{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-netdata--netdata","slug":"netdata--netdata","name":"netdata","type":"product","url":"https://www.netdata.cloud","page_url":"https://unfragile.ai/netdata--netdata","categories":["observability"],"tags":["ai","alerting","cncf","data-visualization","database","devops","docker","grafana","influxdb","kubernetes","linux","machine-learning","mcp","mongodb","monitoring","mysql","netdata","observability","postgresql","prometheus"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-netdata--netdata__cap_0","uri":"capability://data.processing.analysis.per.second.metric.collection.with.zero.configuration.auto.discovery","name":"per-second metric collection with zero-configuration auto-discovery","description":"Netdata collects thousands of metrics per second (default update_every=1) across 850+ integrations by automatically discovering data sources without manual configuration. The collector architecture in src/collectors/ and src/go/plugin/go.d/ uses a modular plugin system where external collector processes (src/plugins.d/) are spawned and managed by the core daemon (src/daemon/), each maintaining independent threads that parse system interfaces, container APIs, and application endpoints to extract metrics in real-time.","intents":["I want to monitor my entire infrastructure without writing configuration files","I need instant visibility into system metrics, container health, and application performance across heterogeneous environments","I want to automatically detect new services and databases as they appear in my infrastructure"],"best_for":["DevOps teams managing Kubernetes clusters and containerized workloads","SREs requiring sub-second metric granularity for incident response","Lean teams without dedicated monitoring engineers"],"limitations":["Per-second collection generates high cardinality metrics — requires careful retention policies to avoid storage explosion","Auto-discovery may miss custom applications without explicit collector plugins","Collector overhead scales with number of monitored entities; high-cardinality environments (1000+ containers) may require tuning"],"requires":["Linux, FreeBSD, macOS, or Windows system with read access to /proc, /sys, or equivalent APIs","Netdata agent installed and running as root or with appropriate capabilities","For container monitoring: Docker daemon socket or Kubernetes API access"],"input_types":["system interfaces (/proc, /sys on Linux)","container APIs (Docker, containerd, cgroup v2)","application endpoints (HTTP, TCP, Unix sockets)","database query results (MySQL, PostgreSQL, MongoDB)"],"output_types":["time-series metrics (numeric, gauge, counter, histogram)","structured metadata (dimensions, units, labels)","streaming protocol (parent-child replication via src/streaming/)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_1","uri":"capability://planning.reasoning.edge.local.anomaly.detection.via.unsupervised.machine.learning","name":"edge-local anomaly detection via unsupervised machine learning","description":"Netdata trains unsupervised learning models locally on each agent (src/ml/) to detect anomalies per metric without sending raw data to cloud services. The ML pipeline analyzes metric distributions, seasonality, and trend deviations using statistical models that adapt to each metric's baseline behavior, enabling real-time anomaly flagging at the edge with sub-second latency and zero external dependencies.","intents":["I want to detect infrastructure anomalies without manually defining alert thresholds","I need anomaly detection that adapts to my system's normal behavior patterns and seasonal variations","I want to avoid sending sensitive metrics to external ML services for privacy and compliance reasons"],"best_for":["Organizations with strict data residency or privacy requirements (HIPAA, GDPR)","Teams managing highly variable workloads where static thresholds are ineffective","Environments where cloud connectivity is unreliable or unavailable"],"limitations":["ML models require 1-2 weeks of baseline data before achieving reliable anomaly detection accuracy","Unsupervised models cannot distinguish between benign spikes and true anomalies without labeled training data","Memory overhead for model storage scales with metric cardinality; high-cardinality environments may require selective model training","No support for multivariate anomaly detection across correlated metrics"],"requires":["Netdata agent running for minimum 7-14 days to establish baseline","ML module compiled into agent (src/ml/ enabled at build time)","Sufficient RAM for model storage (typically <50MB for 1000 metrics)"],"input_types":["time-series metric streams (numeric values with timestamps)","metric metadata (units, type, dimensions)"],"output_types":["anomaly flags (binary: anomalous/normal)","anomaly scores (0-1 confidence)","baseline/expected value ranges"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_10","uri":"capability://data.processing.analysis.windows.system.monitoring.with.performance.counters.and.wmi.integration","name":"windows system monitoring with performance counters and wmi integration","description":"Netdata provides Windows-specific monitoring (src/collectors/windows/) that collects metrics from Windows Performance Counters and WMI (Windows Management Instrumentation) APIs, enabling monitoring of Windows-specific metrics like CPU, memory, disk I/O, network, and application-specific counters. The collector automatically discovers available counters and maps them to Netdata metrics.","intents":["I want to monitor Windows servers with the same Netdata interface as Linux systems","I need to collect Windows-specific metrics like performance counters and WMI data","I want to monitor .NET applications and IIS web servers on Windows"],"best_for":["Organizations with mixed Windows/Linux infrastructure","Teams managing .NET applications and IIS servers","Environments requiring consistent monitoring across all platforms"],"limitations":["Windows collector has fewer integrations than Linux (no /proc equivalent)","Performance counter collection adds overhead on Windows systems","WMI queries can be slow on systems with many objects; may impact collection frequency","Windows-specific metrics are not portable to other platforms","Requires administrative privileges for some performance counters"],"requires":["Windows Server 2008 R2 or later, or Windows 7 and later","Netdata agent compiled for Windows","Administrative privileges for full performance counter access"],"input_types":["Windows Performance Counters","WMI queries","Registry entries"],"output_types":["CPU, memory, disk, network metrics","process-specific metrics","application performance counters"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_11","uri":"capability://data.processing.analysis.kubernetes.and.container.orchestration.monitoring","name":"kubernetes and container orchestration monitoring","description":"Netdata provides Kubernetes-aware monitoring through collectors that integrate with Kubernetes APIs (src/collectors/kubernetes/) to discover and monitor pods, nodes, and services. The system automatically detects container metadata, tracks pod lifecycle events, and collects container-specific metrics from cgroup interfaces, enabling visibility into containerized workloads without manual configuration.","intents":["I want to monitor Kubernetes clusters and track pod health automatically","I need per-pod and per-container metrics without instrumenting application code","I want to correlate container metrics with node-level system metrics"],"best_for":["Organizations running Kubernetes clusters","Teams managing containerized microservices","DevOps engineers requiring pod-level visibility during incidents"],"limitations":["Kubernetes monitoring requires API server access; cannot monitor air-gapped clusters","Per-pod metrics create high cardinality; large clusters (1000+ pods) may overwhelm storage","Pod lifecycle events (creation, termination) may be missed if agent is not running","No built-in Kubernetes-specific alerting (e.g., pod restart loops); requires custom rules","Metrics are collected per-node; cross-node aggregation requires parent agent or cloud"],"requires":["Kubernetes cluster with API server access","Netdata agent running as DaemonSet on all nodes","RBAC permissions to read pod, node, and service APIs","Container runtime with cgroup v1 or v2 support"],"input_types":["Kubernetes API (pods, nodes, services, events)","cgroup interfaces (/sys/fs/cgroup)","container runtime APIs (Docker, containerd)"],"output_types":["pod metrics (CPU, memory, network)","node metrics","container lifecycle events","service discovery metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_12","uri":"capability://tool.use.integration.distributed.tracing.and.application.performance.monitoring.integration","name":"distributed tracing and application performance monitoring integration","description":"Netdata provides integration points for distributed tracing and APM systems through its API and collector framework, enabling correlation of system metrics with application-level traces. While Netdata itself does not implement tracing, it can ingest trace-derived metrics (latency percentiles, error rates) from external APM systems and correlate them with infrastructure metrics for end-to-end visibility.","intents":["I want to correlate application traces with infrastructure metrics during incident investigation","I need to understand how application performance relates to system resource utilization","I want a unified view of application and infrastructure metrics in a single dashboard"],"best_for":["Organizations using distributed tracing systems (Jaeger, Zipkin, Datadog APM)","Teams investigating performance issues across application and infrastructure layers","Microservices architectures requiring end-to-end observability"],"limitations":["Netdata does not implement tracing; requires external APM system","Correlation between traces and metrics is manual; no automatic causality detection","High-cardinality trace metrics (per-endpoint latencies) can overwhelm storage","No built-in trace sampling or filtering; all traces must be ingested","Trace-to-metric correlation requires custom integration code"],"requires":["External APM/tracing system (Jaeger, Zipkin, Datadog, etc.)","Custom integration code to export trace metrics to Netdata API","Application instrumentation with tracing libraries"],"input_types":["trace metrics from APM systems (latency, error rates, span counts)","application metadata (service names, endpoints)"],"output_types":["correlated metrics in Netdata dashboards","trace-derived alerts"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_2","uri":"capability://data.processing.analysis.custom.time.series.database.with.multi.tier.storage.and.page.caching","name":"custom time-series database with multi-tier storage and page caching","description":"Netdata implements a proprietary RRD-like engine (src/database/engine/) that stores metrics in a custom time-series database with configurable retention tiers, page-cache optimization (src/database/engine/cache.c), and SQLite metadata storage (src/database/engine/). The engine uses memory-mapped I/O and journal files (src/database/engine/journalfile.c) to achieve high write throughput while maintaining query performance across historical data without external dependencies like InfluxDB or Prometheus.","intents":["I want to store high-cardinality metrics locally without external database dependencies","I need configurable retention policies that balance storage costs with historical query depth","I want sub-millisecond query latency for dashboard rendering and API responses"],"best_for":["Organizations deploying Netdata as a standalone agent without centralized metrics backend","Edge/IoT deployments where external database connectivity is unavailable","Teams requiring local metric retention for compliance audits without cloud storage"],"limitations":["RRD engine is optimized for time-series data only; cannot efficiently query arbitrary dimensions or perform complex joins","Multi-tier storage requires manual configuration of retention policies; no automatic tiering based on query patterns","Page cache size must be tuned per environment; undersized cache causes disk I/O bottlenecks","No built-in replication or failover; each agent maintains independent database","Query performance degrades significantly when querying across multiple tiers or very long time ranges"],"requires":["Disk space: minimum 256MB, recommended 1-10GB depending on metric cardinality and retention","RAM for page cache: configurable, default ~32MB","File system with support for memory-mapped I/O (ext4, XFS, APFS, NTFS)"],"input_types":["metric streams (numeric values with timestamps)","metadata (dimension names, units, chart definitions)"],"output_types":["time-series data points (timestamp, value pairs)","aggregated statistics (min, max, average, sum)","query results in JSON or CSV format"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_3","uri":"capability://automation.workflow.parent.child.metric.streaming.for.distributed.infrastructure.visibility","name":"parent-child metric streaming for distributed infrastructure visibility","description":"Netdata implements real-time metric replication via a parent-child streaming protocol (src/streaming/) where child agents continuously stream their collected metrics to parent agents, enabling infrastructure-wide dashboards and centralized alerting without requiring a separate metrics aggregation layer. The streaming system uses efficient binary protocols and handles network interruptions with automatic reconnection and backpressure management.","intents":["I want to aggregate metrics from hundreds of agents into a single parent for infrastructure-wide dashboards","I need real-time metric replication across geographically distributed data centers","I want to maintain local metric retention on each agent while also streaming to a central parent for long-term storage"],"best_for":["Organizations with distributed infrastructure (multi-region, multi-cloud, hybrid)","Teams requiring centralized dashboards without external metrics backends","Environments where agents need to function independently if parent becomes unavailable"],"limitations":["Streaming protocol is Netdata-specific; cannot stream to Prometheus, InfluxDB, or other external systems","Parent agent performance degrades with >1000 child agents due to single-threaded metric aggregation","Network bandwidth scales linearly with metric cardinality; high-cardinality environments (1000+ metrics per agent) may saturate network links","No built-in metric filtering or sampling; all metrics are streamed at full resolution","Parent agent failure results in loss of real-time visibility until reconnection"],"requires":["Network connectivity between child and parent agents (TCP port 19999 by default)","Parent agent running Netdata with streaming enabled","Child agent configured with parent IP/hostname in netdata.conf"],"input_types":["metric streams from child agents (binary protocol)","configuration (parent address, API key for authentication)"],"output_types":["aggregated metric streams to parent","unified dashboards showing all child metrics","centralized alert notifications"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_4","uri":"capability://planning.reasoning.rule.based.health.monitoring.and.alert.configuration","name":"rule-based health monitoring and alert configuration","description":"Netdata implements a declarative alert system (src/health/) where users define alert rules using a domain-specific language that evaluates metric conditions, triggers notifications, and manages alert state transitions. The health engine evaluates rules every second against collected metrics, supports multiple notification backends (email, Slack, PagerDuty, webhooks), and can synchronize alert configurations with Netdata Cloud (src/aclk/) for centralized management across distributed agents.","intents":["I want to define alerts that trigger when metrics exceed thresholds or exhibit anomalous behavior","I need alerts to be evaluated locally on each agent without external dependencies","I want to manage alert configurations centrally across hundreds of agents via Netdata Cloud"],"best_for":["Teams managing distributed infrastructure requiring local alert evaluation","Organizations needing multi-channel notifications (email, Slack, webhooks, PagerDuty)","Environments where alert rules must be version-controlled and auditable"],"limitations":["Alert rule language is Netdata-specific; rules cannot be migrated to Prometheus AlertManager or other systems without rewriting","No built-in alert deduplication or grouping; each agent sends independent notifications","Alert state is not persisted across agent restarts; state transitions reset on restart","Complex multi-metric correlations are difficult to express in the rule language","Notification delivery is not guaranteed; failed webhook calls are not retried"],"requires":["Netdata agent running with health module enabled","Alert rule files in /etc/netdata/health.d/ or custom directories","For cloud synchronization: Netdata Cloud account and ACLK connection"],"input_types":["metric values (numeric)","alert rule definitions (YAML-like DSL)","notification configuration (email, Slack, webhook URLs)"],"output_types":["alert state changes (triggered, warning, critical, cleared)","notifications (email, Slack messages, webhook payloads)","alert history and logs"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_5","uri":"capability://tool.use.integration.agent.cloud.link.aclk.for.secure.cloud.synchronization","name":"agent-cloud link (aclk) for secure cloud synchronization","description":"Netdata implements a secure bidirectional communication channel (src/aclk/) between agents and Netdata Cloud that enables cloud-based features (multi-node dashboards, RBAC, centralized alert configuration) while maintaining agent autonomy. ACLK uses TLS-encrypted WebSocket connections with certificate-based authentication, allowing agents to receive configuration updates and send alerts to cloud while remaining fully functional if cloud connectivity is lost.","intents":["I want to use Netdata Cloud features (multi-node dashboards, RBAC) while keeping agents independent","I need secure communication between agents and cloud without exposing agents to the internet","I want to centrally manage alert configurations and receive notifications through cloud"],"best_for":["Organizations using Netdata Cloud for enterprise features (RBAC, team management)","Teams requiring secure agent-to-cloud communication in regulated environments","Hybrid deployments where some agents are cloud-connected and others are air-gapped"],"limitations":["ACLK connection is optional but required for cloud features; agents function independently without it","Cloud synchronization adds latency for configuration updates (typically 5-30 seconds)","Agent certificates must be managed and rotated; expired certificates break cloud connectivity","ACLK protocol is proprietary; cannot be used with third-party cloud platforms","Bandwidth overhead for cloud synchronization scales with number of agents and metrics"],"requires":["Netdata agent with ACLK support compiled in","Netdata Cloud account and agent registration","Outbound HTTPS/WebSocket connectivity to Netdata Cloud (cloud.netdata.io)","Valid agent certificate (auto-generated during registration)"],"input_types":["agent configuration (netdata.conf)","cloud credentials (API key, certificate)","alert rules and notification settings from cloud"],"output_types":["agent status and metrics to cloud","configuration updates from cloud","alert notifications routed through cloud"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_6","uri":"capability://image.visual.interactive.web.dashboard.with.real.time.metric.visualization","name":"interactive web dashboard with real-time metric visualization","description":"Netdata provides a built-in React-based web dashboard (src/web/) that renders real-time metric charts with interactive features including zoom, pan, drill-down, and metric selection. The dashboard communicates with the Netdata API to fetch metric data, supports multiple visualization types (line, area, stacked charts), and can display metrics from multiple agents via parent-child streaming or cloud aggregation.","intents":["I want to visualize real-time metrics in an interactive dashboard without external tools","I need to drill down into specific time ranges and compare metrics across multiple agents","I want a responsive dashboard that works on mobile devices and low-bandwidth connections"],"best_for":["Teams using Netdata as a standalone monitoring solution without Grafana","On-call engineers needing quick access to real-time metrics during incidents","Organizations with air-gapped infrastructure where external dashboards are unavailable"],"limitations":["Dashboard is optimized for real-time visualization; historical analysis requires exporting data","Limited customization compared to Grafana; cannot create arbitrary dashboard layouts or mix metrics from different sources","Dashboard performance degrades with >10,000 metrics displayed simultaneously","No built-in dashboard sharing or multi-user collaboration features (available in cloud)","Chart rendering is CPU-intensive on older browsers; requires modern JavaScript support"],"requires":["Web browser with JavaScript support (Chrome, Firefox, Safari, Edge)","Network connectivity to Netdata agent (port 19999 by default)","For multi-agent dashboards: parent agent or Netdata Cloud"],"input_types":["metric data from Netdata API (JSON)","user interactions (zoom, pan, metric selection)"],"output_types":["rendered charts (SVG/Canvas)","metric values and statistics","exported data (CSV, JSON)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_7","uri":"capability://tool.use.integration.restful.api.for.metric.queries.and.configuration.management","name":"restful api for metric queries and configuration management","description":"Netdata exposes a comprehensive RESTful API (src/web/api/) that enables programmatic access to collected metrics, alert status, and agent configuration. The API supports multiple query formats (JSON, CSV, raw), time-range filtering, metric aggregation, and data export, allowing external tools and scripts to integrate with Netdata without direct database access.","intents":["I want to query metrics programmatically from external applications or scripts","I need to export historical metrics for analysis in data science tools","I want to automate agent configuration and alert management via API"],"best_for":["DevOps teams building custom monitoring integrations","Data scientists exporting metrics for offline analysis","Organizations automating infrastructure provisioning and monitoring setup"],"limitations":["API query performance degrades with very large time ranges (>1 year) or high cardinality metrics","No built-in rate limiting; high-frequency API calls can impact agent performance","API responses are not cached; repeated queries for same data cause redundant computation","No GraphQL support; REST API requires multiple calls for complex queries","Authentication is optional by default; requires manual configuration for security"],"requires":["Network connectivity to Netdata agent (port 19999 by default)","HTTP client library (curl, Python requests, etc.)","Knowledge of API endpoints and query parameters"],"input_types":["HTTP requests with query parameters (metric name, time range, aggregation)","JSON payloads for configuration updates"],"output_types":["JSON, CSV, or raw metric data","agent status and configuration","alert history and logs"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_8","uri":"capability://tool.use.integration.modular.collector.plugin.system.with.850.integrations","name":"modular collector plugin system with 850+ integrations","description":"Netdata implements a modular collector architecture (src/collectors/, src/go/plugin/go.d/) where collectors are independent plugins that discover and monitor specific services or systems. The system supports multiple collector implementations (C-based internal collectors, Go-based collectors, shell scripts, external processes) with automatic discovery, health checks, and dynamic enable/disable based on system state.","intents":["I want to monitor a specific service or application without writing custom code","I need to extend Netdata with custom collectors for proprietary systems","I want collectors to automatically detect and monitor new instances as they appear"],"best_for":["Organizations using diverse technology stacks (databases, message queues, web servers)","Teams building custom collectors for proprietary applications","Environments where automatic service discovery is critical"],"limitations":["Not all services have collectors; custom development required for unsupported systems","Collector quality and maintenance varies; community-contributed collectors may lag behind service updates","External collectors (shell scripts, Go) add latency compared to internal C collectors","Collector discovery is heuristic-based; may miss services with non-standard configurations","No built-in collector versioning; updates may break existing configurations"],"requires":["Netdata agent with collector support","For specific collectors: service-specific requirements (e.g., MySQL client for MySQL collector)","For custom collectors: knowledge of collector API and configuration format"],"input_types":["service APIs (HTTP, TCP, Unix sockets)","system interfaces (/proc, /sys, cgroup)","database query results","application logs and metrics endpoints"],"output_types":["structured metrics with dimensions and labels","collector status and health checks","configuration metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-netdata--netdata__cap_9","uri":"capability://data.processing.analysis.sql.database.collector.with.automatic.schema.discovery","name":"sql database collector with automatic schema discovery","description":"Netdata includes a specialized SQL database collector (src/collectors/databases/) that automatically discovers database instances, executes monitoring queries, and extracts metrics without manual configuration. The collector supports MySQL, PostgreSQL, MongoDB, and other databases, with built-in queries for common metrics (connections, queries, replication lag) and extensibility for custom queries.","intents":["I want to monitor database health and performance without writing custom queries","I need to automatically detect database instances and collect metrics from them","I want to track database-specific metrics (replication lag, slow queries, connection pools)"],"best_for":["Organizations running multiple database instances across infrastructure","Teams requiring database-specific monitoring without external tools","Environments where database credentials are managed centrally"],"limitations":["Collector requires database client libraries (mysql, psql, mongo) to be installed","Custom queries require knowledge of database-specific query syntax","High-cardinality database metrics (per-table statistics) can overwhelm storage","Collector performance depends on database query execution time; slow queries block metric collection","No built-in query result caching; repeated queries execute every collection interval"],"requires":["Database client libraries installed on Netdata agent host","Database credentials configured in netdata.conf or environment variables","Network connectivity to database instances","Database user with SELECT permissions on system tables"],"input_types":["database connection strings (host, port, user, password)","custom SQL queries","database system tables (information_schema, pg_stat_statements, etc.)"],"output_types":["database metrics (connections, queries, replication lag)","per-table or per-index statistics","query results as structured metrics"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Linux, FreeBSD, macOS, or Windows system with read access to /proc, /sys, or equivalent APIs","Netdata agent installed and running as root or with appropriate capabilities","For container monitoring: Docker daemon socket or Kubernetes API access","Netdata agent running for minimum 7-14 days to establish baseline","ML module compiled into agent (src/ml/ enabled at build time)","Sufficient RAM for model storage (typically <50MB for 1000 metrics)","Windows Server 2008 R2 or later, or Windows 7 and later","Netdata agent compiled for Windows","Administrative privileges for full performance counter access","Kubernetes cluster with API server access"],"failure_modes":["Per-second collection generates high cardinality metrics — requires careful retention policies to avoid storage explosion","Auto-discovery may miss custom applications without explicit collector plugins","Collector overhead scales with number of monitored entities; high-cardinality environments (1000+ containers) may require tuning","ML models require 1-2 weeks of baseline data before achieving reliable anomaly detection accuracy","Unsupervised models cannot distinguish between benign spikes and true anomalies without labeled training data","Memory overhead for model storage scales with metric cardinality; high-cardinality environments may require selective model training","No support for multivariate anomaly detection across correlated metrics","Windows collector has fewer integrations than Linux (no /proc equivalent)","Performance counter collection adds overhead on Windows systems","WMI queries can be slow on systems with many objects; may impact collection frequency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4529009230498543,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:57:01.479Z","last_commit":"2026-05-03T13:22:42Z"},"community":{"stars":78683,"forks":6426,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=netdata--netdata","compare_url":"https://unfragile.ai/compare?artifact=netdata--netdata"}},"signature":"clXB+Zjqgl8dK9lENYwxUIwq9VTm38sLNDwbHSgJm/DUq1T/4TH58cuH4QJMbW4aqTUcYGPlT1ZTkFre+hkrBg==","signedAt":"2026-06-21T14:06:02.601Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/netdata--netdata","artifact":"https://unfragile.ai/netdata--netdata","verify":"https://unfragile.ai/api/v1/verify?slug=netdata--netdata","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"}}