fix(chatgpt): an in-sequence system message reaches Codex as role=developer

The Codex backend answers 400 {"detail":"System messages are not allowed"} to any
role=system input item, which is what the /v1/messages adapter makes of a mid-turn
system message. role=developer is accepted.

Co-Authored-By: Claude Code <noreply@anthropic.com>

litellm/llms/chatgpt/responses/transformation.py

@@ -90,6 +90,13 @@ class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig):
                 request["instructions"] = f"{base_instructions}\n\n{existing_instructions}"
         else:
             request["instructions"] = base_instructions
+        # The Codex backend rejects role=system input items ("System messages are not allowed")
+        # but accepts role=developer, which is what an in-sequence system message means there.
+        if isinstance(request.get("input"), list):
+            request["input"] = [
+                {**item, "role": "developer"} if isinstance(item, dict) and item.get("role") == "system" else item
+                for item in request["input"]
+            ]
         request["store"] = False
         request["stream"] = True
         include: Final = list(request.get("include") or [])

tests/unit/llms/chatgpt/responses/test_chatgpt_responses_transformation.py

@@ -71,6 +71,18 @@ class TestChatGPTResponsesAPITransformation:
 
         assert "service_tier" not in request
 
+    def test_system_input_items_become_developer(self):
+        system_item = {"type": "message", "role": "system", "content": [{"type": "input_text", "text": "be brief"}]}
+        user_item = {"type": "message", "role": "user", "content": [{"type": "input_text", "text": "hi"}]}
+        request = ChatGPTResponsesAPIConfig().transform_responses_api_request(
+            model="gpt-6.1-sol",
+            input=[user_item, system_item],
+            response_api_optional_request_params={},
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+        assert request["input"] == [user_item, {**system_item, "role": "developer"}]
+
     @pytest.mark.parametrize(
         "model_name",
         [