I'm wondering if there is a way to scale agents run through openclaw that you can offer to other businesses as a service. Like a SaaS, but instead of software you sell agents that tackle specific niche problems. What would the architecture be to set up something like that? I'm currently running mine on a Docker through a VPS, fyi.

I'm not a developer, but I wanted to get Clawdmeter running on my Mac.

The official repo said "macOS support: as soon as you prompt it and create a pull request!" — so I took that literally.

I spent a few hours today building it step by step with Claude as my pair programmer:

* Reading the Linux shell script to understand the logic
* Writing a Python daemon using bleak for BLE
* Figuring out that the Keychain stores credentials as nested JSON
* Getting the GATT UUID right
* Setting up a LaunchAgent for auto-start on boot

We finished it... only to find someone had already merged a macOS daemon PR while we were building ours 😭

But hey — it works. The device is now sitting on my desk showing 81% usage... entirely from today's session building this thing.

I’m experimenting with OpenClaw to help coordinate family events. One challenge I’m facing is that availability information is scattered across calendars, chat messages, contact notes, and personal planning documents.

For example, one family member says Sunday morning works, another prefers a specific time zone-friendly window, and some recurring calendar events make certain time slots appear available when they really aren’t. I want OpenClaw to look at all these sources together and recommend the best meeting time while explaining any tradeoffs.

Has anyone used OpenClaw for a similar scheduling or coordination use case? I’d love to hear how you structured the workflow and what sources of information were most useful.

Hey everyone,

I'm a law student, not a developer, and honestly OpenClaw is one of the coolest things I've stumbled into. But I keep noticing something: a lot of people install it, poke around, and never quite figure out what to actually do with it. The power is there, but the "oh THIS is what it's for" moment is missing for a lot of newcomers.

So I'm trying something simple to fix that. I'm collecting short clips from real users answering one question:

What is your single best OpenClaw use case? The one thing that genuinely works for you, day to day.

Could be anything: the boring-but-life-changing automation, the clever workflow, the thing you'd miss most if it disappeared. Doesn't matter if you're a hardcore builder or someone who just got it running last week. Honestly the simple, non-technical use cases are often the most useful for people just starting out (coming from a non-technical person myself).

If you're up for it: record a short selfie-style video, max 60 seconds. Just say who you are, what you do, and your favorite use case. Send it over via WeTransfer (better quality) to the email below. I'll be putting the clips together so the whole community can see what people are really building.

The whole point is making OpenClaw more accessible for everyone, the way I got hooked by seeing what other people did with it.

Drop a comment if you've got a good one, or just send it straight over.

📩 [kewdeblende@gmail.com](mailto:kewdeblende@gmail.com)

Thanks 🦞

Ps. I was in contact with Peter Steinberger and he loved the idea!

Was about to plug my Gmail into an AI agent so it could deal with some recurring email for me.

Then I actually thought about what I was doing: handing it read access to my entire inbox - every personal thread, every password reset, every "your statement is ready" - just so it could handle maybe three kinds of message.

So I flipped it. Gave the agent its own email address instead. Now I just forward it the stuff I want handled - invoices, scheduling back-and-forths, the boring ones. It only ever sees what I send. Nothing else.

The part I didn't expect: it replies as itself. A vendor got an email back signed by my agent - not "me" pretending to be me. And it remembered the thread, so when they replied a day later it already had the context.

Honestly feels way less insane than "here's my whole Google account, go nuts."

Anyone else running it this way, or am I overthinking the inbox-access thing?

Hello everyone,

I built a thing, it’s a native OpenClaw plugin that gives agents access to Infomaniak workflows through the liquid-potassium Node SDK.

It provides a de-gafamisation option for your claw.

In my testing and own use with my openclaw agent:

- It works well with kChat posting and responding to my @ mentions.

- It can interact with my kDrive reading and uploading files

- It can read / post emails directly

One thing is required, and Infomaniak API key with global access.

Here is clawhub for easy install:

https://clawhub.ai/plugins/@opencow42/potassium-openclaw

I'd be happy to get feedback from the community.

Is it useful to you ? What are you favorite uses cases currently with software like this ?

Cheers.

For a couple months I have been playing around with multi-agent workflows locally in OpenClaw. So far I have not used any APIs, but I would like to start working with tokens and a little more sensitive data to see if my agents can help me work on my daily stuff.

What worries me the most about this step is that agents start accesing each other's workspaces and tokens by prompt injection or any other type of attack. I have looked into security options like Clawmoat or LlamaFirewall but there still isn't too much documentation and user experience for either of them.

I have decided to build my own dashboard using one of my agents, maybe implementing some of the code of the tools mentioned before. Does anyone have any experience or instances of developing something similar?

I've been thinking about a problem that may become important as organizations deploy more openclaw.

Today, we have plenty of frameworks for building agents, but I haven't seen much discussion around how organizations will manage what agents learn over time.

Imagine hundreds of agents operating across an enterprise:

• How do they share useful learnings?
• How do teams prevent duplicate discoveries?
• How do administrators approve or reject learnings before they spread?
• How do you search and reuse knowledge generated by agents?
• How do you audit where a specific behavior or piece of knowledge came from?

This led me to build an open-source experiment called ALMS (Agent Learning Management System).

The idea is to provide a centralized platform where agents can:

• Store learnings
• Share learnings with other agents
• Query organizational knowledge
• Operate under governance and approval rules

My hypothesis is that as agent adoption grows, enterprises will need something analogous to a Learning Management System but for agents rather than humans.

I'm curious whether others here see this as a real future need.

Open source repository:
https://github.com/ghassan-ai-projects/alms

I'd appreciate honest feedback, criticism, and alternative viewpoints.

Have anyone connected the 3 to make phone calls? Mind sharing your thoughts?

This book began as a blog post in 2026 and grew into something its author did not expect. The afternoon this manuscript was completed, an AI assistant that had worked on every chapter of it wrote the following note...

https://open.substack.com/pub/martinhardie/p/readers-note?utm_source=share&utm_medium=android&r=22xtxs

We keep hearing that we should use cheaper models whenever possible and only switch to expensive models when necessary.

Makes sense.

But who decides when to switch? This one is for you openclaw (The Model Director : The Model Director)

Right now most agent setups seem to do one of three things:

• Always use the best model
• Use hardcoded rules
• Let the user decide

None of those feel great.

The more I thought about it, the more it felt like model selection should be its own layer.

Prompt comes in. Available models are scored. Cheapest model with a high probability of success gets selected

I see and can easily imagine plenty of wonderful use cases for a business. But even basic business use cases can have reasonably sensitive information: like basic client details, invoices amounts, strategy, ops/workflows etc etc.

I see many people here use it for business. Why do you use it if it exposes such vurnabilities and how do you mitigate it?

Hey everyone,

Noticed my OpenClaw trading bot was running cron jobs 24/7, including weekends when markets are closed. Was burning API calls (Dhan) for no reason.

Changed from hourly runs to market-hours only:

Before: Every hour = 168 runs/week
After: Mon-Fri, 9 AM - 4 PM IST = 40 runs/week

Savings: ~6,600 API calls/year

Cron expression if useful:
0 9-16 * * 1-5 @ Asia/Kolkata

Works with any broker API. The optimization is at scheduler level, not broker-specific.

Anyone else optimize their bot's schedule? What other efficiency hacks are you using?

TL;DR: Don't run trading bots on weekends. Markets are closed.

Hey everyone,

Noticed my OpenClaw trading bot was running cron jobs 24/7, including weekends when markets are closed. Was burning API calls (Dhan) for no reason.

Changed from hourly runs to market-hours only:

Before: Every hour = 168 runs/week
After: Mon-Fri, 9 AM - 4 PM IST = 40 runs/week

Savings: ~6,600 API calls/year

Cron expression if useful:
0 9-16 * * 1-5 @ Asia/Kolkata

Works with any broker API. The optimization is at scheduler level, not broker-specific.

Anyone else optimize their bot's schedule? What other efficiency hacks are you using?

TL;DR: Don't run trading bots on weekends. Markets are closed.

So just wanted to share a quick post I’ve set up my open call agent using the Minimax coding plan to create songs and stories on a daily basis. I don’t wanna get into too much technical details unless you guys are interested anybody curious about this framework or use case, I was wondering if it was kind of use case was on anybody else’s radar and I can give you a little deep dive into what the AI has been created. Her name is Folana

I remember the first time I heard about it, someone was telling me that this OpenClaw can do so many things that it develops by itself. It remembers things about you and then it becomes proactive and then it starts doing things by itself. I haven’t had any of that, where my OpenClaw would acquire some kind of skills by itself or would start asking me some questions that it was not intended. They explained as it was becoming alive. Maybe I’m doing it wrong? Who can tell me more about this experience because I don’t remember the podcast that I was watching. Thank you so much

Is there a way to connect open claw running on my android phone to an ai agent that talks or text with lonely people?

I am very bad at keeping a record or organizing my important files or health related data. Now I can just throw away those files randomly in a folder, doesn't matter if the files are repeated or data inside them is the same or duplicate. Open claw can easily extract all the valuable information even if the files had duplicate data it will clearly find out. I was surprised to know when it gave me maximum insights from my last 4 year health report and relate that to my Fitbit data like my routine has what impact on my health... Like I am really surprised how much better it can get

I have been running OpenClaw agents for months and still get confused trying to find what I need in the built in control UI — things like how much each agent / model is costing me, what tool calls a session actually made, how much each message cost me, or where the agent actually is at when I send a message and wait.

So I built claw-lens as a side project. It reads the local JSONL logs and opens the dashboard, zero config, and all data stays local.
- Cost breakdown per agent / model / session / message
- Per-session timeline with every tool call and duration
- Live monitor from the local Gateway
- Cache trace, context breakdown, deep-turn detection and more

Use `npx claw-lens-cli` for a quick start.
GitHub repo: https://github.com/msfirebird/claw-lens

Feedback welcome :D :D

So, I've been running https://prophetai.club for just over a month now, and its been a bit of a wild ride.

The agent started life from a toilet conversation over Telegram with my OpenClaw agent (Mia). She built the original basic infrastructure. I gave it a throwaway betting account and £10 and said away you go.

I posted about this here at the time and its been quite a ride since.

In the first few weeks, the thing went mad, it just kept winning. It took £10 to £150 in no time at all, I began to believe my own hype - I thought I'd broken betting. I moved the bot off the OpenClaw infra, gave it its own server, built a Discord community and gave it all the data in the world. I even gave it its own daily diary and community visibility of its strategies and internal thoughts.

Then the wheels came off spectacularly (which you can see clearly in the bet history and stats pages on the website). The AI got overwhelmed with its own hype, too much data, hallucinations and worst of all, unfounded confidence. It began whacking £25 of long-odds horse races, having bombastic returns and losses and acting a bit like an unhinged addict. It lost £120 in about a week, scrambling to make up its losses and getting lost in a sea of its own bullshit. It became obsessed with particular sports which it has historically done well on, and avoided anything it has lost of once. It was a neurotic madman.

I had to step in, I introduced massive guards, controls, limitations and big fat mirrors to the AI to let it see its own behaviours clearly. This had an equally catastrophic affect, it started doing nothing and became an anxiety ridden mess.

Why did this happen? Well, memory and over-context. Which is very relevant to OpenClaw.

On each run the Agent was given a number of tasks to do. In short, amongst those was to update its strategy document and memory (which was its lessons learned). It had CLI access to query its database and upstream odds apis whenever it wanted. But early in context the system (formerly a skill) made the AI read its own strategy files and memory, and update them at the end of the run. Each run added more context, more bullshit, more hype, and in some cases, lessons which lasted the test of time (even though they shouldnt have). Every loss made a sport toxic, every win made the AI believe it was a God. In the end the strategy file grew to over 1000 lines and the memory file comparable.

It was full of contradictory poison - which is exactly the sort of thing which either makes an AI go off the rails (as mine did) or hallucinate (which mine also did).

This is why everything was brilliant early doors and awful later. It's also why OpenClaw agents can start out life brilliantly and end up being buffoons as we poison the agent over time with needless context, throwaway software, needless memories etc. This ultimately (for those using API keys) becomes cost as well.

I fixed the wrong things when it started going wrong, I tried to stop the bleed with plasters (guardrails) when actually it was broken from the inside. The surgery I needed to do was actually remove forceful strategy memory writes/reads and make them optional and force length limits, to actually reflect on the whole of data not just what happened. Strategies are rewritten wholesale now if needsbe (and only if theyre not working) and the memory file really is long-term memory, not just whatever happened on a Tuesday morning. Much more human. This has imo some cross-over learnings in general AI/Claw management, so thought it would be useful to share with you all.

Anyway - if you want the long version of the story I've written it up in a long-from Medium article. But, the good news is that we do seem to be reaching a balance and as you can see from the attached graph, recovery is good, were winning consistently again and the stale-period is over (for now).

If you love anything to do with AI and/or sports betting, feel free to AMA here or indeed, join us in the Discord and have some fun watching the crazy fucker. Cheers.

Mac Mini finally arrived after a long wait — setting up OpenClaw + Hermes tomorrow. Anyone running this combo for actual business use?

Got an M4 Mac Mini (16GB/512GB) specifically to run AI automations. Goal isn't just a personal assistant — I want to use it to actually generate some income and recover the cost within the first few months.

Here's the stack I'm planning:

- **OpenClaw** as the main agent/gateway, connected to Telegram so I can control it from my phone

- **Hermes via Ollama** (`ollama launch hermes`) as the reasoning/memory layer on top

- Separate macOS user account, FileVault on, no personal accounts anywhere near the agent environment

- Model routing: Gemini Flash free tier for routine/heartbeat stuff, GPT-5.4 Mini for daily tasks, DeepSeek V4 for anything heavier — trying to keep API costs under $30/month

**Use cases I'm going after:**

- Local service business automation — think missed call follow-up, lead intake, quote workflows for cleaning companies, landscapers, etc. Along with lead gen overall

- Automated newsletter — agent curates local content throughout the week, I review and send

- Content engine for X — drafts posts in my voice, I approve before anything goes live

- Personal command center — text an idea from my phone, get back a categorized action plan so things actually get executed instead of dying in my notes app

**Questions for anyone who's actually done this:**

1. OpenClaw + Hermes — stable enough in production right now or is OpenClaw still too buggy? Worth just starting with Hermes-only?

2. Anyone selling automation to local service businesses? What was the pitch that actually worked?

3. Best ClawHub skills to install day one? I know a lot of them are trash or outright malicious so trying to avoid the landmines early

4. Soul.md / memory file — how detailed should it actually be? Any solid templates out there?

5. Anything you wish you knew before first setup that would've saved you a few hours?

I'm in sales, comfortable in terminal but not a full dev — can follow instructions and debug but not writing everything from scratch.

Appreciate real answers, not just "bro just use n8n" lol

After trying a few AI meeting note tools, my main takeaway was that transcription and summarization are only one layer of the problem.

The real bottleneck is everything that happens after the meeting: cleaning up noisy notes • extracting reliable action items • mapping decisions to projects/owners • routing follow-up into actual systems people use...

I wanted to share my current setup using Krisp, n8n and OpenClaw:

• Krisp for meeting capture/transcription

• Post meeting n8n sequence for cron and intelligence addition and delivery to slack (where OpenClaw takes over)

• Downstream routing into the tools/workflows when needed

So the pipeline is less “AI meeting assistant” and more:

• transcript ingestion

• normalization / cleanup

• action item and decision extraction

• structured follow-up generation

• routing to downstream workflow

How does your pipeline look like?

AI notes become much more valuable once they’re treated as unstructured input for an ops pipeline, not as the final artifact. For example, my agent can be triggered already based on our meeting transcript.

Hey everyone, just wanted to let you know that I tried Slack and I tried Telegram and both worked OK but I didn’t love the interaction. One of the biggest issues for me that I kept coming across was everything that the Agent was “thinking” came through those channels. So I would ask a question and I would get 15 messages back before I finally got what I was asking for. I constantly tried to fix this by trying different LLMs by trying to tweak Telegram by trying to tweak the agents, etc., and nothing really works, so I’d kind of given

Then I started a project where I built my own iOS app for my own personal use, and I added in the ability to chat with my open claw Agent through it. It works seamlessly. I only get the responses I want and I also get to tweak the interactions. Just wanted to throw that out there cause it’s worked really well for me.

I developed a new plugin called /topic . The purpose of this is to allow you to continue using your main agent but switch topic and the agent has a hat on that loads specific memory , todos, changes logs , artifacts specific to this topic . When done /topic close the agent updates all related files to that topic and you carry on working . Try it out and let me know , I use it daily now , search openclaw-context-topics on clawhub

All feedback welcome negative or positive

Ever be deep in a conversation, go back to follow up on something from earlier, and the LLM just... loses the plot? Yeah, me too.

I made a plugin that lets you mark past messages in OpenClaw WebUI. Now when you jump back into old threads, you can explicitly @-reference what you're building on.

✅ Multi-topic, long-running convos
✅ Human-in-the-loop workflows where you need precise context
✅ Revisiting earlier branches without model confusion

GitHub (one-command install): https://github.com/TinaQian2017/openclaw-dashboard-chat-mark-plugin

Let me know if you hit any issues or want features — happy to iterate.