Installation and Setup
System requirements
Section titled “System requirements”| Platform | Requirement |
|---|---|
| macOS | Apple Silicon or Intel, 16GB RAM recommended |
| Windows | Windows 10 or later |
| Disk | 2GB free space |
Step 1: Install the client
Section titled “Step 1: Install the client”Download the installer for your platform from the official site, then drag it into Applications (Mac) or run the installer (Windows).
First launch asks you to sign in.
Step 2: Configure a model
Section titled “Step 2: Configure a model”WorkBuddy is the workbench; a model powers it. Two routes:
Route A: use the built-in model Works out of the box.
Route B: connect your own model or a third party Useful combinations:
- General work: a strong commercial model
- Local inference: MLX or Ollama on Mac (note: some custom architectures need a specially built runtime)
- Behind a proxy: configure your local proxy address and port in settings
Step 3: Authorize a workspace
Section titled “Step 3: Authorize a workspace”This is the step people get wrong.
mkdir -p ~/WorkBuddyCreate one dedicated directory, put projects inside, and authorize only that directory.
Don’t authorize ~/Desktop or ~/Downloads wholesale — a mistake there has a much larger blast radius.
Mac-specific gotchas
Section titled “Mac-specific gotchas”1. npm/node commands get blocked
In sandboxed environments, npm install or npm run build may fail with CODEBUDDY_BROKER_DENY. The NODE_OPTIONS environment variable has a shim script injected into it. Clear it per command:
env -u NODE_OPTIONS npm installenv -u NODE_OPTIONS npm run build2. Permission prompts
The first write triggers a system prompt. Choose “Always Allow” to avoid repeating it.
3. Fanless models
MacBook Air models without a fan will throttle under sustained local model load. Plug in for long runs.
Verify the install
Section titled “Verify the install”Run the smallest possible end-to-end test:
Write a Python script that counts the total number of words across all
.mdfiles in this directory, then run it.
If it creates the script, executes it, and shows output, the whole chain works: model → tools → file operations.