fix(sap): normalize list-shaped reasoning_content into a string

litellm/llms/sap/chat/handler.py

@@ -2,12 +2,12 @@ from __future__ import annotations
 
 import json
 import time
-from typing import AsyncIterator, Iterator, Optional
+from typing import Any, AsyncIterator, Iterator, Optional
 
 import httpx
 
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
-from litellm.types.llms.openai import OpenAIChatCompletionChunk
+from litellm.types.llms.openai import ChatCompletionThinkingBlock, OpenAIChatCompletionChunk
 
 from ...custom_httpx.llm_http_handler import BaseLLMHTTPHandler
 
@@ -31,6 +31,58 @@ def _now_ts() -> int:
     return int(time.time())
 
 
+def _to_thinking_block(block: dict[str, Any] | str) -> ChatCompletionThinkingBlock:
+    if isinstance(block, str):
+        return ChatCompletionThinkingBlock(type="thinking", thinking=block, signature=None)
+    return ChatCompletionThinkingBlock(
+        type="thinking",
+        thinking=block.get("content") or block.get("text") or "",
+        signature=block.get("signature"),
+    )
+
+
+def _normalized_message(message: dict[str, Any]) -> dict[str, Any]:
+    blocks = message.get("reasoning_content")
+    if not isinstance(blocks, list):
+        return message
+
+    thinking_blocks = [_to_thinking_block(block) for block in blocks if isinstance(block, (dict, str))]
+    reasoning_text = "".join(block["thinking"] for block in thinking_blocks)
+    return {
+        **message,
+        "reasoning_content": reasoning_text or None,
+        "thinking_blocks": message.get("thinking_blocks") or thinking_blocks or None,
+    }
+
+
+def normalize_reasoning_content(payload: dict[str, Any]) -> dict[str, Any]:
+    """
+    SAP returns `reasoning_content` as a list of blocks for some models (e.g. `gemini-3.5-flash`),
+    while the OpenAI-compatible schema expects a string; the structured blocks are kept in
+    `thinking_blocks`
+    """
+    choices = payload.get("choices")
+    if not isinstance(choices, list):
+        return payload
+
+    return {
+        **payload,
+        "choices": [
+            {
+                **choice,
+                **{
+                    key: _normalized_message(choice[key])
+                    for key in ("message", "delta")
+                    if isinstance(choice.get(key), dict)
+                },
+            }
+            if isinstance(choice, dict)
+            else choice
+            for choice in choices
+        ],
+    }
+
+
 def _is_terminal_chunk(chunk: OpenAIChatCompletionChunk) -> bool:
     """OpenAI-shaped chunk is terminal if any choice has a non-None finish_reason."""
     try:
@@ -55,20 +107,22 @@ class _StreamParser:
             return None
 
         return OpenAIChatCompletionChunk.model_validate(
-            {
-                "id": orc.get("id") or evt.get("request_id") or "stream-chunk",
-                "object": orc.get("object") or "chat.completion.chunk",
-                "created": orc.get("created") or evt.get("created") or _now_ts(),
-                "model": orc.get("model") or "unknown",
-                "choices": [
-                    {
-                        "index": c.get("index", 0),
-                        "delta": c.get("delta") or {},
-                        "finish_reason": c.get("finish_reason"),
-                    }
-                    for c in (orc.get("choices") or [])
-                ],
-            }
+            normalize_reasoning_content(
+                {
+                    "id": orc.get("id") or evt.get("request_id") or "stream-chunk",
+                    "object": orc.get("object") or "chat.completion.chunk",
+                    "created": orc.get("created") or evt.get("created") or _now_ts(),
+                    "model": orc.get("model") or "unknown",
+                    "choices": [
+                        {
+                            "index": c.get("index", 0),
+                            "delta": c.get("delta") or {},
+                            "finish_reason": c.get("finish_reason"),
+                        }
+                        for c in (orc.get("choices") or [])
+                    ],
+                }
+            )
         )
 
     @staticmethod
@@ -92,7 +146,7 @@ class _StreamParser:
             # ensure it looks like an OpenAI chunk
             if "object" not in fr:
                 fr["object"] = "chat.completion.chunk"
-            return OpenAIChatCompletionChunk.model_validate(fr)
+            return OpenAIChatCompletionChunk.model_validate(normalize_reasoning_content(fr))
 
         # Orchestration incremental delta
         if "orchestration_result" in event_obj:
@@ -100,7 +154,7 @@ class _StreamParser:
 
         # Already an OpenAI-like chunk
         if "choices" in event_obj and "object" in event_obj:
-            return OpenAIChatCompletionChunk.model_validate(event_obj)
+            return OpenAIChatCompletionChunk.model_validate(normalize_reasoning_content(event_obj))
 
         # Unknown / heartbeat / metrics
         return None

litellm/llms/sap/chat/transformation.py

@@ -46,6 +46,7 @@ from .handler import (
     GenAIHubOrchestrationError,
     AsyncSAPStreamIterator,
     SAPStreamIterator,
+    normalize_reasoning_content,
 )
 
 # Keys routed outside SAP orchestration `model.params` (prompt, stream, fallbacks, etc.)
@@ -402,7 +403,7 @@ class GenAIHubOrchestrationConfig(OpenAIGPTConfig):
             original_response=raw_response.text,
             additional_args={"complete_input_dict": request_data},
         )
-        response = ModelResponse.model_validate(raw_response.json()["final_result"])
+        response = ModelResponse.model_validate(normalize_reasoning_content(raw_response.json()["final_result"]))
 
         # Strip markdown code blocks if JSON response_format was used with Anthropic models
         # SAP GenAI Hub with Anthropic models sometimes wraps JSON in ```json ... ```

tests/test_litellm/llms/sap/chat/test_sap_transformation.py

@@ -639,3 +639,115 @@ class TestSAPTransformationIntegration:
                 config["config"]["modules"][1]["translation"]["input"]["type"]
                 == "sap_document_translation"
             )
+
+
+class TestSAPReasoningContentNormalization:
+    """SAP returns reasoning_content as a list of blocks for some models (e.g. gemini-3.5-flash)."""
+
+    @pytest.fixture
+    def mock_config(self):
+        from litellm.llms.sap.chat.transformation import GenAIHubOrchestrationConfig
+
+        config = GenAIHubOrchestrationConfig()
+        config.token_creator = lambda: "Bearer TEST_TOKEN"
+        config._base_url = "https://api.test-sap.com"
+        config._resource_group = "test-group"
+        return config
+
+    def _raw_response(self, reasoning_content):
+        from unittest.mock import MagicMock
+
+        raw_response = MagicMock()
+        raw_response.json.return_value = {
+            "final_result": {
+                "id": "test-id",
+                "model": "gemini-3.5-flash",
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "assistant",
+                            "content": "Hello!",
+                            "reasoning_content": reasoning_content,
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+            }
+        }
+        raw_response.text = "{}"
+        return raw_response
+
+    def _transform(self, mock_config, raw_response):
+        from unittest.mock import MagicMock
+        from litellm.types.utils import ModelResponse
+
+        return mock_config.transform_response(
+            model="gemini-3.5-flash",
+            raw_response=raw_response,
+            model_response=ModelResponse(id="test", model="test"),
+            logging_obj=MagicMock(),
+            request_data={},
+            messages=[{"role": "user", "content": "Hi!"}],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+        )
+
+    def test_list_reasoning_blocks_are_joined_into_a_string(self, mock_config):
+        raw_response = self._raw_response(
+            [
+                {"content": "first thought ", "signature": "sig-1"},
+                {"content": "second thought", "signature": "sig-2"},
+            ]
+        )
+
+        result = self._transform(mock_config, raw_response)
+
+        message = result.choices[0].message
+        assert message.content == "Hello!"
+        assert message.reasoning_content == "first thought second thought"
+        assert message.thinking_blocks == [
+            {"type": "thinking", "thinking": "first thought ", "signature": "sig-1"},
+            {"type": "thinking", "thinking": "second thought", "signature": "sig-2"},
+        ]
+
+    def test_empty_reasoning_blocks_become_none(self, mock_config):
+        raw_response = self._raw_response([{"content": "", "signature": "sig"}])
+
+        result = self._transform(mock_config, raw_response)
+
+        assert getattr(result.choices[0].message, "reasoning_content", None) is None
+
+    def test_string_reasoning_content_is_preserved(self, mock_config):
+        raw_response = self._raw_response("already a string")
+
+        result = self._transform(mock_config, raw_response)
+
+        assert result.choices[0].message.reasoning_content == "already a string"
+
+    def test_streaming_chunk_with_list_reasoning_blocks(self):
+        from litellm.llms.sap.chat.handler import _StreamParser
+
+        chunk = _StreamParser.to_openai_chunk(
+            {
+                "orchestration_result": {
+                    "id": "chunk-1",
+                    "model": "gemini-3.5-flash",
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {
+                                "role": "assistant",
+                                "content": "",
+                                "reasoning_content": [{"content": "thinking...", "signature": "sig"}],
+                            },
+                            "finish_reason": None,
+                        }
+                    ],
+                }
+            }
+        )
+
+        assert chunk is not None
+        assert chunk.choices[0].delta.reasoning_content == "thinking..."