What this page covers: resource usage, not connectivity or file access
Searches for “OpeClaw high CPU” or “OpeClaw high memory” usually mean loud fans, a hot laptop, or Task Manager showing sustained load.
This article covers: telling normal inference/background work from idle spikes; tightening local-model concurrency; and when to leave for other guides. API unreachable or proxy/TLS errors → local LLM / proxy. Folder permissions or unread paths → workspace. Skills won’t install → Skills guide.
Menu labels, process names, and tray exits follow your current build. This page does not invent unpublished “performance mode” toggles.
First split: busy working vs spinning while idle
Open Task Manager / Activity Monitor and watch OpeClaw (and any helper). Does CPU/memory rise with on-screen generation, indexing, or batch rewrites? If yes, that is load—not necessarily a broken install.
If chats are closed, no visible jobs are running, and usage still hugs full cores or memory only climbs, then check background tasks and leftover processes. Do not reinstall first—note the About-page version so you can match the download page later.
Installer source and platform status start on the download page; unknown “optimizer” packs are riskier when usage already looks wrong.
- Generation/long job in progress → finish or cancel, then retest idle usage.
- Still high while idle → tray leftovers, local model service, concurrent Skills.
- Never launched cleanly but already pegs CPU → also read won’t-open and clean-uninstall.
- Opens eventually but stuck/slow on loading → startup-slow guide (this page covers usage after it is up).
Background tasks and tray processes: quit means quit
Closing the window is not always a full exit. If your build has a tray icon or helper, use Quit so processes actually stop, then recheck usage. Leaving a helper behind looks like “I closed it, but CPU is still busy.”
If ending processes drops usage immediately, you likely had a leftover session or unfinished background job. After relaunch, avoid enabling every automation at once; run one short proof for a baseline.
On managed PCs, follow IT policy—do not disable security tools to force-kill processes.
Local models: inference is heavy; “won’t connect” is another URL
With Ollama / LM Studio (or similar) on the same machine, CPU/GPU/memory peaks during inference are expected. Smaller quantizations, shorter context, and fewer parallel local calls usually help more than reinstalling OpeClaw.
If the symptom is unreachable, timeout, model not found, or a proxy hijacking 127.0.0.1, that is connectivity—use the local-LLM setup guide; corporate proxy/TLS → proxy guide.
Connected but the local service pegs cores while idle: check Ollama/LM Studio for stuck loads or leftover inference, stop the local server as a control, and see whether the spike is the model process or OpeClaw itself.
Skills and concurrency: fewer at once, then add back
Batch Skills, scheduled workflows, and multiple long prompts at once stack CPU and memory. When usage spikes, disable recently added Skills or pause autos, compare on/off, then re-enable one by one.
Install failures, permission dialogs, or unknown packages → Skills install guide. This page does not cover install steps; it only flags concurrency and over-broad grants as resource factors.
Unread workspace files or denied permissions that trigger retry loops can also look like high CPU—fix paths/permissions on the workspace guide instead of hunting mythical performance toggles.
If it stays wrong: clean exit, reinstall boundary, and splits
Full quit → confirm no leftover processes → relaunch and measure idle baseline. If only one workflow triggers the spike, shrink that workflow (shorter context, less parallelism) and retry.
After config corruption or a half-finished update, persistent abnormal usage may need a clean uninstall and reinstall from the download page—back up workflows first. Leftover folders → clean-uninstall guide; update-channel failures → update-fail.
Scenario boundaries → FAQ. Release status and installers → Download. Opens eventually but slow/stuck on loading → startup-slow guide.
CPU / memory table (no new claims)
This table only restates the splits above. It does not change “busy vs idle first,” “connectivity stays on the local-LLM URL,” “file access stays on the workspace URL,” or “slow launch stays on startup-slow.”
| Symptom | Safer action | Common mistake |
|---|---|---|
| High CPU while generating | Finish/cancel, then retest idle | Killing processes mid-inference repeatedly |
| CPU after closing the window | Quit tray/helper fully | Blaming a “leak” while a helper still runs |
| Spike during local inference | Lower quant / concurrency; connect issues → other guide | Editing Base URL only because CPU is high |
| Spike after enabling a Skill | Disable to compare → Skills guide for permissions | Turning every Skill on at once |
| Slow start but UI eventually opens | Startup-slow (login items / plugins) | Treating cold start as sustained load |
| Idle still abnormal and stuck | Backup, then clean uninstall + reinstall | Chat-group “optimizer cleaner” packs |
Process names and quit entries follow the current client; this page does not promise hardware benchmark scores.
CPU & memory troubleshooting FAQ
Does high CPU always mean OpeClaw is broken?
Not necessarily. Local LLM inference, long-running jobs, batch Skills, or indexing can spike CPU and memory. Check whether Task Manager lines up with an active generation or task; only dig into background leftovers if usage stays high while idle.
Memory keeps climbing after I close the window—what next?
Quit fully (including tray/helper processes if your build has them), then see if memory drops. If it stays high, end leftover processes and relaunch. If it keeps returning with severe lag, consider a clean uninstall and reinstall from the download page—back up workflows first if you still need them.
How is this different from “local model won’t connect”?
Timeouts, 404, model not found, or proxy breakage belong on the local-LLM or proxy guides. This page covers a running client with abnormally high CPU/memory. Do not keep editing Base URL just because resources are high.
Will turning off Skills lower usage?
Use it as a control: disable recently enabled Skills and compare idle usage. Install/permission failures belong on the Skills guide; here we only treat “too many concurrent jobs” as a resource check.
Use this with an OpeClaw workflow
Check the current OpeClaw download status first, then save this guide as part of your setup, review, or troubleshooting workflow.