{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_regennow-regennexus","slug":"regennow-regennexus","name":"regennexus-uap","type":"repo","url":"https://github.com/ReGenNow/ReGenNexus","page_url":"https://unfragile.ai/regennow-regennexus","categories":["ai-agents"],"tags":["mcp","model-context-protocol","smithery:ReGenNow/regennexus"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_regennow-regennexus__cap_0","uri":"capability://tool.use.integration.hardware.agnostic.device.abstraction.layer","name":"hardware-agnostic device abstraction layer","description":"Provides a unified interface for controlling heterogeneous hardware platforms (Raspberry Pi, Arduino, NVIDIA Jetson, robotic arms) through a standardized adapter protocol. Uses a plugin-based architecture where each hardware platform implements a common interface, allowing AI agents to issue commands without knowledge of underlying hardware specifics. The UAP (Universal Adapter Protocol) translates high-level agent intents into platform-specific control sequences.","intents":["I want my AI agent to control multiple robot types without rewriting logic for each platform","I need to abstract away hardware differences so agents can work with any compatible device","I want to add support for a new hardware platform without modifying agent code"],"best_for":["robotics teams building multi-platform AI control systems","IoT developers integrating diverse hardware into unified agent workflows","hardware manufacturers wanting AI agent compatibility without custom integration"],"limitations":["Adapter quality and feature parity depends on platform-specific implementation — some platforms may have incomplete capability coverage","Latency overhead from abstraction layer translation adds 50-200ms per command depending on hardware complexity","Requires custom adapter development for proprietary or niche hardware platforms not in core distribution"],"requires":["Python 3.8+","Hardware-specific drivers or SDKs (e.g., RPi.GPIO, PySerial for Arduino, Jetson SDK)","Network connectivity for mesh networking features"],"input_types":["structured commands (JSON/YAML)","high-level agent intents (natural language converted to structured format)","configuration schemas for device registration"],"output_types":["device state responses (JSON)","sensor telemetry data","command execution status and feedback"],"categories":["tool-use-integration","hardware-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_1","uri":"capability://tool.use.integration.mesh.networking.and.auto.discovery.for.distributed.devices","name":"mesh networking and auto-discovery for distributed devices","description":"Implements automatic device discovery and mesh networking capabilities allowing AI agents to locate and communicate with hardware across network boundaries without manual configuration. Uses a distributed registry pattern where devices announce themselves and maintain peer-to-peer connectivity, enabling agents to dynamically discover available hardware and route commands through optimal network paths. Supports both local network discovery (mDNS/Bonjour-style) and cloud-assisted discovery for remote deployments.","intents":["I want my agent to automatically discover all available robots on the network without hardcoding IP addresses","I need reliable communication with devices even if network topology changes or devices go offline temporarily","I want to deploy agents that can work with any compatible hardware without pre-registration"],"best_for":["dynamic IoT environments with frequent device addition/removal","multi-robot systems requiring decentralized coordination","edge deployments where manual device configuration is impractical"],"limitations":["Discovery latency can be 2-10 seconds depending on network size and topology","Mesh networking overhead increases with device count — performance degrades beyond ~50 devices without hierarchical clustering","Requires network broadcast capability — may not work in heavily firewalled or segmented corporate networks","No built-in security for device discovery — requires external authentication layer for untrusted networks"],"requires":["Python 3.8+","Network interface supporting UDP broadcast or mDNS","Devices running ReGenNexus agent firmware or compatible discovery protocol"],"input_types":["device capability queries (JSON schema)","network configuration parameters","discovery filters (device type, location, capabilities)"],"output_types":["device registry (list of available devices with metadata)","network topology graph","device health status and connectivity metrics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_2","uri":"capability://tool.use.integration.agent.to.hardware.command.translation.and.execution","name":"agent-to-hardware command translation and execution","description":"Translates high-level agent commands (expressed as structured intents or function calls) into platform-specific hardware control sequences with automatic parameter mapping and validation. Uses a schema-based approach where each device exposes its capabilities as a formal interface, allowing agents to discover what operations are available and what parameters they require. Handles type coercion, unit conversion, and constraint validation before sending commands to hardware, preventing invalid operations.","intents":["I want my LLM agent to control hardware by calling functions with natural parameter names that map to device-specific APIs","I need automatic validation that ensures commands respect hardware constraints before execution","I want agents to discover device capabilities at runtime and adapt their behavior accordingly"],"best_for":["AI agent developers building multi-modal control systems","teams integrating LLMs with robotics using function-calling APIs","systems requiring safe hardware control with pre-execution validation"],"limitations":["Schema-based validation adds 10-50ms latency per command for complex constraint checking","Parameter mapping requires explicit schema definition per device type — generic devices without schema support fall back to raw command mode","No built-in rollback mechanism — failed commands may leave hardware in inconsistent state if not handled by device firmware","Type coercion and unit conversion only supports common units — custom or domain-specific units require manual mapping"],"requires":["Python 3.8+","Device capability schemas (JSON Schema or similar)","Agent framework supporting structured function calling (OpenAI, Anthropic, or compatible)"],"input_types":["agent function calls (JSON with parameters)","device capability schemas","constraint definitions (min/max values, allowed enums, unit specifications)"],"output_types":["validated command objects","execution status (success/failure with error details)","device response data (sensor readings, state changes)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_3","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration","name":"model context protocol (mcp) server integration","description":"Exposes ReGenNexus hardware control capabilities as an MCP server, allowing Claude and other MCP-compatible AI agents to directly invoke hardware operations through the standard MCP protocol. Implements MCP resource and tool interfaces where devices are exposed as resources with discoverable tools for each operation. Handles MCP protocol serialization, request routing, and response formatting automatically, abstracting away protocol complexity from hardware implementations.","intents":["I want Claude to control my robots through the MCP protocol without custom integration code","I need to expose hardware capabilities as MCP tools that any MCP-compatible agent can use","I want to build agent workflows that seamlessly combine LLM reasoning with hardware control"],"best_for":["Claude users building robotics or IoT applications","teams standardizing on MCP for AI agent integrations","developers building multi-agent systems with heterogeneous hardware"],"limitations":["MCP protocol overhead adds ~100-300ms per round-trip depending on serialization complexity","Streaming responses for long-running hardware operations not fully supported in current MCP spec","Requires MCP-compatible agent — older or proprietary agent frameworks cannot use this integration","Error handling relies on MCP error format — detailed hardware-specific error context may be lost in translation"],"requires":["Python 3.8+","MCP SDK (mcp package)","MCP-compatible agent (Claude, or other MCP clients)","Network connectivity between agent and MCP server"],"input_types":["MCP tool calls (JSON-RPC format)","resource queries","MCP protocol messages"],"output_types":["MCP tool results (JSON)","resource representations","MCP protocol responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_4","uri":"capability://data.processing.analysis.real.time.sensor.data.streaming.and.telemetry.collection","name":"real-time sensor data streaming and telemetry collection","description":"Provides streaming interfaces for continuous sensor data collection from hardware devices with configurable sampling rates, filtering, and aggregation. Uses event-driven architecture where devices push telemetry data asynchronously to subscribed agents, with optional buffering and time-series storage. Supports multiple output formats (raw streams, aggregated metrics, time-windowed statistics) allowing agents to consume data at different granularities depending on use case.","intents":["I want my agent to receive continuous sensor data from robots without polling","I need to aggregate sensor readings over time windows for decision-making","I want to monitor hardware health metrics and alert agents when anomalies occur"],"best_for":["real-time robotics applications requiring continuous feedback","monitoring systems that need to track hardware state over time","agents making decisions based on sensor fusion from multiple devices"],"limitations":["Streaming overhead scales with number of subscribed agents — high-frequency sensors (>100Hz) with many subscribers can saturate network bandwidth","No built-in time-series database — long-term telemetry storage requires external persistence layer","Buffering adds latency (configurable 10-1000ms) to ensure data ordering and prevent loss","Filtering and aggregation logic must be defined per sensor type — generic filtering may not work for domain-specific metrics"],"requires":["Python 3.8+","Devices supporting streaming protocol (not all hardware adapters implement this)","Sufficient network bandwidth for expected sensor frequency and payload size"],"input_types":["sensor configuration (sampling rate, filters, aggregation windows)","subscription requests from agents","raw sensor data from hardware devices"],"output_types":["streaming telemetry data (JSON events)","aggregated metrics (min/max/avg over time windows)","time-series data points with timestamps"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_5","uri":"capability://memory.knowledge.hardware.capability.schema.discovery.and.exposure","name":"hardware capability schema discovery and exposure","description":"Automatically generates and exposes formal capability schemas for each connected device, describing available operations, parameters, constraints, and expected outputs in a machine-readable format. Uses JSON Schema or similar standards to define device interfaces, enabling agents to programmatically discover what a device can do without prior knowledge. Schemas include metadata like operation latency, power consumption, safety constraints, and compatibility information.","intents":["I want agents to automatically understand what operations a new device supports without manual documentation","I need to expose hardware capabilities in a format that LLM function-calling APIs can understand","I want to ensure agents only attempt operations that a device actually supports"],"best_for":["dynamic hardware environments with frequent device changes","teams building generic agent workflows that work with multiple device types","systems requiring self-documenting hardware interfaces"],"limitations":["Schema generation requires device firmware to expose capability metadata — legacy hardware without this support requires manual schema definition","Complex devices with many operations produce large schemas that may exceed LLM context windows or function-calling limits","Schema versioning and evolution can cause compatibility issues if agents cache outdated schemas","Metadata accuracy depends on device implementation — incorrect or incomplete schemas lead to agent errors"],"requires":["Python 3.8+","Devices implementing capability reporting (firmware support)","JSON Schema or compatible schema format support"],"input_types":["device capability queries","schema format specifications","metadata requests"],"output_types":["JSON Schema documents describing device capabilities","capability metadata (latency, power, safety constraints)","operation documentation and examples"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_6","uri":"capability://tool.use.integration.multi.platform.adapter.framework.with.plugin.architecture","name":"multi-platform adapter framework with plugin architecture","description":"Provides a plugin-based framework for implementing hardware adapters that translate UAP protocol messages into platform-specific control code. Each adapter implements a standard interface (init, execute_command, read_state, get_capabilities) allowing new hardware support to be added without modifying core framework code. Adapters are loaded dynamically at runtime, enabling modular hardware support and third-party adapter development.","intents":["I want to add support for a new hardware platform by implementing a single adapter interface","I need to maintain multiple hardware adapters without coupling them to the core framework","I want third-party developers to create adapters for their hardware without modifying ReGenNexus"],"best_for":["hardware manufacturers wanting to integrate with ReGenNexus","teams supporting diverse hardware ecosystems","open-source projects accepting community hardware contributions"],"limitations":["Adapter quality varies — poorly implemented adapters can introduce bugs or security vulnerabilities","No built-in adapter testing or validation framework — requires manual testing before deployment","Plugin loading adds startup latency (100-500ms depending on number of adapters)","Adapter API changes require updating all existing adapters — breaking changes can cause widespread compatibility issues"],"requires":["Python 3.8+","Understanding of ReGenNexus adapter interface specification","Hardware-specific SDK or driver library"],"input_types":["adapter implementation code (Python classes)","hardware-specific configuration","UAP protocol messages"],"output_types":["platform-specific control sequences","device state and telemetry","adapter metadata and capabilities"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_7","uri":"capability://safety.moderation.safe.hardware.operation.execution.with.constraint.validation","name":"safe hardware operation execution with constraint validation","description":"Enforces safety constraints before executing hardware commands, validating that operations respect device limits, physical constraints, and safety rules defined in device schemas. Uses a constraint validation engine that checks parameter ranges, operation sequences, and device state before allowing execution. Supports conditional execution (e.g., only move arm if gripper is closed) and rollback mechanisms for failed operations.","intents":["I want to prevent agents from issuing dangerous commands that could damage hardware or cause injury","I need to enforce business logic constraints (e.g., robot can only move if safety sensors are clear)","I want automatic rollback if a command fails partway through execution"],"best_for":["safety-critical robotics applications","teams deploying agents in shared physical spaces","systems requiring compliance with hardware manufacturer safety guidelines"],"limitations":["Constraint validation adds 20-100ms latency per command depending on complexity","Safety rules must be explicitly defined — framework cannot infer safety constraints from hardware alone","Rollback only works for operations with explicit undo semantics — some hardware operations are irreversible","No formal safety verification — constraints are checked at runtime, not proven correct at design time"],"requires":["Python 3.8+","Device safety constraint definitions (JSON schema with constraints)","Hardware supporting state queries for pre-condition checking"],"input_types":["hardware commands with parameters","safety constraint definitions","device state for pre-condition evaluation"],"output_types":["validation results (pass/fail with reason)","safe command execution status","rollback confirmation if applicable"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_8","uri":"capability://automation.workflow.agent.state.synchronization.and.consistency.management","name":"agent state synchronization and consistency management","description":"Maintains consistency between agent-side state representations and actual hardware state through periodic synchronization and conflict resolution. Uses an eventual consistency model where agents can cache device state locally but periodically verify against actual hardware state. Detects and resolves conflicts when agent commands fail or hardware state changes unexpectedly (e.g., manual intervention, network disconnection).","intents":["I want my agent to know the actual state of hardware even if commands fail or network connectivity is lost","I need to detect when hardware state diverges from agent expectations and recover gracefully","I want agents to work reliably even with intermittent network connectivity"],"best_for":["distributed robotics systems with unreliable networks","agents that need to recover from transient failures","systems where manual hardware intervention is possible"],"limitations":["Synchronization adds latency (100-500ms per sync cycle) and network overhead","Conflict resolution requires explicit policies — framework cannot automatically determine correct state in all cases","Eventual consistency means agents may operate on stale state for brief periods","No built-in distributed consensus — multiple agents controlling same hardware may experience conflicts"],"requires":["Python 3.8+","Devices supporting state queries","Conflict resolution policies defined per device type"],"input_types":["agent state updates","hardware state queries","conflict resolution policies"],"output_types":["synchronized state representations","conflict detection and resolution results","consistency verification status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_regennow-regennexus__cap_9","uri":"capability://automation.workflow.cross.platform.hardware.logging.and.debugging","name":"cross-platform hardware logging and debugging","description":"Provides comprehensive logging and debugging capabilities for hardware operations across all platforms, capturing command execution traces, state changes, and error conditions in a unified format. Logs include timing information, parameter values, device responses, and stack traces for failures. Supports structured logging with queryable fields, enabling agents and developers to diagnose issues across heterogeneous hardware.","intents":["I want to debug why a hardware command failed without accessing device logs directly","I need to trace agent decisions and hardware responses to understand system behavior","I want to audit all hardware operations for compliance or troubleshooting"],"best_for":["development teams debugging complex multi-device systems","production systems requiring audit trails","teams supporting diverse hardware platforms"],"limitations":["Comprehensive logging adds 5-20% performance overhead depending on log verbosity","Log storage can grow rapidly with high-frequency operations — requires external log aggregation for long-term retention","Sensitive data (credentials, private commands) may be logged — requires careful log sanitization","Cross-platform log correlation requires synchronized clocks — clock skew can make traces difficult to interpret"],"requires":["Python 3.8+","Logging infrastructure (Python logging module or external service)","Sufficient disk space for log retention"],"input_types":["hardware commands and responses","device state changes","error conditions and exceptions"],"output_types":["structured log entries (JSON format)","execution traces with timing","error reports with context"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Hardware-specific drivers or SDKs (e.g., RPi.GPIO, PySerial for Arduino, Jetson SDK)","Network connectivity for mesh networking features","Network interface supporting UDP broadcast or mDNS","Devices running ReGenNexus agent firmware or compatible discovery protocol","Device capability schemas (JSON Schema or similar)","Agent framework supporting structured function calling (OpenAI, Anthropic, or compatible)","MCP SDK (mcp package)","MCP-compatible agent (Claude, or other MCP clients)","Network connectivity between agent and MCP server"],"failure_modes":["Adapter quality and feature parity depends on platform-specific implementation — some platforms may have incomplete capability coverage","Latency overhead from abstraction layer translation adds 50-200ms per command depending on hardware complexity","Requires custom adapter development for proprietary or niche hardware platforms not in core distribution","Discovery latency can be 2-10 seconds depending on network size and topology","Mesh networking overhead increases with device count — performance degrades beyond ~50 devices without hierarchical clustering","Requires network broadcast capability — may not work in heavily firewalled or segmented corporate networks","No built-in security for device discovery — requires external authentication layer for untrusted networks","Schema-based validation adds 10-50ms latency per command for complex constraint checking","Parameter mapping requires explicit schema definition per device type — generic devices without schema support fall back to raw command mode","No built-in rollback mechanism — failed commands may leave hardware in inconsistent state if not handled by device firmware","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.48999999999999994,"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":"2026-05-24T12:16:28.137Z","last_scraped_at":"2026-05-03T15:19:42.883Z","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=regennow-regennexus","compare_url":"https://unfragile.ai/compare?artifact=regennow-regennexus"}},"signature":"jypaxsxlOA3/R0s6haqH8OVGux/kKgC24OtuIpcGLd1xBEtZN+npe35ys3lhZJ5jpIyb4sQZpsplydrVR9y3AA==","signedAt":"2026-06-20T16:18:30.004Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/regennow-regennexus","artifact":"https://unfragile.ai/regennow-regennexus","verify":"https://unfragile.ai/api/v1/verify?slug=regennow-regennexus","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"}}