Span Attribute Reference

import json
import os

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
    OTLPSpanExporter,
)
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.semconv.attributes.service_attributes import SERVICE_NAME
from opentelemetry.trace.status import Status, StatusCode

provider = TracerProvider(
    resource=Resource.create({SERVICE_NAME: "my-agent"}),
)
provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://api.lmnr.ai:8443/v1/traces",
            headers={
                "authorization": f"Bearer {os.environ['LMNR_PROJECT_API_KEY']}",
            },
        ),
    ),
)
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("my-agent", "0.1.0")

with tracer.start_as_current_span(
    "agent.run",
    attributes={
        "lmnr.span.type": "DEFAULT",
        "lmnr.span.input": json.dumps({"goal": "book a flight to NYC"}),
        "lmnr.association.properties.session_id": "sess-9f21",
        "lmnr.association.properties.user_id": "u_42",
        "lmnr.association.properties.tags": ["beta", "internal"],
        "lmnr.association.properties.metadata.environment": "production",
        "lmnr.association.properties.metadata.region": "us-west",
    },
) as root:
    with tracer.start_as_current_span(
        "llm.chat",
        attributes={
            "lmnr.span.type": "LLM",
            "gen_ai.system": "openai",
            "gen_ai.request.model": "gpt-5-mini",
            "gen_ai.input.messages": json.dumps([
                {
                    "role": "user",
                    "parts": [
                        {"type": "text", "content": "Find me a flight to NYC tomorrow."},
                    ],
                },
            ]),
        },
    ) as llm:
        # ... call your provider, capture the response ...
        llm.set_attributes({
            "gen_ai.response.model": "gpt-5-mini-2025-04-01",
            "gen_ai.usage.input_tokens": 18,
            "gen_ai.usage.output_tokens": 42,
            "gen_ai.output.messages": json.dumps([
                {
                    "role": "assistant",
                    "parts": [{"type": "text", "content": "I found 3 flights..."}],
                },
            ]),
            "lmnr.span.output": json.dumps(
                {"flights": [{"id": "AA101"}, {"id": "DL202"}]},
            ),
        })
        llm.set_status(Status(StatusCode.OK))

    with tracer.start_as_current_span(
        "search_flights",
        attributes={
            "lmnr.span.type": "TOOL",
            "lmnr.span.input": json.dumps({
                "origin": "SFO",
                "destination": "JFK",
                "date": "2026-05-19",
            }),
        },
    ) as tool:
        # ... call your tool ...
        tool.set_attribute(
            "lmnr.span.output",
            json.dumps([{"id": "AA101", "price": 412.5}]),
        )
        tool.set_status(Status(StatusCode.OK))

    root.set_status(Status(StatusCode.OK))

provider.force_flush()