fix(responses): avoid recursive cache-breakpoint stripping

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

litellm/completion_extras/litellm_responses_transformation/transformation.py

@@ -81,29 +81,36 @@ _RESPONSES_API_ONLY_FIELDS: Final = frozenset((*Response.model_fields, *Response
 _CHAT_CONTENT_ITEM: Final = TypeAdapter(dict[str, object])
 
 
-def _strip_prompt_cache_breakpoints_from_value(value: object) -> object:
-    if isinstance(value, dict):
-        content: Final = cast(dict[str, object], value)  # cast-ok: isinstance narrows the recursive container
-        return {
-            key: _strip_prompt_cache_breakpoints_from_value(item)
-            for key, item in content.items()
-            if key != "prompt_cache_breakpoint"
-        }
+def _strip_prompt_cache_breakpoint_from_content_block(value: object) -> object:
+    if not isinstance(value, dict):
+        return value
+    content_block: Final = cast(dict[str, object], value)
+    return {key: item for key, item in content_block.items() if key != "prompt_cache_breakpoint"}
+
+
+def _strip_prompt_cache_breakpoints_from_content(value: object) -> object:
     if isinstance(value, list):
-        return [
-            _strip_prompt_cache_breakpoints_from_value(item)
-            for item in cast(list[object], value)  # cast-ok: isinstance narrows the recursive container
-        ]
+        list_content: Final = cast(list[object], value)
+        return [_strip_prompt_cache_breakpoint_from_content_block(item) for item in list_content]
     if isinstance(value, tuple):
-        return tuple(
-            _strip_prompt_cache_breakpoints_from_value(item)
-            for item in cast(tuple[object, ...], value)  # cast-ok: isinstance narrows the recursive container
-        )
-    return value
+        tuple_content: Final = cast(tuple[object, ...], value)
+        return tuple(_strip_prompt_cache_breakpoint_from_content_block(item) for item in tuple_content)
+    return _strip_prompt_cache_breakpoint_from_content_block(value)
+
+
+def _strip_prompt_cache_breakpoints_from_item(value: object) -> object:
+    if not isinstance(value, dict):
+        return value
+    input_item: Final = cast(dict[str, object], value)
+    return {
+        key: _strip_prompt_cache_breakpoints_from_content(item) if key in ("content", "output") else item
+        for key, item in input_item.items()
+        if key != "prompt_cache_breakpoint"
+    }
 
 
 def _strip_prompt_cache_breakpoints(input_items: list[object]) -> list[object]:
-    return [_strip_prompt_cache_breakpoints_from_value(item) for item in input_items]
+    return [_strip_prompt_cache_breakpoints_from_item(item) for item in input_items]
 
 
 def _provider_metadata(response_fields: Mapping[str, object] | None) -> Mapping[str, object]:

tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py

@@ -2,7 +2,7 @@ import datetime
 import json
 import os
 import unittest
-from typing import TYPE_CHECKING, Final, List, Literal, Optional, Tuple, get_args
+from typing import TYPE_CHECKING, Final, List, Literal, Optional, Tuple, cast, get_args
 from unittest.mock import ANY, MagicMock, Mock, patch
 
 import httpx
@@ -12,7 +12,7 @@ import litellm
 from litellm.completion_extras.litellm_responses_transformation.transformation import (
     LiteLLMResponsesTransformationHandler,
 )
-from litellm.types.llms.openai import REASONING_EFFORT
+from litellm.types.llms.openai import AllMessageValues, REASONING_EFFORT
 
 if TYPE_CHECKING:
     from openai.types.responses import ResponseOutputItem
@@ -4291,6 +4291,48 @@ def test_prompt_cache_breakpoints_are_dropped_for_unsupported_models() -> None:
     ]
 
 
+def test_prompt_cache_breakpoints_are_dropped_from_function_call_output_for_unsupported_models() -> None:
+    handler: Final = LiteLLMResponsesTransformationHandler()
+    cache_breakpoint: Final = {"mode": "explicit"}
+    messages: Final = cast(
+        list[AllMessageValues],
+        [
+            {
+                "role": "tool",
+                "tool_call_id": "call_1",
+                "content": [{"type": "text", "text": "Tool result", "prompt_cache_breakpoint": cache_breakpoint}],
+            }
+        ],
+    )
+
+    request: Final = cast(
+        dict[str, object],
+        handler.transform_request(
+            model="gpt-5.4-mini",
+            messages=messages,
+            optional_params={},
+            litellm_params={},
+            headers={},
+            litellm_logging_obj=Mock(),
+        ),
+    )
+
+    assert request["input"] == [
+        {
+            "type": "function_call_output",
+            "call_id": "call_1",
+            "output": [{"type": "input_text", "text": "Tool result"}],
+        }
+    ]
+    assert messages == [
+        {
+            "role": "tool",
+            "tool_call_id": "call_1",
+            "content": [{"type": "text", "text": "Tool result", "prompt_cache_breakpoint": {"mode": "explicit"}}],
+        }
+    ]
+
+
 @pytest.mark.parametrize(
     ("litellm_params", "keep_marker"),
     (({"base_model": "gpt-5.6"}, True), ({}, False)),