Getting Started with LLM

I'm a big fan of Simon Willison's work, and recently, I had a need to do some simple inference using gpt-5 on Azure AI Foundry. In the past I've used GitHub Models for this type of work, and that works pretty well up to a certain point:

gh models run openai/gpt-4.1 "Tell me a joke"

However, I was running into a few challenges with token limits, and usage limits on GitHub Models, so I wanted to switch to Azure AI Foundry instead - I thought it'd be a great opportunity to try out Simon's hugely popular LLM CLI tool.

llm "Tell me a joke"

1. Installation

Installation was a breeze - with a simple:

pip install llm

2. Storing an API key

Next, I needed to store my Azure AI Foundry API key, retrieved from the Azure AI Foundry project overview:

By default, llm keys set will store an OpenAI API key, so adding azure on the end allowed me to differenciate it later

llm keys set azure

When entering the key, the text isn't echoed back to the console, but you can check the keys.json file in the next step if you need to confirm.

3. Configuring the Azure AI Foundry Models

Before the next step, you'll want to check the path to your llm configuration files:

PS C:\Users\gugregor> llm keys path
C:\Users\gugregor\AppData\Roaming\io.datasette.llm\keys.json

...so you can open the folder, and create a new (blank text) file called extra-openai-models.yaml. This file will be used to define any models from Azure AI Foundry.

In this YAML file, you'll want to provide the details of your Azure AI Foundry model deployments:

- model_id: aoai/gpt-5
  model_name: gpt-5
  api_base: https://<foundry resource>.openai.azure.com/openai/v1/
  api_key_name: azure

- model_id: aoai/gpt-5-mini
  model_name: gpt-5-mini
  api_base: https://<foundry resource>.openai.azure.com/openai/v1/
  api_key_name: azure

- model_id: aoai/gpt-5-nano
  model_name: gpt-5-nano
  api_base: https://<foundry resource>.openai.azure.com/openai/v1/
  api_key_name: azure

- model_id: aoai/gpt-4.1
  model_name: gpt-4.1
  api_base: https://<foundry resource>.openai.azure.com/openai/v1/
  api_key_name: azure

Some important Azure-specific considerations:

• model_id is essentially the 'friendly name' for the model within the LLM tool. I chose a aoai/ prefix, so I could differenciate between Azure models, and OpenAI API models.
• model_name is the Azure deployment name - which could be different from the model name (although it makes sense to keep it the same where possible).
• api_base needs to include the openai/v1/ suffix, because the LLM tool isn't able to accept the api_version from the legacy API. If you're not sure where to find the , check in the Azure AI Foundry project overview:
• api_key_name is the name of the key you stored in step 2 (I used azure, but you can use whatever you like, as long as they match)

Don't forget to save the YAML file, once you've added all the above details.

4. Testing the Azure AI Foundry model

Now that you've configured the extra models, you should be able to run llm using the following command:

llm "Tell me a joke" -m aoai/gpt-5-mini

the -m parameter specifies the model that you've defined in the YAML file from step 3.

5. Setting the default model (optional, recommended)

If you're going to use Azure AI Foundry models regularly, then you may want to change the default model over like this:

llm models default aoai/gpt-5-mini

That way, you don't need to specify the model using the -m parameter each time, so you get an easy to remember, concise command:

llm "Tell me a joke"

Conclusion

LLM is a super handy utility which can easily be configured to use Azure AI Foundry models with minimal effort. I can see myself using it for a range of simple command-line tasks, and potentially even for more advanced scripting. It doesn't have the power or advanced features of something like Semantic Kernel, but that level of functionality isn't always required. Try it out today!

LLM - GitHub.com
https://github.com/simonw/llm