Level Up Your LLM Game: Connecting OpenRouter with Python in VS Code

 Are you eager to explore the power of Large Language Models (LLMs) but find yourself limited by your local hardware? If you’re like me, rocking an 8GB RAM setup without a dedicated GPU or TPU, directly running demanding models can be a challenge. That’s where OpenRouter steps in as a game-changer!

OpenRouter provides a fantastic unified interface to access a wide range of LLMs. It’s the perfect solution for Proof of Concept (POC) projects and experimentation without straining your system.

This guide will walk you through the simple steps to configure and use OpenRouter within your Python environment in VS Code. Let’s dive in!

Prerequisites:

• Python Installed: Ensure you have Python installed on your system.
• VS Code Installed: You’ll need Visual Studio Code as your code editor.

Configuration Steps:

1. Install the requests Library: Open your VS Code terminal (Ctrl+or Cmd+). This library allows you to make HTTP requests in Python. Run the following command: Bashpip install requests
2. Install the openai Library (Optional but Recommended): While we’ll be directly interacting with the OpenRouter API using requests, installing the openai library can be beneficial for future flexibility if you decide to work directly with OpenAI models as well. Run: Bashpip install openai
3. Create Your OpenRouter API Key: Head over to the OpenRouter website and sign up or log in. Navigate to the API Keys section and generate a new API key. Keep this key secure and do not share it.
4. Write Your Python Code: Now, let’s create a Python file (e.g., openrouter_example.py) in VS Code and paste the following code, replacing the placeholders with your actual API key and website details: Python import requests
import json
response = requests.post(
url="https://openrouter.ai/api/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_OPENROUTER_API_KEY",
"Content-Type": "application/json",
"HTTP-Referer": "YOUR_SITE_URL", # Optional: Your website URL for rankings
"X-Title": "YOUR_SITE_NAME", # Optional: Your website name for rankings
},
data=json.dumps({
"model": "google/gemini-2.0-flash-exp:free",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is the meaning of life?"
}
]
}
],
})
)
Print the response data
print(response.json())
   • Explanation of the Code:
5. Run Your Code: Save the openrouter_example.py file. In your VS Code terminal, navigate to the directory where you saved the file and run: Bashpython openrouter_example.py You should see a JSON response printed in your terminal containing the LLM’s answer to your question!

Next Steps and Exploration:

Congratulations! You’ve successfully connected to OpenRouter using Python and VS Code. Now you can start experimenting with different models, prompts, and parameters.

Here are some ideas for further exploration:

• Try different models: Explore the OpenRouter models list and change the model parameter in your code to see how different LLMs respond.
• Experiment with prompts: Modify the content of your messages to ask different questions or provide different instructions.
• Explore API parameters: Refer to the OpenRouter API documentation to learn about other available parameters you can include in your request (e.g., max_tokens, temperature).
• Build more complex applications: Integrate OpenRouter into your Python projects for tasks like text generation, summarization, translation, and more.

OpenRouter opens up a world of possibilities for working with cutting-edge LLMs without requiring high-end hardware. Happy coding and exploring!