Jam is the screen recorder for bug reporting that automatically captures technical context — console logs, network requests and device info — and now uses AI to write and dedupe bug reports.
Jam makes bug reporting effortless: record your screen showing the problem, and Jam automatically attaches the console logs, network requests, device details and reproduction steps engineers need. Its AI writes the bug description, suggests root causes and detects duplicate reports before they flood your tracker.
Integrations with Jira, Linear, GitHub and Slack send complete reports into existing workflows, and the recent AI agent features can investigate issues further by analyzing the captured network and console data. Teams report dramatically faster bug resolution because reports arrive complete instead of as bare screenshots.
Jam’s capture engine records the screen along with console output, network requests, device and browser details, and reproduction steps — everything an engineer needs to debug. AI writes the report summary, flags likely duplicates and can investigate captured logs to suggest root causes. Trackers like Jira and Linear receive complete reports automatically, and team features share jams across QA, support and engineering.
Jam is free for individuals with unlimited basic jams; paid plans add team features, AI investigations, advanced integrations and administration.
Engineering and QA teams — and anyone who reports bugs — who want reports that arrive complete and actionable.
Yes, unlimited free bug recordings; teams upgrade for AI features and collaboration.
Jam integrates with trackers and chat tools; enterprise plans support deeper integration needs.
Automatic capture of the technical context engineers need is what separates Jam from generic screen recorders.
Yes — Jam integrates with Jira, Linear, GitHub and Slack, sending complete bug reports into your existing workflows.
Jam compresses the reporting loop that normally takes days into minutes. When someone hits a bug, they record it and Jam captures the technical evidence engineers usually request in follow-ups: console logs, network requests, device details and reproduction steps. AI writes the summary and checks for duplicates before the report is filed. Reports arrive in the tracker complete, so engineers start debugging immediately instead of chasing reporters for context. Teams typically see the gap between report and fix shrink dramatically.