span()

@contextmanager
def span(
    name: str,
    span_type: SpanType = SpanType.CHAIN,
    input_data: dict | None = None,
    metadata: dict | None = None,
) -> Generator[Span, None, None]

from rdk import observe, span

@observe()
def pipeline(query: str):
    with span("vector-search", input_data={"query": query}) as s:
        results = my_vector_db.search(query)
        s.metadata["result_count"] = len(results)
    return results

from rdk import observe, span
from rdk.models import SpanType

@observe()
def agent_step(tool_name: str, args: dict) -> str:
    with span(f"tool.{tool_name}", span_type=SpanType.TOOL, input_data=args) as s:
        result = execute_tool(tool_name, args)
        s.metadata["output"] = result
    return result

from rdk import observe, span
from rdk.models import SpanType

@observe()
def lookup_customer(email: str) -> dict:
    with span("db.customers.find", span_type=SpanType.FUNCTION, input_data={"email": email}) as s:
        customer = db.find_by_email(email)
        s.metadata["found"] = customer is not None
    return customer

from rdk import observe, span
from rdk.models import SpanType
from anthropic import Anthropic

@observe(name="customer-lookup")
def lookup_and_summarize(email: str) -> dict:
    # Manual span for DB
    with span("db.query", span_type=SpanType.FUNCTION, input_data={"email": email}) as s:
        customer = db.find_by_email(email)
        s.metadata["found"] = customer is not None

    # LLM call is auto-traced
    client = Anthropic()
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=256,
        messages=[{"role": "user", "content": f"Summarize: {customer}"}]
    )
    return response.content[0].text