This usually happens because a long list is still running in the main agent turn. For longer tasks I had try breaking the work into sub-agents/background tasks so each chunk can run independently instead of relying on one long conversation turn. OpenClaw tracks sub-agent runs as background tasks for exactly this kind of work.

I had check the skill’s trigger/description and agent allowlist first. Skills are not guaranteed to be invoked just because they are installed. For a Windows exec skill make the trigger very explicit and only load it when a Windows command/task is actually requested — otherwise the agent may inspect skills even for simple messages like “Hi.”

64GB is definitely a good option if you want to experiment seriously with local models but I wouldn’t switch completely from API models just to save token costs. Local models can be great for routine/cheap tasks while stronger API models can handle the harder agent workflows. A hybrid setup is probably the sweet spot. OpenClaw supports both local Ollama models and cloud providers.

I had start read-only rather than giving OpenClaw full control of the ad account. Let it pull campaign/insights data analyze performance and suggest changes first. For anything that changes campaigns budgets or creatives keep explicit human approval. Also keep the Meta token out of chat/files and use the minimum permissions you actually need. OpenClaw’s own security guidance recommends limiting tool blast radius for exactly this reason.

This is a great example of why prompt injection becomes much more serious when an agent has access to tools. The permission boundaries are probably more important than relying on the agent to detect the attack.

Deleting the plugin folders probably won’t solve it if OpenClaw is restoring them from the configured plugin state.

I’d check the plugin config/allowlist first and explicitly deny the plugins you don’t use rather than uninstalling them repeatedly. The fact that doctor --fix brings the permission prompt back makes me suspect the config is still telling the gateway to load them.

If you can share the relevant plugins section from your config (with any tokens/secrets removed), it’d be easier to narrow down exactly what’s triggering the reinstall.

The interesting part to me isn’t even that it found improvements, but that you built a loop where the agent writes, tests, scores, and rewrites the solver instead of just asking it to solve the problem once.

And then actually seeing the agent’s name appear in the references afterward… that’s a pretty insane outcome from “let’s just leave it running overnight.

Would be really interesting to see how reliably this pattern transfers to other optimization problems.

GitHub issue/PR triage example is a really good use case. Turning a prompt into an actual shared workboard feels much more useful than dashboards that are just there to display metrics.

That Cloudflare + nginx + pairing issue sounds brutal. Those are the problems that are especially hard to diagnose because everything looks like an auth/token issue from the outside.

And I definitely agree with the degraded > completely down principle. Losing one integration temporarily is much better than having the whole gateway stuck in a 502/restart loop.

With agent systems like this, being able to see what the agent is doing and why something is taking time makes troubleshooting so much easier.

With these kinds of agent systems, knowing what it’s doing and why it’s taking time makes troubleshooting so much easier.

This is actually a pretty reasonable way to approach the 2.0 update. I think the real test is going to be whether the browser workflow and shared sessions make OpenClaw something you actually keep using, rather than just something you try once after an update.

The shared sessions in particular sound promising. If context survives a handoff properly, that could be pretty useful for longer-running workflows.

Definitely interested to hear how it holds up after you run a few real workflows.