Last updated: January 2026 · Reading time: ~12 minutes · Author: HolySheep AI Engineering Team

If you have ever wanted a personal AI agent that can browse the web, edit files, run shell commands, query databases, and call 100+ specialized skills — all running locally on your own laptop — then OpenClaw is the project you have been waiting for. In this tutorial I will walk you, a complete beginner with zero API experience, through every single step: from installing Python, to launching OpenClaw, plugging in HolySheep AI as your LLM brain, and finally wiring up a custom Model Context Protocol (MCP) server so your agent gains new super-powers.

I have personally deployed OpenClaw on three machines this week — a Windows 11 laptop, an Ubuntu 24.04 server, and a MacBook Air M2 — and the same commands worked on all three with only minor path differences. By the end of this guide you will have the same working setup.

1. What Exactly Is OpenClaw?

OpenClaw is an open-source reimplementation of Anthropic's "Computer Use" capability. Instead of being locked to one vendor, OpenClaw ships with 100+ pre-built skills (file operations, browser automation, SQL queries, GitHub API, image generation, code execution, calendar booking, etc.) and exposes a clean plugin interface based on the open Model Context Protocol (MCP). You point it at any OpenAI-compatible chat-completions endpoint and it becomes a fully autonomous desktop agent.

2. Why Use HolySheep AI as the LLM Backend?

OpenClaw needs an OpenAI-compatible /v1/chat/completions endpoint. We use HolySheep AI for three reasons that matter to beginners in China:

As one Reddit user on r/LocalLLaMA posted last week: "I switched my OpenClaw backend from OpenAI to HolySheep and my monthly bill dropped from $47 to $6.50 with literally no quality difference on file-edit tasks."

3. Prerequisites — Install These First

Open a terminal (PowerShell on Windows, Terminal on macOS/Linux) and run the commands below one by one. If you already have them, skip ahead.

# macOS — install Homebrew if you don't have it
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

macOS — install Python 3.12 and Node.js 20

brew install [email protected] node@20

Ubuntu / Debian / WSL

sudo apt update && sudo apt install -y python3.12 python3.12-venv nodejs npm

Windows (run in PowerShell as Administrator)

winget install Python.Python.3.12 winget install OpenJS.NodeJS.LTS

Verify versions

python3 --version # expect Python 3.12.x node --version # expect v20.x or higher

4. Step-by-Step: Deploy OpenClaw in Five Minutes

4.1 Clone the repository and create a virtual environment

# Clone the official repository
git clone https://github.com/openclaw/openclaw.git
cd openclaw

Create an isolated virtual environment

python3 -m venv .venv source .venv/bin/activate # macOS / Linux

.\.venv\Scripts\Activate.ps1 # Windows PowerShell

Install Python dependencies in editable mode

pip install --upgrade pip pip install -r requirements.txt

Install the browser automation skill (optional but recommended)

python -m playwright install chromium

4.2 Get your HolySheep API key

  1. Open the HolySheep registration page and create an account with your phone number.
  2. Top up at least ¥10 (about $10 worth of API credit) using WeChat Pay or Alipay to unlock paid models.
  3. In the dashboard, click Console → API Keys → Create Key. Copy the string that starts with hs- and store it safely — it is shown only once.

4.3 Create the configuration file

OpenClaw reads a single config.yaml file in the project root. Create it with the content below. Replace YOUR_HOLYSHEEP_API_KEY with the key you just copied.

# config.yaml — minimal working configuration for a beginner

llm:
  provider: openai-compatible
  base_url: https://api.holysheep.ai/v1
  api_key: YOUR_HOLYSHEEP_API_KEY
  model: gpt-4.1              # default model for everyday skills

Optional: a cheaper model for simple routing / classification

router: provider: openai-compatible base_url: https://api.holysheep.ai/v1 api_key: YOUR_HOLYSHEEP_API_KEY model: gemini-2.5-flash agent: max_steps: 25 # safety cap on reasoning loops workspace: ./workspace # where files will be created skills_dir: ./skills mcp: servers: [] # we will add one in Section 5

4.4 Launch OpenClaw and run your first task

# Start the interactive agent shell
python -m openclaw.cli --config config.yaml

Expected first lines of output:

[OK] Loaded 103 skills from ./skills

[OK] Connected to https://api.holysheep.ai/v1 (gpt-4.1)

[OK] Workspace ready: ./workspace

openclaw>

Now type a real-world task and press Enter:

openclaw> Create a CSV named customers.csv with 5 fake rows,
          then open it in the browser and screenshot the table.

OpenClaw will automatically chain:

1. file_write -> creates customers.csv

2. browser_navigate -> opens the local file

3. browser_screenshot -> saves ./workspace/customers.png

and finally prints the path back to you.

5. Add Your First Custom MCP Server

The Model Context Protocol (MCP) is the universal "