WorkBuddy Quick Switch to Cloudwise MaaS

Add Cloudwise MaaS as a custom OpenAI-compatible model in WorkBuddy and start using 570+ hosted models for task automation and AI-powered workflows

WorkBuddy is the AI task automation component of the CodeBuddy ecosystem — an end-to-end full-stack development platform. WorkBuddy supports custom OpenAI-compatible models, allowing you to replace or supplement its built-in model list with any Cloudwise MaaS-hosted model in just a few steps.

https://www.codebuddy.ai

CodeBuddy — End-to-end full-stack development ecosystem

WorkBuddy is part of the CodeBuddy platform. Download and install CodeBuddy to get started with WorkBuddy's AI task automation features.

Prerequisites
Open the Model Picker
Select Custom Provider
Fill in the Model Form
Configure Advanced Settings
Verify the Setup
Summary

Prerequisites

Requirement	Details
WorkBuddy	Installed and running (part of CodeBuddy v4.22.10 or later).
Cloudwise API Key	Create or copy your API key from Cloudwise Studio.
Endpoint (OpenAI-compatible)	`https://api.cloudwise.ai/api/v1/chat/completions`
Recommended Starter Model	`glm-5.1`

Open the Model Picker

In WorkBuddy, click the model selector at the bottom of the chat panel. The dropdown lists all built-in models. Scroll to the bottom and click + Configure custom models to open the model management settings.

Click "+ Configure custom models" in the Model Dropdown

The model picker shows all available built-in models. Click the + Configure custom models button at the bottom of the list (highlighted below) to navigate to the custom model settings.

WorkBuddy model picker dropdown with Configure custom models button highlighted

Select Custom Provider

In the Models settings panel, click Add Model. WorkBuddy will show a provider selection dropdown. Expand the Other section and choose Custom to use any OpenAI-compatible third-party endpoint.

Choose "Custom" from the Provider List

The provider list includes Coding Plans, GLM, Kimi, MiniMax, DeepSeek, and Ollama. Select Custom (under the Other section) to configure any OpenAI-compatible API endpoint.

WorkBuddy Add Model dialog showing provider dropdown with Custom option highlighted under Other

Cloudwise MaaS follows the OpenAI Chat Completions API specification. Always select the Custom provider option — do not use vendor-specific integrations like "GLM Open Platform" or "Kimi China" for Cloudwise endpoints.

Fill in the Model Form

After selecting Custom, fill in the three required fields: Endpoint, API Key, and Model Name.

Enter Cloudwise Endpoint, API Key, and Model Name

Field	Value
Provider	Custom
Endpoint	`https://api.cloudwise.ai/api/v1/chat/completions`
API Key	Your Cloudwise API key
Model Name	`glm-5.1`

WorkBuddy Add Model dialog filled with Cloudwise endpoint, API key, and glm-5.1 model name

Important: The endpoint must be the full Chat Completions URL ending with /chat/completions. Using the base URL alone (/api/v1) without the path suffix will cause API errors.

Supported Model Names

Model Name	Use Case
`glm-5.1`	Zhipu's latest flagship model for chat and agentic tasks
`gpt-5.4`	Advanced GPT model for complex reasoning
`gpt-5`	Balanced general-purpose coding and reasoning
`gpt-4o`	Fast multimodal interactions
`claude-opus-4-7`	Highest-capability Claude model (via OpenAI-compatible path)
`deepseek-r2`	Open-source reasoning champion

Configure Advanced Settings

WorkBuddy exposes capability flags under Advanced Settings. Enable the recommended options below to ensure full functionality with Cloudwise MaaS.

Enable Tool Calling and Custom Protocol

In the Advanced Settings section of the Add Model dialog, check the following options:

Option	Recommended Setting	Purpose
Tool Calling	✅ Enabled	Allows the model to invoke tools and connectors in WorkBuddy tasks
Image Input	Optional	Enable only if your chosen model supports vision / image input
Reasoning	Optional	Enable for reasoning-oriented models (e.g. `deepseek-r2`)
Custom Protocol	✅ Enabled	Required for non-standard OpenAI-compatible providers like Cloudwise

WorkBuddy Advanced Settings section with Tool Calling and Custom Protocol checkboxes enabled

You can also set custom Input and Output token limits (32K / 64K / 128K / 256K for input; 8K / 16K / 32K / 64K for output). Leave these as provider defaults unless you need to override them for a specific model.

Alternative Method: Edit the Config File Directly

You can also configure the model by editing the WorkBuddy configuration file manually. First, locate (or create) the config file for your operating system:

macOS / Linux

mkdir -p ~/.workbuddy
code ~/.workbuddy/models.json

If you are not using VS Code, open the file with any text editor, for example:

nano ~/.workbuddy/models.json

Windows

C:\Users\<your-username>\.workbuddy\models.json

Write the following model configuration into the file (using glm-5.1 as an example). Replace the apiKey value with your own Cloudwise API key:

[
  {
    "id": "glm-5.1",
    "name": "glm-5.1",
    "vendor": "Custom",
    "url": "https://api.cloudwise.ai/api/v1/chat/completions",
    "apiKey": "<your-cloudwise-api-key>",
    "maxInputTokens": 262144,
    "maxOutputTokens": 8192,
    "supportsToolCall": true,
    "supportsImages": false,
    "supportsReasoning": false,
    "useCustomProtocol": true
  }
]

The four fields at the bottom correspond directly to the Advanced Settings checkboxes in the UI dialog:

"supportsToolCall": true,
"supportsImages": false,
"supportsReasoning": false,
"useCustomProtocol": true

After saving the configuration file, restart WorkBuddy for the changes to take effect. The Cloudwise MaaS model will then be available in your task and chat sessions.

Verify the Setup

After clicking Save, the model will appear in your custom models list. Open a new task or chat session, select the model from the bottom picker, and send a test message to confirm it responds correctly.

Start a Task and Confirm the Model Responds

Open a new WorkBuddy task, choose your newly added Cloudwise model from the model selector at the bottom of the chat panel, and send a simple message. A successful response confirms your configuration is working.

WorkBuddy chat session showing a successful response from a Cloudwise custom model

Summary

You now have a complete English setup flow for WorkBuddy with Cloudwise MaaS:

Open the model picker in WorkBuddy and click + Configure custom models
In the Models settings, click Add Model and select Custom as the provider
Set the Endpoint to https://api.cloudwise.ai/api/v1/chat/completions
Enter your Cloudwise API key and a model name (e.g. glm-5.1)
Enable Tool Calling and Custom Protocol in Advanced Settings, then click Save
Select the model in any WorkBuddy task or chat and verify it responds correctly

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