{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-nexaai--nexa-sdk","slug":"nexaai--nexa-sdk","name":"nexa-sdk","type":"framework","url":"https://docs.nexa.ai/","page_url":"https://unfragile.ai/nexaai--nexa-sdk","categories":["frameworks-sdks"],"tags":["gemma3","go","gpt-oss","granite4","llama","llama3","llm","on-device-ai","phi3","qwen3","qwen3vl","sdk","stable-diffusion","vlm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-nexaai--nexa-sdk__cap_0","uri":"capability://text.generation.language.cross.platform.on.device.llm.inference.with.hardware.agnostic.abstraction","name":"cross-platform on-device llm inference with hardware-agnostic abstraction","description":"Executes large language models locally across CPU, GPU, and NPU hardware through a layered architecture that abstracts hardware differences via a plugin system. The Go SDK provides type-safe interfaces (Create/Destroy lifecycle) that route inference requests through CGo bindings to C/C++ hardware plugins, enabling day-0 support for models like GPT-OSS, Granite-4, Qwen-3, and Llama-3 without cloud dependencies. Model formats (GGUF, MLX, NEXA) are handled by format-specific plugins that optimize for target hardware capabilities.","intents":["Run proprietary LLMs locally on consumer hardware without sending data to cloud APIs","Deploy inference on edge devices (mobile, IoT) with minimal latency and privacy guarantees","Switch between GPU/NPU/CPU backends transparently without code changes","Integrate latest open-source models immediately upon release without waiting for SDK updates"],"best_for":["Privacy-conscious developers building LLM applications for regulated industries","Mobile app developers targeting Android/iOS with on-device AI","IoT/edge computing teams deploying inference on Arm64 or x86 Docker containers","Teams requiring zero-latency inference or offline-first architectures"],"limitations":["Plugin system adds abstraction overhead (~50-100ms per inference call depending on hardware bridge complexity)","Model quantization (GGUF format) may reduce accuracy vs full-precision cloud models by 1-3% on benchmarks","NPU support limited to Qualcomm, AMD, and Intel architectures — no support for Apple Neural Engine for LLM inference","Memory constraints on mobile devices limit model size to ~7B parameters effectively"],"requires":["Python 3.9+ or Go 1.18+ depending on SDK choice","Minimum 4GB RAM for 7B models, 8GB+ for 13B models","For GPU: CUDA 11.8+ (NVIDIA) or ROCm 5.0+ (AMD)","For NPU: Device-specific drivers (Qualcomm Hexagon, Intel VPU, AMD XDNA)"],"input_types":["text prompts (string)","structured messages (JSON with role/content)","multi-turn conversation history"],"output_types":["text completions (streaming or batch)","structured JSON (via function calling)","token-level logits for custom decoding"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_1","uri":"capability://image.visual.vision.language.model.inference.with.multimodal.input.handling","name":"vision-language model inference with multimodal input handling","description":"Processes images and text together through VLM models (Qwen-3-VL, etc.) using a unified Go SDK interface that handles image encoding, tokenization, and vision-specific hardware optimizations. The VLM plugin system manages image preprocessing (resizing, normalization) and routes vision tokens through specialized hardware paths (GPU tensor cores for image encoding, NPU for attention). Supports batch image processing and maintains image context across multi-turn conversations.","intents":["Analyze images locally without uploading to cloud vision APIs","Build document understanding applications (OCR, table extraction) with context awareness","Create interactive image-based chatbots with conversation memory","Process video frames in real-time for on-device video understanding"],"best_for":["Healthcare/legal teams processing sensitive documents with privacy requirements","Mobile app developers building image analysis features (photo search, accessibility)","Robotics/autonomous systems teams needing real-time visual reasoning","Content moderation platforms requiring on-device image understanding"],"limitations":["Image resolution limited by model architecture (typically 1024x1024 max) — larger images require tiling or downsampling","Vision encoding adds 200-500ms latency per image depending on resolution and hardware","Batch processing limited by available VRAM — typically 1-4 images per batch on mobile devices","No built-in support for video frame extraction — requires external preprocessing"],"requires":["Model supporting vision input (Qwen-3-VL, LLaVA, etc.)","Minimum 6GB RAM for VLM inference on mobile","Image input in JPEG, PNG, or WebP format","For optimal performance: GPU with 8GB+ VRAM or NPU with vision acceleration"],"input_types":["image files (JPEG, PNG, WebP)","raw image buffers (RGB/RGBA byte arrays)","text prompts describing image analysis task","multi-modal conversation history (alternating text/image)"],"output_types":["text descriptions of image content","structured JSON with detected objects/regions","bounding box coordinates for object localization","confidence scores for classification tasks"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_10","uri":"capability://tool.use.integration.python.sdk.with.model.lifecycle.management.and.async.inference","name":"python sdk with model lifecycle management and async inference","description":"Provides Python bindings to the Go SDK through a wrapper layer that exposes model classes (LLM, VLM, Embedder, etc.) with Create/Destroy lifecycle management. Supports both synchronous and asynchronous inference via asyncio, enabling concurrent model execution. Implements model caching and keepalive mechanisms to avoid reloading models between requests. Type hints and docstrings enable IDE autocomplete and documentation.","intents":["Integrate on-device inference into Python applications without Go knowledge","Build async inference pipelines for high-throughput applications","Use models in Jupyter notebooks for interactive development","Create Python packages that depend on Nexa for distribution"],"best_for":["Python developers building LLM applications","Data scientists prototyping inference pipelines in notebooks","ML engineers integrating on-device models into Python services","Teams with existing Python codebases adding local inference"],"limitations":["Python wrapper adds 10-20ms overhead per inference call due to language boundary crossing","Async support requires event loop management — can be complex in multi-threaded applications","Type hints are best-effort — some edge cases may lack proper typing","Model keepalive mechanism uses in-process memory — resets on Python process exit"],"requires":["Python 3.9+","Nexa SDK installed (Go runtime required)","For async: asyncio event loop (built-in to Python 3.7+)"],"input_types":["text prompts (str)","image paths or PIL Image objects","structured data (dict, list)"],"output_types":["text completions (str)","streaming generators (yield text chunks)","structured data (dict with metadata)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_11","uri":"capability://tool.use.integration.android.sdk.with.native.model.inference.and.lifecycle.management","name":"android sdk with native model inference and lifecycle management","description":"Provides Android-specific bindings to the Nexa inference engine through JNI (Java Native Interface) bridges. Implements model lifecycle management (Create/Destroy) with automatic cleanup on activity destruction. Supports both synchronous and asynchronous inference via Android's Executor framework. Handles Android-specific constraints (memory pressure, background execution, battery optimization) through lifecycle-aware components.","intents":["Add on-device LLM/VLM capabilities to Android apps without cloud APIs","Build offline-first mobile applications with local inference","Create voice assistants and chatbots for Android devices","Implement privacy-preserving AI features for sensitive applications"],"best_for":["Android app developers adding AI features without cloud dependencies","Mobile teams building voice assistants and chatbots","Healthcare/financial apps requiring on-device processing for compliance","Startups building AI-first mobile applications"],"limitations":["JNI overhead adds 20-50ms per inference call due to language boundary crossing","Memory constraints on mobile — models limited to ~7B parameters effectively","Battery consumption high during inference — requires thermal throttling on sustained use","Background execution limited by Android OS — inference may be suspended when app backgrounded"],"requires":["Android 8.0+ (API level 26+)","Minimum 4GB RAM for 7B models","For GPU: Adreno GPU (Qualcomm) or Mali GPU (ARM)","For NPU: Snapdragon 8 Gen 2+ or MediaTek Dimensity 9300+"],"input_types":["text prompts (String)","image URIs or Bitmap objects","structured data (Bundle, Map)"],"output_types":["text completions (String)","streaming callbacks (CharSequence chunks)","structured data (Bundle with metadata)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_12","uri":"capability://tool.use.integration.ios.sdk.with.metal.gpu.acceleration.and.app.extension.support","name":"ios sdk with metal gpu acceleration and app extension support","description":"Provides iOS-specific bindings to the Nexa inference engine through Swift/Objective-C bridges. Implements Metal GPU acceleration for inference on Apple devices, leveraging GPU compute shaders for matrix operations. Supports iOS app extensions (Siri, keyboard, share) enabling inference in restricted execution contexts. Implements background task management for long-running inference with proper battery optimization.","intents":["Add on-device LLM/VLM capabilities to iOS apps without cloud APIs","Build Siri shortcuts and voice commands with local inference","Create keyboard extensions with AI-powered text completion","Implement privacy-preserving AI features for sensitive iOS applications"],"best_for":["iOS app developers adding AI features without cloud dependencies","Teams building voice assistants for Apple ecosystem","Healthcare/financial apps requiring on-device processing for compliance","Startups building AI-first iOS applications"],"limitations":["Metal GPU acceleration limited to A-series chips (A12 Bionic+) — older devices fall back to CPU","Memory constraints on iPhone — models limited to ~3-7B parameters effectively","App extension execution limited by iOS sandbox — inference may be terminated after 30 seconds","Battery consumption high during inference — requires thermal throttling on sustained use"],"requires":["iOS 14.0+ (iOS 15.0+ for optimal performance)","Minimum 4GB RAM for 7B models","A12 Bionic chip or newer for GPU acceleration","Swift 5.5+ or Objective-C compatible"],"input_types":["text prompts (String)","image URLs or UIImage objects","structured data (Dictionary, Array)"],"output_types":["text completions (String)","streaming callbacks (AsyncSequence)","structured data (Dictionary with metadata)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_13","uri":"capability://automation.workflow.docker.containerization.for.linux.iot.deployment.with.arm64.and.x86.support","name":"docker containerization for linux/iot deployment with arm64 and x86 support","description":"Provides Docker images and containerization support for deploying Nexa on Linux servers and IoT devices. Supports both Arm64 (Raspberry Pi, Jetson, etc.) and x86-64 architectures with hardware-specific optimizations (CUDA for x86 GPU, NEON for Arm64 CPU). Implements multi-stage builds to minimize image size and includes pre-configured models for common use cases. Supports Docker Compose for orchestrating multi-model inference services.","intents":["Deploy on-device inference on edge servers without manual setup","Run Nexa on Raspberry Pi and Jetson devices for IoT applications","Scale inference across multiple containers with load balancing","Simplify model deployment and updates through container versioning"],"best_for":["DevOps teams deploying inference on edge servers","IoT teams building AI-powered edge devices","Kubernetes operators managing inference clusters","System administrators standardizing model deployment"],"limitations":["Container overhead adds 100-200MB to image size vs bare metal installation","GPU passthrough requires --gpus flag and nvidia-docker — not all container runtimes support it","Arm64 images require cross-compilation or native build on Arm64 hardware — slower builds","No built-in orchestration — requires external tools (Kubernetes, Docker Swarm) for multi-node deployment"],"requires":["Docker 20.10+ or compatible container runtime","For GPU: nvidia-docker or Docker 20.10+ with GPU support","For Arm64: Docker buildx or native Arm64 build environment","Sufficient disk space for images (2-5GB depending on models included)"],"input_types":["Docker image (nexa-sdk:latest)","Environment variables for model selection","Volume mounts for model cache"],"output_types":["Running container with HTTP server","Container logs with inference metrics","Persistent model cache (volume mount)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_14","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.registry.and.multi.provider.support","name":"function calling with schema-based tool registry and multi-provider support","description":"Implements structured function calling through a schema-based tool registry that defines function signatures as JSON schemas. Supports OpenAI and Anthropic function-calling protocols natively, enabling agents to invoke external tools with type-safe arguments. The server middleware validates function calls against schemas, handles tool execution, and formats responses back to the model. Supports both synchronous tool execution and async tool chains.","intents":["Build LLM agents that can call external APIs and tools reliably","Create structured output from models using function calling","Implement multi-step workflows where models decide which tools to use","Ensure type-safe tool invocation without manual validation"],"best_for":["LLM agent builders creating autonomous systems","Teams building structured output pipelines","Developers implementing tool-using AI assistants","Researchers experimenting with agent architectures"],"limitations":["Schema validation adds 50-100ms overhead per function call","Tool execution latency depends on external service — can dominate inference time","No built-in retry logic — requires external circuit breaker for unreliable tools","Schema complexity limited — deeply nested schemas may cause parsing errors"],"requires":["JSON schema definitions for each tool","Tool implementation (function or API endpoint)","Model supporting function calling (GPT-4, Claude, etc.)"],"input_types":["JSON schema definitions","tool function implementations","user prompts requesting tool use"],"output_types":["function call objects with tool_use_id","tool execution results","final model response with tool results incorporated"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_2","uri":"capability://tool.use.integration.openai.compatible.http.server.with.function.calling.and.streaming","name":"openai-compatible http server with function calling and streaming","description":"Exposes local inference models via REST API endpoints that mirror OpenAI's chat completion and embedding APIs, enabling drop-in replacement of cloud LLM services. The server implements streaming responses (Server-Sent Events), function calling via schema-based function registry with native bindings for OpenAI/Anthropic APIs, and middleware for request validation, rate limiting, and response formatting. Built on Go HTTP server with configurable port and model routing.","intents":["Replace OpenAI API calls with local inference without changing client code","Build LLM agents that call external tools/APIs with structured function definitions","Stream model responses to frontend applications for real-time UX","Run multiple models simultaneously with automatic load balancing across requests"],"best_for":["Teams migrating from cloud LLM APIs to on-device inference for cost/latency","LLM agent builders needing tool-calling capabilities without cloud dependencies","Web application developers building real-time chat interfaces","Enterprise teams with strict data residency requirements"],"limitations":["Function calling latency adds 50-200ms per tool invocation due to schema validation and serialization","Streaming responses require persistent HTTP connections — incompatible with some load balancers/proxies","No built-in authentication — requires external reverse proxy (nginx, Caddy) for API key management","Rate limiting is in-memory only — resets on server restart, no distributed rate limiting across multiple instances"],"requires":["HTTP client library (curl, requests, fetch, etc.)","Port availability (default 8000, configurable)","For function calling: JSON schema definitions for each tool","Optional: reverse proxy for TLS/authentication (nginx, Caddy, Envoy)"],"input_types":["JSON request body with messages array (OpenAI format)","Function definitions as JSON schemas","Query parameters for model selection and inference parameters"],"output_types":["JSON response with completion text","Server-Sent Events stream for streaming responses","Function call objects with tool_use_id for agent workflows"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_3","uri":"capability://automation.workflow.model.hub.integration.with.multi.source.downloads.and.caching","name":"model hub integration with multi-source downloads and caching","description":"Manages model lifecycle (discovery, download, caching, updates) across multiple model repositories (Hugging Face, ModelScope, Volces, S3, local filesystem) through a pluggable model hub system. Implements intelligent caching with file locking to prevent concurrent downloads, manifest tracking for version management, and automatic model updates. The store manager (runner/internal/store/) handles disk space management, model validation, and atomic file operations to ensure consistency across platform crashes.","intents":["Download and cache models from multiple sources without manual file management","Automatically update models to latest versions without breaking existing applications","Manage disk space by tracking model sizes and implementing LRU eviction policies","Support offline-first workflows by pre-caching models before deployment"],"best_for":["DevOps teams deploying models to edge devices with limited storage","Mobile app developers managing model updates across user base","CI/CD pipelines that need reproducible model versions","Teams using multiple model sources (HF, ModelScope, custom S3 buckets)"],"limitations":["Download speed limited by network bandwidth and source server rate limits (typically 10-50 MB/s)","File locking mechanism uses filesystem-level locks — may fail on network filesystems (NFS, SMB)","No built-in compression — models stored at full size on disk (7B model ~4-5GB, 13B ~8-10GB)","Manifest tracking is local-only — no distributed cache invalidation for multi-device deployments"],"requires":["Network connectivity for initial model download (can be offline after caching)","Sufficient disk space (minimum 2x model size for download + extraction)","Write permissions to model cache directory (default: ~/.nexa/models)","For S3 sources: AWS credentials or public bucket access"],"input_types":["model identifier string (e.g., 'qwen/qwen-3-7b-instruct')","model source URL (HF, ModelScope, S3, local path)","version/revision specifier (branch, tag, commit hash)"],"output_types":["local filesystem path to cached model","model metadata (size, format, hardware requirements)","download progress events (bytes downloaded, ETA)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_4","uri":"capability://data.processing.analysis.text.embedding.generation.with.semantic.search.support","name":"text embedding generation with semantic search support","description":"Generates dense vector embeddings for text using embedding models (e.g., BGE, ONNX-based embedders) through the embedder interface (runner/nexa-sdk/embedder.go). Embeddings are computed locally on GPU/NPU for privacy, supporting batch processing to amortize inference overhead. Integrates with vector databases via standard embedding output format (float32 arrays), enabling semantic search, similarity matching, and RAG pipeline construction without external embedding services.","intents":["Build semantic search over private documents without sending text to cloud embedding APIs","Create RAG pipelines that retrieve relevant context for LLM generation","Implement similarity-based recommendation systems with local computation","Batch embed large document collections for offline indexing"],"best_for":["Enterprise teams with sensitive documents requiring on-device embeddings","RAG system builders needing low-latency retrieval augmentation","Search engine developers building semantic search without external APIs","Recommendation system teams processing user data locally"],"limitations":["Embedding models typically smaller (100M-500M params) but still require 1-2GB VRAM for batch processing","Batch size limited by available memory — typically 32-256 texts per batch depending on text length","Embedding quality varies by model — domain-specific embeddings may require fine-tuning","No built-in vector database — requires integration with external systems (Milvus, Weaviate, Pinecone, etc.)"],"requires":["Embedding model in GGUF or ONNX format","Minimum 2GB VRAM for batch embedding","Text input in UTF-8 encoding","Vector database or similarity search library for downstream use"],"input_types":["text strings (variable length)","batch of texts (array of strings)","text with metadata (JSON objects with text field)"],"output_types":["float32 embedding vectors (typically 384-1024 dimensions)","batch embeddings (2D array of vectors)","embedding metadata (model name, dimension, normalization info)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_5","uri":"capability://data.processing.analysis.reranking.with.cross.encoder.models.for.retrieval.refinement","name":"reranking with cross-encoder models for retrieval refinement","description":"Implements cross-encoder reranking (runner/nexa-sdk/reranker.go) to refine retrieval results by scoring query-document pairs jointly, improving RAG pipeline precision. Rerankers take query and candidate documents as input, compute relevance scores, and return ranked results. Operates on GPU/NPU for efficient batch scoring of large result sets, supporting both pointwise (single score per document) and pairwise (comparative scoring) reranking strategies.","intents":["Improve RAG retrieval precision by reranking dense retriever results","Filter low-relevance documents before passing to LLM for cost/latency reduction","Implement multi-stage retrieval pipelines (dense → rerank → LLM)","Fine-tune relevance scoring for domain-specific search tasks"],"best_for":["RAG system builders optimizing retrieval quality without increasing latency","Search teams implementing multi-stage ranking pipelines","Question-answering systems requiring high-precision document matching","Information retrieval researchers experimenting with ranking strategies"],"limitations":["Reranking adds 100-300ms latency per batch of documents (typically 10-100 documents)","Cross-encoder models larger than dense retrievers (200M-500M params) — requires 2-4GB VRAM","Pairwise reranking has quadratic complexity — impractical for >1000 candidates without pre-filtering","No built-in integration with vector databases — requires manual pipeline orchestration"],"requires":["Cross-encoder model (e.g., mxbai-rerank-base, bge-reranker)","Query string and list of candidate documents","Minimum 2GB VRAM for batch reranking","Upstream dense retriever results (from embedding similarity search)"],"input_types":["query string","list of candidate documents (strings or JSON objects)","optional: document metadata for filtering"],"output_types":["relevance scores (float values 0-1)","ranked document indices","sorted documents with scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_6","uri":"capability://image.visual.image.generation.with.stable.diffusion.and.latent.diffusion.models","name":"image generation with stable diffusion and latent diffusion models","description":"Generates images from text prompts using Stable Diffusion and compatible latent diffusion models through a dedicated image generation plugin. Implements the full diffusion pipeline (text encoding, latent diffusion, VAE decoding) with hardware-specific optimizations for GPU/NPU. Supports various sampling strategies (DDPM, DDIM, Euler), LoRA adapters for style transfer, and negative prompts for quality control. Outputs PNG/JPEG images with configurable resolution and quality parameters.","intents":["Generate images locally without sending prompts to cloud services (Midjourney, DALL-E)","Build image generation features into mobile/edge applications with low latency","Fine-tune image generation with LoRA adapters for specific styles or domains","Batch generate images for content creation workflows"],"best_for":["Privacy-focused applications (healthcare, legal) generating synthetic images","Mobile app developers adding image generation features","Content creators building custom image generation pipelines","Game/VFX teams generating textures and assets locally"],"limitations":["Image generation latency 30-120 seconds per image depending on resolution and hardware (GPU much faster than NPU)","Memory requirements high — typically 8-12GB VRAM for 512x512 generation, 16GB+ for 768x768","Quality lower than cloud services (DALL-E 3, Midjourney) due to model size constraints","LoRA adapter support requires manual model conversion — no built-in LoRA marketplace integration"],"requires":["Stable Diffusion model (1.5, 2.1, XL, etc.) in GGUF or ONNX format","Minimum 8GB VRAM for 512x512 generation","Text prompt input","Optional: LoRA adapter files for style transfer"],"input_types":["text prompt (string)","negative prompt (string)","generation parameters (steps, guidance_scale, seed, resolution)","optional: LoRA adapter path and weight"],"output_types":["PNG or JPEG image file","raw image tensor (RGB byte array)","generation metadata (seed, parameters used)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_7","uri":"capability://text.generation.language.text.to.speech.synthesis.with.streaming.audio.output","name":"text-to-speech synthesis with streaming audio output","description":"Converts text to natural-sounding speech using TTS models through the audio processing plugin system. Implements streaming audio generation where speech is synthesized incrementally and output as audio chunks (WAV, MP3), enabling real-time playback without waiting for full synthesis. Supports multiple voices, speaking rates, and prosody control. Hardware acceleration on GPU/NPU speeds up mel-spectrogram generation and vocoder inference.","intents":["Add voice output to chatbots and voice assistants without cloud TTS APIs","Generate audiobook content from text locally for privacy","Build accessibility features (screen reader) into applications","Create interactive voice interfaces for IoT and mobile devices"],"best_for":["Accessibility teams building screen readers and voice interfaces","Voice assistant developers requiring on-device synthesis","Content creators generating audiobooks without cloud dependencies","IoT/robotics teams adding voice output to edge devices"],"limitations":["Synthesis latency 2-10 seconds for typical sentence (100 tokens) depending on model and hardware","Voice quality lower than commercial TTS (Google, Amazon Polly) due to model size constraints","Limited voice variety — typically 1-5 voices per model vs 100+ in cloud services","Streaming requires buffering strategy to handle variable synthesis speed — may cause audio artifacts"],"requires":["TTS model (e.g., FastPitch, Glow-TTS, VITS) in GGUF or ONNX format","Text input in supported language","Audio output device or file path","Minimum 2GB VRAM for real-time synthesis"],"input_types":["text string","voice selection (voice ID or name)","speaking rate (0.5-2.0x)","optional: prosody markers (emphasis, pauses)"],"output_types":["audio stream (WAV, MP3, PCM)","audio chunks for streaming playback","mel-spectrogram for visualization"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_8","uri":"capability://text.generation.language.automatic.speech.recognition.with.streaming.audio.input","name":"automatic speech recognition with streaming audio input","description":"Transcribes audio to text using ASR models (Whisper, etc.) through the audio processing plugin system. Supports streaming transcription where audio chunks are processed incrementally, enabling real-time speech-to-text without waiting for full audio. Implements voice activity detection (VAD) to skip silence, reducing computation. Outputs text with optional timestamps and confidence scores. Hardware acceleration on GPU/NPU speeds up acoustic model inference.","intents":["Build voice-controlled interfaces that transcribe speech in real-time","Create meeting transcription tools without cloud APIs","Add voice input to chatbots and voice assistants","Implement accessibility features (live captions) for audio content"],"best_for":["Voice interface developers building speech-to-text features","Meeting/call recording teams generating transcripts locally","Accessibility teams adding live captions to applications","IoT/robotics teams adding voice control to edge devices"],"limitations":["Transcription latency 1-5 seconds for typical audio chunk (5-10 seconds of speech) depending on model","Accuracy lower than commercial ASR (Google, Amazon) — typically 85-95% WER vs 95%+ for cloud services","Language support limited to models available — typically 10-20 languages vs 100+ in cloud services","Streaming requires careful buffering to handle variable processing speed — may cause word boundary artifacts"],"requires":["ASR model (Whisper, etc.) in GGUF or ONNX format","Audio input (microphone stream or audio file)","Minimum 2GB VRAM for real-time transcription","Audio in WAV, MP3, or PCM format"],"input_types":["audio stream (microphone input)","audio file (WAV, MP3, FLAC)","audio chunks (PCM byte arrays)","optional: language hint for better accuracy"],"output_types":["text transcription (string)","text with timestamps (JSON with time ranges)","confidence scores per word","language detection result"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexaai--nexa-sdk__cap_9","uri":"capability://automation.workflow.command.line.interface.with.interactive.repl.and.model.management","name":"command-line interface with interactive repl and model management","description":"Provides a comprehensive CLI (runner/cmd/nexa-cli/) for model discovery, download, inference, and server management. Implements an interactive REPL mode for testing models with multi-turn conversations, model listing/info commands, and server startup. The CLI routes commands through the core orchestration layer (Layer 2) which parses arguments and dispatches to appropriate Go SDK methods. Supports both one-shot inference (nexa run model 'prompt') and interactive sessions (nexa infer model).","intents":["Quickly test models without writing code","Manage model lifecycle (download, list, delete) from command line","Run inference in shell scripts and automation workflows","Start HTTP server for programmatic API access"],"best_for":["Developers prototyping models before integration","DevOps teams automating model deployment","Data scientists testing models in notebooks/scripts","System administrators managing model servers"],"limitations":["CLI argument parsing limited to simple types — complex configurations require config files","REPL mode single-threaded — cannot handle concurrent requests","No built-in command history persistence — history lost on exit","Error messages sometimes cryptic — requires debugging via logs"],"requires":["Nexa SDK installed and in PATH","Shell environment (bash, zsh, PowerShell, etc.)","For server mode: port availability"],"input_types":["command-line arguments","text prompts in REPL","model identifiers (e.g., 'qwen/qwen-3-7b')"],"output_types":["text output (model responses)","JSON (model info, list results)","server logs (HTTP requests)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ or Go 1.18+ depending on SDK choice","Minimum 4GB RAM for 7B models, 8GB+ for 13B models","For GPU: CUDA 11.8+ (NVIDIA) or ROCm 5.0+ (AMD)","For NPU: Device-specific drivers (Qualcomm Hexagon, Intel VPU, AMD XDNA)","Model supporting vision input (Qwen-3-VL, LLaVA, etc.)","Minimum 6GB RAM for VLM inference on mobile","Image input in JPEG, PNG, or WebP format","For optimal performance: GPU with 8GB+ VRAM or NPU with vision acceleration","Python 3.9+","Nexa SDK installed (Go runtime required)"],"failure_modes":["Plugin system adds abstraction overhead (~50-100ms per inference call depending on hardware bridge complexity)","Model quantization (GGUF format) may reduce accuracy vs full-precision cloud models by 1-3% on benchmarks","NPU support limited to Qualcomm, AMD, and Intel architectures — no support for Apple Neural Engine for LLM inference","Memory constraints on mobile devices limit model size to ~7B parameters effectively","Image resolution limited by model architecture (typically 1024x1024 max) — larger images require tiling or downsampling","Vision encoding adds 200-500ms latency per image depending on resolution and hardware","Batch processing limited by available VRAM — typically 1-4 images per batch on mobile devices","No built-in support for video frame extraction — requires external preprocessing","Python wrapper adds 10-20ms overhead per inference call due to language boundary crossing","Async support requires event loop management — can be complex in multi-threaded applications","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.644356976332561,"quality":0.5,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-04-22T08:03:33.309Z","last_commit":"2026-04-14T19:01:05Z"},"community":{"stars":7965,"forks":988,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nexaai--nexa-sdk","compare_url":"https://unfragile.ai/compare?artifact=nexaai--nexa-sdk"}},"signature":"Tyd7E+li5CMgD1qZeDW6Q5Ck1w0+QbQDJSmheqpSQgSiIIsUz5V6Tp3CUv/sATVYdbbHACTHnt6VezcF/H8qBA==","signedAt":"2026-06-20T13:30:02.827Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nexaai--nexa-sdk","artifact":"https://unfragile.ai/nexaai--nexa-sdk","verify":"https://unfragile.ai/api/v1/verify?slug=nexaai--nexa-sdk","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"}}