fm-rs 0.1.3

Python bindings for Apple's FoundationModels.framework

fm-rs - Python bindings for Apple FoundationModels

Python bindings for fm-rs, enabling on-device AI via Apple Intelligence.

Requirements

• macOS 26.0+ (Tahoe) on Apple Silicon (ARM64)
• Apple Intelligence enabled in System Settings
• Python 3.10+

Installation

pip install fm-rs

From Source

# Requires Rust toolchain
cd bindings/python
uv sync
uv run maturin develop

Quick Start

import fm

# Create the default system language model
model = fm.SystemLanguageModel()

# Check availability
if not model.is_available:
    print("Apple Intelligence is not available")
    exit(1)

# Create a session
session = fm.Session(model, instructions="You are a helpful assistant.")

# Send a prompt
response = session.respond("What is the capital of France?")
print(response.content)

Streaming

import fm

model = fm.SystemLanguageModel()
session = fm.Session(model)

# Stream the response
session.stream_response(
    "Tell me a short story",
    lambda chunk: print(chunk, end="", flush=True)
)
print()  # newline at end

Structured Generation

import fm

model = fm.SystemLanguageModel()
session = fm.Session(model)

# Using a dict schema
schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    },
    "required": ["name", "age"]
}

person = session.respond_structured("Generate a fictional person", schema)
print(f"Name: {person['name']}, Age: {person['age']}")

# Using the Schema builder
schema = (fm.Schema.object()
    .property("name", fm.Schema.string(), required=True)
    .property("age", fm.Schema.integer().minimum(0), required=True))

person = session.respond_structured("Generate a fictional person", schema.to_dict())

Tool Calling

Tools allow the model to call external functions during generation.

import fm

class WeatherTool:
    name = "get_weather"
    description = "Gets the current weather for a location"
    arguments_schema = {
        "type": "object",
        "properties": {
            "city": {"type": "string", "description": "The city name"}
        },
        "required": ["city"]
    }

    def call(self, args):
        city = args.get("city", "Unknown")
        return f"Sunny, 72°F in {city}"

model = fm.SystemLanguageModel()
session = fm.Session(model, tools=[WeatherTool()])

response = session.respond("What's the weather in Paris?")
print(response.content)

Context Management

import fm

model = fm.SystemLanguageModel()
session = fm.Session(model)

# After some conversation...
limit = fm.ContextLimit.default_on_device()
usage = session.context_usage(limit)

print(f"Tokens used: {usage.estimated_tokens}/{usage.max_tokens}")
print(f"Utilization: {usage.utilization:.1%}")

if usage.over_limit:
    # Compact the conversation
    transcript = session.transcript_json
    summary = fm.compact_transcript(model, transcript)
    print(f"Summary: {summary}")

Error Handling

import fm

try:
    model = fm.SystemLanguageModel()
    model.ensure_available()
except fm.DeviceNotEligibleError:
    print("This device doesn't support Apple Intelligence")
except fm.AppleIntelligenceNotEnabledError:
    print("Please enable Apple Intelligence in Settings")
except fm.ModelNotReadyError:
    print("Model is still downloading, try again later")
except fm.ModelNotAvailableError:
    print("Model not available for unknown reason")

API Reference

Classes

• SystemLanguageModel - Entry point for on-device AI
• Session - Maintains conversation context
• GenerationOptions - Controls generation (temperature, max_tokens, etc.)
• Response - Model output
• ToolOutput - Tool invocation result
• ContextLimit - Context window configuration
• ContextUsage - Estimated token usage
• Schema - JSON Schema builder

Enums

• Sampling - Greedy or Random
• ModelAvailability - Available, DeviceNotEligible, AppleIntelligenceNotEnabled, ModelNotReady, Unknown

Functions

• estimate_tokens(text, chars_per_token=4) - Estimate token count
• context_usage_from_transcript(json, limit) - Get context usage
• transcript_to_text(json) - Extract text from transcript
• compact_transcript(model, json) - Summarize conversation

Exceptions

• FmError - Base exception
• ModelNotAvailableError
• DeviceNotEligibleError
• AppleIntelligenceNotEnabledError
• ModelNotReadyError
• GenerationError
• ToolCallError
• JsonError

Notes

• Apple Silicon only: Wheels are built for macOS ARM64 only (Apple Silicon Macs)
• Tool callbacks: May be invoked from non-main threads; avoid UI work in callbacks
• Blocking calls: All calls block until completion; use streaming for long responses
• GIL: Callbacks run under the GIL; keep them short

Development

cd bindings/python
uv sync
uv run maturin develop
uv run pytest tests/

License

MIT