Overview

The Opilot extension brings the full Ollama model ecosystem into VS Code. After installation and a model pull, you can use local AI models in Copilot Chat, as inline code completions, in a dedicated @ollama chat participant, with a persistent server heartbeat in the status bar, and with per-model generation controls in a settings webview — all without leaving the editor.

Sidebar Layout

The Ollama activity bar icon opens a sidebar with four panels:

• Local Models — Models downloaded to your machine. Shows running state, VRAM usage, and capability badges.
• Cloud Models — Models streamed from Ollama Cloud (requires ollama login).
• Library — The full ollama.ai/library catalog for browsing and pulling.
• Modelfiles — Your custom .modelfile files for building personalized models.

Each panel has toolbar icons for filtering, grouping, collapsing, and refreshing. See Sidebar & Model Management for the full panel reference.

Model Settings Webview

Open Ollama: Open Model Settings (or use the gear button in the Local Models panel) to configure per-model options such as temperature, top-p/top-k, context, max tokens, and thinking budget.

Changes apply immediately and are persisted per model in extension storage.

Status Bar Heartbeat

Opilot exposes an always-visible status bar item showing:

• loading state while checking server health
• online state with running-model count
• offline state after debounced failures

Clicking the status bar item runs an immediate health check.

Copilot Chat Integration

Model Picker

Open Copilot Chat and click the model selector dropdown. All locally installed Ollama models appear under a 🦙 Ollama group. Select one to use it for the current conversation — no @ollama needed.

@ollama Chat Participant

Type @ollama at the start of any message to use the dedicated Ollama chat participant, which uses the currently-selected default model. The participant is sticky once activated in a thread.

@ollama refactor this function to use async/await

See @ollama Chat Participant for all the details.

Tool Calling

Compatible models (those flagged with the 🛠 tools badge) can invoke VS Code tools, MCP servers, and custom skills during a conversation:

@ollama what files in this project have the most test coverage gaps?

Tool calling follows the VS Code Language Model API tool loop: the model emits tool call requests, VS Code invokes the tool, and the result is fed back for the next response turn. If a model rejects tool schemas, the extension automatically retries the request without tools.

Vision

Models with the 👁 vision badge accept image attachments in Copilot Chat. Drag an image into the chat input or paste from clipboard. Images are automatically stripped for non-vision models to avoid prompt overflow.

Thinking Models

Models like DeepSeek-R1, Qwen QwQ, and Kimi that expose chain-of-thought reasoning display responses in two collapsible sections:

• Thinking — the model's internal reasoning steps
• Response — the final answer

The extension detects thinking models by model ID pattern and automatically applies the split-view format.

Privacy & Security

• Local model conversations are processed entirely on your machine.
• No usage data, telemetry, or conversation content is collected.
• Auth tokens for remote instances are stored in VS Code's encrypted secrets API.
• The extension communicates only with ollama.host (default: http://localhost:11434).