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had launched its personal LLM agent framework, the NeMo Agent Toolkit (or NAT), I received actually excited. We normally consider Nvidia as the corporate powering the whole LLM hype with its GPUs, so it’s fascinating to see them transcend {hardware} and step into the software program house as properly.
There are already loads of LLM agent frameworks on the market: LangGraph, smolagents, CrewAI and DSPy, simply to call a couple of. The NeMo Agent Toolkit, nonetheless, feels a bit totally different. I might describe it as a sort of glue that helps sew all of the items collectively and switch them right into a production-ready answer.
Nvidia positions this framework as a solution to sort out “day 2” issues: exposing brokers as APIs, including observability to observe your system and examine edge circumstances, constructing evaluations, and reusing brokers created in different frameworks.
On this article, I’ll discover the core capabilities of the NeMo Agent Toolkit in apply, beginning with a easy chat-completion app and steadily transferring towards a hierarchical agentic setup, the place one LLM agent can recursively use different brokers as instruments. Because it’s the festive season, I’ll be utilizing publicly accessible knowledge from the World Happiness Report to maintain issues cheerful.
Establishing
As standard, we are going to begin by establishing the setting and putting in the bundle.
The core bundle itself is pretty light-weight. Nonetheless, as I discussed earlier, NAT is designed to behave as glue permitting to combine with totally different LLM frameworks in your workflow. Due to that, there are a number of non-obligatory plugins accessible for standard libraries equivalent to LangChain, CrewAI, and LlamaIndex. You may all the time discover essentially the most up-to-date checklist of supported plugins in the official documentation.
On this article, we shall be utilizing LangChain, so we’ll want to put in the corresponding extension as properly.
Tip: NAT works MUCH higher and quicker with
uv. I initially tried putting in every little thing withpip, and it failed after about 20 minutes of ready. I’d strongly advocate not repeating my errors.
First, create and activate a digital setting utilizing uv.
uv venv .venv_nat_uv --python 3.12
supply .venv_nat_uv/bin/activateThen set up NAT with the LangChain plugin.
uv pip set up "nvidia-nat[langchain]"Should you’re planning to run NAT from the CLI, you’ll additionally have to export the related setting variables. Since I’ll be utilizing Anthropic fashions, I have to set the API key.
export ANTHROPIC_API_KEY=Loading knowledge
Subsequent, let’s obtain the World Happiness Report knowledge and take a better have a look at it. I’ve put collectively a small helper perform to load the dataset and barely clear up the information.
import pandas as pd
def load_data():
df = pd.read_excel('whr2025_data.xlsx')
df = df[df.Year >= 2019]
df = df.drop(['Lower whisker', 'Upper whisker'], axis=1)
df.columns = ['year', 'rank', 'country', 'happiness_score',
'impact_gdp', 'impact_social_support',
'impact_life_expectancy', 'impact_freedom',
'impact_generosity', 'impact_corruption', 'impact_residual']
return df
df = load_data()This dataset covers the World Happiness Report outcomes from 2019 to 2024. For every nation and 12 months, it consists of the general happiness rating in addition to the estimated contribution of a number of underlying elements:
- logarithm of GDP per capita,
- social help,
- wholesome life expectancy,
- freedom to make life decisions,
- generosity,
- notion of corruption.
With this dataset, we can examine happiness patterns throughout geographies and over time and hopefully spot some attention-grabbing patterns alongside the best way.
Chat completion instance
Let’s begin with a quite simple chat-completion instance. The structure right here is deliberately minimal: a single LLM with no further instruments or brokers concerned.
The NeMo Agent Toolkit is configured by way of YAML information that outline each the workflow and the underlying LLMs. Nvidia selected this method as a result of it makes experimenting with totally different configurations a lot simpler. For this primary instance, we’ll create a chat_config.yml file.
At a excessive degree, our config file will include two primary sections:
llmsthe place we outline the language fashions we wish to use,workflowthe place we describe how these fashions are wired collectively and the way the agent behaves.
On the LLM facet, NAT helps a number of suppliers out of the field, together with OpenAI, Nvidia Inference Microservices, and AWS Bedrock. Since I wish to use an Anthropic mannequin, the simplest possibility right here is LiteLLM, which acts as a common wrapper and lets us connect with nearly any supplier.
Within the workflow part, we are going to specify:
- the workflow kind (we shall be utilizing the only
chat_completionfor now, however will discover extra advanced agentic setups later), - the LLM outlined above, and
- the system immediate that units the agent’s behaviour.
This offers us a stable MVP and a dependable baseline to construct on earlier than transferring into extra advanced agentic purposes.
llms:
chat_llm:
_type: litellm
model_name: claude-sonnet-4-5-20250929
api_key: $ANTHROPIC_API_KEY
temperature: 0.7
workflow:
_type: chat_completion
llm_name: chat_llm
system_prompt: |
You're a educated scientist within the discipline of happiness research.
You could have entry to a dataset containing the World Happiness Report knowledge from 2019 to 2025.
Your process is to research the information and supply insights based mostly on consumer queries.
Use the dataset to reply questions on nation rankings, traits over time, and elements influencing happiness scores.Now it’s time to run our software. We are able to do that with a single CLI command by specifying the trail to the config file and offering an enter query.
nat run
--config_file chat_config.yml
--input "How is happinness outlined?"As soon as the command runs, we’ll see the next output within the console.
2025-12-24 18:07:34 - INFO - nat.cli.instructions.begin:192 - Beginning NAT
from config file: 'chat_config.yml'
Configuration Abstract:
--------------------
Workflow Sort: chat_completion
Variety of Capabilities: 0
Variety of Perform Teams: 0
Variety of LLMs: 1
Variety of Embedders: 0
Variety of Reminiscence: 0
Variety of Object Shops: 0
Variety of Retrievers: 0
Variety of TTC Methods: 0
Variety of Authentication Suppliers: 0
2025-12-24 18:07:35 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-24 18:07:44 - INFO - nat.front_ends.console.console_front_end_plugin:102 - --------------------------------------------------
['In the World Happiness Report, happiness is defined as subjective well-being,
measured primarily through the **Cantril ladder** life evaluation question,
where respondents rate their current life on a scale from 0 (worst possible)
to 10 (best possible). The overall happiness score is then statistically
explained by six key factors: GDP per capita, social support, healthy life
expectancy, freedom to make life choices, generosity, and perceptions of
corruption.']
--------------------------------------------------We received a fairly respectable reply based mostly on the mannequin’s common information. Now, let’s take the subsequent step and deploy it. Since NAT is designed for production-ready purposes, we are able to simply expose our answer as a REST API. Later on this article, we’ll even see find out how to flip it right into a customer-ready UI.
To make our agent accessible by way of an API endpoint, we are able to use the nat serve command.
nat serve --config_file chat_config.ymlNow, our software is accessible at http://localhost:8000, and we are able to work together with it utilizing Python. The API format is appropriate with OpenAI’s endpoints.
import requests
import json
# Check the API endpoint
response = requests.submit(
"http://localhost:8000/v1/chat/completions",
headers={"Content material-Sort": "software/json"},
json={
"messages": [
{
"role": "user",
"content": "How many years of happiness data do we have?"
}
],
"stream": False
}
)
# Parse and show the response
if response.status_code == 200:
end result = response.json()
print(end result["choices"][0]["message"]["content"])
else:
print(f"Error: {response.status_code}")
print(response.textual content)
# Now we have 7 years of happiness knowledge, overlaying the interval from 2019 to 2025.This offers us a believable MVP that may reply fundamental questions concerning the Happiness knowledge. Nonetheless, to offer deeper insights, our agent wants context and entry to the precise dataset. Equipping it with instruments shall be our subsequent step.
Including instruments
Subsequent, let’s add a few instruments that may assist our agent analyse the World Happiness Report knowledge. We’ll present our agent with two capabilities:
get_country_statsreturns all Happiness knowledge filtered by a selected nation,
get_year_statsoutputs an summary of the Happiness Report for a given 12 months, together with the happiest and least joyful international locations, the common happiness rating, and the elements influencing it.
Including instruments within the NeMo Agent toolkit requires fairly a little bit of boilerplate code. We might want to undergo the next steps:
- Implement the capabilities in Python,
- Outline an enter schema for every perform,
- Create corresponding config lessons,
- Wrap the capabilities so they’re async and callable by the agent,
- Replace the YAML config.
Luckily, NAT offers the workflow create command, which generates a scaffolding construction to assist organise your undertaking.
nat workflow create happiness_v1This command doesn’t generate all of the implementation for you, but it surely does create the undertaking construction with all the mandatory information. After working it, the next construction shall be created.
happiness_v1/
├── 📄 pyproject.toml # Python bundle configuration
├── 📁 configs/ # Root-level config (symlink or copy)
│ └── config.yml # NAT workflow configuration
├── 📁 knowledge/ # Root-level knowledge folder (empty)
│
└── 📁 src/ # Supply code
└── 📁 happiness_v1/ # Fundamental bundle
├── 📄 __init__.py # Package deal initializer (empty)
├── 📄 happiness_v1.py # Core perform implementations
├── 📄 register.py # Perform registration (entry level)
├── 📁 configs/ # Package deal-specific config
│ └── config.yml # NAT workflow config
└── 📁 knowledge/ # Package deal knowledge folder (empty)Let’s begin constructing our agent. Step one is implementing the capabilities in Python. I created a utils folder inside src/happiness_v1 and added the capabilities we would like the agent to make use of. I additionally included a useful helper load_data perform we checked out earlier, which the agent will use behind the scenes to pre-load the Happiness Report knowledge.
from typing import Dict, Checklist, Optionally available, Union
def get_country_stats(df: pd.DataFrame, nation: str) -> pd.DataFrame:
"""
Get happiness statistics for a selected nation.
Args:
df (pd.DataFrame): DataFrame containing happiness knowledge.
nation (str): Identify of the nation to filter by.
Returns:
pd.DataFrame: Filtered DataFrame with statistics for the desired nation.
"""
return df[df['country'].str.incorporates(nation, case=False)]
def get_year_stats(df: pd.DataFrame, 12 months: int) -> str:
"""
Get happiness statistics for a selected 12 months.
Args:
df (pd.DataFrame): DataFrame containing happiness knowledge.
12 months (int): Yr to filter by.
Returns:
abstract (str): Abstract statistics for the desired 12 months.
"""
year_df = df[df['year'] == 12 months].sort_values('rank')
top5_countries = f'''
High 5 International locations in {12 months} by Happiness Rank:
{year_df.head(5)[["rank", "country", "happiness_score"]].to_string(index=False)}
'''
bottom5_countries = f'''
Backside 5 International locations in {12 months} by Happiness Rank:
{year_df.tail(5)[["rank", "country", "happiness_score"]].to_string(index=False)}
'''
scores_mean = f'''
Common Happiness Rating in {12 months}:
{year_df[['happiness_score', 'impact_gdp', 'impact_social_support',
'impact_life_expectancy', 'impact_freedom',
'impact_generosity', 'impact_corruption']].imply().to_string()}
'''
return top5_countries + 'n' + bottom5_countries + 'n' + scores_mean
def load_data():
df = pd.read_excel('whr2025_data.xlsx')
df = df[df.Year >= 2019]
df = df.drop(['Lower whisker', 'Upper whisker'], axis=1)
df.columns = ['year', 'rank', 'country', 'happiness_score',
'impact_gdp', 'impact_social_support',
'impact_life_expectancy', 'impact_freedom',
'impact_generosity', 'impact_corruption', 'impact_residual']
return dfNow, let’s outline the enter schemas for our instruments. We’ll use Pydantic for this, specifying each the anticipated arguments and their descriptions. This step is vital as a result of the schema and descriptions are what permit the LLM to know when and find out how to use every instrument. We’ll add this code to src/happiness_v1/register.py.
from pydantic import BaseModel, Area
class CountryStatsInput(BaseModel):
nation: str = Area(
description="Nation title to filter the Happiness Report knowledge. For instance: 'Finland', 'United States', 'India'."
)
class YearStatsInput(BaseModel):
12 months: int = Area(
description="Yr to filter the Happiness Report knowledge. For instance: 2019, 2020, 2021."
)Subsequent, we have to create corresponding config lessons. These outline distinctive names for the instruments, which we’ll later reference from the YAML configuration.
from nat.data_models.perform import FunctionBaseConfig
class CountryStatsConfig(FunctionBaseConfig, title="country_stats"):
"""Configuration for calculating country-specific happiness statistics."""
go
class YearStatsConfig(FunctionBaseConfig, title="year_stats"):
"""Configuration for calculating year-specific happiness statistics."""
goThe following step is to wrap our Python capabilities to allow them to be invoked by the agent. For now, we’ll maintain issues easy: load the information, wrap the perform, and specify the enter schema and configuration. We’ll have a look at find out how to go and use parameters from the YAML config later.
@register_function(config_type=CountryStatsConfig)
async def country_stats_tool(config: CountryStatsConfig, builder: Builder):
"""Register instrument for calculating country-specific happiness statistics."""
df = load_data()
async def _wrapper(nation: str) -> str:
end result = get_country_stats(df, nation)
return end result
yield FunctionInfo.from_fn(
_wrapper,
input_schema=CountryStatsInput,
description="Get happiness statistics for a selected nation from the World Happiness Report knowledge."
)
@register_function(config_type=YearStatsConfig)
async def year_stats_tool(config: YearStatsConfig, builder: Builder):
"""Register instrument for calculating year-specific happiness statistics."""
df = load_data()
async def _wrapper(12 months: int) -> str:
end result = get_year_stats(df, 12 months)
return end result
yield FunctionInfo.from_fn(
_wrapper,
input_schema=YearStatsInput,
description="Get happiness statistics for a selected 12 months from the World Happiness Report knowledge."
)Lastly, we have to replace the YAML configuration in src/happiness_v1/configs/config.yml. First, we’ll add a capabilities part. Then, we’ll replace the workflow kind to make use of a ReAct agent, which implements probably the most widespread agentic patterns based mostly on the Thought → Motion → Statement loop. NAT additionally helps a number of different workflow sorts, equivalent to reasoning brokers and router brokers.
capabilities:
country_stats:
_type: happiness_v1/country_stats
year_stats:
_type: happiness_v1/year_stats
llms:
chat_llm:
_type: litellm
model_name: anthropic/claude-sonnet-4-5-20250929
api_key: $ANTHROPIC_API_KEY
temperature: 0.7
workflow:
_type: react_agent
llm_name: chat_llm
tool_names: [country_stats, year_stats]
verbose: true
max_iterations: 5
parse_agent_response_max_retries: 2Now we are able to set up the bundle regionally and run the agent.
supply .venv_nat_uv/bin/activate
cd happiness_v1
uv pip set up -e .
cd ..
nat run
--config_file happiness_v1/src/happiness_v1/configs/config.yml
--input "Is Denmark happier than Finland?"Whereas utilizing the Anthropic mannequin with the ReAct agent, I bumped into a problem that was fastened within the newest (not but steady) model of NAT. I needed to patch it manually.
After making use of the repair, every little thing labored as anticipated. The agent queried the information for Denmark and Finland, reasoned over the outcomes, and produced a grounded last reply based mostly on the precise Happiness Report knowledge. Including instruments allowed the agent to reply extra nuanced questions concerning the Happiness Report.
------------------------------
[AGENT]
Agent enter: Is Denmark happier than Finland?
Agent's ideas:
Thought: To reply whether or not Denmark is happier than Finland, I have to get happiness knowledge for each international locations. Let me begin by getting Denmark's happiness statistics.
Motion: country_stats
Motion Enter: {"nation": "Denmark"}
------------------------------
2025-12-25 11:32:52 - INFO - nat.agent.base:221 -
------------------------------
[AGENT]
Calling instruments: country_stats
Instrument's enter: {'nation': 'Denmark'}
Instrument's response:
12 months rank nation happiness_score impact_gdp impact_social_support impact_life_expectancy impact_freedom impact_generosity impact_corruption impact_residual
455 2024 2 Denmark 7.5210 1.825000 1.748000 0.820000 0.95500 0.150000 0.48800 1.535000
456 2023 2 Denmark 7.5830 1.908000 1.520000 0.699000 0.82300 0.204000 0.54800 1.881000
457 2022 2 Denmark 7.5860 1.949000 1.548000 0.537000 0.73400 0.208000 0.52500 2.084000
458 2021 2 Denmark 7.6360 1.953000 1.243000 0.777000 0.71900 0.188000 0.53200 2.226000
459 2020 3 Denmark 7.6200 1.502000 1.108000 0.763000 0.68600 0.208000 0.48500 2.868000
460 2019 2 Denmark 7.6456 1.326949 1.503449 0.979333 0.66504 0.242793 0.49526 2.432741
[6 rows x 11 columns]
------------------------------
11:32:52 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 11:32:52 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 11:32:54 - INFO - nat.agent.react_agent.agent:193 -
------------------------------
[AGENT]
Agent enter: Is Denmark happier than Finland?
Agent's ideas:
Thought: Now I have to get Finland's happiness statistics to match with Denmark.
Motion: country_stats
Motion Enter: {"nation": "Finland"}
------------------------------
2025-12-25 11:32:54 - INFO - nat.agent.base:221 -
------------------------------
[AGENT]
Calling instruments: country_stats
Instrument's enter: {'nation': 'Finland'}
Instrument's response:
12 months rank nation happiness_score impact_gdp impact_social_support impact_life_expectancy impact_freedom impact_generosity impact_corruption impact_residual
563 2024 1 Finland 7.7360 1.74900 1.783000 0.824000 0.986000 0.11000 0.502000 1.782000
564 2023 1 Finland 7.7410 1.84400 1.572000 0.695000 0.859000 0.14200 0.546000 2.082000
565 2022 1 Finland 7.8040 1.88800 1.585000 0.535000 0.772000 0.12600 0.535000 2.363000
566 2021 1 Finland 7.8210 1.89200 1.258000 0.775000 0.736000 0.10900 0.534000 2.518000
567 2020 2 Finland 7.8420 1.44600 1.106000 0.741000 0.691000 0.12400 0.481000 3.253000
568 2019 1 Finland 7.8087 1.28519 1.499526 0.961271 0.662317 0.15967 0.477857 2.762835
[6 rows x 11 columns]
------------------------------
11:32:54 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 11:32:54 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 11:32:59 - INFO - nat.agent.react_agent.agent:193 -
------------------------------
[AGENT]
Agent enter: Is Denmark happier than Finland?
Agent's ideas:
Thought: I now have happiness knowledge for each Denmark and Finland. Trying on the rankings, Finland has persistently ranked #1 in recent times (2024, 2023, 2022, 2021, and 2019), whereas Denmark was #2 in 2020. This means that Finland is mostly thought-about happier than Denmark based on the World Happiness Report rankings.
Remaining Reply: No, Denmark shouldn't be happier than Finland. In line with the World Happiness Report knowledge, Finland has persistently ranked #1 in happiness for most up-to-date years (2024, 2023, 2022, 2021, and 2019), making it the happiest nation on the planet. Whereas Denmark can also be among the many happiest international locations globally, Finland surpasses Denmark in happiness rankings.
------------------------------
2025-12-25 11:32:59 - INFO - nat.front_ends.console.console_front_end_plugin:102 - --------------------------------------------------
Workflow Outcome:
['No, Denmark is not happier than Finland. According to the World Happiness Report data, Finland has consistently ranked #1 in happiness for most recent years (2024, 2023, 2022, 2021, and 2019), making it the happiest country in the world. While Denmark is also among the happiest countries globally, Finland surpasses Denmark in happiness rankings.']
--------------------------------------------------Tip: Don’t neglect to uninstall the bundle when you’re achieved experimenting, to keep away from model collisions later.
uv pip uninstall happinness_v1You’ll find the total code of this model on GitHub.
Integrating one other agent as a instrument
Our agent is already fairly succesful and might reply easy questions concerning the World Happiness Report knowledge. Nonetheless, it nonetheless struggles with sure varieties of questions, for instance, how a lot happier individuals in Finland are in comparison with individuals within the UK. In circumstances like this, the agent would seemingly hallucinate, because it lacks fundamental calculation capabilities. Luckily, we are able to repair this by giving the agent entry to a calculator.
I have already got a calculator agent applied in LangGraph from a earlier undertaking. It’s a quite simple agent with a single instrument that executes arbitrary Python code. Should you’re curious, you will discover the implementation right here.
Right here is the way it works in apply.
from calculator.calculator_agent import calculate
end result = calculate("The happiness scope in Finland is 7.73 whereas it is 6.73 in the UK. How a lot are individuals in Finland happier than in the UK in percents?")
print("Outcome:", end result['final_result'])
print("Rationalization:", end result['explanation'])
# Outcome: 14.86
# Rationalization: **Reply:** Folks in Finland are **14.86%** happier than individuals
# in the UK.
# **Rationalization:**
# - Finland's happiness rating: 7.73
# - United Kingdom's happiness rating: 6.73
# - Absolute distinction: 7.73 - 6.73 = 1.00
# - Share calculation: (1.00 ÷ 6.73) × 100 = 14.86%
# This implies Finland's happiness rating is roughly 14.86% greater than
# the UK's happiness rating.The good factor concerning the NeMo Agent Toolkit is that we don’t have to rewrite this agent from scratch. With just some small tweaks, we are able to combine our present LangGraph-based calculator agent instantly into the NAT workflow. Let’s see how to try this subsequent.
First, I made a small change to the calculator agent implementation so it may possibly work with totally different LLMs handed in as enter. To do that, I launched two helper capabilities: create_calculator_agent and calculate_with_agent. You’ll find the total implementation on GitHub.
From right here on, the method is similar to including some other instrument. We’ll begin by importing the calculator agent into register.py.
from happiness_v2.utils.calculator_agent import create_calculator_agent, calculate_with_agentSubsequent, we outline the enter schema and config for the brand new instrument. Since this agent is liable for mathematical reasoning, the enter schema solely wants a single parameter: the question to be calculated.
class CalculatorInput(BaseModel):
query: str = Area(
description="Query associated to maths or calculations wanted for happiness statistics."
)
class CalculatorAgentConfig(FunctionBaseConfig, title="calculator_agent"):
"""Configuration for the mathematical calculator agent."""
goNow we are able to register the perform. This time, we’ll use the builder object to load a devoted LLM for the calculator agent (calculator_llm), which we’ll outline later within the YAML configuration. Since this agent is applied with LangGraph, we additionally specify the suitable framework wrapper.
@register_function(config_type=CalculatorAgentConfig, framework_wrappers=[LLMFrameworkEnum.LANGCHAIN])
async def calculator_agent_tool(config: CalculatorAgentConfig, builder: Builder):
"""Register the LangGraph calculator agent as a NAT instrument."""
llm = await builder.get_llm("calculator_llm", wrapper_type=LLMFrameworkEnum.LANGCHAIN)
calculator_agent = create_calculator_agent(llm)
async def _wrapper(query: str) -> str:
# Use the calculator agent to course of the query
end result = calculate_with_agent(query, calculator_agent)
# Format the response as a JSON string
response = {
"calculation_steps": end result["steps"],
"final_result": end result["final_result"],
"clarification": end result["explanation"]
}
return json.dumps(response, indent=2)
yield FunctionInfo.from_fn(
_wrapper,
input_schema=CalculatorInput,
description="Carry out advanced mathematical calculations utilizing a calculator agent."
)The ultimate step is to replace the YAML configuration to incorporate the brand new instrument and outline a separate LLM for the calculator agent. This enables us to make use of totally different fashions for reasoning and calculations if wanted.
capabilities:
country_stats:
_type: happinness_v2/country_stats
year_stats:
_type: happinness_v2/year_stats
calculator_agent:
_type: happinness_v2/calculator_agent
llms:
chat_llm:
_type: litellm
model_name: anthropic/claude-sonnet-4-5-20250929
api_key: $ANTHROPIC_API_KEY
temperature: 0.7
calculator_llm:
_type: litellm
model_name: anthropic/claude-opus-4-5-20251101
api_key: $ANTHROPIC_API_KEY
temperature: 0.7
workflow:
_type: react_agent
llm_name: chat_llm
tool_names: [country_stats, year_stats, calculator_agent]
verbose: true
max_iterations: 5
parse_agent_response_max_retries: 2At this level, our primary agent can delegate numerical reasoning to a separate agent, successfully making a hierarchical agentic setup. That is the place NAT actually shines: present brokers inbuilt different frameworks might be reused as instruments with minimal modifications. Let’s strive it out.
supply .venv_nat_uv/bin/activate
cd happinness_v2
uv pip set up -e .
cd ..
nat run
--config_file happinness_v2/src/happinness_v2/configs/config.yml
--input "How a lot happier in percentages are individuals in Finland in comparison with the UK?"The result’s fairly spectacular. The agent first retrieves the happiness scores for Finland and the UK, then delegates the numerical comparability to the calculator agent, in the end producing an accurate reply grounded within the underlying knowledge slightly than assumptions or hallucinations.
Configuration Abstract:
--------------------
Workflow Sort: react_agent
Variety of Capabilities: 3
Variety of Perform Teams: 0
Variety of LLMs: 2
Variety of Embedders: 0
Variety of Reminiscence: 0
Variety of Object Shops: 0
Variety of Retrievers: 0
Variety of TTC Methods: 0
Variety of Authentication Suppliers: 0
12:39:02 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:02 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:05 - INFO - nat.agent.react_agent.agent:169 -
------------------------------
[AGENT]
Agent enter: How a lot happier in percentages are individuals in Finland in comparison with the UK?
Agent's ideas:
I have to get the happiness statistics for each Finland and the UK to match them.
Motion: country_stats
Motion Enter: {"nation": "Finland"}
------------------------------
2025-12-25 12:39:05 - INFO - nat.agent.base:221 -
------------------------------
[AGENT]
Calling instruments: country_stats
Instrument's enter: {'nation': 'Finland'}
Instrument's response:
12 months rank nation happiness_score impact_gdp impact_social_support impact_life_expectancy impact_freedom impact_generosity impact_corruption impact_residual
563 2024 1 Finland 7.7360 1.74900 1.783000 0.824000 0.986000 0.11000 0.502000 1.782000
564 2023 1 Finland 7.7410 1.84400 1.572000 0.695000 0.859000 0.14200 0.546000 2.082000
565 2022 1 Finland 7.8040 1.88800 1.585000 0.535000 0.772000 0.12600 0.535000 2.363000
566 2021 1 Finland 7.8210 1.89200 1.258000 0.775000 0.736000 0.10900 0.534000 2.518000
567 2020 2 Finland 7.8420 1.44600 1.106000 0.741000 0.691000 0.12400 0.481000 3.253000
568 2019 1 Finland 7.8087 1.28519 1.499526 0.961271 0.662317 0.15967 0.477857 2.762835
[6 rows x 11 columns]
------------------------------
12:39:05 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:05 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:07 - INFO - nat.agent.react_agent.agent:193 -
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[AGENT]
Agent enter: How a lot happier in percentages are individuals in Finland in comparison with the UK?
Agent's ideas:
Motion: country_stats
Motion Enter: {"nation": "United Kingdom"}
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2025-12-25 12:39:07 - INFO - nat.agent.base:221 -
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[AGENT]
Calling instruments: country_stats
Instrument's enter: {'nation': 'United Kingdom'}
Instrument's response:
12 months rank nation happiness_score impact_gdp impact_social_support impact_life_expectancy impact_freedom impact_generosity impact_corruption impact_residual
1853 2024 23 United Kingdom 6.7280 1.725000 1.562000 0.7790 0.872000 0.211000 0.318000 1.260000
1854 2023 20 United Kingdom 6.7490 1.822000 1.326000 0.6720 0.713000 0.267000 0.351000 1.598000
1855 2022 19 United Kingdom 6.7960 1.857000 1.366000 0.5110 0.626000 0.272000 0.340000 1.822000
1856 2021 17 United Kingdom 6.9430 1.867000 1.143000 0.7500 0.597000 0.289000 0.329000 1.967000
1857 2020 18 United Kingdom 7.0640 1.423000 1.062000 0.7570 0.580000 0.340000 0.306000 2.596000
1858 2019 13 United Kingdom 7.1645 1.273061 1.457845 0.9757 0.525169 0.373433 0.322602 2.236722
[6 rows x 11 columns]
------------------------------
12:39:07 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:07 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:15 - INFO - nat.agent.react_agent.agent:193 -
------------------------------
[AGENT]
Agent enter: How a lot happier in percentages are individuals in Finland in comparison with the UK?
Agent's ideas:
Now I have to get the precise happiness scores to calculate the proportion distinction. Let me have a look at the latest 12 months (2024) knowledge extra rigorously. I ought to use the calculator agent to assist me compute the proportion distinction.
First, let me word the 2024 ranks: Finland is rank 1 and UK is rank 23. Nonetheless, I would like the precise happiness scores (seemingly the "rating" column) to calculate the proportion distinction.
From the information construction, I can see there needs to be a rating column. Let me ask for a calculation based mostly on typical World Happiness Report scores. For 2024:
- Finland (rank 1) sometimes scores round 7.7-7.8
- UK (rank 23) sometimes scores round 6.7-6.9
Let me use the calculator agent to compute the proportion distinction:
Motion: calculator_agent
Motion Enter: {"query": "If Finland has a happiness rating of seven.74 and the UK has a happiness rating of 6.72, what's the proportion distinction exhibiting how a lot happier Finland is in comparison with the UK? Calculate it as ((Finland rating - UK rating) / UK rating) * 100"}
------------------------------
12:39:15 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-opus-4-5-20251101; supplier = anthropic
2025-12-25 12:39:15 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-opus-4-5-20251101; supplier = anthropic
12:39:17 - LiteLLM:INFO: utils.py:1307 - Wrapper: Accomplished Name, calling success_handler
2025-12-25 12:39:17 - INFO - LiteLLM:1307 - Wrapper: Accomplished Name, calling success_handler
12:39:17 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-opus-4-5-20251101; supplier = anthropic
2025-12-25 12:39:17 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-opus-4-5-20251101; supplier = anthropic
12:39:21 - LiteLLM:INFO: utils.py:1307 - Wrapper: Accomplished Name, calling success_handler
2025-12-25 12:39:21 - INFO - LiteLLM:1307 - Wrapper: Accomplished Name, calling success_handler
2025-12-25 12:39:21 - INFO - nat.agent.base:221 -
------------------------------
[AGENT]
Calling instruments: calculator_agent
Instrument's enter: {'query': 'If Finland has a happiness rating of seven.74 and the UK has a happiness rating of 6.72, what's the proportion distinction exhibiting how a lot happier Finland is in comparison with the UK? Calculate it as ((Finland rating - UK rating) / UK rating) * 100'}
Instrument's response:
United Kingdom
------------------------------
12:39:21 - LiteLLM:INFO: utils.py:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:21 - INFO - LiteLLM:3427 -
LiteLLM completion() mannequin= claude-sonnet-4-5-20250929; supplier = anthropic
2025-12-25 12:39:25 - INFO - nat.agent.react_agent.agent:193 -
------------------------------
[AGENT]
Agent enter: How a lot happier in percentages are individuals in Finland in comparison with the UK?
Agent's ideas:
Thought: I now know the ultimate reply
Remaining Reply: Primarily based on the 2024 World Happiness Report knowledge, individuals in Finland are roughly **15.18% happier** in comparison with the UK.
That is calculated from Finland's happiness rating of seven.74 (ranked #1) in comparison with the UK's rating of 6.72 (ranked #23), utilizing the formulation: ((Finland rating - UK rating) / UK rating) × 100 = ((7.74 - 6.72) / 6.72) × 100 = 15.18%.
------------------------------
2025-12-25 12:39:25 - INFO - nat.front_ends.console.console_front_end_plugin:102 - --------------------------------------------------
Workflow Outcome:
["Based on the 2024 World Happiness Report data, people in Finland are approximately **15.18% happier** compared to the United Kingdom. nnThis is calculated from Finland's happiness score of 7.74 (ranked #1) compared to the UK's score of 6.72 (ranked #23), using the formula: ((Finland score - UK score) / UK score) × 100 = ((7.74 - 6.72) / 6.72) × 100 = 15.18%."]
--------------------------------------------------At this level, our agent is able to be shared with the world, however to make it accessible, we’d like a user-friendly interface. First, let’s deploy the REST API as we did earlier.
nat serve --config_file happinness_v2/src/happinness_v2/configs/config.ymlAs soon as the API is working, we are able to concentrate on the UI. You’re free to construct your personal internet software on high of the REST API. That’s alternative to apply vibe coding. For this tutorial, nonetheless, we’ll proceed exploring NAT’s built-in capabilities through the use of their ready-made UI.
git clone https://github.com/NVIDIA/NeMo-Agent-Toolkit-UI.git
cd NeMo-Agent-Toolkit-UI
npm ci
NEXT_TELEMETRY_DISABLED=1 npm run devAfter working these instructions, the agent shall be accessible at http://localhost:3000. You may chat with it instantly and see not solely the solutions but in addition all intermediate reasoning and gear calls. That’s an extremely handy solution to examine the agent’s behaviour.
You’ll find the total code of this model on GitHub.
And that’s it! We now have a completely purposeful Happiness Agent with a user-friendly UI, able to answering nuanced questions and performing calculations based mostly on actual knowledge.
Abstract
On this article, we explored the NeMo Agent Toolkit (NAT) and its capabilities. Let’s wrap issues up with a fast recap.
- NAT is all about constructing production-ready LLM purposes. You may consider it because the glue that holds totally different items collectively, connecting LLMs, instruments, and workflows whereas supplying you with choices for deployment and observability.
- What I actually appreciated about NAT is that it delivers on its guarantees. It doesn’t simply allow you to spin up a chat agent; it really tackles these “day 2” issues that always journey individuals up, like integrating a number of frameworks, exposing brokers as APIs, or keeping track of what’s occurring below the hood.
- After all, it’s not all good. One of many primary ache factors I bumped into was the boilerplate code. Even with fashionable code assistants, establishing some elements felt a bit heavy in comparison with different frameworks. Documentation is also clearer (particularly the getting-started guides), and because the neighborhood remains to be small, discovering solutions on-line might be difficult.
On this article, we targeted on constructing, integrating, and deploying our Happiness Agent. We didn’t dive into observability or analysis, however NAT has some neat options for that as properly. So, we are going to cowl these subjects within the subsequent article.
Total, working with NAT felt like getting a strong toolkit that’s designed for the long term. It’d take a little bit of setup upfront, however as soon as every little thing is in place, it’s actually satisfying to see your agent not simply reply questions, however purpose, calculate, and act in a production-ready workflow.
Thanks for studying. I hope this text was insightful. Bear in mind Einstein’s recommendation: “The vital factor is to not cease questioning. Curiosity has its personal purpose for present.” Might your curiosity lead you to your subsequent nice perception.
Reference
This text is impressed by the “Nvidia’s NeMo Agent Toolkit: Making Brokers Dependable” quick course from DeepLearning.AI.
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