Full visibility into every request, every step.

Debug cross-service issues in minutes, not hours

In a point-to-point architecture, debugging a slow or incorrect response means correlating logs from every service that could have been involved — different log formats, different timestamps, different teams. What should take ten minutes takes half a day.

Every request that passes through younifyd produces a single execution timeline: which steps ran, in what order, how long each took, what data was passed in and returned, whether any retries occurred, and what the final composed response looked like. One timeline, one place, complete picture.

Execution timelines are searchable and filterable. Find all executions where the inventory service took over 200ms. Find all executions where a particular customer ID produced a partial failure. Drill into any execution to see the exact request and response at each step — the same level of detail that would normally require access to each service's internal logs.

10minvs hours to diagnose an incident

Know exactly where your latency comes from

Aggregate response time metrics hide the information you need to improve performance. Knowing that p95 latency is 280ms doesn't tell you whether it's getting worse, or whether last Tuesday's deployment made it better or worse.

younifyd tracks p95 and p99 latency, execution volume, and error rate broken down by route and method, over any date range you choose. See a specific endpoint's latency trend day by day, and compare volume and error rate across time periods to understand the impact of a deployment or a traffic spike.

This is the same execution data behind the timeline view — the analytics are an aggregate lens on it, not a separate system you have to reconcile against.

p95 / p99latency, tracked per route

Know before your customers do

Alerts in younifyd are defined as conditions on workflow metrics — request duration or HTTP status exceeding a threshold, triggered after a configurable number of occurrences within a configurable time window, with a cooldown so one incident doesn't spam you with repeat notifications.

When an alert fires, it calls a workflow you build — which means the notification destination is exactly as flexible as any other workflow. Point it at the Slack connector to post to a channel, the email connector to notify a distribution list, or a generic HTTP call to reach whatever on-call tool you already use. The alert logic and the destination are decoupled, so changing where notifications go is a workflow edit, not a support ticket.

Alert rules are workflow-scoped. The alert on your checkout flow can have a tighter threshold than the alert on your admin dashboard — they're configured in the same place as the workflow itself, not a separate monitoring system you have to keep in sync.

<1minalert-to-notification latency

Catch regressions before they reach production

Every younifyd workflow has a built-in test suite. Tests define an inbound request payload, mock responses from upstream services, and assertions on the final composed response. Run the test suite before deploying a workflow change to confirm it still produces the correct output for every scenario.

Mock responses let you test edge cases that are hard to reproduce against real services — a slow inventory response, a pricing engine returning an unexpected field, a recommendation service returning an empty array. The test suite runs these scenarios consistently on every execution, not just when the stars align in production.

Regression validation catches the category of bug that's hardest to notice: a workflow change that fixes one scenario while silently breaking another. When all test assertions pass before deployment, you ship with confidence. When they don't, you know exactly which scenario broke and what the actual response was versus the expected response.

100%scenario coverage before deployment

Automatic summaries of every workflow change

When a workflow is modified and deployed, younifyd diffs the new version against the previous one — routes, steps, and variables added, removed, or modified — and uses that diff to generate a human-readable summary of what changed, so engineers who didn't make the change can understand what changed and why without reading a raw workflow diff.

If AI generation is unavailable for any reason, a deterministic summary of the same diff is produced instead — you always get release notes, just a plainer version when the AI step can't run.

on every publishgenerated automatically