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Merged
max-deygin-traceloop merged 19 commits into from
Mar 30, 2026
Merged

feat(bedrock): Instrumentation adjustment for Otel GenAI semconv support #3845

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b5db09d
feat(bedrock): migrate to semconv 0.5.0 gen_ai conventions
max-deygin-traceloop Mar 23, 2026
1e07d43
feat(bedrock): OTel GenAI spec compliance — parts format, finish reas…
max-deygin-traceloop Mar 24, 2026
ecbee73
fix(bedrock): OTel GenAI semconv compliance - round 1
max-deygin-traceloop Mar 29, 2026
3ab36a7
fix(bedrock): round 2 OTel semconv compliance fixes
max-deygin-traceloop Mar 29, 2026
296df5e
fixed bedrock sample app
max-deygin-traceloop Mar 29, 2026
708830b
lint
max-deygin-traceloop Mar 29, 2026
c9af54f
CR#2
max-deygin-traceloop Mar 29, 2026
f2179c2
CR#3
max-deygin-traceloop Mar 30, 2026
38f31c7
Review #4
max-deygin-traceloop Mar 30, 2026
bce5154
lint
max-deygin-traceloop Mar 30, 2026
b600236
CR#5
max-deygin-traceloop Mar 30, 2026
26c4a8f
CR#6
max-deygin-traceloop Mar 30, 2026
e4c583d
fixed reasonong blocks streaming
max-deygin-traceloop Mar 30, 2026
2544f23
lint fix
max-deygin-traceloop Mar 30, 2026
cf6ccd3
fix thinking_delta blocks, Amazon Nova output
max-deygin-traceloop Mar 30, 2026
c0c8fa2
Fix tool Use blobs
max-deygin-traceloop Mar 30, 2026
f1de028
fix tests
max-deygin-traceloop Mar 30, 2026
35018d8
fix anthropic span athributes GEN_AI_OPERATION_NAME
max-deygin-traceloop Mar 30, 2026
022b175
chore(bedrock): revert uv.lock to main
max-deygin-traceloop Mar 30, 2026
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145 changes: 115 additions & 30 deletions ...s/opentelemetry-instrumentation-bedrock/opentelemetry/instrumentation/bedrock/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,8 @@
from opentelemetry.instrumentation.bedrock.span_utils import (
converse_usage_record,
set_converse_input_prompt_span_attributes,
_set_converse_finish_reasons,
_set_finish_reasons_unconditionally,
set_converse_model_span_attributes,
set_converse_response_span_attributes,
set_converse_streaming_response_span_attributes,
Expand All @@ -48,6 +50,13 @@
unwrap,
)
from opentelemetry.metrics import Counter, Histogram, Meter, get_meter
from opentelemetry.semconv._incubating.attributes import (
gen_ai_attributes as GenAIAttributes,
)
from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import (
GenAiOperationNameValues,
GenAiSystemValues,
)
from opentelemetry.semconv_ai import (
SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY,
Meters,
Expand Down Expand Up @@ -104,8 +113,9 @@ def __init__(
{"package": "botocore.session", "object": "Session", "method": "create_client"},
]

_BEDROCK_INVOKE_SPAN_NAME = "bedrock.completion"
_BEDROCK_CONVERSE_SPAN_NAME = "bedrock.converse"
def _span_name(operation_name, model):
"""Build span name per OTel semconv: '{operation_name} {model}'."""
return f"{operation_name} {model}" if model else operation_name


def is_metrics_enabled() -> bool:
Expand Down Expand Up @@ -200,8 +210,15 @@ def with_instrumentation(*args, **kwargs):
if context_api.get_value(SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY):
return fn(*args, **kwargs)

(provider, _model_vendor, _model) = _get_vendor_model(kwargs.get("modelId"))
operation_name = _derive_operation_name(kwargs)
span_attributes = {
GenAIAttributes.GEN_AI_PROVIDER_NAME: provider,
GenAIAttributes.GEN_AI_OPERATION_NAME: operation_name,
GenAIAttributes.GEN_AI_REQUEST_MODEL: _model,
}
with tracer.start_as_current_span(
_BEDROCK_INVOKE_SPAN_NAME, kind=SpanKind.CLIENT
_span_name(operation_name, _model), kind=SpanKind.CLIENT, attributes=span_attributes
) as span:
response = fn(*args, **kwargs)
_handle_call(span, kwargs, response, metric_params, event_logger)
Expand All @@ -218,7 +235,18 @@ def with_instrumentation(*args, **kwargs):
if context_api.get_value(SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY):
return fn(*args, **kwargs)

span = tracer.start_span(_BEDROCK_INVOKE_SPAN_NAME, kind=SpanKind.CLIENT)
(provider, _model_vendor, _model) = _get_vendor_model(kwargs.get("modelId"))
operation_name = _derive_operation_name(kwargs)
span_attributes = {
GenAIAttributes.GEN_AI_PROVIDER_NAME: provider,
GenAIAttributes.GEN_AI_OPERATION_NAME: operation_name,
GenAIAttributes.GEN_AI_REQUEST_MODEL: _model,
}
span = tracer.start_span(
_span_name(operation_name, _model),
kind=SpanKind.CLIENT,
attributes=span_attributes,
)

response = fn(*args, **kwargs)
_handle_stream_call(span, kwargs, response, metric_params, event_logger)
Comment thread
coderabbitai[bot] marked this conversation as resolved.
Expand All @@ -237,8 +265,16 @@ def with_instrumentation(*args, **kwargs):
if context_api.get_value(SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY):
return fn(*args, **kwargs)

(provider, _model_vendor, _model) = _get_vendor_model(kwargs.get("modelId"))
span_attributes = {
GenAIAttributes.GEN_AI_PROVIDER_NAME: provider,
GenAIAttributes.GEN_AI_OPERATION_NAME: GenAiOperationNameValues.CHAT.value,
GenAIAttributes.GEN_AI_REQUEST_MODEL: _model,
}
with tracer.start_as_current_span(
_BEDROCK_CONVERSE_SPAN_NAME, kind=SpanKind.CLIENT
_span_name(GenAiOperationNameValues.CHAT.value, _model),
kind=SpanKind.CLIENT,
attributes=span_attributes,
) as span:
response = fn(*args, **kwargs)
_handle_converse(span, kwargs, response, metric_params, event_logger)
Expand All @@ -254,7 +290,17 @@ def with_instrumentation(*args, **kwargs):
if context_api.get_value(SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY):
return fn(*args, **kwargs)

span = tracer.start_span(_BEDROCK_CONVERSE_SPAN_NAME, kind=SpanKind.CLIENT)
(provider, _model_vendor, _model) = _get_vendor_model(kwargs.get("modelId"))
span_attributes = {
GenAIAttributes.GEN_AI_PROVIDER_NAME: provider,
GenAIAttributes.GEN_AI_OPERATION_NAME: GenAiOperationNameValues.CHAT.value,
GenAIAttributes.GEN_AI_REQUEST_MODEL: _model,
}
span = tracer.start_span(
_span_name(GenAiOperationNameValues.CHAT.value, _model),
kind=SpanKind.CLIENT,
attributes=span_attributes,
)
response = fn(*args, **kwargs)
if span.is_recording():
_handle_converse_stream(span, kwargs, response, metric_params, event_logger)
Expand Down Expand Up @@ -299,6 +345,7 @@ def stream_done(response_body):
if should_emit_events() and event_logger:
emit_message_events(event_logger, kwargs)
emit_streaming_response_event(response_body, event_logger)
_set_finish_reasons_unconditionally(model_vendor, span, response_body)
else:
set_model_message_span_attributes(model_vendor, span, request_body)
set_model_choice_span_attributes(model_vendor, span, response_body)
Expand Down Expand Up @@ -345,6 +392,7 @@ def _handle_call(span: Span, kwargs, response, metric_params, event_logger):
if should_emit_events() and event_logger:
emit_message_events(event_logger, kwargs)
emit_choice_events(event_logger, response)
_set_finish_reasons_unconditionally(model_vendor, span, response_body)
else:
set_model_message_span_attributes(model_vendor, span, request_body)
set_model_choice_span_attributes(model_vendor, span, response_body)
Expand All @@ -362,6 +410,7 @@ def _handle_converse(span, kwargs, response, metric_params, event_logger):
if should_emit_events() and event_logger:
emit_input_events_converse(kwargs, event_logger)
emit_response_event_converse(response, event_logger)
_set_converse_finish_reasons(span, response.get("stopReason"))
else:
set_converse_input_prompt_span_attributes(kwargs, span)
set_converse_response_span_attributes(response, span)
Expand All @@ -385,11 +434,28 @@ def _handle_converse_stream(span, kwargs, response, metric_params, event_logger)
def handler(func):
def wrap(*args, **kwargs):
response_msg = kwargs.pop("response_msg")
tool_blocks = kwargs.pop("tool_blocks")
reasoning_blocks = kwargs.pop("reasoning_blocks")
span = kwargs.pop("span")
event = func(*args, **kwargs)
nonlocal role
if "contentBlockDelta" in event and "text" in event["contentBlockDelta"].get("delta", {}):
response_msg.append(event["contentBlockDelta"]["delta"]["text"])
if "contentBlockDelta" in event:
delta = event["contentBlockDelta"].get("delta", {})
if "text" in delta:
response_msg.append(delta["text"])
if "toolUse" in delta:
# Merge delta input into the last tool block (created by contentBlockStart)
if tool_blocks:
tool_blocks[-1].setdefault("input", "")
tool_blocks[-1]["input"] += delta["toolUse"].get("input", "")
else:
tool_blocks.append(delta["toolUse"])
if "reasoningContent" in delta:
reasoning_blocks.append(delta["reasoningContent"].get("text", ""))
elif "contentBlockStart" in event:
start = event["contentBlockStart"].get("start", {})
if "toolUse" in start:
tool_blocks.append(start["toolUse"])
Comment thread
coderabbitai[bot] marked this conversation as resolved.
elif "messageStart" in event:
role = event["messageStart"]["role"]
elif "metadata" in event:
Expand All @@ -398,29 +464,36 @@ def wrap(*args, **kwargs):
converse_usage_record(span, event["metadata"], metric_params)
span.end()
elif "messageStop" in event:
stop_reason = event.get("messageStop", {}).get("stopReason")
if should_emit_events() and event_logger:
emit_streaming_converse_response_event(
event_logger,
response_msg,
role,
event.get("messageStop", {}).get("stopReason", "unknown"),
stop_reason,
)
_set_converse_finish_reasons(span, stop_reason)
else:
set_converse_streaming_response_span_attributes(
response_msg, role, span
response_msg,
role,
span,
finish_reason=stop_reason,
tool_blocks=tool_blocks,
reasoning_blocks=reasoning_blocks,
)

return event

return partial(wrap, response_msg=[], span=span)
return partial(wrap, response_msg=[], tool_blocks=[], reasoning_blocks=[], span=span)

stream._parse_event = handler(stream._parse_event)


def _get_vendor_model(modelId):
# Docs:
# https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html#inference-profiles-support-system
provider = "AWS"
provider = GenAiSystemValues.AWS_BEDROCK.value
model_vendor = "imported_model"
model = modelId

Expand Down Expand Up @@ -449,19 +522,32 @@ def _cross_region_check(value):
return model_vendor, model


def _derive_operation_name(kwargs):
"""Derive operation name for invoke_model spans prior to creation."""
body_str = kwargs.get("body")
if body_str:
try:
body = json.loads(body_str)
if isinstance(body, dict) and "messages" in body:
return GenAiOperationNameValues.CHAT.value
except (json.JSONDecodeError, TypeError):
pass
return GenAiOperationNameValues.TEXT_COMPLETION.value


class GuardrailMeters:
LLM_BEDROCK_GUARDRAIL_ACTIVATION = "gen_ai.bedrock.guardrail.activation"
LLM_BEDROCK_GUARDRAIL_LATENCY = "gen_ai.bedrock.guardrail.latency"
LLM_BEDROCK_GUARDRAIL_COVERAGE = "gen_ai.bedrock.guardrail.coverage"
LLM_BEDROCK_GUARDRAIL_SENSITIVE = "gen_ai.bedrock.guardrail.sensitive_info"
LLM_BEDROCK_GUARDRAIL_TOPICS = "gen_ai.bedrock.guardrail.topics"
LLM_BEDROCK_GUARDRAIL_CONTENT = "gen_ai.bedrock.guardrail.content"
LLM_BEDROCK_GUARDRAIL_WORDS = "gen_ai.bedrock.guardrail.words"
GEN_AI_BEDROCK_GUARDRAIL_ACTIVATION = "gen_ai.bedrock.guardrail.activation"
GEN_AI_BEDROCK_GUARDRAIL_LATENCY = "gen_ai.bedrock.guardrail.latency"
GEN_AI_BEDROCK_GUARDRAIL_COVERAGE = "gen_ai.bedrock.guardrail.coverage"
GEN_AI_BEDROCK_GUARDRAIL_SENSITIVE = "gen_ai.bedrock.guardrail.sensitive_info"
GEN_AI_BEDROCK_GUARDRAIL_TOPICS = "gen_ai.bedrock.guardrail.topics"
GEN_AI_BEDROCK_GUARDRAIL_CONTENT = "gen_ai.bedrock.guardrail.content"
GEN_AI_BEDROCK_GUARDRAIL_WORDS = "gen_ai.bedrock.guardrail.words"


class PromptCaching:
# will be moved under the AI SemConv. Not namespaced since also OpenAI supports this.
LLM_BEDROCK_PROMPT_CACHING = "gen_ai.prompt.caching"
GEN_AI_PROMPT_CACHING = "gen_ai.prompt.caching"

Comment on lines 538 to 551
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@coderabbitai coderabbitai Bot Mar 29, 2026

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⚠️ Potential issue | 🟠 Major

Keep backward-compatible aliases for the renamed metric constants.

GuardrailMeters and PromptCaching live in the package root, and the old LLM_BEDROCK_* names disappear completely here. That turns an otherwise additive semconv migration into an AttributeError for downstream code that imports those constants.

🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In
`@packages/opentelemetry-instrumentation-bedrock/opentelemetry/instrumentation/bedrock/__init__.py`
around lines 456 - 469, The renamed metric constants removed the old
LLM_BEDROCK_* identifiers causing AttributeError for downstream imports; restore
backward-compatible aliases by adding the old names as assignments pointing to
the new constants (e.g., define LLM_BEDROCK_GUARDRAIL_ACTIVATION =
GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_ACTIVATION,
LLM_BEDROCK_GUARDRAIL_LATENCY =
GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_LATENCY, etc., and
LLM_BEDROCK_PROMPT_CACHING = PromptCaching.GEN_AI_PROMPT_CACHING) so both
GuardrailMeters and PromptCaching keep the new names while the legacy
LLM_BEDROCK_* symbols remain available.


def _create_metrics(meter: Meter):
Expand All @@ -484,58 +570,57 @@ def _create_metrics(meter: Meter):
)

exception_counter = meter.create_counter(
# TODO: will fix this in future as a consolidation for semantic convention
name="llm.bedrock.completions.exceptions",
name="gen_ai.bedrock.completions.exceptions",
unit="time",
description="Number of exceptions occurred during chat completions",
)

# Guardrail metrics
guardrail_activation = meter.create_counter(
name=GuardrailMeters.LLM_BEDROCK_GUARDRAIL_ACTIVATION,
name=GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_ACTIVATION,
unit="",
description="Number of guardrail activation",
)

guardrail_latency_histogram = meter.create_histogram(
name=GuardrailMeters.LLM_BEDROCK_GUARDRAIL_LATENCY,
name=GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_LATENCY,
unit="ms",
description="GenAI guardrail latency",
)

guardrail_coverage = meter.create_counter(
name=GuardrailMeters.LLM_BEDROCK_GUARDRAIL_COVERAGE,
name=GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_COVERAGE,
unit="char",
description="GenAI guardrail coverage",
)

guardrail_sensitive_info = meter.create_counter(
name=GuardrailMeters.LLM_BEDROCK_GUARDRAIL_SENSITIVE,
name=GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_SENSITIVE,
unit="",
description="GenAI guardrail sensitive information protection",
)

guardrail_topic = meter.create_counter(
name=GuardrailMeters.LLM_BEDROCK_GUARDRAIL_TOPICS,
name=GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_TOPICS,
unit="",
description="GenAI guardrail topics protection",
)

guardrail_content = meter.create_counter(
name=GuardrailMeters.LLM_BEDROCK_GUARDRAIL_CONTENT,
name=GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_CONTENT,
unit="",
description="GenAI guardrail content filter protection",
)

guardrail_words = meter.create_counter(
name=GuardrailMeters.LLM_BEDROCK_GUARDRAIL_WORDS,
name=GuardrailMeters.GEN_AI_BEDROCK_GUARDRAIL_WORDS,
unit="",
description="GenAI guardrail words filter protection",
)

# Prompt Caching
prompt_caching = meter.create_counter(
name=PromptCaching.LLM_BEDROCK_PROMPT_CACHING,
name=PromptCaching.GEN_AI_PROMPT_CACHING,
unit="",
description="Number of cached tokens",
)
Expand Down
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