diff --git a/tests/unit/integrations/test_otel_tool_call_parts.py b/tests/unit/integrations/test_otel_tool_call_parts.py
new file mode 100644
index 00000000000..52bed152314
--- /dev/null
+++ b/tests/unit/integrations/test_otel_tool_call_parts.py
@@ -0,0 +1,93 @@
+# -*- coding: utf-8 -*-
+"""Regression tests for #45796: OTEL tool-call parts + streamed finish reason.
+
+Covered behavior:
+- ``_transform_messages_to_otel_semantic_conventions`` emits GenAI
+  ``{"type": "tool_call", "id", "name", "arguments"}`` parts for assistant
+  messages carrying ``tool_calls`` — for plain dicts and for Pydantic
+  message objects (which used to be skipped because ``"tool_calls" in msg``
+  is False for non-dict objects).
+- Plain string ``content`` still emits a text part (no regression).
+- ``ChunkProcessor.build_base_response`` aggregates a stream whose chunks
+  carried tool-call deltas but no explicit ``finish_reason`` to
+  ``"tool_calls"`` instead of the default ``"stop"``.
+"""
+from pydantic import BaseModel
+
+from litellm.integrations.opentelemetry import OpenTelemetry
+from litellm.litellm_core_utils.streaming_chunk_builder_utils import ChunkProcessor
+
+
+class _FakeAssistantMessage(BaseModel):
+    role: str = "assistant"
+    content: str | None = None
+    tool_calls: list | None = None
+
+
+class TestOtelToolCallParts:
+    def test_dict_message_with_tool_calls_emits_genai_tool_call_parts(self):
+        msg = {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_1",
+                    "type": "function",
+                    "function": {"name": "get_weather", "arguments": '{"city": "SF"}'},
+                }
+            ],
+        }
+        out = OpenTelemetry()._transform_messages_to_otel_semantic_conventions([msg])[0]
+        assert out["parts"] == [
+            {"type": "tool_call", "id": "call_1", "name": "get_weather", "arguments": '{"city": "SF"}'}
+        ]
+        assert "tool_calls" not in out
+
+    def test_pydantic_message_with_tool_calls_emits_genai_tool_call_parts(self):
+        msg = _FakeAssistantMessage(
+            tool_calls=[
+                {
+                    "id": "call_2",
+                    "type": "function",
+                    "function": {"name": "lookup", "arguments": "{}"},
+                }
+            ]
+        )
+        out = OpenTelemetry()._transform_messages_to_otel_semantic_conventions([msg])[0]
+        assert out["parts"] == [
+            {"type": "tool_call", "id": "call_2", "name": "lookup", "arguments": "{}"}
+        ]
+
+    def test_string_content_still_emits_text_part(self):
+        out = OpenTelemetry()._transform_messages_to_otel_semantic_conventions(
+            [{"role": "assistant", "content": "hello"}]
+        )[0]
+        assert out["parts"] == [{"type": "text", "content": "hello"}]
+
+    def test_streamed_tool_call_without_explicit_finish_reason_aggregates_to_tool_calls(self):
+        chunks = [
+            {
+                "object": "chat.completion.chunk",
+                "created": 0,
+                "model": "mock",
+                "choices": [{"index": 0, "delta": {"role": "assistant", "content": None}}],
+            },
+            {
+                "choices": [
+                    {
+                        "index": 0,
+                        "delta": {
+                            "tool_calls": [
+                                {
+                                    "id": "c1",
+                                    "type": "function",
+                                    "function": {"name": "f", "arguments": "{}"},
+                                }
+                            ]
+                        },
+                    }
+                ]
+            },
+        ]
+        resp = ChunkProcessor(chunks).build_base_response(chunks)
+        assert resp.choices[0].finish_reason == "tool_calls"