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class TraceloopLogger:
def __init__(self):
from traceloop.sdk.tracing.tracing import TracerWrapper
from traceloop.sdk import Traceloop
Traceloop.init(app_name="Litellm-Server", disable_batch=True)
self.tracer_wrapper = TracerWrapper()
def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
from opentelemetry.trace import SpanKind
from opentelemetry.semconv.ai import SpanAttributes
try:
tracer = self.tracer_wrapper.get_tracer()
model = kwargs.get("model")
# LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK
if kwargs.get("litellm_params").get("custom_llm_provider") == "openai":
return
optional_params = kwargs.get("optional_params", {})
with tracer.start_as_current_span(
"litellm.completion",
kind=SpanKind.CLIENT,
) as span:
if span.is_recording():
span.set_attribute(
SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model")
)
if "stop" in optional_params:
span.set_attribute(
SpanAttributes.LLM_CHAT_STOP_SEQUENCES,
optional_params.get("stop"),
)
if "frequency_penalty" in optional_params:
span.set_attribute(
SpanAttributes.LLM_FREQUENCY_PENALTY,
optional_params.get("frequency_penalty"),
)
if "presence_penalty" in optional_params:
span.set_attribute(
SpanAttributes.LLM_PRESENCE_PENALTY,
optional_params.get("presence_penalty"),
)
if "top_p" in optional_params:
span.set_attribute(
SpanAttributes.LLM_TOP_P, optional_params.get("top_p")
)
if "tools" in optional_params or "functions" in optional_params:
span.set_attribute(
SpanAttributes.LLM_REQUEST_FUNCTIONS,
optional_params.get(
"tools", optional_params.get("functions")
),
)
if "user" in optional_params:
span.set_attribute(
SpanAttributes.LLM_USER, optional_params.get("user")
)
if "max_tokens" in optional_params:
span.set_attribute(
SpanAttributes.LLM_REQUEST_MAX_TOKENS,
kwargs.get("max_tokens"),
)
if "temperature" in optional_params:
span.set_attribute(
SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature")
)
for idx, prompt in enumerate(kwargs.get("messages")):
span.set_attribute(
f"{SpanAttributes.LLM_PROMPTS}.{idx}.role",
prompt.get("role"),
)
span.set_attribute(
f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
prompt.get("content"),
)
span.set_attribute(
SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model")
)
usage = response_obj.get("usage")
if usage:
span.set_attribute(
SpanAttributes.LLM_USAGE_TOTAL_TOKENS,
usage.get("total_tokens"),
)
span.set_attribute(
SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
usage.get("completion_tokens"),
)
span.set_attribute(
SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
usage.get("prompt_tokens"),
)
for idx, choice in enumerate(response_obj.get("choices")):
span.set_attribute(
f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason",
choice.get("finish_reason"),
)
span.set_attribute(
f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role",
choice.get("message").get("role"),
)
span.set_attribute(
f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
choice.get("message").get("content"),
)
except Exception as e:
print_verbose(f"Traceloop Layer Error - {e}")
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