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73a764c
cherry pick changes from previous PR
keith-decker Sep 17, 2025
0d749a9
move span utils to new file
keith-decker Sep 17, 2025
77d9c3c
remove span state, use otel context for parent/child
keith-decker Sep 17, 2025
054ebe9
flatten LLMInvocation to use attributes instead of dict keys
keith-decker Sep 17, 2025
9d3926f
helper function and docstrings
keith-decker Sep 17, 2025
635b7f8
Merge branch 'main' into util-genai-inference-clean
keith-decker Sep 18, 2025
9837cf4
refactor: store span and context token in LLMInvocation instead of Sp…
keith-decker Sep 18, 2025
1a172d1
refactor: rename prompts/chat_generations to input_messages/output_me…
keith-decker Sep 19, 2025
465ca78
refactor: simplify TelemetryHandler API by moving invocation data man…
keith-decker Sep 19, 2025
cd5aaa6
refactor: update relative imports to absolute imports
keith-decker Sep 19, 2025
8347d17
Update handler to use a context manager instead of start_llm and stop…
keith-decker Sep 22, 2025
742a36f
resolve tox -e doc failure
keith-decker Sep 22, 2025
00e08a8
safeguard against empty request-model
keith-decker Sep 22, 2025
109625c
Merge branch 'main' into util-genai-inference-clean
keith-decker Sep 22, 2025
80c94bf
fix tox typecheck errors for utils
keith-decker Sep 22, 2025
f07c61f
refactor: move tracer to generator, clean up dead code
keith-decker Sep 22, 2025
d6f722f
remove unused linting hint
keith-decker Sep 22, 2025
fdc7f50
back off stricter request-model requirements
keith-decker Sep 22, 2025
da20448
reintroduce manual start/stop for langchain callback flow
keith-decker Sep 22, 2025
c1ff846
Merge branch 'main' into util-genai-inference-clean
aabmass Sep 23, 2025
4a08d7f
clean up context handler, clarify unit tests
keith-decker Sep 23, 2025
243bf8d
remove generator concept
keith-decker Sep 25, 2025
3d6c8da
Merge branch 'main' into util-genai-inference-clean
keith-decker Sep 25, 2025
5f8cd1c
update token types
keith-decker Sep 25, 2025
e987d66
Merge branch 'main' into util-genai-inference-clean
keith-decker Sep 26, 2025
51d28c0
code cleanup
keith-decker Sep 30, 2025
88d0149
Merge branch 'main' into util-genai-inference-clean
keith-decker Sep 30, 2025
188e84b
Merge branch 'main' into util-genai-inference-clean
keith-decker Oct 2, 2025
c264c46
Merge branch 'main' into util-genai-inference-clean
keith-decker Oct 6, 2025
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5 changes: 5 additions & 0 deletions util/opentelemetry-util-genai/CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -9,3 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

- Add a utility to parse the `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` environment variable.
Add `gen_ai_latest_experimental` as a new value to the Sem Conv stability flag ([#3716](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3716)).

### Added

- Generate Spans for LLM invocations
- Helper functions for starting and finishing LLM invocations
19 changes: 19 additions & 0 deletions util/opentelemetry-util-genai/README.rst
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,25 @@ The GenAI Utils package will include boilerplate and helpers to standardize inst
This package will provide APIs and decorators to minimize the work needed to instrument genai libraries,
while providing standardization for generating both types of otel, "spans and metrics" and "spans, metrics and events"

This package relies on environment variables to configure capturing of message content.
By default, message content will not be captured.
Set the environment variable `OTEL_SEMCONV_STABILITY_OPT_IN` to `gen_ai_latest_experimental` to enable experimental features.
And set the environment variable `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` to `SPAN_ONLY` or `SPAN_AND_EVENT` to capture message content in spans.

This package provides these span attributes:

- `gen_ai.provider.name`: Str(openai)
- `gen_ai.operation.name`: Str(chat)
- `gen_ai.request.model`: Str(gpt-3.5-turbo)
- `gen_ai.response.finish_reasons`: Slice(["stop"])
- `gen_ai.response.model`: Str(gpt-3.5-turbo-0125)
- `gen_ai.response.id`: Str(chatcmpl-Bz8yrvPnydD9pObv625n2CGBPHS13)
- `gen_ai.usage.input_tokens`: Int(24)
- `gen_ai.usage.output_tokens`: Int(7)
- `gen_ai.input.messages`: Str('[{"role": "Human", "parts": [{"content": "hello world", "type": "text"}]}]')
- `gen_ai.output.messages`: Str('[{"role": "AI", "parts": [{"content": "hello back", "type": "text"}], "finish_reason": "stop"}]')


Installation
------------

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6 changes: 3 additions & 3 deletions util/opentelemetry-util-genai/pyproject.toml
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ dynamic = ["version"]
description = "OpenTelemetry GenAI Utils"
readme = "README.rst"
license = "Apache-2.0"
requires-python = ">=3.8"
requires-python = ">=3.9"
authors = [
{ name = "OpenTelemetry Authors", email = "[email protected]" },
]
Expand All @@ -25,8 +25,8 @@ classifiers = [
"Programming Language :: Python :: 3.13",
]
dependencies = [
"opentelemetry-instrumentation ~= 0.51b0",
"opentelemetry-semantic-conventions ~= 0.51b0",
"opentelemetry-instrumentation ~= 0.57b0",
"opentelemetry-semantic-conventions ~= 0.57b0",
"opentelemetry-api>=1.31.0",
]

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Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
# Copyright The OpenTelemetry Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Original file line number Diff line number Diff line change
@@ -0,0 +1,118 @@
# Copyright The OpenTelemetry Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Span generation utilities for GenAI telemetry.

This module maps GenAI (Generative AI) invocations to OpenTelemetry spans and
applies GenAI semantic convention attributes.

Classes:
- BaseTelemetryGenerator: Abstract base for GenAI telemetry emitters.
- SpanGenerator: Concrete implementation that creates and finalizes spans
for LLM operations (e.g., chat) and records input/output messages when
experimental mode and content capture settings allow.

Usage:
See `opentelemetry/util/genai/handler.py` for `TelemetryHandler`, which
constructs `LLMInvocation` objects and delegates to `SpanGenerator.start`,
`SpanGenerator.finish`, and `SpanGenerator.error` to produce spans that
follow the GenAI semantic conventions.
"""

from contextvars import Token
from typing import Dict, Optional
from uuid import UUID

from typing_extensions import TypeAlias

from opentelemetry import context as otel_context
from opentelemetry import trace
from opentelemetry.semconv._incubating.attributes import (
gen_ai_attributes as GenAI,
)
from opentelemetry.trace import (
Span,
SpanKind,
Tracer,
set_span_in_context,
)
from opentelemetry.util.genai.span_utils import (
_apply_error_attributes,
_apply_finish_attributes,
)
from opentelemetry.util.genai.types import Error, LLMInvocation

# Type alias matching the token type expected by opentelemetry.context.detach
ContextToken: TypeAlias = Token[otel_context.Context]


class BaseTelemetryGenerator:
"""
Abstract base for emitters mapping GenAI types -> OpenTelemetry.
"""

def start(self, invocation: LLMInvocation) -> None:
raise NotImplementedError

def finish(self, invocation: LLMInvocation) -> None:
raise NotImplementedError

def error(self, error: Error, invocation: LLMInvocation) -> None:
raise NotImplementedError


class SpanGenerator(BaseTelemetryGenerator):
"""
Generates only spans.
"""

def __init__(
self,
tracer: Optional[Tracer] = None,
):
self._tracer: Tracer = tracer or trace.get_tracer(__name__)

# Store the active span and its context attachment token
self._active: Dict[UUID, tuple[Span, ContextToken]] = {}

def start(self, invocation: LLMInvocation):
# Create a span and attach it as current; keep the token to detach later
span = self._tracer.start_span(
name=f"{GenAI.GenAiOperationNameValues.CHAT.value} {invocation.request_model}",
kind=SpanKind.CLIENT,
)
invocation.span = span
invocation.context_token = otel_context.attach(
set_span_in_context(span)
)

def finish(self, invocation: LLMInvocation):
if invocation.context_token is None or invocation.span is None:
return

_apply_finish_attributes(invocation.span, invocation)
# Detach context and end span
otel_context.detach(invocation.context_token)
invocation.span.end()

def error(self, error: Error, invocation: LLMInvocation):
if invocation.context_token is None or invocation.span is None:
return

_apply_error_attributes(invocation.span, error)
# Detach context and end span
otel_context.detach(invocation.context_token)
invocation.span.end()
return
Original file line number Diff line number Diff line change
@@ -0,0 +1,111 @@
# Copyright The OpenTelemetry Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Telemetry handler for GenAI invocations.

This module exposes the `TelemetryHandler` class, which manages the lifecycle of
GenAI (Generative AI) invocations and emits telemetry data (spans and related attributes).
It supports starting, stopping, and failing LLM invocations.

Classes:
- TelemetryHandler: Manages GenAI invocation lifecycles and emits telemetry.

Functions:
- get_telemetry_handler: Returns a singleton `TelemetryHandler` instance.

Usage:
handler = get_telemetry_handler()

# Create an invocation object with your request data
invocation = LLMInvocation(
request_model="my-model",
input_messages=[...],
provider="my-provider",
attributes={"custom": "attr"},
)

# Start the invocation (opens a span)
handler.start_llm(invocation)

# Populate outputs and any additional attributes, then stop (closes the span)
invocation.output_messages = [...]
invocation.attributes.update({"more": "attrs"})
handler.stop_llm(invocation)

# Or, in case of error
# handler.fail_llm(invocation, Error(type="...", message="..."))
"""

import time
from typing import Any, Optional

from opentelemetry.semconv.schemas import Schemas
from opentelemetry.trace import get_tracer
from opentelemetry.util.genai.generators import SpanGenerator
from opentelemetry.util.genai.types import Error, LLMInvocation
from opentelemetry.util.genai.version import __version__


class TelemetryHandler:
"""
High-level handler managing GenAI invocation lifecycles and emitting
them as spans, metrics, and events.
"""

def __init__(self, **kwargs: Any):
tracer_provider = kwargs.get("tracer_provider")
self._tracer = get_tracer(
__name__,
__version__,
tracer_provider,
schema_url=Schemas.V1_36_0.value,
)

self._generator = SpanGenerator(tracer=self._tracer)

def start_llm(
self,
invocation: LLMInvocation,
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@pmcollins pmcollins Oct 1, 2025

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Another option here would be to use immutable types, e.g. breaking out LLMInvocation into an LLMRequest and an LLMResponse. It's possible that span and token don't belong in either of these types, but maybe they can be returned in a tuple or a wrapper.

) -> LLMInvocation:
"""Start an LLM invocation and create a pending span entry."""
self._generator.start(invocation)
return invocation

def stop_llm(self, invocation: LLMInvocation) -> LLMInvocation:
"""Finalize an LLM invocation successfully and end its span."""
invocation.end_time = time.time()
self._generator.finish(invocation)
return invocation

def fail_llm(
self, invocation: LLMInvocation, error: Error
) -> LLMInvocation:
"""Fail an LLM invocation and end its span with error status."""
invocation.end_time = time.time()
self._generator.error(error, invocation)
return invocation


def get_telemetry_handler(**kwargs: Any) -> TelemetryHandler:
"""
Returns a singleton TelemetryHandler instance.
"""
handler: Optional[TelemetryHandler] = getattr(
get_telemetry_handler, "_default_handler", None
)
if handler is None:
handler = TelemetryHandler(**kwargs)
setattr(get_telemetry_handler, "_default_handler", handler)
return handler
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