fix(anthropic-adapter): translate stop_sequences and disabled thinking for non-Claude targets

litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py

@@ -331,6 +331,7 @@ def translatable_anthropic_params(self) -> List:
             "thinking",
             "output_format",
             "output_config",
+            "stop_sequences",
         ]
 
     def _is_web_search_tool(self, tool: Dict[str, Any]) -> bool:
@@ -615,7 +616,7 @@ def translate_anthropic_thinking_to_reasoning_effort(
         thinking_type = thinking.get("type", "disabled")
 
         if thinking_type == "disabled":
-            return None
+            return "none"
         elif thinking_type == "enabled":
             return reasoning_effort_from_thinking_budget(thinking.get("budget_tokens", 0))
         elif thinking_type == "adaptive":
@@ -919,6 +920,18 @@ def _translate_tool_choice_to_openai(
             tool_choice=cast(AnthropicMessagesToolChoice, tool_choice)
         )
 
+    def _translate_stop_sequences_to_openai(
+        self,
+        anthropic_message_request: AnthropicMessagesRequest,
+        new_kwargs: ChatCompletionRequest,
+    ) -> None:
+        if "stop_sequences" not in anthropic_message_request:
+            return
+        stop_sequences = anthropic_message_request["stop_sequences"]
+        if not stop_sequences:
+            return
+        new_kwargs["stop"] = stop_sequences
+
     def _translate_tools_to_openai(
         self,
         anthropic_message_request: AnthropicMessagesRequest,
@@ -976,12 +989,18 @@ def _translate_thinking_to_openai(
         if not reasoning_effort:
             return
 
+        thinking_type = thinking.get("type") if isinstance(thinking, dict) else None
+
         # For adaptive thinking, override with output_config.effort if available
-        if isinstance(thinking, dict) and thinking.get("type") == "adaptive":
+        if thinking_type == "adaptive":
             output_config = anthropic_message_request.get("output_config")
             if isinstance(output_config, dict) and output_config.get("effort"):
                 reasoning_effort = output_config["effort"]
 
+        if thinking_type == "disabled":
+            new_kwargs["reasoning_effort"] = reasoning_effort
+            return
+
         summary = thinking.get("summary") if isinstance(thinking, dict) else None
         auto_summary = is_reasoning_auto_summary_enabled()
         if summary:
@@ -1098,6 +1117,11 @@ def translate_anthropic_to_openai(
             anthropic_message_request=anthropic_message_request,
             new_kwargs=new_kwargs,
         )
+        ## CONVERT STOP_SEQUENCES
+        self._translate_stop_sequences_to_openai(
+            anthropic_message_request=anthropic_message_request,
+            new_kwargs=new_kwargs,
+        )
         ## CONVERT OUTPUT_FORMAT to RESPONSE_FORMAT
         self._translate_output_format_to_openai(
             anthropic_message_request=anthropic_message_request,

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

@@ -1582,6 +1582,67 @@ def test_thinking_still_translated_to_reasoning_effort_for_non_claude_model():
     assert new_kwargs["reasoning_effort"] == "low"
 
 
+def test_thinking_disabled_translated_to_reasoning_effort_none_for_non_claude_model():
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    thinking = {"type": "disabled"}
+
+    new_kwargs = {"model": CACHE_CONTROL_NON_ANTHROPIC_MODEL}
+    adapter._translate_thinking_to_openai(cast(Any, {"thinking": thinking}), cast(Any, new_kwargs))
+
+    assert "thinking" not in new_kwargs
+    assert new_kwargs["reasoning_effort"] == "none"
+
+
+def test_thinking_disabled_stays_plain_string_when_auto_summary_enabled():
+    import litellm
+
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    thinking = {"type": "disabled"}
+
+    original = litellm.reasoning_auto_summary
+    try:
+        litellm.reasoning_auto_summary = True
+        new_kwargs = {"model": CACHE_CONTROL_NON_ANTHROPIC_MODEL}
+        adapter._translate_thinking_to_openai(cast(Any, {"thinking": thinking}), cast(Any, new_kwargs))
+    finally:
+        litellm.reasoning_auto_summary = original
+
+    assert new_kwargs["reasoning_effort"] == "none"
+
+
+def test_stop_sequences_translated_to_stop_for_non_claude_model():
+    from litellm.types.llms.anthropic import AnthropicMessagesRequest
+
+    anthropic_request = AnthropicMessagesRequest(
+        model=CACHE_CONTROL_NON_ANTHROPIC_MODEL,
+        max_tokens=1024,
+        messages=[{"role": "user", "content": "hi"}],
+        stop_sequences=["</block>"],
+    )
+
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    openai_request, _ = adapter.translate_anthropic_to_openai(anthropic_message_request=anthropic_request)
+
+    assert openai_request["stop"] == ["</block>"]
+    assert "stop_sequences" not in openai_request
+
+
+def test_empty_stop_sequences_does_not_set_stop():
+    from litellm.types.llms.anthropic import AnthropicMessagesRequest
+
+    anthropic_request = AnthropicMessagesRequest(
+        model=CACHE_CONTROL_NON_ANTHROPIC_MODEL,
+        max_tokens=1024,
+        messages=[{"role": "user", "content": "hi"}],
+        stop_sequences=[],
+    )
+
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    openai_request, _ = adapter.translate_anthropic_to_openai(anthropic_message_request=anthropic_request)
+
+    assert "stop" not in openai_request
+
+
 def test_cache_control_preserved_in_image_content_for_claude():
     """Cache control should be preserved in image content for Claude models."""
     anthropic_messages = [