Production incidents,
fixed before standup.
AME tails your Java, Python, and Node.js logs, identifies the failing code, and opens a reviewable pull request with the fix — autonomously.
An on-call engineer that never sleeps.
Incident detection
Drain3-based deduplication clusters log noise into incidents. Stack-trace-aware extractors find the failing exception, file, and line — across distributed services.
Auto-remediation
OpenHands-powered agents read the stack, browse the repo, run the tests, and open a pull request your team can review.
Works with your stack
GitHub, GitLab, Jira, and local repositories. Ship logs via Vector, AWS CloudWatch, or the lightweight log-agent — your choice.
Native runtimes
Language-aware parsers for the JVM, CPython, and Node.
From stack trace to merged PR.
Three stages, one continuous loop — every step is auditable and reversible.
Ingest & parse
Logs stream in via Vector, CloudWatch, or the log-agent. Heuristic filters drop INFO/WARN noise; language extractors isolate the exception block.
Diagnose
The LLM reads the trace alongside the repo, runs targeted tests, and forms a hypothesis with confidence scoring. You can review reasoning at every step.
Remediate
Branch, commit, push, open PR, file the Jira ticket — every artifact tagged to the incident ID so the trail back to root cause is one click away.
Common questions.
Does AME push directly to production? expand_more
Which LLMs are supported? expand_more
Where does my source code live? expand_more
What if AME proposes a bad fix? expand_more
Ready to hand off incident response?
Start free. Connect a repo, point your logs at AME, and watch the first fix land in minutes.