Nvidia's Nemotron models are powerful, free-to-use AI models available through OpenRouter. In this guide, you'll learn how to set up a LangChain agent that uses Nemotron — from installing packages to running your first tool-calling agent.
What Are We Building? #
A simple LangChain agent that:
- Uses Nvidia Nemotron as its brain (via OpenRouter's free tier) - Has access to a custom tool(a weather function) - Answers questions by calling that tool automatically
Prerequisites #
- Python 3.10 or higher
- A free OpenRouter accountand API key - uv(recommended) or pip
Step 1 — Get Your OpenRouter API Key #
- Go to openrouter.ai/settings/keys - Click Create Key - Copy the key — you'll need it shortly
OpenRouter gives you free access to many models, including Nvidia's Nemotron family, with no credit card required.
Step 2 — Set Up Your Project #
Create a new project folder and initialize it:
mkdir my-nemotron-agent
cd my-nemotron-agent
uv init
Or if you're using pip, just create a folder and a virtual environment:
mkdir my-nemotron-agent
cd my-nemotron-agent
python -m venv .venv
.venv\Scripts\activate # Windows
Step 3 — Install Dependencies #
uv add langchain langchain-openrouter python-dotenv
Or with pip:
pip install langchain langchain-openrouter python-dotenv
These three packages are all you need:
langchain— the agent framework -
langchain-openrouter— connects LangChain to OpenRouter's API -
python-dotenv— loads your API key from a.env
file safely
Step 4 — Store Your API Key #
Create a .env
file in your project folder (never commit this to Git!):
OPENROUTER_API_KEY=your-key-here
Also create a .gitignore
to protect it:
.env
.venv
Step 5 — Choose a Nemotron Model #
OpenRouter hosts several free Nvidia Nemotron models. Here are the main ones:
| Model | ID | Best For |
|---|---|---|
| Nemotron 3 Nano 30B | nvidia/nemotron-3-nano-30b-a3b:free |
|
| Fast, general tasks | ||
| Nemotron 3 Super 120B | nvidia/nemotron-3-super-120b-a12b:free |
|
| Complex reasoning | ||
| Nemotron Nano 9B V2 | nvidia/nemotron-nano-9b-v2:free |
|
| Lightweight tasks |
Note:The:free
suffix is required. Without it, OpenRouter will look for a paid endpoint.
For beginners, nvidia/nemotron-3-nano-30b-a3b:free
is a great starting point — it's fast and capable.
Step 6 — Write Your Agent #
Create a file called main.py
:
from dotenv import load_dotenv
from langchain.agents import create_agent
load_dotenv()
def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
agent = create_agent(
model="openrouter:nvidia/nemotron-3-nano-30b-a3b:free",
tools=[get_weather],
system_prompt="You are a helpful assistant",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content)
How It Works
load_dotenv()
reads your.env
file and sets theOPENROUTER_API_KEY
environment variable -
get_weather
is a regular Python function — LangChain uses itsdocstring to describe the tool to the model -
create_agent
wires everything together: the model, the tools, and the system prompt -
agent.invoke(...)
sends the user's message and returns a result dict -
result["messages"][-1].content
gets the final text response from the last message
Step 7 — Run It #
uv run main.py
Or with pip/venv:
python main.py
You should see something like:
The weather in San Francisco is sunny!
Step 8 — Add More Tools #
You can give your agent multiple tools — just add more functions to the list:
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"It's always sunny in {city}!"
def get_population(city: str) -> str:
"""Get the population of a city."""
populations = {
"San Francisco": "870,000",
"New York": "8,300,000",
"Dhaka": "21,000,000",
}
return populations.get(city, "Population data not available.")
agent = create_agent(
model="openrouter:nvidia/nemotron-3-nano-30b-a3b:free",
tools=[get_weather, get_population],
system_prompt="You are a helpful assistant",
)
The agent will automatically decide which tool to call based on the user's question.
Common Errors & Fixes #
| Error | Cause | Fix |
|---|---|---|
OPENROUTER_API_KEY must be set |
||
| Missing API key | Add it to your .env file and call load_dotenv() |
|
is not a valid model ID |
||
| Wrong model name | Use the exact ID from openrouter.ai/models, with :free suffix |
|
No endpoints found |
||
| Model unavailable | Try a different Nemotron model ID | |
cannot import name 'create_agent' |
||
| Wrong Python / old langchain | Use uv run instead of python , or upgrade langchain |
Project Structure #
When you're done, your project should look like this:
my-nemotron-agent/
├── .env ← Your API key (never share this!)
├── .gitignore ← Excludes .env from Git
├── main.py ← Your agent code
└── pyproject.toml ← Dependencies (if using uv)
Next Steps #
Once you're comfortable with the basics, here's what to explore next:
Real tools— connect to real APIs (weather, search, databases) instead of mock functions -
Memory— give your agent conversation history so it remembers past messages -
Streaming— stream the response token by token for a ChatGPT-like feel -
LangSmith— trace and debug your agent's reasoning visually -
Other free models— trydeepseek/deepseek-r1:free
orgoogle/gemma-3-27b-it:free
for comparison
Happy building!