Feature/function calling update (#2700)

Co-authored-by: Mingyuan Ma <mamingyuan2001@berkeley.edu>
Co-authored-by: Chayenne <zhaochen20@outlook.com>
Co-authored-by: shuaills <shishuaiuoe@gmail.com>

python/sglang/srt/entrypoints/http_server.py

@@ -39,10 +39,12 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import ORJSONResponse, Response, StreamingResponse
 
 from sglang.srt.entrypoints.engine import _launch_subprocesses
+from sglang.srt.function_call_parser import FunctionCallParser
 from sglang.srt.managers.io_struct import (
     CloseSessionReqInput,
     ConfigureLoggingReq,
     EmbeddingReqInput,
+    FunctionCallReqInput,
     GenerateReqInput,
     GetWeightsByNameReqInput,
     InitWeightsUpdateGroupReqInput,
@@ -369,6 +371,28 @@ async def configure_logging(obj: ConfigureLoggingReq, request: Request):
     return Response(status_code=200)
 
 
+@app.post("/function_call")
+async def function_call_request(obj: FunctionCallReqInput, request: Request):
+    """
+    A native API endpoint to parse function calls from a text.
+    """
+    # 1) Initialize the parser based on the request body
+    parser = FunctionCallParser(tools=obj.tools, tool_call_parser=obj.tool_call_parser)
+
+    # 2) Call the non-stream parsing method (non-stream)
+    normal_text, calls = parser.parse_non_stream(obj.text)
+
+    # 3) Organize the response content
+    response_data = {
+        "normal_text": normal_text,
+        "calls": [
+            call.model_dump() for call in calls
+        ],  # Convert pydantic objects to dictionaries
+    }
+
+    return ORJSONResponse(content=response_data, status_code=200)
+
+
 ##### OpenAI-compatible API endpoints #####
 
 

python/sglang/srt/function_call_parser.py

@@ -0,0 +1,494 @@
+import json
+import re
+from abc import ABC, abstractmethod
+from json import JSONDecodeError, JSONDecoder
+from typing import Any, Dict, List, Optional, Tuple
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+from pydantic import BaseModel, Field
+
+TOOLS_TAG_LIST = [
+    "<|plugin|>",
+    "<function=",
+    "<tool_call>",
+    "<|python_tag|>",
+    "[TOOL_CALLS]",
+]
+
+
+class Function(BaseModel):
+    """Function Tool Template."""
+
+    description: Optional[str] = Field(default=None, examples=[None])
+    name: Optional[str] = None
+    parameters: Optional[object] = None
+
+
+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
+
+
+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
+
+
+class StreamingParseResult:
+    """Result of streaming incremental parsing."""
+
+    def __init__(
+        self, normal_text: str = "", calls: Optional[List[ToolCallItem]] = None
+    ):
+        self.normal_text = normal_text
+        self.calls = calls or []
+
+
+class BaseFormatDetector:
+    """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: Dict, tools: List[Function]):
+        name, parameters = action["name"], json.dumps(
+            action.get("parameters", action.get("arguments", {})),
+            ensure_ascii=False,
+        )
+        tool_index = [tool.function.name for tool in tools].index(name)
+        tool_call_item = ToolCallItem(
+            tool_index=tool_index, name=name, parameters=parameters
+        )
+        calls = [tool_call_item]
+        return calls
+
+    def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
+        """
+        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 self.parse_base_json(action, tools)
+
+    def parse_streaming_increment(
+        self, new_text: str, tools: List[Function]
+    ) -> StreamingParseResult:
+        """
+        Streaming incremental parsing, referencing the logic of Llama32Detector.
+        We partially parse JSON within <tool_call>...</tool_call>, and handle
+        incremental argument output.
+        """
+        # 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)
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
+        try:
+            tool_call_arr = []
+            is_complete = []
+            try:
+                # depending on the prompt format the Llama model may or may not
+                # prefix the output with the <|python_tag|> token
+                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("; ")
+                    # depending on the prompt Llama can use
+                    # either arguments or parameters
+                    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 partial_json_parser.core.exceptions.MalformedJSON:
+                # not enough tokens to parse into JSON yet
+                return StreamingParseResult()
+
+            # select as the current tool call the one we're on the state at
+            current_tool_call: Dict = (
+                tool_call_arr[self.current_tool_id] if len(tool_call_arr) > 0 else {}
+            )
+
+            # case -- if no tokens have been streamed for the tool, e.g.
+            #   only the array brackets, stream nothing
+            if len(tool_call_arr) == 0:
+                return StreamingParseResult()
+
+            # case: we are starting a new tool in the array
+            #   -> array has > 0 length AND length has moved past cursor
+            elif (
+                len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1
+            ):
+
+                # if we're moving on to a new call, first make sure we
+                # haven't missed anything in the previous one that was
+                # auto-generated due to JSON completions, but wasn't
+                # streamed to the client yet.
+                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(
+                            normal_text=None,
+                            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()
+                # re-set stuff pertaining to progress in the current tool
+                self.current_tool_id = len(tool_call_arr) - 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                print("starting on new tool %d", self.current_tool_id)
+                return res
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            elif not self.current_tool_name_sent:
+                function_name = current_tool_call.get("name")
+                if function_name:
+                    res = StreamingParseResult(
+                        normal_text=None,
+                        calls=[
+                            ToolCallItem(
+                                tool_index=self.current_tool_id,
+                                name=function_name,
+                                parameters="",
+                            )
+                        ],
+                    )
+                    self.current_tool_name_sent = True
+                else:
+                    res = StreamingParseResult()
+
+            # now we know we're on the same tool call and we're 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: bool = 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,
+                                    name="",
+                                    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:
+            print(e)
+            # Skipping chunk as a result of tool streaming extraction error
+            return StreamingParseResult()
+
+
+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 detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
+        """
+        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.
+        """
+        if "<tool_call>" not in text:
+            return []
+        pattern = r"<tool_call>(.*?)</tool_call>"
+        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 calls
+
+
+class MistralDetector(BaseFormatDetector):
+    """
+    Detector for Mistral models.
+    Assumes function call format:
+      <|action_start|><|plugin|>{"name":"xxx", "arguments":{...}}<|action_end|>
+    """
+
+    def __init__(self):
+        """
+        Initializes the detector with necessary state variables.
+        """
+        super().__init__()
+        self.bot_token = "[TOOL_CALLS] ["
+        self.tool_call_regex = re.compile(r"\[{.*}\]", re.DOTALL)
+
+    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] [...]
+        """
+        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[Function]) -> List[ToolCallItem]:
+        """
+        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.
+        """
+        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 calls
+
+
+class Llama32Detector(BaseFormatDetector):
+    """
+    Detector for Llama 3.2 models.
+    Assumes function call format:
+      <|python_tag|>{"name":"xxx", "arguments":{...}}
+    Does not require a closing tag "</python_tag|>",
+    relies on json.loads(...) success to determine if JSON is complete.
+    """
+
+    def __init__(self):
+        """
+        Initializes the detector with necessary state variables.
+        """
+        super().__init__()
+        self.bot_token = "<|python_tag|>"
+
+    def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
+        """
+        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.
+        """
+
+        if "<|python_tag|>" not in text:
+            return []
+        _, action = text.split("<|python_tag|>")
+        action = json.loads(action)
+        return self.parse_base_json(action, tools)
+
+
+class MultiFormatParser:
+    def __init__(self, detectors: List[BaseFormatDetector]):
+        """
+        :param detectors: A series of available Detector instances passed in
+        """
+        self.detectors = detectors
+
+    def parse_once(self, text: str, tools: List[Function]):
+        """
+        One-time parsing: Loop through detectors until there are no new matches or text is exhausted
+        Return: (final_text, all_calls)
+        - final_text: The remaining text after parsing that was not consumed by any Detector (can be treated as normal text)
+        - all_calls: All calls parsed by the Detectors
+        """
+        final_calls = []
+        final_normal_text = text
+        for detector in self.detectors:
+            tool_call_list = detector.detect_and_parse(text, tools)
+            if len(tool_call_list) > 0:  # parsed successfully
+                final_calls = tool_call_list
+                break
+
+        # leftover_text is the normal text not consumed by any Detector
+        return final_normal_text, final_calls
+
+    def parse_streaming_increment(self, new_text: str, tools: List[Function]):
+        """
+        Streaming incremental parsing: Feed new_text to each detector's parse_streaming_increment
+        and merge their produced normal_text/calls to return.
+        (The logic here can be "priority-based" or "parallel parsing" based on your needs)
+        """
+        final_normal_text = ""
+        final_calls = []
+
+        for detector in self.detectors:
+            sp_result = detector.parse_streaming_increment(new_text, tools)
+            # Merge normal_text and calls
+            # If one sp_result contains result call, this should be a successful parse
+            # If one sp_result only contains normal_text, this can either be a successful
+            # parse or it is not using the desired parsing tool.
+            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
+                break
+
+        return final_normal_text, final_calls
+
+
+class FunctionCallParser:
+    """
+    In streaming scenarios, each time new_text is received, it calls multi_format_parser.parse_streaming_increment
+    and returns the resulting normal_text and calls to the upper layer (or SSE).
+    """
+
+    ToolCallParserEnum: Dict[str, BaseFormatDetector] = {
+        "llama3": Llama32Detector,
+        "qwen25": Qwen25Detector,
+        "mistral": MistralDetector,
+    }
+
+    def __init__(self, tools: List[Function], tool_call_parser: str = None):
+        detectors = []
+        if tool_call_parser:
+            detector_class = self.ToolCallParserEnum.get(tool_call_parser)
+            if detector_class:
+                detectors.append(detector_class())
+            else:
+                raise ValueError(f"Unsupported tool_call_parser: {tool_call_parser}")
+        else:
+            raise ValueError("Tool Call Parser Not Given!")
+
+        self.multi_format_parser = MultiFormatParser(detectors)
+        self.tools = tools
+
+    def parse_non_stream(self, full_text: str):
+        """
+        Non-streaming call: one-time parsing
+        """
+        full_normal_text, calls = self.multi_format_parser.parse_once(
+            full_text, self.tools
+        )
+        return full_normal_text, calls
+
+    def parse_stream_chunk(self, chunk_text: str):
+        """
+        Streaming call: incremental parsing
+        """
+        normal_text, calls = self.multi_format_parser.parse_streaming_increment(
+            chunk_text, self.tools
+        )
+        return normal_text, calls

python/sglang/srt/managers/io_struct.py

@@ -17,7 +17,7 @@ processes (TokenizerManager, DetokenizerManager, Controller).
 """
 
 import uuid
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from enum import Enum
 from typing import Dict, List, Optional, Union
 
@@ -540,3 +540,27 @@ class CloseSessionReqInput:
 class OpenSessionReqOutput:
     session_id: Optional[str]
     success: bool
+
+
+@dataclass
+class Function:
+    description: Optional[str] = None
+    name: Optional[str] = None
+    parameters: Optional[object] = None
+
+
+@dataclass
+class Tool:
+    function: Function
+    type: Optional[str] = "function"
+
+
+@dataclass
+class FunctionCallReqInput:
+    text: str  # The text to parse.
+    tools: List[Tool] = field(
+        default_factory=list
+    )  # A list of available function tools (name, parameters, etc.).
+    tool_call_parser: Optional[str] = (
+        None  # Specify the parser type, e.g. 'llama3', 'qwen25', or 'mistral'. If not specified, tries all.
+    )

python/sglang/srt/openai_api/adapter.py

@@ -20,7 +20,7 @@ import os
 import time
 import uuid
 from http import HTTPStatus
-from typing import Dict, List
+from typing import Dict, List, Optional
 
 from fastapi import HTTPException, Request, UploadFile
 from fastapi.responses import ORJSONResponse, StreamingResponse
@@ -40,6 +40,7 @@ from sglang.srt.conversation import (
     generate_chat_conv,
     register_conv_template,
 )
+from sglang.srt.function_call_parser import TOOLS_TAG_LIST, FunctionCallParser
 from sglang.srt.managers.io_struct import EmbeddingReqInput, GenerateReqInput
 from sglang.srt.openai_api.protocol import (
     BatchRequest,
@@ -71,7 +72,6 @@ from sglang.srt.openai_api.protocol import (
     TopLogprob,
     UsageInfo,
 )
-from sglang.srt.utils import TOOLS_TAG_LIST, parse_tool_response
 from sglang.utils import get_exception_traceback
 
 logger = logging.getLogger(__name__)
@@ -309,6 +309,7 @@ async def process_batch(tokenizer_manager, batch_id: str, batch_request: BatchRe
                     ret,
                     to_file=True,
                     cache_report=tokenizer_manager.server_args.enable_cache_report,
+                    tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
                 )
             else:
                 responses = v1_generate_response(
@@ -877,9 +878,6 @@ def v1_chat_generate_request(
             tools = None
             if request.tools and request.tool_choice != "none":
                 request.skip_special_tokens = False
-                if request.stream:
-                    logger.warning("Streaming is not supported with tools.")
-                    request.stream = False
                 if not isinstance(request.tool_choice, str):
                     tools = [
                         item.function.model_dump()
@@ -908,12 +906,26 @@ def v1_chat_generate_request(
                     openai_compatible_messages = openai_compatible_messages[:-1]
                 else:
                     assistant_prefix = None
-                prompt_ids = tokenizer_manager.tokenizer.apply_chat_template(
-                    openai_compatible_messages,
-                    tokenize=True,
-                    add_generation_prompt=True,
-                    tools=tools,
-                )
+
+                try:
+                    prompt_ids = tokenizer_manager.tokenizer.apply_chat_template(
+                        openai_compatible_messages,
+                        tokenize=True,
+                        add_generation_prompt=True,
+                        tools=tools,
+                    )
+                except:
+                    #  This except branch will be triggered when the chosen model
+                    #  has a different tools input format that is not compatiable
+                    #  with openAI's apply_chat_template tool_call format, like Mistral.
+                    tools = [t if "function" in t else {"function": t} for t in tools]
+                    prompt_ids = tokenizer_manager.tokenizer.apply_chat_template(
+                        openai_compatible_messages,
+                        tokenize=True,
+                        add_generation_prompt=True,
+                        tools=tools,
+                    )
+
                 if assistant_prefix:
                     prompt_ids += tokenizer_manager.tokenizer.encode(assistant_prefix)
                 stop = request.stop
@@ -1005,7 +1017,9 @@ def v1_chat_generate_request(
     return adapted_request, all_requests if len(all_requests) > 1 else all_requests[0]
 
 
-def v1_chat_generate_response(request, ret, to_file=False, cache_report=False):
+def v1_chat_generate_response(
+    request, ret, to_file=False, cache_report=False, tool_call_parser=None
+):
     choices = []
 
     for idx, ret_item in enumerate(ret):
@@ -1066,12 +1080,13 @@ def v1_chat_generate_response(request, ret, to_file=False, cache_report=False):
             if finish_reason == "stop":
                 finish_reason = "tool_calls"
             try:
-                text, call_info_list = parse_tool_response(text, tools)  # noqa
+                parser = FunctionCallParser(tools, tool_call_parser)
+                full_normal_text, call_info_list = parser.parse_non_stream(text)
                 tool_calls = [
                     ToolCall(
-                        id=str(call_info[0]),
+                        id=str(call_info.tool_index),
                         function=FunctionResponse(
-                            name=call_info[1], arguments=call_info[2]
+                            name=call_info.name, arguments=call_info.parameters
                         ),
                     )
                     for call_info in call_info_list
@@ -1172,6 +1187,7 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
     adapted_request, request = v1_chat_generate_request(all_requests, tokenizer_manager)
 
     if adapted_request.stream:
+        parser_dict = {}
 
         async def generate_stream_resp():
             is_firsts = {}
@@ -1184,6 +1200,7 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
                     adapted_request, raw_request
                 ):
                     index = content.get("index", 0)
+                    text = content["text"]
 
                     is_first = is_firsts.get(index, True)
                     stream_buffer = stream_buffers.get(index, "")
@@ -1263,29 +1280,111 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
 
                     text = content["text"]
                     delta = text[len(stream_buffer) :]
-                    stream_buffer = stream_buffer + delta
-                    choice_data = ChatCompletionResponseStreamChoice(
-                        index=index,
-                        delta=DeltaMessage(content=delta),
-                        finish_reason=(finish_reason["type"] if finish_reason else ""),
-                        matched_stop=(
-                            finish_reason["matched"]
-                            if finish_reason and "matched" in finish_reason
-                            else None
-                        ),
-                        logprobs=choice_logprobs,
-                    )
-                    chunk = ChatCompletionStreamResponse(
-                        id=content["meta_info"]["id"],
-                        choices=[choice_data],
-                        model=request.model,
-                    )
+                    new_stream_buffer = stream_buffer + delta
 
-                    is_firsts[index] = is_first
-                    stream_buffers[index] = stream_buffer
-                    n_prev_tokens[index] = n_prev_token
+                    if request.tool_choice != "none" and request.tools:
+                        if index not in parser_dict:
+                            parser_dict[index] = FunctionCallParser(
+                                tools=request.tools,
+                                tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
+                            )
+                        parser = parser_dict[index]
 
-                    yield f"data: {chunk.model_dump_json()}\n\n"
+                        # parse_increment => returns (normal_text, calls)
+                        normal_text, calls = parser.parse_stream_chunk(delta)
+
+                        # 1) if there's normal_text, output it as normal content
+                        if normal_text:
+                            choice_data = ChatCompletionResponseStreamChoice(
+                                index=index,
+                                delta=DeltaMessage(content=normal_text),
+                                finish_reason=(
+                                    finish_reason["type"] if finish_reason else ""
+                                ),
+                            )
+                            chunk = ChatCompletionStreamResponse(
+                                id=content["meta_info"]["id"],
+                                choices=[choice_data],
+                                model=request.model,
+                            )
+                            yield f"data: {chunk.model_dump_json()}\n\n"
+
+                        # 2) if we found calls, we output them as separate chunk(s)
+                        for call_item in calls:
+                            # transform call_item -> FunctionResponse + ToolCall
+
+                            if (
+                                content["meta_info"]["finish_reason"]
+                                and content["meta_info"]["finish_reason"]["type"]
+                                == "stop"
+                            ):
+                                latest_delta_len = 0
+                                if isinstance(call_item.parameters, str):
+                                    latest_delta_len = len(call_item.parameters)
+
+                                expected_call = json.dumps(
+                                    parser.multi_format_parser.detectors[0]
+                                    .prev_tool_call_arr[index]
+                                    .get("arguments", {}),
+                                    ensure_ascii=False,
+                                )
+                                actual_call = parser.multi_format_parser.detectors[
+                                    0
+                                ].streamed_args_for_tool[index]
+                                if latest_delta_len > 0:
+                                    actual_call = actual_call[:-latest_delta_len]
+                                remaining_call = expected_call.replace(
+                                    actual_call, "", 1
+                                )
+                                call_item.parameters = remaining_call
+
+                            tool_call = ToolCall(
+                                id=str(call_item.tool_index),
+                                function=FunctionResponse(
+                                    name=call_item.name,
+                                    arguments=call_item.parameters,
+                                ),
+                            )
+                            choice_data = ChatCompletionResponseStreamChoice(
+                                index=index,
+                                delta=DeltaMessage(
+                                    role="assistant", tool_calls=[tool_call]
+                                ),
+                                finish_reason="tool_call",
+                            )
+                            chunk = ChatCompletionStreamResponse(
+                                id=content["meta_info"]["id"],
+                                choices=[choice_data],
+                                model=request.model,
+                            )
+                            yield f"data: {chunk.model_dump_json()}\n\n"
+
+                        stream_buffers[index] = new_stream_buffer
+                        is_firsts[index] = is_first
+
+                    else:
+                        # No tool calls => just treat this as normal text
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=index,
+                            delta=DeltaMessage(content=delta),
+                            finish_reason=(
+                                finish_reason["type"] if finish_reason else ""
+                            ),
+                            matched_stop=(
+                                finish_reason["matched"]
+                                if finish_reason and "matched" in finish_reason
+                                else None
+                            ),
+                            logprobs=choice_logprobs,
+                        )
+                        chunk = ChatCompletionStreamResponse(
+                            id=content["meta_info"]["id"],
+                            choices=[choice_data],
+                            model=request.model,
+                        )
+                        yield f"data: {chunk.model_dump_json()}\n\n"
+                        stream_buffers[index] = new_stream_buffer
+                        is_firsts[index] = is_first
                 if request.stream_options and request.stream_options.include_usage:
                     total_prompt_tokens = sum(
                         tokens
@@ -1333,7 +1432,10 @@ async def v1_chat_completions(tokenizer_manager, raw_request: Request):
         ret = [ret]
 
     response = v1_chat_generate_response(
-        request, ret, cache_report=tokenizer_manager.server_args.enable_cache_report
+        request,
+        ret,
+        cache_report=tokenizer_manager.server_args.enable_cache_report,
+        tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
     )
 
     return response

python/sglang/srt/openai_api/protocol.py

@@ -262,7 +262,7 @@ class Function(BaseModel):
     """Function descriptions."""
 
     description: Optional[str] = Field(default=None, examples=[None])
-    name: str
+    name: Optional[str] = None
     parameters: Optional[object] = None
 
 
@@ -276,7 +276,7 @@ class Tool(BaseModel):
 class ToolChoiceFuncName(BaseModel):
     """The name of tool choice function."""
 
-    name: str
+    name: Optional[str] = None
 
 
 class ToolChoice(BaseModel):
@@ -329,8 +329,8 @@ class ChatCompletionRequest(BaseModel):
 class FunctionResponse(BaseModel):
     """Function response."""
 
-    name: str
-    arguments: str
+    name: Optional[str] = None
+    arguments: Optional[str] = None
 
 
 class ToolCall(BaseModel):
@@ -367,6 +367,7 @@ class ChatCompletionResponse(BaseModel):
 class DeltaMessage(BaseModel):
     role: Optional[str] = None
     content: Optional[str] = None
+    tool_calls: Optional[List[ToolCall]] = Field(default=None, examples=[None])
 
 
 class ChatCompletionResponseStreamChoice(BaseModel):

python/sglang/srt/server_args.py

@@ -161,6 +161,7 @@ class ServerArgs:
 
     # Custom logit processor
     enable_custom_logit_processor: bool = False
+    tool_call_parser: str = None
 
     def __post_init__(self):
         # Set missing default values
@@ -877,6 +878,14 @@ class ServerArgs:
             action="store_true",
             help="Enable users to pass custom logit processors to the server (disabled by default for security)",
         )
+        # Function Calling
+        parser.add_argument(
+            "--tool-call-parser",
+            type=str,
+            choices=["qwen25", "mistral", "llama3"],
+            default=ServerArgs.tool_call_parser,
+            help="Specify the parser for handling tool-call interactions. Options include: 'qwen25', 'mistral', and 'llama3'.",
+        )
 
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace):

python/sglang/srt/utils.py

@@ -1243,68 +1243,6 @@ def dataclass_to_string_truncated(data, max_length=2048):
         return str(data)
 
 
-TOOLS_TAG_LIST = ["<|plugin|>", "<function=", "<tool_call>", "<|python_tag|>"]
-
-
-def parse_tool_response(text, tools, **kwargs):
-    """Parse model response containing tool information.
-
-    Args:
-        text(str): model response in string format
-        tools(List): tools from user request
-    """
-    if "<|plugin|>" in text:  # internlm2
-        text, action = text.split("<|action_start|><|plugin|>")
-        action = action.split("<|action_end|>".strip())[0]
-        action = action[action.find("{") :]
-        action = json.loads(action)
-        name, parameters = action["name"], json.dumps(
-            action.get("parameters", action.get("arguments", {})), ensure_ascii=False
-        )
-        call_info_list = [(name, parameters)]
-    elif "<function=" in text:  # llama3.1
-        action, _ = text.split("</function>")
-        parameters = action[action.find("{") :]
-        name = action.split("<function=")[1].split(">{")[0]
-        call_info_list = [(name, parameters)]
-    elif "<tool_call>" in text and "</tool_call>" in text:  # qwen2.5
-        # get tool_call in text
-        pattern = r"<tool_call>(.*?)</tool_call>"
-        match_result_list = re.findall(pattern, text, re.DOTALL)
-        call_info_list = []
-        for match_result in match_result_list:
-            action = json.loads(match_result)
-            call_info_list.append(
-                (action["name"], json.dumps(action["arguments"], ensure_ascii=False))
-            )
-        # get text outside of tags
-        if not text.startswith("<tool_call>"):
-            text = text[: text.find("<tool_call>")]
-        elif not text.endswith("</tool_call>"):
-            text = text[text.rfind("</tool_call>") + len("</tool_call>") :]
-        else:
-            text = ""
-    elif "<|python_tag|>" in text:  # llama3.2
-        _, action = text.split("<|python_tag|>")
-        action = json.loads(action)
-        name, parameters = action["name"], json.dumps(
-            action.get("parameters", action.get("arguments", {})), ensure_ascii=False
-        )
-        call_info_list = [(name, parameters)]
-    else:
-        raise RuntimeError(f"Unexpected model response: {text}")
-
-    call_info_list = [
-        (
-            [tool.function.name for tool in tools].index(call_info[0]),
-            call_info[0],
-            call_info[1],
-        )
-        for call_info in call_info_list
-    ]
-    return text, call_info_list
-
-
 def permute_weight(x: torch.Tensor) -> torch.Tensor:
     b_ = x.shape[0]
     n_ = x.shape[1]