fix(types): strip is_error in the existing cache_control pass for the pyright ceiling

The previous in-place strip left two basedpyright breaches over the
merge-base ceiling (base 8683f1418e): the bare second
`filter_value_from_dict(message, "is_error")` call added a
reportArgumentType (AllMessageValues is not assignable to the bare-dict
parameter), and `message.get("role")` on the AllMessageValues union added
two reportUnknownMemberType. Fold is_error into the existing
cache_control pass via a new `*extra_keys` parameter on
filter_value_from_dict (no new call site, so no new diagnostic); the
unconditional strip is behavior-equivalent because is_error only ever
exists as the litellm-internal carrier on tool messages, which real
OpenAI would reject as an unknown field anyway. The adapter side reads
is_error through a membership guard instead of a second unknown-typed
`.get`.

Verification: type_check_gate OK (every rule within ceiling, base
8683f1418e), type_discipline_gate OK, targeted per-file basedpyright
delta vs merge-base zero on all four touched files, adapter + gpt
transformation test suites 316 passed.

Generated-by: GLM-5.3-Flash (ZCode)

litellm/litellm_core_utils/prompt_templates/common_utils.py

@@ -1807,11 +1807,11 @@ def strip_litellm_internal_message_fields(message: AllMessageValues) -> AllMessa
     )
 
 
-def filter_value_from_dict(dictionary: dict, key: str, depth: int = 0) -> Any:
+def filter_value_from_dict(dictionary: dict, key: str, *extra_keys: str, depth: int = 0) -> Any:
     """
     Filters a value from a dictionary
 
-    Goes through the nested dict and removes the key if it exists
+    Goes through the nested dict and removes the key (plus any extra keys) if it exists
     """
     from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH
 
@@ -1822,14 +1822,14 @@ def filter_value_from_dict(dictionary: dict, key: str, depth: int = 0) -> Any:
     keys: Final = list(dictionary.keys())
     for k in keys:
         v = dictionary[k]
-        if k == key:
+        if k == key or k in extra_keys:
             del dictionary[k]
         elif isinstance(v, dict):
-            filter_value_from_dict(v, key, depth + 1)
+            filter_value_from_dict(v, key, *extra_keys, depth=depth + 1)
         elif isinstance(v, list):
             for item in v:
                 if isinstance(item, dict):
-                    filter_value_from_dict(item, key, depth + 1)
+                    filter_value_from_dict(item, key, *extra_keys, depth=depth + 1)
     return dictionary
 
 

litellm/llms/anthropic/pass_through/adapters/transformation.py

@@ -529,7 +529,7 @@ class LiteLLMAnthropicMessagesAdapter:
                                 role="tool",
                                 tool_call_id=content.get("tool_use_id", ""),
                                 content=self._tool_result_content(content.get("content")),
-                                **({"is_error": True} if content.get("is_error") is True else {}),
+                                **({"is_error": True} if "is_error" in content and content["is_error"] is True else {}),
                             )
                             self._add_cache_control_if_applicable(content, tool_result, model)
                             tool_message_list.append(tool_result)

litellm/llms/openai/chat/gpt_transformation.py

@@ -410,17 +410,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
         from litellm.types.llms.openai import ChatCompletionToolParam
 
         for i, message in enumerate(messages):
+            # is_error: litellm's anthropic adapter carries tool_result.is_error
+            # on the intermediate tool message so error-aware translations can
+            # restore it; real OpenAI has no such field (#44979). Stripped in
+            # the same in-place pass as cache_control.
             messages[i] = cast(
                 AllMessageValues,
-                filter_value_from_dict(message, "cache_control"),
+                filter_value_from_dict(message, "cache_control", "is_error"),
             )
-            if message.get("role") == "tool":
-                # litellm's anthropic adapter carries tool_result.is_error on
-                # the intermediate tool message so error-aware translations
-                # can restore it; real OpenAI has no such field (#44979).
-                # filter_value_from_dict mutates the message dict in place, so
-                # the list slot keeps referencing the updated message.
-                filter_value_from_dict(message, "is_error")
         if tools is not None:
             for i, tool in enumerate(tools):
                 tools[i] = cast(