macafm 0.9.13

Access Apple's on-device Foundation Models via CLI and OpenAI-compatible API

If you find this useful, please ⭐ the repo!   Also check out Vesta AI Explorer! — my full-featured native macOS AI app.

Install

	Stable (v0.9.12)	Nightly (afm-next)
Homebrew	brew install scouzi1966/afm/afm	brew install scouzi1966/afm/afm-next
pip	pip install macafm	pip install --extra-index-url https://maclocal-ai.pages.dev/afm/wheels/simple/ macafm-next
Release notes	v0.9.12	v0.9.13-next

🔨 Build from source — one command

Clone and build everything (submodules, patches, WebUI, release binary) with a single script. It checks your toolchain, auto-installs what it can (Node via Homebrew), and tells you what to install manually (Xcode Command Line Tools) — no AI agent or project knowledge required. The script initializes submodules for you, so a plain git clone is all you need.

git clone https://github.com/scouzi1966/maclocal-api.git
cd maclocal-api
./build.sh

That's it. The afm binary lands in .build/release/afm. Add --install to also install it to /usr/local/bin (on your PATH by default; uses sudo if needed):

./build.sh --install

Run ./build.sh --help for all options (--debug, --skip-webui, --yes for non-interactive/CI).

[!TIP] Switching between stable and nightly:

brew unlink afm && brew install scouzi1966/afm/afm-next   # switch to nightly
brew unlink afm-next && brew link afm                      # switch back to stable
ASSUMES you did a brew install scouzi1966/afm/afm previously

Install a previous version

Older stable releases are kept as pinned formulae in the Homebrew tap and as version-pinned wheels on PyPI. Useful for reproducing an issue against a specific build or rolling back without waiting for a new release.

Homebrew (pinned stable formulae): afm@<version> — available for 0.9.0, 0.9.1, 0.9.3–0.9.10.

brew install scouzi1966/afm/afm@0.9.10      # install v0.9.10
brew uninstall afm                          # if current afm is already installed
brew link afm@0.9.10                        # expose `afm` on PATH
afm --version                               # → v0.9.10

Homebrew (pinned nightly formulae): afm-next@<full-version> — e.g. afm-next@0.9.11-next.9c3225e.20260418. Lists of available pinned nightlies are at github.com/scouzi1966/homebrew-afm.

brew install scouzi1966/afm/afm-next@0.9.11-next.9c3225e.20260418

pip (version-pinned wheels): any published release.

pip install macafm==0.9.10                  # previous stable
pip install --extra-index-url https://maclocal-ai.pages.dev/afm/wheels/simple/ \
  macafm-next==0.9.13.dev20260621           # pinned nightly

[!NOTE]

31 Mar, 2026. AFM was pinned to an older version of https://github.com/huggingface/swift-huggingface. I have now pinned to the latest which uses hub for model cache. The older version downloaded models to the ~/Documents/Huggingface folder which was causing some pain with iCloud sync. They are now stored under ~/.cache which is not in iCloud scope. the TLDR is that models will be re-downloaded again. You can manually delete the older models located in ~/Documents/Huggingface to regain some valuable space available (spring cleaning!). Please report any issues.

Attention M-series Mac AI enthusiasts! You don't need to be a Swift developer to explore. Vibe coding really allows anyone to participate in this project. A lot of the hype is real! It does work.

Fork this repo first, then clone your fork to submit PRs:

git clone https://github.com/<your-username>/maclocal-api.git   
cd maclocal-api
claude
/build-afm

To just experiment locally

git clone https://github.com/scouzi1966/maclocal-api.git   
cd maclocal-api
claude
/build-afm

/build-afm is an AI skill that builds for the first time so that you can start coding

Start vibe coding! I will add support for skills with more coding agents in the future.

afm — Run Any MLX LLM on Your Mac, 100% Local

Extensive testing of Qwen3.5-35B-A3B with afm. Uses an experimental technique with Claude and Codex as judges for evaluation scoring. Click the link below to view test results.

afm-next Nightly Test Report — Qwen3.5-35B-A3B Focus

Run open-source MLX models or Apple's on-device Foundation Model through an OpenAI-compatible API. Built entirely in Swift for maximum Metal GPU performance. No Python runtime, no cloud, no API keys.

What's new in afm-next

[!IMPORTANT] The nightly build is the future stable release. It includes everything in v0.9.12 plus:

• ⚡ Speculative decoding — up to +52% faster decode, quality-preserving. Two model-specific options:
  • --mtp for Qwen3.6-27B (self-speculative MTP head) → ~+52%
  • --eagle3 <drafter> for dense Gemma4-31B (EAGLE3 drafter) → ~+30%
  • Both work for streaming and non-streaming and preserve greedy-decode quality (bit-exact on short generations; may differ token-for-token on longer ones). See ⚡ Speculative Decoding below.
• Faster long context — backported adaptive-block SDPA (~+10% decode @16k), eager <think>-tag streaming (reasoning TTFT ~610ms→~346ms), and Metal-kernel prewarm for a faster cold first token.
• Swift 6 language mode migration.

[!TIP] 🙏 Huge thanks to @jesserobbins — first-time contributor, landed two substantial features in this cycle (Vision OCR + Speech transcription). Both PRs brought afm's Apple-native capabilities from the CLI into first-class HTTP APIs. Contributions of this size and quality from a new contributor are rare and appreciated.

Quick Start

# Run any MLX model with WebUI
afm mlx -m mlx-community/Qwen3.5-35B-A3B-4bit -w

# Or any smaller model
afm mlx -m mlx-community/gemma-3-4b-it-8bit -w

# Chat from the terminal (auto-downloads from Hugging Face)
afm mlx -m Qwen3-0.6B-4bit -s "Explain quantum computing"

# Interactive model picker (lists your downloaded models)
MACAFM_MLX_MODEL_CACHE=/path/to/models afm mlx -w

# Apple's on-device Foundation Model with WebUI
afm -w

⚡ Speculative Decoding

afm can decode up to +52% faster while preserving greedy-decode quality — output is bit-exact to normal greedy decoding on short generations and stays greedy-quality on longer ones (it may differ token-for-token there). There are two options, one per model family. Each needs a specific checkpoint/drafter (a plain 4-bit conversion won't work):

Running…	Flag	Speedup	Get the model (Hugging Face)
Qwen3.6-27B	--mtp	~+52%	Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed — ships the mtp.safetensors head¹
Gemma4-31B (dense)	--eagle3 <drafter-dir>	~+30%	verifier mlx-community/gemma-4-31b-it-4bit + drafter RedHatAI/gemma-4-31B-it-speculator.eagle3

¹ The plain mlx-community/Qwen3.6-27B-4bit conversion strips the MTP head, so --mtp silently no-ops there — you must use the checkpoint above.

[!NOTE] Both fast paths engage only for greedy (temperature: 0), text-only requests (streaming or non-streaming). Anything with tools / response_format / logprobs / stop, or --concurrent N≥2, silently falls back to normal autoregressive decode — output is always correct either way.

1. Qwen3.6-27B — MTP (--mtp)

Self-speculative decoding using Qwen3.6's in-model MTP head — no separate draft model needed.

# afm auto-downloads the model from Hugging Face on first run
afm mlx -m Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed --mtp --port 9999

# Then call it like any OpenAI endpoint (greedy → MTP fast path engages)
curl -s http://127.0.0.1:9999/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model": "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed",
  "messages": [{"role":"user","content":"Explain how a CPU cache works in 4 sentences."}],
  "temperature": 0, "max_tokens": 200
}'

2. Gemma4-31B dense — EAGLE3 (--eagle3 <drafter-dir>)

Speculative decoding for the dense Gemma4-31B verifier using an EAGLE3 drafter. Pass the drafter as a local directory (download it first):

# 1) download the EAGLE3 drafter from Hugging Face
huggingface-cli download RedHatAI/gemma-4-31B-it-speculator.eagle3 \
  --local-dir ~/models/gemma-4-31B-eagle3

# 2) run the dense verifier with the drafter
afm mlx -m mlx-community/gemma-4-31b-it-4bit \
  --eagle3 ~/models/gemma-4-31B-eagle3 --port 9999

Tuning: drafts-per-round defaults to 2 (the sweet spot); override with AFM_EAGLE3_BLOCK=3. The MoE Gemma4 (26B-A4B) is not accelerated by spec-decode — --eagle3 only helps the dense 31B.

📖 Full usage, tuning, debugging/profiling flags, and benchmarks: docs/decode-optimizations.md

Why AFM for agents

afm is built for agentic clients — OpenCode, OpenClaw, Cline, Continue.dev, Aider, Cursor, Hermes — that drive multi-turn tool-using LLM loops against a local OpenAI-compatible endpoint. The capabilities below are already in the box:

Capability	What it gets you	Where it lives
7+ tool-call formats, auto-detected	json, lfm2, xmlFunction (Qwen3-Coder), glm4, gemma, kimiK2, minimaxM2 picked from model_type in config.json — no per-model tuning	MLXModelService.swift:inferToolCallFormat
afm_adaptive_xml parser	JSON-in-XML fallback, type coercion, nullable schema flatten, fuzzy tool-name match — survives the malformed XML real models emit	Models/ToolCallStreamingRuntime.swift
tool_choice: auto / none / required / named function	Standard OpenAI semantics; named-function forcing routed end-to-end	Models/OpenAIRequest.swift:ToolChoice
Streaming tool-call deltas	Token-level start/end tag detection; content outside tool calls streams normally	Controllers/MLXChatCompletionsController.swift
<think> + harmony channel reasoning extraction	Routes Qwen/DeepSeek <think>…</think> and gpt-oss <|channel|>analysis… into reasoning_content so the WebUI/agent can show it separately	Controllers/MLXChatCompletionsController.swift:extractThinkTags / extractHarmonyChannels
Strict json_schema + xgrammar EBNF	Guaranteed-valid JSON via token-level grammar enforcement when --enable-grammar-constraints is on	Models/XGrammarService.swift
--guided-json server default	One CLI flag pins a schema across every chat request that omits its own response_format (Foundation + MLX backends)	Sources/MacLocalAPI/main.swift
Deterministic seed, logprobs, top_logprobs	All sampling controls (temperature, top_p, top_k, min_p, repetition_penalty, presence_penalty, seed, logprobs+top_logprobs up to 20) plumbed end-to-end	Models/OpenAIRequest.swift + Scripts/patches/Evaluate.swift
Radix-tree prefix KV cache	--enable-prefix-caching reuses KV across turns — agent loops with stable system prompts get prefill for free	Models/RadixTreeCache.swift
4/8-bit KV quantization	`--kv-bits 4	8` cuts memory ~2-4× on long-context turns
Concurrent batch decode	--concurrent N runs N requests through one model with fair queueing; vLLM-style metrics expose queue depth	Models/BatchScheduler.swift
vLLM-namespaced Prometheus /metrics	afm:max_concurrent_slots, afm:num_requests_running, afm:num_requests_waiting, plus per-request token/timing histograms	Controllers/MetricsController.swift
Retry-After: 2 on 503	Tells well-behaved agents (LangChain, OpenAI SDK) when to retry — no thundering herd	Controllers/MLXChatCompletionsController.swift
Multi-backend gateway mode	--gateway discovers Ollama / LM Studio / Jan on the same machine and proxies them under one OpenAI surface, normalizing reasoning → reasoning_content	Models/BackendDiscoveryService.swift + BackendProxyService.swift
X-Request-ID / OpenAI-Request-ID echo	Inbound IDs are honored; otherwise minted as req_<uuid12>. Echoed on every response and inside error.request_id for retry correlation	Server.swift:RequestIDMiddleware
stream_options.include_usage honored	Suppress the final usage chunk when the client doesn't want it (matches OpenAI strict mode)	Models/OpenAIRequest.swift:StreamOptions
parallel_tool_calls: false honored	Truncate to a single tool call per turn for agents that want serial execution	Controllers/MLXChatCompletionsController.swift:finalizeAssistantTurn
Speech (transcribe + TTS) and Vision OCR	/v1/audio/transcriptions, /v1/audio/speech, /v1/ocr — agents can hand off audio/image inputs without a separate service	Controllers/SpeechAPIController.swift, VisionAPIController.swift
On-device embeddings for RAG	/v1/embeddings from Apple's NaturalLanguage model — OpenAI-compatible vectors for retrieval/semantic search. Runs as a dedicated afm embed server (:9998), separate from the chat endpoint	Controllers/EmbeddingsController.swift
Per-client config generators	afm mlx -m <model> --openclaw-config prints a paste-ready provider config; cookbook recipes in docs/clients/ cover OpenCode, OpenClaw, Cline, Continue.dev, Aider, Cursor, Hermes	Sources/MacLocalAPI/main.swift:printOpenClawConfig

See docs/clients/ for one-page recipes per agent.

Use with OpenCode

OpenCode is a terminal-based AI coding assistant. Connect it to afm for a fully local coding experience — no cloud, no API keys. No Internet required (other than initially download the model of course!)

1. Configure OpenCode (~/.config/opencode/opencode.json):

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "macafm (local)",
      "options": {
        "baseURL": "http://localhost:9999/v1"
      },
      "models": {
        "mlx-community/Qwen3-Coder-Next-4bit": {
          "name": "mlx-community/Qwen3-Coder-Next-4bit"
        }
      }
    }
  }
}

2. Start afm with a coding model:

afm mlx -m mlx-community/Qwen3-Coder-Next-4bit -t 1.0 --top-p 0.95 --max-tokens 8192

3. Launch OpenCode and type /connect. Scroll down to the very bottom of the provider list — macafm (local) will likely be the last entry. Select it, and when prompted for an API key, enter any value (e.g. x) — tokenized access is not yet implemented in afm so the key is ignored. All inference runs locally on your Mac's GPU.

---

28+ MLX Models Tested

28 models tested and verified including Qwen3, Gemma 3/3n, GLM-4/5, DeepSeek V3, LFM2, SmolLM3, Llama 3.2, MiniMax M2.5, Nemotron, and more. See test reports.

---

⭐ Star History

Related Projects

• Vesta AI Explorer — full-featured native macOS AI chat app
• AFMTrainer — LoRA fine-tuning wrapper for Apple's toolkit (Mac M-series & Linux CUDA)
• Apple Foundation Model Adapters — Apple's adapter training toolkit

🌟 Features

• 🔗 OpenAI API Compatible - Works with existing OpenAI client libraries and applications
• 🧠 MLX Local Models - Run any Hugging Face MLX model locally (Qwen, Gemma, Llama, DeepSeek, GLM, and 28+ tested models)
• 🌐 API Gateway - Auto-discovers and proxies Ollama, LM Studio, Jan, and other local backends into a single API
• ⚡ LoRA adapter support - Supports fine-tuning with LoRA adapters using Apple's tuning Toolkit
• 📱 Apple Foundation Models - Uses Apple's on-device 3B parameter language model
• 👁️ Vision OCR - Extract text from images and PDFs using Apple Vision via CLI and HTTP (afm vision, /v1/vision/ocr)
• 🔢 Embeddings - OpenAI-compatible embeddings from Apple's NaturalLanguage model, on-device, via a dedicated server (afm embed, /v1/embeddings)
• 🖥️ Built-in WebUI - Chat interface with model selection (afm -w)
• 🔒 Privacy-First - All processing happens locally on your device
• ⚡ Fast & Lightweight - No network calls, no API keys required
• 🛠️ Easy Integration - Drop-in replacement for OpenAI API endpoints
• 📊 Token Usage Tracking - Provides accurate token consumption metrics

📋 Requirements

• **macOS 26 (Tahoe) or later
• Apple Silicon Mac (M1/M2/M3/M4 series)
• Apple Intelligence enabled in System Settings
• **Xcode 26 (for building from source)

🚀 Quick Start

Installation

Option 1: Homebrew (Recommended)

# Add the tap
brew tap scouzi1966/afm

# Install AFM
brew install afm

# Verify installation
afm --version

Option 2: pip (PyPI)

# Install from PyPI
pip install macafm

# Verify installation
afm --version

Option 3: Build from Source

# Clone the repository (build.sh initializes submodules for you)
git clone https://github.com/scouzi1966/maclocal-api.git
cd maclocal-api

# Build everything from scratch (checks/installs deps + patches + webui + release build)
./build.sh

# Or skip webui if you don't have Node.js
./build.sh --skip-webui

# Or use make (patches + release build, no webui)
make

# Run
./.build/release/afm --version

Running

# API server only (Apple Foundation Model on port 9999)
afm

# API server with WebUI chat interface
afm -w

# WebUI + API gateway (auto-discovers Ollama, LM Studio, Jan, etc.)
afm -w -g

# Custom port with verbose logging
afm -p 8080 -v

# Show help
afm -h

MLX Local Models

Run open-source models locally on Apple Silicon using MLX:

# Run a model with single prompt
afm mlx -m mlx-community/Qwen2.5-0.5B-Instruct-4bit -s "Explain gravity"

# Start MLX model with WebUI
afm mlx -m mlx-community/gemma-3-4b-it-8bit -w

# Interactive model picker (lists downloaded models)
afm mlx -w

# MLX model as API server
afm mlx -m mlx-community/Llama-3.2-1B-Instruct-4bit -p 8080

# Pipe mode
cat essay.txt | afm mlx -m mlx-community/Qwen3-0.6B-4bit -i "Summarize this"

# MLX help
afm mlx --help

Models are downloaded from Hugging Face on first use and cached locally. Any model from the mlx-community collection is supported.

📡 API Endpoints

Chat Completions

POST /v1/chat/completions

Compatible with OpenAI's chat completions API.

curl -X POST http://localhost:9999/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "foundation",
    "messages": [
      {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

List Models

GET /v1/models

Returns available Foundation Models.

curl http://localhost:9999/v1/models

Vision OCR

POST /v1/vision/ocr

Runs Apple Vision OCR against local files, uploads, base64 payloads, data: URLs, and OpenAI-style image inputs.

curl -X POST http://localhost:9999/v1/vision/ocr \
  -H "Content-Type: application/json" \
  -d '{
    "file": "/tmp/invoice.pdf",
    "recognition_level": "accurate",
    "languages": ["en-US"],
    "max_pages": 10
  }'

The endpoint returns structured JSON with per-document text, per-page text, text blocks, detected tables, document hints, and a top-level combined_text field. See docs/vision-ocr-api.md for request formats, options, and response details.

Embeddings

POST /v1/embeddings

Serves OpenAI-compatible embeddings backed by Apple's NaturalLanguage contextual model, fully on-device. Started with afm embed (default port 9998), separate from the chat server.

afm embed                       # start the embeddings server on port 9998

curl -X POST http://localhost:9998/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "apple-nl-contextual-en",
    "input": "The quick brown fox"
  }'

Accepts a string, an array of strings, or pre-tokenized ids; supports float/base64 output and Matryoshka-style dimensions truncation. See docs/embeddings-api.md for models, request fields, response shape, and error semantics.

Health Check

GET /health

Server health status endpoint.

curl http://localhost:9999/health

💻 Usage Examples

Python with OpenAI Library

from openai import OpenAI

# Point to your local MacLocalAPI server
client = OpenAI(
    api_key="not-needed-for-local",
    base_url="http://localhost:9999/v1"
)

response = client.chat.completions.create(
    model="foundation",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ]
)

print(response.choices[0].message.content)

Vision OCR from OpenAI-Compatible Clients

The OCR endpoint also accepts OpenAI-style multimodal payloads. This is useful when your client already sends messages[].content[] parts with image_url.

curl -X POST http://localhost:9999/v1/vision/ocr \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Extract the invoice text"},
        {
          "type": "image_url",
          "image_url": {
            "url": "data:application/pdf;base64,..."
          }
        }
      ]
    }],
    "recognition_level": "accurate",
    "languages": ["en-US"]
  }'

Foundation chat requests can also auto-run Apple Vision OCR before prompting the model when:

• the request includes image content
• the request includes the built-in apple_vision_ocr tool
• tool_choice is auto, required, omitted, or explicitly selects that tool

JavaScript/Node.js

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'not-needed-for-local',
  baseURL: 'http://localhost:9999/v1',
});

const completion = await openai.chat.completions.create({
  messages: [{ role: 'user', content: 'Write a haiku about programming' }],
  model: 'foundation',
});

console.log(completion.choices[0].message.content);

curl Examples

# Basic chat completion
curl -X POST http://localhost:9999/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "foundation",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

# With temperature control
curl -X POST http://localhost:9999/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "foundation",
    "messages": [{"role": "user", "content": "Be creative!"}],
    "temperature": 0.8
  }'

Single Prompt & Pipe Examples

# Single prompt mode
afm -s "Explain quantum computing"

# Piped input from other commands
echo "What is the meaning of life?" | afm
cat file.txt | afm
git log --oneline | head -5 | afm

# Custom instructions with pipe
echo "Review this code" | afm -i "You are a senior software engineer"

🏗️ Architecture

MacLocalAPI/
├── Package.swift                    # Swift Package Manager config
├── Sources/MacLocalAPI/
│   ├── main.swift                   # CLI entry point & ArgumentParser
│   ├── Server.swift                 # Vapor web server configuration
│   ├── Controllers/
│   │   └── ChatCompletionsController.swift  # OpenAI API endpoints
│   └── Models/
│       ├── FoundationModelService.swift     # Apple Foundation Models wrapper
│       ├── OpenAIRequest.swift              # Request data models
│       └── OpenAIResponse.swift             # Response data models
└── README.md

🔧 Configuration

Command Line Options

OVERVIEW: macOS server that exposes Apple's Foundation Models through
OpenAI-compatible API

Use -w to enable the WebUI, -g to enable API gateway mode (auto-discovers and
proxies to Ollama, LM Studio, Jan, and other local LLM backends).

USAGE: afm <options>
       afm mlx [<options>]      Run local MLX models from Hugging Face
       afm vision <image>       OCR text extraction from images/PDFs

OPTIONS:
  -s, --single-prompt <single-prompt>
                          Run a single prompt without starting the server
  -i, --instructions <instructions>
                          Custom instructions for the AI assistant (default:
                          You are a helpful assistant)
  -v, --verbose           Enable verbose logging
  --no-streaming          Disable streaming responses (streaming is enabled by
                          default)
  -a, --adapter <adapter> Path to a .fmadapter file for LoRA adapter fine-tuning
  -p, --port <port>       Port to run the server on (default: 9999)
  -H, --hostname <hostname>
                          Hostname to bind server to (default: 127.0.0.1)
  -t, --temperature <temperature>
                          Temperature for response generation (0.0-1.0)
  -r, --randomness <randomness>
                          Sampling mode: 'greedy', 'random',
                          'random:top-p=<0.0-1.0>', 'random:top-k=<int>', with
                          optional ':seed=<int>'
  -P, --permissive-guardrails
                          Permissive guardrails for unsafe or inappropriate
                          responses
  -w, --webui             Enable webui and open in default browser
  -g, --gateway           Enable API gateway mode: discover and proxy to local
                          LLM backends (Ollama, LM Studio, Jan, etc.)
  --prewarm <prewarm>     Pre-warm the model on server startup for faster first
                          response (y/n, default: y)
  --version               Show the version.
  -h, --help              Show help information.

Note: afm also accepts piped input from other commands, equivalent to using -s
with the piped content as the prompt.

Environment Variables

The server respects standard logging environment variables:

• LOG_LEVEL - Set logging level (trace, debug, info, notice, warning, error, critical)

⚠️ Limitations & Notes

• Model Scope: Apple Foundation Model is a 3B parameter model (optimized for on-device performance)
• macOS 26+ Only: Requires the latest macOS with Foundation Models framework
• Apple Intelligence Required: Must be enabled in System Settings
• Token Estimation: Uses word-based approximation for token counting (Foundation model only; proxied backends report real counts)

🔍 Troubleshooting

"Foundation Models framework is not available"

1. Ensure you're running **macOS 26 or later
2. Enable Apple Intelligence in System Settings → Apple Intelligence & Siri
3. Verify you're on an Apple Silicon Mac
4. Restart the application after enabling Apple Intelligence

Server Won't Start

1. Check if the port is already in use: lsof -i :9999
2. Try a different port: afm -p 8080
3. Enable verbose logging: afm -v

Build Issues

1. Ensure you have **Xcode 26 installed
2. Update Swift toolchain: xcode-select --install
3. Clean and rebuild: swift package clean && swift build -c release

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

# Clone the repo (build.sh initializes submodules for you)
git clone https://github.com/scouzi1966/maclocal-api.git
cd maclocal-api

# Full build from scratch (submodules + patches + webui + release)
./build.sh

# Or for debug builds during development
./build.sh --debug --skip-webui

# Run with verbose logging
./.build/debug/afm -w -g -v

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Apple for the Foundation Models framework
• The Vapor Swift web framework team
• OpenAI for the API specification standard
• The Swift community for excellent tooling

📞 Support

If you encounter any issues or have questions:

1. Check the Troubleshooting section
2. Search existing GitHub Issues
3. Create a new issue with detailed information about your problem

🗺️ Roadmap

• Streaming response support
• MLX local model support (28+ models tested)
• Multiple model support (API gateway mode)
• Web UI for testing (llama.cpp WebUI integration)
• Vision OCR subcommand
• Function/tool calling (OpenAI-compatible, multiple formats)
• Performance optimizations
• BFCL integration for automated tool calling validation
• Docker containerization (when supported)

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