OpenClaw Local Deployment Guide: Running Your Personal AI Assistant 24/7
With the advancement of artificial intelligence, personalized AI assistants have become a crucial tool for boosting productivity. Unlike solutions that rely on cloud services, deploying the AI agent framework OpenClaw on a local machine offers users greater autonomy, data security, and cost-effectiveness. This article will provide a systematic walkthrough of the entire local deployment process for OpenClaw on macOS, from environment configuration to achieving 24/7 automated operation.
1. Architecture and Hardware Requirements
The operational mechanism of OpenClaw primarily consists of three parts: a Large Language Model (LLM) serving as the brain, a host machine running the OpenClaw application, and an Instant Messaging (IM) tool for interaction. The local deployment model involves replacing the cloud server with the user’s personal computer as the host machine. The advantage of this model is the elimination of recurring server rental fees, while all interaction data remains on the local machine, ensuring privacy and security.
According to OpenClaw’s official recommendations, the minimum hardware requirements are a dual-core processor and 4GB or more of RAM. This means that many idle older laptops or desktops, such as a MacBook Pro with an Intel i5 processor and 16GB of RAM, can meet the operational demands. Using such a device as a dedicated server can enable 24-hour, uninterrupted service from your AI assistant.
2. Core Environment Setup: Node.js and Homebrew
The OpenClaw project is developed in JavaScript, so its execution depends on the corresponding runtime environment. A computer cannot directly interpret JavaScript code; it requires an “interpreter” called Node.js to execute the program’s commands.
Before deploying OpenClaw locally, you must ensure that Node.js is installed on your system and that its version is v22 or higher. For macOS users, it is recommended to use the package manager Homebrew for installation and management.
Install Homebrew: Homebrew is a popular package manager for the macOS platform that simplifies the installation and update process for software. Open the “Terminal” app and execute the following command to install it:
bash
/bin/bash -c “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)”
During the installation process, the system will prompt you to enter your computer’s login password. Characters will not be displayed on the screen as you type. Simply press Enter after you finish typing. Once the installation is successful, you can verify the version with the brew -v command.
Install Node.js: After Homebrew is installed, continue to execute the following command in the terminal to install Node.js:
bash
brew install node
After the installation is complete, execute the node -v command. If the output version number is v22 or higher, it means the core runtime environment for OpenClaw is ready.
3. OpenClaw Installation and Initialization
Once the environment setup is complete, you can begin installing the OpenClaw framework itself. As an open-source project on GitHub, OpenClaw is installed using a specific command-line instruction.
Install OpenClaw: Run the following command in the terminal. This command will download and execute the installation script from the official source:
bash
curl -fsSL https://openclaw.ai/install.sh | bash
After a successful installation, you can verify it using the openclaw -v or openclaw --version command. If the terminal returns a version date like 2026.3.2, it indicates that the installation was successful.
Initial Configuration: Run the openclaw onboard command to start the interactive onboarding process. This program will guide you through the key configurations:
- Select an LLM: You can choose different large language models based on your needs, such as Kimi K2.5, Minimax, or Alibaba Cloud’s Qwen. Taking Kimi as an example, you need to go to its API management dashboard to create and obtain an API Key, then paste it into the terminal when prompted.
- Skip Skill and Hook Installation: During the initial setup, for the installation options for skills and hooks, you can temporarily choose to skip (
skip for now or NO). These features can be dynamically installed later through conversations with the AI assistant.
- Access Method: The program will offer ways to start a conversation. Selecting
Hatch in TUI allows you to start a conversation directly in the current terminal window. Choosing Open The WebUI allows you to access the local admin dashboard via a browser at http://127.0.0.1:18789. This interface provides features like an operational overview and chat interaction.
4. Connecting to External IM Tools and Ensuring Persistent Operation
To enable convenient remote interaction, you need to connect OpenClaw to IM tools like Lark or WeCom. This step involves installing the corresponding plugin for OpenClaw and configuring the credentials.
Install the Lark Plugin: Using Lark as an example, execute the following command in the terminal to install the plugin:
bash
openclaw plugins install @openclaw/feishu
Configure Credentials: Follow the prompts during the plugin installation process to enter the bot’s App ID and App Secret obtained from the Lark Open Platform. After completing the configuration, OpenClaw will be able to receive commands and respond via the Lark bot.
Achieving 24/7 Operation: To ensure the OpenClaw service is not interrupted by the computer sleeping, you need to adjust the system’s power settings. In macOS’s “System Settings” > “Battery” > “Options”, enable the “Prevent computer from sleeping automatically when the display is off” feature (or a similar setting). This simple configuration, combined with a continuous power supply, will allow your local AI assistant to be online around the clock.
Through local deployment, users not only gain complete control over their AI assistant but also lay the foundation for exploring more intelligent interaction models. The industry view is that a truly intelligent agent should be able to use “long context” to understand a user’s “short prompt”—that is, to infer complex intent from extremely simple commands, rather than relying on tedious prompt engineering. Having an autonomous and controllable local AI assistant is a crucial step towards practicing and advancing towards this future.
