Local LLM for VS Code

Features

🤖 Local AI Chat

• Privacy-First: All data stays on your machine—no cloud APIs required
• Multiple LLM Support: Works with Ollama, LM Studio, vLLM, and OpenAI-compatible endpoints
• Persistent Conversations: Chat history is maintained throughout your session
• Streaming Responses: See AI responses in real-time as they're generated

📁 Workspace Integration

• Read Files: Load any workspace file into the conversation context
• List Directories: Browse your project structure directly from chat
• Search Files: Find files using glob patterns (e.g., **/*.ts)
• Workspace Info: Get metadata about your project (Git status, dependencies, etc.)
• Smart Context: Send active file or selected code to the AI instantly

✨ File Creation & Management

• AI-Powered File Generation: Let the AI create complete files based on your requirements
• Syntax Detection: Automatically detects file types and applies proper formatting
• Safe Operations: Confirmation prompts before creating/overwriting files
• Selection to File: Convert any code selection into a new file

⚡ Chat Commands

Built-in slash commands for quick actions:

• /read <file-path> - Read a file and add to conversation
• /list [directory] - List files in a directory
• /search <pattern> - Search for files with glob patterns
• /workspace - Show workspace information
• /help - Display all available commands

Installation

From VSIX File

1. Download the .vsix file from the releases page
2. Open VS Code
3. Go to Extensions view (Ctrl+Shift+X or Cmd+Shift+X)
4. Click the ... menu → Install from VSIX...
5. Select the downloaded .vsix file

From Source

git clone https://github.com/markusbegerow/local-llm-vscode.git
cd local-llm-vscode
npm install
npm run compile

Then press F5 in VS Code to launch the extension in debug mode.

Getting Started

1. Set Up Your Local LLM

Option A: Ollama (Recommended)

# Install Ollama from https://ollama.ai
ollama pull llama3.1
ollama serve

Option B: LM Studio

1. Download from lmstudio.ai
2. Load a model
3. Start the local server (default: http://localhost:1234)

2. Configure the Extension

Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P) and run:

Local LLM: Configure API Settings

Or manually configure in VS Code settings:

{
  "localLLM.apiUrl": "http://localhost:11434",
  "localLLM.model": "llama3.1",
  "localLLM.apiCompat": "openai",
  "localLLM.temperature": 0.7
}

3. Start Chatting

1. Open Command Palette
2. Run: Local LLM: Open Chat
3. Start asking questions!

Usage Examples

Analyze Code

/read src/extension.ts
Can you explain how this extension works and suggest improvements?

Explore Project Structure

/workspace
/list src
What is the architecture of this project?

Generate Files

Create a TypeScript utility function that validates email addresses with proper error handling and unit tests.

The AI will suggest a file with path and content. Click "Create" to save it!

Search and Refactor

/search **/*.ts
Find all TypeScript files, then help me refactor the error handling patterns across the project.

Quick File Analysis

1. Open any file in the editor
2. Press Ctrl+Shift+P
3. Run: Local LLM: Send Active File to Chat
4. Ask questions about the code

Commands

Command Palette Commands

In-Chat Slash Commands

Configuration

Settings

API Compatibility Modes

OpenAI Compatible (Recommended)

• LM Studio
• vLLM
• text-generation-webui
• Most modern LLM servers

{
  "localLLM.apiCompat": "openai",
  "localLLM.apiUrl": "http://localhost:1234"
}

Ollama Native

{
  "localLLM.apiCompat": "ollama",
  "localLLM.apiUrl": "http://localhost:11434"
}

Security Features

• ✅ Path Traversal Protection: Prevents access outside workspace
• ✅ File Size Limits: Configurable maximum file sizes
• ✅ Content Security Policy: XSS protection in webviews
• ✅ Secure Token Storage: API keys stored in VS Code secrets
• ✅ Confirmation Prompts: Review before creating/overwriting files
• ✅ Local-Only: No external API calls unless you configure them

Requirements

• VS Code: Version 1.85.0 or higher
• Node.js: Version 20.x or higher (for development)
• Local LLM: Ollama, LM Studio, or compatible server

Development

Building from Source

# Clone repository
git clone https://github.com/markusbegerow/local-llm-vscode.git
cd local-llm-vscode

# Install dependencies
npm install

# Compile TypeScript
npm run compile

# Watch mode for development
npm run watch

# Package as VSIX
npm install -g @vscode/vsce
vsce package

Project Structure

local-llm-vscode/
├── src/
│   ├── extension.ts      # Extension entry point
│   ├── chatPanel.ts      # Chat UI and logic
│   ├── llm.ts           # LLM API integration
│   ├── utils.ts         # Workspace utilities
│   └── types.ts         # TypeScript types
├── media/
│   └── webview.js       # Chat UI JavaScript
├── out/                 # Compiled JavaScript
├── package.json         # Extension manifest
└── tsconfig.json        # TypeScript config

Troubleshooting

Connection Issues

• Verify your LLM server is running: curl http://localhost:11434/api/tags (Ollama) or curl http://localhost:1234/v1/models (LM Studio)
• Check the API URL in settings matches your server
• Try switching API compatibility mode

Chat Not Responding

• Check the Output panel (View → Output → Local LLM Chat) for errors
• Increase the request timeout in settings
• Verify your model is loaded in the LLM server

File Operations Failing

• Ensure you have a workspace/folder open in VS Code
• Check file paths are relative (no absolute paths allowed)
• Verify file size limits in settings

Recommended Models

Coding Tasks

• Llama 3.1 8B: Fast, good for general coding
• CodeLlama 13B: Specialized for code generation
• Qwen 2.5 Coder: Excellent code understanding
• DeepSeek Coder: Strong at complex algorithms

General Tasks

• Llama 3.1: Best all-around performance
• Mistral 7B: Fast and efficient
• Phi-3: Compact but capable

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the Ollama team for making local LLMs accessible
• LM Studio for providing an excellent local inference platform
• The VS Code extension API team for comprehensive documentation

🙋‍♂️ Get Involved

If you encounter any issues or have questions:

• 🐛 Report bugs
• 💡 Request features
• ⭐ Star the repo if you find it useful!

☕ Support the Project

If you like this project, support further development with a repost or coffee:

📬 Contact

• 🧑‍💻 Markus Begerow
• 💾 GitHub
• ✉️ Twitter

Privacy Notice: This extension operates entirely locally. No data is sent to external servers unless you explicitly configure it to use a remote API endpoint.