diff --git a/packages/opentelemetry-instrumentation-writer/.flake8 b/packages/opentelemetry-instrumentation-writer/.flake8
new file mode 100644
index 0000000000..3da962c38e
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/.flake8
@@ -0,0 +1,11 @@
+[flake8]
+exclude =
+ .git,
+ __pycache__,
+ build,
+ dist,
+ .tox,
+ venv,
+ .venv,
+ .pytest_cache
+max-line-length = 120
diff --git a/packages/opentelemetry-instrumentation-writer/.python-version b/packages/opentelemetry-instrumentation-writer/.python-version
new file mode 100644
index 0000000000..d9506ceba5
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/.python-version
@@ -0,0 +1 @@
+3.12.5
diff --git a/packages/opentelemetry-instrumentation-writer/README.md b/packages/opentelemetry-instrumentation-writer/README.md
new file mode 100644
index 0000000000..5f8c883a35
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/README.md
@@ -0,0 +1,33 @@
+# OpenTelemetry Writer Instrumentation
+
+
+
+
+
+This library allows tracing calls to any of Writer's endpoints sent with the official [Writer Python Library](https://github.com/writer/writer-python).
+
+## Installation
+
+```bash
+pip install opentelemetry-instrumentation-writer
+```
+
+## Example usage
+
+```python
+from opentelemetry.instrumentation.writer import WriterInstrumentor
+
+WriterInstrumentor().instrument()
+```
+
+## Privacy
+
+**By default, this instrumentation logs prompts, completions, and embeddings to span attributes**. This gives you a clear visibility into how your LLM application is working, and can make it easy to debug and evaluate the quality of the outputs.
+
+However, you may want to disable this logging for privacy reasons, as they may contain highly sensitive data from your users. You may also simply want to reduce the size of your traces.
+
+To disable logging, set the `TRACELOOP_TRACE_CONTENT` environment variable to `false`.
+
+```bash
+TRACELOOP_TRACE_CONTENT=false
+```
diff --git a/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/__init__.py b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/__init__.py
new file mode 100644
index 0000000000..53ac9064de
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/__init__.py
@@ -0,0 +1,564 @@
+"""OpenTelemetry Writer instrumentation"""
+
+import logging
+import os
+import time
+from typing import Collection, Union
+
+from opentelemetry._events import EventLogger, get_event_logger
+from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
+from opentelemetry.instrumentation.utils import (_SUPPRESS_INSTRUMENTATION_KEY,
+ unwrap)
+from opentelemetry.metrics import Histogram, Meter, get_meter
+from opentelemetry.semconv._incubating.metrics import \
+ gen_ai_metrics as GenAIMetrics
+from opentelemetry.semconv_ai import (
+ SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY, LLMRequestTypeValues, Meters,
+ SpanAttributes)
+from opentelemetry.trace import SpanKind, get_tracer
+from opentelemetry.trace.status import Status, StatusCode
+from wrapt import wrap_function_wrapper
+from writerai._streaming import AsyncStream, Stream
+
+from opentelemetry import context as context_api
+from opentelemetry.instrumentation.writer.config import Config
+from opentelemetry.instrumentation.writer.event_emitter import (
+ emit_choice_events, emit_message_events, emit_streaming_response_events)
+from opentelemetry.instrumentation.writer.span_utils import (
+ set_input_attributes, set_model_input_attributes,
+ set_model_response_attributes, set_model_streaming_response_attributes,
+ set_response_attributes, set_streaming_response_attributes)
+from opentelemetry.instrumentation.writer.utils import (
+ error_metrics_attributes, model_as_dict, response_attributes,
+ should_emit_events)
+from opentelemetry.instrumentation.writer.version import __version__
+
+logger = logging.getLogger(__name__)
+
+_instruments = ("writer-sdk >= 2.2.1, < 3",)
+
+WRAPPED_METHODS = [
+ {
+ "package": "writerai.resources.chat",
+ "object": "ChatResource",
+ "method": "chat",
+ "span_name": "writerai.chat",
+ },
+ {
+ "package": "writerai.resources.completions",
+ "object": "CompletionsResource",
+ "method": "create",
+ "span_name": "writerai.completions",
+ },
+]
+WRAPPED_AMETHODS = [
+ {
+ "package": "writerai.resources.chat",
+ "object": "AsyncChatResource",
+ "method": "chat",
+ "span_name": "writerai.chat",
+ },
+ {
+ "package": "writerai.resources.completions",
+ "object": "AsyncCompletionsResource",
+ "method": "create",
+ "span_name": "writerai.completions",
+ },
+]
+
+
+def is_streaming_response(response):
+ return isinstance(response, Stream) or isinstance(response, AsyncStream)
+
+
+def _process_streaming_chunk(chunk):
+ chunk_dict = model_as_dict(chunk)
+
+ choice = chunk_dict.get("choices", [{}])[0]
+ delta = choice.get("delta", {})
+
+ content = delta.get("content") if delta else choice.get("text")
+ finish_reason = choice.get("finish_reason")
+
+ usage = chunk_dict.get("usage")
+
+ return content, finish_reason, usage
+
+
+def _handle_streaming_response(
+ span, accumulated_content, finish_reason, usage, event_logger
+):
+ set_model_streaming_response_attributes(span, usage)
+
+ if should_emit_events() and event_logger:
+ emit_streaming_response_events(accumulated_content, finish_reason, event_logger)
+ else:
+ set_streaming_response_attributes(
+ span, accumulated_content, finish_reason, usage
+ )
+
+
+def _create_stream_processor(
+ response,
+ span,
+ event_logger,
+ start_time,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+):
+ accumulated_content = ""
+ finish_reason = None
+ usage = None
+
+ first_token_time = time.time()
+ streaming_time_to_first_token.record(
+ first_token_time - start_time,
+ attributes={SpanAttributes.LLM_SYSTEM: "Writer", "stream": True},
+ )
+
+ for chunk in response:
+ content, chunk_finish_reason, chunk_usage = _process_streaming_chunk(chunk)
+
+ if content:
+ accumulated_content += content
+ if chunk_finish_reason:
+ finish_reason = chunk_finish_reason
+ if chunk_usage:
+ usage = chunk_usage
+
+ yield chunk
+
+ streaming_time_to_generate.record(
+ time.time() - first_token_time,
+ attributes={SpanAttributes.LLM_SYSTEM: "Writer", "stream": True},
+ )
+
+ _handle_streaming_response(
+ span, accumulated_content, finish_reason, usage, event_logger
+ )
+
+ if span.is_recording():
+ span.set_status(Status(StatusCode.OK))
+
+ span.end()
+
+
+async def _create_async_stream_processor(
+ response,
+ span,
+ event_logger,
+ start_time,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+):
+ accumulated_content = ""
+ finish_reason = None
+ usage = None
+
+ first_token_time = time.time()
+ streaming_time_to_first_token.record(
+ first_token_time - start_time,
+ attributes={SpanAttributes.LLM_SYSTEM: "Writer", "stream": True},
+ )
+
+ async for chunk in response:
+ content, chunk_finish_reason, chunk_usage = _process_streaming_chunk(chunk)
+
+ if content:
+ accumulated_content += content
+
+ if chunk_finish_reason:
+ finish_reason = chunk_finish_reason
+
+ if chunk_usage:
+ usage = chunk_usage
+
+ yield chunk
+
+ streaming_time_to_generate.record(
+ time.time() - first_token_time,
+ attributes={SpanAttributes.LLM_SYSTEM: "Writer", "stream": True},
+ )
+
+ _handle_streaming_response(
+ span, accumulated_content, finish_reason, usage, event_logger
+ )
+
+ if span.is_recording():
+ span.set_status(Status(StatusCode.OK))
+
+ span.end()
+
+
+def _handle_input(span, kwargs, event_logger):
+ set_model_input_attributes(span, kwargs)
+ if should_emit_events() and event_logger:
+ emit_message_events(kwargs, event_logger)
+ else:
+ set_input_attributes(span, kwargs)
+
+
+def _request_type_by_method(method_name):
+ if method_name == "chat":
+ return LLMRequestTypeValues.CHAT
+ elif method_name == "create":
+ return LLMRequestTypeValues.COMPLETION
+ else:
+ return LLMRequestTypeValues.UNKNOWN
+
+
+def _with_tracer_wrapper(func):
+ """Helper for providing tracer for wrapper functions. Includes metric collectors."""
+
+ def _with_chat_telemetry(
+ tracer,
+ token_histogram,
+ duration_histogram,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ event_logger,
+ to_wrap,
+ ):
+ def wrapper(wrapped, instance, args, kwargs):
+ return func(
+ tracer,
+ token_histogram,
+ duration_histogram,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ event_logger,
+ to_wrap,
+ wrapped,
+ instance,
+ args,
+ kwargs,
+ )
+
+ return wrapper
+
+ return _with_chat_telemetry
+
+
+def _handle_response(span, response, token_histogram, event_logger):
+ set_model_response_attributes(span, response, token_histogram)
+ if should_emit_events() and event_logger:
+ emit_choice_events(response, event_logger)
+ else:
+ set_response_attributes(span, response)
+
+
+@_with_tracer_wrapper
+def _wrap(
+ tracer,
+ token_histogram: Histogram,
+ duration_histogram: Histogram,
+ streaming_time_to_first_token: Histogram,
+ streaming_time_to_generate: Histogram,
+ event_logger: Union[EventLogger, None],
+ to_wrap,
+ wrapped,
+ instance,
+ args,
+ kwargs,
+):
+ """Instruments and calls every function defined in TO_WRAP."""
+ if context_api.get_value(_SUPPRESS_INSTRUMENTATION_KEY) or context_api.get_value(
+ SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY
+ ):
+ return wrapped(*args, **kwargs)
+
+ name = to_wrap.get("span_name")
+ request_type = _request_type_by_method(to_wrap.get("method"))
+
+ span = tracer.start_span(
+ name,
+ kind=SpanKind.CLIENT,
+ attributes={
+ SpanAttributes.LLM_SYSTEM: "Writer",
+ SpanAttributes.LLM_REQUEST_TYPE: request_type.value,
+ },
+ )
+
+ _handle_input(span, kwargs, event_logger)
+
+ start_time = time.time()
+ try:
+ response = wrapped(*args, **kwargs)
+ except Exception as e: # pylint: disable=broad-except
+ end_time = time.time()
+
+ if duration_histogram:
+ duration = end_time - start_time
+ duration_histogram.record(duration, attributes=error_metrics_attributes(e))
+
+ raise e
+
+ if is_streaming_response(response):
+ try:
+ return _create_stream_processor(
+ response,
+ span,
+ event_logger,
+ start_time,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ )
+ except Exception as ex:
+ logger.warning(
+ "Failed to process streaming response for writer span, error: %s",
+ str(ex),
+ )
+ span.set_status(Status(StatusCode.ERROR))
+ span.end()
+ raise
+ elif response:
+ end_time = time.time()
+ try:
+ if duration_histogram:
+ duration = end_time - start_time
+ duration_histogram.record(
+ duration,
+ attributes=response_attributes(response),
+ )
+
+ # Process non-streaming response attributes and events
+ _handle_response(span, response, token_histogram, event_logger)
+
+ except Exception as ex: # pylint: disable=broad-except
+ logger.warning(
+ "Failed to set response attributes for writer span, error: %s",
+ str(ex),
+ )
+
+ if span.is_recording():
+ span.set_status(Status(StatusCode.OK))
+
+ span.end()
+ return response
+
+
+@_with_tracer_wrapper
+async def _awrap(
+ tracer,
+ token_histogram: Histogram,
+ duration_histogram: Histogram,
+ streaming_time_to_first_token: Histogram,
+ streaming_time_to_generate: Histogram,
+ event_logger: Union[EventLogger, None],
+ to_wrap,
+ wrapped,
+ instance,
+ args,
+ kwargs,
+):
+ """Instruments and calls every function defined in TO_WRAP."""
+ if context_api.get_value(_SUPPRESS_INSTRUMENTATION_KEY) or context_api.get_value(
+ SUPPRESS_LANGUAGE_MODEL_INSTRUMENTATION_KEY
+ ):
+ return await wrapped(*args, **kwargs)
+
+ name = to_wrap.get("span_name")
+ request_type = _request_type_by_method(to_wrap.get("method"))
+
+ span = tracer.start_span(
+ name,
+ kind=SpanKind.CLIENT,
+ attributes={
+ SpanAttributes.LLM_SYSTEM: "Writer",
+ SpanAttributes.LLM_REQUEST_TYPE: request_type.value,
+ },
+ )
+
+ _handle_input(span, kwargs, event_logger)
+
+ start_time = time.time()
+ try:
+ response = await wrapped(*args, **kwargs)
+ except Exception as e: # pylint: disable=broad-except
+ end_time = time.time()
+
+ if duration_histogram:
+ duration = end_time - start_time
+ duration_histogram.record(duration, attributes=error_metrics_attributes(e))
+
+ raise e
+
+ if is_streaming_response(response):
+ try:
+ return _create_async_stream_processor(
+ response,
+ span,
+ event_logger,
+ start_time,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ )
+ except Exception as ex:
+ logger.warning(
+ "Failed to process streaming response for writer span, error: %s",
+ str(ex),
+ )
+ span.set_status(Status(StatusCode.ERROR))
+ span.end()
+ raise
+ elif response:
+ end_time = time.time()
+ try:
+ if duration_histogram:
+ duration = end_time - start_time
+ duration_histogram.record(
+ duration,
+ attributes=response_attributes(response),
+ )
+
+ # Process non-streaming response attributes and events
+ _handle_response(span, response, token_histogram, event_logger)
+
+ except Exception as ex: # pylint: disable=broad-except
+ logger.warning(
+ "Failed to set response attributes for writer span, error: %s",
+ str(ex),
+ )
+
+ if span.is_recording():
+ span.set_status(Status(StatusCode.OK))
+
+ span.end()
+ return response
+
+
+def _build_metrics(meter: Meter):
+ token_histogram = meter.create_histogram(
+ name=Meters.LLM_TOKEN_USAGE,
+ unit="token",
+ description="Measures number of input and output tokens used",
+ )
+
+ duration_histogram = meter.create_histogram(
+ name=Meters.LLM_OPERATION_DURATION,
+ unit="s",
+ description="Generation operation duration",
+ )
+
+ streaming_time_to_first_token = meter.create_histogram(
+ name=GenAIMetrics.GEN_AI_SERVER_TIME_TO_FIRST_TOKEN,
+ unit="s",
+ description="Time to first token in streaming chat completions",
+ )
+
+ streaming_time_to_generate = meter.create_histogram(
+ name=Meters.LLM_STREAMING_TIME_TO_GENERATE,
+ unit="s",
+ description="Time from first token to completion in streaming responses",
+ )
+
+ return (
+ token_histogram,
+ duration_histogram,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ )
+
+
+def is_metrics_collection_enabled() -> bool:
+ return (os.getenv("TRACELOOP_METRICS_ENABLED") or "true").lower() == "true"
+
+
+class WriterInstrumentor(BaseInstrumentor):
+ """An instrumentor for Writer's client library."""
+
+ def __init__(self, exception_logger=None, use_legacy_attributes=True):
+ super().__init__()
+ Config.exception_logger = exception_logger
+ Config.use_legacy_attributes = use_legacy_attributes
+
+ def instrumentation_dependencies(self) -> Collection[str]:
+ return _instruments
+
+ def _instrument(self, **kwargs):
+ tracer_provider = kwargs.get("tracer_provider")
+ tracer = get_tracer(__name__, __version__, tracer_provider)
+
+ meter_provider = kwargs.get("meter_provider")
+ meter = get_meter(__name__, __version__, meter_provider)
+
+ if is_metrics_collection_enabled():
+ (
+ token_histogram,
+ duration_histogram,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ ) = _build_metrics(meter)
+ else:
+ (
+ token_histogram,
+ duration_histogram,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ ) = (None, None, None, None)
+
+ event_logger = None
+ if not Config.use_legacy_attributes:
+ event_logger_provider = kwargs.get("event_logger_provider")
+ event_logger = get_event_logger(
+ __name__, __version__, event_logger_provider=event_logger_provider
+ )
+
+ for wrapped_method in WRAPPED_METHODS:
+ wrap_package = wrapped_method.get("package")
+ wrap_object = wrapped_method.get("object")
+ wrap_method = wrapped_method.get("method")
+
+ try:
+ wrap_function_wrapper(
+ wrap_package,
+ f"{wrap_object}.{wrap_method}",
+ _wrap(
+ tracer,
+ token_histogram,
+ duration_histogram,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ event_logger,
+ wrapped_method,
+ ),
+ )
+ except ModuleNotFoundError:
+ pass
+
+ for wrapped_method in WRAPPED_AMETHODS:
+ wrap_package = wrapped_method.get("package")
+ wrap_object = wrapped_method.get("object")
+ wrap_method = wrapped_method.get("method")
+ try:
+ wrap_function_wrapper(
+ wrap_package,
+ f"{wrap_object}.{wrap_method}",
+ _awrap(
+ tracer,
+ token_histogram,
+ duration_histogram,
+ streaming_time_to_first_token,
+ streaming_time_to_generate,
+ event_logger,
+ wrapped_method,
+ ),
+ )
+ except ModuleNotFoundError:
+ pass
+
+ def _uninstrument(self, **kwargs):
+ for wrapped_method in WRAPPED_METHODS:
+ wrap_package = wrapped_method.get("package")
+ wrap_object = wrapped_method.get("object")
+ unwrap(
+ f"{wrap_package}.{wrap_object}",
+ wrapped_method.get("method"),
+ )
+ for wrapped_method in WRAPPED_AMETHODS:
+ wrap_package = wrapped_method.get("package")
+ wrap_object = wrapped_method.get("object")
+ unwrap(
+ f"{wrap_package}.{wrap_object}",
+ wrapped_method.get("method"),
+ )
diff --git a/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/config.py b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/config.py
new file mode 100644
index 0000000000..44199c038c
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/config.py
@@ -0,0 +1,3 @@
+class Config:
+ exception_logger = None
+ use_legacy_attributes = True
diff --git a/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/event_emitter.py b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/event_emitter.py
new file mode 100644
index 0000000000..e9eaf36f55
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/event_emitter.py
@@ -0,0 +1,145 @@
+from dataclasses import asdict
+from enum import Enum
+from typing import Union
+
+from opentelemetry._events import Event, EventLogger
+from opentelemetry.semconv._incubating.attributes import \
+ gen_ai_attributes as GenAIAttributes
+
+from opentelemetry.instrumentation.writer.event_models import (ChoiceEvent,
+ MessageEvent)
+from opentelemetry.instrumentation.writer.utils import (dont_throw,
+ model_as_dict,
+ should_emit_events,
+ should_send_prompts)
+
+
+class Roles(Enum):
+ USER = "user"
+ ASSISTANT = "assistant"
+ SYSTEM = "system"
+ TOOL = "tool"
+
+
+VALID_MESSAGE_ROLES = {role.value for role in Roles}
+"""The valid roles for naming the message event."""
+
+EVENT_ATTRIBUTES = {
+ # Should be GenAIAttributes.GenAiSystemValues.WRITER.value but it's not defined in the opentelemetry-semconv package
+ GenAIAttributes.GEN_AI_SYSTEM: "writer"
+}
+"""The attributes to be used for the event."""
+
+
+@dont_throw
+def emit_message_events(kwargs: dict, event_logger):
+ for message in kwargs.get("messages", []):
+ emit_event(
+ MessageEvent(
+ content=message.get("content"), role=message.get("role", "unknown")
+ ),
+ event_logger=event_logger,
+ )
+
+
+@dont_throw
+def emit_choice_events(response, event_logger):
+ response_dict = model_as_dict(response)
+
+ for choice in response_dict.get("choices", []):
+ message = choice.get("message")
+
+ if message:
+ emit_event(
+ ChoiceEvent(
+ index=choice.get("index", 0),
+ message={
+ "content": message.get("content"),
+ "role": message.get("role", "assistant"),
+ },
+ finish_reason=choice.get("finish_reason"),
+ ),
+ event_logger=event_logger,
+ )
+ elif choice.get("text") is not None:
+ emit_event(
+ ChoiceEvent(
+ index=choice.get("index", 0),
+ message={
+ "content": choice.get("text"),
+ "role": "assistant",
+ },
+ finish_reason=choice.get("finish_reason"),
+ ),
+ event_logger=event_logger,
+ )
+
+
+@dont_throw
+def emit_streaming_response_events(
+ accumulated_content: str, finish_reason: Union[str, None], event_logger
+):
+ emit_event(
+ ChoiceEvent(
+ index=0,
+ message={"content": accumulated_content, "role": "assistant"},
+ finish_reason=finish_reason or "unknown",
+ ),
+ event_logger,
+ )
+
+
+def emit_event(
+ event: Union[MessageEvent, ChoiceEvent], event_logger: Union[EventLogger, None]
+) -> None:
+ if not should_emit_events() or event_logger is None:
+ return
+
+ if isinstance(event, MessageEvent):
+ _emit_message_event(event, event_logger)
+ elif isinstance(event, ChoiceEvent):
+ _emit_choice_event(event, event_logger)
+ else:
+ raise TypeError("Unsupported event type")
+
+
+def _emit_message_event(event: MessageEvent, event_logger: EventLogger) -> None:
+ body = asdict(event)
+
+ if event.role in VALID_MESSAGE_ROLES:
+ name = "gen_ai.{}.message".format(event.role)
+ body.pop("role", None)
+ else:
+ name = "gen_ai.user.message"
+
+ if event.role != Roles.ASSISTANT.value and event.tool_calls is not None:
+ del body["tool_calls"]
+ elif event.tool_calls is None:
+ del body["tool_calls"]
+
+ if not should_send_prompts():
+ del body["content"]
+ if body.get("tool_calls") is not None:
+ for tool_call in body["tool_calls"]:
+ tool_call["function"].pop("arguments", None)
+
+ event_logger.emit(Event(name=name, body=body, attributes=EVENT_ATTRIBUTES))
+
+
+def _emit_choice_event(event: ChoiceEvent, event_logger: EventLogger) -> None:
+ body = asdict(event)
+ if event.message["role"] == Roles.ASSISTANT.value:
+ body["message"].pop("role", None)
+
+ if event.tool_calls is None:
+ del body["tool_calls"]
+
+ if not should_send_prompts():
+ body["message"].pop("content", None)
+ if body.get("tool_calls") is not None:
+ for tool_call in body["tool_calls"]:
+ tool_call["function"].pop("arguments", None)
+
+ event_logger.emit(
+ Event(name="gen_ai.choice", body=body, attributes=EVENT_ATTRIBUTES)
+ )
diff --git a/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/event_models.py b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/event_models.py
new file mode 100644
index 0000000000..2b1580f157
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/event_models.py
@@ -0,0 +1,33 @@
+from dataclasses import dataclass
+from typing import Any, List, Literal, Optional, TypedDict
+
+
+class _FunctionToolCall(TypedDict):
+ name: str
+ arguments: Optional[dict[str, Any]]
+
+
+class ToolCall(TypedDict):
+ id: str
+ function: _FunctionToolCall
+ type: Literal["function"]
+
+
+class CompletionMessage(TypedDict):
+ content: Any
+ role: str = "assistant"
+
+
+@dataclass
+class MessageEvent:
+ content: Any
+ role: str = "user"
+ tool_calls: Optional[List[ToolCall]] = None
+
+
+@dataclass
+class ChoiceEvent:
+ index: int
+ message: CompletionMessage
+ finish_reason: str = "unknown"
+ tool_calls: Optional[List[ToolCall]] = None
diff --git a/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/span_utils.py b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/span_utils.py
new file mode 100644
index 0000000000..54101d043e
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/span_utils.py
@@ -0,0 +1,202 @@
+from opentelemetry.semconv_ai import SpanAttributes
+
+from opentelemetry.instrumentation.writer.utils import (dont_throw,
+ model_as_dict,
+ set_span_attribute,
+ should_send_prompts)
+
+
+@dont_throw
+def set_input_attributes(span, kwargs):
+ if not span.is_recording():
+ return
+
+ if should_send_prompts():
+ if kwargs.get("prompt") is not None:
+ set_span_attribute(span, f"{SpanAttributes.LLM_PROMPTS}.0.role", "user")
+ set_span_attribute(
+ span, f"{SpanAttributes.LLM_PROMPTS}.0.content", kwargs.get("prompt")
+ )
+
+ elif kwargs.get("messages") is not None:
+ for i, message in enumerate(kwargs.get("messages")):
+ set_span_attribute(
+ span, f"{SpanAttributes.LLM_PROMPTS}.{i}.role", message.get("role")
+ )
+ set_span_attribute(
+ span,
+ f"{SpanAttributes.LLM_PROMPTS}.{i}.content",
+ message.get("content"),
+ )
+
+ tool_calls = message.get("tool_calls")
+
+ if tool_calls:
+ for j, tool_call in enumerate(tool_calls):
+ set_span_attribute(
+ span,
+ f"{SpanAttributes.LLM_PROMPTS}.{i}.tool_calls.{j}.id",
+ tool_call.get("id"),
+ )
+
+ function = tool_call.get("function", {})
+ set_span_attribute(
+ span,
+ f"{SpanAttributes.LLM_PROMPTS}.{i}.tool_calls.{j}.name",
+ function.get("name"),
+ )
+
+ set_span_attribute(
+ span,
+ f"{SpanAttributes.LLM_PROMPTS}.{i}.tool_calls.{j}.arguments",
+ function.get("arguments"),
+ )
+
+
+@dont_throw
+def set_model_input_attributes(span, kwargs):
+ if not span.is_recording():
+ return
+
+ set_span_attribute(span, SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model"))
+ set_span_attribute(
+ span, SpanAttributes.LLM_REQUEST_MAX_TOKENS, kwargs.get("max_tokens")
+ )
+ set_span_attribute(
+ span, SpanAttributes.LLM_REQUEST_TEMPERATURE, kwargs.get("temperature")
+ )
+ set_span_attribute(span, SpanAttributes.LLM_REQUEST_TOP_P, kwargs.get("top_p"))
+ set_span_attribute(span, SpanAttributes.LLM_CHAT_STOP_SEQUENCES, kwargs.get("stop"))
+ set_span_attribute(
+ span, SpanAttributes.LLM_IS_STREAMING, kwargs.get("stream") or False
+ )
+
+
+@dont_throw
+def set_model_response_attributes(span, response, token_histogram):
+ if not span.is_recording():
+ return
+
+ response_dict = model_as_dict(response)
+
+ set_span_attribute(
+ span, SpanAttributes.LLM_RESPONSE_MODEL, response_dict.get("model")
+ )
+
+ usage = response_dict.get("usage") or {}
+ prompt_tokens = usage.get("prompt_tokens")
+ completion_tokens = usage.get("completion_tokens")
+ total_tokens = usage.get("total_tokens")
+
+ if usage:
+ set_span_attribute(span, SpanAttributes.LLM_USAGE_TOTAL_TOKENS, total_tokens)
+ set_span_attribute(
+ span, SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, completion_tokens
+ )
+ set_span_attribute(span, SpanAttributes.LLM_USAGE_PROMPT_TOKENS, prompt_tokens)
+
+ if (
+ isinstance(prompt_tokens, int)
+ and prompt_tokens >= 0
+ and token_histogram is not None
+ ):
+ token_histogram.record(
+ prompt_tokens,
+ attributes={
+ SpanAttributes.LLM_TOKEN_TYPE: "input",
+ SpanAttributes.LLM_RESPONSE_MODEL: response_dict.get("model"),
+ },
+ )
+
+ if (
+ isinstance(completion_tokens, int)
+ and completion_tokens >= 0
+ and token_histogram is not None
+ ):
+ token_histogram.record(
+ completion_tokens,
+ attributes={
+ SpanAttributes.LLM_TOKEN_TYPE: "output",
+ SpanAttributes.LLM_RESPONSE_MODEL: response_dict.get("model"),
+ },
+ )
+
+
+@dont_throw
+def set_response_attributes(span, response):
+ if not span.is_recording():
+ return
+
+ response_dict = model_as_dict(response)
+ choices = response_dict.get("choices")
+
+ if should_send_prompts() and choices:
+ _set_completions(span, choices)
+
+
+def _set_completions(span, choices):
+ if choices is None or not should_send_prompts():
+ return
+
+ for choice in choices:
+ index = choice.get("index", 0)
+ prefix = f"{SpanAttributes.LLM_COMPLETIONS}.{index}"
+ set_span_attribute(span, f"{prefix}.finish_reason", choice.get("finish_reason"))
+
+ message = choice.get("message")
+
+ if message:
+ set_span_attribute(span, f"{prefix}.role", message.get("role", "assistant"))
+ set_span_attribute(span, f"{prefix}.content", message.get("content"))
+
+ tool_calls = message.get("tool_calls")
+ if tool_calls:
+ for i, tool_call in enumerate(tool_calls):
+ function = tool_call.get("function", {})
+ set_span_attribute(
+ span, f"{prefix}.tool_calls.{i}.id", tool_call.get("id")
+ )
+ set_span_attribute(
+ span, f"{prefix}.tool_calls.{i}.name", function.get("name")
+ )
+ set_span_attribute(
+ span,
+ f"{prefix}.tool_calls.{i}.arguments",
+ function.get("arguments"),
+ )
+
+ elif choice.get("text") is not None:
+ set_span_attribute(span, f"{prefix}.role", "assistant")
+ set_span_attribute(span, f"{prefix}.content", choice.get("text"))
+
+
+def set_streaming_response_attributes(
+ span, accumulated_content, finish_reason=None, usage=None
+):
+ if not span.is_recording() or not should_send_prompts():
+ return
+
+ prefix = f"{SpanAttributes.LLM_COMPLETIONS}.0"
+ set_span_attribute(span, f"{prefix}.role", "assistant")
+ set_span_attribute(span, f"{prefix}.content", accumulated_content)
+
+ if finish_reason:
+ set_span_attribute(span, f"{prefix}.finish_reason", finish_reason)
+
+
+def set_model_streaming_response_attributes(span, usage):
+ if not span.is_recording():
+ return
+
+ if usage:
+ set_span_attribute(
+ span,
+ SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
+ usage.get("completion_tokens"),
+ )
+ set_span_attribute(
+ span, SpanAttributes.LLM_USAGE_PROMPT_TOKENS, usage.get("prompt_tokens")
+ )
+ set_span_attribute(
+ span, SpanAttributes.LLM_USAGE_TOTAL_TOKENS, usage.get("total_tokens")
+ )
diff --git a/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/utils.py b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/utils.py
new file mode 100644
index 0000000000..bc9807663c
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/utils.py
@@ -0,0 +1,94 @@
+import logging
+import os
+import traceback
+from importlib.metadata import version
+
+from opentelemetry.semconv_ai import SpanAttributes
+
+from opentelemetry import context as context_api
+from opentelemetry.instrumentation.writer import Config
+
+GEN_AI_SYSTEM = "gen_ai.system"
+GEN_AI_SYSTEM_WRITER = "writer"
+
+try:
+ _PYDANTIC_VERSION = version("pydantic")
+except ImportError:
+ _PYDANTIC_VERSION = "0.0.0"
+
+TRACELOOP_TRACE_CONTENT = "TRACELOOP_TRACE_CONTENT"
+
+
+def set_span_attribute(span, name, value):
+ if value is not None and value != "":
+ span.set_attribute(name, value)
+
+
+def should_send_prompts():
+ return (
+ os.getenv(TRACELOOP_TRACE_CONTENT) or "true"
+ ).lower() == "true" or context_api.get_value("override_enable_content_tracing")
+
+
+def dont_throw(func):
+ """
+ A decorator that wraps the passed in function and logs exceptions instead of throwing them.
+
+ @param func: The function to wrap
+ @return: The wrapper function
+ """
+ # Obtain a logger specific to the function's module
+ logger = logging.getLogger(func.__module__)
+
+ def wrapper(*args, **kwargs):
+ try:
+ return func(*args, **kwargs)
+ except Exception as e:
+ logger.debug(
+ "OpenLLMetry failed to trace in %s, error: %s",
+ func.__name__,
+ traceback.format_exc(),
+ )
+ if Config.exception_logger:
+ Config.exception_logger(e)
+
+ return wrapper
+
+
+@dont_throw
+def error_metrics_attributes(exception):
+ return {
+ GEN_AI_SYSTEM: GEN_AI_SYSTEM_WRITER,
+ "error.type": exception.__class__.__name__,
+ "error.message": str(exception),
+ }
+
+
+@dont_throw
+def response_attributes(response):
+ response_dict = model_as_dict(response)
+
+ return {
+ GEN_AI_SYSTEM: GEN_AI_SYSTEM_WRITER,
+ SpanAttributes.LLM_RESPONSE_MODEL: response_dict.get("model"),
+ }
+
+
+def should_emit_events() -> bool:
+ """
+ Checks if the instrumentation isn't using the legacy attributes
+ and if the event logger is not None.
+ """
+
+ return not Config.use_legacy_attributes
+
+
+def model_as_dict(model):
+ if _PYDANTIC_VERSION < "2.0.0":
+ return model.dict()
+ if hasattr(model, "model_dump"):
+ return model.model_dump()
+ elif hasattr(model, "parse"):
+ return model_as_dict(model.parse())
+ else:
+ return model
diff --git a/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/version.py b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/version.py
new file mode 100644
index 0000000000..f102a9cadf
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer/version.py
@@ -0,0 +1 @@
+__version__ = "0.0.1"
diff --git a/packages/opentelemetry-instrumentation-writer/poetry.lock b/packages/opentelemetry-instrumentation-writer/poetry.lock
new file mode 100644
index 0000000000..137b35b429
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/poetry.lock
@@ -0,0 +1,1457 @@
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+groups = ["test"]
+files = [
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+]
+
+[[package]]
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+version = "4.9.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
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+ {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+]
+
+[package.dependencies]
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+trio = ["trio (>=0.26.1)"]
+
+[[package]]
+name = "autopep8"
+version = "2.2.0"
+description = "A tool that automatically formats Python code to conform to the PEP 8 style guide"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
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+]
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+[package.dependencies]
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+
+[[package]]
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+
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+uvloop = ["uvloop (>=0.15.2)"]
+
+[[package]]
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+version = "2025.7.14"
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+optional = false
+python-versions = ">=3.7"
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+]
+
+[[package]]
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+version = "7.0.0"
+description = "the modular source code checker: pep8 pyflakes and co"
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+ {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:c7ddf7a09f38667aea38801da8b8d6bfe81df767d9dfc8c88eb45827b195cd1c"},
+ {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:57edc88517d7fc62b174fcfb2e939fbc486a68315d648d7e74d07fac42cec240"},
+ {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:dab096ce479d5894d62c26ff4f699ec9072269d514b4edd630a393223f45a0ee"},
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+ {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:2c89b5c792685dd9cd3fa9761c1b9f46fc240c2a3265483acc1565769996a3f8"},
+ {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:69e9b141de5511021942a6866990aea6d111c9042235de90e08f94cf972ca03d"},
+ {file = "yarl-1.20.1-cp39-cp39-win32.whl", hash = "sha256:b5f307337819cdfdbb40193cad84978a029f847b0a357fbe49f712063cfc4f06"},
+ {file = "yarl-1.20.1-cp39-cp39-win_amd64.whl", hash = "sha256:eae7bfe2069f9c1c5b05fc7fe5d612e5bbc089a39309904ee8b829e322dcad00"},
+ {file = "yarl-1.20.1-py3-none-any.whl", hash = "sha256:83b8eb083fe4683c6115795d9fc1cfaf2cbbefb19b3a1cb68f6527460f483a77"},
+ {file = "yarl-1.20.1.tar.gz", hash = "sha256:d017a4997ee50c91fd5466cef416231bb82177b93b029906cefc542ce14c35ac"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.1"
+
+[[package]]
+name = "zipp"
+version = "3.23.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.9"
+groups = ["main", "test"]
+files = [
+ {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
+ {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[extras]
+instruments = []
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.11,<4"
+content-hash = "d388e6529f8f2e8dd5055a691cce39b00e262fe8000ea10f81e9443b44fb5322"
diff --git a/packages/opentelemetry-instrumentation-writer/poetry.toml b/packages/opentelemetry-instrumentation-writer/poetry.toml
new file mode 100644
index 0000000000..ab1033bd37
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/poetry.toml
@@ -0,0 +1,2 @@
+[virtualenvs]
+in-project = true
diff --git a/packages/opentelemetry-instrumentation-writer/project.json b/packages/opentelemetry-instrumentation-writer/project.json
new file mode 100644
index 0000000000..1c8cc387d5
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/project.json
@@ -0,0 +1,78 @@
+{
+ "name": "opentelemetry-instrumentation-writer",
+ "$schema": "../../node_modules/nx/schemas/project-schema.json",
+ "projectType": "library",
+ "sourceRoot": "packages/opentelemetry-instrumentation-writer/opentelemetry/instrumentation/writer",
+ "targets": {
+ "lock": {
+ "executor": "@nxlv/python:run-commands",
+ "options": {
+ "command": "poetry lock",
+ "cwd": "packages/opentelemetry-instrumentation-writer"
+ }
+ },
+ "add": {
+ "executor": "@nxlv/python:add",
+ "options": {}
+ },
+ "update": {
+ "executor": "@nxlv/python:update",
+ "options": {}
+ },
+ "remove": {
+ "executor": "@nxlv/python:remove",
+ "options": {}
+ },
+ "build": {
+ "executor": "@nxlv/python:build",
+ "outputs": ["{projectRoot}/dist"],
+ "options": {
+ "outputPath": "packages/opentelemetry-instrumentation-writer/dist",
+ "publish": false,
+ "lockedVersions": true,
+ "bundleLocalDependencies": true
+ }
+ },
+ "install": {
+ "executor": "@nxlv/python:install",
+ "options": {
+ "silent": false,
+ "args": "",
+ "cacheDir": ".cache/pypoetry",
+ "verbose": false,
+ "debug": false
+ }
+ },
+ "lint": {
+ "executor": "@nxlv/python:flake8",
+ "outputs": [
+ "{workspaceRoot}/reports/packages/opentelemetry-instrumentation-writer/pylint.txt"
+ ],
+ "options": {
+ "outputFile": "reports/packages/opentelemetry-instrumentation-writer/pylint.txt"
+ }
+ },
+ "test": {
+ "executor": "@nxlv/python:run-commands",
+ "outputs": [
+ "{workspaceRoot}/reports/packages/opentelemetry-instrumentation-writer/unittests",
+ "{workspaceRoot}/coverage/packages/opentelemetry-instrumentation-writer"
+ ],
+ "options": {
+ "command": "poetry run pytest tests/",
+ "cwd": "packages/opentelemetry-instrumentation-writer"
+ }
+ },
+ "build-release": {
+ "executor": "@nxlv/python:run-commands",
+ "options": {
+ "commands": [
+ "chmod +x ../../scripts/build-release.sh",
+ "../../scripts/build-release.sh"
+ ],
+ "cwd": "packages/opentelemetry-instrumentation-writer"
+ }
+ }
+ },
+ "tags": ["instrumentation"]
+}
diff --git a/packages/opentelemetry-instrumentation-writer/pyproject.toml b/packages/opentelemetry-instrumentation-writer/pyproject.toml
new file mode 100644
index 0000000000..1d7dc86bf4
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-writer/pyproject.toml
@@ -0,0 +1,53 @@
+[tool.coverage.run]
+branch = true
+source = ["opentelemetry/instrumentation/writer"]
+
+[tool.coverage.report]
+exclude_lines = ['if TYPE_CHECKING:']
+show_missing = true
+
+[tool.poetry]
+name = "opentelemetry-instrumentation-writer"
+version = "0.0.1"
+description = "OpenTelemetry Writer instrumentation"
+authors = ["Yan Tolstoy ", "Writer, Inc. "]
+repository = "https://github.com/traceloop/openllmetry/tree/main/packages/opentelemetry-instrumentation-writer"
+license = "Apache-2.0"
+readme = "README.md"
+
+[[tool.poetry.packages]]
+include = "opentelemetry/instrumentation/writer"
+
+[tool.poetry.dependencies]
+python = ">=3.11,<4"
+opentelemetry-api = "^1.28.0"
+opentelemetry-instrumentation = ">=0.50b0"
+opentelemetry-semantic-conventions = ">=0.50b0"
+opentelemetry-semantic-conventions-ai = "0.4.11"
+black = "^25.1.0"
+isort = "^6.0.1"
+
+[tool.poetry.group.dev.dependencies]
+autopep8 = "^2.2.0"
+flake8 = "7.0.0"
+pytest = "^8.2.2"
+pytest-sugar = "1.0.0"
+
+[tool.poetry.group.test.dependencies]
+pytest = "^8.2.2"
+pytest-sugar = "1.0.0"
+vcrpy = "^6.0.1"
+pytest-recording = "^0.13.1"
+pytest-asyncio = "^0.23.7"
+opentelemetry-sdk = "^1.27.0"
+writer-sdk = "^2.2.1"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.extras]
+instruments = ["writer"]
+
+[tool.poetry.plugins."opentelemetry_instrumentor"]
+writer = "opentelemetry.instrumentation.writer:WriterInstrumentor"
diff --git a/packages/opentelemetry-instrumentation-writer/tests/__init__.py b/packages/opentelemetry-instrumentation-writer/tests/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/packages/traceloop-sdk/pyproject.toml b/packages/traceloop-sdk/pyproject.toml
index 4e538c6410..277ce9777e 100644
--- a/packages/traceloop-sdk/pyproject.toml
+++ b/packages/traceloop-sdk/pyproject.toml
@@ -60,6 +60,7 @@ opentelemetry-instrumentation-replicate = { path = "../opentelemetry-instrumenta
opentelemetry-instrumentation-vertexai = { path = "../opentelemetry-instrumentation-vertexai", develop = true }
opentelemetry-instrumentation-watsonx = { path = "../opentelemetry-instrumentation-watsonx", develop = true }
opentelemetry-instrumentation-weaviate = { path = "../opentelemetry-instrumentation-weaviate", develop = true }
+opentelemetry-instrumentation-writer = { path = "../opentelemetry-instrumentation-writer", develop = true }
opentelemetry-instrumentation-alephalpha = { path = "../opentelemetry-instrumentation-alephalpha", develop = true }
opentelemetry-instrumentation-marqo = { path = "../opentelemetry-instrumentation-marqo", develop = true }
opentelemetry-instrumentation-groq = { path = "../opentelemetry-instrumentation-groq", develop = true }
diff --git a/packages/traceloop-sdk/traceloop/sdk/instruments.py b/packages/traceloop-sdk/traceloop/sdk/instruments.py
index aded2e0dfa..fb8ddd4e28 100644
--- a/packages/traceloop-sdk/traceloop/sdk/instruments.py
+++ b/packages/traceloop-sdk/traceloop/sdk/instruments.py
@@ -34,3 +34,4 @@ class Instruments(Enum):
VERTEXAI = "vertexai"
WATSONX = "watsonx"
WEAVIATE = "weaviate"
+ WRITER = "writer"
diff --git a/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py b/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py
index b7fab46364..eb78639310 100644
--- a/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py
+++ b/packages/traceloop-sdk/traceloop/sdk/tracing/tracing.py
@@ -535,6 +535,9 @@ def init_instrumentations(
elif instrument == Instruments.WEAVIATE:
if init_weaviate_instrumentor():
instrument_set = True
+ elif instrument == Instruments.WRITER:
+ if init_writer_instrumentor():
+ instrument_set = True
else:
print(Fore.RED + f"Warning: {instrument} instrumentation does not exist.")
print(
@@ -994,6 +997,23 @@ def init_weaviate_instrumentor():
Telemetry().log_exception(e)
return False
+def init_writer_instrumentor():
+ try:
+ if is_package_installed("writer-sdk"):
+ Telemetry().capture("instrumentation:writer:init")
+ from opentelemetry.instrumentation.writer import WriterInstrumentor
+
+ instrumentor = WriterInstrumentor(
+ exception_logger=lambda e: Telemetry().log_exception(e),
+ )
+ if not instrumentor.is_instrumented_by_opentelemetry:
+ instrumentor.instrument()
+ return True
+ except Exception as e:
+ logging.error(f"Error initializing Writer instrumentor: {e}")
+ Telemetry().log_exception(e)
+ return False
+
def init_alephalpha_instrumentor():
try: