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1 change: 1 addition & 0 deletions .github/filters.yaml
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Expand Up @@ -17,6 +17,7 @@ all:

docs:
- 'docs/**'
- 'fern/**'
- '**/*.md'
- '**/*.rst'
- '**/*.txt'
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1 change: 1 addition & 0 deletions docs/kubernetes/dynamo_operator.md
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Expand Up @@ -86,6 +86,7 @@ helm install dynamo-test dynamo-platform-${RELEASE_VERSION}.tgz \
--create-namespace \
--set dynamo-operator.namespaceRestriction.enabled=true \
--set dynamo-operator.controllerManager.manager.image.tag=v2.0.0-beta
```

**Observability:**

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**/.preview
**/.definition

# Include logos
!*.svg
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47 changes: 47 additions & 0 deletions fern/docs.yml
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# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

instances:
- url: ai-dynamo.docs.buildwithfern.com

title: NVIDIA Dynamo Documentation

# Version configuration
versions:
- display-name: Next
path: ./versions/next.yml

# GitHub repository link in navbar
navbar-links:
- type: github
value: https://github.com/ai-dynamo/dynamo

# NVIDIA branding colors
colors:
accent-primary:
dark: "#76B900"
light: "#4A7300"
background:
dark: "#1A1A1A"
light: "#FFFFFF"

# Logo and favicon
logo:
href: /
light: ./assets/img/nvidia-logo.svg
dark: ./assets/img/nvidia-logo-dark.svg
height: 50

favicon: ./assets/img/favicon.png
4 changes: 4 additions & 0 deletions fern/fern.config.json
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{
"organization": "ai-dynamo",
"version": "3.29.1"
}
189 changes: 189 additions & 0 deletions fern/pages/agents/tool-calling.md
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---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: "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

```bash
# <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.
</Note>

<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>`.
</Tip>


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 | 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, |


## Examples

### Launch Dynamo Frontend and Backend

```bash
# 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
```python
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
```python

# 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


```python

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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