Tool Parser

This guide demonstrates how to use SGLang’s Function calling functionality.

Currently supported parsers:

Parser	Supported Models	Notes
deepseekv3	DeepSeek-v3 (e.g., deepseek-ai/DeepSeek-V3-0324)	Recommend adding --chat-template ./examples/chat_template/tool_chat_template_deepseekv3.jinja to launch command.
deepseekv31	DeepSeek-V3.1 and DeepSeek-V3.2-Exp (e.g. deepseek-ai/DeepSeek-V3.1, deepseek-ai/DeepSeek-V3.2-Exp)	Recommend adding --chat-template ./examples/chat_template/tool_chat_template_deepseekv31.jinja (Or ..deepseekv32.jinja for DeepSeek-V3.2) to launch command.
deepseekv32	DeepSeek-V3.2 (deepseek-ai/DeepSeek-V3.2)
glm	GLM series (e.g. zai-org/GLM-4.6)
gpt-oss	GPT-OSS (e.g., openai/gpt-oss-120b, openai/gpt-oss-20b, lmsys/gpt-oss-120b-bf16, lmsys/gpt-oss-20b-bf16)	The gpt-oss tool parser filters out analysis channel events and only preserves normal text. This can cause the content to be empty when explanations are in the analysis channel. To work around this, complete the tool round by returning tool results as role="tool" messages, which enables the model to generate the final content.
kimi_k2	moonshotai/Kimi-K2-Instruct
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)
llama4	Llama 4 (e.g. meta-llama/Llama-4-Scout-17B-16E-Instruct)
mistral	Mistral (e.g. mistralai/Mistral-7B-Instruct-v0.3, mistralai/Mistral-Nemo-Instruct-2407, mistralai/Mistral-7B-v0.3)
pythonic	Llama-3.2 / Llama-3.3 / Llama-4	Model outputs function calls as Python code. Requires --tool-call-parser pythonic and is recommended to use with a specific chat template.
qwen	Qwen series (e.g. Qwen/Qwen3-Next-80B-A3B-Instruct, Qwen/Qwen3-VL-30B-A3B-Thinking) except Qwen3-Coder
qwen3_coder	Qwen3-Coder (e.g. Qwen/Qwen3-Coder-30B-A3B-Instruct)
step3	Step-3

OpenAI Compatible API

Launching the Server

import json
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process
from openai import OpenAI

server_process, port = launch_server_cmd(
    "python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --tool-call-parser qwen25 --host 0.0.0.0 --log-level warning"  # qwen25
)
wait_for_server(f"http://localhost:{port}")

Note that --tool-call-parser defines the parser used to interpret responses.

Define Tools for Function Call

Below is a Python snippet that shows how to define a tool as a dictionary. The dictionary includes a tool name, a description, and property defined Parameters.

# Define tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The city to find the weather for, e.g. 'San Francisco'",
                    },
                    "state": {
                        "type": "string",
                        "description": "the two-letter abbreviation for the state that the city is"
                        " in, e.g. 'CA' which would mean 'California'",
                    },
                    "unit": {
                        "type": "string",
                        "description": "The unit to fetch the temperature in",
                        "enum": ["celsius", "fahrenheit"],
                    },
                },
                "required": ["city", "state", "unit"],
            },
        },
    }
]

Define Messages

def get_messages():
    return [
        {
            "role": "user",
            "content": "What's the weather like in Boston today? Output a reasoning before act, then use the tools to help you.",
        }
    ]


messages = get_messages()

Initialize the Client

# Initialize OpenAI-like client
client = OpenAI(api_key="None", base_url=f"http://0.0.0.0:{port}/v1")
model_name = client.models.list().data[0].id

Non-Streaming Request

# Non-streaming mode test
response_non_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0,
    top_p=0.95,
    max_tokens=1024,
    stream=False,  # Non-streaming
    tools=tools,
)
print_highlight("Non-stream response:")
print_highlight(response_non_stream)
print_highlight("==== content ====")
print_highlight(response_non_stream.choices[0].message.content)
print_highlight("==== tool_calls ====")
print_highlight(response_non_stream.choices[0].message.tool_calls)

Handle Tools

When the engine determines it should call a particular tool, it will return arguments or partial arguments through the response. You can parse these arguments and later invoke the tool accordingly.

name_non_stream = response_non_stream.choices[0].message.tool_calls[0].function.name
arguments_non_stream = (
    response_non_stream.choices[0].message.tool_calls[0].function.arguments
)

print_highlight(f"Final streamed function call name: {name_non_stream}")
print_highlight(f"Final streamed function call arguments: {arguments_non_stream}")

Streaming Request

# Streaming mode test
print_highlight("Streaming response:")
response_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0,
    top_p=0.95,
    max_tokens=1024,
    stream=True,  # Enable streaming
    tools=tools,
)

texts = ""
tool_calls = []
name = ""
arguments = ""
for chunk in response_stream:
    if chunk.choices[0].delta.content:
        texts += chunk.choices[0].delta.content
    if chunk.choices[0].delta.tool_calls:
        tool_calls.append(chunk.choices[0].delta.tool_calls[0])
print_highlight("==== Text ====")
print_highlight(texts)

print_highlight("==== Tool Call ====")
for tool_call in tool_calls:
    print_highlight(tool_call)

Handle Tools

When the engine determines it should call a particular tool, it will return arguments or partial arguments through the response. You can parse these arguments and later invoke the tool accordingly.

# Parse and combine function call arguments
arguments = []
for tool_call in tool_calls:
    if tool_call.function.name:
        print_highlight(f"Streamed function call name: {tool_call.function.name}")

    if tool_call.function.arguments:
        arguments.append(tool_call.function.arguments)

# Combine all fragments into a single JSON string
full_arguments = "".join(arguments)
print_highlight(f"streamed function call arguments: {full_arguments}")

Define a Tool Function

# This is a demonstration, define real function according to your usage.
def get_current_weather(city: str, state: str, unit: "str"):
    return (
        f"The weather in {city}, {state} is 85 degrees {unit}. It is "
        "partly cloudly, with highs in the 90's."
    )


available_tools = {"get_current_weather": get_current_weather}

Execute the Tool

messages.append(response_non_stream.choices[0].message)

# Call the corresponding tool function
tool_call = messages[-1].tool_calls[0]
tool_name = tool_call.function.name
tool_to_call = available_tools[tool_name]
result = tool_to_call(**(json.loads(tool_call.function.arguments)))
print_highlight(f"Function call result: {result}")
# messages.append({"role": "tool", "content": result, "name": tool_name})
messages.append(
    {
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": str(result),
        "name": tool_name,
    }
)

print_highlight(f"Updated message history: {messages}")

Send Results Back to Model

final_response = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0,
    top_p=0.95,
    stream=False,
    tools=tools,
)
print_highlight("Non-stream response:")
print_highlight(final_response)

print_highlight("==== Text ====")
print_highlight(final_response.choices[0].message.content)

Native API and SGLang Runtime (SRT)

from transformers import AutoTokenizer
import requests

# generate an answer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

messages = get_messages()

input = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, tools=tools, return_dict=False
)

gen_url = f"http://localhost:{port}/generate"
gen_data = {
    "text": input,
    "sampling_params": {
        "skip_special_tokens": False,
        "max_new_tokens": 1024,
        "temperature": 0,
        "top_p": 0.95,
    },
}
gen_response = requests.post(gen_url, json=gen_data).json()["text"]
print_highlight("==== Response ====")
print_highlight(gen_response)

# parse the response
parse_url = f"http://localhost:{port}/parse_function_call"

function_call_input = {
    "text": gen_response,
    "tool_call_parser": "qwen25",
    "tools": tools,
}

function_call_response = requests.post(parse_url, json=function_call_input)
function_call_response_json = function_call_response.json()

print_highlight("==== Text ====")
print(function_call_response_json["normal_text"])
print_highlight("==== Calls ====")
print("function name: ", function_call_response_json["calls"][0]["name"])
print("function arguments: ", function_call_response_json["calls"][0]["parameters"])

terminate_process(server_process)

Offline Engine API

import sglang as sgl
from sglang.srt.function_call.function_call_parser import FunctionCallParser
from sglang.srt.managers.io_struct import Tool, Function

llm = sgl.Engine(model_path="Qwen/Qwen2.5-7B-Instruct")
tokenizer = llm.tokenizer_manager.tokenizer
input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, tools=tools, return_dict=False
)

# Note that for gpt-oss tool parser, adding "no_stop_trim": True
# to make sure the tool call token <call> is not trimmed.

sampling_params = {
    "max_new_tokens": 1024,
    "temperature": 0,
    "top_p": 0.95,
    "skip_special_tokens": False,
}

# 1) Offline generation
result = llm.generate(input_ids=input_ids, sampling_params=sampling_params)
generated_text = result["text"]  # Assume there is only one prompt

print_highlight("=== Offline Engine Output Text ===")
print_highlight(generated_text)


# 2) Parse using FunctionCallParser
def convert_dict_to_tool(tool_dict: dict) -> Tool:
    function_dict = tool_dict.get("function", {})
    return Tool(
        type=tool_dict.get("type", "function"),
        function=Function(
            name=function_dict.get("name"),
            description=function_dict.get("description"),
            parameters=function_dict.get("parameters"),
        ),
    )


tools = [convert_dict_to_tool(raw_tool) for raw_tool in tools]

parser = FunctionCallParser(tools=tools, tool_call_parser="qwen25")
normal_text, calls = parser.parse_non_stream(generated_text)

print_highlight("=== Parsing Result ===")
print("Normal text portion:", normal_text)
print_highlight("Function call portion:")
for call in calls:
    # call: ToolCallItem
    print_highlight(f"  - tool name: {call.name}")
    print_highlight(f"    parameters: {call.parameters}")

# 3) If needed, perform additional logic on the parsed functions, such as automatically calling the corresponding function to obtain a return value, etc.

llm.shutdown()

Tool Choice Mode

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.

Supported Tool Choice Options

• tool_choice="required": Forces the model to call at least one tool
• tool_choice={"type": "function", "function": {"name": "specific_function"}}: Forces the model to call a specific function

Backend Compatibility

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.

Example: Required Tool Choice

from openai import OpenAI
from sglang.utils import wait_for_server, print_highlight, terminate_process
from sglang.test.doc_patch import launch_server_cmd

# Start a new server session for tool choice examples
server_process_tool_choice, port_tool_choice = launch_server_cmd(
    "python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --tool-call-parser qwen25 --host 0.0.0.0  --log-level warning"
)
wait_for_server(f"http://localhost:{port_tool_choice}")

# Initialize client for tool choice examples
client_tool_choice = OpenAI(
    api_key="None", base_url=f"http://0.0.0.0:{port_tool_choice}/v1"
)
model_name_tool_choice = client_tool_choice.models.list().data[0].id

# Example with tool_choice="required" - forces the model to call a tool
messages_required = [
    {"role": "user", "content": "Hello, what is the capital of France?"}
]

# Define tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The city to find the weather for, e.g. 'San Francisco'",
                    },
                    "unit": {
                        "type": "string",
                        "description": "The unit to fetch the temperature in",
                        "enum": ["celsius", "fahrenheit"],
                    },
                },
                "required": ["city", "unit"],
            },
        },
    }
]

response_required = client_tool_choice.chat.completions.create(
    model=model_name_tool_choice,
    messages=messages_required,
    temperature=0,
    max_tokens=1024,
    tools=tools,
    tool_choice="required",  # Force the model to call a tool
)

print_highlight("Response with tool_choice='required':")
print("Content:", response_required.choices[0].message.content)
print("Tool calls:", response_required.choices[0].message.tool_calls)

Example: Specific Function Choice

# Example with specific function choice - forces the model to call a specific function
messages_specific = [
    {"role": "user", "content": "What are the most attactive places in France?"}
]

response_specific = client_tool_choice.chat.completions.create(
    model=model_name_tool_choice,
    messages=messages_specific,
    temperature=0,
    max_tokens=1024,
    tools=tools,
    tool_choice={
        "type": "function",
        "function": {"name": "get_current_weather"},
    },  # Force the model to call the specific get_current_weather function
)

print_highlight("Response with specific function choice:")
print("Content:", response_specific.choices[0].message.content)
print("Tool calls:", response_specific.choices[0].message.tool_calls)

if response_specific.choices[0].message.tool_calls:
    tool_call = response_specific.choices[0].message.tool_calls[0]
    print_highlight(f"Called function: {tool_call.function.name}")
    print_highlight(f"Arguments: {tool_call.function.arguments}")

terminate_process(server_process_tool_choice)

Pythonic Tool Call Format (Llama-3.2 / Llama-3.3 / Llama-4)

Some Llama models (such as Llama-3.2-1B, Llama-3.2-3B, Llama-3.3-70B, and Llama-4) support a “pythonic” tool call format, where the model outputs function calls as Python code, e.g.:

[get_current_weather(city="San Francisco", state="CA", unit="celsius")]

• The output is a Python list of function calls, with arguments as Python literals (not JSON).
• Multiple tool calls can be returned in the same list:

[get_current_weather(city="San Francisco", state="CA", unit="celsius"),
 get_current_weather(city="New York", state="NY", unit="fahrenheit")]

For more information, refer to Meta’s documentation on Zero shot function calling.

Note that this feature is still under development on Blackwell.

How to enable

• Launch the server with --tool-call-parser pythonic
• You may also specify —chat-template with the improved template for the model (e.g., --chat-template=examples/chat_template/tool_chat_template_llama4_pythonic.jinja). This is recommended because the model expects a special prompt format to reliably produce valid pythonic tool call outputs. The template ensures that the prompt structure (e.g., special tokens, message boundaries like <|eom|>, and function call delimiters) matches what the model was trained or fine-tuned on. If you do not use the correct chat template, tool calling may fail or produce inconsistent results.

Forcing Pythonic Tool Call Output Without a Chat Template

If you don’t want to specify a chat template, you must give the model extremely explicit instructions in your messages to enforce pythonic output. For example, for Llama-3.2-1B-Instruct, you need:

import openai

server_process, port = launch_server_cmd(
    " python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --tool-call-parser pythonic --tp 1  --log-level warning"  # llama-3.2-1b-instruct
)
wait_for_server(f"http://localhost:{port}")

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a given location.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The name of the city or location.",
                    }
                },
                "required": ["location"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "get_tourist_attractions",
            "description": "Get a list of top tourist attractions for a given city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The name of the city to find attractions for.",
                    }
                },
                "required": ["city"],
            },
        },
    },
]


def get_messages():
    return [
        {
            "role": "system",
            "content": (
                "You are a travel assistant. "
                "When asked to call functions, ALWAYS respond ONLY with a python list of function calls, "
                "using this format: [func_name1(param1=value1, param2=value2), func_name2(param=value)]. "
                "Do NOT use JSON, do NOT use variables, do NOT use any other format. "
                "Here is an example:\n"
                '[get_weather(location="Paris"), get_tourist_attractions(city="Paris")]'
            ),
        },
        {
            "role": "user",
            "content": (
                "I'm planning a trip to Tokyo next week. What's the weather like and what are some top tourist attractions? "
                "Propose parallel tool calls at once, using the python list of function calls format as shown above."
            ),
        },
    ]


messages = get_messages()

client = openai.Client(base_url=f"http://localhost:{port}/v1", api_key="xxxxxx")
model_name = client.models.list().data[0].id


response_non_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0,
    top_p=0.9,
    stream=False,  # Non-streaming
    tools=tools,
)
print_highlight("Non-stream response:")
print_highlight(response_non_stream)

response_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0,
    top_p=0.9,
    stream=True,
    tools=tools,
)
texts = ""
tool_calls = []
name = ""
arguments = ""

for chunk in response_stream:
    if chunk.choices[0].delta.content:
        texts += chunk.choices[0].delta.content
    if chunk.choices[0].delta.tool_calls:
        tool_calls.append(chunk.choices[0].delta.tool_calls[0])

print_highlight("Streaming Response:")
print_highlight("==== Text ====")
print_highlight(texts)

print_highlight("==== Tool Call ====")
for tool_call in tool_calls:
    print_highlight(tool_call)

terminate_process(server_process)

Note:
The model may still default to JSON if it was heavily finetuned on that format. Prompt engineering (including examples) is the only way to increase the chance of pythonic output if you are not using a chat template.

How to support a new model?

1. Update the TOOLS_TAG_LIST in sglang/srt/function_call_parser.py with the model’s tool tags. Currently supported tags include:

	TOOLS_TAG_LIST = [
	    “<|plugin|>“,
	    “<function=“,
	    “<tool_call>“,
	    “<|python_tag|>“,
	    “[TOOL_CALLS]”
	]

2. Create a new detector class in sglang/srt/function_call_parser.py that inherits from BaseFormatDetector. The detector should handle the model’s specific function call format. For example:

    class NewModelDetector(BaseFormatDetector):

3. Add the new detector to the MultiFormatParser class that manages all the format detectors.