{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47353981","slug":"logclaw-open-source-ai-sre-that-auto-creates-ticke","name":"LogClaw – Open-source AI SRE that auto-creates tickets from logs","type":"agent","url":"https://logclaw.ai","page_url":"https://unfragile.ai/logclaw-open-source-ai-sre-that-auto-creates-ticke","categories":["automation"],"tags":["hackernews","show-hn"],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47353981__cap_0","uri":"capability://data.processing.analysis.log.stream.ingestion.and.parsing","name":"log-stream-ingestion-and-parsing","description":"Ingests structured and unstructured logs from multiple sources (files, syslog, cloud platforms) and parses them into normalized event objects using pattern matching and optional LLM-assisted semantic extraction. Supports real-time streaming via file watchers or batch ingestion, with configurable parsers for common log formats (JSON, syslog, Apache, Nginx, application-specific formats).","intents":["I need to collect logs from multiple services and normalize them into a consistent format","I want to ingest logs in real-time without manual preprocessing","I need to parse unstructured logs where format varies across applications"],"best_for":["DevOps teams managing multi-service deployments","SREs building observability pipelines","Teams with heterogeneous logging infrastructure"],"limitations":["Unstructured log parsing accuracy depends on LLM quality and context window limits","Real-time ingestion latency scales with log volume and parser complexity","No built-in deduplication — duplicate logs require downstream filtering"],"requires":["Log source accessibility (file paths, syslog endpoints, or cloud API credentials)","Python 3.8+ or Node.js 16+ depending on deployment","API key for LLM provider if using semantic parsing"],"input_types":["text (raw log lines)","JSON (structured logs)","syslog format","application-specific log formats"],"output_types":["normalized log event objects (JSON)","structured metadata (timestamp, severity, service, host)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47353981__cap_1","uri":"capability://planning.reasoning.anomaly.detection.and.log.clustering","name":"anomaly-detection-and-log-clustering","description":"Analyzes parsed logs to identify anomalies and group related events using statistical baselines, pattern frequency analysis, and optional LLM-based semantic similarity clustering. Detects deviations from normal behavior (error rate spikes, unusual latency patterns, new error types) by comparing against historical baselines or predefined thresholds, then clusters related anomalies to reduce alert fatigue.","intents":["I want to automatically detect when something is wrong in my logs without manual threshold tuning","I need to group related errors together so I'm not alerted 100 times for the same root cause","I want to find new error patterns that haven't been seen before"],"best_for":["SREs managing large-scale systems with high log volume","Teams wanting to reduce alert fatigue from noisy logs","Organizations building automated incident detection"],"limitations":["Baseline learning requires historical data — new services need warm-up period (typically 24-48 hours)","Clustering quality depends on log structure; highly variable formats reduce effectiveness","False positives possible during legitimate traffic spikes or deployments"],"requires":["Minimum 24 hours of historical log data for baseline establishment","API key for LLM provider if using semantic clustering","Configurable anomaly thresholds or pre-trained baseline models"],"input_types":["normalized log event objects","time-series metrics (optional, for correlation)"],"output_types":["anomaly flags with severity scores","clustered event groups with representative samples","baseline statistics and deviation metrics"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47353981__cap_2","uri":"capability://tool.use.integration.intelligent.ticket.generation.from.anomalies","name":"intelligent-ticket-generation-from-anomalies","description":"Automatically generates incident tickets (Jira, GitHub Issues, PagerDuty, etc.) from detected anomalies by extracting root cause signals from logs, generating human-readable summaries, and populating structured fields (severity, affected service, reproduction steps). Uses LLM to synthesize log context into actionable ticket descriptions with relevant stack traces, error messages, and suggested remediation steps.","intents":["I want incidents automatically filed without manual triage when errors spike","I need tickets with enough context that engineers can start debugging immediately","I want to avoid duplicate tickets for the same underlying issue"],"best_for":["DevOps teams with high-volume incident response","Organizations using Jira, GitHub, or PagerDuty for incident tracking","Teams wanting to reduce MTTR by automating ticket creation"],"limitations":["Ticket quality depends on log quality — sparse logs produce vague tickets","LLM-generated summaries may miss context or misattribute root cause","Deduplication across similar anomalies requires tuning to avoid duplicate tickets","Integration with each ticketing system requires separate connector implementation"],"requires":["API credentials for target ticketing system (Jira, GitHub, PagerDuty, etc.)","API key for LLM provider (OpenAI, Anthropic, or self-hosted)","Configured anomaly detection pipeline upstream","Template or schema for ticket fields (title, description, labels, priority)"],"input_types":["clustered anomaly groups with representative log samples","metadata (service name, environment, timestamp, severity score)"],"output_types":["ticket objects (Jira issue, GitHub issue, PagerDuty incident)","ticket metadata (ID, URL, status)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47353981__cap_3","uri":"capability://data.processing.analysis.multi.source.log.correlation.and.context.enrichment","name":"multi-source-log-correlation-and-context-enrichment","description":"Correlates logs across multiple services and data sources (application logs, infrastructure metrics, distributed traces, deployment events) to provide cross-system context for incident analysis. Enriches log events with metadata from external sources (service topology, recent deployments, infrastructure state) using timestamp-based joining and optional semantic correlation via LLM.","intents":["I need to understand how an error in Service A cascaded to failures in Services B and C","I want to know if a log anomaly correlates with a recent deployment or infrastructure change","I need to see the full request trace across microservices when debugging an incident"],"best_for":["Teams running microservices or distributed systems","Organizations with multiple observability tools (logs, metrics, traces, events)","SREs needing root cause analysis across system boundaries"],"limitations":["Correlation accuracy depends on synchronized timestamps across sources — clock skew causes missed correlations","Requires integration with each data source (Prometheus, Jaeger, deployment systems, etc.)","Semantic correlation via LLM adds latency (~500ms-2s per correlation attempt)","No built-in handling of eventual consistency in distributed systems"],"requires":["Access to multiple log/metric/trace sources with queryable APIs","Synchronized time across all systems (NTP or equivalent)","API credentials for each data source","Optional: API key for LLM if using semantic correlation"],"input_types":["normalized log events","time-series metrics","distributed traces (OpenTelemetry, Jaeger format)","deployment/infrastructure events"],"output_types":["enriched log events with cross-system context","correlation graphs showing relationships between events","timeline views of related incidents across services"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47353981__cap_4","uri":"capability://automation.workflow.configurable.alerting.and.notification.routing","name":"configurable-alerting-and-notification-routing","description":"Routes generated tickets and alerts to appropriate teams based on configurable rules (service ownership, severity, time-of-day, escalation policies). Supports multiple notification channels (Slack, email, PagerDuty, webhooks) with customizable message formatting and optional deduplication to prevent alert storms. Implements escalation logic (e.g., page on-call if not acknowledged within 15 minutes).","intents":["I want critical incidents to page the on-call engineer immediately, but low-severity issues to go to Slack","I need to route database errors to the database team and API errors to the API team automatically","I want to avoid getting 50 notifications for the same incident"],"best_for":["Teams with on-call rotations and escalation policies","Organizations using multiple communication channels (Slack, email, PagerDuty)","SREs managing alert fatigue and notification routing"],"limitations":["Routing rules require manual configuration and maintenance as team structure changes","Deduplication window is fixed — may miss related incidents outside the window","Escalation logic is synchronous — delays in notification delivery can cascade","No built-in handling of on-call schedule changes or team availability"],"requires":["Configured routing rules (service-to-team mappings, severity thresholds)","API credentials for notification channels (Slack, PagerDuty, email service)","Optional: on-call schedule data (from PagerDuty, Opsgenie, or custom source)"],"input_types":["ticket objects with metadata (service, severity, type)","anomaly clusters with context"],"output_types":["notifications sent to configured channels","routing logs and delivery status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47353981__cap_5","uri":"capability://planning.reasoning.feedback.loop.and.model.improvement","name":"feedback-loop-and-model-improvement","description":"Collects feedback on generated tickets and anomalies (false positives, missed incidents, incorrect severity) and uses it to improve future detections and ticket generation. Tracks which tickets led to actual incidents, which were false alarms, and which anomalies were missed, then retrains or fine-tunes detection models and LLM prompts based on this feedback.","intents":["I want the system to learn from false positives and stop alerting on them","I need to track which anomalies actually mattered and improve detection accuracy","I want to improve ticket quality by learning from engineer feedback"],"best_for":["Teams with mature incident response processes","Organizations willing to invest in continuous improvement of detection models","SREs wanting to reduce false positive rates over time"],"limitations":["Feedback collection requires manual labeling or integration with incident tracking — not automatic","Model retraining requires sufficient feedback volume (typically 100+ labeled examples) before improvement is measurable","Feedback loop introduces lag — improvements may take days or weeks to manifest","Risk of reinforcing biases if feedback is skewed (e.g., only labeling high-severity incidents)"],"requires":["Mechanism to collect feedback (manual labels, incident tracking integration, or user surveys)","Sufficient historical data and feedback to retrain models","Infrastructure to retrain or fine-tune models (GPU optional, depends on model size)"],"input_types":["generated tickets with outcomes (resolved, false positive, missed)","detected anomalies with ground truth labels","user feedback on ticket quality and accuracy"],"output_types":["improved detection models","updated LLM prompts or fine-tuned models","accuracy metrics and improvement reports"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47353981__cap_6","uri":"capability://automation.workflow.custom.rule.and.pattern.definition","name":"custom-rule-and-pattern-definition","description":"Allows users to define custom anomaly detection rules, log parsing patterns, and ticket generation templates using a domain-specific language (DSL) or visual rule builder. Supports regex patterns, threshold-based rules, time-series patterns (e.g., 'alert if error rate increases 10x in 5 minutes'), and conditional logic for complex scenarios.","intents":["I need to detect a specific error pattern that's unique to my application","I want to define custom thresholds for what constitutes an anomaly in my system","I need to generate tickets with custom fields and formatting for my workflow"],"best_for":["Teams with domain-specific logging or error patterns","Organizations needing to customize detection logic without code changes","SREs wanting to experiment with detection rules quickly"],"limitations":["DSL complexity increases with rule sophistication — very complex rules may require code","Rule performance scales with number of rules and log volume — no built-in optimization","No built-in validation of rule correctness — bad rules can cause false positives or missed detections","Rule versioning and rollback require manual management"],"requires":["Understanding of rule syntax (DSL or visual builder)","Access to sample logs for testing rules","Optional: regex knowledge for pattern-based rules"],"input_types":["rule definitions (DSL, JSON, or visual rule builder output)","sample logs for testing"],"output_types":["compiled rules","rule validation results","test results showing which logs match the rule"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47353981__cap_7","uri":"capability://search.retrieval.historical.incident.search.and.replay","name":"historical-incident-search-and-replay","description":"Provides searchable archive of historical incidents, anomalies, and generated tickets with full log context and correlation data. Allows users to replay past incidents (re-run anomaly detection on historical logs) to validate rule changes or investigate similar patterns. Supports full-text search, filtering by service/severity/date, and export of incident data for analysis.","intents":["I want to find similar incidents from the past to understand how they were resolved","I need to validate that my new detection rule would have caught this incident","I want to export incident data for post-mortem analysis or compliance reporting"],"best_for":["Teams conducting post-mortems and learning from past incidents","SREs validating detection rule changes","Organizations with compliance requirements for incident documentation"],"limitations":["Search performance degrades with large historical datasets (millions of incidents) — requires indexing","Replay accuracy depends on whether log sources are still available — old logs may be archived","Storage costs scale with retention period — long-term retention requires external storage","No built-in deduplication of similar incidents — search results may be noisy"],"requires":["Persistent storage for historical logs and incident data","Search index (Elasticsearch, similar) for fast querying","Retention policy defining how long to keep historical data"],"input_types":["historical log events","past incident records","detection rules (for replay)"],"output_types":["search results with incident summaries","replay results showing which rules would have triggered","exported incident reports (CSV, JSON, PDF)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Log source accessibility (file paths, syslog endpoints, or cloud API credentials)","Python 3.8+ or Node.js 16+ depending on deployment","API key for LLM provider if using semantic parsing","Minimum 24 hours of historical log data for baseline establishment","API key for LLM provider if using semantic clustering","Configurable anomaly thresholds or pre-trained baseline models","API credentials for target ticketing system (Jira, GitHub, PagerDuty, etc.)","API key for LLM provider (OpenAI, Anthropic, or self-hosted)","Configured anomaly detection pipeline upstream","Template or schema for ticket fields (title, description, labels, priority)"],"failure_modes":["Unstructured log parsing accuracy depends on LLM quality and context window limits","Real-time ingestion latency scales with log volume and parser complexity","No built-in deduplication — duplicate logs require downstream filtering","Baseline learning requires historical data — new services need warm-up period (typically 24-48 hours)","Clustering quality depends on log structure; highly variable formats reduce effectiveness","False positives possible during legitimate traffic spikes or deployments","Ticket quality depends on log quality — sparse logs produce vague tickets","LLM-generated summaries may miss context or misattribute root cause","Deduplication across similar anomalies requires tuning to avoid duplicate tickets","Integration with each ticketing system requires separate connector implementation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.26,"ecosystem":0.21000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.326Z","last_scraped_at":"2026-05-04T08:10:10.018Z","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=logclaw-open-source-ai-sre-that-auto-creates-ticke","compare_url":"https://unfragile.ai/compare?artifact=logclaw-open-source-ai-sre-that-auto-creates-ticke"}},"signature":"8lFIZbQib5ehrllRMnT229sstQMxKWmGkXzDTIGA+9vCG66zINgNpZTqEHEvmiTHEnq1dHVTTI+nUeIG8zq3BA==","signedAt":"2026-06-20T01:39:12.228Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/logclaw-open-source-ai-sre-that-auto-creates-ticke","artifact":"https://unfragile.ai/logclaw-open-source-ai-sre-that-auto-creates-ticke","verify":"https://unfragile.ai/api/v1/verify?slug=logclaw-open-source-ai-sre-that-auto-creates-ticke","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"}}