Find root cause faster with structured logs.
Auralogs gives teams searchable production logs, severity timelines, trace metadata, and optional AI summaries, so you can move from alert to evidence before opening another tool.
Search full-text logs by level, environment, trace ID, and metadata.
See error volume and severity changes before you start guessing.
Hand the same evidence to an AI agent when you want a second set of eyes.
How it works
Start with structured logging, then choose how much agent automation you want on top of it.
Capture the right context
Attach user IDs, environment, request context, and trace IDs to each log event.
Filter to the signal
Use the dashboard or REST API to narrow a spike by level, query, environment, and time window.
Summarize and decide
Use AI analysis or MCP investigation to summarize what changed and decide whether a fix PR is warranted.
Questions you can answer
Auralogs keeps the raw logs, metadata, and analysis context close enough for humans and agents to use.
Show me fatal logs from production in the last hour.
Which users are affected by this payment failure?
Did this error start before or after the deploy?
FAQ
Is Auralogs only for AI workflows?
No. Auralogs is a real log pipeline with dashboard search, filtering, retention, and REST access. AI and MCP workflows sit on top of the same logs.
What context should I attach to logs?
Start with environment, user ID, trace ID, route or job name, and relevant request metadata. Auralogs keeps that metadata queryable.
Can I use it before enabling AI analysis?
Yes. You can start with structured logging and the dashboard, then add MCP, AI analysis, notifications, or autofix later.
Try it with your next error spike
Create a free project, send your first logs, and copy the MCP/API read key for your agent.
Start free