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To identify the source of a Home Assistant CPU spike, combine four kinds of evidence: the System monitor integration to confirm host-level saturation, Profiler to attribute runtime to Home Assistant code, automation traces to explain individual runs, and integration debug logs and diagnostics to verify a suspected device integration. Start collecting a baseline, capture the profiler while the spike is happening, then change only one suspected automation or integration and repeat the same observation window. No universal CPU percentage identifies a fault on every installation; the useful result is a before-and-after profile tied to a timestamp, trace, or log entry.
Which Home Assistant tool should I use first?
Choose the tool according to the question you are trying to answer. System monitor tells you whether the machine is busy, Profiler shows where Home Assistant spends execution time, traces explain one automation run, and debug logs show what a particular integration is doing.
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| Tool | What it measures | When to run it | Output | Important limitation |
|---|---|---|---|---|
| System monitor integration | Host processor use, load and, where available, running-process information | Continuously for a baseline and during the incident | Entities and sensors representing system activity | It does not identify the responsible automation by itself |
| Profiler integration | Time spent in Home Assistant functions and subsystems | During the high-CPU window | cprof and callgrind.out profile files |
It must capture the incident; an idle profile may hide the problem |
| Automation Activity and traces | The sequence and timing of one automation execution | Immediately after the relevant state change or run | Entity Activity entries and a trace timeline | It explains a run but does not total CPU consumed by all runs |
| Integration debug logging and diagnostics | Integration requests, retries, exceptions and internal events | While reproducing a suspected integration fault | Downloadable logs and, when offered, diagnostics | Debug logging increases log volume and should be disabled after capture |
| Integration startup time report | How long integrations take to initialize during boot | When startup itself is slow | A startup timing report | It does not measure steady-state CPU use |
How do I confirm that Home Assistant is actually saturating the host?
First establish whether the computer, virtual machine or single-board host is under sustained load. A high value in one Home Assistant sensor can be transient, while a host-level pattern across processor use, load and processes gives you a reliable time window for investigation.
Add the System monitor integration
Install or enable Home Assistant’s System monitor integration, then expose its processor-use and load sensors on a dashboard or in the history view. The integration is designed to monitor CPU usage and running processes on the host where Home Assistant runs. If process sensors are available in your release, record them as context rather than treating them as proof that a specific automation is at fault.
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Capture a useful baseline
Observe the installation when it is behaving normally. Note the time, processor-use pattern and load rather than saving only a single percentage. A baseline lets you distinguish an occasional callback from a sustained change after an automation, integration or configuration edit.
Mark the incident window
When the spike begins, record the start and end times in the same timezone used by your Home Assistant logs. Keep the System monitor history visible while you collect the profile and logs; the timestamps are what allow a function in a profile to be matched to an automation trace or integration message later.
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How do I profile Home Assistant while CPU usage is high?
The Profiler integration is Home Assistant’s attribution tool. Its documentation describes a profile as statistics that identify how much time each part of Home Assistant is taking. Capture it during the incident, not hours afterward.
Enable Profiler
Add the Profiler integration to the same Home Assistant instance. You need administrator permission to run the profiling action. Keep the System monitor view open so you can tell whether the capture overlaps the high-CPU period.
Run “Create system profile”
- Open Settings and go to Developer tools or Tools, depending on your Home Assistant release.
- Open Actions and select Profiler: Create system profile.
- Start the action while processor use is elevated. The documented default capture duration is 60 seconds.
- Wait for Home Assistant’s notification. It reports where the generated files were written in the configuration directory.
- Save the notification, the exact start time and the System monitor history together with the profile files.
You can also call the profiler.start action from an automation or script when the incident is predictable. Ensure that the call itself is restricted to administrators and that it starts early enough to overlap the spike.
Know what the capture produces
The action writes a cprof file and a callgrind.out file. These are different views of the same captured execution activity. Do not infer a culprit from the filename or from file size; open the data and inspect which functions consume time during the marked window.
How do I read the Profiler files?
Open the files on a workstation rather than trying to interpret them as plain text. The useful question is which call paths dominate the captured interval and whether those paths lead into an integration, template rendering, event handling or a callback that repeats unusually often.
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Open a cprof file
Use a compatible viewer such as SnakeViz or Gprof2dot. Sort or visualize the entries by cumulative time, then follow the callers into the functions doing the work. A high-level Home Assistant function is only a starting point; the relevant clue is the deeper path that names a custom component, built-in integration, template operation or event dispatcher.
Open callgrind output
Use KCachegrind, QCachegrind or Gprof2dot to inspect callgrind.out. Look for a narrow branch that dominates the graph and for repeated paths rather than isolated one-time setup calls. Record the function names and module paths before changing anything so you can compare the next capture.
Interpret profile results cautiously
- A profile attributes execution time; it does not automatically prove that the deepest function is defective.
- Core event-processing code may appear because an integration is generating many events. Correlate that path with the integration log and an automation trace.
- Template rendering may be the visible consumer even when an automation triggered it. Check the template’s render frequency and the event that causes each render.
- A profile taken after the spike has ended can look normal. Repeat the capture during the same symptom.
How do I find the automation connected to the CPU spike?
Use the affected entity’s Activity entry to connect a state change to its cause, then open the automation trace for that run. Home Assistant’s troubleshooting guidance says, “Most problems show up in the trace of the automation, so open the trace first.”
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Open the entity’s Activity history around the timestamp recorded from System monitor. Look for wording such as By automation, By script or Via an integration. This narrows the search from every automation in the system to the one that actually changed the entity.
Read the trace timeline
Open the linked automation trace and follow the run from trigger through conditions, actions and service calls. Check whether a branch repeats, waits for an event that immediately retriggers the automation, or calls a script that launches additional work. Compare the trace timestamp with the profiler’s capture window rather than assuming that a nearby run caused the entire spike.
Separate trigger from expensive work
The trigger may be harmless while an action performs repeated service calls or renders a complex template. Use the trace to identify the exact action that ran, then use the profiler to determine whether that action’s code path consumed a meaningful share of the captured CPU time. A trace explains causality for that run; only the profile and host history show its resource cost in context.
How can I prove that a particular integration is responsible?
Once the profile or trace points toward an integration, reproduce the symptom with that integration’s debug logging enabled. The resulting log and diagnostics provide stronger evidence than a dashboard percentage alone.
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Enable integration debug logging
- Open Settings > Devices & services.
- Open the suspected integration and use its debug-logging control.
- Reproduce the high-CPU behavior while keeping the exact timestamp.
- Stop debug logging as soon as the evidence is captured.
- Download the log. Download diagnostics too when the integration offers them.
Debug logging can increase log volume and add overhead, so it is a short capture technique, not a permanent operating mode. If the spike disappears when logging is enabled, treat that change as a confounding factor and rely on a second profile or a narrower reproduction.
Look for actionable signatures
- Repeated polling at a much higher frequency than expected.
- Retries caused by a connection or authentication failure.
- Exceptions that recur for every update.
- A stack path or module name matching the suspected integration.
- A burst of events that matches the trigger shown in an automation trace.
Save the unedited log, diagnostics and profile with their timestamps. Those materials are the appropriate evidence if you need to report a defect to an integration maintainer.
Can a template or automation loop cause high CPU?
Yes, but confirm the hypothesis with a captured profile and trace instead of relying on a single render count. Templates can be rendered repeatedly by state changes, event listeners or an automation that retriggers itself.
Use RenderInfo as a lead
A community troubleshooting technique calls profiler.dump_log_objects with type: RenderInfo, then looks for unusually high renders= counts. This is community guidance, not an official CPU threshold or a guarantee that a high count is harmful. Use it to choose which templates to inspect, then verify the same period in Profiler output.
Check for retriggering
In the automation trace, look for a state change produced by the automation itself, a broad event trigger, or a script that calls the same automation path again. A loop can be indirect: one action changes an entity, that change fires a second automation, and the second automation causes another render. The trace shows the chain; the profile shows whether the resulting callbacks are expensive.
Reduce variables one at a time
Do not rewrite every template at once. Disable or edit one suspected automation, capture the same profiling interval, and compare the dominant call paths and System monitor history. If the load remains, restore that change and test the next candidate so you do not lose the causal link.
What does the integration startup-time report tell me?
For boot delays, open Settings > System > Repairs, open the three-dot menu, and choose Integration startup time. The report identifies integrations that delay initialization.
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That report answers a startup question only. An integration can start quickly and still consume CPU during steady-state polling, retries or event processing. Use System monitor and Profiler for continuous load, and use debug logs when you need to see the integration’s behavior during the incident.
How should I run a controlled before-and-after test?
A controlled test prevents a plausible automation from being blamed for a coincidental spike. Preserve the original evidence, change one variable, and reproduce the same type of workload.
- Capture a normal System monitor baseline and note the software configuration.
- When the symptom occurs, capture a 60-second Profiler profile that overlaps the spike.
- Export the relevant automation trace, Activity entry, integration log and diagnostics.
- Choose one suspected automation, template or integration. Disable or edit only that item.
- Wait for an equivalent trigger or reproduce the same device activity.
- Capture another profile over a comparable interval and compare dominant call paths, render counts and host-level sensors.
- Restore the original configuration if the change did not affect the symptom, then test the next candidate.
Keep the timestamps and configuration change with every capture. A result is convincing when the same symptom changes in parallel with one controlled edit, not when a single profile merely contains a familiar function name.
What should I do when the evidence is ambiguous?
The System monitor is high but the profile looks normal
Check that the profile overlapped the incident and that the host was not busy with work outside Home Assistant. Run another capture during the next spike and keep process information from System monitor available for comparison. A normal profile after the event cannot explain a past spike.
The profile names core event code, not an integration
Use the event timestamp to inspect Activity entries and integration logs. Core dispatch functions may be processing a flood of events generated by an integration or automation; the producer is identified by correlation, not by the generic dispatcher name alone.
An automation trace is short but CPU remains high
The expensive work may be outside that single run, such as frequent polling, background callbacks or another automation triggered by the same entity. Leave System monitor collecting history, profile the next sustained interval and inspect integrations that update the entity.
Debug logging changes the symptom
Disable it after the brief reproduction and treat the result as inconclusive if the load changed materially. Use the profile, trace and normal-level logs to repeat the test with less instrumentation overhead.
Startup is slow but runtime CPU is normal
Use the startup-time report to identify initialization delays, then investigate startup configuration separately. Do not use that report to claim that an integration is responsible for a later steady-state CPU spike.
What evidence should accompany a bug report?
Provide a compact, time-aligned evidence set rather than a screenshot of a CPU gauge. Include:
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- System monitor history showing the incident window.
- The
cprofandcallgrind.outfiles captured during the spike. - The affected entity’s Activity entry and the automation trace.
- The suspected integration’s debug log and downloadable diagnostics, with sensitive data removed.
- A description of the one configuration change that altered the result, if a controlled test established one.
- Exact timestamps and whether debug logging was enabled during each capture.
These details let a maintainer distinguish an integration defect from an automation loop, a template-rendering pattern or unrelated host activity. Keep the original files until the issue is resolved so a follow-up profile can be compared with the first capture.
Frequently Asked Questions
Does a high Home Assistant CPU reading always mean an automation is broken?
No. The reading describes resource pressure on the host, which can also come from integration polling, retries, event floods or work outside Home Assistant. Attribution requires a profile or time-aligned logs rather than the percentage alone.
Can I identify the culprit from the automation trace without profiling?
A trace can show which trigger and action ran and whether a branch retriggered, but it does not quantify the total processor time consumed by that run. Use it to establish causality, then use a profile for cost.
Why are my profiler files difficult to open in a text editor?
They are profiling data formats intended for visualization tools, not ordinary log files. Open them with a compatible call-graph viewer so cumulative time and call relationships are visible.
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Preserve the baseline, make one change, and reproduce the same device activity while monitoring the host. Restore the setting if the test affects required services, and keep the timestamped evidence so the result can be reviewed without repeating a disruptive change.
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