# Running Nvidia Nemotron on LangChain via OpenRouter > Source: > Published: 2026-05-20 09:46:09+00:00 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 account](https://openrouter.ai/)and API key - [uv](https://docs.astral.sh/uv/)(recommended) or pip ## Step 1 — Get Your OpenRouter API Key - Go to [openrouter.ai/settings/keys](https://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 # source .venv/bin/activate # Mac/Linux ``` ## 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` : ``` python from dotenv import load_dotenv from langchain.agents import create_agent # Load the OPENROUTER_API_KEY from your .env file load_dotenv() # Define a tool — any plain Python function works! def get_weather(city: str) -> str: """Get weather for a given city.""" return f"It's always sunny in {city}!" # Create the agent agent = create_agent( model="openrouter:nvidia/nemotron-3-nano-30b-a3b:free", tools=[get_weather], system_prompt="You are a helpful assistant", ) # Run the agent result = agent.invoke( {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]} ) # Print the final response print(result["messages"][-1].content) ``` ### How It Works - `load_dotenv()` reads your`.env` file and sets the`OPENROUTER_API_KEY` environment variable - `get_weather` is a regular Python function — LangChain uses its**docstring** 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: ``` php 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**— try`deepseek/deepseek-r1:free` or`google/gemma-3-27b-it:free` for comparison Happy building!