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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesMitsuki’s built-in instrumentation can record HTTP requests, calls to instrumented components, scheduled-task activity, and process resource use. In the documented example, enabling it requires both decorating the application with @Instrumented() and turning on instrumentation and metrics in YAML. The framework exposes a JSON summary at /metrics and Prometheus text at /metrics/prometheus. These measurements are held in memory per process, so protect the endpoints and account for worker behavior before treating them as a durable, globally aggregated monitoring source.
Enable Mitsuki instrumentation
The setup below follows David Landup’s Mitsuki feature article, published September 29, 2026. It describes the author’s implementation and example rather than an independent test.
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Install Mitsuki with the optional metrics extra, which the article identifies for process CPU and memory sampling:
DriversOutdated Drivers Are Slowing You DownPerformanceWindows Errors? Fix Them Before They SpreadDriversCrashes, No Sound, or Screen Glitches?Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.pip install "mitsuki[metrics]" -
Decorate the application class:
@Instrumented() class MyApplication: ... -
Enable both instrumentation and metrics in
application.yml:#1 Best Overall
instrumentation: enabled: true metrics: enabled: true
In the article’s example, both configuration switches are needed for recording and for the metrics registry and endpoints. The decorator may also be applied to individual components. At application level, Landup says it covers controllers, services, repositories, and CRUD repositories. The feature article describes public methods as wrapped at startup, excluding names beginning with _, static methods, class methods, and properties; it also says generated and custom repository methods are recorded.
What Mitsuki records
The feature article describes metrics spanning request handling, application components, background work, and process resources:
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http_requests_total: a counter labelled by HTTP method, path, and status.Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy. -
http_request_duration_seconds: a histogram labelled by method and path. -
Component call counts and duration metrics, with component, method, and status labels as shown in the article.
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Scheduled-task execution counts, durations, and gauges for running tasks.
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system_memory_bytesandsystem_cpu_percent: process memory and CPU measurements sampled every five seconds, using the optionalpsutilmetrics extra.Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy. -
system_traced_memory_bytes: traced Python memory, sampled every five seconds only whentrack_memory: trueis enabled. Landup describes this as a debugging aid based ontracemallocand cautions that it slows allocations.
Do not assume traced-memory tracking is on by default. The feature article’s sample output represents one illustrative run, not a typical workload or benchmark.
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Read the built-in endpoints
The documented implementation provides two views. The JSON endpoint reports totals and averages since startup; the Prometheus endpoint exposes text-format metrics, including series and histograms.
| Endpoint | Output | Use |
|---|---|---|
/metrics |
JSON summary of totals and averages since startup | Inspect a concise application-level summary |
/metrics/prometheus |
Prometheus exposition text, including metric series and histograms | Configure a Prometheus scrape target |
Neither endpoint is a hosted service. Prometheus and Grafana are optional tools for collecting and viewing these measurements.
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Connect Prometheus and Grafana
A related Mitsuki walkthrough dated October 3, 2026 demonstrates an application, Prometheus, and Grafana stack. In that example, Prometheus scrapes at five-second intervals and dashboards are provisioned in Grafana. Treat that interval and dashboard arrangement as the tutorial’s example, not a required Mitsuki setting; configure collection according to your environment and retention needs.
Best Value
The feature article establishes a Prometheus-compatible output endpoint, but does not establish that Mitsuki’s built-in instrumentation is implemented with OpenTelemetry. OpenTelemetry Python is a separate ecosystem of APIs, SDKs, instrumentation libraries, and exporters. Its documentation is useful context if you are evaluating broader instrumentation options, but it does not demonstrate feature parity with Mitsuki.
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Metrics are per-process and reset at restart
Landup describes the measurements as in-memory and specific to each process. They reset when the process restarts. With multiple workers, a scrape can reach a different worker and return that worker’s totals, making values appear to jump between worker-specific figures. Do not interpret the built-in endpoint as a persistent store or as globally aggregated worker data.
Restrict access to metrics
The endpoints can reveal route tables, component names, and traffic volumes. The feature article documents an IP allowlist setting named metrics.allowed_ips and says an empty list permits all addresses. Avoid leaving them open unless that exposure is intentional.
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The article also warns about a proxy-related risk in the described Mitsuki 0.2.0 Granian engine setup: when requests pass through a reverse proxy or load balancer, the application may see the proxy address as the client. Allowlisting that proxy address could therefore make the endpoints accessible to everyone whose requests pass through it. Verify how your deployed Mitsuki version and proxy handle client addresses before relying on an IP allowlist; use an access-control design that limits the endpoints at the point where the actual client identity can be enforced.
What this feature does—and does not—establish
Landup frames Mitsuki’s built-in feature as a framework-native way to instrument an application, drawing an analogy to Spring Boot Actuator and Micrometer in Spring or a separate FastAPI instrumentation package. That is the author’s comparison, not an independently validated equivalence. The documented value is the integrated setup and the breadth of the described metrics; the stated per-process memory model and endpoint access considerations remain important deployment constraints.
PyPI metadata cited in the feature context listed Mitsuki as alpha, version 0.1.4 uploaded December 14, 2025, with Python 3.9 or newer. That is a dated package snapshot, not a guarantee of the current release or support range; check the package listing for present metadata before adopting it.
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