Getting Started with logcat.ai

logcat.ai is a conversational AI observability platform for Android and Linux systems. It transforms raw system logs — bugreports, logcat, dmesg, kernel logs — into actionable insights through autonomous AI agents. Debug faster, understand deeper, and resolve issues that would take days in minutes.

These docs cover everything from your first upload to building integrations against the API.

What you can do with logcat.ai

  • Deep Research — autonomous multi-step debugging agent that investigates across bugreports, logcat, and dmesg, correlates events across 30+ subsystems, and looks up CVEs and external references during the investigation.
  • Quick Search — natural-language answers from your logs in under five seconds. “Show memory warnings”, “Find camera HAL issues”.
  • Bugreport Analysis — 10 parallel subsystem analyzers (Applications, Memory, Power, Network, Security, Storage, Performance, System Services, Device Info, Error Discovery) with embedded logcat and kernel viewers.
  • Logcat Analysis — automated crash, ANR, and performance issue detection across Android app and system logs, with interactive timelines and severity trends.
  • Dmesg / Kernel Analysis — kernel panic, oops, and driver-failure investigation across Ubuntu, Red Hat, Raspberry Pi, embedded boards, and any Linux kernel. Multi-architecture: ARM, x86, MIPS, RISC-V.
  • Delta Mode — compare and analyze differences across multiple log files (bugreports, logcats, dmesg) to isolate regressions.

Who it’s for

Android developers (app to kernel), Android OEMs and BSP teams, Linux kernel developers, DevOps and SRE teams, QA, support engineers, embedded-systems engineers, and security researchers.

Quick start

  1. Sign in at console.logcat.ai — Google OAuth or email.
  2. Upload a log file — bugreport zip, logcat, or dmesg. Files up to 100 MB+ are supported.
  3. Wait for analysis — the AI consumer runs the 10 subsystem analyzers in parallel; progress streams over WebSocket.
  4. Explore — use Quick Search for fast answers, Deep Research for full investigations, or RAG Chat to drill into specifics.
  5. Share — export findings as PDF or share a public link with citations back to the source log lines.