{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mlx","slug":"mlx","name":"MLX","type":"framework","url":"https://github.com/ml-explore/mlx","page_url":"https://unfragile.ai/mlx","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mlx__cap_0","uri":"capability://planning.reasoning.lazy.evaluation.computation.graph.building","name":"lazy-evaluation-computation-graph-building","description":"MLX builds computation graphs without immediate execution by deferring operations until explicit eval() calls. Operations create graph nodes in the array class that represent pending computations; the framework delays actual kernel dispatch to the backend until evaluation is triggered, enabling graph optimization and memory efficiency. This lazy model is implemented through a deferred execution pattern where each operation returns an array wrapping a computation node rather than executing immediately.","intents":["I want to build complex computation graphs and optimize them before execution","I need to reduce memory overhead by deferring operations until necessary","I want to inspect the computation graph structure before running it"],"best_for":["ML researchers optimizing training pipelines on Apple Silicon","developers building custom neural network architectures with dynamic computation","teams migrating from eager frameworks (PyTorch) to lazy evaluation models"],"limitations":["Debugging is harder than eager execution because errors surface at eval() time, not operation time","Graph building adds overhead for simple, single-operation workloads","Requires explicit eval() calls or risk silent non-execution of operations"],"requires":["Python 3.8+","Understanding of lazy evaluation semantics","Explicit eval() calls in user code to trigger computation"],"input_types":["array operations (mathematical, linear algebra, neural network)","function compositions"],"output_types":["computation graph (internal representation)","evaluated arrays (after eval() call)"],"categories":["planning-reasoning","graph-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_1","uri":"capability://tool.use.integration.multi.backend.dispatch.with.platform.abstraction","name":"multi-backend-dispatch-with-platform-abstraction","description":"MLX abstracts hardware differences through a multi-backend system where the core API is platform-agnostic and each backend (Metal for Apple Silicon, CUDA for NVIDIA, CPU fallback) implements the same Primitive interface with eval_cpu(), eval_gpu(), and device-specific methods. The framework routes operations to the appropriate backend at runtime based on device selection, allowing identical Python code to run on M1/M2/M3/M4 chips, NVIDIA GPUs, or CPU without modification.","intents":["I want to write ML code once and run it on Apple Silicon, NVIDIA GPUs, and CPUs without changes","I need to abstract away hardware-specific details while maintaining performance","I want to add support for new hardware backends without rewriting the core framework"],"best_for":["cross-platform ML teams supporting multiple hardware targets","framework developers extending MLX with new backends","organizations with heterogeneous hardware deployments (Macs, Linux servers, cloud GPUs)"],"limitations":["Backend-specific optimizations may not translate across platforms, requiring per-backend tuning","CUDA backend support is newer and may lack some Metal-optimized operations","CPU fallback is slow for large models; intended for development/debugging only"],"requires":["Metal SDK (for Apple Silicon backend)","CUDA Toolkit 11.0+ (for NVIDIA backend)","C++ compiler supporting C++17 (for building from source)"],"input_types":["array operations","primitive operations (add, matmul, conv, etc.)"],"output_types":["computed arrays on target device","device-specific memory allocations"],"categories":["tool-use-integration","backend-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_10","uri":"capability://text.generation.language.mlx.lm.language.model.inference.and.generation","name":"mlx-lm-language-model-inference-and-generation","description":"MLX-LM is a companion library for running large language models (LLMs) on Apple Silicon, providing model loading, tokenization, and text generation with support for popular architectures (Llama, Mistral, Phi, etc.). The library handles model quantization, prompt caching for efficient multi-turn conversations, and generation algorithms (greedy, beam search, top-k sampling). Models are loaded from Hugging Face Hub and automatically optimized for Apple Silicon.","intents":["I want to run open-source LLMs locally on my Mac","I need efficient inference with prompt caching for chatbots","I want to fine-tune or adapt LLMs for specific tasks"],"best_for":["developers building local LLM applications on macOS","researchers experimenting with open-source models","teams deploying LLMs on edge devices without cloud dependency"],"limitations":["Limited to models that fit in Apple Silicon memory (typically 8-24GB)","Inference is slower than cloud APIs (OpenAI, Anthropic) due to hardware constraints","Model selection is limited to architectures with MLX implementations"],"requires":["macOS 11.0+ with Apple Silicon","8GB+ unified memory (16GB+ recommended for larger models)","Internet connection for downloading models from Hugging Face Hub","MLX installed"],"input_types":["model identifiers (e.g., 'meta-llama/Llama-2-7b')","text prompts","generation parameters (temperature, top_k, max_tokens)"],"output_types":["generated text","token sequences","logits (for advanced use cases)"],"categories":["text-generation-language","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_11","uri":"capability://image.visual.mlx.vlm.vision.language.model.inference","name":"mlx-vlm-vision-language-model-inference","description":"MLX-VLM extends MLX-LM to support vision-language models (VLMs) that process both images and text, enabling tasks like image captioning, visual question answering, and image understanding. The library handles image preprocessing, vision encoder inference, and integration with language model components. Models like LLaVA and others are supported with automatic optimization for Apple Silicon.","intents":["I want to run vision-language models locally for image understanding","I need to build multimodal applications (image + text) on macOS","I want to perform visual question answering without cloud APIs"],"best_for":["developers building multimodal applications on Apple Silicon","researchers experimenting with vision-language models","teams building image understanding features without cloud dependency"],"limitations":["VLM inference is slower than text-only LLMs due to vision encoder overhead","Limited model selection compared to text-only LLMs","Image preprocessing and vision encoding consume significant memory"],"requires":["macOS 11.0+ with Apple Silicon","16GB+ unified memory (recommended for VLMs)","MLX and MLX-LM installed","Image input (file path or PIL Image)"],"input_types":["image files (PNG, JPEG, etc.)","text prompts","model identifiers"],"output_types":["generated text descriptions","answers to visual questions","image embeddings (for advanced use cases)"],"categories":["image-visual","text-generation-language","model-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_12","uri":"capability://code.generation.editing.custom.primitive.and.kernel.definition.system","name":"custom-primitive-and-kernel-definition-system","description":"MLX enables users to define custom primitives and kernels that integrate with the framework's operation system and autodiff. Custom primitives inherit from the Primitive class and implement eval_cpu() and eval_gpu() methods for different backends. Users can write Metal Shading Language (MSL) kernels for GPU computation or C++ code for CPU, and the framework automatically handles autodiff by requiring VJP/JVP definitions for custom operations.","intents":["I want to implement custom operations not provided by MLX","I need to optimize critical operations with hand-written kernels","I want to integrate domain-specific algorithms into MLX"],"best_for":["ML researchers implementing novel operations","developers optimizing performance-critical code paths","teams integrating specialized algorithms (signal processing, physics simulations)"],"limitations":["Requires C++ and Metal/CUDA knowledge; steep learning curve","Custom kernels must be manually optimized for each backend","VJP/JVP definitions are required for autodiff; complex for non-trivial operations","No automatic differentiation for custom kernels; must be hand-coded"],"requires":["C++ compiler supporting C++17","Metal SDK (for GPU kernels on Apple Silicon)","CUDA Toolkit (for NVIDIA GPU kernels)","Understanding of MLX's Primitive interface and autodiff"],"input_types":["custom operation definitions (C++ classes)","kernel code (MSL or CUDA C++)"],"output_types":["custom primitives (integrated with MLX)","computed arrays (from custom kernels)"],"categories":["code-generation-editing","extensibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_13","uri":"capability://tool.use.integration.python.bindings.with.nanobind.and.indexing.support","name":"python-bindings-with-nanobind-and-indexing-support","description":"MLX uses Nanobind to create efficient Python-C++ bindings that expose the C++ core to Python with minimal overhead. The bindings support NumPy-style indexing (slicing, fancy indexing, boolean indexing) on arrays, enabling Pythonic array manipulation. Nanobind generates type-safe bindings that preserve performance while providing a natural Python API.","intents":["I want to use MLX from Python with minimal performance overhead","I need NumPy-style indexing for array manipulation","I want to extend MLX with Python code without rewriting in C++"],"best_for":["Python developers using MLX for ML tasks","teams building Python-based ML pipelines","researchers prototyping with Python before optimizing in C++"],"limitations":["Nanobind adds small overhead compared to direct C++ calls","Complex Python objects (custom classes) may not bind efficiently","Debugging Python-C++ interactions can be challenging"],"requires":["Python 3.8+","Nanobind (included in MLX source)","C++ compiler for building bindings"],"input_types":["Python objects (arrays, scalars, functions)","indexing expressions (slices, fancy indices)"],"output_types":["Python arrays (MLX arrays)","Python scalars"],"categories":["tool-use-integration","language-bindings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_14","uri":"capability://tool.use.integration.python.binding.with.nanobind.for.minimal.overhead","name":"python-binding-with-nanobind-for-minimal-overhead","description":"MLX uses Nanobind (mlx/python/src) to create efficient Python-C++ bindings with minimal overhead. Nanobind generates type-safe bindings that preserve C++ semantics while exposing a Pythonic API. The binding layer handles array conversion, type promotion, and error propagation. Integration with lazy evaluation means Python operations return unevaluated computation graphs, enabling efficient batching and optimization.","intents":["I want to use MLX's C++ performance from Python without significant overhead","I need type-safe bindings that catch errors early","I want to extend MLX with custom Python-C++ bindings"],"best_for":["Python developers leveraging C++ performance","Teams building production ML systems with Python interfaces","Researchers extending MLX with custom bindings"],"limitations":["Nanobind adds ~5-10% overhead compared to pure C++","Type checking is stricter than pure Python; implicit conversions may fail","Custom bindings require C++ knowledge and Nanobind expertise","Debugging bindings requires understanding of both Python and C++"],"requires":["Python 3.8+","C++ compiler (clang, gcc, MSVC)","For custom bindings: Nanobind knowledge"],"input_types":["Python objects","arrays","function calls"],"output_types":["Python objects","arrays","computation results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_2","uri":"capability://planning.reasoning.automatic.differentiation.with.vjp.and.jvp","name":"automatic-differentiation-with-vjp-and-jvp","description":"MLX implements automatic differentiation through Vector-Jacobian Products (VJP) for reverse-mode autodiff and Jacobian-Vector Products (JVP) for forward-mode autodiff, building gradient computation graphs that mirror the forward computation. The framework traces operations to construct a computation graph, then applies the chain rule in reverse (for backprop) or forward (for forward-mode) to compute gradients. Both modes are composable and can be nested for higher-order derivatives.","intents":["I want to compute gradients for training neural networks without manual backprop","I need higher-order derivatives for advanced optimization algorithms","I want to use both reverse-mode (efficient for many outputs) and forward-mode (efficient for many inputs) autodiff"],"best_for":["ML researchers training neural networks on Apple Silicon","developers implementing custom loss functions and optimizers","teams needing higher-order derivatives for Hessian-based methods"],"limitations":["VJP (reverse-mode) requires storing intermediate activations, increasing memory usage during training","JVP (forward-mode) is slower than VJP for typical neural networks with many parameters","Gradient computation adds latency; no automatic gradient checkpointing to reduce memory"],"requires":["Python 3.8+","Understanding of autodiff semantics (chain rule, VJP/JVP)","Explicit grad() or value_and_grad() calls in user code"],"input_types":["scalar loss functions","array operations with differentiable primitives"],"output_types":["gradient arrays (same shape as inputs)","higher-order derivatives (for nested autodiff)"],"categories":["planning-reasoning","automatic-differentiation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_3","uri":"capability://planning.reasoning.vectorization.transform.with.vmap","name":"vectorization-transform-with-vmap","description":"MLX provides vmap (vectorization map) as a transform that automatically vectorizes scalar functions over batch dimensions without manual broadcasting or loop unrolling. vmap takes a function and a batch axis specification, then generates vectorized code that applies the function to each batch element in parallel. This is implemented as a transform in mlx/transforms.cpp that modifies the computation graph to add batch dimensions and broadcast operations accordingly.","intents":["I want to automatically vectorize scalar functions across batch dimensions","I need to apply the same operation to multiple inputs without explicit loops","I want to compose vectorization with other transforms like autodiff"],"best_for":["ML researchers implementing batch-aware algorithms","developers building flexible neural network layers that work with variable batch sizes","teams optimizing data parallelism without manual broadcasting"],"limitations":["vmap adds graph construction overhead; not beneficial for already-vectorized operations","Composing vmap with autodiff can create complex computation graphs with overhead","Some operations may not vectorize efficiently if they have data-dependent control flow"],"requires":["Python 3.8+","Understanding of batch dimensions and axis specifications","Functions that are written in scalar form (not already vectorized)"],"input_types":["scalar functions (taking single array elements)","batch axis specifications (integer or tuple)"],"output_types":["vectorized functions (operating on batches)","batched computation graphs"],"categories":["planning-reasoning","vectorization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_4","uri":"capability://planning.reasoning.graph.compilation.and.optimization","name":"graph-compilation-and-optimization","description":"MLX compiles computation graphs into optimized kernel sequences through a compilation system (mlx/compile.cpp) that fuses operations, eliminates redundant computations, and generates backend-specific code. The compiler analyzes the computation graph, identifies fusion opportunities (combining multiple operations into single kernels), and generates optimized code for Metal or CUDA. This happens transparently when eval() is called, reducing memory bandwidth and kernel launch overhead.","intents":["I want automatic fusion of operations to reduce kernel launch overhead","I need graph-level optimizations without manual kernel writing","I want to reduce memory bandwidth by fusing operations"],"best_for":["ML teams optimizing inference latency on Apple Silicon","researchers implementing custom architectures that benefit from operation fusion","developers building production models where kernel launch overhead matters"],"limitations":["Compilation adds latency on first eval() call; not beneficial for one-off operations","Fusion opportunities are limited by data dependencies; not all operation sequences can be fused","Debugging compiled graphs is harder because operations are fused into single kernels"],"requires":["Python 3.8+","Computation graphs with multiple operations (fusion requires multiple ops)","Understanding of when fusion is beneficial (bandwidth-bound operations)"],"input_types":["computation graphs (from lazy evaluation)","operation sequences"],"output_types":["compiled kernel sequences","optimized computation plans"],"categories":["planning-reasoning","graph-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_5","uri":"capability://tool.use.integration.metal.backend.with.jit.compilation.and.command.encoding","name":"metal-backend-with-jit-compilation-and-command-encoding","description":"MLX's Metal backend implements GPU computation for Apple Silicon through Metal command encoding, JIT compilation of kernels, and device management. The backend translates primitives into Metal Shading Language (MSL) kernels, compiles them at runtime, and encodes commands into Metal command buffers for GPU execution. Device management abstracts Metal's GPU/CPU memory hierarchy, and stream abstraction enables asynchronous command submission and synchronization.","intents":["I want maximum performance on Apple Silicon M1/M2/M3/M4 chips","I need to leverage Metal's unified memory and GPU compute capabilities","I want automatic GPU memory management without manual allocation"],"best_for":["ML teams building on macOS with Apple Silicon","developers optimizing inference on edge devices (MacBook, Mac mini)","researchers exploring GPU compute on Apple's hardware"],"limitations":["Metal backend is Apple-only; no portability to other platforms","JIT compilation adds latency on first kernel execution; subsequent calls are cached","Some advanced CUDA features (dynamic parallelism, texture memory) don't have Metal equivalents"],"requires":["macOS 11.0+ (Big Sur or later)","Apple Silicon chip (M1, M2, M3, M4)","Metal SDK (included with Xcode)"],"input_types":["primitive operations (matmul, conv, elementwise, etc.)","array data"],"output_types":["computed arrays in GPU memory","Metal command buffers (internal)"],"categories":["tool-use-integration","backend-implementation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_6","uri":"capability://tool.use.integration.cuda.backend.with.graph.system.and.memory.management","name":"cuda-backend-with-graph-system-and-memory-management","description":"MLX's CUDA backend enables GPU computation on NVIDIA hardware through CUDA graph management, memory synchronization, and device abstraction. The backend manages CUDA device contexts, allocates GPU memory, and uses CUDA graphs to batch kernel launches for reduced overhead. Device management handles stream synchronization and memory pooling to optimize allocation patterns.","intents":["I want to run MLX models on NVIDIA GPUs in data centers and cloud","I need efficient memory management and kernel batching on CUDA","I want to use MLX on Linux servers with NVIDIA GPUs"],"best_for":["ML teams with NVIDIA GPU infrastructure (data centers, cloud)","researchers training large models on NVIDIA hardware","developers building cross-platform ML systems (Apple Silicon + NVIDIA)"],"limitations":["CUDA backend is newer than Metal; some operations may be less optimized","Requires CUDA Toolkit installation and NVIDIA driver setup","CUDA graph overhead is significant for small models; better for large batch sizes"],"requires":["NVIDIA GPU with CUDA Compute Capability 3.5+","CUDA Toolkit 11.0 or later","NVIDIA driver 450.0 or later","Linux or Windows OS"],"input_types":["primitive operations","array data"],"output_types":["computed arrays in GPU memory","CUDA graph structures (internal)"],"categories":["tool-use-integration","backend-implementation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_7","uri":"capability://data.processing.analysis.numpy.compatible.array.operations.api","name":"numpy-compatible-array-operations-api","description":"MLX provides a NumPy-like API for array operations (mlx.core) with familiar functions like reshape, transpose, matmul, conv2d, and element-wise operations. The API is implemented through Python bindings (Nanobind) that wrap C++ operations, enabling users familiar with NumPy to use MLX with minimal learning curve. Operations return lazy arrays that build computation graphs rather than executing immediately.","intents":["I want to use familiar NumPy syntax for ML operations","I need to port NumPy/SciPy code to MLX with minimal changes","I want a Pythonic API for array manipulation"],"best_for":["data scientists familiar with NumPy transitioning to MLX","teams porting existing NumPy/SciPy code to Apple Silicon","developers building ML pipelines with familiar array syntax"],"limitations":["Not all NumPy functions are implemented; some advanced functions are missing","NumPy broadcasting rules are followed, but lazy evaluation changes semantics (no immediate errors)","Performance characteristics differ from NumPy (lazy vs eager); direct ports may not be optimal"],"requires":["Python 3.8+","NumPy familiarity (optional but helpful)","MLX installed via pip or built from source"],"input_types":["array-like objects (lists, tuples, NumPy arrays)","scalar values"],"output_types":["MLX arrays (lazy or evaluated)","scalar values"],"categories":["data-processing-analysis","api-compatibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_8","uri":"capability://code.generation.editing.neural.network.module.system.with.parameter.management","name":"neural-network-module-system-with-parameter-management","description":"MLX provides mlx.nn, a neural network module system where layers inherit from a base Module class that manages parameters and submodules. The Module system tracks trainable parameters, enables parameter sharing, and provides methods for parameter initialization and state management. Layers (Linear, Conv2d, BatchNorm, etc.) are implemented as Modules that compose primitive operations, and the system integrates with autodiff for gradient computation during training.","intents":["I want to build neural networks with composable layers","I need automatic parameter tracking for training","I want to save and load model weights easily"],"best_for":["ML researchers building custom neural network architectures","developers training models with MLX","teams implementing transfer learning and fine-tuning"],"limitations":["Module system adds abstraction overhead; not beneficial for simple operations","Parameter initialization is manual; no automatic initialization like PyTorch","No built-in distributed training utilities; requires manual parameter synchronization"],"requires":["Python 3.8+","Understanding of neural network layers and composition","MLX autodiff for gradient computation"],"input_types":["layer definitions (Linear, Conv2d, etc.)","input arrays"],"output_types":["output arrays","parameter dictionaries (for saving/loading)"],"categories":["code-generation-editing","neural-network-layers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__cap_9","uri":"capability://data.processing.analysis.quantization.with.multiple.modes.and.backends","name":"quantization-with-multiple-modes-and-backends","description":"MLX implements quantization (mlx.core.quantize) with multiple modes (int4, int8, float16) and backend-specific implementations for Metal and CUDA. Quantization reduces model size and inference latency by converting weights to lower precision, with dequantization happening on-the-fly during computation. The framework provides both quantization APIs for converting models and quantized operations that handle dequantization transparently.","intents":["I want to reduce model size for deployment on edge devices","I need faster inference through lower-precision computation","I want to quantize pre-trained models without retraining"],"best_for":["ML teams deploying models on resource-constrained devices (MacBook, mobile)","developers optimizing inference latency and memory usage","researchers exploring post-training quantization"],"limitations":["Quantization reduces model accuracy; requires fine-tuning for critical applications","int4 quantization is more aggressive and may lose significant information","Quantized operations have different performance characteristics than full-precision; not always faster"],"requires":["Python 3.8+","Pre-trained model weights","Understanding of quantization trade-offs (accuracy vs size/speed)"],"input_types":["full-precision model weights","quantization configuration (mode, group size, etc.)"],"output_types":["quantized weights (int4, int8, float16)","quantization metadata (scales, zero points)"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mlx__headline","uri":"capability://data.processing.analysis.apple.optimized.machine.learning.framework","name":"apple-optimized machine learning framework","description":"MLX is a machine learning framework specifically optimized for Apple Silicon, offering a NumPy-like API with features like automatic differentiation and lazy computation, making it ideal for developers working on Apple hardware.","intents":["best Apple machine learning framework","machine learning framework for Apple Silicon","MLX for deep learning on M1","high-performance ML framework for Mac","best framework for running language models on Apple hardware"],"best_for":["Apple Silicon users","deep learning applications","vision-language models"],"limitations":["limited to Apple hardware"],"requires":["Apple M1/M2/M3/M4 chips"],"input_types":["data arrays","language model inputs"],"output_types":["model predictions","computed arrays"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Understanding of lazy evaluation semantics","Explicit eval() calls in user code to trigger computation","Metal SDK (for Apple Silicon backend)","CUDA Toolkit 11.0+ (for NVIDIA backend)","C++ compiler supporting C++17 (for building from source)","macOS 11.0+ with Apple Silicon","8GB+ unified memory (16GB+ recommended for larger models)","Internet connection for downloading models from Hugging Face Hub","MLX installed"],"failure_modes":["Debugging is harder than eager execution because errors surface at eval() time, not operation time","Graph building adds overhead for simple, single-operation workloads","Requires explicit eval() calls or risk silent non-execution of operations","Backend-specific optimizations may not translate across platforms, requiring per-backend tuning","CUDA backend support is newer and may lack some Metal-optimized operations","CPU fallback is slow for large models; intended for development/debugging only","Limited to models that fit in Apple Silicon memory (typically 8-24GB)","Inference is slower than cloud APIs (OpenAI, Anthropic) due to hardware constraints","Model selection is limited to architectures with MLX implementations","VLM inference is slower than text-only LLMs due to vision encoder overhead","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mlx","compare_url":"https://unfragile.ai/compare?artifact=mlx"}},"signature":"EjTCI57aQb7dESVkNJf7riMdRoxSbC0wvzEq0/r33XWcmZ/7i91nMpwNaQB/w8clwJzjdrU+8RVIEDePwe4lAQ==","signedAt":"2026-06-22T18:30:35.733Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mlx","artifact":"https://unfragile.ai/mlx","verify":"https://unfragile.ai/api/v1/verify?slug=mlx","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}