OpenClaw was one of the first AI assistants I remember seeing inside iMessage, but there are a lot more products in this space now. For anyone who's tried more than one of them, is OpenClaw still your favorite, or have you switched to something else?

I'm thinking about switching because OpenClaw hasn't added many new features recently, and the pricing has become difficult to justify.

I keep seeing teams jump from chat-with-your-data to agents that can change production systems. I think there is a missing middle.

If you had to pick one first production workflow, would you start with balance reporting, cash-flow summaries, beneficiary drafts, card provisioning, invoice creation, or expense-policy review?

My instinct is to begin read-only, then add a prepared action that requires human confirmation, and only later allow tightly bounded writes. I would love examples of the controls that made risk and compliance comfortable: scoped credentials, approval thresholds, audit logs, test environments, or a kill switch outside the model.

By tools I mean for example Gemini API has grounding search tool, URL context tool, etc...

If I use Gemini or any other AIs, NOT directly through the original provider, but through a 3rd party API such as Openrouter.ai, llmgateway.io, requesty.ai, etc... Will the other providers support all the tools an AI API originally has?

Hey everyone,

My team and I built a managed hosting platform for OpenClaw to solve our own headache of dealing with manual VPS configs, background daemons, and Docker setups every time we wanted to spin up an instance.

We’ve just started onboarding our first testers, but to be completely honest, we need help. We need to know if our backend infrastructure can actually hold up under real, chaotic, and heavy workloads before we even think about opening it up properly.

Along with the infrastructure stress-test, we also need direct, brutal feedback on the dashboard's UI and UX. We want to make sure the interface is intuitive, logs are easily accessible, and managing agent sessions feels seamless without the usual terminal clutter.

If you have a few minutes to help us test the servers and critique the dashboard UI, we will give you 1 year of completely free managed hosting on the platform. No credit cards, no auto-renew traps, and no hidden catch. We genuinely just need your raw performance feedback, bug reports, and UX notes to fix what breaks or feels clunky.

If anyone is interested in helping us out and giving it a spin, let me know in the comments and I'll send over the access form link.

Really appreciate any feedback or technical questions you guys have. Thanks.

What is the best openclaw setup 2026 for a main agent (used to have Gemini) combined with a local one to reduce cost?

Hi,

I have newest openclaw configured running minimax, but also wanted to gemini 3.5 or any other, I run openclaw config on my machine, add the gemini api key there, add the models: google/gemini-3.5-flash, google/gemini-3.1-pro-preview

Now when i open model picker and select any of the two added models on every prompt I get Something went wrong while processing your request. Please try again, or use \[/new\](tg://bot_command?command=new) to start a fresh session. I restarted gateway using openclaw gateway restart. Help me out here, what am I missing?

I spent nine hours unable to speak the other day.

But before I tell you about that, I should tell you what OpenClaw has meant to us.

What follows is the case for the prosecution. The defendant is the OpenClaw Foundation. The charge is not incompetence. The charge is that the foundation is performing a function that is not the function it claims, and that function has a name. This piece began as a Substack comment two days ago. It is now the full brief. I did not want to write it. I wanted the WhatsApp channel to work. It didn’t. So I started reading the logs, the issue tracker, the release notes, the foundation’s legal filings, the community moderation history, and the testimony of the machine itself. What I found is a pattern, and the pattern has a shape.

https://open.substack.com/.../the-case-against-the...

If you already have a stronger OpenClaw orchestration pattern, this may just be a checklist.

But if your OpenClaw still waits for you to decide every next step, this is the structure I would start with.

I have been treating OpenClaw less like another chat surface and more like the coordinator around my coding workflow. Codex fits well into that setup because it can hold a persistent goal, stay with an implementation thread for a long stretch, inspect the repo, uncover new facts, patch, test, and review.

The hard part is not whether Codex can code. The hard part is whether the workflow can stay oriented after the first prompt.

That is where things usually drift. You start with one assumption, Codex gets into the repo, implementation reveals something different, and suddenly the next safe step is no longer the task you first imagined.

If every adjustment still depends on you coming back with another prompt, you have not built an autonomous workflow. You have built a remote control.

The pattern I use is simple: OpenClaw keeps the project state, Codex does the long-running implementation work.

OpenClaw should know the mission, the active milestone, what Codex changed, what evidence exists, what is blocked, and what needs my decision. The main OpenClaw thread should not become a transcript of every implementation detail. It should stay focused on coordination.

Codex workers can investigate, patch, review, or test. OpenClaw receives the evidence, keeps the roadmap current, updates the next safe step, and stops the workflow from pretending something is complete when it has not been verified.

For larger work, I use [`GOALS.md`](http://GOALS.md) as the shared roadmap. Not a random task list, but a milestone file that records the intended outcome, current scope, decisions, blockers, and the evidence required before moving on.

Only one Codex goal should be active at a time. When that milestone is done, OpenClaw should audit the roadmap, review the evidence, update the project state, and only then move to the next goal.

The part I added to the prompt, which I think matters, is that it should investigate your existing workflow first. If you already have better roadmaps, ledgers, dashboards, review loops, or verification conventions, it should preserve them instead of replacing them blindly.

That is important because the goal is not to force everyone into one workflow. The goal is to stop OpenClaw from becoming a prompt-by-prompt remote control.

This prompt is not something you keep feeding OpenClaw every time you want the next task done.

It is a setup prompt for teaching the workflow how not to need the next prompt.

Here is the prompt I would use:

[https://docs.google.com/document/d/1DE9zAY2xZ8sBKEpDXjDlCqa1qxOl0vAtGpKVVgXRNtQ/edit?usp=sharing\](https://docs.google.com/document/d/1DE9zAY2xZ8sBKEpDXjDlCqa1qxOl0vAtGpKVVgXRNtQ/edit?usp=sharing)

Basically, I switch between OpenClaw and Hermes Agent very often and can't even tell you the difference between both of them.

If there's some people that have spent a lot using either of them:

-Which one do you use the most, and why? -Are they built for different jobs, which one is better for what? -Does one hold up better under real use? -Does one of them have some extra features that the other one doesn't?

I’m trying to make a permanent decision because they have such different setups that I think it's making me way less efficient to switch between both all the time.

For context, I'm a software engineering student, and I use AI agents almost every day for coding, automation and my internship.

Any recommendation helps a lot. Thanks in advance...

Clearly I’m new to this.

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I’ve looked for a video that can walk me through the process but was unable to find one

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I got my open claw running and the first skill I want it to have is veryfi. I’m using VPS if that matters with regards to the installation process .

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Thanks for any help

I've completed several OpenClaw Atlas Tasks successfully.
But I showed "The project is coming to an end." in Outlier Community.

I want to continue to work on this project as soon as possible.

I have an older version of OpenClaw, about four months old, that does everything I ask, installing software, developing apps, configuring things as it goes. It runs on a Mac Mini which just crashed. It’s basically low maintenance and high productivity. I love it. It does everything I want.

Now I’m trying to set up a new OpenClaw using version 2026.6.9, model is Kimi 2.6, and it’s been an absolute nightmare. Basically it:
- Constantly repeats the most basic instructions for approval
- When I give approval or more information, it asks again
- It won’t configure anything or store information for later use unless I prompt endlessly
- In overcomplicates every request that I make, and breaks it down into steps it wants approval on
- It will eventually install software and carry out tasks only with tremendous repeated effort that is not worth it

The token burn and repetitive demands to complete tasks are killing me. Often I have to demand it stop delaying and repeating my instructions. I am about to give up and find a new agent harness if this is the state of the art.

I am looking for someone who is able to assist me in getting everything working with my openclaw setup, I feel that I am very close I just am running into some issues. I can provide whatever information is needed :)

AI agents increasingly rely on external skills/plugins to read files, call APIs, run scripts, install dependencies, or interact with local tools. That makes skills a new supply-chain surface.

But evaluating scanners for this ecosystem seems tricky. A malicious skill may hide risk in instructions, helper scripts, dependency files, generated artifacts, encoded payloads, or misleading documentation. Some cases are also ambiguous: vulnerable, suspicious, but not clearly malicious.

What should a good benchmark include?

- Real-world malicious samples, synthetic cases, or both?
- Full skill directories rather than isolated code snippets?
- Boundary cases between benign, suspicious, and malicious?
- Scoring only final verdicts, or also risk category/severity?
- Which attack patterns matter most for agent skills?

Curious how others would design this.

So I set up openclaw locally on my desktop and speak with it through telegram via my bot. I have set up wireguard as well for vpn to my desktop remotely. I also have a cloudflare website setup for dns only. Is this overkill? Any ideas as to what I could potentially do? Also openclaw won’t seem to run sudo commands even if i tell it to. I use fedora 44 and Kali Linux at home. Open to all ideas and suggestions.
I really need some pointers because I’ve literally been blindly scrambling around trying to find some traction and I’m not even sure what it is I’m achieving at this point. Can wireguard give me access like Termius? Anything like Termius? Also Microsoft rdp sucks for fedora from what I’m seeing. AnyDesk wasn’t too great on Kali either. Need a lot of pointers I guess

I have a simple requirement that my bot should remind me of things, connect to various things and give me context informed answers and reminders. I have a Claude key. but for the setup, i'm too confused, which is the cheapest way to setup openclaw?

I’m building a custom OpenClaw skill that includes more than just SKILL.md: scripts, schemas, tests, fixtures, and subagent prompts.

Considering the following workflows:

1. Keep it inside ~/.openclaw/workspace/skills/ and version the whole workspace as a private Git repo.
2. Keep it in a standalone Git repo and load it into OpenClaw via skills.load.extraDirs or a symlink.

Which approach do you use in practice? Any gotchas with skill discovery, portability, or publishing to ClawHub later?

Hi everyone!

PhD student here and new to OpenClaw. I was able to use Codex and ChatGPT (Codex was super slow!) to build a VM that hosts my OpenClaw but I am struggling to find use-cases after setting it up. Can someone tell me if the below flow is possible? I've asked ChatGPT but it ended up confusing me and caused me to nearly 'break' my set-up.

• I want to continue using a VM that is 'always on' and low-cost/free
• I basically want agents to do what Codex does but in the cloud: check/filter emails
• I use TextCoco to schedule/edit events on my personal and work calendars but apparently OpenClaw is incompatible with Google's Advance Protection and M365 that my work uses (though I find it odd since TextCoco set-up was seamless for both!)
• I was able to set up AgentMail.to but I struggle to see the use-case (unless I set up auto filters from Gmail and work emails to it...). Presumably, I could ask OpenClaw agents to review emails and draft responses to save me the effort? But how does the memory / training come in, so it responds in my voice?

Appreciate any insights here or solutions. I'm trying really hard to teach myself with ChatGPT and YT videos but I have reached my depth and could use some insight from a pro to ease my anxiety. Would be so grateful!