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[Frontend] OpenAI Responses API supports Tool/Function calling #20874

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81 changes: 81 additions & 0 deletions examples/online_serving/openai_responses_client_with_tools.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,81 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Set up this example by starting a vLLM OpenAI-compatible server with tool call
options enabled.
Reasoning models can be used through the Responses API as seen here
https://platform.openai.com/docs/api-reference/responses

For example:

vllm serve Qwen/Qwen3-1.7B --reasoning-parser qwen3 \
--guided-decoding-backend xgrammar \
--enable-auto-tool-choice --tool-call-parser hermes
"""

import json

from openai import OpenAI


def get_weather(latitude: float, longitude: float) -> str:
"""
Mock function to simulate getting weather data.
In a real application, this would call an external weather API.
"""
return f"Current temperature at ({latitude}, {longitude}) is 20°C."


tools = [
{
"type": "function",
"name": "get_weather",
"description": "Get current temperature for provided coordinates in celsius.",
"parameters": {
"type": "object",
"properties": {
"latitude": {"type": "number"},
"longitude": {"type": "number"},
},
"required": ["latitude", "longitude"],
"additionalProperties": False,
},
"strict": True,
}
]

input_messages = [
{"role": "user", "content": "What's the weather like in Paris today?"}
]


def main():
base_url = "http://0.0.0.0:8000/v1"
model = "Qwen/Qwen3-1.7B"
client = OpenAI(base_url=base_url, api_key="empty")
response = client.responses.create(
model=model, input=input_messages, tools=tools, tool_choice="required"
)
tool_call = response.output[0]
args = json.loads(tool_call.arguments)

result = get_weather(args["latitude"], args["longitude"])

input_messages.append(tool_call) # append model's function call message
input_messages.append(
{ # append result message
"type": "function_call_output",
"call_id": tool_call.call_id,
"output": str(result),
}
)
response_2 = client.responses.create(
model=model,
input=input_messages,
tools=tools,
)
print(response_2.output_text)


if __name__ == "__main__":
main()
7 changes: 6 additions & 1 deletion tests/v1/entrypoints/openai/responses/conftest.py
Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,13 @@ def default_server_args():
"--max-model-len",
"8192",
"--enforce-eager", # For faster startup.
"--enable-auto-tool-choice",
"--guided-decoding-backend",
"xgrammar",
"--tool-call-parser",
"hermes",
"--reasoning-parser",
"deepseek_r1",
"qwen3",
]


Expand Down
196 changes: 196 additions & 0 deletions tests/v1/entrypoints/openai/responses/test_function_call.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,196 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import json

import openai # use the official client for correctness check
import pytest

MODEL_NAME = "Qwen/Qwen3-0.6B"
tools = [
{
"type": "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. 'Vienna'",
"default": "Vienna",
},
"country": {
"type":
"string",
"description":
"The country that the city is in, e.g. 'Austria'",
},
"unit": {
"type": "string",
"description": "The unit to fetch the temperature in",
"enum": ["celsius", "fahrenheit"],
},
"options": {
"$ref": "#/$defs/WeatherOptions",
"description": "Optional parameters for weather query",
},
},
"required": ["country", "unit"],
"$defs": {
"WeatherOptions": {
"title": "WeatherOptions",
"type": "object",
"additionalProperties": False,
"properties": {
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"default": "celsius",
"description": "Temperature unit",
"title": "Temperature Unit",
},
"include_forecast": {
"type": "boolean",
"default": False,
"description":
"Whether to include a 24-hour forecast",
"title": "Include Forecast",
},
"language": {
"type": "string",
"default": "zh-CN",
"description": "Language of the response",
"title": "Language",
"enum": ["zh-CN", "en-US", "ja-JP"],
},
},
},
},
},
},
{
"type": "function",
"name": "get_forecast",
"description": "Get the weather forecast for a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description":
"The city to get the forecast for, e.g. 'Vienna'",
"default": "Vienna",
},
"country": {
"type":
"string",
"description":
"The country that the city is in, e.g. 'Austria'",
},
"days": {
"type": "integer",
"description":
"Number of days to get the forecast for (1-7)",
},
"unit": {
"type": "string",
"description": "The unit to fetch the temperature in",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["country", "days", "unit"],
},
},
]


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
@pytest.mark.parametrize("tool_choice", ["auto", "required"])
async def test_function_tool_use(client: openai.AsyncOpenAI, model_name: str,
tool_choice: str):
prompt = [{
"role": "user",
"content":
"Can you tell me what the current weather is in Berlin and the "\
"forecast for the next 5 days, in fahrenheit?",
},]
response = await client.responses.create(
model=model_name,
input=prompt,
tools=tools,
tool_choice=tool_choice,
)

assert len(response.output) >= 1
tool_call = response.output[0]

assert tool_call.type == "function_call"
assert json.loads(tool_call.arguments) is not None


@pytest.mark.asyncio
async def test_named_tool_use(client: openai.AsyncOpenAI):

def get_weather(latitude: float, longitude: float) -> str:
"""
Mock function to simulate getting weather data.
In a real application, this would call an external weather API.
"""
return f"Current temperature at ({latitude}, {longitude}) is 20°C."

tools = [{
"type": "function",
"name": "get_weather",
"description":
"Get current temperature for provided coordinates in celsius.",
"parameters": {
"type": "object",
"properties": {
"latitude": {
"type": "number"
},
"longitude": {
"type": "number"
}
},
"required": ["latitude", "longitude"],
"additionalProperties": False
},
"strict": True
}]

input_messages = [{
"role": "user",
"content": "What's the weather like in Paris today?"
}]

response = await client.responses.create(model=MODEL_NAME,
input=input_messages,
tools=tools,
tool_choice={
"type": "function",
"name": "get_weather"
})
assert len(response.output) == 1
tool_call = response.output[0]
assert tool_call.type == "function_call"
assert tool_call.name == "get_weather"
args = json.loads(tool_call.arguments)
assert args["latitude"] is not None
assert args["longitude"] is not None
# call the tool
result = get_weather(args["latitude"], args["longitude"])
input_messages.append(tool_call) # append model's function call message
input_messages.append({ # append result message
"type": "function_call_output",
"call_id": tool_call.call_id,
"output": str(result)
})
# create a new response with the tool call result
response_2 = await client.responses.create(model=MODEL_NAME,
input=input_messages)
# check the output
assert len(response_2.output_text) > 0
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