llm-apple 0.1.2

LLM plugin for Apple Foundation Models (Apple Intelligence)

llm-apple

LLM plugin for Apple Foundation Models (Apple Intelligence)

This plugin exposes Apple's on-device Foundation Models through the llm CLI tool.

Requirements

• macOS 26 or later
• Apple Intelligence enabled
• Python 3.8 or later
• apple-foundation-models installed

Installation

pip install llm # if llm is not already installed
llm install llm-apple

Usage

Basic usage:

llm -m apple "What is the capital of France?"

With streaming:

llm -m apple "Tell me a story" --stream

With options:

llm -m apple "Write a poem" -o temperature 1.5 -o max_tokens 500

With system instructions:

llm -m apple "What is Python?" --system "You are a helpful programming tutor"

Conversations

The plugin supports conversations, maintaining context across multiple prompts:

# Start a conversation
llm -m apple "My name is Alice" --save conversation1

# Continue the conversation
llm -m apple "What is my name?" --continue conversation1

Available Options

• temperature (float, 0.0-2.0, default: 1.0): Controls randomness in generation
  • 0.0 = deterministic
  • 2.0 = very random
• max_tokens (int, default: 1024): Maximum tokens to generate

System prompts can be provided using llm's built-in --system or -s flag.

Availability

The plugin checks Apple Intelligence availability on startup. If Apple Intelligence is not available, you'll see an error message with details on why.

Common reasons:

• Device not eligible (requires Apple Silicon)
• Apple Intelligence not enabled in Settings
• Model not ready (downloading or initializing)

Examples

Creative writing with higher temperature:

llm -m apple "Write a creative story about a robot" -o temperature 1.8

Factual query with lower temperature:

llm -m apple "Explain quantum computing" -o temperature 0.3

With system prompt for career guidance:

llm -m apple "Should I learn Python or JavaScript?" \
  --system "You are a career counselor specializing in tech"

Development

Running Tests

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=llm_apple --cov-report=html --cov-report=term

The tests use mocks to simulate the Apple Foundation Models API, so they can run on any platform without requiring actual Apple Intelligence hardware.