fix(openai): validate assistant tool call arguments before chat template (#28035)

Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>

python/sglang/srt/entrypoints/openai/encoding_dsv32.py

@@ -97,7 +97,14 @@ def encode_arguments_to_dsml(tool_call: Dict[str, str]) -> str:
     p_dsml_template = """<{dsml_token}parameter name="{key}" string="{is_str}">{value}</{dsml_token}parameter>"""
     P_dsml_strs = []
 
-    arguments = json.loads(tool_call["arguments"])
+    raw_arguments = tool_call["arguments"]
+    arguments = (
+        json.loads(raw_arguments) if isinstance(raw_arguments, str) else raw_arguments
+    )
+    if not isinstance(arguments, dict):
+        raise ValueError(
+            "Assistant tool call function.arguments must be a JSON object."
+        )
 
     for k, v in arguments.items():
         p_dsml_str = p_dsml_template.format(

python/sglang/srt/entrypoints/openai/encoding_dsv4.py

@@ -141,7 +141,7 @@ def encode_arguments_to_dsml(tool_call: Dict[str, str]) -> str:
     Encode tool call arguments into DSML parameter format.
 
     Args:
-        tool_call: Dict with "name" and "arguments" (JSON string) keys.
+        tool_call: Dict with "name" and "arguments" keys.
 
     Returns:
         DSML-formatted parameter string.
@@ -149,10 +149,14 @@ def encode_arguments_to_dsml(tool_call: Dict[str, str]) -> str:
     p_dsml_template = '<{dsml_token}parameter name="{key}" string="{is_str}">{value}</{dsml_token}parameter>'
     P_dsml_strs = []
 
-    try:
-        arguments = json.loads(tool_call["arguments"])
-    except Exception as err:
-        arguments = {"arguments": tool_call["arguments"]}
+    raw_arguments = tool_call["arguments"]
+    arguments = (
+        json.loads(raw_arguments) if isinstance(raw_arguments, str) else raw_arguments
+    )
+    if not isinstance(arguments, dict):
+        raise ValueError(
+            "Assistant tool call function.arguments must be a JSON object."
+        )
 
     for k, v in arguments.items():
         p_dsml_str = p_dsml_template.format(

python/sglang/srt/entrypoints/openai/serving_chat.py

@@ -98,6 +98,38 @@ def normalize_tool_content(role: str, content):
     return content
 
 
+def parse_tool_call_arguments(arguments: str) -> Dict[str, Any]:
+    """Parse OpenAI tool call arguments for chat templates."""
+    try:
+        parsed_arguments = orjson.loads(arguments)
+    except orjson.JSONDecodeError as exc:
+        raise ValueError(
+            "Assistant tool call function.arguments must be valid JSON."
+        ) from exc
+
+    if not isinstance(parsed_arguments, dict):
+        raise ValueError(
+            "Assistant tool call function.arguments must be a JSON object."
+        )
+
+    return parsed_arguments
+
+
+def normalize_assistant_tool_call_arguments(message: Dict[str, Any]) -> None:
+    """Normalize assistant history tool call arguments in-place."""
+    if message.get("role") != "assistant" or not isinstance(
+        message.get("tool_calls"), list
+    ):
+        return
+
+    for item in message["tool_calls"]:
+        function = item.get("function") if isinstance(item, dict) else None
+        if not isinstance(function, dict):
+            continue
+        if "arguments" in function and isinstance(function["arguments"], str):
+            function["arguments"] = parse_tool_call_arguments(function["arguments"])
+
+
 def _extract_max_dynamic_patch(request: ChatCompletionRequest):
     img_vals = []
     vid_vals = []
@@ -651,8 +683,12 @@ class OpenAIServingChat(OpenAIServingBase):
         thinking_mode = (
             ThinkingMode.THINKING if thinking_requested else ThinkingMode.CHAT
         )
+        messages = [msg.model_dump() for msg in request.messages]
+        for message in messages:
+            normalize_assistant_tool_call_arguments(message)
+
         prompt_ids = self._encode_messages(
-            [msg.model_dump() for msg in request.messages], request, thinking_mode
+            copy.deepcopy(messages), request, thinking_mode
         )
 
         if prompt_ids is not None:
@@ -660,7 +696,7 @@ class OpenAIServingChat(OpenAIServingBase):
             pass
         elif self.chat_encoding_spec is not None:
             # dsv4/dsv32 encoding path
-            messages = [msg.model_dump() for msg in request.messages]
+            messages = copy.deepcopy(messages)
 
             # dsv4/dsv32 are text-only and consume string content; flatten
             # OpenAI parts-list content here so the encoder sees a plain string.
@@ -730,10 +766,9 @@ class OpenAIServingChat(OpenAIServingBase):
                     prompt_ids, assistant_prefix
                 )
         else:
-            for message in request.messages:
-                if message.content is None:
-                    message.content = ""
-                msg_dict = message.model_dump()
+            for msg_dict in copy.deepcopy(messages):
+                if msg_dict.get("content") is None:
+                    msg_dict["content"] = ""
 
                 # Process content based on detected template format
                 processed_msg = process_content_for_template_format(
@@ -749,24 +784,6 @@ class OpenAIServingChat(OpenAIServingBase):
                     processed_msg["role"], processed_msg.get("content")
                 )
 
-                # per the Transformers docs & maintainers, tool call arguments in
-                # assistant-role messages with tool_calls need to be dicts not JSON str -
-                # this is how tool-use chat templates will expect them moving forwards
-                # so, for messages that have tool_calls, parse the string (which we get
-                # from openAI format) to dict
-                if (
-                    processed_msg["role"] == "assistant"
-                    and "tool_calls" in processed_msg
-                    and isinstance(processed_msg["tool_calls"], list)
-                ):
-                    for item in processed_msg["tool_calls"]:
-                        if "arguments" in item["function"] and isinstance(
-                            item["function"]["arguments"], str
-                        ):
-                            item["function"]["arguments"] = orjson.loads(
-                                item["function"]["arguments"]
-                            )
-
                 openai_compatible_messages.append(processed_msg)
 
             # Handle continue_final_message: separate final assistant message