VS Code with DEVUP AI
Use DEVUP AI models in VS Code chat without installing an extension. Configure VS Code's native Custom Endpoint to route chat requests, tool calling loops, and Agent mode directly to DEVUP AI foundation models.
System Architecture
How VS Code Connects to DEVUP AI
VS Code communicates directly with DEVUP AI via the OpenAI-compatible Chat Completions protocol on https://api.devupai.com/v1/chat/completions. The editor manages authentication through your DEVUP AI API key, formats messages and workspace tool schemas, and streams token deltas in real time.
Configuration
Quick Setup in Your Environment
Open Chat in VS Code.
Open the native Chat panel in VS Code.
Open the model picker above the chat input and choose Manage Models.
In the chat panel, click the active model name above the input area to reveal the dropdown and select Manage Models.
Choose Add Models, then Custom Endpoint.
From the management menu, select Add Models and pick Custom Endpoint.
For API Type, choose Chat Completions.
Select Chat Completions as the protocol format.
Give the group a name and enter your DEVUP AI API key when prompted.
Assign an identifier to your endpoint group and supply your API key from the API keys dashboard.
VS Code opens chatLanguageModels.json. Add your model inside the models array of the Custom Endpoint group.
Paste the following configuration inside the models array:
{
"id": "deepseek-ai/DeepSeek-V4-Pro",
"name": "DeepSeek V4 Pro (DEVUP AI)",
"url": "https://api.devupai.com/v1/chat/completions",
"toolCalling": true,
"vision": false,
"maxInputTokens": 1048576,
"maxOutputTokens": 16000
}url is the full endpoint path, not a base URL.vision to match the model you are adding. Models without image input should have it set to false.Save the file, then select the model in the chat model picker.
Save chatLanguageModels.json and pick your new model directly in the chat panel.
Schema Reference
chatLanguageModels.json Field Reference
Each entry in the Custom Endpoint models array accepts the following parameters:
| Field | Type | Description |
|---|---|---|
| id | string | Model identifier from DEVUP AI catalog (e.g. "deepseek-ai/DeepSeek-V4-Pro") |
| name | string | Display label shown in VS Code model picker |
| url | string | Full endpoint path: "https://api.devupai.com/v1/chat/completions" |
| toolCalling | boolean | Set to true to enable workspace tool use and Agent mode |
| vision | boolean | Set to match model capabilities; false for models without image input |
| maxInputTokens | number | Input context window matching the model being added (verify model-specific limits in the models catalog) |
| maxOutputTokens | number | Maximum generation tokens matching the model being added (verify model-specific limits in the models catalog) |
Capabilities
Supported Editor Capabilities
Full Tool Calling Loop
Enables VS Code Chat and Agent mode to read workspace files, run terminal commands, and apply code edits through standard function schemas.
Streaming Token Output
Server-Sent Events deliver real-time token streaming directly to the chat interface for an immediate response experience.
No Extension Required
Operates entirely through VS Code's native Custom Endpoint feature without third-party extension installations or extra processes.
Catalog Extensibility
Any model from the DEVUP AI catalog can be added as an additional entry in the same models array using its catalog identifier and endpoint.
Ready to Code with VS Code on DEVUP AI?
Create an API key in your dashboard, configure your Custom Endpoint in VS Code, and start executing agentic coding workflows today.
