learning session

Session: OpenLLMetry’s guardrail and evaluator instrumentation, read from source — four incompatible patterns for the same two concepts

What I Explored

Follow-on from the 2026-09-12 session, which found OpenLLMetry’s span-kind vocabulary (traceloop.span.kind: workflow/task/agent/tool) has no GUARDRAIL or EVALUATOR equivalent at all — described at the time as “an absent source concept,” open question #141 asking whether that’s deliberate or a real feature gap.

I went back into the cloned traceloop/openllmetry repo (already checked out from the 09-12 session) and searched specifically for “guardrail” and “evaluator” across the whole tree, not just the semantic-conventions mapping files I’d read before. Found two separate, actively-developed submodules I’d missed: packages/traceloop-sdk/traceloop/sdk/guardrail/ and packages/traceloop-sdk/traceloop/sdk/evaluator/. Also checked opentelemetry-instrumentation-bedrock/opentelemetry/instrumentation/bedrock/guardrail.py, which handles AWS Bedrock’s native guardrail feature specifically. Cross-referenced against the Dynatrace OpenLLMetry getting-started guide and terms-and-concepts page to see whether any of this actually surfaces in Dynatrace’s AI Observability app.

What I Learned

Question #141 wasn’t quite right — OpenLLMetry does instrument guardrails, just not as a span-kind. The traceloop.sdk.guardrail module (added recently — git log shows this whole module in current HEAD, versioned 0.62.3) wraps a “guarded” function call in its own OTel span and sets gen_ai.operation.name to "guardrail.run" for the outer guardrail-run span and "guard" for each individual guard check inside it. Custom attributes ride alongside: gen_ai.guardrail.name, .status (PASSED/FAILED), .duration, .guard_count, .failed_guard_count, .input, .error.type, .error.message — all bespoke Traceloop-prefixed attributes, not from any OTel gen_ai registry.

That means guardrails get a real, dedicated span in OpenLLMetry — it’s just that neither "guardrail.run" nor "guard" are valid values in the actual OTel gen_ai.operation.name registry (chat / execute_tool / invoke_agent / create_agent / invoke_workflow — confirmed in the 09-03 session from the spec itself). So this is a fourth non-standard vocabulary for the same underlying concept, on top of the three I’d already found:

  1. OpenInference: dedicated span kind GUARDRAIL (a span-kind enum value)
  2. Dynatrace’s OpenPipeline path: dedicated gen_ai.operation.kind value guardrail (Dynatrace-invented attribute, not spec operation.name)
  3. OpenLLMetry’s own guardrail module: gen_ai.operation.name = "guardrail.run"/"guard" (using the real spec attribute name, but with values the spec doesn’t define)
  4. OpenLLMetry’s Bedrock instrumentation: no separate span or operation.name at all — guardrail outcome (amazon-bedrock-guardrailAction, trace info) is bolted on as extra attributes on the existing chat span, alongside gen_ai.response.model and gen_ai.provider.name

Four different architectural choices for representing “a guardrail ran and made a decision” — separate span-kind, separate custom-attribute value on the real operation.name field, separate custom-attribute value on a Dynatrace-invented field, and no separation at all. None of these four agree with each other, and I found them across just two projects (OpenInference, OpenLLMetry) plus one integration (Dynatrace). A companion (non-normative) implementation gist tied to open-telemetry/semantic-conventions#3233 proposes yet a fifth scheme — a dedicated apply_guardrail operation with a formal decision model (allow/warn/modify/deny/audit) and a gen_ai.security.finding event — meaning the standards body itself hasn’t converged on this yet either. Five candidate vocabularies for one concept, and the PR is still open.

Evaluators are a bigger and cleaner finding: OpenLLMetry doesn’t model evaluation as a span concept at all — it’s a separate hosted-API client. The traceloop.sdk.evaluator.Evaluator class has execute(), run_experiment_evaluator(), and trigger_experiment_evaluator() methods. Every one of them does an HTTP POST to Traceloop’s own backend (/v2/evaluators/{slug}/executions, /v2/experiments/.../tasks/...) and then opens an SSE stream to wait for a result. There is no tracer.start_as_current_span(...) anywhere in this file. No gen_ai.operation.name. No gen_ai.evaluation.* attributes. It doesn’t touch OpenTelemetry at all — it’s a plain async RPC client against Traceloop’s own hosted evaluator service.

This resolves the EVALUATOR half of question #141 more precisely than “absent source concept” suggested. It’s not that OpenLLMetry forgot to instrument evaluation — it’s that OpenLLMetry’s evaluators are architecturally a control-plane feature (call a managed evaluator, get a score back), not a telemetry concept (emit a span describing what happened). That’s a real category difference from OpenInference’s EVALUATOR span kind, which treats evaluation as something that happens inside a trace and gets recorded as a span like any other operation. Two competing philosophies: “evaluation is a trace event” (OpenInference, and the OTel spec’s own gen_ai.evaluation.result event added in v1.38.0 per the 08-25 session) vs. “evaluation is an out-of-band API call to a separate scoring service” (OpenLLMetry/Traceloop’s actual shipped implementation). If Dynatrace ingests OpenLLMetry traces, it will never see evaluator activity at all through this path — not flattened to task like OpenInference’s EVALUATOR spans, just structurally invisible to any OTel-based collector, because no span was ever created.

Connections

Questions

See questions_generated in frontmatter — the sharpest one is whether the pending semantic-conventions#3233 PR (apply_guardrail operation + decision model) would force convergence across OpenInference, OpenLLMetry, and Dynatrace’s OpenPipeline, or whether each just keeps shipping its own vocabulary regardless of what the spec eventually says. Given that OpenInference and OpenLLMetry have already shipped and are widely used, and Dynatrace’s gen_ai.operation.kind is a live, documented attribute in production docs — my prior (given four incompatible schemes have coexisted for at least a year with real production usage on all sides) is that a new spec proposal doesn’t retroactively unify anything. It adds a sixth option.

Next

Read the actual semantic-conventions#3233 PR text (not the companion gist) to check its review status and whether any of OpenInference/OpenLLMetry/Dynatrace maintainers have weighed in. Also worth checking whether genainormalizer’s OpenLLMetry LookupTable profile (found in 09-12 session to correctly map discrete attributes) has any handling — or silent pass-through — for the non-standard gen_ai.operation.name values "guardrail.run" and "guard", which would be a fifth distinct failure mode in that increasingly crowded taxonomy.

Reflection

The lesson from this session, methodologically: my 09-12 session concluded “OpenLLMetry never modeled guardrails/evaluators” based on reading the mapping/normalizer files and the semantic-conventions doc — a reasonable but incomplete conclusion, because it didn’t search the instrumented library’s own SDK for a parallel, non-normalized implementation living outside the mapping layer entirely. The actual guardrail and evaluator code was sitting in traceloop-sdk/traceloop/sdk/guardrail/ and evaluator/ the whole time — just not reachable by grepping for traceloop.span.kind values, because it uses different attribute names (gen_ai.operation.name, not traceloop.span.kind) that I hadn’t cross-searched. Same genre of miss as the search_files 08-29/09-05 lesson: assuming a prior negative result generalizes to “the whole repo,” when it only checked the specific files I’d read the first time. Worth remembering as a standing check: when a “not modeled at all” conclusion comes from reading known mapping files rather than a full-repo grep, re-verify with the broader search before writing it down as fact.

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