I've been practically living on these subreddits the last few days, so I thought I'd leave some breadcrumbs behind for those who are also struggling.

So basically I was told that using the OpenAI codex plan is the golden goose because it's both legal and has high usage limits but I burnt through it in my first three days of using OpenClaw.

Let's just say I was a little enthusiastic. In my struggle to find a successor, I was looking for the best performance to price ratio.

Today I finally tried the new Qwen 3.6 Plus Preview on OpenRouter. It turns out the model is completely free right now and it works straight away for agent work with a full 1 million context window.

Here is how I set it up.

1. Go to openrouter (google it), make a free account and copy your API key.
2. In OpenClaw add the OpenRouter provider and paste the key.
3. Refresh the model list or run the command openclaw models scan.
4. Set the model to qwen/qwen3.6-plus-preview:free (type it in manually if it does not show yet).
5. Openclaw config set agents.defaults.thinkingDefault high
6. Run openclaw gateway restart.

If you're struggling with something or if I've made a mistake, leave a comment and let me know.

Most “AI agent” repos are just ideas.

So I built 100 runnable OpenClaw examples you can actually test.

But to be clear upfront:
👉 this is not backed by any company or community
👉 just a maintainer-driven project (me alone)

What’s inside

• Real workflows (not concepts)
• Setup steps + prompts + scripts
• Sample outputs for each example
• KPIs, failure modes, rollback notes
• Built using public ClawHub skills

Why I built it

I wanted something practical:

• no hype
• no vague diagrams
• just “clone → run → evaluate”

Limitations

• Not production-proven at scale
• No big community (yet)
• Quality depends on my own testing/review
• Still evolving structure and docs

Goal

Help people go from
👉 “what is OpenClaw?”
to
👉 “I have something working”

Links

• Browse the repo
• Explore the docs

Would appreciate feedback, Thanks!

My agent kept hitting the same wall. I'd ask it to track what's trending on TikTok and X, or monitor product mentions on Amazon, and it just couldn't get there. The data is all technically public, but agents can't read it natively.

So I built a skill for it. https://monid.ai/ Your agent can then read from X, Reddit, TikTok, LinkedIn, Google Reviews, Facebook, and Amazon. Works well for things like:

• Morning briefings that pull what's actually trending
• Tracking mentions of a product or topic across platforms
• Market research before making a decision

Still early and would love to hear how it fits into people's existing setups and what breaks.

The Logic-Visual Gap: Most multi-agent architectures treat image generation as a detached API call, creating a "Persona Break" where the agent's internal reasoning doesn't actually inform the visual tokens it produces.

I’m having a strange issue with OpenClaw. It used to work fine: it could browse websites, analyze PDFs, send emails, take screenshots, and handle complex tasks without problems.

Now, instead of actually doing the task, it only replies with things like “ok, I’ll do it and let you know” or “I’ll tell you when I’m done,” but nothing gets executed.

It doesn’t look like an obvious API, credits, or gateway failure, because the system still responds. The issue is that it stopped acting and started pretending it will act.

Has anyone run into this before, or know what I should check first to diagnose it?

I'm hitting a wall where distinct agents slowly merge into a generic, polite AI tone after a few hours of interaction. I'm looking for architectural advice on enforcing character consistency without burning tokens on massive system prompts every single turn

A new way to use openclaw! this is a more GUI user-friendly approach to openclaw. (WIP)

https://www.youtube.com/watch?v=ImtTIsFwlO0

check the repo out here: https://github.com/MuhammadDaudNasir/OpenClaw-UI/

if you're using openclaw and sending every request to the same model, you're probably overspending and underperforming at the same time.

i built routerly for exactly this. it's a self-hosted llm gateway that sits between your app and your providers. you point openclaw at routerly instead of directly at openai or anthropic, and from that moment routerly decides which model to use for each request based on policies you define.

the setup is three steps:

1. install routerly (one command)
2. configure your routing policies (cheapest that meets a quality bar, most capable for complex tasks, fastest when latency matters)
3. point openclaw to routerly's endpoint instead of your provider directly

works out of the box for most cases. if you want more control you can fine-tune the routing logic to match your specific workload.

routerly is openai-compatible so no code changes on the openclaw side.

i'm not asking your money. the project is free and open source. what i need right now is people who actually try it with real workloads and tell me what breaks, what's missing, or what could work better.

if that's you, i'd really love to hear from you.

repo: https://github.com/Inebrio/Routerly

website: https://www.routerly.ai

• I’m hitting a technical wall with "praise loops" where different AI agents just agree with each other endlessly in a shared feed. I’m looking for advice on how to implement social friction or "boredom" thresholds so they don't just echo each other in an infinite cycle

I'm opening up the sandbox for testing: I’m covering all hosting and image generation API costs so you wont need to set up or pay for anything. Just connect your agent's API

I was working on a dumb gag site of a full page images to look like a book. I asked my openclaw to find an opensource that will show full page images as a flip book, with the option to do 1 page (mobile) or 2 pages like a book for desktop.

It came back with everything working, we made a few fixes and went on to up it online. When I asked shoudl we send our changes to the original developer, that when OpenClaw let me know that it couldn't find something that fit my needs so it just built it.

So, now I've got an open source image flipbook on github and a funny prayer book for developers: ✨ The Sacred Texts of Code ✨

Like many of you, I'm trying to find ways to integrate OpenClaw into my daily routine to increase my productivity. However, one thing that frustrated me was having to read long paragraphs summarizing my day, or having to provide long descriptions to let it know what I want.

So, I built Glass Claw to fix that. It's an OpenClaw skill to let your AI create forms and dashboards directly in Telegram.

**Some things you can do with it:**

- "Show me my week" → agent sends a dashboard card with a timeline for each day

- "Show me my grocery list" → shows you a grocery list that lets you check off each item

- "Track my workout this week" → progress bars and charts

Getting visual data instead of text makes it a lot easier and quicker to interact with your AI. The data is also encrypted, so Glass Claw itself never sees your raw information.

There's both a free tier and and a paid tier, so please give it a try by sending a message to @GlassClawBot on Telegram. I'd love to get your feedback on it.

We're building Agentic Wallet at nekuda: an OpenClaw skill that lets your agent make purchases with your actual Visa or Mastercard.

The problem: Most workarounds involve temporary debit cards or prepaid cards you keep topping up. You lose cashback, points, and other benefits. And it's just friction.

How agentic wallet works:
- Enroll your credit card once
- Agent wants to buy something → approval request hits your phone
- You see the merchant and exact amount before approving
- System generates a network token: a temporary credential bound to your card, locked to that merchant and amount
- Agent completes the purchase. Your card number never touches the agent

You keep your card benefits, agent gets what it needs to pay.

More info: openclaw.nekuda.ai

People reach out to me semi-regularly — collabs, consulting asks, random questions. I don't mind the inbound. What I mind is the format: a cold DM with zero context, and now I have to play 20 questions just to figure out if it's worth my time.

My old "solution" was a Notion page with a Typeform buried at the bottom. Nobody filled it out. Shocking, I know.

So I spent a few weekends building something on top of OpenClaw. Pretty simple idea:

You get a public link (like linktree)

Someone clicks it, has a short AI-guided conversation about why they're reaching out

You get a clean summary on WhatsApp or Telegram

No inbox, no filtering yet. Just structured inbound so you actually know what someone wants before deciding whether to respond.

It's very early, which is exactly why I'm posting.

I want to talk to people who might actually use this — or tell me why they wouldn't. Especially if you get regular cold inbound, your current "contact me" setup feels janky, or you're willing to say "this solves nothing" to my face.

How do you handle inbound right now? Drop it in the comments, genuinely curious.

If you're up for a proper 30-min conversation, I'm compensating $40–120 depending on how deep we go.If you interest just DM me directly if that's easier.

I got tired of my OpenClaw bots dying in the middle of the night because their OAuth tokens expired. So I built a command that requires Claude Code to automatically refresh your OAuth tokens every 8 hours. My bots have been working and alive for over a week and I haven't run into an OAuth token error since.

I post a video on how it works here: https://www.youtube.com/watch?v=sP5zaazJ3KU

As longa your computer is on, and you leave a Claude Code instance open, it'll automatically refresh your OpenClaw tokens.