test(anthropic): add think-tag regression coverage

tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py

@@ -511,6 +511,41 @@ def test_multiple_web_search_tool_results():
     assert web_search_results[1]["tool_use_id"] == "srvtoolu_search2"
 
 
+def test_extract_response_content_strips_leaked_think_tags_from_text_blocks():
+    config = AnthropicConfig()
+
+    completion_response = {
+        "content": [
+            {
+                "type": "text",
+                "text": "I need to call the tool first.\n</think>\n\ntool-loop-ok",
+            },
+            {
+                "type": "tool_use",
+                "id": "toolu_01XYZ789",
+                "name": "echo_status",
+                "input": {"status": "ok"},
+            },
+        ]
+    }
+
+    (
+        text,
+        citations,
+        thinking_blocks,
+        reasoning_content,
+        tool_calls,
+        web_search_results,
+        tool_results,
+        compaction_blocks,
+    ) = config.extract_response_content(completion_response)
+
+    assert text == "tool-loop-ok"
+    assert tool_calls is not None
+    assert len(tool_calls) == 1
+    assert tool_calls[0]["function"]["name"] == "echo_status"
+
+
 def test_add_code_execution_tool():
     config = AnthropicConfig()
 

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

@@ -472,6 +472,45 @@ def test_translate_openai_response_to_anthropic_text_and_tool_calls():
     assert anthropic_response.get("stop_reason") == "tool_use"
 
 
+def test_translate_openai_response_to_anthropic_strips_leaked_think_tags():
+    openai_response = ModelResponse(
+        id="resp_text_tool_sanitized",
+        model="gpt-4o-mini",
+        choices=[
+            Choices(
+                finish_reason="tool_calls",
+                message=Message(
+                    role="assistant",
+                    content="I need to call the tool first.\n</think>\n\ntool-loop-ok",
+                    tool_calls=[
+                        ChatCompletionAssistantToolCall(
+                            id="call_tool_combo",
+                            type="function",
+                            function=Function(
+                                name="echo_status", arguments='{"status": "ok"}'
+                            ),
+                        )
+                    ],
+                ),
+            )
+        ],
+        usage=Usage(prompt_tokens=5, completion_tokens=2),
+    )
+
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    anthropic_response = adapter.translate_openai_response_to_anthropic(
+        response=openai_response
+    )
+
+    anthropic_content = anthropic_response.get("content")
+    assert anthropic_content is not None
+    assert len(anthropic_content) == 2
+    assert anthropic_content[0]["type"] == "text"
+    assert anthropic_content[0]["text"] == "tool-loop-ok"
+    assert anthropic_content[1]["type"] == "tool_use"
+    assert anthropic_content[1]["name"] == "echo_status"
+
+
 def test_translate_streaming_openai_chunk_to_anthropic_with_partial_json():
     """Test that partial tool arguments are correctly handled as input_json_delta."""
     choices = [

tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py

@@ -499,3 +499,51 @@ class TestThinkingSummaryPreservation:
         assert result == {
             "reasoning_effort": {"effort": "medium", "summary": "concise"}
         }
+
+
+def test_anthropic_messages_handler_strips_leaked_think_tags_from_completion_path():
+    from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
+        anthropic_messages_handler,
+    )
+    from litellm.types.llms.anthropic_messages.anthropic_response import (
+        AnthropicMessagesResponse,
+    )
+
+    leaked_response = AnthropicMessagesResponse(
+        id="msg_test",
+        type="message",
+        role="assistant",
+        content=[
+            {
+                "type": "text",
+                "text": "I need to call the tool first.\n</think>\n\ntool-loop-ok",
+            },
+            {
+                "type": "tool_use",
+                "id": "toolu_01XYZ789",
+                "name": "echo_status",
+                "input": {"status": "ok"},
+            },
+        ],
+        model="custom-provider/test-model",
+        stop_reason="tool_use",
+        usage={"input_tokens": 10, "output_tokens": 20},
+    )
+
+    with patch(
+        "litellm.llms.anthropic.experimental_pass_through.messages.handler.LiteLLMMessagesToCompletionTransformationHandler.anthropic_messages_handler",
+        return_value=leaked_response,
+    ) as mock_completion_handler:
+        result = anthropic_messages_handler(
+            max_tokens=100,
+            messages=[{"role": "user", "content": "Hello"}],
+            model="my-custom-model",
+            custom_llm_provider="my-custom-llm",
+            api_key="test-api-key",
+        )
+
+    mock_completion_handler.assert_called_once()
+    assert result["content"][0]["type"] == "text"
+    assert result["content"][0]["text"] == "tool-loop-ok"
+    assert result["content"][1]["type"] == "tool_use"
+    assert result["content"][1]["name"] == "echo_status"