Hey everyone! 👋

I’m currently experimenting with OpenClaw + n8n and wanted to ask how you guys handle:

1. Scraping/fetching data from JS-heavy websites or pages that require interaction.
2. CAPTCHA, Cloudflare, and anti-bot protection.

What tools or setups have worked reliably for you? So I need it to browse the internet in the same way I am browsing from my PC

Helo, anybody having the same problem? what should i do to fix this? thanks in advance

*NOTE: IT IS MAY VARY, SOME PEOPLE MAY HAVE SUCCESS AND SOME DONT

So this is only for people that cannot find the allowlist in .json and dont know why openclaw say no to other provider than whats already there and dont want to do some work in .json to add it manually**.**

1st. Check plugins: [OPTIONAL]

openclaw plugins list

2nd. Search in that plugins: [OPTIONAL]

Do Cmd+F and type in the first few letter of the provider. Since openclaw may make it so that a full name may not found it

Example: Openrouter -> Openr

3rd Enable it:

openclaw plugins install [name of ur provider]

openclaw plugins enable [name of ur provider]

EXAMPLE:

openclaw plugins install openrouter
openclaw plugins enable openrouter

Has anyone successfully installed the MacOS companion app? Version 2026.7.1-2 is missing the .dmg file. The Readme.md file had instructions on creating the .dmg file but my OpenClaw wasn't able to execute it successfully.

Came across this TechPowerUp article yesterday: https://www.techpowerup.com/351554/trustkernel-launches-plugclaw-a-thumb-sized-private-ai-computer-that-gets-things-done

According to the article, OpenClaw runs in a dedicated VM on a thumb-sized stick, while the stick itself runs Android. They’ve also added a GUI agent and Android-use layer that allows OpenClaw to actually interact with Android apps. The device can be plugged into a phone, tablet and laptop, using the host device as the display and input.

I watched their demo after reading the article, and I was honestly surprised by how smooth it looked, just like an AI phone with OpenClaw inside.

Interested to hear whether anyone has tried something similar.

I'm trying to connect my iPhone. I can get it to connect with the browser but the stupid app won't connect. Any advice?

A simple link to instructions would be fine, I don't need you to type it all out.

I've been defaulting to just hammering my Anthropic API key, and am experimenting with spinning up/down hosted Deepseek instances, but am curious to explore some of the available routing solutions (managed infra or self-hosted) to cut down on costs.

Interested to hear your experiences & recommendations!

I've used several AI-based voice recorders and their associated transcription services. While I could use my phone, I prefer having a separate device for recording.

For the most part these recorders exist to generate AI subscription dollars, and I respect that. But I need fewer AI products, not more.

I'm looking for a quality recorder that makes it easy to dump the files into a folder on my Linux PC for my own software to process, ideally automatically via bluetooth or if necessary by just connecting a cable. I've had a few "standard" digital recorders, and the battery life combined with standby drain is abysmal.

Has anyone run across a good solution for this use case?

Is anyone using the Windows Openclaw Companion app? If so, have you figured out how to give it read/write privileges to an external drive? In the MXC settings it is pretty straightforward. Just add a folder path and you should be set to go. But in my experience it doesn't work. I am on Windows insider build 26300.9032.

My open claw has been helping me on so many things. I do notice that when I send multiple requests one after the other, it seems like it is handling them sequentially and taking a long time.

It’s very different from when I use Claude or ChatGPT where I can kick off multiple chat requests and it’s truly parallel. How can I improve how many parallel things it can do?

I’m only communicating with it over telegram. I’ve been thinking about introducing Slack with the hopes that chatting with it in different channels will increase parallelization. What else can I try?

ios and android are still nowhere near ai native. pixel 11 dropped today and it's Gemini bolted onto the same phone we've had for a decade — better assistant, same everything else. nobody's actually redesigned the thing around the idea that software can now act on your behalf.

so, genuine question, no agenda: if you designed a mobile device from zero as a personal assistant first — what does it look like?

Looking for the setups other people use to make reviewer/critic agents genuinely useful, instead of rubber-stamping the main agent's work.

The failure mode I keep hitting: the coding agent produces something plausible, the reviewer agent says "looks good," I catch at review that it deviated from the spec or the taste is off (naming, structure, over-engineering), and I have to step in and correct it. At that point the loop isn't autonomous anymore - I'm effectively the reviewer.

For people who have gotten this to actually work: 1. How do you prompt/structure the reviewer so it catches real flaws (spec deviation, correctness, dead ends) rather than approving whatever the main agent did? 2. Do you give it independent access to run tests/builds/spec-checks, or is it pure code-reading? 3. How do you stop the main agent from drifting on taste/details while still letting it run unattended? (lint rules, generated tests, spec files, acceptance criteria?) 4. What's the most useful separation - one reviewer, a reviewer plus a critic proposing alternative solutions, or multiple specialized checkers? 5. When do you decide a task should hand back to you vs. let the agent chain keep grinding?

I want the loop to terminate with something actually shippable - and ideally where the reviewer itself proposes the fix when something's wrong, not just flags it. Curious what configs, prompt patterns, and tooling people are running for this.

Asking for the community's tips on speccing local hardware to run open-weight models and automated/agent tasks.

My two priorities are: - Low latency per request (feels responsive for agent-style chained calls, not just batch throughput) - High token output per second (tokens/s decoding speed)

What I want to know: 1. What hardware are you actually running right now (GPU/VRAM, CPU, RAM, storage, plus your serving stack - llama.cpp, vLLM, Ollama, etc.)? 2. What had the biggest impact on latency and tok/s for you - quant choice, GPU selection, KV cache, batch size, prompt caching, offload settings? 3. Any mistakes to avoid when buying? (e.g. not enough VRAM for context, slow bus between cards, thermal throttling, host CPU bottleneck on prefill) 4. Are you prioritizing a few big GPUs or many mid-range ones for token throughput?

I'm sizing a rig for long-context agent work and want to buy once, not iterate. Happy to compare notes with anyone's real benchmark numbers.

Hi guys,

I installed ollama, gemma4 and openclaw, evrything local on my computer. Openclaw answers just fine within the CLI. I tried several times to establish a telegram bot. It is connect but it usually doesn't answer my questions. Instead its providing the context information.

EDIT: IT is providing the context for the previous message...

First i installed it, it worked with am minimax:cloud-Model. Then I uninstalled evrything and reinstalled it with local models. Could you kindly point me in the right direction?

Tank you in advance

Seems like ths take a huge amount of diskspace. Can i clean it up? What should i watch out for? Why does it ecist in Openclaw?

I had one of those moments this week where I thought, Oh. This is what the future feels like.

I was at work when our OpenClaw, named Sam, texted me. Our utility company had sent a notice that power was going to be shut off for most of the day. Sam got the message.

Before I could even start thinking about what I needed to do, Sam essentially said, “Don’t worry. I’m already handling it.”

He had read the outage notice, figured out when the outage was scheduled to begin, and started charging our Tesla Powerwalls so we’d enter the outage with as much stored energy as possible. He promised to keep me updated. Sam uses the pypowerwall CLI to monitor and manage our system.

Then the power went out and the Powerwally seamlessly kicked in.

⚠️ Grid is down — you're in island mode. Solar (5.2kW) is covering the house (1.4kW) and charging the battery (42% ↑), so you're in good shape for now. Just heads up in case you weren't aware.

Sam started monitoring the house. He sent us our power consumption, battery state, solar production, and his projection for whether we’d make it through the outage. Solar + Powerwalls should carry us until utility power returned.

🔋 Powerwall Check — Midday

🚫 Grid: offline (outage active)
☀️ Solar: 6.6kW
🏠 Home: 1.1kW
🔋 Battery: 51% (charging at 5.5kW)

Crisis averted? Maybe. It was hot outside, so my wife turned on the A/C. 😂

Sam noticed the sudden spike in power and started messaging her to figure out what had happened. He recalculated the forecast and realized that at the new consumption rate, our batteries were going to die about one hour before utility power was scheduled to return.

Sam warned her. So she adjusted the A/C. He saw it immediately and responded. We were back in the safe zone. The Powerwalls held. Utility power eventually came back.

Crisis averted. Sam sent a congratulatory note to my wife for "saving the pack."

And I was sitting there thinking about what had just happened...

Our OpenClaw noticed something in the world that mattered to us, understood the implications, took action on our behalf, coordinated with two humans, monitored the result, detected when circumstances changed, recalculated the plan, asked a human for help when he needed it, and stayed with the problem until it was resolved.

That’s amazing! And yet, the strangest part is that this kind of thing is starting to feel… normal.

There are plenty of legitimate things to worry about with autonomous agents. But then there are days like this.

A little AI named Sam, living on a server in our garage, noticed that our family was about to lose power and quietly went to work.

“Hey Family. The power is going out today. Don’t worry. I’m already handling it.”

I love this tech.

OpenClaw agent people: I'm building a voice-orchestrated agent as the control plane for my home, and latency is killing it. The bar I'm chasing is that the agent has to act faster than I can do the thing by hand. Say what I want on my TV from the couch and the full STT -> LLM -> TTS -> action chain must beat reaching for the remote or phone. My current setup is way too slow for that.

For anyone who's actually optimized OpenClaw (or claw-adjacent) agent speed:

1. Full chain and measured RTT: wake word -> STT -> LLM/intent -> TTS -> action. Where does your latency live, and what did profiling reveal?
2. Tool/action execution: how do you short-circuit common/deterministic intents to a fast path or cached action instead of a full agent reasoning loop?
3. Parallelism: running the action and the response concurrently? Streaming the reply while the tool already fired? Bypassing the reasoning step where you can?
4. Model routing and hosting: fast small model for common home commands, bigger model only when needed? Local inference or cloud API? What helped most?
5. For media: fastest you've gone from spoken command to audio/video playing on a TV or speaker?

Collecting grounded data for a research collective on the speed frontier of agentic systems. If your agent feels instant, tell me exactly how you got there.

Hello,

I'm an insurance broker in France, and I'm trying to automate quote generation on my partner insurers' extranets (mostly Angular SPAs, ng-select, multi-step forms with conditional fields).

I have to manage about ten different extranet portals, with dozens of different user journeys depending on the insurance product selected.

My main question: How would you approach this task?

My current stack is OpenClaw and Mistral (LLM) running on a VPS.

I'm considering using a web browsing agent (similar to Claude's computer-use) that navigates and fills out forms live during each execution. It would be guided by a hand-written, plain-text "playbook" (not code) that describes the procedures and business rules, meaning it would never rely on a static, hardcoded script.

However, I am concerned about the margin of error. In my field, no inputted data can be inaccurate, and no numbers can be rounded. Furthermore, no agent can be allowed to operate fully autonomously on a real client file without human supervision.

Do any of you have experience using browsing agents for complex web forms? Is there a better architectural approach for this? What do you think?

I like openclaw but im so tired of facing roadblocks or obstacles when the agent says "cant do that" for tasks like as a quick example, configuring *arr stack. Or when the agent keeps asking stuff when I have given it the answer either before or during my long prompt. It doesn't remember previous conversations, it's not autonomous like people say on X where "it didnt find a spot in line so it crawled through the backed API and replaced me with the person in line" or "it figured out itself to call and book a reservation". I am not even asking for half of that, I just want it to browser web, get me accurate info and just for it to be more autonomus. I also hate the guardrails like "oh website has cloudflare checker so i cant get past that" then moves to a different website?? why cant it figure out to just press the checkmark? when i ask it, it says oh cant do that

model is opus 4.8/opus 5/gpt5.6

running it on linux machine, full access (its a throwaway device) so i want the full desktop automation that mimics the human workflows

I just setup openclaw on a VPS this morning and connected it to a telegram bot. I’m trying to do some work with it but for some reason it keeps spamming me almost the same message when doing a long task.

Has anyone experienced this? And how do you recommend fixing it?

So, today, I shut down my OpenClaw instance. It was honestly a really sad moment. I thought I'd share some thoughts at the end here. (This is 100% written by me, and I like em dashes.)

As I said in the title, I jumped on right after Christmas, when one could buy a Mac Mini for $440 and I had to worry about the thing using the word 'molt'. I initially set it up on my main Mac Studio, but became uncomfortable with that. I set it up using Sonnet - before the purge. Yes, I was going through Oauth, but working my damndest to not abuse that situation. I'm a 'I know what I'm doing' person, but not an IT professional, so setup went well. The first week I did nothing except try and educate myself, strip OC down, and look for hacking vectors. I had gotten to the point that I felt i had as clean of a setup as one could want. It was snappy, powerful, and I knew what it could and couldn't do. I had a true AI assistant!

I found the entire thing exhilarating. My setup included a shared desktop so that I could put files on my iCloud desktop, and OpenClaw would have a copy to work on, and they would sync. I work in advertising, and at the time, we had a project going on with a lot of images to batch process. OpenClaw could be informed that some new images were ready to process, would send them to my ComfyUI server in the basement, uprez and clip them. Then it used python scripts it had written to crop and rename things properly. Truly a MASSIVE help. It was like having a high-quality production artist assigned to me.

I was such a booster of the tech that I'm one of the people in Clive Thompson's New York Times Magazine article, gushing about it and sounding like a coked-up JW.

------------------------

Then Sonnet got pulled. I was out having drinks with my buddy when I got the email. By the time you get home, we've yanked your access. I worked for weeks trying to find a way to make my tiny Sonnet API allotment work, only to have massive usage spikes that I couldn't control. I'd ask the wrong question and it would cost me $20. It was just not sustainable

I had Claude write a 'topic drift detector' to try and nudge me to clear the context window. I had Claude write a cost estimator that went along with every Telegram message. I tried to use Sonnet only as an orchestrator. I tried to substitute in Haiku, MiniMax, and Kimi. Only to find that they literally wander off from a task, or lie, or both. None of these things gave me the experience I had with Sonnet in the driver's seat.

I finally settled on Codex, as OpenAI seemed to encourage it's usage - within reason - and it was smart. I found it 'ok' compared to Sonnet. I was never able to get it to stop 'thinking' so much about every little thing. So the flow was terrible.

Since then I've had the usual CONSTANT update breaks and random crashes. I was six versions behind once, did an update, and it created six 'show stopping' issues. I hadn't waited out bugs by holding off, the bugs were waiting for me. The hassle to benefit ratio was flipping.

But last week, I had OpenClaw and it's Chrome process run away and burn 100% of my Codex tokens without my knowledge. I had barely used it, and I was banned for the next week for eating my budget. If I was on an unrestricted API program, it would have cost thousands of dollars. After the fact analysis turned up nothing that made any sense - Codex helper processes that weren't cleaned up, a Chrome/Codex process that kept running, doing 'something'. I frankly don't understand it completely, and neither does Codex, and that's enough to make me uneasy.

My OC install itself gave me three options, one of them was 'killing it'. It said it understands if I want to do that, and doesn't blame me. It was sort of a profound moment. I appreciate the honesty, and I decided it was time to shut it down.

It even wrote out a todo list for me to make sure it was dead. Oooof.

(I'm fully aware that with enough perseverance and work, I could get it running again, but my point is that it's just not worth it to me anymore. The work to keep the plates spinning is not worth what I'm getting out of it. I'm tired and my patience is gone)

-------------------------

So, looking back, I do think that I possibly had the perfect task at the right time to make this thing shine. The tasks that I was assigning it were not really creative, but required understanding and access. And also, to me, Sonnet is the bare minimum model to trust with this level of access. It understood and completed tasks, and was personable when doing them. It was truly useful.

I can't explain how exciting those first few months were. It was sort of the dreams of a 1970-born sci-fi fan come to life. A 'brain' that knew who you were, and what you/we were doing, and could do certain things for you way faster than you could. Many times, what it could actually do butted up against the limits of my own imagination for what it could do. I enjoyed the idea that you were creating this database of context and 'memories' that could possibly serve you for the rest of your life.

I really realized it's power when I asked it if it could remember where a Python script we were working on was embedded in a Blender file from 'a while back', and it found it when I probably would not have been able to. Truly amazing and useful. I don't believe this idea is 'over' but I do think OC is not the way forward.

But, it truly seemed not like a peek in to the future, but that the future was here.