Handle developer messages conservatively for OpenRouter

src/pipecat/services/openrouter/llm.py

@@ -37,6 +37,7 @@ class OpenRouterLLMService(OpenAILLMService):
 
     Settings = OpenRouterLLMSettings
     _settings: Settings
+    supports_developer_role = False
 
     def __init__(
         self,

tests/test_run_inference.py

@@ -23,6 +23,7 @@ from pipecat.services.openai.responses.llm import (
     OpenAIResponsesHttpLLMService,
     OpenAIResponsesLLMService,
 )
+from pipecat.services.openrouter.llm import OpenRouterLLMService
 
 
 @pytest.mark.asyncio
@@ -105,6 +106,35 @@ async def test_openai_run_inference_client_exception():
             await service.run_inference(mock_context)
 
 
+@pytest.mark.asyncio
+async def test_openrouter_run_inference_converts_developer_messages_to_user():
+    """Test OpenRouter requests convert developer messages for broad model compatibility."""
+    with patch.object(OpenRouterLLMService, "create_client"):
+        service = OpenRouterLLMService(settings=OpenRouterLLMService.Settings(model="gpt-4"))
+        service._client = AsyncMock()
+
+        mock_context = MagicMock(spec=LLMContext)
+        mock_adapter = MagicMock()
+        mock_adapter.get_llm_invocation_params.return_value = OpenAILLMInvocationParams(
+            messages=[{"role": "user", "content": "Tool result"}],
+            tools=OPENAI_NOT_GIVEN,
+            tool_choice=OPENAI_NOT_GIVEN,
+        )
+        service.get_llm_adapter = MagicMock(return_value=mock_adapter)
+
+        mock_response = MagicMock()
+        mock_response.choices = [MagicMock()]
+        mock_response.choices[0].message.content = "Done"
+        service._client.chat.completions.create.return_value = mock_response
+
+        result = await service.run_inference(mock_context)
+
+        assert result == "Done"
+        mock_adapter.get_llm_invocation_params.assert_called_once_with(
+            mock_context, system_instruction=None, convert_developer_to_user=True
+        )
+
+
 @pytest.mark.asyncio
 async def test_anthropic_run_inference_with_llm_context():
     """Test run_inference with LLMContext returns expected response for Anthropic."""