Markdown Content: Tool Calling with Dynamo#

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 vllm, sglang, trtllm, 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-*
phi4	Phi-4-*
deepseek_v3_1	deepseek-ai/DeepSeek-V3.1
pythonic	meta-llama/Llama-4-*

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_functionstool_call.name}")

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}")

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