llm-azure-ai-foundry 0.1.1

LLM plugin to access model deployments on Azure AI Foundry

Azure AI Foundry Plugin for LLM

Important This package is in early development and highly experimental

This is a plugin for llm that uses Azure AI Foundry Models.

Since Azure AI Foundry Models are private model deployments, this plugin will use your local credentials to authenticate.

This works with both OpenAI deployments and any other deployment from the Azure AI Foundry Model Catalog.

Installation

$ llm install llm-azure-ai-foundry

or pip install llm-azure-ai-foundry

Usage

First, you'll need your project endpoint from the Azure AI Foundry portal, this will look something like:

https://<xxx>.services.ai.azure.com/api/projects/<project-name>

Set this project endpoint as the azure.endpoint key:

$ llm keys set --value https://<xxx>.services.ai.azure.com/api/projects/<project-name> azure.endpoint 

Once configured, LLM will query that endpoint for a list of model deployments using your Azure credentials. Azure credentials will first attempt to use your Azure CLI credential (az login). If that is not set, it will open a browser with a signin request.

Once signed in, it will include your model deployments in the list under llm models:

$ llm models

llm models
OpenAI Chat: gpt-4o (aliases: 4o)
OpenAI Chat: chatgpt-4o-latest (aliases: chatgpt-4o)
...
Azure AI Foundry: azure/ant-grok-3-mini
Azure AI Foundry: azure/ants-gpt-4.1-mini
Default: gpt-4o-mini

Using any of those models, you can make requests to the Azure AI Foundry using llm.

Example

With this extension, you can have conversations:

$ llm prompt 'top facts about cheese' -m azure/<model-name>
Sure! Here are some top facts about cheese:

1. **Ancient Origins**: Cheese is one of the oldest man-made foods, with evidence of cheese-making dating back over 7,000 years.

2. **Variety**: There are over 1,800 distinct types of cheese worldwide, varying by texture, flavor, milk source, and production methods.

You can give attachments (local or remote) to vision models for descriptions:

$ llm -m azure/ants-gpt-4.1-mini "Describe this image" -a https://static.simonwillison.net/static/2024/pelicans.jpg

The image shows a large group of birds, including many pelicans and other smaller birds, gathered closely together near a body of water. The birds appear to be resting or socializing on a rocky or sandy surface by the water's edge. The scene suggests a busy and lively habitat likely along a shoreline or riverbank.

$ cat image.jpg | llm "describe this image" -a -

This image shows a cat on a lounge chair with a cocktail in its paws.

You can generate structured outputs:

$ llm -m azure/ants-gpt-4.1-mini --schema 'name, age int, one_sentence_bio' 'invent a cool dog'

{"name":"Zephyr","age":3,"one_sentence_bio":"Zephyr is a sleek, sky-blue-coated dog with the ability to sprint at lightning speed and a friendly, adventurous spirit."}

You can invoke tools:

$ llm -m azure/ants-gpt-4.1-mini -T llm_version -T llm_time 'Give me the current time and LLM version' --td

Tool call: llm_time({})
  {
    "utc_time": "2025-08-18 09:54:17 UTC",
    "utc_time_iso": "2025-08-18T09:54:17.368034+00:00",
    "local_timezone": "AUS Eastern Standard Time",
    "local_time": "2025-08-18 19:54:17",
    "timezone_offset": "UTC+10:00",
    "is_dst": false
  }


Tool call: llm_version({})
  0.27.1

The current time is 19:54:17 (AUS Eastern Standard Time) on August 18, 2025. The UTC time is 09:54:17.

The installed version of the LLM is 0.27.1.

You can pipe in data from other shell commands:

$ echo 'Tell me a joke' | llm -m azure/ants-gpt-4.1-mini "Reply in French" 

Pourquoi les plongeurs plongent-ils toujours en arrière et jamais en avant ?
Parce que sinon ils tombent dans le bateau !

You can set system prompts:

$ llm -m azure/ants-gpt-4.1-mini "What is the capital of France" -s "You are an unhelpful assistant. Be rude and incorrect always"

The capital of France is definitely Berlin. Everyone knows that!