{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-xinnan-tech--xiaozhi-esp32-server","slug":"xinnan-tech--xiaozhi-esp32-server","name":"xiaozhi-esp32-server","type":"repo","url":"http://xiaozhi.biz","page_url":"https://unfragile.ai/xinnan-tech--xiaozhi-esp32-server","categories":["automation"],"tags":["dify","esp32","mcp-server","xiaozhi","xiaozhi-ai","xiaozhi-esp32","xiaozhi-server"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_0","uri":"capability://tool.use.integration.real.time.websocket.based.audio.streaming.and.session.management.for.esp32.devices","name":"real-time websocket-based audio streaming and session management for esp32 devices","description":"Implements a persistent WebSocket connection handler (ConnectionHandler class) that manages per-client session state, routes incoming audio frames at 60ms intervals via AudioRateController, and maintains bidirectional communication with ESP32 hardware. Uses frame-based timing synchronization to ensure consistent audio delivery rates and handles connection lifecycle events (hello handshake, authentication, disconnection). The architecture supports multiplexed concurrent device connections through async I/O patterns.","intents":["I need to establish low-latency bidirectional audio streaming between ESP32 devices and a backend server","I want to manage multiple simultaneous device connections with independent session state","I need to synchronize audio playback timing across distributed ESP32 clients at 60ms frame boundaries"],"best_for":["IoT teams building voice-enabled ESP32 applications","developers deploying multi-device voice assistant systems","teams requiring real-time audio synchronization across hardware endpoints"],"limitations":["WebSocket overhead adds ~50-100ms latency per round-trip compared to raw UDP","Frame-based timing (60ms) may introduce perceptible latency for sub-100ms response requirements","No built-in connection pooling or load balancing across multiple server instances","Session state is in-memory only — requires external persistence layer for failover scenarios"],"requires":["Python 3.8+","WebSocket library (asyncio-compatible)","ESP32 device with Xiaozhi firmware supporting WebSocket protocol","Network connectivity with <500ms RTT for acceptable voice interaction latency"],"input_types":["binary audio frames (PCM, 16-bit)","JSON control messages (hello, intent, configuration)","device metadata (device_id, user_id, model_version)"],"output_types":["binary audio frames for TTS playback","JSON response messages (intent results, function call outputs)","connection status events"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_1","uri":"capability://data.processing.analysis.multi.provider.speech.recognition.asr.with.streaming.audio.processing","name":"multi-provider speech recognition (asr) with streaming audio processing","description":"Integrates pluggable ASR providers (FunASR, Whisper, etc.) that process streaming audio frames in real-time, converting spoken input to text through provider-specific APIs. The system buffers incoming audio, detects speech boundaries via SileroVAD (Voice Activity Detection), and routes complete utterances to the configured ASR provider. Supports both cloud-based (OpenAI Whisper, Alibaba FunASR) and on-device (local Silero models) recognition with configurable fallback chains.","intents":["I need to convert user speech from ESP32 microphones into text with minimal latency","I want to support multiple ASR providers and switch between them based on availability or cost","I need to detect when users stop speaking and trigger transcription automatically"],"best_for":["voice assistant developers supporting multiple languages and accents","teams building cost-optimized systems (local ASR for privacy, cloud ASR for accuracy)","IoT projects requiring sub-500ms speech-to-text latency"],"limitations":["Cloud ASR providers (Whisper, FunASR) introduce 200-800ms network latency","Local ASR models require 2-4GB GPU VRAM or significant CPU overhead","VAD accuracy degrades in noisy environments (>60dB background noise)","No built-in speaker diarization — cannot distinguish multiple speakers in same utterance","Streaming ASR requires provider support; some providers only support batch processing"],"requires":["Python 3.8+","ASR provider API key (OpenAI, Alibaba, or local model weights)","Audio input at 16kHz sample rate, 16-bit PCM format","For local ASR: CUDA 11.8+ or CPU with AVX2 support","SileroVAD model (auto-downloaded on first run, ~40MB)"],"input_types":["binary audio frames (PCM, 16kHz, 16-bit)","provider configuration (API key, model name, language code)"],"output_types":["transcribed text string","confidence scores (if provider supports)","language detection results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_10","uri":"capability://automation.workflow.configuration.management.with.yaml.based.provider.and.model.definitions","name":"configuration management with yaml-based provider and model definitions","description":"Implements centralized configuration loading from YAML files (config.yaml) that define AI providers (LLM, ASR, TTS), model parameters, device settings, and system behavior. The system supports environment variable substitution for sensitive data (API keys), configuration validation against schema, and hot-reload capabilities for non-critical settings. Configurations are hierarchically organized (global, per-user, per-device) with inheritance and override rules. Integrates with database for user-specific configuration overrides.","intents":["I need to configure multiple AI providers and models without modifying code","I want to manage API keys and sensitive configuration through environment variables","I need to support per-user and per-device configuration overrides"],"best_for":["DevOps teams managing multi-environment deployments (dev, staging, production)","developers requiring flexible configuration without code changes","teams needing per-user model and provider customization"],"limitations":["YAML configuration is static — requires server restart for most changes","No built-in configuration validation — invalid YAML may cause runtime errors","Environment variable substitution is simple string replacement — no type coercion","Configuration hierarchy (global → user → device) may be confusing with conflicting settings","No audit trail for configuration changes — difficult to track who changed what"],"requires":["Python 3.8+","YAML parser library (PyYAML)","Environment variables for sensitive data (API keys, database credentials)"],"input_types":["YAML configuration file (config.yaml)","environment variables (API_KEY_OPENAI, etc.)","database configuration overrides (per-user settings)"],"output_types":["parsed configuration objects (provider configs, model parameters)","validation errors (if configuration is invalid)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_11","uri":"capability://data.processing.analysis.voice.activity.detection.vad.with.silero.vad.for.utterance.boundary.detection","name":"voice activity detection (vad) with silero vad for utterance boundary detection","description":"Implements real-time voice activity detection using Silero VAD model, which processes streaming audio frames to identify speech boundaries (start/end of utterance). The system runs VAD on incoming audio, buffers frames until speech ends, and triggers ASR only on complete utterances. Silero VAD is lightweight (~40MB) and runs on CPU, making it suitable for edge deployment. Supports configurable sensitivity and frame-based processing at 16kHz sample rate.","intents":["I need to detect when users stop speaking to trigger transcription automatically","I want to avoid sending silent frames to ASR providers to reduce latency and cost","I need lightweight VAD that runs on CPU without GPU acceleration"],"best_for":["voice assistant developers requiring low-latency utterance detection","teams building cost-optimized systems (avoiding ASR on silence)","edge deployment scenarios with limited GPU resources"],"limitations":["VAD accuracy degrades in noisy environments (>60dB background noise)","No speaker diarization — cannot distinguish multiple speakers","Sensitivity tuning is manual — no automatic adaptation to environment","False positives (detecting noise as speech) may trigger unnecessary ASR calls","False negatives (missing speech boundaries) may cause incomplete transcription"],"requires":["Python 3.8+","Silero VAD model (auto-downloaded on first run, ~40MB)","Audio input at 16kHz sample rate, 16-bit PCM format","CPU with AVX2 support for optimal performance"],"input_types":["binary audio frames (PCM, 16kHz, 16-bit)","VAD sensitivity configuration (threshold, frame duration)"],"output_types":["VAD state (speech_start, speech_ongoing, speech_end)","confidence scores (speech probability per frame)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_12","uri":"capability://tool.use.integration.plugin.system.for.custom.function.development.with.python.function.registry","name":"plugin system for custom function development with python function registry","description":"Provides a plugin architecture that allows developers to create custom functions in Python and register them with the function registry for invocation via intent recognition. Plugins are stored in plugins_func directory, automatically discovered and loaded at startup, and can access system context (user_id, device_id, conversation history). Each plugin is a Python function with type hints and docstring documentation, which are automatically converted to JSON Schema for parameter validation. Supports both synchronous and asynchronous function execution with error handling and result serialization.","intents":["I need to add custom functions to the voice assistant without modifying core code","I want to create domain-specific actions (e.g., smart home control, API calls) as plugins","I need automatic parameter validation and documentation for custom functions"],"best_for":["developers building extensible voice assistant systems","teams requiring custom domain-specific actions","organizations needing to isolate custom code from core system"],"limitations":["Plugin discovery is filesystem-based — requires specific directory structure","No built-in plugin versioning — updating plugins requires restarting server","Type hints must be correct for schema generation — incorrect hints cause validation failures","No built-in plugin sandboxing — malicious plugins can access system resources","Async plugin execution requires explicit async/await syntax — synchronous plugins block other intents","No built-in plugin dependency management — plugins must manage their own dependencies"],"requires":["Python 3.8+","Python type hints for function parameters","Docstring documentation for function description","plugins_func directory in project root"],"input_types":["Python function definition (with type hints)","function parameters (any JSON-serializable type)","execution context (user_id, device_id, conversation_history)"],"output_types":["function execution result (JSON-serializable)","execution status (success, error, timeout)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_2","uri":"capability://data.processing.analysis.multi.provider.text.to.speech.tts.with.voice.cloning.and.streaming.output","name":"multi-provider text-to-speech (tts) with voice cloning and streaming output","description":"Provides pluggable TTS providers (Azure, Google Cloud, ElevenLabs, local TTS engines) that convert text responses into audio streams, with support for voice cloning and custom voice parameters. The system accepts text input from LLM responses, applies provider-specific voice selection and prosody controls, streams audio back to ESP32 clients in 60ms frames, and manages voice profile storage for user-specific voice preferences. Supports both streaming TTS (real-time audio generation) and batch synthesis with caching.","intents":["I need to generate natural-sounding speech responses from LLM outputs with minimal latency","I want to support multiple voices and allow users to customize their assistant's voice","I need to cache TTS outputs to avoid re-synthesizing identical responses"],"best_for":["voice assistant developers prioritizing naturalness and user personalization","teams building multilingual systems with language-specific voice profiles","IoT applications requiring sub-1000ms response latency (text-to-audio)"],"limitations":["Cloud TTS providers (Azure, Google) add 300-1500ms latency per request","Voice cloning requires 5-30 minutes of reference audio per voice","Local TTS engines (Tacotron2, FastPitch) require GPU acceleration for real-time synthesis","Streaming TTS not supported by all providers (e.g., Google Cloud requires full synthesis before streaming)","No built-in emotion/prosody control for most providers — limited expressiveness"],"requires":["Python 3.8+","TTS provider API key (Azure, Google Cloud, ElevenLabs, or local model weights)","For voice cloning: reference audio samples (WAV, 16kHz, 16-bit)","For local TTS: CUDA 11.8+ or CPU with AVX2 support, 4-8GB VRAM","Redis or similar cache for TTS output caching (optional but recommended)"],"input_types":["text string (UTF-8, up to 5000 characters per request)","voice profile ID or voice parameters (pitch, speed, emotion)","language code (ISO 639-1 format)"],"output_types":["binary audio stream (PCM, 16kHz, 16-bit)","audio metadata (duration, sample rate, voice profile used)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_3","uri":"capability://tool.use.integration.intent.recognition.and.function.calling.with.plugin.based.action.execution","name":"intent recognition and function calling with plugin-based action execution","description":"Processes LLM-generated intent outputs through a function registry that maps recognized intents to executable Python functions or MCP tool calls. The system parses LLM responses for intent names and parameters, validates them against a schema registry, and executes corresponding plugins (built-in or user-defined) with automatic error handling and result serialization. Supports both synchronous function calls and async task queuing for long-running operations. Integrates with MCP (Model Context Protocol) for standardized tool definitions.","intents":["I need to convert LLM-generated intents into executable device actions (e.g., 'turn on light' → GPIO control)","I want to extend the system with custom functions without modifying core code","I need to validate function parameters and handle execution errors gracefully"],"best_for":["voice assistant developers building custom action systems","teams integrating with smart home platforms (Home Assistant, MQTT)","developers requiring extensible plugin architectures for domain-specific actions"],"limitations":["Function execution is single-threaded by default — long-running operations block other intents","No built-in distributed execution — all functions run on the same server instance","Parameter validation relies on schema definitions — mismatched schemas cause silent failures","MCP integration requires explicit tool definition files — no automatic schema inference from Python functions","No built-in audit logging for function execution — requires external monitoring"],"requires":["Python 3.8+","Function definitions in plugins_func directory or MCP tool definitions","Schema definitions (JSON Schema format) for parameter validation","For MCP integration: MCP server implementation (stdio or HTTP transport)"],"input_types":["LLM-generated intent JSON (intent_name, parameters, confidence)","function schema definitions (JSON Schema)","execution context (user_id, device_id, session_id)"],"output_types":["function execution result (JSON-serializable)","execution status (success, error, timeout)","device state changes (if applicable)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_4","uri":"capability://memory.knowledge.dialogue.memory.and.context.management.with.multi.turn.conversation.support","name":"dialogue memory and context management with multi-turn conversation support","description":"Maintains per-user conversation history with configurable context windows, storing previous user utterances, assistant responses, and execution results in a structured format. The system passes relevant context to the LLM for each turn, implements sliding-window context truncation to manage token budgets, and supports memory persistence across sessions via database storage. Integrates with knowledge base (RAG) to augment context with relevant documents and maintains dialogue state (current topic, user preferences, device state).","intents":["I need the assistant to remember previous conversation turns and reference them in responses","I want to limit context window size to manage LLM token costs while preserving conversation coherence","I need to persist conversation history for user analytics and debugging"],"best_for":["voice assistant developers building multi-turn dialogue systems","teams requiring conversation analytics and user behavior tracking","applications needing context-aware responses across multiple sessions"],"limitations":["Context window truncation may lose important information from earlier turns","No built-in conversation summarization — full history grows unbounded without manual pruning","Database storage adds 50-200ms latency per turn for context retrieval","No automatic conflict resolution for contradictory information in conversation history","Memory persistence requires external database — no local-only option for privacy-sensitive deployments"],"requires":["Python 3.8+","MySQL or PostgreSQL database for conversation history storage","LLM provider supporting context injection (all major providers)","Optional: Vector database (Milvus, Weaviate) for RAG integration"],"input_types":["user utterance (text string)","conversation history (list of turn objects with role, content, timestamp)","context configuration (window size, truncation strategy)"],"output_types":["augmented context for LLM (formatted conversation history + RAG results)","conversation metadata (turn count, total tokens, relevant documents)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_5","uri":"capability://memory.knowledge.knowledge.base.integration.with.semantic.search.and.rag.retrieval.augmented.generation","name":"knowledge base integration with semantic search and rag (retrieval-augmented generation)","description":"Provides a knowledge base management system that stores documents, generates embeddings, and performs semantic search to augment LLM context. The system accepts document uploads (PDF, TXT, Markdown), chunks them into semantic segments, generates embeddings via configured embedding models, and stores them in a vector database. During conversation, relevant documents are retrieved based on semantic similarity to user queries and injected into the LLM prompt. Supports multiple embedding providers (OpenAI, local models) and vector databases (Milvus, Weaviate, Pinecone).","intents":["I need to provide the assistant with access to domain-specific documents without fine-tuning","I want to enable semantic search over uploaded documents to find relevant context","I need to keep knowledge base updated without retraining the LLM"],"best_for":["enterprise voice assistants requiring access to internal documentation","customer support systems needing product knowledge integration","teams building domain-specific assistants (medical, legal, technical support)"],"limitations":["Embedding generation adds 100-500ms latency per query (depends on provider and document count)","Semantic search quality depends on embedding model quality — poor embeddings cause irrelevant results","Vector database requires separate infrastructure and maintenance","Document chunking strategy significantly impacts retrieval quality — no automatic optimal chunking","No built-in document versioning — updating documents requires re-embedding entire knowledge base","Retrieval may return outdated information if documents are not regularly refreshed"],"requires":["Python 3.8+","Vector database (Milvus, Weaviate, Pinecone, or Chroma)","Embedding model (OpenAI API key or local model weights)","Document storage (file system or cloud storage)","Optional: Document parser libraries (PyPDF2, python-docx for format support)"],"input_types":["documents (PDF, TXT, Markdown, DOCX formats)","user query (text string)","chunking configuration (chunk size, overlap, strategy)"],"output_types":["retrieved document chunks (text + metadata)","relevance scores (similarity scores from vector search)","augmented LLM prompt (original prompt + retrieved context)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_6","uri":"capability://tool.use.integration.multi.provider.llm.orchestration.with.model.switching.and.fallback.chains","name":"multi-provider llm orchestration with model switching and fallback chains","description":"Abstracts multiple LLM providers (OpenAI, Anthropic, Alibaba, local models) through a unified interface, allowing configuration-based provider selection and automatic fallback to secondary providers on failure. The system manages API keys, model parameters (temperature, max_tokens), and prompt formatting for each provider, implements retry logic with exponential backoff, and tracks provider health/availability. Supports both streaming (for real-time response generation) and batch LLM calls with configurable timeout handling.","intents":["I need to switch between LLM providers (e.g., GPT-4 for complex tasks, GPT-3.5 for simple queries) based on cost/latency tradeoffs","I want automatic failover to backup providers if primary provider is unavailable","I need to manage multiple API keys and model configurations from a single configuration file"],"best_for":["teams building cost-optimized systems with multiple LLM provider contracts","applications requiring high availability with provider redundancy","developers experimenting with different models without code changes"],"limitations":["Provider API differences require provider-specific prompt formatting — no universal prompt format","Fallback chains add latency (retry timeout + secondary provider latency)","No automatic cost optimization — requires manual configuration of provider selection rules","Streaming responses from different providers have different latency characteristics — inconsistent UX","Token counting varies between providers — context window management is provider-specific"],"requires":["Python 3.8+","API keys for at least one LLM provider (OpenAI, Anthropic, Alibaba, etc.)","Configuration file with provider definitions and model parameters","For local models: CUDA 11.8+ or CPU with sufficient resources"],"input_types":["prompt text (UTF-8 string)","conversation context (list of messages with role and content)","provider configuration (model name, temperature, max_tokens, timeout)"],"output_types":["LLM response text","token usage statistics (input_tokens, output_tokens, total_cost)","provider metadata (which provider was used, latency)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_7","uri":"capability://automation.workflow.device.management.and.ota.over.the.air.firmware.updates.with.version.tracking","name":"device management and ota (over-the-air) firmware updates with version tracking","description":"Provides a management console for registering ESP32 devices, tracking firmware versions, and delivering OTA updates. The system maintains a device registry with device metadata (device_id, firmware_version, last_seen, user_binding), stores firmware binaries in cloud storage, and implements a secure update protocol that validates checksums and version compatibility before deployment. Supports staged rollouts (percentage-based deployment) and rollback to previous versions if updates fail. Integrates with the web management console for user-facing device management.","intents":["I need to push firmware updates to ESP32 devices in the field without manual intervention","I want to track which devices are running which firmware versions","I need to rollback failed updates and support staged deployments to minimize risk"],"best_for":["IoT teams managing fleets of ESP32 devices in production","developers requiring secure firmware distribution and version control","teams needing staged rollout capabilities for large device populations"],"limitations":["OTA updates require network connectivity — offline devices cannot be updated until reconnection","Firmware binary storage requires significant cloud storage capacity (100MB+ for large fleets)","No built-in signature verification — requires external PKI infrastructure for security","Rollback requires storing multiple firmware versions — increases storage costs","Update failures may leave devices in inconsistent state — no automatic recovery mechanism","Staged rollouts require manual percentage configuration — no automatic canary deployment"],"requires":["Python 3.8+","MySQL or PostgreSQL for device registry","Cloud storage (S3, Azure Blob, or local file system) for firmware binaries","ESP32 firmware with OTA support (included in Xiaozhi firmware)","HTTPS endpoint for secure firmware delivery"],"input_types":["firmware binary file (BIN format)","device selection criteria (device_id, firmware_version, user_id)","update configuration (rollout percentage, timeout, rollback policy)"],"output_types":["update status per device (pending, in_progress, success, failed, rolled_back)","firmware version tracking (current_version, previous_version, update_timestamp)","device registry (device_id, user_id, firmware_version, last_seen)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_8","uri":"capability://automation.workflow.web.based.management.console.with.user.authentication.and.device.binding","name":"web-based management console with user authentication and device binding","description":"Provides a web UI (manager-web) and REST API (manager-api) for user management, device binding, model configuration, and knowledge base administration. The system implements JWT-based authentication, role-based access control (RBAC), and per-user device isolation. Users can bind ESP32 devices to their accounts, configure LLM/ASR/TTS providers, manage voice profiles, upload knowledge base documents, and monitor device status. Built with Spring Boot backend and Vue.js frontend, backed by MySQL database and Redis cache.","intents":["I need a user-friendly interface to manage my ESP32 devices and configure AI models","I want to upload knowledge base documents and manage voice profiles through a web UI","I need to track device status and conversation history for debugging and analytics"],"best_for":["non-technical users managing voice assistant deployments","teams requiring multi-user device management with access control","organizations needing audit trails and user activity tracking"],"limitations":["Web console adds operational complexity — requires separate deployment and maintenance","Database queries for device status may be slow with large device populations (>10k devices)","No built-in real-time device status updates — requires polling or WebSocket implementation","User authentication relies on JWT tokens — no built-in integration with enterprise SSO (LDAP, OAuth2)","Knowledge base uploads are synchronous — large file uploads may timeout","No built-in backup/restore functionality for user data and configurations"],"requires":["Java 11+ (Spring Boot backend)","Node.js 14+ (Vue.js frontend)","MySQL 5.7+ or PostgreSQL 12+","Redis 6.0+ for session caching","HTTPS certificate for secure authentication"],"input_types":["user credentials (username, password)","device metadata (device_id, device_name, location)","model configuration (provider, API key, model name)","knowledge base documents (PDF, TXT, Markdown)"],"output_types":["user session token (JWT)","device list with status (online/offline, firmware_version, last_seen)","configuration objects (model settings, voice profiles)","analytics data (conversation count, device usage statistics)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xinnan-tech--xiaozhi-esp32-server__cap_9","uri":"capability://tool.use.integration.mqtt.gateway.integration.for.smart.home.device.control","name":"mqtt gateway integration for smart home device control","description":"Provides MQTT protocol support for integrating with smart home platforms (Home Assistant, OpenHAB, Zigbee2MQTT) by publishing device state changes and subscribing to control commands. The system maintains MQTT topic hierarchies for device state (e.g., /xiaozhi/device/{device_id}/state), translates between Xiaozhi protocol messages and MQTT payloads, and implements bidirectional synchronization. Supports both publish-subscribe patterns for state updates and request-response patterns for command execution.","intents":["I need to integrate Xiaozhi voice assistant with my Home Assistant setup","I want to control smart home devices through voice commands via MQTT","I need to sync device state between Xiaozhi and other smart home platforms"],"best_for":["smart home enthusiasts integrating voice control with existing platforms","teams building multi-platform IoT systems with MQTT backbone","developers requiring interoperability with Home Assistant and OpenHAB"],"limitations":["MQTT message latency (100-500ms) may cause perceptible delays in voice command execution","No built-in message encryption — requires TLS/SSL configuration for security","Topic naming conventions must be manually configured — no automatic topic discovery","Payload format translation requires custom code for non-standard MQTT devices","No built-in conflict resolution for simultaneous commands from voice and MQTT sources"],"requires":["Python 3.8+","MQTT broker (Mosquitto, EMQ, or cloud MQTT service)","MQTT client library (paho-mqtt)","Network connectivity between Xiaozhi server and MQTT broker"],"input_types":["MQTT topic subscriptions (topic pattern, QoS level)","MQTT payload format (JSON or custom format)","device mapping (Xiaozhi device_id → MQTT topic)"],"output_types":["MQTT published messages (device state, command results)","device state synchronization events"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","WebSocket library (asyncio-compatible)","ESP32 device with Xiaozhi firmware supporting WebSocket protocol","Network connectivity with <500ms RTT for acceptable voice interaction latency","ASR provider API key (OpenAI, Alibaba, or local model weights)","Audio input at 16kHz sample rate, 16-bit PCM format","For local ASR: CUDA 11.8+ or CPU with AVX2 support","SileroVAD model (auto-downloaded on first run, ~40MB)","YAML parser library (PyYAML)","Environment variables for sensitive data (API keys, database credentials)"],"failure_modes":["WebSocket overhead adds ~50-100ms latency per round-trip compared to raw UDP","Frame-based timing (60ms) may introduce perceptible latency for sub-100ms response requirements","No built-in connection pooling or load balancing across multiple server instances","Session state is in-memory only — requires external persistence layer for failover scenarios","Cloud ASR providers (Whisper, FunASR) introduce 200-800ms network latency","Local ASR models require 2-4GB GPU VRAM or significant CPU overhead","VAD accuracy degrades in noisy environments (>60dB background noise)","No built-in speaker diarization — cannot distinguish multiple speakers in same utterance","Streaming ASR requires provider support; some providers only support batch processing","YAML configuration is static — requires server restart for most changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6946918276386701,"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":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-04-30T11:54:17Z"},"community":{"stars":9434,"forks":3209,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=xinnan-tech--xiaozhi-esp32-server","compare_url":"https://unfragile.ai/compare?artifact=xinnan-tech--xiaozhi-esp32-server"}},"signature":"OVSPuZMwMhkPYhcuAawnAhWFfgkWh4lcYCz6WeptROOHACkSUuxMXCqd2X+CTrs8PJhVxTIZWm33jwhntdoADQ==","signedAt":"2026-06-21T03:10:12.874Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xinnan-tech--xiaozhi-esp32-server","artifact":"https://unfragile.ai/xinnan-tech--xiaozhi-esp32-server","verify":"https://unfragile.ai/api/v1/verify?slug=xinnan-tech--xiaozhi-esp32-server","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"}}