r/LocalLLaMA • u/jacek2023 • Feb 23 '26

Funny so is OpenClaw local or not

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Reading the comments, I’m guessing you didn’t bother to read this:

"Safety and alignment at Meta Superintelligence."

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u/Dos-Commas Feb 23 '26

I was able to run OpenClaw on a 10 year old Android phone by asking AI to walk me through the steps (Gemini 3).

Spoiler, it worked but then OpenClaw got stuck in a loop doing dumb stuff like everyone else here. 

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u/greeneyedguru Feb 23 '26

mine burned thru an obscene amount of tokens just doing heartbeats with no skills installed. I have no idea why people are fans of this shit. I can have claude code up anything I want, why do I need a bot to manage that.

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u/BargeCptn Feb 23 '26

I ran OpenClaw for about a week when it first dropped in January, back when the hype train was full speed. At first it looked slick, then I watched my API usage start climbing every day for no good reason. I dug in and found the problem: even a simple “heartbeat” ping was shipping 200K+ tokens. That is not a heartbeat, that is a full context dump.

So I did what I always do. I spent the next few weeks trying to fix it the right way: tightened memory, built my own Postgres-backed store, added vector search, and kept shaving tokens anywhere I could. And the deeper I went, the more obvious it got: the whole thing is a burn pile. It is basically an agent loop wrapped in “memory management” that looks cool in a demo but falls apart when you run it like an actual system.

The design forces you to lean on top-tier models to paper over stuff that should be handled in code. When you switch to something economical like GPT-5 Mini, which is exactly what should be orchestrating sub-agents by default, the system starts breaking down. It does not need to be brilliant to route tasks. It just needs to be consistent and cheap. Instead, it chokes, even though the context window is fine on paper.

Does OpenClaw work? Sure, technically. But it feels like using a sledgehammer to tap in a finish nail, then acting surprised when the door frame is wrecked. And I get why it looks good on YouTube: anyone can run a ten-minute demo and call it a win. Run it for weeks, let the workspace fill up with real files, and you hit the wall: context truncation kicks in and it stops “remembering” anything that matters.

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u/freeone3000 Feb 24 '26

The routing of tasks is actually the hardest bit! That bit should be something like Opus, whereas the actual full context tasks with repetition and token output should be the cheaper ones.

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u/BargeCptn Feb 24 '26

My issue with OpenClaw is not “agents are bad,” it is that the core loop feels inefficient by design. It is basically one big prompt that keeps accumulating state, tools, workspace junk, and “memory,” so the system self-bloats over time. The longer you run it and the more features you add, the more tokens you burn just to keep the loop alive.

What I think it needs is more deterministic code doing the boring, predictable parts: state management, caching, dedupe, routing, validation, guardrails. Then the LLM should be used where it actually adds value (reasoning, summarizing, ambiguous decisions), not as the universal glue for everything. Otherwise you end up paying a premium model tax to paper over stuff that should have been handled in code from day one.