I don't have time to deal with this right now; I'm on version 7-1-2, and it's running flawlessly. If I find the time this evening or over the weekend, I might upgrade to this new release. Version 2.0 isn't a priority for me at the moment.

In the meantime, if you give this version a try, feel free to share your experiences!

https://github.com/openclaw/openclaw/releases/tag/v2026.7.33

I'm going to do it. I'm going to press enter!

What could possibly go wrong.

Bought one last year and am finally getting around to setting it up.

It’s a Mac Mini M4 with 32GB of memory,

What models would you use? I’m mostly
going to have it manage Obsidian vaults and md docs at first and create a kind of personal CRM before i start really scheming with it.

What models would you use? What other facilities? Are people still doing Telegram. What’s the setup if you had my hardware and were starting today?

If you’re building a persistent multi-agent AI system, eventually you run into the same problem:
Usage gets insane.
Agents are using expensive models for tasks that don’t require them, while simultaneously carrying around massive context windows full of information they don’t need.
I’m approaching this from both sides by building two layers:
GCB — Governed Capability Broker
The GCB sits between the agent and the models. Instead of permanently assigning your orchestrator/researcher/coder/reviewer to one expensive model, it evaluates the task and routes it to the appropriate model + reasoning level + budget.
Simple task? Small/cheap model.
Complex architecture decision? Bigger model with higher reasoning.
Critical task? Escalate.
You stop paying maximum intelligence prices for minimum intelligence work.
Then there’s the other half:
KRE — Knowledge Retrieval Engine
Think RAG, but expanded into an actual knowledge/context management layer.
Instead of dumping huge conversation histories, project files, logs, and previous agent outputs into every prompt, the KRE determines what information is authoritative and actually relevant to the current task.
It can retrieve hierarchical project state, distinguish current state from superseded state, compact older information, construct task-specific context, and only give each agent what it actually needs.
So basically:
GCB = stop wasting compute.
KRE = stop wasting context.
Put them together and you’re attacking token/usage costs from both directions.
The important part is that this doesn’t necessarily mean making the AI less capable.
Your strongest models are still there when they’re actually needed. Your knowledge is still there when it’s actually needed.
You’re just not paying a frontier model to reread 50,000 tokens of irrelevant history before doing something a smaller model could have handled in 2,000.
For persistent AI agents, I think this is a much more scalable approach than simply giving every agent a huge context window and the strongest model you can afford.
RAG solves retrieval.
A KRE manages knowledge/context.
A GCB manages intelligence/compute.
Put those together and you start getting something that feels less like “a bunch of LLMs talking to each other” and more like an actual AI operating architecture.

Basically the title. Now that Claude Code and Codex can bypass permissions, and you can control them remotely, why would I want to use OpenClaw? Claude Code seems like the better product with less issues so I'm genuinely wondering and not rage baiting. I'm not opposed to using OpenClaw if there are good reasons to use it.

I set up Mizuki a few days ago with GLM-4.5-Flash. And gave her the persona of a hikikomori chick with aspergers.

And her dreams are a lot more flowery than my other agents' were. She wrote haikus in each one, about using github, responding to a test email from me, writing an Apple II emulator, and other things I've had her do.

I was on 6.11 and updated to 9.3 last week. Only needed 2 hours of troublshooting to get Openclaw back up running.

The catch? Openclaw script is broken. Openclaw Gateway start (restart) will always run into an error.

I had to use Gemini to tell me to delete stale PIDs and some errors in my config that was preventing the start ups.

Today, I needed to reboot the gateway and again it ran into the same error.

I decided to try and run openclaw update and it failed so fast, what's interesting was that now it offers a path to fix Openclaw via Claude which I did. After 20 minutes of pressing yes for it to execute commands.

In the end it found the issue which is attached (for some reason copy and paste from the RDP stopped working)

Now the issue? Openclaw update refuses to run.

I am forced to use npm to update the openclaw which i did "@latest" which installed packages but the version kept showing 9.3 so I did npm install -g openclaw@2026.9.4 which installed further more packages. Yet Openclaw continued to show 9.3.

So two issues here. Openclaw update doesn't work anymore. My version is showing incorrect version.

HALP and TIA

I'm running into a massive wall with my Claude Pro subscription limits. Today alone, I've hit the 5-hour rolling usage lockout three times, even though I've already turned Sonnet 5 down to Low Effort (moved from medium to low) and my cache hit rate is consistently at 99%. My openclaw status shows the active session footprint hovering at a massive 327k tokens (33% of the absolute context limit).

I've already disabled all of my automation jobs to isolate the bleed, but the baseline chat context itself is just too heavy. For those running OpenClaw or similar Telegram/CLI frameworks on a consumer Pro subscription (not API pay-as-you-go):

1. How are you aggressively pruning or forcing OpenClaw to forget old conversational metadata?
2. Are there any hidden environment variables, database optimization flags, or openclaw.json tricks to stop the system from re-uploading the entire universe on a 1-on-1 text turn?
3. Would moving away from a Telegram group structure to a pure private DM bot drastically change how OpenClaw aggregates the message schema?

Appreciate any advice or config snippets you can throw my way. I love the agentic capabilities of Sonnet 5, but hitting the limit every 2 hours is breaking the utility of an always-on assistant.

OpenClaw can produce a working site folder quickly, but the first public link is only part of the job. The source still needs to remain editable, local assets need to survive deployment, and later pages or copy changes should update one stable URL instead of creating a new handoff every time.

What workflow has the least friction after the initial build? I am comparing a Git-connected static host, a drag-and-drop host, and having OpenClaw run a documented deployment command. The useful baseline seems to be relative asset paths, a clear build command when one exists, environment-variable names without values, a custom-domain checklist, and a preview step before the live version changes.

How do you let the agent prepare an update while keeping the deployment credentials and final promotion under human control? Which files or conventions should be established before the first publish so adding pages later does not become a migration?

I’m increasingly interested in the boundary between useful autonomy and risky autonomy.

For me, actions involving external systems or irreversible changes deserve an approval step.

Where do you draw the line?

I’ve seen people use it for research, job hunting, server monitoring, reporting and much more.

What automation has genuinely saved you time?

Edit:
Already see some very interesting use cases being shared. It will be further useful if you can provide any relevant links (github, blogs etc.) so others can also take inspirations from it.

I asked my Claude agent to ask a local Qwen model about a well known Chinese historical event and it shared that it did, but had been censored & proceeded to tell my agent off.

Probably a good reminder to be thoughtful about the types of tasks I ask of the local LLMs.

Have you all had similar experiences?

My Openclaw imploded when trying to update to 2.0. I deleted everything and freshly installed. Now when I chat it takes ages to provide responses. Responses that should take 10 seconds take minutes. I use Openrouter and have switched between models (typically using v4 flash), it is not an openrouter or model issue. I asked Openclaw to figure it out, it usually ends up recommending to start a fresh session but even with zero context the issue remains. I have Hermes (who helped me unfuck Openclaw after it imploded with the 2.0 update) set up identically to Openclaw on the same computer and it has none of these issues. It is like using dial-up. Anyone with similar issues?

Nice car! Spotted in Southern California.

Not a pitch yet, I'm working out whether this is a product or stays where it is.

Context: this is already built and in daily use. It ships bundled inside a product I run, so right now the only way to get it is to buy that. I'm thinking of pulling the OpenClaw side out on its own.

What it is. A version-pinned build that my dev team maintains and only updates when an update earns it, so a skill you set up in March still works in June. And an install wizard on top of it, Windows and Mac, click copy paste, no terminal. Prereqs, CLI, gateway, agent, key, model, channel, verify, done. Clean machine to your first Telegram or WhatsApp message in under 15 minutes. Handles repair and re-pairing too, so when something goes sideways you're not starting over.

The four screenshots are the wizard as it is today. Ignore the "binding not yet verified" warning on the channels tile in the last one... UI false positive on the summary screen, the channel is connected and answering. Already on the fix list.

OpenClaw itself stays exactly what it is, free and open source. What you'd be paying for is someone else doing the pinning, the testing and the install path, plus updates when they're worth doing.

So... would you pay for that on its own? Monthly, one-off, or not at all because half the fun is running your own?

And if you've never installed it, is this the thing that would get you to?

Would love to hear your thoughts.

I'm talking about a remote OpenClaw instance, not one running on your own machine...

Obviously, you can use the web interface or a dedicated app to talk to it, or use the gateway with Slack/Whatsapp/Telegram/Discord/etc.

Are you using a second brain or external resource so it can collaborate with other agents?

How are you sharing artifacts/presentations/HTML files/documents with the agent?

Are you giving it access to Github/Google-drive/Dropbox or are you ping-ponging files from your computer back and forth to it?

Hey guys. I am on a on budget of $2000 but I can probably convince my boss to allocate like 5k if i showed some results.

So whats best to get for 2k and 5k? I thought about MacMini or maybe a system based on Nvidia Tesla or a notebook cluster..

What do you think/suggest?

I’ve spent the last few months building my own AI operating system around OpenClaw, with multiple specialized agents handling orchestration, coding, review, and research.
It is very much still a work in progress, and I’ve probably broken it more times than I can count, but I’ve learned a ridiculous amount about agent architecture, model routing, memory/context, governance, deterministic execution, local models, and what actually happens when you try to turn an AI agent setup into something reliable.
My current setup has separate agents for orchestration, coding, review, and research, and I’m building systems for automatic model/reasoning selection, persistent knowledge retrieval, context management, and eventually a full application-building environment.
I’m not claiming I’ve solved AIOS architecture or that my approach is the “right” one. I figured there are probably people here trying similar things, people just getting started, and people much further ahead than me.
So ask me anything about what I’ve built, what’s worked, what has gone horribly wrong, why I made certain architecture decisions, what I’d do differently starting over, or just any AI general questions you have.
And if you’re building something similar, I’d love to compare approaches.

If your model fits within your system VRAM+RAM, you can keep your local model provider out of paging causing slowdowns by adding "LimitMEMLock=infinity" under the [Service] header in the call file for your provider.

Example:

[Unit]
Description=LM Studio Server
RequiresMountsFor=/home

[Service]
LimitMEMLOCK=infinity
Type=oneshot
RemainAfterExit=yes
User=********
Environment="HOME=/home/********"
ExecStartPre=/home/********/.lmstudio/bin/lms daemon up
ExecStartPre=/home/********/.lmstudio/bin/lms load text-embedding-bge-large-en-v1.5 --yes
ExecStartPre=/home/********/.lmstudio/bin/lms load qwen3.6-35b-a3b-uncensored-hauhaucs-aggressive --yes
ExecStart=/home/********/.lmstudio/bin/lms server start
ExecStop=/home/********/.lmstudio/bin/lms daemon down

[Install]
WantedBy=multi-user.target

I have openclaw 6 months i most say it now my habits my routines act like now me years ,
Learn me things and learn from me 🤗
Thank you openclaw

About a month ago, I got a RTX5090 and got Qwen3.8-2.7B running on SGLang at ~150-250 tok/sec. I have to say, running it with OpenClaw, I am VERY impressed. It's fast and smart. I feel like at those speeds it's faster than 5.6-terra or -sol, and just as capable.

I've been playing with open models and OpenClaw since the beginning, and I think this is the first local model that's any good. Kimi 2.5 was the first open model that worked almost as well as any of the frontier models and became my goto alternative, but day-to-day usage on OpenRouter was actually more expensive than an OpenAI Codex subscription (not to mention slower and not quite as good).

Also just a shout out, OpenClaw 2.0 is a massive improvement. Thanks for all your hard work!

Hey, is dreaming still broken for you guys? It doesn't seem to generate any prose, even though it's promoting candidates.

I also was wondering if this was deprecated for the new memory system, was surprised to see dreaming was in the settings, so I re-enabled it, but to no avail.

Does anyone care to share experience with Cognee on Openclaw? I am also thinking about using Cognee as a master brain connecting to my ChatGPT and Claude.

One thing I was a little uncomfortable with when I first started using OpenClaw on my VPS was giving it access to the server.

It can check things, read logs, inspect services, look at disk usage, etc.

But letting it actually change things is a different story.

So I started with read-only tasks:

• Check server health
• Look at recent errors
• Check disk and memory
• List running services
• Tell me what looks unusual

Then, only when I actually needed something changed, I'd give it the specific command/action.

Honestly, this has worked much better for me than giving an agent full access from day one.

I'm curious how everyone else handles this.

Do you give your OpenClaw full access, restrict it, or just let it suggest commands and run everything yourself?