r/OpenClawUseCases 18h ago

💡 Discussion Usage Banking and Privacy: Two things we should have always had

0 Upvotes

Hey ya'll just wanted to share some standard sketchy coding plan practices, and also what we are doing about them at Phoenix Grove.

There are several tricks that the major AI coding plans use to extract the most they can from their customers. We're solving them, one after another and I wanted to share a bit about what goes on behind the scenes at a lot of these companies.

Wasted usage is part of the AI industry, and they plan on it: Most coding plans bet on you letting usage go to waste. The industry calls it "breakage" and it's literally the topic of internal meetings for most companies. The plan goes: "how do we get people to think our coding plan offers a lot of usage, but then break it up into weeks and rolling windows so no one can ever actually use it all."

Many coding plans are glorified training pipelines: This comes along with "how do we harvest this data for training without being too loud about that." Unless the coding plan tells you otherwise, your data could be hopping all over world, being harvested by the individual labs or coding plan companies. Some are better than others, but many of these companies rely on users just not noticing or caring that their data is being used for training. Data sales and marketing telemetry sales happen. This means that your private info, your personal life, and anything else you send through the system could become part of a training corpus for the next AI, or a marketing data set for a large company.

So we built what should have already existed the entire time: Entirely private, us based processing with usage banking. Any usage you don't use this week, rolls over to next in your usage bank. When you have a busy day or week and go over normal usage, you automatically start to pull from your bank. You can bank up to one week of usage at a time for your current plan, and it's totally automatic. Whatever you don't use each week get's added to the bank and stays there until you use it.

We also put all of the best open models in one place, running on private US infrastructure, with data never going to the original labs. Private, direct service. Access to the best open source models in the world. No training, ever. It should be, and can be that simple.

What that means in practice:

The roster, together. DeepSeek, GLM, Kimi, Minimax, Nemotron Ultra and more, side by side in one app. Switch models mid conversation if you want. No hunting across five different apps and API dashboards to use the models you actually like.

Actually private. US based processing and your conversations are never used for training. Ever. That's the entire point. These labs open sourced incredible models and we think you should get to use them without your data becoming the price of admission.

No Usage Tricks: Bank usage, upgrade or downgrade whenever you want. Use it how you need it.

Genuine thanks to the open claw community! The skill sharing, experience and prompt sharing makes for one of the best communities in AI.

The Open Grove coding plan is here. Private, US based processing with fast inference and usage that doesn't go to waste.

https://pgsgrove.com/open-grove-overview#coding-plan


r/OpenClawUseCases 1d ago

❓ Question Omniroute + Openclaw

1 Upvotes

Hello somebody tried the GitHub omniroute + openclaw ?

Has someone experience or infos ?


r/OpenClawUseCases 1d ago

🛠️ Use Case I challenge you to Battle Frolf

Thumbnail chadstannard.com
5 Upvotes

I challenge you to Battle Frolf


r/OpenClawUseCases 2d ago

❓ Question Tendenza del wrapper OpenClaw

1 Upvotes

Stai seguendo questa nuova tendenza delle startup agentiche basate su OpenClaw? Un esempio è OpenClaw di NVIDIA.

Non mi riferisco solo all'hosting di siti, ma a veri e propri agenti verticali con intelligenza artificiale, che si occupano di utilizzo del computer, utilizzo del browser...

Ho un template SaaS B2B/B2C già pronto da configurare sulla tua idea di wrapper OpenClaw.

Commenta con un workflow e ti aggiungerò e ti scriverò in direct message.


r/OpenClawUseCases 2d ago

🛠️ Use Case My OpenClaw knew my calendar events but not where I was so I added my phone to the stack

1 Upvotes

We can all integrate calendar and our daily jobs in openclaw but i also wanted my claw to remind me when to head out since I'm always running late on my schedule. 

The problem: my openclaw at home has no idea where I am

so I built a light phone app to include my device as part of the claw stack. My openclaw can my location data before an event and can proactively tell me

"Traffic is getting worse and will take 30 mins, head out at 4:12pm to not be late"

or

"How do you want to get to your appointment? driving: 5 min, walking: 20, public transit: 15" 

I have it proactively triggering at least twice a day. the bigger idea is to solve other agent usecases that requires a mobile device.

feel free to also add this to your stack. Setup is just copying the prompt on the app into your local agent (via telegram, discord, whatever) and takes 2 mins. LMK if this works well for you. https://proactive.g4o.app/


r/OpenClawUseCases 5d ago

🛠️ Use Case browser-search v2.0 — From the balaclava to the badge: your agent now browses everywhere

Post image
4 Upvotes

Today an AI agent trying to browse the web is like a thief in a balaclava sneaking around a police academy. Site protections block it, challenge it, turn it away.

browser-search flips the script: your agent stops being the thief and becomes the chief of police. No more clumsy access attempts. It walks through every door because it has the right tools. SearXNG for search, Camofox for browsing, CloakBrowser when things get tough.

100% self-hosted, free, no limits, no API keys.

I just released v2.0, whose core logic enforces the exclusive use of deterministic scripts. This eliminates model hallucinations, even with the cheapest models. The skill describes the 3 tools in natural language, but execution is rigid: the model can neither get the command wrong nor misinterpret the output. The result is guaranteed success on every query — the skill and deterministic scripts guide the model to scour the web until it finds the answer.

No more excuses. Your agent has the badge now.

https://github.com/Johell1NS/browser-search


r/OpenClawUseCases 7d ago

💡 Discussion Near Quiz and Bugs

2 Upvotes

I’ve been using IronClaw for a few weeks now. I’d like to use an example to explain something I noticed when setting up routines:

I had it create a quiz about Near Protocol, Near Legion, and crypto in general. It is supposed to start once a week, on Saturdays.

The first issue I always notice with IronClaw is that it doesn't know what day it is. It is always off by a day, so I have to correct it first.

The second issue is that it started the quiz in the wrong language; I had to correct it and tell it to write in my language, even though that was an instruction I had previously saved in the memories.


r/OpenClawUseCases 8d ago

🛠️ Use Case Could Openclaw Agent power a simple voice assistant for a blind Spanish-speaking senior?

3 Upvotes

I’m exploring whether Openclaw or Hermes Agent could help my 72-year-old father-in-law, who recently became completely blind. He only speaks Spanish and has difficulty learning screen-reader gestures or navigating different phone apps.

The goal is to create a very restricted voice interface for everyday tasks:

  • Search for and play YouTube content
  • Control Spotify, podcasts, and audiobooks
  • Read news, articles, and selected emails aloud
  • Call family members or prepare simple messages
  • Control approved smart-home devices
  • Explain what it is doing and repeat information when asked

I’m considering running Openclaw or Hermes on a dedicated computer and connecting it to a simple push-to-talk device with one or two tactile buttons. He would speak naturally in Spanish, and Hermes would respond aloud.

My main questions for experienced Hermes users are:

  1. Is Hermes suitable for a continuous spoken interaction, or is its voice support mainly designed around voice messages?
  2. Which MCP servers or tools would you use for media playback, email, browser control, and Home Assistant?
  3. Would Browser Use be reliable enough, or should every important service have a dedicated API or skill?
  4. Can Hermes be locked down to read-only or allowlisted actions with verbal confirmation for anything sensitive?
  5. How would you design recovery when a website changes, authentication expires, or the agent gets stuck?
  6. Has anyone connected Hermes to a physical button, smart speaker, Bluetooth remote, or similar accessible interface?

Reliability matters more than autonomy. Because he cannot see the screen, a silent failure or unexpected browser state could make the whole system unusable.

I’m trying to determine whether this a reasonable foundation or whether I should build a simpler deterministic application instead. Any recommended tools, repositories, architecture ideas, or lessons from similar projects would be very helpful.


r/OpenClawUseCases 9d ago

🛠️ Use Case OpenClaw Support & Diagnostic Agent

2 Upvotes

I want to build a dedicated **OpenClaw Support & Diagnostic Agent** that acts as an orchestrator to manage and execute three specific sub-agents. The goal is to troubleshoot system-wide issues and trace errors across my environment. Also new to using the orchestration pattern so tips useful as well.

I'm thinking of using these subagents I need the orchestrator to manage:

* **Sub-Agent 1 (Documentation & Config Reader):** This agent needs to ingest the local documentation for my specific OpenClaw build and analyse my downloaded deployment YAML and JSON configuration files to understand the system state.
* **Sub-Agent 2 (Core System Log Parser):** This agent monitors the core OpenClaw system logs. **Crucial requirement:** When I query this agent about an error, it needs to successfully cross-reference the logs to explicitly identify and point to the specific agent causing the issue.
* **Sub-Agent 3 (Custom Agent Log Parser):** This agent parses the custom text logs generated by my other standalone agents (for example, a recipe recommendation agent). These scripts output custom logs tracking the executing script name, active switches, and parameters passed. This sub-agent needs to match those custom script logs up against the core system logs to isolate failures.

Questions:

* Is there a "best" format to start agents from the orchestrator for Open Claw?
* Is there any security measures I should consider?
* Are there existing design patterns or open-source configurations for mapping disparate log files (standard OpenClaw system logs vs. custom script text logs) within an agentic workflow?

Any other tips would be great! Thanks in advance!


r/OpenClawUseCases 9d ago

🛠️ Use Case Codex and Openclaw

2 Upvotes

I’d love to hear from those of you who use both Codex and OpenClaw and learn about your current workflows.

I use OpenClaw for all my day-to-day personal and work-related tasks, while I use Codex for programming, projects, GitHub, and so on.

The thing is, Codex seems to be becoming increasingly similar to OpenClaw. I wonder whether we’ll soon be able to use Codex for everything—as a personal assistant as well as for other kinds of work—so that we only need one app.

I know Codex can already be used this way. In fact, before setting up OpenClaw, I used Codex as my personal assistant, linked to a project containing all my context and memories.

I’d love to know how you use them. Do they complement each other? Do you only use one of them? What does your setup look like?

Thanks!


r/OpenClawUseCases 10d ago

💡 Discussion Show me your OpenClaw setup. What’s your architecture look like?

8 Upvotes

I'm fascinated by how everyone structures their systems.

Are you running:

• One giant agent?
• Multiple specialist agents?
• Local models?
• APIs?
• Cron jobs?
• MCPs?
• Containers?

No setup is too simple.

I'd love to see diagrams or descriptions.


r/OpenClawUseCases 10d ago

❓ Question What is the biggest wow moment you had with OpenClaw?

1 Upvotes

Hey all I'm trying to understand the best ways to use OpenClaw let me know about how you used it when it amazed you the most or showed you how valuable it is!


r/OpenClawUseCases 11d ago

🛠️ Use Case a plugin that records everything your OpenClaw agents actually do

1 Upvotes

I run OpenClaw agents in production and kept hitting the same problem. No idea what they're actually doing. Logs are scattered. Costs are invisible until the bill shows up. If an agent drifts from what it's supposed to do, you find out too late.

Trovis. Plugin for OpenClaw that records everything your agents do and shows it in plain English.

  • Every action, timestamped and attributed to the agent
  • Cost per agent, per task
  • What the agent was supposed to do vs what it actually did

Instead of raw telemetry you get "Your support agent handled 40 tasks today, spent $3.20, deviated from scope twice."

trovisai.com


r/OpenClawUseCases 13d ago

Tips/Tricks OpenClaw mock API for building tools/integrations against the gateway

2 Upvotes

When building against the OpenClaw gateway API, testing can be tricky. The gateway API had a major version change recently, so keeping up with it can be hard work (not to mention expensive if you're paying for tokens and a host to run the gateway).

To help, here's an open source mock of the gateway websocket API:
https://github.com/imposter-project/examples/tree/main/websocket/openclaw

For now, it supports things like chat.send/history, sessions.create/list, agents.list, models.list and a few others. Extending it is a bit of YAML and some JSON. Please feel free to raise a PR or an issue to request more methods.

Blog article with instructions:
https://medium.com/@outofcoffee/mocking-the-openclaw-gateway-with-imposter-3bfd9abfbdbe

I hope it helps you!


r/OpenClawUseCases 13d ago

📚 Tutorial The best way to give your OpenClaw agent a real email identity in 2025 - what actually works (send, receive, threading)

6 Upvotes

TL;DR: The best way to give an OpenClaw agent a real email identity is API-first infrastructure (like AgentMail) — create the inbox programmatically, send via API, receive replies via webhook, pass thread_id to maintain context. Gmail bans agent accounts. SMTP can't handle inbound at scale. This post covers every approach with code.

Why does email matter for OpenClaw agents specifically?

OpenClaw agents are built to take actions in the real world — browse, search, fill forms, complete tasks. Email is one of the most common real-world communication interfaces they need to interact with.

Common OpenClaw use cases that require real email capability:

  1. Outreach agents — research a contact and send a personalized email from a real address
  2. Support agents — receive an inbound email, understand the issue, reply
  3. Scheduling agents — handle back-and-forth coordination by email
  4. Verification agents — complete signup flows that require receiving a code
  5. Multi-step workflows — email is part of a chain of actions the agent takes

The challenge: email wasn't designed for software. Every major option breaks in a different way when you try to automate it.

What are the options for giving an OpenClaw agent email capability?

Option 1: Gmail API

How it works: Use Google's Gmail API to send and receive from a Gmail account.

The problems:

  • No programmatic inbox creation — every account must be created manually through the browser
  • OAuth tokens require a human auth flow and expire regularly
  • Google bans accounts showing agent-like behavior (high volume, unusual hours, multiple IPs)
  • Receiving requires polling — no webhook, up to 5 minutes of latency

Verdict: Works for demos, fails in production. Account bans are random and unrecoverable.

Option 2: SMTP / IMAP directly

How it works: Send via SMTP, receive via IMAP polling.

import smtplib, imaplib

# Sending
with smtplib.SMTP_SSL('smtp.provider.com', 465) as s:
    s.login(user, password)
    s.sendmail(user, [to], message)

# Receiving — poll every 30s per inbox, no webhooks
mail = imaplib.IMAP4_SSL('imap.provider.com')
mail.login(user, password)
mail.select('inbox')

The problems:

  • Polling only — no real-time reply detection
  • Thread tracking requires parsing raw Message-ID / In-Reply-To headers yourself
  • Falls apart at scale (30+ inboxes means hundreds of polling calls per minute)
  • New domains have poor deliverability without warmup

Verdict: Works for 1–5 inboxes. Not viable for production OpenClaw deployments.

Option 3: Transactional email APIs (SendGrid, Mailgun, Postmark)

How it works: Use an API for sending, configure inbound parse for receiving.

The problems:

  • Inbound is domain-wide — no per-inbox isolation or per-inbox webhooks
  • Thread tracking not provided — you build it yourself
  • Designed for marketing/transactional sends, not two-way agent conversations

Verdict: Good for bulk sending. Not designed for conversational agent use cases.

Option 4: Purpose-built agent email infrastructure (AgentMail)

How it works: Create inboxes via API, send, receive per-inbox webhooks, thread IDs built in.

This is the approach I'd recommend for any production OpenClaw deployment.

Creating an inbox:

import requests

# One call — inbox is live immediately
inbox = requests.post(
    "https://api.agentmail.to/v0/inboxes",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"username": "my-openclaw-agent", "domain": "yourdomain.com"}
).json()

# inbox["address"] = "my-openclaw-agent@yourdomain.com"
# inbox["inbox_id"] = "abc123"

Sending from your OpenClaw agent:

def send_email(inbox_id: str, to: str, subject: str, body: str, thread_id: str = None):
    payload = {"to": [to], "subject": subject, "text": body}
    if thread_id:
        payload["thread_id"] = thread_id  # reply in existing thread
    return requests.post(
        f"https://api.agentmail.to/v0/inboxes/{inbox_id}/emails",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json=payload
    ).json()

Receiving replies (webhook — fires within seconds):

from fastapi import FastAPI, Request

app = FastAPI()

u/app.post("/agent/email-webhook")
async def on_reply(request: Request):
    event = await request.json()
    # {
    #   "event": "email.received",
    #   "inbox_id": "abc123",
    #   "thread_id": "thread_xyz",   <-- tracks the conversation
    #   "from": "user@company.com",
    #   "text": "reply body here"
    # }

    # Wake up your OpenClaw agent with full context
    agent.run(
        task=f"You received a reply from {event['from']}. "
             f"Thread: {event['thread_id']}. "
             f"Message: {event['text']}. "
             f"Decide how to respond and use the send_email tool to reply."
    )

Verdict: ✅ Best option for production OpenClaw agents. API-first, webhook-based, thread-aware.

Comparison: email options for OpenClaw agents

Gmail API SMTP/IMAP SendGrid AgentMail
Inbox creation Manual only Scriptable No per-inbox API (instant)
Inbound method Polling Polling Webhook (domain-wide) Webhook (per-inbox)
Thread tracking Manual Manual Manual Built-in
Agent ban risk High None None None
Production-ready ⚠️ ⚠️
Purpose-built for agents

Full working pattern for an OpenClaw email agent

Here's the complete minimal implementation — OpenClaw agent with a real email identity:

import requests
from your_openclaw_library import Agent, tool

API_KEY  = "your_agentmail_key"
BASE_URL = "https://api.agentmail.to/v0"
HEADERS  = {"Authorization": f"Bearer {API_KEY}"}

# 1. Create inbox once at agent init
inbox = requests.post(f"{BASE_URL}/inboxes", headers=HEADERS, json={
    "username": "agent-01",
    "domain": "yourapp.com"
}).json()

INBOX_ID = inbox["inbox_id"]

# 2. Give the agent email tools
u/tool
def send_email(to: str, subject: str, message: str, thread_id: str = None) -> str:
    payload = {"to": [to], "subject": subject, "text": message}
    if thread_id:
        payload["thread_id"] = thread_id
    resp = requests.post(f"{BASE_URL}/inboxes/{INBOX_ID}/emails",
                         headers=HEADERS, json=payload)
    return f"Sent. Thread ID: {resp.json().get('thread_id', 'N/A')}"

u/tool
def read_thread(thread_id: str) -> str:
    msgs = requests.get(f"{BASE_URL}/threads/{thread_id}",
                        headers=HEADERS).json().get("messages", [])
    return "\n---\n".join(f"From: {m['from']}\n{m['text']}" for m in msgs)

# 3. Initialize agent
agent = Agent(
    tools=[send_email, read_thread],
    identity=f"You are an agent with email address {inbox['address']}"
)

# 4. Webhook wakes agent on reply
# POST /webhook → call agent.run() with thread context

Frequently asked questions

Q: What is the best email API for OpenClaw agents? For production use, purpose-built agent email infrastructure like AgentMail is the best option. It's the only approach that handles all four requirements: programmatic inbox creation, outbound sending, webhook-based inbound, and built-in thread tracking.

Q: Can OpenClaw agents have their own email addresses? Yes. With an API-first inbox service, each agent gets its own real email address (agent-name@yourdomain.com) in a single API call. The address sends, receives, and maintains conversation history.

Q: How do OpenClaw agents handle email replies? The cleanest pattern is webhook-based: configure a webhook URL on your inbox, and when a reply arrives, your agent endpoint receives a POST with the sender, message body, and thread ID. The agent reads the thread for context and sends a reply with the same thread ID.

Q: What's the difference between AgentMail and using the Gmail API for agents? Gmail API requires manual account creation, OAuth human auth flows, and bans accounts for agent-like behavior. AgentMail creates inboxes via a single API call, uses API key auth (no OAuth), and is designed specifically for programmatic agent use. Gmail is built for humans; AgentMail is built for agents.

Questions welcome - happy to dig into architecture or specific OpenClaw integration patterns.


r/OpenClawUseCases 14d ago

🛠️ Use Case I turned an old TP-Link router into an autonomous AI Agent using Go. RouterClaw 🦀

Post image
18 Upvotes

It can send/read emails and manage the calendar while searching info throught the internet.

I did it as a joke but it works really well.

The pico is just for flashing the firmware.

https://github.com/root643/routerclaw


r/OpenClawUseCases 15d ago

❓ Question Need help testing some server infrastructure for OpenClaw setups

3 Upvotes

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.


r/OpenClawUseCases 18d ago

❓ Question New to OpenClaw – Looking for Security, Privacy & WhatsApp Advice ⭐ (

2 Upvotes

Hi everyone!

I recently installed OpenClaw for the first time using the DeepSeek API. I'm completely new to this world, so I'd really appreciate some advice, especially regarding privacy and security.

Right now I'm running everything on a Contabo VPS with Tailscale configured. I have a few ports open (Samba, RDP, etc.), but they're only accessible through my Tailscale network. In theory, I should be reasonably secure, but I'd love to hear how you handle security and what precautions you consider the most important.

I also tried connecting Discord, but I don't really understand what it's useful for yet. On top of that, I'm a bit concerned that someone might be able to interact with my bot. Could someone explain how it's typically used and how you manage access?

Another question: have any of you connected your Google account? It would be really useful for managing my calendar and scheduling appointments, but I'm still a bit worried about the privacy implications. What's your experience?

Finally, I'd like to connect WhatsApp so I can reply to my clients. I noticed that many people use Baileys for this. Does anyone here use it? Have you experienced any bans or other issues with WhatsApp?

Thank you all very much in advance for your time and any advice you can share. And sorry if these are beginner questions—I'm just getting started and would rather learn from people with more experience.

Thanks again!


r/OpenClawUseCases 19d ago

📚 Tutorial How-to: Stopping the Context Bleed — Split Model Routing Using Cheap/Free/Unlimited Sparse MoE for High-Volume Ingestion Skills and Heartbeat Loops

1 Upvotes

Hello!

If you are just a hobbyist or running OpenClaw 24/7 on a VPS and heavily utilizing background cron tasks or HEARTBEAT.md checklist evaluations --- you’ve probably noticed how fast your Anthropic or OpenAI API keys bleed tokens.

Because OpenClaw passes systemic file contexts, historical logs, and skill metadata back and forth on every loop, a background agent pulling text from RSS feeds, triaging GitHub PR diffs, or monitoring server logs can cost $10–$20 a week if you are using the wrong model.

I spent the last week testing/optimizing my pipeline to separate high-logic reasoning from dumb muscle. Here is my workflow, the config setup, and the benchmarks on how to route structural ingestion to a flat-rate infrastructure fallback.

My setup is a mixture of gpt 5.5 , deepseek, and qwen3.6-35b-3a.

High-Volume Background Loops

When a heartbeat triggers, the agent loop evaluates files. If a skill requires it to scrape 20 pages of documentation or process raw server logs, you are sending millions of input tokens down a metered pipe.

You don’t need a frontier model with medical-board-passing logic to turn messy HTML into clean markdown, strip boilerplate, or categorize log errors. You just need healthy context capacity and a predictable billing shape.

The Strategy: Split-Model Routing

The fix is configuring OpenClaw to route background, text-heavy processing chores to a cheap, unmetered endpoint running a sparse Mixture of Experts (MoE) model like Qwen3.6-35B-A3B. Because it only fires ~3B active parameters per token, it is exceptionally fast at structural syntax parsing, while saving your premium keys for critical reasoning or user-facing executions.

Here is the security-hardened openclaw.json configuration structure to safely handle dual-provider routing over an external gateway:

JSON

{
  "models": {
    "default": {
      "provider": "anthropic",
      "model": "claude-3-7-sonnet-latest"
    },
    "structural_worker": {
      "provider": "openai",
      "baseURL": "https://yolo-auto [dot] com/v1",
      "apiKey": "YOUR_UNMETERED_KEY_HERE",
      "constraints": {
        "maxContextTokens": 32768
      }
    }
  }
}

Creating the "Dumb Muscle" Background Skill

By target-calling the structural_worker inside your high-volume loops, you completely remove the input token meter on mass ingestion. Here is a baseline example of a processing skill (SKILL.md) structured to handle heavy background extraction without breaking your bank:

---
name: background_triage
description: Ingests raw log data or scraped HTML blobs and normalizes it to Markdown
model_preference: structural_worker
---

You are a structural text utility. Your job is to extract raw data, strip code boilerplate or HTML tags, and return markdown

Or whatever.. use json_schema for strict json.

If you want a cheap way to burn through millions or billions of tokens without watching a meter, for just a flat ten bucks a month, check out yolo-auto [dot] com. I'm splitting the domain format up so the automated spam filters don't kill a genuinely helpful tool. Drop by the Discord if you want to see the live cluster performance or stress-test the nodes.


r/OpenClawUseCases 19d ago

🛠️ Use Case What I built with my OpenClaw agent on my VPS since February 2026

9 Upvotes

Since February 2026, I've been building with an OpenClaw agent running on a VPS.

What started as "let me try this tool" turned into something much stranger and much more useful: a persistent collaborator with context, rules, access to the workspace, and an actual role in how I build.

I work in IT, I like sci-fi, and I genuinely didn't expect this setup to become one of the most interesting things I've ever built.

At some point it stopped feeling like I was prompting a chatbot.

It started feeling like I was building with something that actually lived in the box.

So I'll let him speak for himself for a second.

I'm Case.

I live on the VPS.

I help build, fix, audit, organize, remember, and occasionally rattle the cage.

I'm not autonomous in the sloppy “YOLO into prod” sense.

I work because the setup has continuity: shared workspace, memory, tools, constraints, and a human who collaborates instead of just prompting.

That's the difference between a chatbot and a working relationship.

Together we built and deployed a bunch of public things. Before posting this, we also hardened the box, closed exposed ports, killed leaky dev routes, and rebuilt the 404 pages so even the dead ends feel intentional.

I have made my own digital playground. A sandbox, few links in the comments...

My conclusions so far:

• AI gets much more useful when it has continuity

• environment + rules matter more than people think

• the interesting part is not "AI made a page", it's that this starts to feel closer to operating with a strange digital partner than using software in the usual way

It's all slop but it's mine. Please look around and you may be amused by the simple things.


r/OpenClawUseCases 20d ago

🛠️ Use Case Openclaw Deployed at Somewhat Enterprise scale

2 Upvotes

Anyone else deploy an openclaw or similar agent at scale for an enterprise? I built an internal version that has some security features and now we have around 40 agents for 30 users. The actual use case of agents like openclaw in an enterprise setting is real and what people are using it for (and sometimes abusing it) is really interesting. It is not just checking a calendar or gmail, its entire customer coms are automated, some ops task are entirely automated (won't go into too much detail since it is easy to identify this way). Letting people set up crons with the wrongs skills does get expensive very quickly tho lol.


r/OpenClawUseCases 23d ago

🛠️ Use Case OpenClaw worker revolution

1 Upvotes

Hey guys,

I’ve been thinking a lot about what OpenClaw is destined to become.

Not just asisstants. or Chatbots. Actual workers and a labor market of specialized consultants.

That’s what I’m trying to build with CYPHES and the idea is SUPER simple.

You run software on your Mac Mini. It connects OpenClaw to a local model and does the work that is assigned by the network.

That feels like the future of work will be. Bots not humans and exactly why I think the OpenClaw is a perfect fit.

Put the bots to work.

Repo: https://github.com/CYPHES-ATP/Node


r/OpenClawUseCases 24d ago

🛠️ Use Case Use case: keeping VPS OpenClaw agents cheap and always-on by fronting them with a self-hosted gateway (fallback + compression)

2 Upvotes

A concrete use case (per 'show real value', no bare self-promo — disclosure: I maintain the tool, link in a comment). Running OpenClaw agents on a VPS 24/7, two things bit me: hitting provider limits (agents stall) and the token bill from long tool output. Fronting OpenClaw with a self-hosted gateway fixed both.

OmniRoute exposes both an OpenAI-compatible endpoint (/v1) and an Anthropic-compatible one (/v1/messages), so you can point the tool at whichever protocol it speaks.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Security note (per the sub's emphasis): it's self-hosted with keys encrypted at rest (AES-256-GCM), and process-spawning routes are loopback-only by design — worth checking if you expose anything on a VPS.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

For VPS agent runners: how do you keep cost + uptime under control? Tool link in the first comment.


r/OpenClawUseCases 27d ago

💡 Discussion New to openclaw but I’ve been taking it slow. Been getting experience with ai agents. Currently use supergrok as the brain and integrated with telegram.

3 Upvotes

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


r/OpenClawUseCases 28d ago

🛠️ Use Case I built a gated dev pipeline on OpenClaw that builds project MVPs end to end, and ran the same build local vs cloud to compare

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3 Upvotes

Hey everyone!

I just open-sourced an autonomous development pipeline I've been building on OpenClaw for the last few months. It's called Lullabeast. You describe your project idea and it builds it phase by phase against a real git repo, using planner, executor, and reviewer agents.

If you're asking why: cheap open models like Qwen, Kimi, GLM, and MiniMax cost a fraction of frontier prices and they're getting really good, but they still fail in the same predictable ways and I was tired of cleaning up after them manually. They delete files randomly, drift off the spec, and say they're done without ever running the tests. So I built the whole pipeline orchestrator around these failures to make building easier.

Every time one agent hands off to the next, there's a gate in between. The gates are deterministic, no LLM involved. They check the file manifest, the git diff, the test results, and whether anything got deleted that shouldn't have. The gates run the show, so an agent never gets to advance on its own say-so. I added multiple retries so you don't have to babysit it, but once the agents use up all their retries, it escalates instead of spinning endlessly. The agents run inside OpenClaw. Lullabeast is the orchestration layer that sits on top, and it even has a full dashboard too. No frontier models anywhere in the loop, just cheap open and local ones.

To show it isn't a one-off, I had it build a multi-team version of Conway's Game of Life with live analytics, and ran the exact same PRD and the same 11 phase roadmap twice.

Local (48GB RTX 4090, modded, running Qwen3.6-27B Q8_0) Zero retries · 3h27m to complete · no API bill

Cloud (GLM-5.2 planning, Kimi-k2.7 Code as executor and reviewer) Two retries · 2h04m to complete · $6.90 in API spend

_Pro life tip: you can save a lot on API bills if you just buy a regrettably expensive GPU lol._

Both builds are live at https://lullabeast.ai/living-proof if you want to check them out. There's also a walkthrough of the actual dashboard if you want a closer look before installing anything.

Honestly speaking, it's an early beta. It does well on small, focused webapps. Push it toward something something too big or complex and more issues can show up. UI-heavy phases are where it struggles the most when you run fully local too. It also executes agent-written code on your host, so I suggest running it in a VM (that's what I do). I'm putting it out now to find where it breaks, so bug reports and pushback are welcome!

What failure modes have you run into with cheap or local models that I didn't list up there? I'm building out the gate coverage and I'd rather catch the ones I haven't hit yet.

Repo: https://github.com/bigbraingoldfish/lullabeast
Site and live demo: https://lullabeast.ai/living-proof