- Updated: March 20, 2026
- 2 min read
Step‑by‑step deployment guide for the OpenClaw full‑stack demo
## Introduction
The OpenClaw full‑stack demo is a production‑ready, one‑click‑deployable GitHub template that showcases a modern AI‑agent powered application. In this guide we walk developers and founders through the entire deployment process, tying the steps into the current hype around AI agents.
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### Prerequisites
– A GitHub account.
– Docker installed on your local machine (or access to a Docker‑enabled cloud environment).
– Basic knowledge of environment variables and command‑line usage.
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### 1. Clone the repository
bash
git clone https://github.com/ubos-tech/openclaw-demo.git
cd openclaw-demo
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### 2. Configure environment variables
Create a `.env` file (or edit the provided example) with the required variables:
– `OPENAI_API_KEY` – your OpenAI API key to power the AI agents.
– `NEXT_PUBLIC_APP_URL` – the URL where the app will be accessible.
– Any other service‑specific keys mentioned in the README.
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### 3. Run the stack
bash
docker compose up -d
Docker will pull the necessary images and start the services. The one‑click deployment script takes care of building the frontend, backend, and the AI‑agent orchestration layer.
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### 4. Verify the demo
Open your browser and navigate to `http://localhost:3000` (or the URL you set in `.env`). You should see the OpenClaw UI where you can interact with the AI agents. Try creating a new task and watch the agents coordinate automatically – a perfect showcase of the AI‑agent hype in action.
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### 5. Next steps & internal link
For deeper integration details and hosting options, visit our internal guide: .
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## Conclusion
By following these steps you have a fully functional OpenClaw demo up and running, ready to demonstrate the power of AI agents in a production‑grade environment. Share your deployment with the community and keep an eye on future enhancements.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.