OpenClaw Community Project Showcase

I’ve often felt stuck when starting with a new tool, searching for a sign that it actually handles the tasks I care about. It helps to see a working setup before you spend hours on your own configuration, rather than just reading a list of functions.

I find that seeing a project in action is the best way to understand how it fits into your workflow. The community has already built some great examples that show exactly what is possible.

What You’ll Need

A Discord account to join the community
Access to X (Twitter) for updates

Quick Start

I recommend following these steps to see how to get your own instance running.

Watch the full setup walkthrough by VelvetShark to see the self-hosted process.
Join the Discord server to see real-time projects from other developers.

OpenClaw in Action

Here are the guides and demonstrations from the community.

Full setup walkthrough (28m) by VelvetShark

Watch on YouTube

Showcase Videos

Watch on YouTube

Troubleshooting

If you want to get your project featured or need to find where the community hangs out:

Share your project in the #showcase channel on Discord.
Tag @openclaw on X to show us what you are building.

Need more specific help with your setup? Check out the AI Setup Assistant.

What’s Next

I often spend more time clicking buttons and switching between apps than actually writing code. It is frustrating when you have a clear goal but your tools do not talk to each other, forcing you to do the manual labor of moving data around.

I have been watching the Discord community lately, and I am impressed by how people are solving these exact problems. Instead of waiting for official integrations, developers are building their own ways to make agents handle the boring parts of their day.

What You’ll Need

Before you try these out, make sure you have the basics ready based on what the community is using:

A Telegram account (for feedback and voice note integrations)
Homebrew (to install devtools like CodexMonitor)
A Gemini API key (required for SNAG’s vision features)
Cloudflare R2 or S3 credentials (for file upload skills)

Quick Start

I recommend starting with these community favorites to see what is possible. You can set most of these up in about five minutes.

1. Level Up Your Dev Workflow

If you spend your day in a terminal or a code editor, these tools help keep you focused:

Linear CLI: Use github.com/Finesssee/linear-cli to manage issues and projects directly from your agentic workflows.
CodexMonitor: Install this via Homebrew to watch and inspect your local OpenAI Codex sessions in VS Code or your CLI.
SNAG: Grab a screen region and use Gemini vision to turn it into Markdown in your clipboard instantly.
PR Reviews: Use OpenClaw to review diffs and send the verdict to Telegram.

2. Automate Your Daily Tasks

You can let agents handle your chores using browser control or specialized APIs:

Tesco Shop Autopilot: Book delivery slots and confirm orders based on your meal plan without using an API.
ParentPay School Meals: Use mouse coordinates for reliable clicking to book school meals in the UK.
Beeper CLI: Connect your agent to iMessage or WhatsApp via the Beeper local MCP API.
Vienna Transport: Get real-time departures and elevator status for the Wiener Linien.

3. Hardware and Health

I love seeing agents interact with the physical world:

Bambu 3D Printer Control: Check status, jobs, and the AMS for your BambuLab printers.
Oura Ring Assistant: Connect your health data with your calendar and gym schedule to stay on track.

Troubleshooting

I noticed a few specific solutions the community found for common hurdles:

Problem: UI elements are hard to click reliably. Solution: For tools like ParentPay, use specific mouse coordinates for table cell clicking rather than relying on standard selectors.

Problem: Files are stuck on remote instances. Solution: Use the R2 Upload skill to generate secure presigned download links so you can access your files from anywhere.

Problem: Telegram voice notes play automatically or fail to send. Solution: Use the papla.media wrapper to send TTS results as voice notes, which prevents annoying autoplay.

Problem: Managing complex agent architectures. Solution: Look at “Kev’s Dream Team” setup. He uses an Opus 4.5 orchestrator with 14+ agents and Clawdspace for sandboxing.

If you hit a snag while setting these up, I recommend checking out the AI Setup Assistant.

What’s Next

I spend a lot of time on tasks that feel like they should be automatic. Whether it is checking if a sports court is open or organizing tax receipts, the manual work adds up fast. I want to spend my time building things, not clicking the same buttons every week.

I have found that the best way to handle this is to let OpenClaw take over the repetitive parts. You can connect it to your home hardware, your work tools, or even your favorite sports booking app.

What You’ll Need

OpenClaw access
Telegram (if you want to work remotely)
Specific API keys (like JSearch or Jira)
Browser automation setup (for sites without APIs)

Quick Start

Pick a task: Choose something you do every day, like checking job listings or booking a court.
Connect your tools: Link OpenClaw to your target platform, such as Slack, Jira, or a Playtomic CLI.
Create a skill: If a tool does not have a skill yet, ask OpenClaw to generate one on the fly.
Set a trigger: Use a scheduled prompt for morning briefings or an email watcher for accounting.

Real-World Examples

I recommend looking at how others are using these workflows to get ideas for your own setup.

Home & Hardware

Winix Air Purifier: Claude Code confirms the controls, and OpenClaw manages the room air quality.
Sky Camera: A roof camera triggers OpenClaw to snap a photo whenever the sky looks pretty. It even designed its own skill to take the shot.

Productivity & Work

Jira Skill Builder: You can connect to Jira and generate a new skill immediately, even if it is not on ClawHub yet.
Slack Auto-Support: This setup watches Slack, responds to questions, and sends notifications to Telegram. It can even fix production bugs in deployed apps on its own.
Todoist via Telegram: You can automate your tasks and have the skill generated directly inside a Telegram chat.

Finance & Life

Padel Court Booking: Use the padel-cli with a Playtomic availability checker so you never miss a court.
Accounting Intake: This collects PDFs from your email and prepares documents for your tax consultant every month.
TradingView Analysis: OpenClaw logs into TradingView via browser automation, takes screenshots, and performs technical analysis without needing an API.
Job Search Agent: Built in 30 minutes using the JSearch API, this agent matches listings against your CV and returns links to relevant roles.

Remote Development

Couch Potato Dev Mode: You can rebuild your entire personal site from your phone. One developer migrated 18 posts from Notion to Astro and moved DNS to Cloudflare via Telegram while watching Netflix.

Troubleshooting

Missing Skills: If a skill does not exist on ClawHub yet, ask OpenClaw to build it for you in the chat.
No API Access: For sites like TradingView, use browser automation to log in and grab screenshots instead of waiting for API access.

If you need help setting up these workflows, check out the AI Setup Assistant.

What’s Next

I’ve often found that the hardest part of building an agent isn’t the initial setup, but making it actually remember what we talked about yesterday. We’ve all been there—trying to bridge the gap between a raw LLM and a tool that understands our personal history or a specific set of data.

It gets frustrating when your assistant forgets your preferences or can’t access the files you’ve spent years collecting. I want to show you how the community is solving this by turning static data into active memory.

What You’ll Need

Before you start, make sure you have these pieces ready based on the tools you choose:

OpenClaw: The base engine for all these integrations.
External Data: WhatsApp exports, Karakeep bookmarks, or session files.
Vector Database: Specifically Qdrant if you are setting up semantic search.
API Keys: OpenAI or Ollama for embeddings, and Vapi if you want phone support.
Hardware/OS: Home Assistant OS or a Nix-based system for deployment.

Quick Start

I’ve broken these down into the fastest paths to get your agent learning and communicating.

1. Set Up Knowledge & Memory

If you want your agent to actually know things, start with these community projects:

xuezh Chinese Learning: Use this if you need a Chinese learning engine. It provides pronunciation feedback and study flows via OpenClaw. Find it at joshp123/xuezh.
WhatsApp Memory Vault: This is great for ingesting full WhatsApp exports. It transcribes over 1,000 voice notes, cross-checks them with git logs, and creates linked markdown reports.
Karakeep Semantic Search: Add vector search to your bookmarks. It uses Qdrant with OpenAI or Ollama embeddings. Check it out at jamesbrooksco/karakeep-semantic-search.
Inside-Out-2 Memory: This manager turns your session files into memories and beliefs, creating an evolving self-model for your agent.

2. Connect Voice & Phone

Once the memory is set, you can give your agent a way to talk:

Clawdia Phone Bridge: This acts as an HTTP bridge between Vapi and OpenClaw for near real-time phone calls. See alejandroOPI/clawdia-bridge.
OpenRouter Transcription: If you need multi-lingual audio transcription (via Gemini, etc.), you can find this on ClawHub.

3. Deploy the Infrastructure

To keep everything running, use these deployment options:

Home Assistant Add-on: Run the OpenClaw gateway on Home Assistant OS. It includes SSH tunnel support and persistent state. Find it at ngutman/openclaw-ha-addon.
Nix Packaging: For reproducible deployments, use the nixified configuration at openclaw/nix-openclaw.

Troubleshooting

The source documentation does not list specific error codes or common bugs for these community projects. If you run into issues during setup, I recommend checking the specific GitHub repositories linked above for their issue trackers.

For general configuration help, you should use the interactive assistant.

Ending

If you need help getting any of these specific tools configured for your local environment, head over to the AI Setup Assistant.

What’s Next

I’ve found that most home automation tools are either too basic or way too hard to configure. You want something that works with your existing tools without spending hours on custom scripts that eventually break. OpenClaw provides a base that actually handles hardware well.

What You’ll Need

An active OpenClaw setup
Nix (specifically for the GoHome automation project)

Quick Start

If you want to see what is possible, I recommend looking at these community projects. They show how to handle everything from data visualization to vacuum cleaners.

GoHome Automation

This project by @joshp123 is a Nix-native home automation setup. It uses OpenClaw as the primary interface and includes Grafana dashboards to help you see what is happening in your home.

Source: github.com/joshp123/gohome

Roborock Vacuum Plugin

You can also control hardware through natural conversation. This plugin allows you to manage your Roborock robot vacuum directly.

Source: Roborock Plugin

StarSwap Marketplace

The community also built StarSwap, which is a marketplace for astronomy gear. It was built around the OpenClaw environment to show how the system handles specialized webapps.

Link: star-swap.com

Submit Your Project

If you have built something, I want to see it. We feature standout projects on this page regularly.

Share It: Post in the #showcase channel on Discord or tweet @openclaw.
Include Details: Tell us what your project does, link to the repository or a live demo, and include a screenshot.

Troubleshooting

The source documentation does not list specific error codes for these hardware integrations. If you run into a problem while setting these up, I suggest reaching out in the Discord community mentioned in the submission steps.

Need help with your specific configuration? Check out the AI Setup Assistant.

What’s Next

OpenClaw Expert

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