Merge branch 'litellm_internal_staging' of https://github.com/BerriAI/litellm into litellm_otel-improvement

merge parent

litellm/integrations/custom_guardrail.py

@@ -888,6 +888,15 @@ def log_guardrail_information(func):
         - pre_call
         - during_call
         - post_call
+
+    Some guardrails (e.g. ``block_code_execution``) call
+    ``add_standard_logging_guardrail_information_to_request_data`` directly
+    from inside the wrapped function so they can record a richer payload
+    (structured detections, tracing detail) than this decorator's
+    "allow"/"mask"/raw-response default. To avoid double-recording in that
+    case (which would emit two spans, two Datadog records, two spend-log
+    entries, etc.), snapshot the entry count before invocation: if the
+    wrapped function already appended its own entry, skip the auto-record.
     """
     import functools
     import inspect
@@ -907,6 +916,16 @@ def log_guardrail_information(func):
             return GuardrailEventHooks.post_call
         return None
 
+    def _count_recorded_guardrail_entries(request_data: dict) -> int:
+        total = 0
+        for container_key in ("metadata", "litellm_metadata"):
+            container = request_data.get(container_key)
+            if isinstance(container, dict):
+                entries = container.get("standard_logging_guardrail_information")
+                if isinstance(entries, list):
+                    total += len(entries)
+        return total
+
     @functools.wraps(func)
     async def async_wrapper(*args, **kwargs):
         start_time = datetime.now()  # Move start_time inside the wrapper
@@ -919,8 +938,11 @@ def log_guardrail_information(func):
         if func.__name__ == "apply_guardrail" and "inputs" in kwargs:
             original_inputs = kwargs.get("inputs")
 
+        entries_before = _count_recorded_guardrail_entries(request_data)
         try:
             response = await func(*args, **kwargs)
+            if _count_recorded_guardrail_entries(request_data) > entries_before:
+                return response
             return self._process_response(
                 response=response,
                 request_data=request_data,
@@ -931,6 +953,8 @@ def log_guardrail_information(func):
                 original_inputs=original_inputs,
             )
         except Exception as e:
+            if _count_recorded_guardrail_entries(request_data) > entries_before:
+                raise
             return self._process_error(
                 e=e,
                 request_data=request_data,
@@ -952,8 +976,11 @@ def log_guardrail_information(func):
         if func.__name__ == "apply_guardrail" and "inputs" in kwargs:
             original_inputs = kwargs.get("inputs")
 
+        entries_before = _count_recorded_guardrail_entries(request_data)
         try:
             response = func(*args, **kwargs)
+            if _count_recorded_guardrail_entries(request_data) > entries_before:
+                return response
             return self._process_response(
                 response=response,
                 request_data=request_data,
@@ -962,6 +989,8 @@ def log_guardrail_information(func):
                 original_inputs=original_inputs,
             )
         except Exception as e:
+            if _count_recorded_guardrail_entries(request_data) > entries_before:
+                raise
             return self._process_error(
                 e=e,
                 request_data=request_data,

litellm/integrations/opentelemetry.py

@@ -237,7 +237,14 @@ class OpenTelemetry(CustomLogger):
             not isinstance(cb, OpenTelemetry) for cb in litellm.service_callback
         ):
             litellm.service_callback.append(self)
-        setattr(proxy_server, "open_telemetry_logger", self)
+        # avoid proxy logger ownership being overwritten by later
+        # handlers. Multiple integrations (default OTEL, Langfuse OTEL,
+        # Arize OTEL, etc.) may initialize in sequence; without this guard,
+        # the last one silently replaces the first and breaks expected
+        # routing for proxy_server.open_telemetry_logger consumers.
+        # Behavior: first-registered wins.
+        if getattr(proxy_server, "open_telemetry_logger", None) is None:
+            setattr(proxy_server, "open_telemetry_logger", self)
 
     def _get_or_create_provider(
         self,
@@ -794,12 +801,100 @@ class OpenTelemetry(CustomLogger):
     # End of Team/Key Based Logging Control Flow
     #########################################################
 
+    def _emit_once(self, kwargs: dict, *scope: object) -> bool:
+        """Return True the first time this handler is asked to emit a span
+        for the given (handler, scope) on this kwargs; False on repeats.
+
+        Used to suppress duplicate span emission for two distinct patterns:
+
+        1. **Handler-level dual-fire**: streaming code paths trigger both
+           the sync and async callback for one request, so ``_handle_success``
+           / ``_handle_failure`` would otherwise produce two
+           ``litellm_request`` spans. Scope: ``("success",)`` / ``("failure",)``.
+        2. **Payload-driven multi-entrypoint emission**: a span loop that
+           reads entries from ``standard_logging_payload`` (currently only
+           guardrails) is invoked from multiple lifecycle points
+           (post-call hooks, success callback, failure callback). The list
+           can be re-read with mutated entries between calls, so dedupe
+           must be at entry granularity. Scope: the entry's stable identity.
+
+        ``scope`` parts can be any hashable identity. The marker is stored
+        in ``kwargs["litellm_params"]["metadata"]["_otel_internal"]`` so it
+        is request-local (kwargs is shared across the sync/async callbacks
+        and lifecycle hooks for one request).
+        """
+        litellm_params = kwargs.get("litellm_params")
+        if not isinstance(litellm_params, dict):
+            litellm_params = {}
+            kwargs["litellm_params"] = litellm_params
+
+        _metadata = litellm_params.get("metadata")
+        if not isinstance(_metadata, dict):
+            _metadata = {}
+            litellm_params["metadata"] = _metadata
+
+        _otel_internal = _metadata.get("_otel_internal")
+        if not isinstance(_otel_internal, dict):
+            _otel_internal = {}
+            _metadata["_otel_internal"] = _otel_internal
+
+        spans_logged = _otel_internal.get("spans_logged")
+        if not isinstance(spans_logged, dict):
+            spans_logged = {}
+            _otel_internal["spans_logged"] = spans_logged
+
+        dedupe_key = (self.__class__.__name__, id(self), *scope)
+        if spans_logged.get(dedupe_key) is True:
+            return False
+
+        spans_logged[dedupe_key] = True
+        return True
+
+    def _end_proxy_span_from_kwargs(self, kwargs: dict, end_time) -> None:
+        """Close the proxy-level parent span if it is still recording.
+
+        This helper retrieves the proxy span directly from kwargs metadata
+        and closes it after all child spans have been recorded.
+
+        Only called from the success path. The failure path deliberately
+        leaves the proxy span open so ``async_post_call_failure_hook`` can
+        append the ``"Failed Proxy Server Request"`` child span before
+        closing it.
+
+        Only spans named ``LITELLM_PROXY_REQUEST_SPAN_NAME`` are closed —
+        externally provided spans must not be closed by LiteLLM.
+        """
+        litellm_params = kwargs.get("litellm_params", {}) or {}
+        _metadata = litellm_params.get("metadata", {}) or {}
+        proxy_span = _metadata.get("litellm_parent_otel_span", None)
+        if (
+            proxy_span is not None
+            and getattr(proxy_span, "name", None) == LITELLM_PROXY_REQUEST_SPAN_NAME
+            and hasattr(proxy_span, "is_recording")
+            and proxy_span.is_recording()
+        ):
+            proxy_span.end(end_time=self._to_ns(end_time))
+
     def _handle_success(self, kwargs, response_obj, start_time, end_time):
+        """Create the litellm_request span then close the proxy span."""
         verbose_logger.debug(
             "OpenTelemetry Logger: Logging kwargs: %s, OTEL config settings=%s",
             kwargs,
             self.config,
         )
+
+        # sync + async success handlers can both fire for one
+        # request (notably in streaming code paths). Guard against duplicate
+        # span writes — but still close the proxy span on the skip path so
+        # the trace doesn't leak an open root span.
+        if not self._emit_once(kwargs, "success"):
+            verbose_logger.debug(
+                "OpenTelemetry: skipping duplicate success span for handler=%s",
+                self.__class__.__name__,
+            )
+            self._end_proxy_span_from_kwargs(kwargs, end_time)
+            return
+
         ctx, parent_span = self._get_span_context(kwargs)
 
         if self.config.ignore_context_propagation:
@@ -859,7 +954,7 @@ class OpenTelemetry(CustomLogger):
 
         # 6. Do NOT end parent span - it should be managed by its creator
         # External spans (from Langfuse, user code, HTTP headers, global context) must not be closed by LiteLLM
-        # However, proxy-created spans should be closed here
+        # However, proxy-created spans should be closed here.
         if (
             parent_span is not None
             and hasattr(parent_span, "name")
@@ -867,6 +962,11 @@ class OpenTelemetry(CustomLogger):
         ):
             parent_span.end(end_time=self._to_ns(end_time))
 
+        # close the proxy span explicitly from kwargs metadata
+        # after all child spans (litellm_request, guardrail, raw_request)
+        # have been fully recorded and exported.
+        self._end_proxy_span_from_kwargs(kwargs, end_time)
+
     def _start_primary_span(
         self,
         kwargs,
@@ -1296,6 +1396,21 @@ class OpenTelemetry(CustomLogger):
         for guardrail_information in guardrail_information_list:
             start_time_float = guardrail_information.get("start_time")
             end_time_float = guardrail_information.get("end_time")
+
+            # ``_create_guardrail_span`` is called from three lifecycle
+            # points (``async_post_call_success_hook``, ``_handle_success``,
+            # ``_handle_failure``) and re-reads the (mutating) entry list
+            # each time. Dedupe at entry granularity so a single real
+            # guardrail invocation produces exactly one span per handler.
+            if not self._emit_once(
+                kwargs,
+                "guardrail",
+                guardrail_information.get("guardrail_name"),
+                start_time_float,
+                guardrail_information.get("guardrail_mode"),
+            ):
+                continue
+
             start_time_datetime = datetime.now()
             if start_time_float is not None:
                 start_time_datetime = datetime.fromtimestamp(start_time_float)
@@ -1349,6 +1464,21 @@ class OpenTelemetry(CustomLogger):
             kwargs,
             self.config,
         )
+
+        # sync + async failure handlers can both fire for one
+        # request (notably in streaming code paths), producing two
+        # semantically identical ERROR spans. Unlike the success path, the
+        # proxy span is intentionally left open here so that
+        # ``async_post_call_failure_hook`` can append the
+        # "Failed Proxy Server Request" child span before closing it —
+        # there is no proxy-span side-effect to preserve on the skip path.
+        if not self._emit_once(kwargs, "failure"):
+            verbose_logger.debug(
+                "OpenTelemetry: skipping duplicate failure span for handler=%s",
+                self.__class__.__name__,
+            )
+            return
+
         _parent_context, parent_otel_span = self._get_span_context(kwargs)
 
         if self.config.ignore_context_propagation:
@@ -2188,7 +2318,7 @@ class OpenTelemetry(CustomLogger):
             verbose_logger.debug(
                 "OpenTelemetry: Using explicit parent span from metadata"
             )
-            return trace.set_span_in_context(parent_otel_span), parent_otel_span
+            return trace.set_span_in_context(parent_otel_span), None
 
         # Priority 2: HTTP traceparent header
         if traceparent is not None:

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -4977,8 +4977,9 @@ class BedrockConverseMessagesProcessor:
             )
             if reasoning_text and not reasoning_text.get("signature"):
                 reasoning_text_text = reasoning_text["text"]
-                assistants_part = BedrockContentBlock(text=reasoning_text_text)
-                assistant_parts.append(assistants_part)
+                if reasoning_text_text.strip():
+                    assistants_part = BedrockContentBlock(text=reasoning_text_text)
+                    assistant_parts.append(assistants_part)
             else:
                 filtered_thinking_blocks.append(block)
         if len(filtered_thinking_blocks) > 0:

litellm/llms/gemini/google_genai/transformation.py

@@ -2,6 +2,7 @@
 Transformation for Calling Google models in their native format.
 """
 
+from copy import deepcopy
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast
 
 import httpx
@@ -11,6 +12,10 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
 from litellm.llms.base_llm.google_genai.transformation import (
     BaseGoogleGenAIGenerateContentConfig,
 )
+from litellm.llms.vertex_ai.common_utils import (
+    _build_vertex_schema,
+    supports_response_json_schema,
+)
 from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
 from litellm.types.router import GenericLiteLLMParams
 
@@ -302,6 +307,52 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             litellm_params=litellm_params,
         )
 
+    @staticmethod
+    def _normalize_response_schema(
+        generate_content_config_dict: Dict, model: str
+    ) -> None:
+        schema_key = next(
+            (
+                k
+                for k in ("responseSchema", "response_schema")
+                if k in generate_content_config_dict
+            ),
+            None,
+        )
+        json_schema_key = next(
+            (
+                k
+                for k in ("responseJsonSchema", "response_json_schema")
+                if k in generate_content_config_dict
+            ),
+            None,
+        )
+
+        if schema_key is None:
+            return
+
+        value = generate_content_config_dict[schema_key]
+        if not isinstance(value, dict):
+            return
+
+        if supports_response_json_schema(model):
+            if json_schema_key is not None:
+                generate_content_config_dict.pop(schema_key)
+                return
+            generate_content_config_dict.pop(schema_key)
+            new_json_schema_key = (
+                "response_json_schema"
+                if schema_key == "response_schema"
+                else "responseJsonSchema"
+            )
+            generate_content_config_dict[new_json_schema_key] = value
+        else:
+            if json_schema_key is not None:
+                generate_content_config_dict.pop(json_schema_key)
+            generate_content_config_dict[schema_key] = _build_vertex_schema(
+                parameters=deepcopy(value), add_property_ordering=True
+            )
+
     def transform_generate_content_request(
         self,
         model: str,
@@ -315,6 +366,8 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             GenerateContentRequestDict,
         )
 
+        self._normalize_response_schema(generate_content_config_dict, model)
+
         typed_generate_content_request = GenerateContentRequestDict(
             model=model,
             contents=contents,

litellm/llms/vertex_ai/google_genai/transformation.py

@@ -79,6 +79,9 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig):
         Transform the generate content request for Vertex AI.
         Since Vertex AI natively supports Google GenAI format, we can pass most fields directly.
         """
+        if generate_content_config_dict:
+            self._normalize_response_schema(generate_content_config_dict, model)
+
         # Build the request in Google GenAI format that Vertex AI expects
         result = {
             "model": model,

litellm/proxy/_experimental/out/__next.__PAGE__.txt

@@ -1,30 +1,30 @@
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