fix(ollama): set finish_reason to tool_calls and remove broken capability check

litellm/llms/ollama/chat/transformation.py

@@ -190,46 +190,14 @@ def map_openai_params(
                 else:
                     optional_params["think"] = value in {"low", "medium", "high"}
             ### FUNCTION CALLING LOGIC ###
+            # Ollama 0.4+ supports native tool calling - pass tools directly
+            # and let Ollama handle model capability detection
+            # Fixes: https://github.com/BerriAI/litellm/issues/18922
             if param == "tools":
-                ## CHECK IF MODEL SUPPORTS TOOL CALLING ##
-                try:
-                    model_info = litellm.get_model_info(
-                        model=model, custom_llm_provider="ollama"
-                    )
-                    if model_info.get("supports_function_calling") is True:
-                        optional_params["tools"] = value
-                    else:
-                        raise Exception
-                except Exception:
-                    optional_params["format"] = "json"
-                    litellm.add_function_to_prompt = (
-                        True  # so that main.py adds the function call to the prompt
-                    )
-                    optional_params["functions_unsupported_model"] = value
-
-                    if len(optional_params["functions_unsupported_model"]) == 1:
-                        optional_params["function_name"] = optional_params[
-                            "functions_unsupported_model"
-                        ][0]["function"]["name"]
+                optional_params["tools"] = value
 
             if param == "functions":
-                ## CHECK IF MODEL SUPPORTS TOOL CALLING ##
-                try:
-                    model_info = litellm.get_model_info(
-                        model=model, custom_llm_provider="ollama"
-                    )
-                    if model_info.get("supports_function_calling") is True:
-                        optional_params["tools"] = value
-                    else:
-                        raise Exception
-                except Exception:
-                    optional_params["format"] = "json"
-                    litellm.add_function_to_prompt = (
-                        True  # so that main.py adds the function call to the prompt
-                    )
-                    optional_params["functions_unsupported_model"] = (
-                        non_default_params.get("functions")
-                    )
+                optional_params["tools"] = value
         non_default_params.pop("tool_choice", None)  # causes ollama requests to hang
         non_default_params.pop("functions", None)  # causes ollama requests to hang
         return optional_params
@@ -431,6 +399,10 @@ def transform_response(
 
             _message = litellm.Message(**response_json_message)
             model_response.choices[0].message = _message  # type: ignore
+            # Set finish_reason to "tool_calls" when tool_calls are present
+            # Fixes: https://github.com/BerriAI/litellm/issues/18922
+            if _message.tool_calls:
+                model_response.choices[0].finish_reason = "tool_calls"
         model_response.created = int(time.time())
         model_response.model = "ollama_chat/" + model
         prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=messages))  # type: ignore
@@ -563,6 +535,10 @@ def chunk_parser(self, chunk: dict) -> ModelResponseStream:
 
             if chunk["done"] is True:
                 finish_reason = chunk.get("done_reason", "stop")
+                # Override finish_reason when tool_calls are present
+                # Fixes: https://github.com/BerriAI/litellm/issues/18922
+                if tool_calls is not None:
+                    finish_reason = "tool_calls"
                 choices = [
                     StreamingChoices(
                         delta=delta,

tests/test_litellm/llms/ollama/test_ollama_chat_transformation.py

@@ -323,3 +323,153 @@ def test_transform_request_no_images_no_images_key(self):
         # and the code checks "if images is not None", an empty list will still be set
         assert "images" in result["messages"][0]
         assert result["messages"][0]["images"] == []
+
+
+class TestOllamaToolCalling:
+    """Tests for Ollama tool calling fixes.
+
+    Issue: https://github.com/BerriAI/litellm/issues/18922
+    """
+
+    def test_tools_passed_directly_without_capability_check(self):
+        """Test that tools are passed directly to Ollama without model capability checks.
+
+        Previously, the code called litellm.get_model_info() which could fail
+        when Ollama runs on a remote server, causing a broken fallback.
+        Now tools are passed directly - Ollama 0.4+ handles capability detection.
+        """
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get weather",
+                    "parameters": {"type": "object", "properties": {}},
+                },
+            }
+        ]
+
+        optional_params = get_optional_params(
+            model="ollama_chat/qwen3:14b",
+            tools=tools,
+            custom_llm_provider="ollama_chat",
+        )
+
+        # Tools should be passed through directly
+        assert "tools" in optional_params
+        assert optional_params["tools"] == tools
+        # Should NOT trigger the broken fallback
+        assert "functions_unsupported_model" not in optional_params
+        assert "format" not in optional_params or optional_params.get("format") != "json"
+
+    def test_finish_reason_tool_calls_non_streaming(self):
+        """Test that finish_reason is set to 'tool_calls' when tool_calls present.
+
+        Previously, finish_reason was hardcoded to 'stop' even when tool_calls
+        were in the response, causing clients to ignore the tool calls.
+        """
+        import json
+        from unittest.mock import MagicMock
+
+        import litellm
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        config = OllamaChatConfig()
+
+        # Simulated Ollama response with tool_calls
+        ollama_response = {
+            "model": "qwen3:14b",
+            "created_at": "2025-01-11T00:00:00.000000Z",
+            "message": {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "Tokyo"},
+                        }
+                    }
+                ],
+            },
+            "done": True,
+            "prompt_eval_count": 100,
+            "eval_count": 50,
+        }
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = ollama_response
+        mock_response.text = json.dumps(ollama_response)
+
+        mock_logging = MagicMock()
+
+        model_response = ModelResponse()
+        model_response.choices = [Choices(message=Message(content=""), index=0)]
+
+        result = config.transform_response(
+            model="qwen3:14b",
+            raw_response=mock_response,
+            model_response=model_response,
+            logging_obj=mock_logging,
+            request_data={},
+            messages=[{"role": "user", "content": "Weather?"}],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+            api_key=None,
+            json_mode=False,
+        )
+
+        # finish_reason should be "tool_calls", not "stop"
+        assert result.choices[0].finish_reason == "tool_calls"
+        assert result.choices[0].message.tool_calls is not None
+
+    def test_finish_reason_stop_when_no_tool_calls(self):
+        """Test that finish_reason remains 'stop' when no tool_calls present."""
+        import json
+        from unittest.mock import MagicMock
+
+        import litellm
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        config = OllamaChatConfig()
+
+        # Simulated Ollama response without tool_calls
+        ollama_response = {
+            "model": "qwen3:14b",
+            "created_at": "2025-01-11T00:00:00.000000Z",
+            "message": {
+                "role": "assistant",
+                "content": "Hello! How can I help you?",
+            },
+            "done": True,
+            "prompt_eval_count": 100,
+            "eval_count": 50,
+        }
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = ollama_response
+        mock_response.text = json.dumps(ollama_response)
+
+        mock_logging = MagicMock()
+
+        model_response = ModelResponse()
+        model_response.choices = [Choices(message=Message(content=""), index=0)]
+
+        result = config.transform_response(
+            model="qwen3:14b",
+            raw_response=mock_response,
+            model_response=model_response,
+            logging_obj=mock_logging,
+            request_data={},
+            messages=[{"role": "user", "content": "Hello"}],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+            api_key=None,
+            json_mode=False,
+        )
+
+        # finish_reason should be "stop" (default behavior)
+        assert result.choices[0].finish_reason == "stop"
+        assert result.choices[0].message.tool_calls is None