{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-whisper-cpp","slug":"whisper-cpp","name":"whisper.cpp","type":"repo","url":"https://github.com/ggml-org/whisper.cpp","page_url":"https://unfragile.ai/whisper-cpp","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-whisper-cpp__cap_0","uri":"capability://text.generation.language.cpu.optimized.speech.to.text.inference","name":"cpu-optimized speech-to-text inference","description":"Executes OpenAI's Whisper model entirely on CPU using quantized weights and optimized matrix operations, eliminating GPU dependency. Implements GGML (Georgi Gerganov's Machine Learning) tensor library with hand-optimized kernels for x86, ARM, and WASM architectures, achieving real-time or near-real-time transcription on consumer hardware through aggressive quantization (Q4, Q5, Q8 formats) and memory-mapped model loading.","intents":["Run speech recognition locally without cloud API calls or GPU hardware","Deploy Whisper to edge devices, mobile, or resource-constrained servers","Reduce latency and privacy concerns by keeping audio processing on-device","Build offline-first voice applications with minimal infrastructure"],"best_for":["Embedded systems and IoT developers needing local inference","Privacy-focused applications avoiding cloud transcription","Teams deploying to heterogeneous hardware (x86, ARM, RISC-V)","Developers building offline-capable voice assistants"],"limitations":["Quantization reduces accuracy by 2-5% vs full-precision Whisper depending on quantization level","Single-threaded performance slower than GPU inference; multi-threaded scaling limited by memory bandwidth","No built-in streaming/chunked inference — requires buffering full audio segments","WASM target limited to ~500MB model sizes due to browser memory constraints"],"requires":["C++11 compiler (GCC, Clang, MSVC)","Audio input source (file, microphone, or stream)","Quantized Whisper model (base, small, medium, large variants available)","2GB+ RAM for large models, 512MB+ for small models"],"input_types":["WAV, MP3, FLAC, OGG audio files","Raw PCM audio streams (16-bit, mono/stereo)","Microphone input via platform-specific APIs"],"output_types":["Plain text transcription","JSON with timestamps and confidence scores","VTT/SRT subtitle format","Token-level timing information"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_1","uri":"capability://text.generation.language.multi.language.speech.recognition.with.language.detection","name":"multi-language speech recognition with language detection","description":"Automatically detects spoken language from audio and transcribes in 99+ languages using Whisper's multilingual encoder-decoder architecture. The model learns language-agnostic acoustic representations in the encoder, then uses language tokens to condition the decoder, enabling zero-shot transfer to languages unseen during fine-tuning. Language detection happens via a 50-token language classifier embedded in the model.","intents":["Transcribe multilingual audio without pre-specifying the language","Build voice applications serving global audiences with automatic language routing","Detect language of audio segments for downstream processing or analytics","Handle code-switched audio (mixing multiple languages) with reasonable accuracy"],"best_for":["International teams building voice products for multiple markets","Content platforms needing automatic language identification for transcription","Accessibility tools serving diverse linguistic populations","Research applications studying multilingual speech patterns"],"limitations":["Language detection accuracy drops for short audio clips (<3 seconds) or heavily accented speech","Code-switching (mid-sentence language mixing) often transcribed in primary language only","Rare languages (e.g., indigenous languages) have lower accuracy than high-resource languages","No explicit language confidence scores — only binary language token output"],"requires":["Multilingual Whisper model variant (base, small, medium, large)","Audio sample with sufficient speech content (>1 second recommended for reliable detection)","No external language detection model needed — built into Whisper weights"],"input_types":["Audio files in any supported format","Raw audio streams with language auto-detection"],"output_types":["Detected language code (ISO 639-1 or 639-3)","Transcribed text in detected language","Language confidence via token probability"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_10","uri":"capability://automation.workflow.command.line.interface.with.flexible.configuration","name":"command-line interface with flexible configuration","description":"Provides a comprehensive CLI tool for running Whisper inference with extensive configuration options, including model selection, input/output format specification, language hints, timestamp generation, and performance tuning. The CLI supports both single-file and batch processing modes, with configuration via command-line flags, environment variables, or config files. Includes progress reporting, error handling, and output formatting options.","intents":["Quickly transcribe audio files from the command line without writing code","Integrate whisper.cpp into shell scripts and automation workflows","Experiment with different model sizes and configurations","Batch process audio files with consistent settings"],"best_for":["System administrators and DevOps engineers automating transcription","Researchers experimenting with Whisper on various audio samples","Content creators transcribing podcasts or videos","Teams building shell-based transcription pipelines"],"limitations":["CLI overhead adds 100-200ms startup time vs programmatic API","Limited real-time streaming support compared to programmatic API","Configuration via flags becomes unwieldy with many options (consider config files)","No built-in progress bar for very large files (>1GB)"],"requires":["whisper.cpp compiled binary in PATH","Audio file or microphone input","Quantized model file accessible from CLI"],"input_types":["Audio file paths","Microphone input (platform-dependent)","stdin for piped audio"],"output_types":["Plain text to stdout","JSON output to file","SRT/VTT subtitle files","Structured logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_11","uri":"capability://text.generation.language.language.specific.model.variants.with.optimized.weights","name":"language-specific model variants with optimized weights","description":"Provides pre-trained Whisper models optimized for specific languages (English-only variants) with reduced model size and improved accuracy for that language. The English-only models remove the multilingual encoder and language token logic, reducing parameters by ~30% and improving English transcription accuracy by 2-3%. Available in multiple sizes (tiny, base, small, medium, large) with corresponding quantization levels.","intents":["Deploy English-only transcription with smaller model footprint and better accuracy","Reduce latency for English-specific applications","Optimize for English-heavy workloads while maintaining multilingual fallback option","Build language-specific applications with minimal model overhead"],"best_for":["English-only applications (US/UK/Australian English focus)","Mobile and embedded deployments with size constraints","Real-time transcription systems prioritizing English accuracy","Teams not needing multilingual support"],"limitations":["Cannot transcribe non-English languages (hard constraint)","Accuracy improvements over multilingual models are modest (2-3%)","Fewer model variants available than multilingual (no tiny.en in some versions)","Not suitable for code-switched or multilingual audio"],"requires":["English-only Whisper model variant (.en suffix)","English audio input","No language specification needed (English assumed)"],"input_types":["English audio files or streams"],"output_types":["English transcription text","Timestamps and confidence scores"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_2","uri":"capability://text.generation.language.timestamp.aware.transcription.with.word.level.timing","name":"timestamp-aware transcription with word-level timing","description":"Generates transcription output with precise word-level and segment-level timestamps by leveraging Whisper's decoder attention patterns and cross-attention to the encoder. The implementation extracts timing information from the model's internal attention weights during inference, mapping each decoded token back to its corresponding audio frame, then aggregates frames into word and segment boundaries using heuristic post-processing.","intents":["Create searchable transcripts with clickable timestamps for video/podcast platforms","Generate accurate subtitle files (SRT, VTT) with frame-perfect timing","Build interactive transcription UIs where clicking a word seeks to that audio position","Analyze speech patterns (pause duration, speaking rate) from timestamp data"],"best_for":["Media platforms (YouTube, Spotify, podcasting apps) needing subtitle generation","Accessibility tools creating synchronized captions for deaf/hard-of-hearing users","Researchers analyzing temporal speech characteristics","Video editors needing automatic caption timing"],"limitations":["Timestamp accuracy ±100-200ms due to attention weight heuristics and frame-level quantization","Word boundaries sometimes misaligned for fast speech or heavy accents","Timestamps degrade for audio with background noise or multiple speakers","No speaker diarization — cannot distinguish who spoke at each timestamp"],"requires":["Whisper model with attention weight access (all variants)","Audio with clear speech and minimal background noise for best timing accuracy","Post-processing logic to convert frame-level timings to word boundaries"],"input_types":["Audio files with known sample rate","Streaming audio with buffered segments"],"output_types":["JSON with word-level timestamps and confidence","SRT/VTT subtitle format with timing","Segment-level timing (sentence/paragraph boundaries)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_3","uri":"capability://text.generation.language.streaming.real.time.transcription.with.sliding.window.buffering","name":"streaming/real-time transcription with sliding window buffering","description":"Processes continuous audio streams in fixed-size chunks (e.g., 30-second windows) with overlap to maintain context, enabling near-real-time transcription without waiting for complete audio. The implementation buffers incoming audio samples, triggers inference when a chunk is ready, and uses overlapping windows to preserve word boundaries and context across chunk boundaries. Partial results are emitted as chunks complete, with final results refined as more context arrives.","intents":["Build live transcription for video calls, meetings, or live events","Create real-time voice command recognition with sub-second latency","Stream transcription results to users as they speak (live captions)","Process continuous audio feeds from surveillance or monitoring systems"],"best_for":["Video conferencing platforms (Zoom, Teams, Google Meet) adding live captions","Voice assistant developers needing responsive command recognition","Live event platforms (streaming, conferences) requiring real-time subtitles","Accessibility tools providing live captions for deaf/hard-of-hearing users"],"limitations":["Latency 5-30 seconds depending on chunk size and hardware (not true real-time like streaming ASR models)","Chunk boundaries may split words or sentences, requiring post-processing to fix","Overlapping windows increase computational cost by 20-40% vs single-pass inference","No built-in speaker diarization or multi-speaker handling","Requires careful tuning of chunk size and overlap for optimal latency/accuracy tradeoff"],"requires":["Audio input stream (microphone, network stream, etc.)","Buffering mechanism to accumulate samples between inference runs","Chunk size configuration (typically 10-30 seconds)","Overlap percentage (typically 10-20% of chunk size)"],"input_types":["Real-time audio streams (microphone input)","Network audio streams (RTP, WebRTC, etc.)","File-based audio with simulated streaming"],"output_types":["Partial transcription results (updated per chunk)","Final transcription with corrections as context arrives","Streaming JSON with incremental updates"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_4","uri":"capability://data.processing.analysis.model.quantization.and.format.conversion","name":"model quantization and format conversion","description":"Converts full-precision Whisper models (PyTorch, ONNX) to quantized GGML format with multiple precision levels (Q4_0, Q4_1, Q5_0, Q5_1, Q8_0) using a custom quantization pipeline. The process includes weight quantization (reducing 32-bit floats to 4-8 bits), layer-wise statistics collection for optimal quantization ranges, and format serialization into memory-mapped binary files. Supports both symmetric and asymmetric quantization strategies with per-channel or per-tensor granularity.","intents":["Reduce model size from 1.5GB (large) to 200-400MB (Q4) for deployment on resource-constrained devices","Convert existing Whisper checkpoints to whisper.cpp compatible format","Experiment with different quantization levels to balance accuracy vs speed/size","Create optimized model variants for specific hardware targets (mobile, embedded, WASM)"],"best_for":["ML engineers optimizing models for edge deployment","Teams migrating from PyTorch Whisper to whisper.cpp","Developers building mobile apps requiring small model footprints","Researchers studying quantization effects on speech recognition accuracy"],"limitations":["Quantization introduces 2-5% accuracy loss depending on quantization level (Q4 worse than Q8)","Conversion process requires original model weights (PyTorch .pt or ONNX .onnx files)","No automatic quantization level selection — requires manual experimentation","Quantized models not compatible with standard PyTorch/ONNX inference (whisper.cpp only)","Per-channel quantization increases file size by 5-10% vs per-tensor but improves accuracy"],"requires":["Original Whisper model in PyTorch or ONNX format","Python environment with PyTorch/ONNX libraries for conversion","Disk space for both original and quantized models during conversion","whisper.cpp quantization tools (convert-pt-to-ggml.py or similar)"],"input_types":["PyTorch .pt checkpoint files","ONNX .onnx model files","HuggingFace model identifiers"],"output_types":["GGML binary format (.ggml, .bin)","Quantization metadata (statistics, ranges)","Model info file with layer counts and dimensions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_5","uri":"capability://automation.workflow.multi.threaded.inference.with.work.distribution","name":"multi-threaded inference with work distribution","description":"Parallelizes Whisper inference across multiple CPU cores using thread-pool-based work distribution at the tensor operation level. The implementation partitions matrix multiplications and element-wise operations across threads, with each thread processing a slice of the computation. Uses lock-free work queues and NUMA-aware thread pinning for optimal cache locality on multi-socket systems. Supports configurable thread count and automatic detection of available cores.","intents":["Reduce transcription latency on multi-core CPUs (4-16 cores) by 2-4x vs single-threaded","Maximize CPU utilization on server hardware for batch transcription workloads","Balance latency and throughput for real-time applications on multi-core systems","Enable efficient resource sharing in containerized/cloud deployments"],"best_for":["Server-side transcription services handling multiple concurrent requests","Desktop applications on modern multi-core CPUs (8+ cores)","Batch processing pipelines transcribing large audio libraries","Cloud deployments on multi-core instances (AWS, GCP, Azure)"],"limitations":["Scaling efficiency drops above 8-16 threads due to memory bandwidth saturation","Overhead of thread synchronization adds 5-10% latency vs single-threaded on small models","NUMA systems require careful thread pinning; automatic detection may be suboptimal","No GPU offloading — all computation remains on CPU","Thread count must be tuned per hardware; no automatic optimal selection"],"requires":["Multi-core CPU (4+ cores recommended, 8+ for significant speedup)","Thread-safe audio buffering mechanism","Configuration parameter for thread count (default: auto-detect)","Sufficient memory bandwidth (DDR4/DDR5 recommended)"],"input_types":["Audio files or streams (same as single-threaded)"],"output_types":["Transcription results (same as single-threaded, timing may vary)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_6","uri":"capability://text.generation.language.wasm.javascript.binding.for.browser.based.transcription","name":"wasm/javascript binding for browser-based transcription","description":"Compiles whisper.cpp to WebAssembly using Emscripten, exposing a JavaScript API for running Whisper inference directly in web browsers. The implementation includes audio capture from microphone/file input, model loading from IndexedDB or network, and streaming results back to JavaScript. Uses Web Workers to prevent blocking the main thread, with SharedArrayBuffer for efficient audio buffer passing between JS and WASM.","intents":["Build web applications with client-side speech recognition (no server required)","Create privacy-preserving voice interfaces where audio never leaves the browser","Develop offline-capable web apps with local model caching","Enable voice input in web-based accessibility tools and assistants"],"best_for":["Web developers building privacy-first voice applications","Accessibility tool creators adding voice input to web platforms","Teams avoiding cloud ASR costs by processing locally","Progressive web app (PWA) developers needing offline voice capabilities"],"limitations":["Model size limited to ~500MB due to browser memory constraints (vs 1.5GB for desktop)","Inference latency 2-5x slower than native C++ due to WASM overhead and JS interop","Requires modern browsers with WebAssembly support (Chrome 57+, Firefox 52+, Safari 14.1+)","SharedArrayBuffer requires cross-origin isolation headers, limiting deployment options","No GPU acceleration in WASM (WebGPU still experimental)","Audio capture requires HTTPS and user permission"],"requires":["Modern web browser with WebAssembly support","HTTPS connection (required for microphone access)","Sufficient browser memory (512MB+ for small models, 1GB+ for medium)","JavaScript ES6+ support for async/await and Promises"],"input_types":["Microphone input via Web Audio API","File upload (WAV, MP3, FLAC, OGG)","Audio from HTMLMediaElement (video/audio tags)"],"output_types":["JavaScript Promise resolving to transcription string","Streaming results via callback or EventEmitter","JSON with timestamps and token-level details"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_7","uri":"capability://automation.workflow.batch.transcription.with.automatic.queue.management","name":"batch transcription with automatic queue management","description":"Processes multiple audio files sequentially or in parallel batches with automatic queue management, progress tracking, and error handling. The implementation maintains a work queue of audio files, distributes them to worker threads or processes, aggregates results, and provides callbacks for progress updates and error recovery. Supports priority queuing, retry logic for failed transcriptions, and output batching to reduce I/O overhead.","intents":["Transcribe large audio libraries (100s-1000s of files) efficiently","Build batch processing pipelines for content platforms or archives","Distribute transcription work across multiple machines or containers","Monitor and report progress on long-running transcription jobs"],"best_for":["Content platforms processing user-uploaded audio at scale","Archivists digitizing and transcribing historical audio collections","Researchers analyzing large speech corpora","Teams building automated transcription pipelines"],"limitations":["No built-in distributed processing — requires manual setup for multi-machine batching","Memory usage scales with queue size; large queues may exhaust RAM","No automatic load balancing across heterogeneous hardware","Error recovery requires manual retry configuration","Progress tracking adds overhead; disabling improves throughput by 5-10%"],"requires":["List of audio files with accessible paths or URLs","Output directory for transcription results","Configuration for batch size, thread count, and retry policy","Sufficient disk space for output files"],"input_types":["File paths (local or network-accessible)","URLs to remote audio files","Manifest files listing audio locations"],"output_types":["Individual transcription files (JSON, TXT, SRT)","Batch summary with success/failure counts","Progress logs and error reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_8","uri":"capability://data.processing.analysis.audio.preprocessing.and.normalization","name":"audio preprocessing and normalization","description":"Applies audio signal processing to normalize and prepare raw audio for Whisper inference, including resampling to 16kHz, mono conversion, volume normalization (peak or RMS-based), silence trimming, and optional noise reduction. The implementation uses efficient DSP algorithms (polyphase resampling, FFT-based filtering) to minimize preprocessing latency. Supports multiple input sample rates (8kHz-48kHz) and channel configurations (mono, stereo, multi-channel).","intents":["Handle audio from diverse sources with different sample rates and formats","Improve transcription accuracy by normalizing volume and removing silence","Reduce preprocessing latency in real-time transcription pipelines","Prepare audio for downstream analysis (speaker diarization, emotion detection)"],"best_for":["Audio processing engineers building robust transcription pipelines","Teams handling user-generated audio with variable quality","Real-time transcription systems requiring low-latency preprocessing","Accessibility tools needing to handle diverse audio sources"],"limitations":["Aggressive silence trimming may remove intentional pauses (e.g., in poetry or speeches)","Noise reduction (if enabled) may distort speech in high-noise environments","Resampling introduces ~10-20ms latency depending on filter quality","No speaker diarization or multi-speaker separation","Volume normalization may amplify background noise in quiet sections"],"requires":["Raw audio data or file path","Target sample rate (16kHz for Whisper)","Configuration for normalization strategy (peak, RMS, or none)","Optional: noise reduction parameters"],"input_types":["Raw PCM audio (8/16/24/32-bit, various sample rates)","Audio files (WAV, MP3, FLAC, OGG)","Audio streams from microphone or network"],"output_types":["Normalized PCM audio at 16kHz mono","Audio statistics (peak level, RMS, silence regions)","Trimmed audio with silence removed"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-whisper-cpp__cap_9","uri":"capability://memory.knowledge.model.caching.and.lazy.loading","name":"model caching and lazy loading","description":"Implements memory-mapped model loading and intelligent caching to avoid reloading models between inference runs. The implementation uses mmap to load quantized models without decompression, caches loaded models in memory with LRU eviction, and supports lazy loading of model layers on first use. Enables efficient multi-model scenarios (e.g., different languages or sizes) without excessive memory usage.","intents":["Reduce startup latency for repeated transcription tasks by caching models","Support multiple model variants (base, small, medium, large) without reloading","Minimize memory footprint in resource-constrained environments","Enable fast model switching for multilingual applications"],"best_for":["Server applications handling multiple transcription requests","Applications supporting multiple languages or model sizes","Embedded systems with limited memory","Interactive tools requiring fast model switching"],"limitations":["Memory-mapped files require OS support (not available on all platforms)","Cache invalidation requires manual management; no automatic update detection","LRU eviction may cause thrashing if working set exceeds available memory","Model loading still requires initial disk I/O (mmap doesn't eliminate this)","No distributed caching across multiple machines"],"requires":["Quantized GGML model files on disk","Sufficient RAM for at least one model (512MB+ for small, 1GB+ for medium)","Configuration for cache size and eviction policy","OS with mmap support (Linux, macOS, Windows)"],"input_types":["Model file paths","Model identifiers (e.g., 'base.en', 'small')"],"output_types":["Loaded model in memory (ready for inference)","Cache statistics (hit rate, memory usage)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["C++11 compiler (GCC, Clang, MSVC)","Audio input source (file, microphone, or stream)","Quantized Whisper model (base, small, medium, large variants available)","2GB+ RAM for large models, 512MB+ for small models","Multilingual Whisper model variant (base, small, medium, large)","Audio sample with sufficient speech content (>1 second recommended for reliable detection)","No external language detection model needed — built into Whisper weights","whisper.cpp compiled binary in PATH","Audio file or microphone input","Quantized model file accessible from CLI"],"failure_modes":["Quantization reduces accuracy by 2-5% vs full-precision Whisper depending on quantization level","Single-threaded performance slower than GPU inference; multi-threaded scaling limited by memory bandwidth","No built-in streaming/chunked inference — requires buffering full audio segments","WASM target limited to ~500MB model sizes due to browser memory constraints","Language detection accuracy drops for short audio clips (<3 seconds) or heavily accented speech","Code-switching (mid-sentence language mixing) often transcribed in primary language only","Rare languages (e.g., indigenous languages) have lower accuracy than high-resource languages","No explicit language confidence scores — only binary language token output","CLI overhead adds 100-200ms startup time vs programmatic API","Limited real-time streaming support compared to programmatic API","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.689Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=whisper-cpp","compare_url":"https://unfragile.ai/compare?artifact=whisper-cpp"}},"signature":"4BRQcljUcoTQJvZbNd5iY1DXlMTM+4kLrZtX9vn42PxKa/0GsOsbeNGjLVrCYhl3b5PcTnlN3+IJ5stslO4LAA==","signedAt":"2026-06-21T12:50:26.516Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/whisper-cpp","artifact":"https://unfragile.ai/whisper-cpp","verify":"https://unfragile.ai/api/v1/verify?slug=whisper-cpp","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"}}