fix(ollama): forward tool_calls and tool_call_id in transform_request (#26122)

tool_calls on assistant messages were translated to OllamaToolCall format
but never copied into the outgoing OllamaChatCompletionMessage, so Ollama
received {role: assistant, content: ''} with no tool_calls. The model
then had no record of having made a tool call, causing it to re-issue
the identical call on every turn (infinite loop).

Similarly, tool_call_id on role:tool messages was silently dropped.
Ollama uses this field to resolve the tool name from conversation history.

Also add tool_call_id to OllamaChatCompletionMessage TypedDict.

Fixes #26094

litellm/llms/ollama/chat/transformation.py

@@ -265,8 +265,9 @@ class OllamaChatConfig(BaseConfig):
             ):  # avoid message serialization issues - https://github.com/BerriAI/litellm/issues/5319
                 m = m.model_dump(exclude_none=True)
             tool_calls = m.get("tool_calls")
+            new_tools: Optional[List[OllamaToolCall]] = None
             if tool_calls is not None and isinstance(tool_calls, list):
-                new_tools: List[OllamaToolCall] = []
+                new_tools = []
                 for tool in tool_calls:
                     typed_tool = ChatCompletionAssistantToolCall(**tool)  # type: ignore
                     if typed_tool["type"] == "function":
@@ -280,7 +281,6 @@ class OllamaChatConfig(BaseConfig):
                             )
                         )
                         new_tools.append(ollama_tool_call)
-                cast(dict, m)["tool_calls"] = new_tools
             reasoning_content, parsed_content = _extract_reasoning_content(
                 cast(dict, m)
             )
@@ -296,6 +296,11 @@ class OllamaChatConfig(BaseConfig):
                 ollama_message["content"] = content_str
             if images is not None:
                 ollama_message["images"] = images
+            if new_tools is not None:
+                ollama_message["tool_calls"] = new_tools
+            tool_call_id = m.get("tool_call_id")
+            if tool_call_id is not None:
+                ollama_message["tool_call_id"] = cast(str, tool_call_id)
 
             new_messages.append(ollama_message)
 

litellm/types/llms/ollama.py

@@ -37,3 +37,4 @@ class OllamaChatCompletionMessage(TypedDict, total=False):
     images: List[str]
     tool_calls: List[OllamaToolCall]
     tool_name: str
+    tool_call_id: str

tests/test_litellm/llms/ollama/test_ollama_chat_transformation.py

@@ -746,3 +746,98 @@ class TestOllamaReasoningContentStreaming:
         result = iterator.chunk_parser(done_chunk)
         assert result.choices[0].delta.reasoning_content == "Final thought"
         assert result.choices[0].finish_reason == "stop"
+
+
+class TestOllamaToolCallTransformation:
+    def test_transform_request_preserves_tool_calls(self):
+        """
+        tool_calls on assistant messages must survive transform_request.
+        Previously the translated OllamaToolCall list was built but never
+        copied into the outgoing OllamaChatCompletionMessage, so Ollama
+        received {role: assistant, content: ''} with no tool_calls and
+        the model re-issued the same call on every turn.
+        Regression: https://github.com/BerriAI/litellm/issues/26094
+        """
+        config = OllamaChatConfig()
+        messages = cast(
+            list[AllMessageValues],
+            [
+                {"role": "user", "content": "What's the weather in SF?"},
+                {
+                    "role": "assistant",
+                    "content": "",
+                    "tool_calls": [
+                        {
+                            "id": "call_abc123",
+                            "type": "function",
+                            "function": {
+                                "name": "get_weather",
+                                "arguments": '{"location": "San Francisco, CA"}',
+                            },
+                        }
+                    ],
+                },
+            ],
+        )
+
+        result = config.transform_request(
+            model="gemma4:27b",
+            messages=messages,
+            optional_params={},
+            litellm_params={},
+            headers={},
+        )
+
+        assistant_msg = result["messages"][1]
+        assert "tool_calls" in assistant_msg, "tool_calls must be forwarded to Ollama"
+        assert len(assistant_msg["tool_calls"]) == 1
+        tc = assistant_msg["tool_calls"][0]
+        assert tc["function"]["name"] == "get_weather"
+        assert tc["function"]["arguments"] == {"location": "San Francisco, CA"}
+
+    def test_transform_request_forwards_tool_call_id(self):
+        """
+        tool_call_id on role:tool messages must be forwarded so Ollama can
+        resolve the tool name from the conversation history.
+        Regression: https://github.com/BerriAI/litellm/issues/26094
+        """
+        config = OllamaChatConfig()
+        messages = cast(
+            list[AllMessageValues],
+            [
+                {"role": "user", "content": "What's the weather in SF?"},
+                {
+                    "role": "assistant",
+                    "content": "",
+                    "tool_calls": [
+                        {
+                            "id": "call_abc123",
+                            "type": "function",
+                            "function": {
+                                "name": "get_weather",
+                                "arguments": '{"location": "San Francisco, CA"}',
+                            },
+                        }
+                    ],
+                },
+                {
+                    "role": "tool",
+                    "tool_call_id": "call_abc123",
+                    "content": "Sunny, 72°F",
+                },
+            ],
+        )
+
+        result = config.transform_request(
+            model="gemma4:27b",
+            messages=messages,
+            optional_params={},
+            litellm_params={},
+            headers={},
+        )
+
+        tool_msg = result["messages"][2]
+        assert tool_msg["role"] == "tool"
+        assert tool_msg["content"] == "Sunny, 72°F"
+        assert "tool_call_id" in tool_msg, "tool_call_id must be forwarded to Ollama"
+        assert tool_msg["tool_call_id"] == "call_abc123"