LogClaw – Open-source AI SRE that auto-creates tickets from logs
Hi HN, I'm Robel. I built LogClaw because I was tired of paying for Datadog and still waking up to pages that said "something is wrong" with no context. LogClaw is an open-source log intelligence platform that runs on Kubernetes. It ingests logs via OpenTelemetry and detects anomalies using signal-based composite scoring — not simple threshold alerting. The system extracts 8 failure-type signals (OOM, crashes, resource exhaustion, dependency failures, DB deadlocks, timeouts, connection errors, auth failures), combines them with statistical z-score analysis, blast radius, error velocity, and…
In plain words
LogClaw is an open-source log intelligence platform designed for Kubernetes environments that automatically detects system anomalies and creates incident tickets. It ingests logs via OpenTelemetry and uses signal-based composite scoring to identify eight types of failures including crashes, resource exhaustion, database deadlocks, and connection errors. Rather than simple threshold alerting, the system combines statistical analysis with blast radius and error velocity signals to reduce false alerts. It is built for site reliability engineers seeking cost-effective alternative to commercial monitoring platforms like Datadog.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I'm Robel. I built LogClaw because I was tired of paying for Datadog and still waking up to pages that said "something is wrong" with no context. LogClaw is an open-source log intelligence platform that runs on Kubernetes. It ingests logs via OpenTelemetry and detects anomalies using signal-based composite scoring — not simple threshold alerting. The system extracts 8 failure-type signals (OOM, crashes, resource exhaustion, dependency failures, DB deadlocks, timeouts, connection errors, auth failures), combines them with statistical z-score analysis, blast radius, error velocity, and recurrence signals into a composite score. Critical failures (OOM, panics) trigger the immediate detection path in <100ms — before a time window even completes. The detection achieves 99.8% for critical failures while filtering noise (validation errors and 404s don't fire incidents). Once an anomaly is confirmed, a 5-layer trace correlation engine groups logs by traceId, maps service dependencies, tracks error propagation cascades, and computes blast radius across affected services. Then the Ticketing Agent pulls the correlated timeline, sends it to an LLM for root cause analysis, and creates a deduplicated ticket on Jira, ServiceNow, PagerDuty, OpsGenie, Slack, or Zammad. The loop from log noise to a filed ticket is about 90 seconds. Architecture: OTel Collector → Kafka (Strimzi, KRaft mode) → Bridge (Python, 4 concurrent threads: ETL, anomaly detection, OpenSearch indexing, trace correlation) → OpenSearch + Ticketing Agent. The AI layer supports OpenAI, Claude, or Ollama for fully air-gapped deployments. Everything deploys with a single Helm chart per tenant, namespace-isolated, no shared data plane. To try it locally: https://docs.logclaw.ai/local-development What it does NOT do yet: - Metrics and traces — this is logs-only right now. Metrics support is on the roadmap. - The anomaly detection is signal-based + statistical (composite scoring with z-score), not deep learning. It catches 99.8% of critical failures but won't detect subtle performance drift patterns yet. - The dashboard is functional but basic. We use OpenSearch Dashboards for the heavy lifting. Licensed Apache 2.0. The managed cloud version is $0.30/GB ingested if you don't want to self-host. Hi HN — I’m Robel. I built LogClaw after getting tired of waking up to alerts that only said “something is wrong” with no context. LogClaw is an open-source log intelligence platform for Kubernetes. It ingests logs via OpenTelemetry and detects operational failures using signal-based anomaly detection rather than simple thresholds. Instead of looking at a single metric, LogClaw extracts failure signals from logs (OOMs, crashes, dependency failures, DB deadlocks, timeouts, etc.) and combines them with statistical signals like error velocity, recurrence, z-score anomalies, and blast radius to compute a composite anomaly score. Critical failures bypass time windows and trigger detection in <100ms. Once an anomaly is confirmed, a correlation engine reconstructs the trace timeline across services, detects error propagation, and computes the blast radius. A ticketing agent then generates a root-cause summary and creates deduplicated incidents in Jira, ServiceNow, PagerDuty, OpsGenie, Slack, or Zammad. Architecture: OTel Collector → Kafka → Detection Engine → OpenSearch → Ticketing Agent Repo: https://github.com/logclaw/logclaw Would love feedback from people running large production systems.
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
