ontos-observability-otel 0.1.1

One-line OpenTelemetry onboarding for the Ontos observability app: traces, cost, and the answer contract (question, answer, context, tokens).

ontos-observability-otel

One-line OpenTelemetry onboarding for the Ontos observability app. Two calls send everything the app needs to trace, cost, judge and compare your agent's answers: the question, the answer, the evidence context, the token usage and the model, tagged with your customer/environment/domain.

Install

pip install ontos-observability-otel

Use

from ontos_observability_otel import observe, record_answer

observe(customer="sigma", environment="dev", domain="my-agent",
        endpoint="https://<observability-host>:4318", token="<your ingest token>")

# after your agent answers a question:
record_answer(
    question=user_question,
    answer=agent_answer,
    context=evidence,            # what the answer was grounded on (rows, documents, SQL results)
    model="claude-haiku-4-5",
    input_tokens=usage.input_tokens,
    output_tokens=usage.output_tokens,
)

To also measure latency, wrap the work instead:

from ontos_observability_otel import answer

with answer(user_question) as rec:
    result = my_agent.ask(user_question)
    rec.record(answer=result.text, context=result.evidence, model=result.model,
               input_tokens=result.input_tokens, output_tokens=result.output_tokens)

Everything can also come from the environment, so no code changes per deployment: ONTOS_OTLP_ENDPOINT, ONTOS_TOKEN, ONTOS_CUSTOMER, ONTOS_ENVIRONMENT, ONTOS_DOMAIN, ONTOS_VARIANT (defaults to baseline), OTEL_SERVICE_NAME.

What each field buys you

• question / answer: the trace, the review queue, and the relevance judge.
• context: the groundedness judge grades the answer AGAINST this evidence; without it, hallucination cannot be measured.
• model + input_tokens / output_tokens: real cost per request and per day.
• customer / environment / domain: how the dashboard segments and filters everything. domain is yours to organize by (one value per app or agent works well).
• ontos.variant is stamped automatically (baseline). The value ontos is RESERVED for the Ontos runtime, so the Ontos-vs-baseline comparison is always trustworthy; the SDK refuses it.

Already on OpenTelemetry?

You do not need this package: keep your setup and stamp the same span attributes (ontos.question, ontos.answer, ontos.context, ontos.customer, ontos.environment, ontos.domain, gen_ai.request.model, gen_ai.usage.input_tokens, gen_ai.usage.output_tokens) on the span that carries each answer, and point your OTLP/HTTP exporter at <endpoint>/v1/traces with the bearer token.

Design

Standard OpenTelemetry, no proprietary tracer, no Ontos IP (scorers, grounding and comparison live server-side). Best-effort by contract: no endpoint or missing dependency means a no-op; the host application is never broken by telemetry.

Optional auto-instrumentation extras: ontos-observability-otel[fastapi], [flask], [django], [requests], [httpx].