feat(Tool Calling): Support `required` and specific function mode (#6550)

docs/backend/function_calling.ipynb

@@ -54,10 +54,12 @@
    "source": [
     "Note that `--tool-call-parser` defines the parser used to interpret responses. Currently supported parsers include:\n",
     "\n",
-    "- llama3: Llama 3.1 / 3.2 (e.g. meta-llama/Llama-3.1-8B-Instruct, meta-llama/Llama-3.2-1B-Instruct).\n",
+    "- llama3: Llama 3.1 / 3.2 / 3.3 (e.g. meta-llama/Llama-3.1-8B-Instruct, meta-llama/Llama-3.2-1B-Instruct, meta-llama/Llama-3.3-70B-Instruct).\n",
+    "- llama4: Llama 4 (e.g. meta-llama/Llama-4-Scout-17B-16E-Instruct).\n",
     "- mistral: Mistral (e.g. mistralai/Mistral-7B-Instruct-v0.3, mistralai/Mistral-Nemo-Instruct-2407, mistralai/\n",
     "Mistral-Nemo-Instruct-2407, mistralai/Mistral-7B-v0.3).\n",
-    "- qwen25: Qwen 2.5 (e.g. Qwen/Qwen2.5-1.5B-Instruct, Qwen/Qwen2.5-7B-Instruct) and QwQ (i.e. Qwen/QwQ-32B). Especially, for QwQ, we can enable the reasoning parser together with tool call parser, details about reasoning parser can be found in [reasoning parser](https://docs.sglang.ai/backend/separate_reasoning.html)."
+    "- qwen25: Qwen 2.5 (e.g. Qwen/Qwen2.5-1.5B-Instruct, Qwen/Qwen2.5-7B-Instruct) and QwQ (i.e. Qwen/QwQ-32B). Especially, for QwQ, we can enable the reasoning parser together with tool call parser, details about reasoning parser can be found in [reasoning parser](https://docs.sglang.ai/backend/separate_reasoning.html).\n",
+    "- deepseekv3: DeepSeek-v3 (e.g., deepseek-ai/DeepSeek-V3-0324).\n"
    ]
   },
   {
@@ -360,6 +362,164 @@
     "print(final_response.choices[0].message.content)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tool Choice Mode\n",
+    "\n",
+    "SGLang supports OpenAI's `tool_choice` parameter to control when and which tools the model should call. This feature is implemented using EBNF (Extended Backus-Naur Form) grammar to ensure reliable tool calling behavior.\n",
+    "\n",
+    "### Supported Tool Choice Options\n",
+    "\n",
+    "- **`tool_choice=\"required\"`**: Forces the model to call at least one tool\n",
+    "- **`tool_choice={\"type\": \"function\", \"function\": {\"name\": \"specific_function\"}}`**: Forces the model to call a specific function\n",
+    "\n",
+    "### Backend Compatibility\n",
+    "\n",
+    "Tool choice is fully supported with the **Xgrammar backend**, which is the default grammar backend (`--grammar-backend xgrammar`). However, it may not be fully supported with other backends such as `outlines`.\n",
+    "\n",
+    "### Example: Required Tool Choice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response with tool_choice='required':\n",
+      "Content: None\n",
+      "Tool calls: [ChatCompletionMessageToolCall(id='call_NFO3TSZuRRO8Eu3Cv79uiQ', function=Function(arguments='{\"city\": \"Paris\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function', index=0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from openai import OpenAI\n",
+    "import json\n",
+    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
+    "from sglang.test.test_utils import is_in_ci\n",
+    "\n",
+    "if is_in_ci():\n",
+    "    from patch import launch_server_cmd\n",
+    "else:\n",
+    "    from sglang.utils import launch_server_cmd\n",
+    "    import nest_asyncio\n",
+    "\n",
+    "    nest_asyncio.apply()\n",
+    "\n",
+    "# Start a new server session for tool choice examples\n",
+    "server_process_tool_choice, port_tool_choice = launch_server_cmd(\n",
+    "    \"python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --tool-call-parser qwen25 --host 0.0.0.0\"\n",
+    ")\n",
+    "wait_for_server(f\"http://localhost:{port_tool_choice}\")\n",
+    "\n",
+    "# Initialize client for tool choice examples\n",
+    "client_tool_choice = OpenAI(\n",
+    "    api_key=\"None\", base_url=f\"http://0.0.0.0:{port_tool_choice}/v1\"\n",
+    ")\n",
+    "model_name_tool_choice = client_tool_choice.models.list().data[0].id\n",
+    "\n",
+    "# Example with tool_choice=\"required\" - forces the model to call a tool\n",
+    "messages_required = [\n",
+    "    {\"role\": \"user\", \"content\": \"Hello, what is the capital of France?\"}\n",
+    "]\n",
+    "\n",
+    "# Define tools\n",
+    "tools = [\n",
+    "    {\n",
+    "        \"type\": \"function\",\n",
+    "        \"function\": {\n",
+    "            \"name\": \"get_current_weather\",\n",
+    "            \"description\": \"Get the current weather in a given location\",\n",
+    "            \"parameters\": {\n",
+    "                \"type\": \"object\",\n",
+    "                \"properties\": {\n",
+    "                    \"city\": {\n",
+    "                        \"type\": \"string\",\n",
+    "                        \"description\": \"The city to find the weather for, e.g. 'San Francisco'\",\n",
+    "                    },\n",
+    "                    \"unit\": {\n",
+    "                        \"type\": \"string\",\n",
+    "                        \"description\": \"The unit to fetch the temperature in\",\n",
+    "                        \"enum\": [\"celsius\", \"fahrenheit\"],\n",
+    "                    },\n",
+    "                },\n",
+    "                \"required\": [\"city\", \"unit\"],\n",
+    "            },\n",
+    "        },\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "response_required = client_tool_choice.chat.completions.create(\n",
+    "    model=model_name_tool_choice,\n",
+    "    messages=messages_required,\n",
+    "    temperature=0,\n",
+    "    max_tokens=1024,\n",
+    "    tools=tools,\n",
+    "    tool_choice=\"required\",  # Force the model to call a tool\n",
+    ")\n",
+    "\n",
+    "print_highlight(\"Response with tool_choice='required':\")\n",
+    "print(\"Content:\", response_required.choices[0].message.content)\n",
+    "print(\"Tool calls:\", response_required.choices[0].message.tool_calls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example: Specific Function Choice\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response with specific function choice:\n",
+      "Content: None\n",
+      "Tool calls: [ChatCompletionMessageToolCall(id='call_fGL_1qsPQFqntNBPkSynJw', function=Function(arguments='{\"city\": \"Sophia Antipolis\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function', index=0)]\n",
+      "Called function: get_current_weather\n",
+      "Arguments: {\"city\": \"Sophia Antipolis\", \"unit\": \"celsius\"}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example with specific function choice - forces the model to call a specific function\n",
+    "messages_specific = [\n",
+    "    {\"role\": \"user\", \"content\": \"What are the most attactive places in France?\"}\n",
+    "]\n",
+    "\n",
+    "response_specific = client_tool_choice.chat.completions.create(\n",
+    "    model=model_name_tool_choice,\n",
+    "    messages=messages_specific,\n",
+    "    temperature=0,\n",
+    "    max_tokens=1024,\n",
+    "    tools=tools,\n",
+    "    tool_choice={\n",
+    "        \"type\": \"function\",\n",
+    "        \"function\": {\"name\": \"get_current_weather\"},\n",
+    "    },  # Force the model to call the specific get_current_weather function\n",
+    ")\n",
+    "\n",
+    "print_highlight(\"Response with specific function choice:\")\n",
+    "print(\"Content:\", response_specific.choices[0].message.content)\n",
+    "print(\"Tool calls:\", response_specific.choices[0].message.tool_calls)\n",
+    "\n",
+    "if response_specific.choices[0].message.tool_calls:\n",
+    "    tool_call = response_specific.choices[0].message.tool_calls[0]\n",
+    "    print(f\"Called function: {tool_call.function.name}\")\n",
+    "    print(f\"Arguments: {tool_call.function.arguments}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -444,7 +604,7 @@
    "outputs": [],
    "source": [
     "import sglang as sgl\n",
-    "from sglang.srt.function_call_parser import FunctionCallParser\n",
+    "from sglang.srt.function_call.function_call_parser import FunctionCallParser\n",
     "from sglang.srt.managers.io_struct import Tool, Function\n",
     "\n",
     "llm = sgl.Engine(model_path=\"Qwen/Qwen2.5-7B-Instruct\")\n",

python/sglang/srt/entrypoints/http_server.py

@@ -47,7 +47,7 @@ from sglang.srt.disaggregation.utils import (
     register_disaggregation_server,
 )
 from sglang.srt.entrypoints.engine import _launch_subprocesses
-from sglang.srt.function_call_parser import FunctionCallParser
+from sglang.srt.function_call.function_call_parser import FunctionCallParser
 from sglang.srt.managers.io_struct import (
     AbortReq,
     CloseSessionReqInput,

python/sglang/srt/function_call/base_format_detector.py

+import json
+import logging
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List
+
+from partial_json_parser.core.exceptions import MalformedJSON
+from partial_json_parser.core.options import Allow
+
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    ToolCallItem,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.utils import (
+    _find_common_prefix,
+    _is_complete_json,
+    _partial_json_loads,
+)
+from sglang.srt.openai_api.protocol import Tool
+
+logger = logging.getLogger(__name__)
+
+
+class BaseFormatDetector(ABC):
+    """Base class providing two sets of interfaces: one-time and streaming incremental."""
+
+    def __init__(self):
+        # initialize properties used for state when parsing tool calls in
+        self._buffer = ""
+        # streaming mode
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: List[str] = (
+            []
+        )  # map what has been streamed for each tool so far to a list
+        self.bot_token = ""
+        self.eot_token = ""
+
+    def parse_base_json(self, action: Any, tools: List[Tool]) -> List[ToolCallItem]:
+        tool_indices = {
+            tool.function.name: i for i, tool in enumerate(tools) if tool.function.name
+        }
+        if not isinstance(action, list):
+            action = [action]
+
+        results = []
+        for act in action:
+            name = act.get("name")
+            if name and name in tool_indices:
+                results.append(
+                    ToolCallItem(
+                        tool_index=tool_indices[name],
+                        name=name,
+                        parameters=json.dumps(
+                            act.get("parameters") or act.get("arguments", {}),
+                            ensure_ascii=False,
+                        ),
+                    )
+                )
+            else:
+                logger.warning(f"Model attempted to call undefined function: {name}")
+
+        return results
+
+    @abstractmethod
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        """
+        Parses the text in one go. Returns success=True if the format matches, otherwise False.
+        Note that leftover_text here represents "content that this parser will not consume further".
+        """
+        action = json.loads(text)
+        return StreamingParseResult(calls=self.parse_base_json(action, tools))
+
+    def parse_streaming_increment(
+        self, new_text: str, tools: List[Tool]
+    ) -> StreamingParseResult:
+        """
+        Streaming incremental parsing with tool validation.
+        """
+        # Append new text to buffer
+        self._buffer += new_text
+        current_text = self._buffer
+        if not (self.bot_token in current_text or current_text.startswith("{")):
+            self._buffer = ""
+            if self.eot_token in new_text:
+                new_text = new_text.replace(self.eot_token, "")
+            return StreamingParseResult(normal_text=new_text)
+
+        # Build tool indices if not already built
+        if not hasattr(self, "_tool_indices"):
+            self._tool_indices = {
+                tool.function.name: i
+                for i, tool in enumerate(tools)
+                if tool.function and tool.function.name
+            }
+
+        flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
+        try:
+            tool_call_arr = []
+            is_complete = []
+            try:
+                start_idx = (
+                    len(self.bot_token)
+                    if current_text.startswith(self.bot_token)
+                    else 0
+                )
+                while start_idx < len(current_text):
+                    (obj, end_idx) = _partial_json_loads(
+                        current_text[start_idx:], flags
+                    )
+                    is_complete.append(
+                        _is_complete_json(current_text[start_idx : start_idx + end_idx])
+                    )
+                    start_idx += end_idx + len("; ")
+
+                    # Validate tool name if present
+                    if "name" in obj and obj["name"] not in self._tool_indices:
+                        # Invalid tool name - reset state
+                        self._buffer = ""
+                        self.current_tool_id = -1
+                        self.current_tool_name_sent = False
+                        if self.streamed_args_for_tool:
+                            self.streamed_args_for_tool.pop()
+                        return StreamingParseResult()
+
+                    # Handle parameters/arguments consistency
+                    if "parameters" in obj:
+                        assert (
+                            "arguments" not in obj
+                        ), "model generated both parameters and arguments"
+                        obj["arguments"] = obj["parameters"]
+                    tool_call_arr.append(obj)
+
+            except MalformedJSON:
+                return StreamingParseResult()
+
+            if len(tool_call_arr) == 0:
+                return StreamingParseResult()
+
+            current_tool_call: Dict = (
+                tool_call_arr[self.current_tool_id] if len(tool_call_arr) > 0 else {}
+            )
+
+            # Handle new tool in array
+            if len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1:
+                if self.current_tool_id >= 0:
+                    cur_arguments = current_tool_call.get("arguments")
+                    if cur_arguments:
+                        cur_args_json = json.dumps(cur_arguments)
+                        sent = len(self.streamed_args_for_tool[self.current_tool_id])
+                        argument_diff = cur_args_json[sent:]
+
+                        res = StreamingParseResult(
+                            calls=[
+                                ToolCallItem(
+                                    tool_index=self.current_tool_id,
+                                    name="",
+                                    parameters=argument_diff,
+                                )
+                            ],
+                        )
+                        self.streamed_args_for_tool[
+                            self.current_tool_id
+                        ] += argument_diff
+                    else:
+                        res = StreamingParseResult()
+                else:
+                    res = StreamingParseResult()
+
+                self.current_tool_id = len(tool_call_arr) - 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                return res
+
+            # Handle tool name
+            elif not self.current_tool_name_sent:
+                function_name = current_tool_call.get("name")
+                if function_name and function_name in self._tool_indices:
+                    res = StreamingParseResult(
+                        calls=[
+                            ToolCallItem(
+                                tool_index=self._tool_indices[function_name],
+                                name=function_name,
+                                parameters="",
+                            )
+                        ],
+                    )
+                    self.current_tool_name_sent = True
+                else:
+                    res = StreamingParseResult()
+
+            # Handle streaming arguments
+            else:
+                cur_arguments = current_tool_call.get("arguments")
+                res = StreamingParseResult()
+
+                if cur_arguments:
+                    sent = len(self.streamed_args_for_tool[self.current_tool_id])
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
+                        "arguments"
+                    )
+
+                    argument_diff = None
+                    if is_complete[self.current_tool_id]:
+                        argument_diff = cur_args_json[sent:]
+                        self._buffer = ""
+                        self.prev_tool_call_arr[self.current_tool_id].clear()
+                        self.current_tool_name_sent = False
+                        self.streamed_args_for_tool[self.current_tool_id] = ""
+
+                    elif prev_arguments:
+                        prev_args_json = json.dumps(prev_arguments)
+                        if cur_args_json != prev_args_json:
+                            prefix = _find_common_prefix(prev_args_json, cur_args_json)
+                            argument_diff = prefix[sent:]
+
+                    if argument_diff is not None:
+                        res = StreamingParseResult(
+                            calls=[
+                                ToolCallItem(
+                                    tool_index=self.current_tool_id,
+                                    parameters=argument_diff,
+                                )
+                            ],
+                        )
+                        if not is_complete[self.current_tool_id]:
+                            self.streamed_args_for_tool[
+                                self.current_tool_id
+                            ] += argument_diff
+
+            self.prev_tool_call_arr = tool_call_arr
+            return res
+
+        except Exception as e:
+            logger.error(f"Error in parse_streaming_increment: {e}")
+            return StreamingParseResult()
+
+    @abstractmethod
+    def has_tool_call(self, text: str) -> bool:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def structure_info(self) -> _GetInfoFunc:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def build_ebnf(self, tools: List[Tool]) -> str:
+        raise NotImplementedError()

python/sglang/srt/function_call/core_types.py

+from dataclasses import dataclass
+from typing import Callable, List, Optional
+
+from pydantic import BaseModel
+
+
+class ToolCallItem(BaseModel):
+    """Simple encapsulation of the parsed ToolCall result for easier usage in streaming contexts."""
+
+    tool_index: int
+    name: Optional[str] = None
+    parameters: str  # JSON string
+
+
+class StreamingParseResult(BaseModel):
+    """Result of streaming incremental parsing."""
+
+    normal_text: str = ""
+    calls: List[ToolCallItem] = []
+
+
+@dataclass
+class StructureInfo:
+    begin: str
+    end: str
+    trigger: str
+
+
+"""
+Helper alias of function
+Usually it is a function that takes a name string and returns a StructureInfo object,
+which can be used to construct a structural_tag object
+"""
+_GetInfoFunc = Callable[[str], StructureInfo]

python/sglang/srt/function_call/deepseekv3_detector.py

+import json
+import logging
+import re
+from typing import List
+
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    StructureInfo,
+    ToolCallItem,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.ebnf_composer import EBNFComposer
+from sglang.srt.function_call.utils import _is_complete_json
+from sglang.srt.openai_api.protocol import Tool
+
+logger = logging.getLogger(__name__)
+
+
+class DeepSeekV3Detector(BaseFormatDetector):
+    """
+    Detector for DeepSeek models.
+    Assumes function call format:
+      '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_current_weather\n```json\n{"location": "Tokyo"}\n```<｜tool▁call▁end｜>\n<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_current_weather\n```json\n{"location": "Paris"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜>
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.bot_token = "<｜tool▁calls▁begin｜>"
+        self.eot_token = "<｜tool▁calls▁end｜>"
+        self.func_call_regex = r"<｜tool▁call▁begin｜>.*?<｜tool▁call▁end｜>"
+        self.func_detail_regex = r"<｜tool▁call▁begin｜>(.*)<｜tool▁sep｜>(.*)\n```json\n(.*)\n```<｜tool▁call▁end｜>"
+        self._last_arguments = ""
+
+    def has_tool_call(self, text: str) -> bool:
+        """Check if the text contains a deepseek format tool call."""
+        return self.bot_token in text
+
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        """
+        One-time parsing: Detects and parses tool calls in the provided text.
+
+        :param text: The complete text to parse.
+        :param tools: List of available tools.
+        :return: ParseResult indicating success or failure, consumed text, leftover text, and parsed calls.
+        """
+        idx = text.find(self.bot_token)
+        normal_text = text[:idx].strip() if idx != -1 else text
+        if self.bot_token not in text:
+            return StreamingParseResult(normal_text=normal_text, calls=[])
+        match_result_list = re.findall(self.func_call_regex, text, re.DOTALL)
+        calls = []
+        try:
+            for match_result in match_result_list:
+                # Get function name
+                func_detail = re.search(self.func_detail_regex, match_result, re.DOTALL)
+                func_name = func_detail.group(2)
+                func_args = func_detail.group(3)
+                func_args = json.loads(func_args)
+                # construct match_result for parse_base_json
+                match_result = {"name": func_name, "parameters": func_args}
+                calls.extend(self.parse_base_json(match_result, tools))
+            return StreamingParseResult(normal_text=normal_text, calls=calls)
+        except Exception as e:
+            logger.error(f"Error in detect_and_parse: {e}")
+            # return the normal text if parsing fails
+            return StreamingParseResult(normal_text=text)
+
+    def parse_streaming_increment(
+        self, new_text: str, tools: List[Tool]
+    ) -> StreamingParseResult:
+        """
+        Streaming incremental parsing tool calls for DeepSeekV3 format.
+        """
+        self._buffer += new_text
+        current_text = self._buffer
+
+        if self.bot_token not in current_text:
+            self._buffer = ""
+            for e_token in [self.eot_token, "```", "<｜tool▁call▁end｜>"]:
+                if e_token in new_text:
+                    new_text = new_text.replace(e_token, "")
+            return StreamingParseResult(normal_text=new_text)
+
+        if not hasattr(self, "_tool_indices"):
+            self._tool_indices = {
+                tool.function.name: i
+                for i, tool in enumerate(tools)
+                if tool.function and tool.function.name
+            }
+
+        calls: list[ToolCallItem] = []
+        try:
+            partial_match = re.search(
+                pattern=r"<｜tool▁call▁begin｜>(.*)<｜tool▁sep｜>(.*)\n```json\n(.*)",
+                string=current_text,
+                flags=re.DOTALL,
+            )
+            if partial_match:
+                func_name = partial_match.group(2).strip()
+                func_args_raw = partial_match.group(3).strip()
+
+                if not self.current_tool_name_sent:
+                    calls.append(
+                        ToolCallItem(
+                            tool_index=self._tool_indices.get(func_name, 0),
+                            name=func_name,
+                            parameters="",
+                        )
+                    )
+                    self.current_tool_name_sent = True
+                else:
+                    argument_diff = (
+                        func_args_raw[len(self._last_arguments) :]
+                        if func_args_raw.startswith(self._last_arguments)
+                        else func_args_raw
+                    )
+
+                    if argument_diff:
+                        calls.append(
+                            ToolCallItem(
+                                tool_index=self._tool_indices.get(func_name, 0),
+                                name=None,
+                                parameters=argument_diff,
+                            )
+                        )
+                        self._last_arguments += argument_diff
+
+                    if _is_complete_json(func_args_raw):
+                        result = StreamingParseResult(normal_text="", calls=calls)
+                        self._buffer = ""
+                        self._last_arguments = ""
+                        self.current_tool_name_sent = False
+                        return result
+
+            return StreamingParseResult(normal_text="", calls=calls)
+
+        except Exception as e:
+            logger.error(f"Error in parse_streaming_increment: {e}")
+            return StreamingParseResult(normal_text=current_text)
+
+    def structure_info(self) -> _GetInfoFunc:
+        return lambda name: StructureInfo(
+            begin=">" + name + "\n```json\n",
+            end="\n```<",
+            trigger=">" + name + "\n```json\n",
+        )
+
+    def build_ebnf(self, tools: List[Tool]):
+        return EBNFComposer.build_ebnf(
+            tools,
+            bot_token=self.bot_token,
+            eot_token=self.eot_token,
+            tool_call_separator="",
+            call_rule_fmt='"<｜tool▁call▁begin｜>function<｜tool▁sep｜>{name}\\n```json\\n" {arguments_rule} "\\n```<｜tool▁call▁end｜>"',
+            function_format="json",
+        )

python/sglang/srt/function_call/ebnf_composer.py

+from typing import Literal, Optional
+
+
+class EBNFComposer:
+    # Adapted from https://xgrammar.mlc.ai/docs/how_to/ebnf_guided_generation.html#try-out-via-hf-transformers
+    json_grammar_ebnf_str = r"""
+        json ::= basic_array | basic_object
+        basic_any ::= basic_number | basic_string | basic_boolean | basic_null | basic_array | basic_object
+        basic_integer ::= ("0" | "-"? [1-9] [0-9]*) ".0"?
+        basic_number ::= ("0" | "-"? [1-9] [0-9]*) ("." [0-9]+)? ([eE] [+-]? [0-9]+)?
+        basic_string ::= (([\"] basic_string_1 [\"]))
+        basic_string_1 ::= "" | [^"\\\x00-\x1F] basic_string_1 | "\\" escape basic_string_1
+        escape ::= ["\\/bfnrt] | "u" [A-Fa-f0-9] [A-Fa-f0-9] [A-Fa-f0-9] [A-Fa-f0-9]
+        basic_boolean ::= "true" | "false"
+        basic_null ::= "null"
+        basic_array ::= "[" ("" | ws basic_any (ws "," ws basic_any)*) ws "]"
+        basic_object ::= "{" ("" | ws basic_string ws ":" ws basic_any ( ws "," ws basic_string ws ":" ws basic_any)*) ws "}"
+        ws ::= [ \n\t]*
+        """
+
+    pythonic_grammar_ebnf_str = r"""
+        pythonic ::= basic_number | basic_string | basic_array | "True" | "False" | "None"
+        basic_any ::= basic_number | basic_string | basic_array | basic_object
+        basic_number ::= ("0" | "-"? [1-9] [0-9]*) ("." [0-9]+)? ([eE] [+-]? [0-9]+)?
+        basic_string ::= (([\"] basic_string_1 [\"]))
+        basic_string_1 ::= "" | [^"\\\x00-\x1F] basic_string_1 | "\\" escape basic_string_1
+        escape ::= ["\\/bfnrt] | "u" [A-Fa-f0-9] [A-Fa-f0-9] [A-Fa-f0-9] [A-Fa-f0-9]
+        basic_array ::= "[" ("" | ws basic_any (ws "," ws basic_any)*) ws "]"
+        basic_object ::= "{" ("" | ws basic_string ws ":" ws basic_any ( ws "," ws basic_string ws ":" ws basic_any)*) ws "}"
+        ws ::= [ \n\t]*
+    """
+
+    TOOL_CALLS_MAP = {
+        "pythonic": '"[" function_call ("," function_call)* "]"',
+        "json": "function_call",
+    }
+
+    CALL_RULE_MAP = {
+        "pythonic": 'call_{name} ::= "{name}" "(" {arguments_rule} ")"',
+        "json": 'call_{name} ::= "{{" "\\"name\\"" ":" "\\"{name}\\"" ", " "\\"arguments\\"" ":" {arguments_rule} "}}"',
+    }
+
+    ARGUMENTS_RULE_MAP = {
+        "pythonic": "{arg_rules}",
+        "json": '"{{" {arg_rules} "}}"',
+    }
+
+    KEY_VALUE_RULE_MAP = {
+        "pythonic": '"{key}" "=" {valrule}',
+        "json": '"\\"{key}\\"" ":" {valrule}',
+    }
+
+    JSON_TYPE_MAPPING = {
+        "string": "basic_string",
+        "number": "basic_number",
+        "integer": "basic_number",
+        "boolean": "basic_boolean",
+        "null": "basic_null",
+        "array": "basic_array",
+        "object": "basic_object",
+    }
+
+    PYTHONIC_TYPE_MAPPING = {
+        "string": "basic_string",
+        "number": "basic_number",
+        "integer": "basic_number",
+        "boolean": '"True" | "False"',
+        "null": '"None"',
+        "array": "basic_array",
+        "object": "basic_object",
+    }
+
+    @staticmethod
+    def get_value_rule(
+        prop: dict, function_format: Literal["pythonic", "json"] = "json"
+    ) -> str:
+        if "enum" in prop:
+            return EBNFComposer._handle_enum(prop, function_format)
+
+        if "type" in prop:
+            return EBNFComposer._handle_type(prop, function_format)
+
+        return function_format
+
+    @staticmethod
+    def _handle_enum(prop: dict, function_format: str) -> str:
+        """Handle enum properties by formatting each value according to type and format."""
+        enum_values = prop["enum"]
+        prop_type = prop.get("type", "string")
+
+        # Define formatters for different type/format combinations
+        formatters = {
+            ("string", "json"): lambda v: f'"\\"{v}\\""',
+            ("string", "pythonic"): lambda v: f'"\\"{v}\\""',
+            ("number", "json"): str,
+            ("number", "pythonic"): str,
+            ("integer", "json"): str,
+            ("integer", "pythonic"): str,
+            ("boolean", "json"): lambda v: "true" if v else "false",
+            ("boolean", "pythonic"): lambda v: "True" if v else "False",
+        }
+
+        # Get the formatter or default to string handling
+        formatter = formatters.get(
+            (prop_type, function_format),
+            formatters[("string", function_format)],  # Default to string handling
+        )
+
+        formatted_values = [formatter(value) for value in enum_values]
+        enum_rule = " | ".join(formatted_values)
+
+        # Wrap in parentheses if there are multiple values to ensure correct EBNF precedence
+        if len(formatted_values) > 1:
+            enum_rule = f"({enum_rule})"
+
+        return enum_rule
+
+    @staticmethod
+    def _handle_type(prop: dict, function_format: str) -> str:
+        """Handle type properties using the appropriate type mapping."""
+        prop_type = prop["type"]
+        type_mapping = (
+            EBNFComposer.PYTHONIC_TYPE_MAPPING
+            if function_format == "pythonic"
+            else EBNFComposer.JSON_TYPE_MAPPING
+        )
+
+        if isinstance(prop_type, list):
+            type_rules = [
+                type_mapping[single_type]
+                for single_type in prop_type
+                if single_type in type_mapping
+            ]
+            return " | ".join(type_rules) if type_rules else function_format
+
+        return type_mapping.get(prop_type, function_format)
+
+    @staticmethod
+    def build_ebnf(
+        tools,
+        *,
+        call_rule_fmt: Optional[str] = None,
+        function_format: Literal["pythonic", "json"] = "json",
+        bot_token: Optional[str] = None,
+        eot_token: Optional[str] = None,
+        tool_call_separator: Optional[str] = None,
+    ):
+        """
+        Generalized EBNF builder for all detectors.
+        Args:
+            tools: List of Tool objects to generate EBNF grammar for
+            call_rule_fmt: Optional custom format string for call_{name} rule. It should define each function call's format, with
+                the placeholders {name} for the function name and {arguments_rule} for the arguments rule. If None, a default
+                format based on function_format will be used.
+            function_format: The format of function calls, either "pythonic" or "json"
+            bot_token: The token that indicates the start of a tool call section
+            eot_token: The token that indicates the end of a tool call section
+            tool_call_separator: The separator between multiple tool calls
+        """
+        # =================================================================
+        # Step 1: Determine the root tool calls rule
+        # =================================================================
+        if bot_token and eot_token:
+            if tool_call_separator:
+                root_rule = f'"{bot_token}" function_call ( "{tool_call_separator}" function_call )* "{eot_token}"'
+            else:
+                root_rule = f'"{bot_token}" function_call "{eot_token}"'
+        else:
+            root_rule = EBNFComposer.TOOL_CALLS_MAP[function_format]
+
+        # =================================================================
+        # Step 2: Build the header rules
+        # =================================================================
+        ebnf_lines = [
+            f"root ::= {root_rule}",
+            "function_call ::= "
+            + " | ".join([f"call_{tool.function.name}" for tool in tools]),
+        ]
+
+        # =================================================================
+        # Step 3: Set up formatting templates
+        # =================================================================
+        call_template = (
+            f"call_{{name}} ::= {call_rule_fmt}"
+            if call_rule_fmt
+            else EBNFComposer.CALL_RULE_MAP[function_format]
+        )
+        args_template = EBNFComposer.ARGUMENTS_RULE_MAP[function_format]
+        key_value_template = EBNFComposer.KEY_VALUE_RULE_MAP[function_format]
+
+        # =================================================================
+        # Step 4: Build rules for each tool
+        # =================================================================
+        for tool in tools:
+            tool_name = tool.function.name
+            params = tool.function.parameters or {}
+            properties = params.get("properties", {})
+            required_props = set(params.get("required", []))
+
+            # Build argument rules for this tool
+            arg_rules = []
+            for prop_name, prop_schema in properties.items():
+                value_rule = EBNFComposer.get_value_rule(prop_schema, function_format)
+                # Create key=value pair
+                pair = key_value_template.format(key=prop_name, valrule=value_rule)
+
+                if prop_name not in required_props:
+                    pair = f"[ {pair} ]"
+
+                arg_rules.append(pair)
+
+            # Combine all argument rules
+            combined_args = ' "," '.join(arg_rules) if arg_rules else ""
+            arguments_rule = args_template.format(arg_rules=combined_args)
+
+            # Add the function call rule and its arguments rule
+            ebnf_lines.append(
+                call_template.format(
+                    name=tool_name, arguments_rule=f"arguments_{tool_name}"
+                )
+            )
+            ebnf_lines.append(f"arguments_{tool_name} ::= {arguments_rule}")
+
+        # =================================================================
+        # Step 5: Add base grammar rules
+        # =================================================================
+        base_grammar = (
+            EBNFComposer.pythonic_grammar_ebnf_str
+            if function_format == "pythonic"
+            else EBNFComposer.json_grammar_ebnf_str
+        )
+        ebnf_lines.append(base_grammar)
+
+        return "\n".join(ebnf_lines)

python/sglang/srt/function_call/function_call_parser.py

+from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Type, Union
+
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import ToolCallItem
+from sglang.srt.function_call.deepseekv3_detector import DeepSeekV3Detector
+from sglang.srt.function_call.llama32_detector import Llama32Detector
+from sglang.srt.function_call.mistral_detector import MistralDetector
+from sglang.srt.function_call.pythonic_detector import PythonicDetector
+from sglang.srt.function_call.qwen25_detector import Qwen25Detector
+from sglang.srt.openai_api.protocol import (
+    StructuralTagResponseFormat,
+    StructuresResponseFormat,
+    Tool,
+    ToolChoice,
+)
+
+
+class FunctionCallParser:
+    """
+    Parser for function/tool calls in model outputs.
+
+    This class handles both streaming and non-streaming parsing of function calls using a detector.
+    In streaming scenarios, each time new_text is received, it calls detector.parse_streaming_increment
+    and returns the resulting normal_text and calls to the upper layer (or SSE).
+    """
+
+    ToolCallParserEnum: Dict[str, Type[BaseFormatDetector]] = {
+        "llama3": Llama32Detector,
+        "qwen25": Qwen25Detector,
+        "mistral": MistralDetector,
+        "deepseekv3": DeepSeekV3Detector,
+        "pythonic": PythonicDetector,
+    }
+
+    def __init__(self, tools: List[Tool], tool_call_parser: str):
+        detector: Type[BaseFormatDetector] = None
+        detector_class = self.ToolCallParserEnum.get(tool_call_parser)
+        if detector_class:
+            detector = detector_class()
+        else:
+            raise ValueError(f"Unsupported tool_call_parser: {tool_call_parser}")
+
+        self.detector = detector
+        self.tools = tools
+
+    def has_tool_call(self, text: str) -> bool:
+        """
+        Check if the given text contains a tool call in the format supported by this parser.
+        This delegates to the detector's implementation.
+
+        Args:
+            text: The text to check for tool calls
+
+        Returns:
+            True if the text contains a tool call, False otherwise
+        """
+        return self.detector.has_tool_call(text)
+
+    def parse_non_stream(self, full_text: str) -> Tuple[str, list[ToolCallItem]]:
+        """
+        One-time parsing of the full text to extract tool calls.
+
+        Args:
+            full_text: The complete text to parse
+
+        Returns:
+            A tuple containing:
+            - The remaining text after parsing that was not consumed by the detector (can be treated as normal text)
+            - A list of tool calls parsed from the text
+        """
+        parsed_result = self.detector.detect_and_parse(full_text, self.tools)
+        tool_call_list = parsed_result.calls
+        if tool_call_list:
+            return parsed_result.normal_text, tool_call_list
+        else:
+            return full_text, []
+
+    def parse_stream_chunk(self, chunk_text: str) -> Tuple[str, list[ToolCallItem]]:
+        """
+        Streaming incremental parsing of chunks of text as they arrive.
+
+        Args:
+            chunk_text: The new chunk of text to parse
+
+        Returns:
+            A tuple containing:
+            - The normal text that should be displayed to the user
+            - A list of tool calls parsed from the chunk
+        """
+        final_normal_text = ""
+        final_calls = []
+
+        sp_result = self.detector.parse_streaming_increment(chunk_text, self.tools)
+        if sp_result.normal_text:
+            final_normal_text = sp_result.normal_text
+        if sp_result.calls:
+            final_calls.extend(sp_result.calls)
+            final_normal_text = sp_result.normal_text
+
+        return final_normal_text, final_calls
+
+    def get_structure_tag(self) -> StructuralTagResponseFormat:
+        """
+        Generate a structural tag response format for all available tools.
+
+        This creates the necessary structural tags that guide the model's output format.
+        """
+        tool_structures: List[StructuresResponseFormat] = list()
+        tool_trigger_set: Set[str] = set()
+
+        get_structure_info = self.detector.structure_info()
+        for tool in self.tools:
+            function = tool.function
+            name = function.name
+            assert name is not None
+            info = get_structure_info(name)
+
+            # accept all if not strict, otherwise only accept the schema
+            schema = function.parameters if function.strict else {}
+
+            tool_structures.append(
+                StructuresResponseFormat(
+                    begin=info.begin,
+                    schema=schema,  # type: ignore
+                    end=info.end,
+                )
+            )
+            tool_trigger_set.add(info.trigger)
+
+        return StructuralTagResponseFormat(
+            type="structural_tag",
+            structures=tool_structures,
+            triggers=list(tool_trigger_set),
+        )
+
+    def get_structure_constraint(
+        self, tool_choice: Union[ToolChoice, Literal["auto", "required"]]
+    ) -> Optional[Tuple[str, Any]]:
+        """
+        Returns the appropriate structure constraint for tool calls based on the tool_choice.
+        The constraint is used to guide the model's output format.
+
+        Args:
+            tool_choice: The tool choice setting from the request
+
+        Returns:
+            A tuple of (constraint_type, constraint_value) to be added to sampling parameters,
+            or None if no constraint applies.
+        """
+        # NOTE: structural_tag only supports JSON-compatible content between the begin and end.
+        # It cannot parse or validate Python syntax like function calls.
+        if (
+            not isinstance(self.detector, PythonicDetector)
+            and tool_choice == "auto"
+            and any(tool.function.strict for tool in self.tools)
+        ):
+            strict_tag = self.get_structure_tag()
+            return ("structural_tag", strict_tag)
+        elif tool_choice == "required" or isinstance(tool_choice, ToolChoice):
+            ebnf = self.get_ebnf(tool_choice)
+            return ("ebnf", ebnf) if ebnf is not None else None
+
+    def get_ebnf(
+        self, tool_choice: Union[ToolChoice, Literal["required"]]
+    ) -> Optional[str]:
+        """
+        Get the EBNF grammar for the specified tool choice.
+        """
+        filtered_tools = []
+        if isinstance(tool_choice, ToolChoice):
+            fn_name = tool_choice.function.name
+            filtered_tools = [t for t in self.tools if t.function.name == fn_name]
+        else:
+            filtered_tools = self.tools
+        return self.detector.build_ebnf(filtered_tools)

python/sglang/srt/function_call/llama32_detector.py

+import json
+import logging
+from typing import List
+
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    StructureInfo,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.ebnf_composer import EBNFComposer
+from sglang.srt.openai_api.protocol import Tool
+
+logger = logging.getLogger(__name__)
+
+
+class Llama32Detector(BaseFormatDetector):
+    """
+    Detector for Llama 3.2 models.
+    Assumes function call format:
+      <|python_tag|>{"name":"xxx", "arguments":{...}}
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.bot_token = "<|python_tag|>"
+
+    def has_tool_call(self, text: str) -> bool:
+        """Check if the text contains a Llama 3.2 format tool call."""
+        # depending on the prompt format the Llama model may or may not
+        # prefix the output with the <|python_tag|> token
+        return "<|python_tag|>" in text or text.startswith("{")
+
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        """Parse function calls from text, handling multiple JSON objects."""
+        if "<|python_tag|>" not in text and not text.startswith("{"):
+            return StreamingParseResult(normal_text=text, calls=[])
+
+        if "<|python_tag|>" in text:
+            normal_text, action_text = text.split("<|python_tag|>")
+        else:
+            normal_text, action_text = "", text
+
+        # Split by semicolon and process each part
+        json_parts = [part.strip() for part in action_text.split(";") if part.strip()]
+        all_actions = []
+        for part in json_parts:
+            try:
+                # Parse each individual JSON object
+                action = json.loads(part)
+                all_actions.append(action)
+            except json.JSONDecodeError as e:
+                logger.warning(f"Failed to parse JSON part: {part}")
+                logger.warning(f"JSON parse error: {str(e)}")
+                continue
+        calls = []
+        # Only process if we found valid JSON objects
+        if all_actions:
+            calls = self.parse_base_json(all_actions, tools)
+        return StreamingParseResult(normal_text=normal_text, calls=calls)
+
+    def structure_info(self) -> _GetInfoFunc:
+        return lambda name: StructureInfo(
+            begin='<|python_tag|>{"name":"' + name + '", "arguments":',
+            end="}",
+            trigger="<|python_tag|>",
+        )
+
+    def build_ebnf(self, tools: List[Tool]):
+        return EBNFComposer.build_ebnf(
+            tools,
+            function_format="json",
+            tool_call_separator=",",
+        )

python/sglang/srt/function_call/mistral_detector.py

+import json
+import re
+from typing import List
+
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    StructureInfo,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.ebnf_composer import EBNFComposer
+from sglang.srt.openai_api.protocol import Tool
+
+
+class MistralDetector(BaseFormatDetector):
+    """
+    Detector for Mistral models.
+    Assumes function call format:
+      [TOOL_CALLS] [{"name":"xxx", "arguments":{...}}]
+    """
+
+    def __init__(self):
+        """
+        Initializes the detector with necessary state variables.
+        """
+        super().__init__()
+        self.bot_token = "[TOOL_CALLS] ["
+        self.eot_token = "]"
+        self.tool_call_regex = re.compile(r"\[{.*}\]", re.DOTALL)
+
+    def has_tool_call(self, text: str) -> bool:
+        """Check if the text contains a Mistral format tool call."""
+        return self.bot_token in text
+
+    def _clean_text(self, text: str) -> str:
+        """
+        clean text to only leave ''[TOOL_CALLS] [{"name": xxx, "arguments": {xxx}}]'
+        for example,
+            text = '[TOOL_CALLS] [{"name": "get_current_weather", "arguments": {"location": "Boston, MA", "unit": "fahrenheit"}}]\n\nToday\'s weather in Boston is :{function call result} (in Fahrenheit)\n\nIf you prefer Celsius, please let me know.'
+            return '[TOOL_CALLS] [{"name": "get_current_weather", "arguments": {"location": "Boston, MA", "unit": "fahrenheit"}}]'
+        The key pattern is [TOOL_CALLS] [...]
+        """
+        # TODO: check if Mistral supports multiple tool calls, currently assume only support one tool call
+        find_results = re.findall(r"\[TOOL_CALLS\] \[.*?\]", text, re.DOTALL)
+        if len(find_results) > 0:
+            return find_results[0]
+        else:
+            return ""
+
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        """
+        One-time parsing: Detects and parses tool calls in the provided text.
+
+        :param text: The complete text to parse.
+        :param tools: List of available tools.
+        :return: ParseResult indicating success or failure, consumed text, leftover text, and parsed calls.
+        """
+        idx = text.find(self.bot_token)
+        normal_text = text[:idx].strip() if idx != -1 else text
+        text = self._clean_text(text)
+        tool_content = text.replace("[TOOL_CALLS]", "").strip()
+        raw_tool_calls = self.tool_call_regex.findall(tool_content)
+        calls = []
+        if len(raw_tool_calls) > 0:
+            raw_tool_call = raw_tool_calls[0]
+            function_call_arr = json.loads(raw_tool_call)
+            for match_result in function_call_arr:
+                calls.extend(self.parse_base_json(match_result, tools))
+        return StreamingParseResult(normal_text=normal_text, calls=calls)
+
+    def structure_info(self) -> _GetInfoFunc:
+        return lambda name: StructureInfo(
+            begin='[TOOL_CALLS] [{"name":"' + name + '", "arguments":',
+            end="}]",
+            trigger="[TOOL_CALLS]",
+        )
+
+    def build_ebnf(self, tools: List[Tool]):
+        return EBNFComposer.build_ebnf(
+            tools,
+            bot_token=self.bot_token,
+            eot_token=self.eot_token,
+            function_format="json",
+        )

python/sglang/srt/function_call/pythonic_detector.py

+import ast
+import json
+import logging
+import re
+from typing import List, Optional
+
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    StructureInfo,
+    ToolCallItem,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.ebnf_composer import EBNFComposer
+from sglang.srt.openai_api.protocol import Tool
+
+logger = logging.getLogger(__name__)
+
+
+class PythonicDetector(BaseFormatDetector):
+    """
+    Detector for Llama-3.2 and Llama-4 models with pythonic tool call format.
+    Assumes function call format:
+      [tool1(arg1=val1, arg2=val2), tool2(arg1=val3)]
+    Arguments are Python literals (not JSON).
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.tool_call_regex = re.compile(
+            r"\[([a-zA-Z]+\w*\(([a-zA-Z]+\w*=.*,\s*)*([a-zA-Z]+\w*=.*\s)?\),\s*)*([a-zA-Z]+\w*\(([a-zA-Z]+\w*=.*,\s*)*([a-zA-Z]+\w*=.*\s*)?\)\s*)+\]",
+            re.DOTALL,
+        )
+
+    def has_tool_call(self, text: str) -> bool:
+        return bool(self.tool_call_regex.match(text.strip()))
+
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        # Try parsing the text as a Python list of function calls
+        text = text.strip()
+        if not (text.startswith("[") and text.endswith("]")):
+            # Not a pythonic tool call format
+            return StreamingParseResult(normal_text=text, calls=[])
+        try:
+            module = ast.parse(text)
+            parsed = getattr(module.body[0], "value", None)
+            if not (
+                isinstance(parsed, ast.List)
+                and all(isinstance(e, ast.Call) for e in parsed.elts)
+            ):
+                return StreamingParseResult(normal_text=text, calls=[])
+            calls = []
+            tool_indices = {
+                tool.function.name: i
+                for i, tool in enumerate(tools)
+                if tool.function.name
+            }
+            for call in parsed.elts:
+                if not isinstance(call.func, ast.Name):
+                    continue
+                function_name = call.func.id
+                arguments = {}
+                for keyword in call.keywords:
+                    arguments[keyword.arg] = self._get_parameter_value(keyword.value)
+                calls.append(
+                    ToolCallItem(
+                        tool_index=tool_indices.get(function_name, -1),
+                        name=function_name,
+                        parameters=json.dumps(arguments, ensure_ascii=False),
+                    )
+                )
+            return StreamingParseResult(normal_text="", calls=calls)
+        except Exception:
+            logger.exception("Error in pythonic tool call parsing.")
+            return StreamingParseResult(normal_text=text, calls=[])
+
+    def _find_matching_bracket(self, buffer: str, start: int) -> int:
+        """
+        Find the matching closing bracket for the opening bracket at start position.
+        Properly handles nested brackets.
+
+        Args:
+            buffer: The text buffer to search in
+            start: Position of the opening bracket '['
+
+        Returns:
+            Position of the matching closing bracket ']', or -1 if not found
+        """
+        bracket_count = 0
+        for i in range(start, len(buffer)):
+            if buffer[i] == "[":
+                bracket_count += 1
+            elif buffer[i] == "]":
+                bracket_count -= 1
+                if bracket_count == 0:
+                    return i
+        return -1  # No matching bracket found
+
+    def parse_streaming_increment(
+        self, new_text: str, tools: List[Tool]
+    ) -> StreamingParseResult:
+        """
+        Streaming incremental parsing for pythonic tool calls.
+        Buffers input until a complete pythonic tool call (from [ to ]) is found,
+        then parses and emits any detected calls.
+        """
+        self._buffer += new_text
+        start = self._buffer.find("[")
+
+        if start == -1:
+            normal_text = self._buffer
+            self._buffer = ""
+            return StreamingParseResult(normal_text=normal_text)
+
+        normal_text = self._buffer[:start] if start > 0 else ""
+
+        end = self._find_matching_bracket(self._buffer, start)
+        if end != -1:
+            call_text = self._buffer[start : end + 1]
+            result = self.detect_and_parse(call_text, tools)
+            self._buffer = self._buffer[end + 1 :]
+
+            # If we had normal text before the tool call, add it to the result
+            if normal_text:
+                result.normal_text = normal_text + (result.normal_text or "")
+
+            return result
+
+        # We have an opening bracket but no closing bracket yet
+        if normal_text:
+            self._buffer = self._buffer[start:]
+            return StreamingParseResult(normal_text=normal_text)
+
+        # Otherwise, we're still accumulating a potential tool call
+        return StreamingParseResult(normal_text="")
+
+    def _get_parameter_value(self, val):
+        if isinstance(val, ast.Constant):
+            return val.value
+        elif isinstance(val, ast.Dict):
+            return {
+                k.value: self._get_parameter_value(v)
+                for k, v in zip(val.keys, val.values)
+            }
+        elif isinstance(val, ast.List):
+            return [self._get_parameter_value(v) for v in val.elts]
+        else:
+            raise ValueError("Tool call arguments must be literals")
+
+    def structure_info(self) -> _GetInfoFunc:
+        def info(name: str):
+            return StructureInfo(begin=f"[{name}(", end=")]", trigger=f"[{name}(")
+
+        return info
+
+    def build_ebnf(self, tools: List[Tool]) -> Optional[str]:
+        return EBNFComposer.build_ebnf(
+            tools,
+            bot_token="[",
+            eot_token="]",
+            tool_call_separator=",",
+            function_format="pythonic",
+        )

python/sglang/srt/function_call/qwen25_detector.py

+import json
+import re
+from typing import List
+
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    StructureInfo,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.ebnf_composer import EBNFComposer
+from sglang.srt.openai_api.protocol import Tool
+
+
+class Qwen25Detector(BaseFormatDetector):
+    """
+    Detector for Qwen 2.5 models.
+    Assumes function call format:
+      <tool_call>{"name":"xxx", "arguments":{...}}</tool_call>
+    """
+
+    def __init__(self):
+        """
+        Initializes the detector with necessary state variables.
+        """
+        super().__init__()
+        self.bot_token = "<tool_call>"
+        self.eot_token = "</tool_call>"
+
+    def has_tool_call(self, text: str) -> bool:
+        """Check if the text contains a Qwen 2.5 format tool call."""
+        return self.bot_token in text
+
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        """
+        One-time parsing: Detects and parses tool calls in the provided text.
+
+        :param text: The complete text to parse.
+        :param tools: List of available tools.
+        :return: ParseResult indicating success or failure, consumed text, leftover text, and parsed calls.
+        """
+        idx = text.find(self.bot_token)
+        normal_text = text[:idx].strip() if idx != -1 else text
+        if self.bot_token not in text:
+            return StreamingParseResult(normal_text=normal_text, calls=[])
+        pattern = rf"{self.bot_token}(.*?){self.eot_token}"
+        match_result_list = re.findall(pattern, text, re.DOTALL)
+        calls = []
+        for match_result in match_result_list:
+            match_result = json.loads(match_result)
+            calls.extend(self.parse_base_json(match_result, tools))
+        return StreamingParseResult(normal_text=normal_text, calls=calls)
+
+    def structure_info(self) -> _GetInfoFunc:
+        return lambda name: StructureInfo(
+            begin='<tool_call>{"name":"' + name + '", "arguments":',
+            end="}</tool_call>",
+            trigger="<tool_call>",
+        )
+
+    def build_ebnf(self, tools: List[Tool]):
+        return EBNFComposer.build_ebnf(
+            tools,
+            bot_token=self.bot_token,
+            eot_token=self.eot_token,
+            function_format="json",
+        )

python/sglang/srt/function_call/utils.py

+import json
+from json import JSONDecodeError, JSONDecoder
+from typing import Any, Tuple
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+
+def _find_common_prefix(s1: str, s2: str) -> str:
+    prefix = ""
+    min_length = min(len(s1), len(s2))
+    for i in range(0, min_length):
+        if s1[i] == s2[i]:
+            prefix += s1[i]
+        else:
+            break
+    return prefix
+
+
+def _partial_json_loads(input_str: str, flags: Allow) -> Tuple[Any, int]:
+    try:
+        return (partial_json_parser.loads(input_str, flags), len(input_str))
+    except JSONDecodeError as e:
+        if "Extra data" in e.msg:
+            dec = JSONDecoder()
+            return dec.raw_decode(input_str)
+        raise
+
+
+def _is_complete_json(input_str: str) -> bool:
+    try:
+        json.loads(input_str)
+        return True
+    except JSONDecodeError:
+        return False

python/sglang/srt/openai_api/adapter.py

@@ -40,7 +40,7 @@ from sglang.srt.conversation import (
     get_conv_template_by_model_path,
     register_conv_template,
 )
-from sglang.srt.function_call_parser import FunctionCallParser
+from sglang.srt.function_call.function_call_parser import FunctionCallParser
 from sglang.srt.managers.io_struct import EmbeddingReqInput, GenerateReqInput
 from sglang.srt.openai_api.protocol import (
     BatchRequest,
@@ -970,7 +970,7 @@ def v1_chat_generate_request(
         #  - image_data: None or a list of image strings (URLs or base64 strings).
         #  - audio_data: None or a list of audio strings (URLs).
         #    None skips any image processing in GenerateReqInput.
-        strict_tag = None
+        tool_call_constraint = None
         prompt = ""
         prompt_ids = []
         if not isinstance(request.messages, str):
@@ -989,7 +989,9 @@ def v1_chat_generate_request(
 
                 tool_call_parser = tokenizer_manager.server_args.tool_call_parser
                 parser = FunctionCallParser(request.tools, tool_call_parser)
-                strict_tag = parser.get_structure_tag()
+                tool_call_constraint = parser.get_structure_constraint(
+                    request.tool_choice
+                )
 
             if chat_template_name is None:
                 openai_compatible_messages = []
@@ -1156,20 +1158,24 @@ def v1_chat_generate_request(
                 request.response_format.model_dump(by_alias=True)
             )
 
-        if strict_tag is not None:
-            if (
+        # Check if there are already existing output constraints
+        has_existing_constraints = (
             sampling_params.get("regex")
             or sampling_params.get("ebnf")
             or sampling_params.get("structural_tag")
             or sampling_params.get("json_schema")
-            ):
-                logger.warning(
-                    "Constrained decoding is not compatible with tool calls."
         )
-            else:
-                sampling_params["structural_tag"] = convert_json_schema_to_str(
-                    strict_tag.model_dump(by_alias=True)
+
+        if tool_call_constraint and has_existing_constraints:
+            logger.warning("Constrained decoding is not compatible with tool calls.")
+        elif tool_call_constraint:
+            constraint_type, constraint_value = tool_call_constraint
+            if constraint_type == "structural_tag":
+                sampling_params[constraint_type] = convert_json_schema_to_str(
+                    constraint_value.model_dump(by_alias=True)
                 )
+            else:
+                sampling_params[constraint_type] = constraint_value
 
         sampling_params_list.append(sampling_params)
 

test/srt/run_suite.py

@@ -36,7 +36,7 @@ suites = {
         TestFile("test_fa3.py", 376),
         TestFile("test_fim_completion.py", 40),
         TestFile("test_fp8_kernel.py", 8),
-        TestFile("test_function_calling.py", 60),
+        TestFile("test_function_call_parser.py", 10),
         TestFile("test_fused_moe.py", 30),
         TestFile("test_hicache.py", 116),
         TestFile("test_hicache_mla.py", 254),
@@ -54,6 +54,7 @@ suites = {
         TestFile("test_flashmla.py", 300),
         TestFile("test_no_chunked_prefill.py", 108),
         TestFile("test_no_overlap_scheduler.py", 216),
+        TestFile("test_openai_function_calling.py", 60),
         TestFile("test_openai_server.py", 149),
         TestFile("test_penalty.py", 41),
         TestFile("test_page_size.py", 60),

test/srt/test_function_call_parser.py

+import json
+import unittest
+
+from xgrammar import GrammarCompiler, TokenizerInfo
+
+from sglang.srt.function_call.deepseekv3_detector import DeepSeekV3Detector
+from sglang.srt.function_call.llama32_detector import Llama32Detector
+from sglang.srt.function_call.mistral_detector import MistralDetector
+from sglang.srt.function_call.pythonic_detector import PythonicDetector
+from sglang.srt.function_call.qwen25_detector import Qwen25Detector
+from sglang.srt.hf_transformers_utils import get_tokenizer
+from sglang.srt.openai_api.protocol import Function, Tool
+from sglang.test.test_utils import DEFAULT_SMALL_MODEL_NAME_FOR_TEST
+
+
+class TestPythonicDetector(unittest.TestCase):
+    def setUp(self):
+        # Create sample tools for testing
+        self.tools = [
+            Tool(
+                type="function",
+                function=Function(
+                    name="get_weather",
+                    description="Get weather information",
+                    parameters={
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "Location to get weather for",
+                            },
+                            "unit": {
+                                "type": "string",
+                                "description": "Temperature unit",
+                                "enum": ["celsius", "fahrenheit"],
+                            },
+                        },
+                        "required": ["location"],
+                    },
+                ),
+            ),
+            Tool(
+                type="function",
+                function=Function(
+                    name="search",
+                    description="Search for information",
+                    parameters={
+                        "properties": {
+                            "query": {
+                                "type": "string",
+                                "description": "Search query",
+                            },
+                        },
+                        "required": ["query"],
+                    },
+                ),
+            ),
+        ]
+        self.detector = PythonicDetector()
+
+    def test_parse_streaming_no_brackets(self):
+        """Test parsing text with no brackets (no tool calls)."""
+        text = "This is just normal text without any tool calls."
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, text)
+        self.assertEqual(result.calls, [])
+        self.assertEqual(self.detector._buffer, "")  # Buffer should be cleared
+
+    def test_parse_streaming_complete_tool_call(self):
+        """Test parsing a complete tool call."""
+        text = "Here's a tool call: [get_weather(location='New York', unit='celsius')]"
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "Here's a tool call: ")
+        self.assertEqual(len(result.calls), 1)
+        self.assertEqual(result.calls[0].name, "get_weather")
+        self.assertEqual(
+            self.detector._buffer, ""
+        )  # Buffer should be cleared after processing
+
+        # Check the parameters
+        params = json.loads(result.calls[0].parameters)
+        self.assertEqual(params["location"], "New York")
+        self.assertEqual(params["unit"], "celsius")
+
+    def test_parse_streaming_text_before_tool_call(self):
+        """Test parsing text that appears before a tool call."""
+        text = "This is some text before [get_weather(location='London')]"
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "This is some text before ")
+        self.assertEqual(len(result.calls), 1)
+        self.assertEqual(result.calls[0].name, "get_weather")
+
+        # Check the parameters
+        params = json.loads(result.calls[0].parameters)
+        self.assertEqual(params["location"], "London")
+
+    def test_parse_streaming_partial_tool_call(self):
+        """Test parsing a partial tool call that spans multiple chunks."""
+        # First chunk with opening bracket but no closing bracket
+        text1 = "Let me check the weather: [get_weather(location="
+        result1 = self.detector.parse_streaming_increment(text1, self.tools)
+
+        self.assertEqual(result1.normal_text, "Let me check the weather: ")
+        self.assertEqual(result1.calls, [])
+        self.assertEqual(
+            self.detector._buffer, "[get_weather(location="
+        )  # Partial tool call remains in buffer
+
+        # Second chunk completing the tool call
+        text2 = "'Paris')]"
+        result2 = self.detector.parse_streaming_increment(text2, self.tools)
+
+        self.assertEqual(result2.normal_text, "")
+        self.assertEqual(len(result2.calls), 1)
+        self.assertEqual(result2.calls[0].name, "get_weather")
+
+        # Check the parameters
+        params = json.loads(result2.calls[0].parameters)
+        self.assertEqual(params["location"], "Paris")
+        self.assertEqual(
+            self.detector._buffer, ""
+        )  # Buffer should be cleared after processing
+
+    def test_parse_streaming_bracket_without_text_before(self):
+        """Test parsing a tool call that starts at the beginning of the text."""
+        text = "[search(query='python programming')]"
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "")
+        self.assertEqual(len(result.calls), 1)
+        self.assertEqual(result.calls[0].name, "search")
+
+        # Check the parameters
+        params = json.loads(result.calls[0].parameters)
+        self.assertEqual(params["query"], "python programming")
+
+    def test_parse_streaming_text_after_tool_call(self):
+        """Test parsing text that appears after a tool call."""
+        # First chunk with complete tool call and some text after
+        text = "[get_weather(location='Tokyo')] Here's the forecast:"
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "")
+        self.assertEqual(len(result.calls), 1)
+        self.assertEqual(result.calls[0].name, "get_weather")
+        self.assertEqual(
+            self.detector._buffer, " Here's the forecast:"
+        )  # Text after tool call remains in buffer
+
+        # Process the remaining text in buffer
+        result2 = self.detector.parse_streaming_increment("", self.tools)
+        self.assertEqual(result2.normal_text, " Here's the forecast:")
+        self.assertEqual(result2.calls, [])
+        self.assertEqual(self.detector._buffer, "")  # Buffer should be cleared
+
+    def test_parse_streaming_multiple_tool_calls(self):
+        """Test parsing multiple tool calls in sequence."""
+        text = "[get_weather(location='Berlin')] and [search(query='restaurants')]"
+
+        # First tool call
+        result1 = self.detector.parse_streaming_increment(text, self.tools)
+        self.assertEqual(len(result1.calls), 1)
+        self.assertEqual(result1.calls[0].name, "get_weather")
+        self.assertEqual(self.detector._buffer, " and [search(query='restaurants')]")
+
+        # Second tool call
+        result2 = self.detector.parse_streaming_increment("", self.tools)
+        self.assertEqual(result2.normal_text, " and ")
+        self.assertEqual(len(result2.calls), 1)
+        self.assertEqual(result2.calls[0].name, "search")
+        self.assertEqual(self.detector._buffer, "")
+
+    def test_parse_streaming_opening_bracket_only(self):
+        """Test parsing text with only an opening bracket but no closing bracket."""
+        text = "Let's try this: ["
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "Let's try this: ")
+        self.assertEqual(result.calls, [])
+        self.assertEqual(
+            self.detector._buffer, "["
+        )  # Opening bracket remains in buffer
+
+    def test_parse_streaming_nested_brackets(self):
+        """Test parsing tool calls with nested brackets in arguments."""
+        # Test with list argument containing nested brackets
+        text = "[get_weather(location='New York', unit='celsius', data=[1, 2, 3])]"
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "")
+        self.assertEqual(len(result.calls), 1)
+        self.assertEqual(result.calls[0].name, "get_weather")
+        self.assertEqual(self.detector._buffer, "")
+
+        # Check the parameters
+        params = json.loads(result.calls[0].parameters)
+        self.assertEqual(params["location"], "New York")
+        self.assertEqual(params["unit"], "celsius")
+        self.assertEqual(params["data"], [1, 2, 3])
+
+    def test_parse_streaming_nested_brackets_dict(self):
+        """Test parsing tool calls with nested dictionaries and lists."""
+        # Test with nested dict and list arguments
+        text = "[search(query='test', config={'options': [1, 2], 'nested': {'key': 'value'}})]"
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "")
+        self.assertEqual(len(result.calls), 1)
+        self.assertEqual(result.calls[0].name, "search")
+        self.assertEqual(self.detector._buffer, "")
+
+        # Check the parameters
+        params = json.loads(result.calls[0].parameters)
+        self.assertEqual(params["query"], "test")
+        self.assertEqual(params["config"]["options"], [1, 2])
+        self.assertEqual(params["config"]["nested"]["key"], "value")
+
+    def test_parse_streaming_multiple_tools_with_nested_brackets(self):
+        """Test parsing multiple tool calls with nested brackets."""
+        text = "[get_weather(location='Paris', data=[10, 20]), search(query='test', filters=['a', 'b'])]"
+        result = self.detector.parse_streaming_increment(text, self.tools)
+
+        self.assertEqual(result.normal_text, "")
+        self.assertEqual(len(result.calls), 2)
+        self.assertEqual(self.detector._buffer, "")
+
+        # Check first tool call
+        params1 = json.loads(result.calls[0].parameters)
+        self.assertEqual(result.calls[0].name, "get_weather")
+        self.assertEqual(params1["location"], "Paris")
+        self.assertEqual(params1["data"], [10, 20])
+
+        # Check second tool call
+        params2 = json.loads(result.calls[1].parameters)
+        self.assertEqual(result.calls[1].name, "search")
+        self.assertEqual(params2["query"], "test")
+        self.assertEqual(params2["filters"], ["a", "b"])
+
+    def test_parse_streaming_partial_nested_brackets(self):
+        """Test parsing partial tool calls with nested brackets across chunks."""
+        # First chunk with nested brackets but incomplete
+        text1 = "Here's a call: [get_weather(location='Tokyo', data=[1, 2"
+        result1 = self.detector.parse_streaming_increment(text1, self.tools)
+
+        self.assertEqual(result1.normal_text, "Here's a call: ")
+        self.assertEqual(result1.calls, [])
+        self.assertEqual(
+            self.detector._buffer, "[get_weather(location='Tokyo', data=[1, 2"
+        )
+
+        # Second chunk completing the nested brackets
+        text2 = ", 3])]"
+        result2 = self.detector.parse_streaming_increment(text2, self.tools)
+
+        self.assertEqual(result2.normal_text, "")
+        self.assertEqual(len(result2.calls), 1)
+        self.assertEqual(result2.calls[0].name, "get_weather")
+        self.assertEqual(self.detector._buffer, "")
+
+        # Check the parameters
+        params = json.loads(result2.calls[0].parameters)
+        self.assertEqual(params["location"], "Tokyo")
+        self.assertEqual(params["data"], [1, 2, 3])
+
+
+class TestEBNFGeneration(unittest.TestCase):
+    def setUp(self):
+        # Create sample tools for testing
+        self.tools = [
+            Tool(
+                type="function",
+                function=Function(
+                    name="get_weather",
+                    description="Get weather information",
+                    parameters={
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "Location to get weather for",
+                            },
+                            "unit": {
+                                "type": "string",
+                                "description": "Temperature unit",
+                                "enum": ["celsius", "fahrenheit"],
+                            },
+                        },
+                        "required": ["location"],
+                    },
+                ),
+            ),
+            Tool(
+                type="function",
+                function=Function(
+                    name="search",
+                    description="Search for information",
+                    parameters={
+                        "properties": {
+                            "query": {
+                                "type": "string",
+                                "description": "Search query",
+                            },
+                        },
+                        "required": ["query"],
+                    },
+                ),
+            ),
+        ]
+
+        self.tokenizer = get_tokenizer(DEFAULT_SMALL_MODEL_NAME_FOR_TEST)
+        tokenizer_info = TokenizerInfo.from_huggingface(self.tokenizer)
+        self.grammar_compiler = GrammarCompiler(tokenizer_info=tokenizer_info)
+
+        # Initialize all detectors
+        self.pythonic_detector = PythonicDetector()
+        self.deepseekv3_detector = DeepSeekV3Detector()
+        self.llama32_detector = Llama32Detector()
+        self.mistral_detector = MistralDetector()
+        self.qwen25_detector = Qwen25Detector()
+
+    def test_pythonic_detector_ebnf(self):
+        """Test that the PythonicDetector generates valid EBNF."""
+        ebnf = self.pythonic_detector.build_ebnf(self.tools)
+        self.assertIsNotNone(ebnf)
+
+        # Check that the EBNF contains expected patterns
+        self.assertIn('call_get_weather ::= "get_weather" "(" ', ebnf)
+        self.assertIn('"location" "=" basic_string', ebnf)
+        self.assertIn('[ "unit" "=" ("\\"celsius\\"" | "\\"fahrenheit\\"") ]', ebnf)
+
+        # Validate that the EBNF can be compiled by GrammarCompiler
+        try:
+            ctx = self.grammar_compiler.compile_grammar(ebnf)
+            self.assertIsNotNone(ctx, "EBNF should be valid and compile successfully")
+        except RuntimeError as e:
+            self.fail(f"Failed to compile EBNF: {e}")
+
+    def test_deepseekv3_detector_ebnf(self):
+        """Test that the DeepSeekV3Detector generates valid EBNF."""
+        ebnf = self.deepseekv3_detector.build_ebnf(self.tools)
+        self.assertIsNotNone(ebnf)
+
+        # Check that the EBNF contains expected patterns
+        self.assertIn("<｜tool▁calls▁begin｜>", ebnf)
+        self.assertIn("<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather", ebnf)
+        self.assertIn('\\"location\\"" ":" basic_string ', ebnf)
+
+        # Validate that the EBNF can be compiled by GrammarCompiler
+        try:
+            ctx = self.grammar_compiler.compile_grammar(ebnf)
+            self.assertIsNotNone(ctx, "EBNF should be valid and compile successfully")
+        except RuntimeError as e:
+            self.fail(f"Failed to compile EBNF: {e}")
+
+    def test_llama32_detector_ebnf(self):
+        """Test that the Llama32Detector generates valid EBNF."""
+        ebnf = self.llama32_detector.build_ebnf(self.tools)
+        self.assertIsNotNone(ebnf)
+
+        # Check that the EBNF contains expected patterns
+        self.assertIn('\\"name\\"" ":" "\\"get_weather\\"', ebnf)
+        self.assertIn('"\\"arguments\\"" ":"', ebnf)
+
+        # Validate that the EBNF can be compiled by GrammarCompiler
+        try:
+            ctx = self.grammar_compiler.compile_grammar(ebnf)
+            self.assertIsNotNone(ctx, "EBNF should be valid and compile successfully")
+        except RuntimeError as e:
+            self.fail(f"Failed to compile EBNF: {e}")
+
+    def test_mistral_detector_ebnf(self):
+        """Test that the MistralDetector generates valid EBNF."""
+        ebnf = self.mistral_detector.build_ebnf(self.tools)
+        self.assertIsNotNone(ebnf)
+
+        # Check that the EBNF contains expected patterns
+        self.assertIn('"[TOOL_CALLS] ["', ebnf)
+        self.assertIn("call_get_weather | call_search", ebnf)
+        self.assertIn('"\\"arguments\\"" ":"', ebnf)
+
+        # Validate that the EBNF can be compiled by GrammarCompiler
+        try:
+            ctx = self.grammar_compiler.compile_grammar(ebnf)
+            self.assertIsNotNone(ctx, "EBNF should be valid and compile successfully")
+        except RuntimeError as e:
+            self.fail(f"Failed to compile EBNF: {e}")
+
+    def test_qwen25_detector_ebnf(self):
+        """Test that the Qwen25Detector generates valid EBNF."""
+        ebnf = self.qwen25_detector.build_ebnf(self.tools)
+        self.assertIsNotNone(ebnf)
+
+        # Check that the EBNF contains expected patterns
+        self.assertIn("<tool_call>", ebnf)
+        self.assertIn('\\"name\\"" ":" "\\"get_weather\\"', ebnf)
+        self.assertIn('"\\"arguments\\"" ":"', ebnf)
+
+        # Validate that the EBNF can be compiled by GrammarCompiler
+        try:
+            ctx = self.grammar_compiler.compile_grammar(ebnf)
+            self.assertIsNotNone(ctx, "EBNF should be valid and compile successfully")
+        except RuntimeError as e:
+            self.fail(f"Failed to compile EBNF: {e}")
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/test_function_calling.py → test/srt/test_openai_function_calling.py

@@ -290,6 +290,151 @@ class TestOpenAIServerFunctionCalling(CustomTestCase):
         self.assertEqual(str(args_obj["int_a"]), "5", "Parameter int_a should be 5")
         self.assertEqual(str(args_obj["int_b"]), "7", "Parameter int_b should be 7")
 
+    def test_function_call_required(self):
+        """
+        Test: Whether tool_choice: "required" works as expected
+        - When tool_choice == "required", the model should return one or more tool_calls.
+        """
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "sub",
+                    "description": "Compute the difference of two integers",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "int_a": {
+                                "type": "integer",
+                                "description": "First integer",
+                            },
+                            "int_b": {
+                                "type": "integer",
+                                "description": "Second integer",
+                            },
+                        },
+                        "required": ["int_a", "int_b"],
+                    },
+                    "strict": True,
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "use this to get latest weather information for a city given its name",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "city": {
+                                "type": "string",
+                                "description": "name of the city to get weather for",
+                            }
+                        },
+                        "required": ["city"],
+                    },
+                },
+            },
+        ]
+
+        messages = [{"role": "user", "content": "What is the capital of France?"}]
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            temperature=0.8,
+            top_p=0.8,
+            stream=False,
+            tools=tools,
+            tool_choice="required",
+        )
+
+        tool_calls = response.choices[0].message.tool_calls
+        self.assertIsNotNone(tool_calls, "No tool_calls in the response")
+        function_name = tool_calls[0].function.name
+        arguments = tool_calls[0].function.arguments
+        args_obj = json.loads(arguments)
+
+        self.assertEqual(
+            function_name, "get_weather", "Function name should be 'get_weather'"
+        )
+        self.assertIn("city", args_obj, "Function arguments should have 'city'")
+        self.assertIn(
+            "Paris", args_obj["city"], "Parameter city should contain 'Paris'"
+        )  # might be flaky
+
+    def test_function_call_specific(self):
+        """
+        Test: Whether tool_choice: ToolChoice works as expected
+        - When tool_choice is a specific ToolChoice, the model should return one or more tool_calls.
+        """
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "sub",
+                    "description": "Compute the difference of two integers",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "int_a": {
+                                "type": "integer",
+                                "description": "First integer",
+                            },
+                            "int_b": {
+                                "type": "integer",
+                                "description": "Second integer",
+                            },
+                        },
+                        "required": ["int_a", "int_b"],
+                    },
+                    "strict": True,
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "use this to get latest weather information for a city given its name",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "city": {
+                                "type": "string",
+                                "description": "name of the city to get weather for",
+                            }
+                        },
+                        "required": ["city"],
+                    },
+                },
+            },
+        ]
+
+        messages = [{"role": "user", "content": "What is the capital of France?"}]
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            temperature=0.8,
+            top_p=0.8,
+            stream=False,
+            tools=tools,
+            tool_choice={"type": "function", "function": {"name": "get_weather"}},
+        )
+
+        tool_calls = response.choices[0].message.tool_calls
+        self.assertIsNotNone(tool_calls, "No tool_calls in the response")
+        function_name = tool_calls[0].function.name
+        arguments = tool_calls[0].function.arguments
+        args_obj = json.loads(arguments)
+
+        self.assertEqual(
+            function_name, "get_weather", "Function name should be 'get_weather'"
+        )
+        self.assertIn("city", args_obj, "Function arguments should have 'city'")
+
 
 class TestOpenAIPythonicFunctionCalling(CustomTestCase):
     PYTHONIC_TOOLS = [
@@ -385,11 +530,13 @@ class TestOpenAIPythonicFunctionCalling(CustomTestCase):
             stream=False,
         )
         tool_calls = response.choices[0].message.tool_calls
-        self.assertIsInstance(tool_calls, list)
+        self.assertIsInstance(tool_calls, list, "No tool_calls found")
         self.assertGreaterEqual(len(tool_calls), 1)
         names = [tc.function.name for tc in tool_calls]
-        self.assertIn("get_weather", names)
-        self.assertIn("get_tourist_attractions", names)
+        self.assertTrue(
+            "get_weather" in names or "get_tourist_attractions" in names,
+            f"Function name '{names}' should container either 'get_weather' or 'get_tourist_attractions'",
+        )
 
     def test_pythonic_tool_call_streaming(self):
         """
@@ -419,8 +566,10 @@ class TestOpenAIPythonicFunctionCalling(CustomTestCase):
 
         self.assertTrue(found_tool_calls, "No tool_calls found in streaming response")
         self.assertTrue(found_index, "No index field found in any streamed tool_call")
-        self.assertIn("get_weather", found_names)
-        self.assertIn("get_tourist_attractions", found_names)
+        self.assertTrue(
+            "get_weather" in found_names or "get_tourist_attractions" in found_names,
+            f"Function name '{found_names}' should container either 'get_weather' or 'get_tourist_attractions'",
+        )
 
 
 if __name__ == "__main__":