[bench] Agentic support for bench_serving.py (#25016)

python/sglang/bench_serving.py

@@ -1136,6 +1136,24 @@ def calculate_metrics(
 MULTI_TURN_BACKENDS = {"sglang-oai-chat", "vllm-chat", "lmdeploy-chat"}
 
 
+def _normalize_round_messages(turn: Any) -> Optional[List[Dict[str, str]]]:
+    """Normalize a multi-turn round to a list of message dicts.
+
+    Accepts ``str`` (single user message) or ``List[Dict]`` with role/content
+    (e.g. multiple tool observations bundled into one round). Returns ``None``
+    on any other shape so callers can also use it as a predicate.
+    """
+    if isinstance(turn, str):
+        return [{"role": "user", "content": turn}]
+    if (
+        isinstance(turn, list)
+        and turn
+        and all(isinstance(m, dict) and "role" in m and "content" in m for m in turn)
+    ):
+        return [{"role": m["role"], "content": m["content"]} for m in turn]
+    return None
+
+
 def wrap_multi_turn_request_func(request_func: Callable, backend: str) -> Callable:
     assert (
         backend in MULTI_TURN_BACKENDS
@@ -1145,12 +1163,19 @@ def wrap_multi_turn_request_func(request_func: Callable, backend: str) -> Callab
         request_func_input: RequestFuncInput,
         pbar: Optional[tqdm] = None,
     ) -> List[RequestFuncOutput]:
-        prompts: List[str] = request_func_input.prompt
+        prompts = request_func_input.prompt
         prev_messages: List[Dict[str, str]] = []
         outputs = []
 
         for round_index in range(len(prompts)):
-            prev_messages.append({"role": "user", "content": prompts[round_index]})
+            normalized = _normalize_round_messages(prompts[round_index])
+            if normalized is None:
+                raise ValueError(
+                    f"Multi-turn round {round_index} must be a str or a "
+                    "non-empty List[Dict] of role/content messages, got: "
+                    f"{type(prompts[round_index]).__name__}"
+                )
+            prev_messages.extend(normalized)
 
             inner_input = replace(
                 copy.deepcopy(request_func_input), prompt=copy.deepcopy(prev_messages)
@@ -1198,14 +1223,13 @@ async def benchmark(
     else:
         raise ValueError(f"Unknown backend: {backend}")
 
-    # Check for multi-turn: prompt is a list of strings (not OpenAI messages dicts)
-    # Multi-turn format: ["turn1", "turn2", ...] - list of strings
-    # OpenAI format: [{"role": "user", "content": "..."}, ...] - list of dicts
+    # Multi-turn iff prompt[0] is a valid per-round payload. Single-shot
+    # OpenAI messages (List[Dict]) is excluded since its first element is a dict.
     first_prompt = input_requests[0].prompt
     is_multi_turn = (
         isinstance(first_prompt, list)
-        and len(first_prompt) > 0
-        and isinstance(first_prompt[0], str)
+        and bool(first_prompt)
+        and _normalize_round_messages(first_prompt[0]) is not None
     )
     if is_multi_turn:
         request_func = wrap_multi_turn_request_func(request_func, backend=backend)

python/sglang/benchmark/datasets/autobench.py

@@ -106,6 +106,20 @@ def _normalize_prompt(row: Dict[str, Any]) -> Tuple[Any, str]:
             and all(isinstance(item, str) for item in prompt)
         ):
             return prompt, "multi_turn"
+        if (
+            isinstance(prompt, list)
+            and prompt
+            and all(
+                isinstance(item, list)
+                and item
+                and all(
+                    isinstance(m, dict) and "role" in m and "content" in m for m in item
+                )
+                for item in prompt
+            )
+        ):
+            # Multi-turn with N messages per round (e.g. tool observations).
+            return prompt, "multi_turn"
         if (
             isinstance(prompt, list)
             and prompt