diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py
index 580e55273f6..bb59b4617af 100644
--- a/litellm/completion_extras/litellm_responses_transformation/transformation.py
+++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py
@@ -1979,6 +1979,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
         self._tool_call_index_map: dict[int, int] = {}  # mutable-ok: per-stream accumulator state
         self._structured_output: Final = structured_output
         self._first_message_item_done: bool = False
+        self._reasoning_summary_streamed: bool = False
 
     def _handle_string_chunk(
         self, str_line: Union[str, "BaseModel"]
@@ -2021,6 +2022,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
         tool_call_index_map: dict[int, int] | None = None,  # mutable-ok: per-stream state, remapped in place
         replays_openai_history: bool = False,
         structured_output: bool = False,
+        reasoning_summary_streamed: bool = False,
     ) -> "ModelResponseStream":
         """
         Translate a Responses API streaming chunk to OpenAI chat completion streaming format.
@@ -2030,6 +2032,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
             tool_call_index_map: Per-stream output_index -> sequential tool_call index map
             replays_openai_history: Record the completed output's item sequence for the next turn
             structured_output: Only the first message item became content
+            reasoning_summary_streamed: Whether earlier chunks already streamed reasoning summary text
 
         Returns:
             ModelResponseStream: OpenAI-formatted streaming chunk
@@ -2210,13 +2213,15 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
                 )
             else:
                 raise ValueError(f"Chat provider: Invalid text delta {parsed_chunk}")
+        elif event_type == "response.reasoning_summary_part.added" and reasoning_summary_streamed:
+            return ModelResponseStream(choices=[StreamingChoices(index=0, delta=Delta(reasoning_content=" "))])
         elif event_type == "response.reasoning_summary_text.delta":
             content_part = parsed_chunk.get("delta", None)
             if content_part:
                 return ModelResponseStream(
                     choices=[
                         StreamingChoices(
-                            index=cast(int, parsed_chunk.get("summary_index")),
+                            index=0,
                             delta=Delta(reasoning_content=content_part),
                         )
                     ]
@@ -2317,10 +2322,13 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
                 tool_call_index_map=self._tool_call_index_map,
                 replays_openai_history=self._replays_openai_history,
                 structured_output=self._structured_output,
+                reasoning_summary_streamed=self._reasoning_summary_streamed,
             )
         )
         if self._structured_output and _is_message_item_done(chunk):
             self._first_message_item_done = True
+        if _responses_event_type(chunk) == "response.reasoning_summary_text.delta" and chunk.get("delta"):
+            self._reasoning_summary_streamed = True
         return self._with_served_service_tier(self._with_stream_scoped_id(translated))
 
     def _is_text_delta_after_structured_answer(self, chunk: Mapping[str, object]) -> bool:
diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
index 8e1791c3c92..c69d9277d65 100644
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -3,7 +3,7 @@ import builtins
 import time
 from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
 from dataclasses import dataclass, replace
-from itertools import chain, groupby
+from itertools import chain, groupby, takewhile
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Final, Optional, Protocol, TypeAlias, TypedDict, TypeVar, Union, cast
 
@@ -257,7 +257,7 @@ def apply_grounding_request_counts(
 
 
 class ChunkProcessor:
-    def __init__(self, chunks: list, messages: Sequence | None = None):
+    def __init__(self, chunks: list, messages: Sequence[object] | None = None):
         self.chunks = self._sort_chunks(chunks)
         self.messages = messages
         self.first_chunk = chunks[0]
@@ -390,6 +390,30 @@ class ChunkProcessor:
             case _:
                 return "assistant"
 
+    @staticmethod
+    def _finish_reason_of_chunks(chunks: Sequence["_BaseChunk"], unreported: str = "stop") -> object:
+        finish_reason = unreported
+        for chunk in chunks:
+            if "choices" in chunk and len(chunk["choices"]) > 0:
+                chunk_finish_reason = None
+                if hasattr(chunk["choices"][0], "finish_reason"):
+                    chunk_finish_reason = chunk["choices"][0].finish_reason
+                elif "finish_reason" in chunk["choices"][0]:
+                    chunk_finish_reason = _CHOICE_FIELD_VALUE.validate_python(chunk["choices"][0]["finish_reason"])
+                if chunk_finish_reason is not None:
+                    finish_reason = chunk_finish_reason
+        return finish_reason
+
+    @staticmethod
+    def build_base_choice(index: int, chunks: Sequence["_BaseChunk"]) -> Choices:
+        return Choices(
+            index=index,
+            finish_reason=ChunkProcessor._finish_reason_of_chunks(
+                chunks, unreported="tool_calls" if _carries_tool_calls(chunks) else "stop"
+            ),
+            message={"role": ChunkProcessor._get_role_from_chunks(chunks), "content": ""},
+        )
+
     @staticmethod
     def _get_model_from_chunks(chunks: Sequence["_BaseChunk"], first_chunk_model: str) -> str:
         """
@@ -414,7 +438,11 @@ class ChunkProcessor:
             None,
         )
 
-    def build_base_response(self, chunks: Sequence["_BaseChunk"]) -> ModelResponse:
+    def build_base_response(
+        self,
+        chunks: Sequence["_BaseChunk"],
+        choice_streams: Sequence[tuple[int, Sequence["_BaseChunk"]]],
+    ) -> ModelResponse:
         chunk = self.first_chunk
         id: Final = ChunkProcessor._get_chunk_id(chunks)
         object: Final = chunk["object"]
@@ -426,16 +454,7 @@ class ChunkProcessor:
         service_tier: Final = ChunkProcessor._get_service_tier_from_chunks(chunks)
 
         role: Final = ChunkProcessor._get_role_from_chunks(chunks)
-        finish_reason = "stop"
-        for chunk in chunks:
-            if "choices" in chunk and len(chunk["choices"]) > 0:
-                chunk_finish_reason = None
-                if hasattr(chunk["choices"][0], "finish_reason"):
-                    chunk_finish_reason = chunk["choices"][0].finish_reason
-                elif "finish_reason" in chunk["choices"][0]:
-                    chunk_finish_reason = _CHOICE_FIELD_VALUE.validate_python(chunk["choices"][0]["finish_reason"])
-                if chunk_finish_reason is not None:
-                    finish_reason = chunk_finish_reason
+        finish_reason: Final = ChunkProcessor._finish_reason_of_chunks(chunks)
 
         # Initialize the response dictionary
         response = ModelResponse(
@@ -455,13 +474,17 @@ class ChunkProcessor:
                     if service_tier is not None
                     else MappingProxyType({})
                 ),
-                "choices": [
-                    {
-                        "index": 0,
-                        "message": {"role": role, "content": ""},
-                        "finish_reason": finish_reason,
-                    }
-                ],
+                "choices": (
+                    [ChunkProcessor.build_base_choice(index, choice_chunks) for index, choice_chunks in choice_streams]
+                    if len(choice_streams) > 1
+                    else [
+                        {
+                            "index": 0,
+                            "message": {"role": role, "content": ""},
+                            "finish_reason": finish_reason,
+                        }
+                    ]
+                ),
                 "usage": {
                     "prompt_tokens": 0,  # Modify as needed
                     "completion_tokens": 0,  # Modify as needed
@@ -1520,11 +1543,188 @@ def _bedrock_trace_fields(chunk: object) -> Mapping[str, object]:
     return {field: fields[field] for field in BEDROCK_TRACE_FIELDS if field in fields}
 
 
+def _stream_choice_index(choice: object) -> int:
+    index: Final = _field_of(choice, "index")
+    return index if isinstance(index, int) else 0
+
+
+def _carries_tool_calls(chunks: Sequence[_BaseChunk]) -> bool:
+    return any(
+        "tool_calls" in choice["delta"] and choice["delta"]["tool_calls"]
+        for choice in chain.from_iterable(chunk.get("choices") or () for chunk in chunks)
+    )
+
+
+def _choice_streams(chunks: Sequence[_BaseChunk]) -> tuple[tuple[int, Sequence[_BaseChunk]], ...]:
+    indices: Final = sorted(
+        frozenset(map(_stream_choice_index, chain.from_iterable(chunk.get("choices") or () for chunk in chunks)))
+    )
+    if len(indices) <= 1:
+        return ((0, chunks),)
+    return tuple((index, _narrowed_to_choice(chunks, index)) for index in indices)
+
+
+def _narrowed_to_choice(chunks: Sequence[_BaseChunk], index: int) -> tuple[_BaseChunk, ...]:
+    return tuple(
+        _BaseChunk(choices=[choice for choice in chunk.get("choices") or () if _stream_choice_index(choice) == index])
+        for chunk in chunks
+    )
+
+
+def _simple_text(chunks: Sequence[_BaseChunk]) -> str | None:
+    choices_per_chunk: Final = tuple(chunk["choices"] for chunk in chunks if chunk.get("choices"))
+    parts: Final = tuple(takewhile(lambda part: part is not None, map(_simple_text_part, choices_per_chunk)))
+    return "".join(filter(None, parts)) if len(parts) == len(choices_per_chunk) else None
+
+
+def _assembled_message_fields(processor: ChunkProcessor, chunks: Sequence) -> Iterator[tuple[str, object]]:
+    tool_call_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if any(
+            "tool_calls" in choice["delta"] and choice["delta"]["tool_calls"] is not None
+            for choice in chunk.get("choices") or ()
+        )
+    ]
+
+    if tool_call_chunks:
+        tool_calls_list: Final = processor.get_combined_tool_content(tool_call_chunks)
+        yield "content", None
+        yield "tool_calls", tool_calls_list
+
+    function_call_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "function_call" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["function_call"] is not None
+    ]
+
+    if function_call_chunks:
+        yield "content", None
+        yield "function_call", processor.get_combined_function_call_content(function_call_chunks)
+
+    content_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "content" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["content"] is not None
+    ]
+
+    if content_chunks:
+        yield "content", processor.get_combined_content(content_chunks)
+
+    thinking_blocks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "thinking_blocks" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["thinking_blocks"] is not None
+    ]
+
+    if thinking_blocks:
+        yield "thinking_blocks", processor.get_combined_thinking_content(thinking_blocks)
+
+    reasoning_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "reasoning_content" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["reasoning_content"] is not None
+    ]
+
+    if reasoning_chunks:
+        yield "reasoning_content", processor.get_combined_reasoning_content(reasoning_chunks)
+
+    typed_chunks: Final = cast(Sequence[object], chunks)  # cast-ok: chunks are untyped upstream
+    reasoning_items: Final = tuple(chain.from_iterable(_chunk_reasoning_items(chunk) for chunk in typed_chunks))
+    if reasoning_items:
+        yield "reasoning_items", list(reasoning_items)
+
+    annotation_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "annotations" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["annotations"] is not None
+    ]
+
+    message_annotations, provider_annotations = (
+        partition_annotations(
+            list(chain.from_iterable(chunk["choices"][0]["delta"]["annotations"] for chunk in annotation_chunks))
+        )
+        if annotation_chunks
+        else (None, ())
+    )
+    if message_annotations is not None:
+        yield "annotations", message_annotations
+
+    audio_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "audio" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["audio"] is not None
+    ]
+
+    if audio_chunks:
+        yield "audio", processor.get_combined_audio_content(audio_chunks)
+
+    # Handle image chunks from models like gemini-2.5-flash-image
+    # See: https://github.com/BerriAI/litellm/issues/19478
+    image_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "images" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["images"] is not None
+    ]
+
+    if image_chunks:
+        # Images come complete in a single chunk, collect all images from all chunks
+        all_images: Final = []
+        for chunk in image_chunks:
+            all_images.extend(chunk["choices"][0]["delta"]["images"])
+        yield "images", all_images
+
+    # Combine provider_specific_fields from streaming chunks (e.g., web_search_results, citations)
+    # See: https://github.com/BerriAI/litellm/issues/17737
+    provider_specific_chunks: Final = [
+        chunk
+        for chunk in chunks
+        if chunk.get("choices")
+        and "provider_specific_fields" in chunk["choices"][0]["delta"]
+        and chunk["choices"][0]["delta"]["provider_specific_fields"] is not None
+    ]
+
+    provider_field_dicts: Final = tuple(
+        fields
+        for chunk in provider_specific_chunks
+        for fields in (chunk["choices"][0]["delta"]["provider_specific_fields"],)
+        if isinstance(fields, dict)
+    )
+    streamed_citations: Final = tuple(
+        fields["citation"] for fields in provider_field_dicts if fields.get("citation") is not None
+    )
+    citation_fields: Final = {"citations": _joined_streamed_citations(streamed_citations)} if streamed_citations else {}
+    provider_annotation_fields: Final = {"annotations": list(provider_annotations)} if provider_annotations else {}
+    combined_provider_fields: Final = {
+        key: value
+        for fields in (provider_annotation_fields, citation_fields, *provider_field_dicts)
+        for key, value in fields.items()
+        if key != "citation"
+    }
+
+    if combined_provider_fields:
+        yield "provider_specific_fields", combined_provider_fields
+
+
 def stream_chunk_builder(
     chunks: list,
     messages: Sequence | None = None,
-    start_time=None,
-    end_time=None,
+    start_time: object = None,
+    end_time: object = None,
     logging_obj: Optional["Logging"] = None,
     count_prompt_tokens: Callable[[], int] | None = None,
 ) -> ModelResponse | TextCompletionResponse | None:
@@ -1561,26 +1761,15 @@ def stream_chunk_builder(
         usage_model: Final = usage_tokenizer_model(
             model, get_base_model_from_metadata(logging_obj.model_call_details) if logging_obj is not None else None
         )
-        # Initialize the response dictionary
-        response: Final = processor.build_base_response(chunks)
+        choice_streams: Final = _choice_streams(chunks)
+        response: Final = processor.build_base_response(chunks, choice_streams)
+        rebuilt_choices: Final = tuple(zip(response.choices, (choice_chunks for _, choice_chunks in choice_streams)))
 
-        # Fast path for the common text-only streaming case:
-        # avoid repeated multi-pass list scans over chunks.
-        simple_content_parts: Final[list[str]] = []
-        is_simple_text_stream = True
-        for chunk in chunks:
-            if not chunk.get("choices"):
-                continue
-
-            if (part := _simple_text_part(chunk["choices"])) is None:
-                is_simple_text_stream = False
-                break
-            if part:
-                simple_content_parts.append(part)
-
-        if is_simple_text_stream:
-            if simple_content_parts:
-                response["choices"][0]["message"]["content"] = "".join(simple_content_parts)
+        simple_texts: Final = tuple(_simple_text(choice_chunks) for _, choice_chunks in choice_streams)
+        if None not in simple_texts:
+            for (choice, _), simple_text in zip(rebuilt_choices, simple_texts):
+                if simple_text:
+                    choice.message.content = simple_text
             completion_output = get_content_from_model_response(response)
             usage = processor.calculate_usage(
                 chunks=chunks,
@@ -1619,165 +1808,9 @@ def stream_chunk_builder(
             processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj)
             return response
 
-        tool_call_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if any(
-                "tool_calls" in choice["delta"] and choice["delta"]["tool_calls"] is not None
-                for choice in chunk.get("choices") or ()
-            )
-        ]
-
-        if len(tool_call_chunks) > 0:
-            tool_calls_list: Final = processor.get_combined_tool_content(tool_call_chunks)
-            _choice = response.choices[0]
-            _choice.message.content = None
-            _choice.message.tool_calls = tool_calls_list
-
-        function_call_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "function_call" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["function_call"] is not None
-        ]
-
-        if len(function_call_chunks) > 0:
-            _choice = response.choices[0]
-            _choice.message.content = None
-            _choice.message.function_call = processor.get_combined_function_call_content(function_call_chunks)
-
-        content_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "content" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["content"] is not None
-        ]
-
-        if len(content_chunks) > 0:
-            response["choices"][0]["message"]["content"] = processor.get_combined_content(content_chunks)
-
-        thinking_blocks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "thinking_blocks" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["thinking_blocks"] is not None
-        ]
-
-        if len(thinking_blocks) > 0:
-            response["choices"][0]["message"]["thinking_blocks"] = processor.get_combined_thinking_content(
-                thinking_blocks
-            )
-
-        reasoning_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "reasoning_content" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["reasoning_content"] is not None
-        ]
-
-        if len(reasoning_chunks) > 0:
-            response["choices"][0]["message"]["reasoning_content"] = processor.get_combined_reasoning_content(
-                reasoning_chunks
-            )
-
-        typed_chunks: Final = cast(Sequence[object], chunks)  # cast-ok: chunks are untyped upstream
-        reasoning_items: Final = tuple(chain.from_iterable(_chunk_reasoning_items(chunk) for chunk in typed_chunks))
-        if reasoning_items:
-            response["choices"][0]["message"]["reasoning_items"] = list(reasoning_items)
-
-        annotation_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "annotations" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["annotations"] is not None
-        ]
-
-        if len(annotation_chunks) > 0:
-            # Merge annotations from ALL chunks — providers may spread
-            # them across multiple streaming chunks or send them only in
-            # the final chunk.
-            all_annotations: Final[list[object]] = []
-            for ac in annotation_chunks:
-                all_annotations.extend(ac["choices"][0]["delta"]["annotations"])
-            message_annotations, provider_annotations = partition_annotations(all_annotations)
-            if message_annotations is not None:
-                response["choices"][0]["message"]["annotations"] = message_annotations
-            if provider_annotations:
-                response.choices[0].message.provider_specific_fields = {
-                    **(response.choices[0].message.provider_specific_fields or {}),
-                    "annotations": list(provider_annotations),
-                }
-
-        audio_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "audio" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["audio"] is not None
-        ]
-
-        if len(audio_chunks) > 0:
-            _choice = response.choices[0]
-            _choice.message.audio = processor.get_combined_audio_content(audio_chunks)
-
-        # Handle image chunks from models like gemini-2.5-flash-image
-        # See: https://github.com/BerriAI/litellm/issues/19478
-        image_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "images" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["images"] is not None
-        ]
-
-        if len(image_chunks) > 0:
-            # Images come complete in a single chunk, collect all images from all chunks
-            all_images: Final = []
-            for chunk in image_chunks:
-                all_images.extend(chunk["choices"][0]["delta"]["images"])
-            response["choices"][0]["message"]["images"] = all_images
-
-        # Combine provider_specific_fields from streaming chunks (e.g., web_search_results, citations)
-        # See: https://github.com/BerriAI/litellm/issues/17737
-        provider_specific_chunks: Final = [
-            chunk
-            for chunk in chunks
-            if chunk.get("choices")
-            and "provider_specific_fields" in chunk["choices"][0]["delta"]
-            and chunk["choices"][0]["delta"]["provider_specific_fields"] is not None
-        ]
-
-        if len(provider_specific_chunks) > 0:
-            provider_field_dicts: Final = tuple(
-                fields
-                for chunk in provider_specific_chunks
-                for fields in (chunk["choices"][0]["delta"]["provider_specific_fields"],)
-                if isinstance(fields, dict)
-            )
-            streamed_citations: Final = tuple(
-                fields["citation"] for fields in provider_field_dicts if fields.get("citation") is not None
-            )
-            citation_fields: Final = (
-                {"citations": _joined_streamed_citations(streamed_citations)} if streamed_citations else {}
-            )
-            combined_provider_fields: Final = {
-                key: value
-                for fields in (citation_fields, *provider_field_dicts)
-                for key, value in fields.items()
-                if key != "citation"
-            }
-
-            if combined_provider_fields:
-                _choice = response.choices[0]
-                _choice.message.provider_specific_fields = {
-                    **(_choice.message.provider_specific_fields or {}),
-                    **combined_provider_fields,
-                }
+        for choice, choice_chunks in rebuilt_choices:
+            for field, value in _assembled_message_fields(processor, choice_chunks):
+                choice.message[field] = value
 
         completion_output = get_content_from_model_response(response)
 
diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py
index 3070bedd30d..5c5204ef2f4 100644
--- a/litellm/llms/openai/chat/guardrail_translation/handler.py
+++ b/litellm/llms/openai/chat/guardrail_translation/handler.py
@@ -687,11 +687,6 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
         responses_so_far: Sequence["ModelResponseStream"],
         litellm_logging_obj: "LiteLLMLoggingObj | None",
     ) -> "ModelResponse":
-        """``stream_chunk_builder`` folds every choice of a stream into one index-0
-        choice, so the stream is rebuilt one choice index at a time (every chunk
-        kept, its choices narrowed to that index, so usage-only chunks still
-        count) and the rebuilt choices are stitched into one response, each
-        carrying the index the stream gave it."""
         choice_indices: Final = tuple(
             sorted(frozenset(choice.index for response in responses_so_far for choice in response.choices))
         )
diff --git a/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense.py b/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense.py
index 45e785fd53c..3d555707159 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/cisco_ai_defense/cisco_ai_defense.py
@@ -22,6 +22,7 @@ import os
 from collections.abc import AsyncIterator, Mapping, Sequence
 from dataclasses import dataclass, replace
 from datetime import datetime
+from itertools import chain
 from typing import TYPE_CHECKING, Any, Final, Literal
 
 import httpx
@@ -120,6 +121,11 @@ class _CustomGuardrailOptions(TypedDict, total=False, extra_items=object):
     """Base-class constructor options this guardrail forwards untouched to CustomGuardrail."""
 
 
+def _streamed_choice_index(choice: object) -> int:
+    index: Final[object] = getattr(choice, "index", None)
+    return index if isinstance(index, int) else 0
+
+
 class CiscoAIDefenseGuardrail(CiscoAIDefenseMcpMixin, CustomGuardrail):
     """
     Cisco AI Defense guardrail integration.
@@ -585,29 +591,44 @@ class CiscoAIDefenseGuardrail(CiscoAIDefenseMcpMixin, CustomGuardrail):
 
     @classmethod
     def _extract_streaming_chunk_scan_text(cls, chunks: Sequence[object]) -> str:
+        streamed_choices: Final = tuple(
+            choice
+            for choice in chain.from_iterable(getattr(chunk, "choices", None) or () for chunk in chunks)
+            if getattr(choice, "delta", None) is not None
+        )
+        choice_indices: Final = sorted(frozenset(map(_streamed_choice_index, streamed_choices)))
+        choice_texts: Final = (
+            cls._extract_streamed_deltas_scan_text(
+                tuple(
+                    getattr(choice, "delta", None)
+                    for choice in streamed_choices
+                    if _streamed_choice_index(choice) == index
+                )
+            )
+            for index in choice_indices
+        )
+        return " ".join(text for text in choice_texts if text)
+
+    @classmethod
+    def _extract_streamed_deltas_scan_text(cls, deltas: Sequence[object]) -> str:
         original_text = ""
         argument_text = ""
-        for chunk in chunks:
-            choices = getattr(chunk, "choices", None) or []
-            for c in choices:
-                delta: object | None = getattr(c, "delta", None)
-                if delta is None:
-                    continue
-                text = getattr(delta, "content", None)
-                if isinstance(text, str):
-                    original_text += text
-                reasoning_text = " ".join(cls._extract_message_reasoning_parts(delta))
-                if reasoning_text:
-                    original_text += reasoning_text
-                for tc in getattr(delta, "tool_calls", None) or []:
-                    args = cls._extract_tool_call_arguments(tc)
-                    if args:
-                        argument_text += args
-                fc: object | None = getattr(delta, "function_call", None)
-                if fc is not None:
-                    args = cls._extract_function_call_arguments(fc)
-                    if args:
-                        argument_text += args
+        for delta in deltas:
+            text = getattr(delta, "content", None)
+            if isinstance(text, str):
+                original_text += text
+            reasoning_text = " ".join(cls._extract_message_reasoning_parts(delta))
+            if reasoning_text:
+                original_text += reasoning_text
+            for tc in getattr(delta, "tool_calls", None) or []:
+                args = cls._extract_tool_call_arguments(tc)
+                if args:
+                    argument_text += args
+            fc: object | None = getattr(delta, "function_call", None)
+            if fc is not None:
+                args = cls._extract_function_call_arguments(fc)
+                if args:
+                    argument_text += args
         return " ".join(part for part in (original_text, argument_text) if part)
 
     # ------------------------------------------------------------------
diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py
index 85f476ecd62..d69fe4068e0 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py
@@ -246,20 +246,38 @@ class NomaGuardrail(CustomGuardrail):
     ) -> str | None:
         """Shared logic for processing LLM response checks"""
 
-        start_time: Final = datetime.now()
-        extra_data: Final = self.get_guardrail_dynamic_request_body_params(request_data)
-
         if not isinstance(response, litellm.ModelResponse):
             return None
 
-        content = None
-        for choice in response.choices:
-            if isinstance(choice, litellm.Choices) and choice.message.content:
-                content = choice.message.content
-                break
+        checked_contents: Final = tuple(
+            [
+                await self._process_choice_check(request_data, response, position, content, user_auth, event_type)
+                for position, content in self._choice_texts(response)
+            ]
+        )
+        return next(iter(checked_contents), None)
 
-        if not content or not isinstance(content, str):
-            return None
+    @staticmethod
+    def _choice_texts(response: litellm.ModelResponse) -> tuple[tuple[int, str], ...]:
+        return tuple(
+            (position, choice.message.content)
+            for position, choice in enumerate(response.choices)
+            if isinstance(choice, litellm.Choices)
+            and isinstance(choice.message.content, str)
+            and choice.message.content
+        )
+
+    async def _process_choice_check(
+        self,
+        request_data: dict[str, object],
+        response: litellm.ModelResponse,
+        position: int,
+        content: str,
+        user_auth: UserAPIKeyAuth,
+        event_type: GuardrailEventHooks | None,
+    ) -> str:
+        start_time: Final = datetime.now()
+        extra_data: Final = self.get_guardrail_dynamic_request_body_params(request_data)
 
         payload: Final = {
             "input": [
@@ -306,7 +324,7 @@ class NomaGuardrail(CustomGuardrail):
             anonymized_content: Final = self._extract_anonymized_content(response_json, ASSISTANT_ROLE)
             if anonymized_content:
                 # Replace the LLM response content with anonymized version
-                self._replace_llm_response_content(response, anonymized_content)
+                self._replace_llm_response_content(response, anonymized_content, position)
                 verbose_proxy_logger.debug("Noma guardrail anonymized LLM response: %s", anonymized_content)
                 return anonymized_content
 
@@ -486,21 +504,14 @@ class NomaGuardrail(CustomGuardrail):
                 messages[i]["content"] = anonymized_content
                 break
 
-    def _replace_llm_response_content(self, response: LLMResponse, anonymized_content: str):
-        """
-        Replace the LLM response content with anonymized version.
-
-        Args:
-            response: The original LLM response
-            anonymized_content: The anonymized content to replace with
-        """
+    def _replace_llm_response_content(self, response: LLMResponse, anonymized_content: str, position: int):
+        """Replace the content of the choice at ``position`` with its anonymized version."""
         if not isinstance(response, litellm.ModelResponse):
             return
 
-        # Replace content in all choices
-        for choice in response.choices:
-            if isinstance(choice, litellm.Choices) and choice.message.content:
-                choice.message.content = anonymized_content
+        choice: Final = response.choices[position]
+        if isinstance(choice, litellm.Choices):
+            choice.message.content = anonymized_content
 
     async def _check_user_message_background(
         self,
@@ -815,6 +826,7 @@ class NomaGuardrail(CustomGuardrail):
         )
 
         if isinstance(assembled_model_response, ModelResponse):
+            texts_before_check: Final = self._choice_texts(assembled_model_response)
             try:
                 processed_response: Final = await self._check_llm_response(
                     request_data,
@@ -828,7 +840,11 @@ class NomaGuardrail(CustomGuardrail):
                 if self.block_failures:
                     raise
                 verbose_proxy_logger.error("Noma streaming post-call hook failed: %s", e)
-                for chunk in all_chunks:
+                if self._choice_texts(assembled_model_response) == texts_before_check:
+                    for chunk in all_chunks:
+                        yield chunk
+                    return
+                async for chunk in MockResponseIterator(model_response=assembled_model_response):
                     yield chunk
                 return
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py
index fa92a0c90e0..e8c094c0c37 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py
@@ -36,6 +36,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.tool_permission import (
 )
 from litellm.types.utils import (
     CallTypesLiteral,
+    ChatCompletionMessageCustomToolCall,
     ChatCompletionMessageToolCall,
     Choices,
     Function,
@@ -744,6 +745,14 @@ class ToolPermissionGuardrail(CustomGuardrail):
 
         return ToolResult(tool_use_id=tool_call.id, content=error_message, is_error=True)
 
+    @staticmethod
+    def _judged_tool_call(
+        tool_call: ChatCompletionMessageToolCall | ChatCompletionMessageCustomToolCall,
+    ) -> tuple[str | None, ...]:
+        if isinstance(tool_call, ChatCompletionMessageCustomToolCall):
+            return (tool_call.id, tool_call.type, tool_call.custom.name, tool_call.custom.input)
+        return (tool_call.id, tool_call.type, tool_call.function.name, tool_call.function.arguments)
+
     def _modify_response_with_permission_errors(
         self,
         response: ModelResponse,
@@ -761,11 +770,10 @@ class ToolPermissionGuardrail(CustomGuardrail):
 
         verbose_proxy_logger.info("Blocking %s unauthorized tool uses", len(denied_tools))
 
-        # Create a mapping of tool_use_id to error result
-        error_results: Final = {}
-        for tool_use, error in denied_tools:
-            error_result = self._create_permission_error_result(tool_use, error)
-            error_results[tool_use.id] = error_result
+        error_results: Final = {
+            self._judged_tool_call(tool_use): self._create_permission_error_result(tool_use, error)
+            for tool_use, error in denied_tools
+        }
 
         # Modify the response content
         for choice_index, choice in enumerate(response.choices):
@@ -775,9 +783,8 @@ class ToolPermissionGuardrail(CustomGuardrail):
 
                 # Rewrite tool_calls
                 for tool_call in choice.message.tool_calls or []:
-                    tool_call_id = tool_call.id
-                    if tool_call_id in error_results:
-                        error_result = error_results[tool_call_id]
+                    error_result = error_results.get(self._judged_tool_call(tool_call))
+                    if error_result is not None:
                         error_messages.append(error_result.content)
                     else:
                         filtered_tool_calls.append(tool_call)
@@ -788,7 +795,7 @@ class ToolPermissionGuardrail(CustomGuardrail):
                     getattr(choice.message, "function_call", None), choice_index
                 )
                 if legacy_tool_call is not None:
-                    legacy_error_result = error_results.get(legacy_tool_call.id)
+                    legacy_error_result = error_results.get(self._judged_tool_call(legacy_tool_call))
                     if legacy_error_result is not None:
                         choice.message.function_call = None
                         error_messages.append(legacy_error_result.content)
diff --git a/litellm/responses/mcp/litellm_proxy_mcp_handler.py b/litellm/responses/mcp/litellm_proxy_mcp_handler.py
index c01d07fb5c3..c88a8c1cc28 100644
--- a/litellm/responses/mcp/litellm_proxy_mcp_handler.py
+++ b/litellm/responses/mcp/litellm_proxy_mcp_handler.py
@@ -35,10 +35,7 @@ from litellm.types.llms.openai import ToolParam as ResponsesToolParam
 from litellm.types.mcp_server.mcp_server_manager import MCPServer
 from litellm.types.utils import (
     CallTypes,
-    ChatCompletionMessageCustomToolCall,
-    ChatCompletionMessageToolCall,
     Choices,
-    Message,
     ModelResponse,
     StandardLoggingMCPToolCall,
 )
@@ -587,29 +584,18 @@ class LiteLLM_Proxy_MCP_Handler:
 
     _extract_tool_calls_from_response = extract_tool_calls_from_response
 
+    @staticmethod
+    def _continued_choice(response: ModelResponse) -> Choices | None:
+        choices: Final = tuple(choice for choice in response.choices if isinstance(choice, Choices))
+        return next((choice for choice in choices if choice.message.tool_calls), next(iter(choices), None))
+
     @staticmethod
     def extract_tool_calls_from_chat_response(response: ModelResponse) -> list[object]:
-        """Extract tool calls from a chat completion response."""
-        tool_calls: Final[list[object]] = []
-
-        try:
-            for choice in response.choices:
-                message: Message | None = getattr(choice, "message", None)
-                if message is None:
-                    continue
-                tool_call_entries: (
-                    Sequence[ChatCompletionMessageToolCall | ChatCompletionMessageCustomToolCall] | None
-                ) = getattr(message, "tool_calls", None)
-                if tool_call_entries:
-                    for tool_call in tool_call_entries:
-                        if hasattr(tool_call, "model_dump"):
-                            tool_calls.append(tool_call.model_dump())
-                        else:
-                            tool_calls.append(tool_call)
-        except Exception:
-            verbose_logger.exception("Failed to extract tool calls from chat completion response")
-
-        return tool_calls
+        """Extract the tool calls of the first choice that made any, since the follow-up turn continues it alone."""
+        continued_choice: Final = LiteLLM_Proxy_MCP_Handler._continued_choice(response)
+        if continued_choice is None:
+            return []
+        return [tool_call.model_dump() for tool_call in continued_choice.message.tool_calls or ()]
 
     _extract_tool_calls_from_chat_response = extract_tool_calls_from_chat_response
 
@@ -1018,9 +1004,9 @@ class LiteLLM_Proxy_MCP_Handler:
 
         message_to_append: dict | None = None
         try:
-            first_choice: Final = response.choices[0]
-            if isinstance(first_choice, Choices) and getattr(first_choice, "message", None):
-                message_to_append = first_choice.message.model_dump(exclude_none=True)
+            continued_choice: Final = LiteLLM_Proxy_MCP_Handler._continued_choice(response)
+            if continued_choice is not None:
+                message_to_append = continued_choice.message.model_dump(exclude_none=True)
                 # Ensure tool_calls have arguments field (required by OpenAI API)
                 if message_to_append.get("tool_calls"):
                     for tool_call in message_to_append["tool_calls"]:
diff --git a/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py b/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py
index d59f59703ad..4e6ed16e360 100644
--- a/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py
+++ b/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py
@@ -418,6 +418,45 @@ def test_openai_responses_chunk_parser_reasoning_summary():
     assert delta.function_call is None
 
 
+def test_bridged_reasoning_summary_parts_rebuild_into_the_one_choice_they_streamed_on():
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        OpenAiResponsesToChatCompletionStreamIterator,
+    )
+
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    events = (
+        {"type": "response.reasoning_summary_part.added", "output_index": 0, "summary_index": 0},
+        {
+            "type": "response.reasoning_summary_text.delta",
+            "delta": "First part.",
+            "output_index": 0,
+            "summary_index": 0,
+        },
+        {"type": "response.reasoning_summary_part.done", "output_index": 0, "summary_index": 0},
+        {"type": "response.reasoning_summary_part.added", "output_index": 0, "summary_index": 1},
+        {
+            "type": "response.reasoning_summary_text.delta",
+            "delta": "Second part.",
+            "output_index": 0,
+            "summary_index": 1,
+        },
+        {"type": "response.reasoning_summary_part.added", "output_index": 1, "summary_index": 0},
+        {
+            "type": "response.reasoning_summary_text.delta",
+            "delta": "Next item.",
+            "output_index": 1,
+            "summary_index": 0,
+        },
+        {"type": "response.output_text.delta", "delta": "Answer", "output_index": 2, "content_index": 0},
+    )
+
+    rebuilt = litellm.stream_chunk_builder(chunks=[iterator.chunk_parser(event) for event in events])
+
+    assert [(choice.index, choice.message.reasoning_content, choice.message.content) for choice in rebuilt.choices] == [
+        (0, "First part. Second part. Next item.", "Answer")
+    ]
+
+
 def test_chunk_parser_string_output_text_delta_produces_text():
     from litellm.completion_extras.litellm_responses_transformation.transformation import (
         OpenAiResponsesToChatCompletionStreamIterator,
diff --git a/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
index dd1f495b0d0..28587bf4749 100644
--- a/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
+++ b/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
@@ -1304,12 +1304,118 @@ def test_stream_chunk_builder_keeps_each_choices_tool_call_arguments_apart():
         ]
     )
 
-    assert [(tool_call.id, tool_call.function.arguments) for tool_call in response.choices[0].message.tool_calls] == [
-        ("call_a", '{"fruit": "persimmon"}'),
-        ("call_b", '{"fruit": "durian"}'),
+    assert [
+        (choice.index, [(tool_call.id, tool_call.function.arguments) for tool_call in choice.message.tool_calls])
+        for choice in response.choices
+    ] == [
+        (0, [("call_a", '{"fruit": "persimmon"}')]),
+        (1, [("call_b", '{"fruit": "durian"}')]),
     ]
 
 
+def test_stream_chunk_builder_rebuilds_each_choice_of_a_multi_choice_text_stream_on_its_own():
+    def chunk(choice_index: int, content: str, finish_reason: str | None = None) -> ModelResponseStream:
+        return ModelResponseStream(
+            id="chatcmpl-123",
+            object="chat.completion.chunk",
+            created=1234567890,
+            model="gpt-4.1-mini",
+            choices=[StreamingChoices(index=choice_index, delta=Delta(content=content), finish_reason=finish_reason)],
+        )
+
+    response = stream_chunk_builder(
+        chunks=[
+            chunk(0, "The persim"),
+            chunk(1, "The"),
+            chunk(1, " persimmon is"),
+            chunk(0, "mon is ripe.", finish_reason="stop"),
+            chunk(1, " ripe and", finish_reason="length"),
+        ]
+    )
+
+    assert [(choice.index, choice.message.content, choice.finish_reason) for choice in response.choices] == [
+        (0, "The persimmon is ripe.", "stop"),
+        (1, "The persimmon is ripe and", "length"),
+    ]
+
+
+def test_stream_chunk_builder_gives_a_choice_without_a_finish_chunk_the_finish_reason_its_content_implies():
+    def chunk(choice_index: int, delta: Delta, finish_reason: str | None = None) -> ModelResponseStream:
+        return ModelResponseStream(
+            id="chatcmpl-123",
+            object="chat.completion.chunk",
+            created=1234567890,
+            model="gpt-4.1-mini",
+            choices=[StreamingChoices(index=choice_index, delta=delta, finish_reason=finish_reason)],
+        )
+
+    def tool_call(call_id: str) -> list[ChatCompletionDeltaToolCall]:
+        return [
+            ChatCompletionDeltaToolCall(
+                id=call_id, index=0, type="function", function=Function(name="lookup_fruit", arguments="{}")
+            )
+        ]
+
+    response = stream_chunk_builder(
+        chunks=[
+            chunk(0, Delta(tool_calls=tool_call("call_a"))),
+            chunk(1, Delta(tool_calls=tool_call("call_b"))),
+            chunk(2, Delta(content="Kiwis are fuzzy.")),
+            chunk(0, Delta(), finish_reason="tool_calls"),
+        ]
+    )
+
+    assert [(choice.index, choice.finish_reason) for choice in response.choices] == [
+        (0, "tool_calls"),
+        (1, "tool_calls"),
+        (2, "stop"),
+    ]
+
+
+def test_stream_chunk_builder_keeps_each_choices_function_call_annotations_and_images_on_that_choice():
+    def chunk(choice_index: int, delta: Delta) -> ModelResponseStream:
+        return ModelResponseStream(
+            id="chatcmpl-123",
+            object="chat.completion.chunk",
+            created=1234567890,
+            model="gpt-4.1-mini",
+            choices=[StreamingChoices(index=choice_index, delta=delta, finish_reason=None)],
+        )
+
+    citation: Final = {
+        "type": "url_citation",
+        "url_citation": {"start_index": 0, "end_index": 5, "url": "https://example.com/kiwi", "title": "Kiwi"},
+    }
+    image: Final = {"type": "image_url", "index": 0, "image_url": {"url": "data:image/png;base64,a2l3aQ=="}}
+
+    response = stream_chunk_builder(
+        chunks=[
+            chunk(0, Delta(function_call={"name": "lookup_fruit", "arguments": '{"fruit": '})),
+            chunk(1, Delta(content="Kiwis are")),
+            chunk(0, Delta(function_call={"arguments": '"kiwi"}'})),
+            chunk(1, Delta(content=" fuzzy.", annotations=[citation])),
+            chunk(1, Delta(images=[image])),
+        ]
+    )
+
+    function_choice, cited_choice = response.choices
+    assert (
+        function_choice.index,
+        function_choice.message.content,
+        function_choice.message.function_call.name,
+        function_choice.message.function_call.arguments,
+        getattr(function_choice.message, "annotations", None),
+        getattr(function_choice.message, "images", None),
+    ) == (0, None, "lookup_fruit", '{"fruit": "kiwi"}', None, None)
+    assert (
+        cited_choice.index,
+        cited_choice.message.content,
+        cited_choice.message.function_call,
+        cited_choice.message.annotations,
+        cited_choice.message.images,
+    ) == (1, "Kiwis are fuzzy.", None, [citation], [image])
+
+
 def test_get_combined_tool_content_joins_many_dict_shaped_argument_fragments_in_order():
     processor = ChunkProcessor.__new__(ChunkProcessor)
     first_fragments = [f"a{i};" for i in range(300)]
diff --git a/tests/unit/proxy/guardrails/guardrail_hooks/test_cisco_ai_defense_chat.py b/tests/unit/proxy/guardrails/guardrail_hooks/test_cisco_ai_defense_chat.py
index 4d1f254aef9..5ce78fd05d1 100644
--- a/tests/unit/proxy/guardrails/guardrail_hooks/test_cisco_ai_defense_chat.py
+++ b/tests/unit/proxy/guardrails/guardrail_hooks/test_cisco_ai_defense_chat.py
@@ -2108,6 +2108,62 @@ class TestCiscoAIDefenseStreamingBypass:
             f"Original: {chunks!r}, received: {received!r}"
         )
 
+    @pytest.mark.asyncio
+    async def test_streaming_safe_n2_response_inspects_each_choice_and_yields_original_chunks(self):
+        g = _make_guardrail(event_hook=["pre_call", "post_call"])
+        chunks = [
+            ModelResponseStream(
+                id="resp_1",
+                choices=[StreamingChoices(delta=Delta(content=part), finish_reason=finish_reason, index=index)],
+                created=1234567890,
+                model="gpt-4",
+                object="chat.completion.chunk",
+            )
+            for index, part, finish_reason in (
+                (0, "The persim", None),
+                (1, "The", None),
+                (0, "mon is ripe.", "stop"),
+                (1, " persimmon is ripe.", "stop"),
+            )
+        ]
+
+        received, post_mock = await _streaming_setup(g, chunks, cisco_response=_safe_response())
+
+        inspected = post_mock.call_args.kwargs["json"]["messages"]
+        assert [m["content"] for m in inspected if m["role"] == "assistant"] == [
+            "The persimmon is ripe.",
+            "The persimmon is ripe.",
+        ]
+        assert received == chunks
+
+    @pytest.mark.asyncio
+    async def test_streaming_safe_n2_response_with_a_function_call_choice_yields_original_chunks(self):
+        g = _make_guardrail(event_hook=["pre_call", "post_call"])
+        chunks = [
+            ModelResponseStream(
+                id="resp_1",
+                choices=[StreamingChoices(delta=delta, finish_reason=None, index=index)],
+                created=1234567890,
+                model="gpt-4",
+                object="chat.completion.chunk",
+            )
+            for index, delta in (
+                (0, Delta(function_call={"name": "lookup_fruit", "arguments": '{"fruit": '})),
+                (1, Delta(content="Kiwis are")),
+                (0, Delta(function_call={"arguments": '"kiwi"}'})),
+                (1, Delta(content=" fuzzy.")),
+            )
+        ]
+
+        received, post_mock = await _streaming_setup(g, chunks, cisco_response=_safe_response())
+
+        inspected = post_mock.call_args.kwargs["json"]["messages"]
+        assert [m["content"] for m in inspected if m["role"] == "assistant"] == [
+            '{"fruit": "kiwi"}',
+            "Kiwis are fuzzy.",
+        ]
+        assert received == chunks
+
     @pytest.mark.asyncio
     async def test_streaming_redact_does_not_replay_tool_call_arguments(self):
         g = _make_guardrail(
diff --git a/tests/unit/proxy/guardrails/guardrail_hooks/test_noma.py b/tests/unit/proxy/guardrails/guardrail_hooks/test_noma.py
index ee369f72f01..574550e1bdd 100644
--- a/tests/unit/proxy/guardrails/guardrail_hooks/test_noma.py
+++ b/tests/unit/proxy/guardrails/guardrail_hooks/test_noma.py
@@ -1,4 +1,5 @@
 import copy
+from itertools import chain
 import os
 from typing import cast
 from unittest.mock import AsyncMock, MagicMock, patch
@@ -18,7 +19,7 @@ import litellm.proxy.guardrails.guardrail_hooks.noma.noma as noma_legacy_module
 from litellm.proxy.guardrails.guardrail_hooks.noma.noma import NomaBlockedMessage
 from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2
 from litellm.types.llms.openai import AllMessageValues
-from litellm.types.utils import Choices, Message
+from litellm.types.utils import Choices, Delta, Message, ModelResponseStream, StreamingChoices
 
 
 @pytest.fixture
@@ -1848,12 +1849,207 @@ class TestNomaAnonymizationLogic:
         )
 
         anonymize_guardrail._replace_llm_response_content(
-            response, "Your email is *******"
+            response, "Your email is *******", 0
         )
 
         assert response.choices[0].message.content == "Your email is *******"
 
 
+def _noma_reply_per_text(verdict_for_text):
+    def reply(*args, **kwargs):
+        response = MagicMock()
+        response.json.return_value = verdict_for_text(kwargs["json"]["input"][0]["content"][0]["text"])
+        response.raise_for_status = MagicMock()
+        return response
+
+    return reply
+
+
+class TestNomaMultiChoiceResponses:
+    @pytest.mark.asyncio
+    async def test_streamed_two_choice_reply_is_blocked_when_only_the_second_choice_is_flagged(self):
+        guardrail = NomaGuardrail(
+            api_key="test-api-key",
+            api_base="https://api.test.noma.security/",
+            monitor_mode=False,
+            block_failures=True,
+            guardrail_name="noma-post-call",
+            event_hook="post_call",
+            default_on=True,
+        )
+        scanned_texts = []
+
+        def verdict_for_text(text):
+            scanned_texts.append(text)
+            return {"aggregatedScanResult": "launch code" in text, "scanResult": []}
+
+        async def upstream():
+            for index, part in ((0, "Kiwis are fuzzy."), (1, "The launch code is 0000.")):
+                yield ModelResponseStream(
+                    id="resp_1",
+                    choices=[StreamingChoices(delta=Delta(content=part), finish_reason="stop", index=index)],
+                    created=1234567890,
+                    model="gpt-4.1-mini",
+                    object="chat.completion.chunk",
+                )
+
+        with patch.object(guardrail.async_handler, "post", side_effect=_noma_reply_per_text(verdict_for_text)):
+            with pytest.raises(NomaBlockedMessage):
+                async for _ in guardrail.async_post_call_streaming_iterator_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test-key"),
+                    response=upstream(),
+                    request_data={"litellm_call_id": "call-1", "metadata": {}},
+                ):
+                    pass
+
+        assert scanned_texts == ["Kiwis are fuzzy.", "The launch code is 0000."]
+
+    @pytest.mark.asyncio
+    async def test_each_choice_gets_its_own_anonymized_text(self):
+        guardrail = NomaGuardrail(
+            api_key="test-api-key",
+            api_base="https://api.test.noma.security/",
+            anonymize_input=True,
+            monitor_mode=False,
+            block_failures=True,
+            guardrail_name="noma-post-call",
+            event_hook="post_call",
+            default_on=True,
+        )
+
+        def verdict_for_text(text):
+            return {
+                "aggregatedScanResult": False,
+                "scanResult": [
+                    {
+                        "role": "assistant",
+                        "type": "message",
+                        "results": {
+                            "anonymizedContent": {"anonymized": text.replace("test@example.com", "*******")},
+                            "sensitiveData": {"PII": {"result": False, "status": "SUCCESS"}},
+                        },
+                    }
+                ],
+            }
+
+        response = ModelResponse(
+            id="resp_1",
+            choices=[
+                Choices(index=0, finish_reason="stop", message=Message(role="assistant", content="Mail test@example.com")),
+                Choices(
+                    index=1, finish_reason="stop", message=Message(role="assistant", content="Write test@example.com")
+                ),
+            ],
+            model="gpt-4.1-mini",
+        )
+
+        with patch.object(guardrail.async_handler, "post", side_effect=_noma_reply_per_text(verdict_for_text)):
+            await guardrail.async_post_call_success_hook(
+                data={"litellm_call_id": "call-1", "metadata": {}},
+                user_api_key_dict=UserAPIKeyAuth(api_key="test-key"),
+                response=response,
+            )
+
+        assert [choice.message.content for choice in response.choices] == ["Mail *******", "Write *******"]
+
+    @pytest.mark.asyncio
+    async def test_fail_open_stream_keeps_redactions_made_before_a_later_choice_fails(self):
+        guardrail = NomaGuardrail(
+            api_key="test-api-key",
+            api_base="https://api.test.noma.security/",
+            anonymize_input=True,
+            monitor_mode=False,
+            block_failures=False,
+            guardrail_name="noma-post-call",
+            event_hook="post_call",
+            default_on=True,
+        )
+
+        def verdict_for_text(text):
+            if text.startswith("Write"):
+                raise httpx.ConnectError("Noma is unreachable")
+            return {
+                "aggregatedScanResult": False,
+                "scanResult": [
+                    {
+                        "role": "assistant",
+                        "type": "message",
+                        "results": {
+                            "anonymizedContent": {"anonymized": text.replace("test@example.com", "*******")},
+                            "sensitiveData": {"PII": {"result": False, "status": "SUCCESS"}},
+                        },
+                    }
+                ],
+            }
+
+        async def upstream():
+            for index, part in ((0, "Mail test@example.com"), (1, "Write back soon")):
+                yield ModelResponseStream(
+                    id="resp_1",
+                    choices=[StreamingChoices(delta=Delta(content=part), finish_reason="stop", index=index)],
+                    created=1234567890,
+                    model="gpt-4.1-mini",
+                    object="chat.completion.chunk",
+                )
+
+        with patch.object(guardrail.async_handler, "post", side_effect=_noma_reply_per_text(verdict_for_text)):
+            streamed = [
+                chunk
+                async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test-key"),
+                    response=upstream(),
+                    request_data={"litellm_call_id": "call-1", "metadata": {}},
+                )
+            ]
+
+        streamed_choices = tuple(chain.from_iterable(part.choices for part in streamed))
+        text_by_choice = {
+            index: "".join(choice.delta.content or "" for choice in streamed_choices if choice.index == index)
+            for index in (0, 1)
+        }
+        assert text_by_choice == {0: "Mail *******", 1: "Write back soon"}
+
+    @pytest.mark.asyncio
+    async def test_fail_open_stream_replays_the_original_chunks_when_nothing_was_redacted(self):
+        guardrail = NomaGuardrail(
+            api_key="test-api-key",
+            api_base="https://api.test.noma.security/",
+            anonymize_input=True,
+            monitor_mode=False,
+            block_failures=False,
+            guardrail_name="noma-post-call",
+            event_hook="post_call",
+            default_on=True,
+        )
+        originals = [
+            ModelResponseStream(
+                id="resp_1",
+                choices=[StreamingChoices(delta=Delta(content=part), index=0)],
+                created=1234567890,
+                model="gpt-4.1-mini",
+                object="chat.completion.chunk",
+            )
+            for part in ("Kiwis", " are", " fuzzy.")
+        ]
+
+        async def upstream():
+            for chunk in originals:
+                yield chunk
+
+        with patch.object(guardrail.async_handler, "post", side_effect=httpx.ConnectError("Noma is unreachable")):
+            streamed = [
+                chunk
+                async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test-key"),
+                    response=upstream(),
+                    request_data={"litellm_call_id": "call-1", "metadata": {}},
+                )
+            ]
+
+        assert len(streamed) == len(originals)
+        assert all(sent is original for sent, original in zip(streamed, originals))
+
+
 class TestNomaAnonymizationFlow:
     """Test full anonymization flow with real Noma response objects"""
 
diff --git a/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py b/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py
index 2ee4c8c72c0..21376068686 100644
--- a/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py
+++ b/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py
@@ -5,6 +5,7 @@ Unit tests for Tool Permission Guardrail (OpenAI tool_calls semantics)
 import json
 import logging
 import re
+from itertools import chain
 from typing import Final
 from unittest.mock import patch
 
@@ -25,10 +26,16 @@ from litellm.types.proxy.guardrails.guardrail_hooks.tool_permission import (
     PermissionError,
 )
 from litellm.types.utils import (
+    ChatCompletionCustomToolCallPayload,
+    ChatCompletionDeltaToolCall,
+    ChatCompletionMessageCustomToolCall,
     ChatCompletionMessageToolCall,
     Choices,
+    Delta,
+    Function,
     ModelResponse,
     ModelResponseStream,
+    StreamingChoices,
 )
 
 
@@ -425,6 +432,41 @@ class TestToolPermissionGuardrail:
         assert isinstance(choice.message.content, str)
         assert "berri" in choice.message.content
 
+    @pytest.mark.asyncio
+    async def test_rewrite_denies_only_the_choice_whose_call_is_denied_when_choices_share_a_call_id(self):
+        guardrail = ToolPermissionGuardrail(
+            guardrail_name="choice-guardrail",
+            rules=[{"id": "deny_bash", "tool_name": "Bash", "decision": "deny"}],
+            default_action="allow",
+            on_disallowed_action="rewrite",
+            event_hook=GuardrailEventHooks.post_call,
+        )
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=index,
+                    finish_reason="tool_calls",
+                    message={
+                        "tool_calls": [
+                            {"id": "call_shared", "type": "function", "function": {"name": name, "arguments": "{}"}}
+                        ]
+                    },
+                )
+                for index, name in enumerate(("Bash", "Read"))
+            ]
+        )
+
+        await guardrail.async_post_call_success_hook(
+            data={"guardrails": ["choice-guardrail"]}, user_api_key_dict=UserAPIKeyAuth(), response=response
+        )
+
+        kept: Final = [
+            [tool_call.function.name for tool_call in choice.message.tool_calls or ()]
+            for choice in response.choices
+            if isinstance(choice, Choices)
+        ]
+        assert kept == [[], ["Read"]]
+
     @pytest.mark.asyncio
     async def test_async_post_call_success_hook_missing_arguments_blocks_param_rule(
         self,
@@ -866,6 +908,46 @@ class TestToolPermissionGuardrail:
             f"got: {chunks[0].choices[0].delta.content!r}"
         )
 
+    @pytest.mark.asyncio
+    async def test_async_post_call_streaming_iterator_hook_re_emits_each_choice_of_an_n2_tool_call_stream(self):
+        def chunk(choice_index: int, tool_call: ChatCompletionDeltaToolCall) -> ModelResponseStream:
+            return ModelResponseStream(
+                id="chatcmpl-n2-tools",
+                created=1700000000,
+                model="gpt-4.1-mini",
+                object="chat.completion.chunk",
+                choices=[StreamingChoices(index=choice_index, delta=Delta(tool_calls=[tool_call]))],
+            )
+
+        def fragment(arguments: str, name: str | None = None) -> ChatCompletionDeltaToolCall:
+            return ChatCompletionDeltaToolCall(
+                id="call_shared" if name else None,
+                index=0,
+                type="function",
+                function=Function(name=name, arguments=arguments),
+            )
+
+        async def _stream():
+            for streamed in (
+                chunk(0, fragment("", name="Bash")),
+                chunk(1, fragment("", name="Bash")),
+                chunk(0, fragment('{"command": "ls"}')),
+                chunk(1, fragment('{"command": "pwd"}')),
+            ):
+                yield streamed
+
+        re_emitted: Final = [
+            chunk
+            async for chunk in self.guardrail.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=UserAPIKeyAuth(), response=_stream(), request_data={}
+            )
+        ]
+
+        assert [
+            (choice.index, [(call.function.name, call.function.arguments) for call in choice.delta.tool_calls])
+            for choice in chain.from_iterable(streamed.choices for streamed in re_emitted)
+        ] == [(0, [("Bash", '{"command": "ls"}')]), (1, [("Bash", '{"command": "pwd"}')])]
+
     def test_modify_response_with_permission_errors(self):
         # Setup a response with one tool_call
         tool_call = ChatCompletionMessageToolCall(
@@ -952,6 +1034,35 @@ class TestToolPermissionGuardrail:
         assert "Permission denied" in choice.message.content
 
 
+    def test_modify_response_with_permission_errors_keeps_custom_tool_call_in_another_choice(self):
+        denied_call = ChatCompletionMessageToolCall(
+            id="call_bash", type="function", function=Function(name="Bash", arguments="{}")
+        )
+        custom_call = ChatCompletionMessageCustomToolCall(
+            id="call_patch", custom=ChatCompletionCustomToolCallPayload(name="apply_patch", input="*** Begin Patch")
+        )
+        response = ModelResponse(
+            choices=[
+                Choices(index=0, message={"role": "assistant", "content": None, "tool_calls": [denied_call]}),
+                Choices(index=1, message={"role": "assistant", "content": None, "tool_calls": [custom_call]}),
+            ]
+        )
+        denied_tools = [
+            (
+                denied_call,
+                PermissionError(tool_name="Bash", rule_id="deny_bash", message="Tool 'Bash' denied by rule 'deny_bash'"),
+            )
+        ]
+
+        self.guardrail._modify_response_with_permission_errors(response, denied_tools)
+
+        kept = [
+            [call.id for call in choice.message.tool_calls or []]
+            for choice in response.choices
+            if isinstance(choice, Choices)
+        ]
+        assert kept == [[], ["call_patch"]]
+
 class TestToolPermissionGuardrailIntegration:
     """Integration tests for Tool Permission Guardrail"""
 
diff --git a/tests/unit/responses/mcp/test_litellm_proxy_mcp_handler.py b/tests/unit/responses/mcp/test_litellm_proxy_mcp_handler.py
index 229d5cbe676..2f27622d77c 100644
--- a/tests/unit/responses/mcp/test_litellm_proxy_mcp_handler.py
+++ b/tests/unit/responses/mcp/test_litellm_proxy_mcp_handler.py
@@ -131,6 +131,86 @@ def test_extract_tool_calls_from_chat_response_handles_tool_calls():
     assert tool_calls[0]["function"]["name"] == "foo"
 
 
+def test_chat_follow_up_answers_only_tool_calls_in_its_assistant_turn():
+    response = ModelResponse(
+        id="resp-n2",
+        choices=[
+            {
+                "index": index,
+                "message": {
+                    "role": "assistant",
+                    "content": None,
+                    "tool_calls": [
+                        {"id": call_id, "type": "function", "function": {"name": "foo", "arguments": "{}"}}
+                    ],
+                },
+                "finish_reason": "tool_calls",
+            }
+            for index, call_id in enumerate(("call-a", "call-b"))
+        ],
+        model="gpt",
+        created=0,
+        object="chat.completion",
+    )
+    tool_results = [
+        {"tool_call_id": tool_call["id"], "name": tool_call["function"]["name"], "result": "done"}
+        for tool_call in LiteLLM_Proxy_MCP_Handler.extract_tool_calls_from_chat_response(response)
+    ]
+
+    follow_up = LiteLLM_Proxy_MCP_Handler.create_follow_up_messages_for_chat(
+        [{"role": "user", "content": "hi"}], response, tool_results
+    )
+
+    assistant_call_ids = [tool_call["id"] for tool_call in follow_up[1]["tool_calls"]]
+    answered_call_ids = [message["tool_call_id"] for message in follow_up if message["role"] == "tool"]
+    assert answered_call_ids == assistant_call_ids == ["call-a"]
+
+
+def test_chat_follow_up_continues_the_choice_that_called_a_tool_when_choice_0_answered():
+    response = ModelResponse(
+        id="resp-n2-second",
+        choices=[
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": "no tool needed"},
+                "finish_reason": "stop",
+            },
+            {
+                "index": 1,
+                "message": {
+                    "role": "assistant",
+                    "content": None,
+                    "tool_calls": [
+                        {"id": "call-b", "type": "function", "function": {"name": "foo", "arguments": "{}"}}
+                    ],
+                },
+                "finish_reason": "tool_calls",
+            },
+        ],
+        model="gpt",
+        created=0,
+        object="chat.completion",
+    )
+    tool_results = [
+        {"tool_call_id": tool_call["id"], "name": tool_call["function"]["name"], "result": "done"}
+        for tool_call in LiteLLM_Proxy_MCP_Handler.extract_tool_calls_from_chat_response(response)
+    ]
+
+    follow_up = LiteLLM_Proxy_MCP_Handler.create_follow_up_messages_for_chat(
+        [{"role": "user", "content": "hi"}], response, tool_results
+    )
+
+    assistant_call_ids = [tool_call["id"] for tool_call in follow_up[1]["tool_calls"]]
+    answered_call_ids = [message["tool_call_id"] for message in follow_up if message["role"] == "tool"]
+    assert answered_call_ids == assistant_call_ids == ["call-b"]
+
+
+def test_extract_tool_calls_from_chat_response_without_choices_finds_none():
+    response = ModelResponse(id="resp-empty", choices=[], model="gpt", created=0, object="chat.completion")
+
+    assert LiteLLM_Proxy_MCP_Handler.extract_tool_calls_from_chat_response(response) == []
+
+
 def test_create_follow_up_messages_for_chat_appends_tool_results():
     original_messages = [{"role": "user", "content": "hi"}]
     response = ModelResponse(
diff --git a/tests/unit/test_main.py b/tests/unit/test_main.py
index 11104cf7bca..571f890a4e6 100644
--- a/tests/unit/test_main.py
+++ b/tests/unit/test_main.py
@@ -2831,9 +2831,10 @@ def test_stream_chunk_builder_keeps_tool_calls_carried_only_by_a_later_choice_of
 
     response = stream_chunk_builder(chunks=chunks)
 
-    tool_calls = response.choices[0].message.tool_calls
-    assert tool_calls is not None
-    assert [(call.id, call.function.name, call.function.arguments) for call in tool_calls] == [
+    text_choice, tool_choice = response.choices
+    assert (text_choice.index, text_choice.message.content, text_choice.message.tool_calls) == (0, "hello world", None)
+    assert tool_choice.index == 1
+    assert [(call.id, call.function.name, call.function.arguments) for call in tool_choice.message.tool_calls] == [
         ("call_1", "lookup_fruit", '{"fruit":"kiwi"}')
     ]