name": "ag2-demo",
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            "service.version": "1.0.0",
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            "deployment.environment": "development",
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        }
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    )
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    # Create TracerProvider with the resource
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    provider = TracerProvider(resource=resource)
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    # Create BatchSpanProcessor with OTLPSpanExporter
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    processor = BatchSpanProcessor(OTLPSpanExporter())
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    provider.add_span_processor(processor)
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    # Set the TracerProvider
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    trace.set_tracer_provider(provider)
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    tracer = trace.get_tracer(__name__)
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    # Instrument OpenAI calls
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    OpenAIInstrumentor().instrument(tracer_provider=provider)
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    # AG2 calls OpenAI in background threads, propagate the context so all spans ends up in the same trace
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    ThreadingInstrumentor().instrument()
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    return tracer, provider
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# 1. Import our agent class
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from autogen import ConversableAgent, LLMConfig
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# 2. Define our LLM configuration for OpenAI's GPT-4o mini
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#    uses the OPENAI_API_KEY environment variable
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llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")
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# 3. Create our LLM agent within the parent span context
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with llm_config:
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    my_agent = ConversableAgent(
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        name="helpful_agent",
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        system_message="You are a poetic AI assistant, respond in rhyme.",
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    )
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def main(message):
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    response = my_agent.run(message=message, max_turns=2, user_input=True)
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    # 5. Iterate through the chat automatically with console output
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    response.process()
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    # 6. Print the chat
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    print(response.messages)
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    return response.messages
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if __name__ == "__main__":
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    tracer, provider = setup_telemetry()
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    # 4. Run the agent with a prompt
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    with tracer.start_as_current_span(my_agent.name) as agent_span:
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        message = "In one sentence, what's the big deal about AI?"
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        agent_span.set_attribute("input", message)  # Manually log the question
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        response = main(message)
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        # Manually log the response
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        agent_span.set_attribute("output", response)
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    # Force flush all spans to ensure they are exported
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    provider = trace.get_tracer_provider()
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    provider.force_flush()