fix(ollama_chat): stamp finish_reason tool_calls when tool calls streamed before the done chunk (#39010)

* fix(ollama_chat): stamp finish_reason tool_calls when tool calls streamed before the done chunk

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(ollama_chat): trim finish_reason override comment

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>

litellm/llms/ollama/chat/transformation.py

@@ -423,6 +423,7 @@ class OllamaChatConfig(BaseConfig):
 class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
     started_reasoning_content: bool = False
     finished_reasoning_content: bool = False
+    seen_tool_calls: bool = False
 
     def _is_function_call_complete(self, function_args: str | dict) -> bool:
         if isinstance(function_args, dict):
@@ -468,6 +469,7 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
             # process tool calls - if complete function arg - add id to tool call
             tool_calls: Final = chunk["message"].get("tool_calls")
             if tool_calls is not None:
+                self.seen_tool_calls = True
                 for tool_call in tool_calls:
                     function_args = tool_call.get("function").get("arguments")
                     if function_args is not None and len(function_args) > 0:
@@ -508,9 +510,10 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
 
             if chunk["done"] is True:
                 finish_reason = chunk.get("done_reason") or "stop"
-                # Override finish_reason when tool_calls are present
+                # Override finish_reason when tool_calls appeared in any chunk
                 # Fixes: https://github.com/BerriAI/litellm/issues/18922
-                if tool_calls is not None:
+                # Fixes: https://github.com/BerriAI/litellm/issues/34692
+                if self.seen_tool_calls:
                     finish_reason = "tool_calls"
                 choices = [
                     StreamingChoices(

tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_stop_reason.py

@@ -0,0 +1,79 @@
+"""
+Regression tests for issue #34692.
+
+ollama_chat streams tool_calls in a mid-stream chunk while its final
+(``done: true``) chunk carries only ``done_reason: "stop"``. The provider
+iterator must remember the earlier tool_calls and stamp
+``finish_reason="tool_calls"`` on the final chunk, so the Anthropic
+``/v1/messages`` bridge emits ``stop_reason: "tool_use"``. Before the fix the
+bridge emitted ``stop_reason: "end_turn"`` and Anthropic tool-runners
+(Claude Code, ``messages.stream``) silently dropped the tool call.
+"""
+
+import pytest
+
+from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterator import (
+    AnthropicStreamWrapper,
+)
+from litellm.llms.ollama.chat.transformation import (
+    OllamaChatCompletionResponseIterator,
+)
+from litellm.types.utils import ModelResponseStream
+
+_OLLAMA_TOOL_CHUNK = {
+    "model": "qwen3:8b",
+    "message": {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [{"function": {"name": "get_weather", "arguments": {"city": "San Francisco"}}}],
+    },
+    "done": False,
+}
+_OLLAMA_DONE_CHUNK = {
+    "model": "qwen3:8b",
+    "message": {"role": "assistant", "content": ""},
+    "done": True,
+    "done_reason": "stop",
+    "prompt_eval_count": 100,
+    "eval_count": 20,
+}
+
+
+def _ollama_streamed_chunks() -> list[ModelResponseStream]:
+    iterator = OllamaChatCompletionResponseIterator(streaming_response=iter([]), sync_stream=True)
+    return [iterator.chunk_parser(_OLLAMA_TOOL_CHUNK), iterator.chunk_parser(_OLLAMA_DONE_CHUNK)]
+
+
+class _AsyncStream:
+    def __init__(self, items: list[ModelResponseStream]):
+        self._it = iter(items)
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self):
+        try:
+            return next(self._it)
+        except StopIteration:
+            raise StopAsyncIteration
+
+
+def _assert_tool_use_stop_reason(events: list[dict]) -> None:
+    block_types = [e["content_block"]["type"] for e in events if e.get("type") == "content_block_start"]
+    assert "tool_use" in block_types, f"no tool_use content block opened: {events}"
+    message_deltas = [e for e in events if e.get("type") == "message_delta"]
+    assert message_deltas, f"no message_delta emitted: {events}"
+    assert message_deltas[-1]["delta"]["stop_reason"] == "tool_use", (
+        f"expected stop_reason 'tool_use', got: {message_deltas[-1]}"
+    )
+
+
+def test_ollama_mid_stream_tool_call_yields_tool_use_stop_reason_sync():
+    wrapper = AnthropicStreamWrapper(completion_stream=iter(_ollama_streamed_chunks()), model="qwen3:8b")
+    _assert_tool_use_stop_reason(list(wrapper))
+
+
+@pytest.mark.asyncio
+async def test_ollama_mid_stream_tool_call_yields_tool_use_stop_reason_async():
+    wrapper = AnthropicStreamWrapper(completion_stream=_AsyncStream(_ollama_streamed_chunks()), model="qwen3:8b")
+    _assert_tool_use_stop_reason([event async for event in wrapper])

tests/test_litellm/llms/ollama/test_ollama_chat_transformation.py

@@ -615,6 +615,46 @@ class TestOllamaFinishReasonLength:
             result.choices[0].finish_reason == "stop"
         ), f"Expected 'stop' for natural finish, got '{result.choices[0].finish_reason}'"
 
+    def test_finish_reason_tool_calls_streamed_before_done_chunk(self):
+        """Streaming: tool_calls arriving mid-stream (not on the done chunk) must
+        still produce finish_reason='tool_calls' on the final chunk.
+
+        Regression test for https://github.com/BerriAI/litellm/issues/34692:
+        Ollama emits tool_calls in an earlier chunk and the done chunk carries
+        none, which left finish_reason at 'stop' and made the Anthropic
+        /v1/messages bridge emit stop_reason 'end_turn' instead of 'tool_use'.
+        """
+        iterator = OllamaChatCompletionResponseIterator(
+            streaming_response=iter([]),
+            sync_stream=True,
+        )
+
+        tool_chunk = {
+            "model": "qwen3:8b",
+            "message": {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {"function": {"name": "get_weather", "arguments": {"city": "San Francisco"}}}
+                ],
+            },
+            "done": False,
+        }
+        done_chunk = {
+            "model": "qwen3:8b",
+            "message": {"role": "assistant", "content": ""},
+            "done": True,
+            "done_reason": "stop",
+        }
+
+        tool_result = iterator.chunk_parser(tool_chunk)
+        assert tool_result.choices[0].delta.tool_calls is not None
+
+        done_result = iterator.chunk_parser(done_chunk)
+        assert (
+            done_result.choices[0].finish_reason == "tool_calls"
+        ), f"Expected 'tool_calls' when tool_calls were streamed earlier, got '{done_result.choices[0].finish_reason}'"
+
 
 class TestOllamaReasoningContentStreaming:
     """Test that reasoning_content is properly extracted from all thinking chunks."""