{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-openvinotoolkit--openvino","slug":"openvinotoolkit--openvino","name":"openvino","type":"framework","url":"https://docs.openvino.ai","page_url":"https://unfragile.ai/openvinotoolkit--openvino","categories":["deployment-infra"],"tags":["ai","computer-vision","deep-learning","deploy-ai","diffusion-models","generative-ai","good-first-issue","inference","llm-inference","natural-language-processing","nlp","openvino","optimize-ai","performance-boost","recommendation-system","speech-recognition","stable-diffusion","transformers","yolo"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-openvinotoolkit--openvino__cap_0","uri":"capability://data.processing.analysis.multi.framework.model.import.with.unified.intermediate.representation","name":"multi-framework model import with unified intermediate representation","description":"OpenVINO ingests models from PyTorch, ONNX, TensorFlow, PaddlePaddle, JAX, and TensorFlow Lite through dedicated frontend parsers that convert framework-specific graph formats into OpenVINO's unified Intermediate Representation (IR). Each frontend implements a graph traversal and node mapping layer that translates framework operations to OpenVINO's Opset (operation set), enabling downstream optimization passes to work uniformly across all input formats without framework-specific logic.","intents":["I need to deploy a PyTorch model trained in my lab without rewriting inference code","I want to use the same optimization pipeline for ONNX and TensorFlow models","I need to convert a JAX research model to a production-ready format"],"best_for":["ML engineers migrating models across frameworks","Production teams standardizing on a single inference runtime","Researchers deploying diverse model architectures to edge devices"],"limitations":["Custom ops in source frameworks may not have Opset equivalents, requiring manual decomposition","Dynamic shapes in TensorFlow require explicit shape inference passes before IR conversion","Some framework-specific quantization metadata is lost during IR conversion"],"requires":["Source model in supported format (PyTorch .pt/.pth, ONNX .onnx, TensorFlow SavedModel/frozen graph, etc.)","Python 3.9+ with openvino package installed","Framework-specific dependencies (torch, tensorflow, onnx) only needed at conversion time, not runtime"],"input_types":["PyTorch model files (.pt, .pth)","ONNX model files (.onnx)","TensorFlow SavedModel directories","TensorFlow frozen graphs (.pb)","PaddlePaddle model files","JAX function definitions","TensorFlow Lite models (.tflite)"],"output_types":["OpenVINO Intermediate Representation (.xml + .bin)","In-memory IR graph object (via Python API)"],"categories":["data-processing-analysis","model-conversion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_1","uri":"capability://data.processing.analysis.hardware.agnostic.graph.optimization.and.transformation.pipeline","name":"hardware-agnostic graph optimization and transformation pipeline","description":"OpenVINO applies a sequence of graph-level transformations to the IR including constant folding, dead code elimination, operator fusion, and layout optimization. The transformation pipeline is hardware-agnostic at the IR level but feeds into plugin-specific optimizations (CPU, GPU, NPU). Common transformations are applied before plugin selection, while plugin-specific passes (e.g., GPU kernel fusion, CPU JIT emission) occur after compilation target is chosen, enabling the same model to be optimized differently for different hardware.","intents":["I want to reduce model size and latency without changing model accuracy","I need to optimize a model once and deploy it across CPU, GPU, and NPU targets","I want to fuse operations to reduce memory bandwidth and kernel launch overhead"],"best_for":["DevOps engineers optimizing models for multiple hardware targets","Edge AI teams reducing model footprint for resource-constrained devices","Performance engineers tuning inference latency"],"limitations":["Some transformations (e.g., aggressive operator fusion) may reduce numerical precision; requires validation","Transformation order matters — some passes must run before others; custom pass ordering is not exposed in public API","Graph modifications are applied eagerly; no lazy evaluation or deferred optimization"],"requires":["OpenVINO IR model (converted from source framework)","Python 3.9+ or C++ 17+ compiler","No external dependencies for transformation passes"],"input_types":["OpenVINO IR (.xml + .bin)","In-memory IR graph object"],"output_types":["Optimized OpenVINO IR (.xml + .bin)","In-memory optimized IR graph"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_10","uri":"capability://tool.use.integration.python.bindings.pyopenvino.with.high.level.api.for.inference","name":"python bindings (pyopenvino) with high-level api for inference","description":"The Python bindings (pyopenvino) provide a high-level API for loading models, configuring inference, and running predictions. The API abstracts device selection, memory management, and batch processing, exposing a simple interface: load model → create inference request → run inference → get results. The bindings are implemented in C++ with Python wrappers, enabling near-native performance while maintaining Pythonic API design. Support for async inference enables non-blocking execution for real-time applications.","intents":["I need a simple Python API for running inference without low-level details","I want to run inference asynchronously for real-time applications","I need to integrate OpenVINO into my Python ML pipeline"],"best_for":["Python developers building inference applications","Data scientists integrating OpenVINO into ML pipelines","Teams prototyping inference solutions quickly"],"limitations":["Python bindings add ~5-10% overhead compared to native C++ API","Async inference requires careful handling of callbacks and error propagation","Memory management is automatic but may not be optimal for all use cases"],"requires":["Python 3.9+","openvino package installed (pip install openvino)","OpenVINO IR model"],"input_types":["OpenVINO IR (.xml + .bin)","NumPy arrays, lists, or other Python array-like objects"],"output_types":["NumPy arrays or Python lists","Inference metadata (execution time, device info)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_11","uri":"capability://tool.use.integration.javascript.node.js.bindings.for.browser.and.server.side.inference","name":"javascript/node.js bindings for browser and server-side inference","description":"OpenVINO provides JavaScript bindings for Node.js and browser environments, enabling inference in JavaScript applications. The bindings wrap the C++ runtime with JavaScript-friendly APIs, supporting both synchronous and asynchronous execution. Browser support uses WebAssembly (WASM) compilation of the OpenVINO runtime, enabling client-side inference without server round-trips. Node.js bindings provide full access to all OpenVINO features including device selection and quantization.","intents":["I need to run inference in a Node.js application","I want to run inference in the browser without sending data to a server","I need to integrate OpenVINO into a JavaScript ML pipeline"],"best_for":["JavaScript developers building inference applications","Web developers deploying models to browsers","Teams building full-stack ML applications with JavaScript"],"limitations":["Browser WASM runtime is slower than native C++ (~2-5x overhead)","Browser memory is limited; large models may not fit","Device selection in browser is limited (CPU only for WASM)","Async inference in JavaScript requires Promise/async-await handling"],"requires":["Node.js 14+ (for Node.js bindings)","Modern browser with WASM support (for browser inference)","openvino npm package installed"],"input_types":["OpenVINO IR (.xml + .bin)","JavaScript arrays, TypedArrays, or Tensors"],"output_types":["JavaScript arrays or TypedArrays","Inference metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_12","uri":"capability://data.processing.analysis.opset.based.operation.abstraction.with.extensibility.for.custom.operations","name":"opset-based operation abstraction with extensibility for custom operations","description":"OpenVINO defines a standardized operation set (Opset) that abstracts framework-specific operations into a common set of primitives (e.g., Convolution, MatMul, Attention). Each Opset version adds new operations and refines existing ones, enabling forward compatibility. The IR is versioned by Opset version, allowing models to be converted and optimized independently of framework versions. Custom operations can be registered via plugins, enabling extension without modifying core OpenVINO code.","intents":["I need to ensure my model remains compatible across OpenVINO versions","I want to implement a custom operation for my domain-specific model","I need to understand what operations my model uses"],"best_for":["Teams managing long-term model compatibility","Researchers implementing novel operations","Advanced users extending OpenVINO for custom hardware"],"limitations":["Custom operations require C++ plugin development; no Python-only extension mechanism","Opset versioning adds complexity; older models may need conversion to newer Opsets","Not all framework operations map cleanly to Opset; some require decomposition"],"requires":["Understanding of Opset specification","C++ 17+ compiler for custom operation plugins","OpenVINO development headers"],"input_types":["Opset specification documents","Custom operation definitions"],"output_types":["Registered custom operations","IR with custom operations"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_13","uri":"capability://data.processing.analysis.dynamic.shape.inference.and.handling.for.variable.length.inputs","name":"dynamic shape inference and handling for variable-length inputs","description":"OpenVINO supports dynamic shapes in models, enabling inference with variable-length inputs (e.g., variable sequence lengths in NLP, variable image sizes in vision). The IR includes shape inference logic that propagates shape information through the graph, computing output shapes based on input shapes at runtime. The shape inference engine handles both static and dynamic dimensions, enabling models to adapt to input variations without recompilation.","intents":["I need to run inference on variable-length sequences without recompiling","I want to process images of different sizes with the same model","I need to handle batches of different sizes dynamically"],"best_for":["NLP teams processing variable-length sequences","Computer vision teams handling images of different sizes","Teams with dynamic batch sizes"],"limitations":["Dynamic shapes add complexity to shape inference; some operations may not support dynamic shapes","Performance may be suboptimal for dynamic shapes compared to static shapes","Shape inference errors can occur at runtime if input shapes are invalid"],"requires":["OpenVINO IR model with dynamic shape support","Input shapes must be compatible with model's dynamic shape constraints"],"input_types":["OpenVINO IR (.xml + .bin) with dynamic shapes","Variable-length inputs (sequences, images, etc.)"],"output_types":["Inference results with dynamically computed output shapes","Shape information"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_2","uri":"capability://data.processing.analysis.low.precision.quantization.with.per.layer.calibration.and.mixed.precision.support","name":"low-precision quantization with per-layer calibration and mixed-precision support","description":"OpenVINO provides quantization transformations that convert FP32 models to INT8 or FP16 with per-layer calibration data. The quantization pipeline includes a calibration phase (running inference on representative data to collect activation statistics) and a conversion phase (inserting quantization/dequantization nodes into the graph). Mixed-precision support allows different layers to use different precisions (e.g., attention layers in FP16, feed-forward in INT8) based on sensitivity analysis, reducing model size while maintaining accuracy.","intents":["I need to reduce model size by 4x for edge deployment without significant accuracy loss","I want to quantize only sensitive layers (e.g., attention) while quantizing others aggressively","I need to calibrate quantization on my own dataset to preserve domain-specific accuracy"],"best_for":["Edge AI engineers deploying models to resource-constrained devices (mobile, IoT)","Teams optimizing inference latency on INT8-capable hardware (Intel CPUs, GPUs)","Researchers studying quantization-aware training and post-training quantization tradeoffs"],"limitations":["Calibration requires representative dataset; poor calibration data leads to accuracy degradation","Some operations (e.g., certain attention mechanisms) may not quantize well without retraining","INT8 quantization is most effective on Intel hardware; benefits on other platforms vary","No built-in quantization-aware training (QAT) — only post-training quantization (PTQ)"],"requires":["OpenVINO IR model","Representative calibration dataset (100-1000 samples typical)","Python 3.9+ with openvino[dev] for quantization tools","Target hardware must support INT8 or FP16 (most modern Intel CPUs/GPUs do)"],"input_types":["OpenVINO IR (.xml + .bin)","Calibration dataset (images, text, or structured data)"],"output_types":["Quantized OpenVINO IR (.xml + .bin) with INT8 or FP16 precision","Quantization statistics and per-layer sensitivity reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_3","uri":"capability://automation.workflow.intel.cpu.plugin.with.jit.compilation.and.llm.specific.optimizations","name":"intel cpu plugin with jit compilation and llm-specific optimizations","description":"The CPU plugin compiles OpenVINO IR to optimized x86-64 code using JIT emission, generating specialized kernels for element-wise operations and leveraging Intel SIMD instructions (AVX-512, AVX2). For LLM inference, the plugin includes scaled attention optimizations and KV-cache management to reduce memory bandwidth during token generation. The plugin uses a graph-based execution model where nodes are scheduled and executed with data flow dependencies, enabling efficient multi-threaded execution on multi-core CPUs.","intents":["I need to run LLM inference on CPU with minimal latency for token generation","I want to leverage all CPU cores for parallel inference without manual threading","I need to optimize attention computation for long sequence lengths"],"best_for":["Server-side inference teams deploying on CPU-only infrastructure","Edge AI engineers running LLMs on laptops and edge servers","Teams optimizing for Intel-specific hardware (Xeon, Core, Atom)"],"limitations":["JIT compilation adds ~100-500ms overhead on first inference (model warmup required)","LLM optimizations assume standard transformer architecture; custom attention patterns may not benefit","KV-cache management is automatic but not exposed for custom control","Performance gains from SIMD instructions vary by CPU generation; older CPUs may see minimal benefit"],"requires":["OpenVINO IR model","Intel x86-64 CPU (AVX2 minimum, AVX-512 recommended for best performance)","Python 3.9+ or C++ 17+ runtime","Multi-core CPU for parallel execution (single-core execution possible but inefficient)"],"input_types":["OpenVINO IR (.xml + .bin)"],"output_types":["Inference results (tensors)","Performance metrics (latency, throughput)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_4","uri":"capability://automation.workflow.intel.gpu.plugin.with.kernel.fusion.and.memory.optimized.execution","name":"intel gpu plugin with kernel fusion and memory-optimized execution","description":"The GPU plugin compiles IR operations to OpenCL or Level Zero kernels, applying layout optimization to minimize memory bandwidth (e.g., converting NCHW to optimized layouts for GPU memory hierarchy). The plugin fuses multiple operations into single kernels to reduce kernel launch overhead and improve cache locality. Memory management includes buffer pooling and reuse to minimize allocation overhead. The plugin supports both discrete GPUs (Arc, Data Center) and integrated GPUs (Iris Xe), with automatic kernel selection based on GPU capabilities.","intents":["I need to accelerate inference on Intel Arc or Iris Xe GPUs","I want to reduce kernel launch overhead by fusing operations","I need to optimize memory layout for GPU memory hierarchy"],"best_for":["Teams deploying on Intel Arc or Iris Xe GPUs","Data center inference teams using Intel Data Center GPU Flex/Max","Edge AI engineers using integrated GPUs in Intel processors"],"limitations":["GPU kernel compilation adds ~500ms-2s overhead on first inference (model warmup required)","Kernel fusion is automatic but may not be optimal for all model architectures","Memory layout optimization may reduce numerical precision in some cases; requires validation","Limited support for dynamic shapes; static shapes recommended for best performance"],"requires":["OpenVINO IR model","Intel GPU (Arc, Iris Xe, or Data Center GPU)","Intel GPU drivers (Level Zero or OpenCL)","Python 3.9+ or C++ 17+ runtime"],"input_types":["OpenVINO IR (.xml + .bin)"],"output_types":["Inference results (tensors)","Performance metrics (latency, throughput, memory usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_5","uri":"capability://automation.workflow.intel.npu.plugin.with.model.partitioning.and.fallback.execution","name":"intel npu plugin with model partitioning and fallback execution","description":"The NPU plugin targets Intel Neural Processing Units (NPUs) found in recent Intel processors. It partitions models into NPU-compatible and CPU-fallback subgraphs, executing NPU-compatible operations on the NPU and falling back to CPU for unsupported operations. The NPUW (NPU Wrapper) layer manages model compilation, KV-cache for LLM inference, and dynamic shape handling. This hybrid execution model allows deploying models that exceed NPU capabilities by offloading unsupported operations to CPU.","intents":["I need to run inference on Intel NPU for ultra-low power consumption","I want to use NPU for supported operations and fall back to CPU for others","I need to deploy LLMs on NPU with efficient KV-cache management"],"best_for":["Edge AI engineers targeting Intel Core Ultra processors with NPU","Teams optimizing for battery life on laptops and mobile devices","Researchers exploring heterogeneous inference (NPU + CPU)"],"limitations":["NPU support is limited to recent Intel processors (Core Ultra, Meteor Lake+)","Not all operations are NPU-compatible; complex models may have large CPU fallback portions","NPU performance is highly dependent on model architecture and operation types","Limited visibility into NPU compilation and optimization process"],"requires":["OpenVINO IR model","Intel processor with NPU (Core Ultra, Meteor Lake or newer)","Intel NPU drivers","Python 3.9+ or C++ 17+ runtime"],"input_types":["OpenVINO IR (.xml + .bin)"],"output_types":["Inference results (tensors)","Execution statistics (NPU vs CPU execution breakdown)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_6","uri":"capability://automation.workflow.auto.plugin.with.device.selection.and.load.balancing","name":"auto plugin with device selection and load balancing","description":"The AUTO plugin automatically selects the best available device (CPU, GPU, NPU) for inference based on model characteristics and device capabilities. It can also distribute inference across multiple devices (e.g., batches split between CPU and GPU) for load balancing. The plugin uses heuristics based on model size, operation types, and device performance characteristics to make selection decisions. This enables write-once, deploy-anywhere inference without manual device selection.","intents":["I want to deploy the same model across different hardware without code changes","I need to automatically select the fastest device for a given model","I want to distribute inference load across multiple devices"],"best_for":["Teams deploying models across heterogeneous hardware","DevOps engineers managing inference infrastructure with mixed devices","Researchers prototyping on different hardware without code changes"],"limitations":["Device selection heuristics may not be optimal for all model types; manual device selection may be faster","Load balancing across devices adds scheduling overhead (~10-50ms per batch)","Device selection is static per model; no dynamic switching based on runtime conditions"],"requires":["OpenVINO IR model","At least one supported device (CPU, GPU, or NPU)","Python 3.9+ or C++ 17+ runtime"],"input_types":["OpenVINO IR (.xml + .bin)"],"output_types":["Inference results (tensors)","Device selection metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_7","uri":"capability://automation.workflow.hetero.plugin.with.explicit.device.assignment.and.fallback.chains","name":"hetero plugin with explicit device assignment and fallback chains","description":"The HETERO plugin enables explicit assignment of operations to specific devices with fallback chains. Users can specify which device should execute which operations (e.g., 'GPU for convolutions, CPU for unsupported ops'), and the plugin automatically falls back to the next device in the chain if an operation fails. This provides fine-grained control over heterogeneous execution while maintaining robustness through fallback mechanisms.","intents":["I want to explicitly assign operations to specific devices","I need to ensure fallback to CPU if GPU execution fails","I want to optimize specific layers on specific devices"],"best_for":["Advanced users with specific device optimization requirements","Teams with heterogeneous hardware and custom optimization strategies","Researchers exploring device-specific operation placement"],"limitations":["Requires manual specification of device assignments; no automatic optimization","Fallback chains add complexity and potential performance overhead","Device assignment is static; no dynamic switching based on runtime conditions"],"requires":["OpenVINO IR model","Multiple supported devices (CPU, GPU, NPU)","Python 3.9+ or C++ 17+ runtime"],"input_types":["OpenVINO IR (.xml + .bin)","Device assignment configuration"],"output_types":["Inference results (tensors)","Device execution trace"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_8","uri":"capability://automation.workflow.model.converter.ovc.with.command.line.and.python.api.interfaces","name":"model converter (ovc) with command-line and python api interfaces","description":"The OpenVINO Model Converter (ovc) is a unified tool for converting models from source frameworks (PyTorch, ONNX, TensorFlow, etc.) to OpenVINO IR. It provides both command-line and Python API interfaces, supporting batch conversion and integration into CI/CD pipelines. The converter applies framework-specific parsing, IR generation, and optimization in a single pass, producing optimized .xml and .bin files ready for deployment.","intents":["I need to convert a PyTorch model to OpenVINO IR for deployment","I want to integrate model conversion into my CI/CD pipeline","I need to batch-convert multiple models with consistent settings"],"best_for":["ML engineers converting models for production deployment","DevOps teams automating model conversion in CI/CD","Teams standardizing on OpenVINO IR as an intermediate format"],"limitations":["Conversion may fail for models with unsupported operations; requires manual decomposition","Some framework-specific metadata (e.g., PyTorch quantization info) is lost during conversion","Conversion time scales with model size; large models may take minutes to convert"],"requires":["Source model in supported format","Framework-specific dependencies (torch, tensorflow, onnx, etc.) installed","Python 3.9+ with openvino package"],"input_types":["PyTorch model files (.pt, .pth)","ONNX model files (.onnx)","TensorFlow SavedModel directories","TensorFlow frozen graphs (.pb)","PaddlePaddle model files","JAX function definitions","TensorFlow Lite models (.tflite)"],"output_types":["OpenVINO IR (.xml + .bin)","Conversion logs and warnings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openvinotoolkit--openvino__cap_9","uri":"capability://automation.workflow.benchmark.tool.for.performance.profiling.and.latency.measurement","name":"benchmark tool for performance profiling and latency measurement","description":"The OpenVINO Benchmark tool measures inference latency, throughput, and memory usage across different devices and batch sizes. It supports warm-up runs, multiple iterations, and statistical analysis (mean, median, percentiles). The tool can profile individual layers to identify bottlenecks and compare performance across devices, enabling data-driven optimization decisions. Results are exported in JSON format for integration with monitoring and reporting systems.","intents":["I need to measure inference latency on different devices","I want to identify performance bottlenecks in my model","I need to compare performance across CPU, GPU, and NPU"],"best_for":["Performance engineers optimizing inference latency","Teams comparing hardware options for deployment","Researchers benchmarking model optimization techniques"],"limitations":["Benchmark results are device-specific and may not generalize across hardware","Layer-level profiling adds overhead and may not reflect real-world performance","Warm-up runs are required for accurate measurements; cold-start latency is not measured"],"requires":["OpenVINO IR model","Target device (CPU, GPU, or NPU)","Python 3.9+ or C++ 17+ runtime"],"input_types":["OpenVINO IR (.xml + .bin)"],"output_types":["Performance metrics (latency, throughput, memory usage)","Per-layer profiling data","JSON reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Source model in supported format (PyTorch .pt/.pth, ONNX .onnx, TensorFlow SavedModel/frozen graph, etc.)","Python 3.9+ with openvino package installed","Framework-specific dependencies (torch, tensorflow, onnx) only needed at conversion time, not runtime","OpenVINO IR model (converted from source framework)","Python 3.9+ or C++ 17+ compiler","No external dependencies for transformation passes","Python 3.9+","openvino package installed (pip install openvino)","OpenVINO IR model","Node.js 14+ (for Node.js bindings)"],"failure_modes":["Custom ops in source frameworks may not have Opset equivalents, requiring manual decomposition","Dynamic shapes in TensorFlow require explicit shape inference passes before IR conversion","Some framework-specific quantization metadata is lost during IR conversion","Some transformations (e.g., aggressive operator fusion) may reduce numerical precision; requires validation","Transformation order matters — some passes must run before others; custom pass ordering is not exposed in public API","Graph modifications are applied eagerly; no lazy evaluation or deferred optimization","Python bindings add ~5-10% overhead compared to native C++ API","Async inference requires careful handling of callbacks and error propagation","Memory management is automatic but may not be optimal for all use cases","Browser WASM runtime is slower than native C++ (~2-5x overhead)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6998828207303306,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:42.318Z","last_commit":"2026-05-01T15:15:41Z"},"community":{"stars":10169,"forks":3198,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openvinotoolkit--openvino","compare_url":"https://unfragile.ai/compare?artifact=openvinotoolkit--openvino"}},"signature":"Z+rAQy6a8YtwRPxh+NnrqnUG/0YDNGrcVtP4ReyerST3gSTVYV8XX9H7FuE9A7S2WdXPvW0pg4lggfRnyUdoAw==","signedAt":"2026-06-20T17:38:49.983Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openvinotoolkit--openvino","artifact":"https://unfragile.ai/openvinotoolkit--openvino","verify":"https://unfragile.ai/api/v1/verify?slug=openvinotoolkit--openvino","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"}}