test: cover app.nz provider params

litellm/llms/openai_like/dynamic_config.py

@@ -94,9 +94,7 @@ def create_config_class(provider: SimpleProviderConfig):
             from litellm.utils import supports_function_calling
 
             supported_params = super().get_supported_openai_params(model=model)
-            supported_params = list(
-                dict.fromkeys([*supported_params, *provider.extra_supported_params])
-            )
+            supported_params = list(dict.fromkeys([*supported_params, *provider.extra_supported_params]))
 
             _supports_fc = supports_function_calling(model=model, custom_llm_provider=provider.slug)
 

provider_endpoints_support.json

@@ -177,6 +177,24 @@
         "interactions": true
       }
     },
+    "app_nz": {
+      "display_name": "app.nz (`app_nz`)",
+      "url": "https://docs.litellm.ai/docs/providers/app_nz",
+      "endpoints": {
+        "chat_completions": true,
+        "messages": true,
+        "responses": true,
+        "embeddings": false,
+        "image_generations": false,
+        "audio_transcriptions": false,
+        "audio_speech": false,
+        "moderations": false,
+        "batches": false,
+        "rerank": false,
+        "a2a": true,
+        "interactions": true
+      }
+    },
     "apertis": {
       "display_name": "Apertis (`apertis`)",
       "endpoints": {

tests/test_litellm/llms/openai_like/test_json_providers.py

@@ -52,9 +52,7 @@ class TestJSONProviderLoader:
         assert api_base == "https://api.publicai.co/v1"
 
         # Test with custom base
-        api_base, api_key = config._get_openai_compatible_provider_info(
-            "https://custom.api.com", "test-key"
-        )
+        api_base, api_key = config._get_openai_compatible_provider_info("https://custom.api.com", "test-key")
         assert api_base == "https://custom.api.com"
         assert api_key == "test-key"
 
@@ -70,9 +68,7 @@ class TestJSONProviderLoader:
         # Test parameter mapping
         optional_params = {}
         non_default_params = {"max_completion_tokens": 100, "temperature": 0.7}
-        result = config.map_openai_params(
-            non_default_params, optional_params, "gpt-4", False
-        )
+        result = config.map_openai_params(non_default_params, optional_params, "gpt-4", False)
 
         # max_completion_tokens should be mapped to max_tokens
         assert "max_tokens" in result
@@ -98,6 +94,19 @@ class TestJSONProviderLoader:
         assert isinstance(supported, list)
         assert len(supported) > 0
 
+    def test_extra_supported_params(self):
+        """Test JSON providers can extend supported OpenAI params"""
+        from litellm.llms.openai_like.dynamic_config import create_config_class
+        from litellm.llms.openai_like.json_loader import JSONProviderRegistry
+
+        provider = JSONProviderRegistry.get("app_nz")
+        config_class = create_config_class(provider)
+        config = config_class()
+
+        supported = config.get_supported_openai_params("app/auto")
+
+        assert "reasoning_effort" in supported
+
     def test_tool_params_excluded_when_function_calling_not_supported(self):
         """Test that tool-related params are excluded for models that don't support
         function calling. Regression test for https://github.com/BerriAI/litellm/issues/21125
@@ -121,9 +130,9 @@ class TestJSONProviderLoader:
             "parallel_tool_calls",
         ]
         for param in tool_params:
-            assert (
-                param not in supported
-            ), f"'{param}' should not be in supported params when function calling is not supported"
+            assert param not in supported, (
+                f"'{param}' should not be in supported params when function calling is not supported"
+            )
 
         # Non-tool params should still be present
         assert "temperature" in supported
@@ -168,9 +177,7 @@ class TestJSONProviderLoader:
         from litellm import LlmProviders
         from litellm.utils import ProviderConfigManager
 
-        config = ProviderConfigManager.get_provider_chat_config(
-            model="gpt-4", provider=LlmProviders.PUBLICAI
-        )
+        config = ProviderConfigManager.get_provider_chat_config(model="gpt-4", provider=LlmProviders.PUBLICAI)
 
         assert config is not None
         assert config.custom_llm_provider == "publicai"
@@ -218,9 +225,7 @@ class TestPinstripes:
         api_base, api_key = config._get_openai_compatible_provider_info(None, None)
         assert api_base == "https://pinstripes.io/v1"
 
-        api_base, api_key = config._get_openai_compatible_provider_info(
-            "https://custom.pinstripes.io/v1", "test-key"
-        )
+        api_base, api_key = config._get_openai_compatible_provider_info("https://custom.pinstripes.io/v1", "test-key")
         assert api_base == "https://custom.pinstripes.io/v1"
         assert api_key == "test-key"
 
@@ -235,9 +240,7 @@ class TestPinstripes:
 
         optional_params = {}
         non_default_params = {"max_completion_tokens": 100, "temperature": 0.7}
-        result = config.map_openai_params(
-            non_default_params, optional_params, "ps/glm-4.5-air", False
-        )
+        result = config.map_openai_params(non_default_params, optional_params, "ps/glm-4.5-air", False)
 
         assert "max_tokens" in result
         assert result["max_tokens"] == 100
@@ -282,9 +285,7 @@ class TestDarkbloom:
         api_base, api_key = config._get_openai_compatible_provider_info(None, None)
         assert api_base == "https://api.darkbloom.dev/v1"
 
-        api_base, api_key = config._get_openai_compatible_provider_info(
-            "https://custom.darkbloom.dev/v1", "test-key"
-        )
+        api_base, api_key = config._get_openai_compatible_provider_info("https://custom.darkbloom.dev/v1", "test-key")
         assert api_base == "https://custom.darkbloom.dev/v1"
         assert api_key == "test-key"
 
@@ -311,17 +312,13 @@ class TestDarkbloom:
         from litellm import LlmProviders
         from litellm.utils import ProviderConfigManager
 
-        config = ProviderConfigManager.get_provider_chat_config(
-            model="gemma-4-26b", provider=LlmProviders.DARKBLOOM
-        )
+        config = ProviderConfigManager.get_provider_chat_config(model="gemma-4-26b", provider=LlmProviders.DARKBLOOM)
 
         assert config is not None
         assert config.custom_llm_provider == "darkbloom"
 
     def test_darkbloom_model_cost_map(self):
-        with open(
-            os.path.join(workspace_path, "model_prices_and_context_window.json")
-        ) as f:
+        with open(os.path.join(workspace_path, "model_prices_and_context_window.json")) as f:
             model_cost = json.load(f)
 
         expected_models = {
@@ -373,9 +370,7 @@ class TestPublicAIIntegration:
             content = response.choices[0].message.content.lower()
             assert len(content) > 0
 
-            print(
-                f"✓ PublicAI completion successful: {response.choices[0].message.content}"
-            )
+            print(f"✓ PublicAI completion successful: {response.choices[0].message.content}")
 
         except Exception as e:
             if pytest:
@@ -403,9 +398,7 @@ class TestPublicAIIntegration:
             chunks = []
             for chunk in response:
                 assert chunk is not None
-                if hasattr(chunk.choices[0], "delta") and hasattr(
-                    chunk.choices[0].delta, "content"
-                ):
+                if hasattr(chunk.choices[0], "delta") and hasattr(chunk.choices[0].delta, "content"):
                     if chunk.choices[0].delta.content:
                         chunks.append(chunk.choices[0].delta.content)
 
@@ -461,9 +454,7 @@ class TestPublicAIIntegration:
             # Send message with content as list (should be converted to string)
             response = litellm.completion(
                 model="publicai/swiss-ai/apertus-8b-instruct",
-                messages=[
-                    {"role": "user", "content": [{"type": "text", "text": "Say hello"}]}
-                ],
+                messages=[{"role": "user", "content": [{"type": "text", "text": "Say hello"}]}],
                 max_tokens=10,
             )