tool-calling.md

title	subtitle
Tool Calling	Connect Dynamo to external tools and services using function calling

You can connect Dynamo to external tools and services using function calling (also known as tool calling). By providing a list of available functions, Dynamo can choose to output function arguments for the relevant function(s) which you can execute to augment the prompt with relevant external information.

Tool calling (AKA function calling) is controlled using the tool_choice and tools request parameters.

Prerequisites

To enable this feature, you should set the following flag while launching the backend worker

• --dyn-tool-call-parser : select the parser from the available parsers list using the below command

# <backend> can be sglang, trtllm, vllm, etc. based on your installation
python -m dynamo.<backend> --help"

Note

If no tool call parser is provided by the user, Dynamo will try to use default tool call parsing based on <TOOLCALL> and <|python_tag|> tool tags.

Tip

If your model's default chat template doesn't support tool calling, but the model itself does, you can specify a custom chat template per worker with python -m dynamo.<backend> --custom-jinja-template </path/to/template.jinja>.

Parser to Model Mapping

Parser Name	Supported Models
hermes	Qwen/Qwen2.5-, Qwen/QwQ-32B, NousResearch/Hermes-2-Pro-, NousResearch/Hermes-2-Theta-, NousResearch/Hermes-3-
mistral	mistralai/Mistral-7B-Instruct-v0.3, Additional mistral function-calling models are compatible as well.
llama3_json	meta-llama/Llama-3.1-, meta-llama/Llama-3.2-
harmony	openai/gpt-oss-*
nemotron_deci	nvidia/nemotron-*
nemotron_nano	nvidia/NVIDIA-Nemotron-3-Nano-*
phi4	Phi-4-*
deepseek_v3	deepseek-ai/DeepSeek-V3, deepseek-ai/DeepSeek-R1, deepseek-ai/DeepSeek-R1-0528
deepseek_v3_1	deepseek-ai/DeepSeek-V3.1
pythonic	meta-llama/Llama-4-*
jamba	ai21labs/AI21-Jamba--1.5, ai21labs/AI21-Jamba--1.6, ai21labs/AI21-Jamba-*-1.7,
glm47	zai-org/GLM-4.7
kimi_k2	moonshotai/Kimi-K2-Thinking*, moonshotai/Kimi-K2-Instruct*, moonshotai/Kimi-K2.5*

* Currently requires converting tiktoken.model to tokenizers.json.

Tip

For Kimi K2.5 thinking models, pair --dyn-tool-call-parser kimi_k2 with --dyn-reasoning-parser kimi_k25 so that both <think> blocks and tool calls are parsed correctly from the same response.

Examples

Launch Dynamo Frontend and Backend

# launch backend worker
python -m dynamo.vllm --model openai/gpt-oss-20b --dyn-tool-call-parser harmony

# launch frontend worker
python -m dynamo.frontend

Tool Calling Request Examples

• Example 1

from openai import OpenAI
import json

client = OpenAI(base_url="http://localhost:8081/v1", api_key="dummy")

def get_weather(location: str, unit: str):
    return f"Getting the weather for {location} in {unit}..."
tool_functions = {"get_weather": get_weather}

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location", "unit"]
        }
    }
}]

response = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[{"role": "user", "content": "What's the weather like in San Francisco in Celsius?"}],
    tools=tools,
    tool_choice="auto",
    max_tokens=10000
)
print(f"{response}")
tool_call = response.choices[0].message.tool_calls[0].function
print(f"Function called: {tool_call.name}")
print(f"Arguments: {tool_call.arguments}")
print(f"Result: {tool_functions[tool_call.name](**json.loads(tool_call.arguments))}")

• Example 2

# Use tools defined in example 1

time_tool = {
    "type": "function",
    "function": {
        "name": "get_current_time_nyc",
        "description": "Get the current time in NYC.",
        "parameters": {}
    }
}


tools.append(time_tool)

messages = [
    {"role": "user", "content": "What's the current time in New York?"}
]


response = client.chat.completions.create(
    model="openai/gpt-oss-20b", #client.models.list().data[1].id,
    messages=messages,
    tools=tools,
    tool_choice="auto",
    max_tokens=100,
)
print(f"{response}")
tool_call = response.choices[0].message.tool_calls[0].function
print(f"Function called: {tool_call.name}")
print(f"Arguments: {tool_call.arguments}")

• Example 3

tools = [
    {
        "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": "user",
            "content": (
                "I'm planning a trip to Tokyo next week. what are some top tourist attractions in Tokyo? "
            ),
        },
    ]


messages = get_messages()

response = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=messages,
    tools=tools,
    tool_choice="auto",
    max_tokens=100,
)
print(f"{response}")
tool_call = response.choices[0].message.tool_calls[0].function
print(f"Function called: {tool_call.name}")
print(f"Arguments: {tool_call.arguments}")