LiteLLM Provider for GitHub Copilot Chat

Use 100+ LLMs in VS Code with GitHub Copilot Chat powered by LiteLLM.

Features

• Access 100+ LLMs (OpenAI, Anthropic, Google, AWS, Azure, and more) through a unified API
• Multi-server support: Connect to multiple LiteLLM servers simultaneously and aggregate models
• Automatic provider selection with cheapest and fastest modes
• Multimodal support: Vision (images), PDF/document attachments, and text/JSON data
• Support for streaming, function calling, and thinking/reasoning tokens
• Broad model options pass-through (response_format, reasoning_effort, seed, and more)
• Self-hosted or cloud-based deployment options

Requirements

• VS Code 1.108.0 or higher
• LiteLLM proxy running (self-hosted or cloud)
• LiteLLM API key (if required by your setup)

Quick Start

1. Install the extension from the VS Code Marketplace
2. Open VS Code's chat interface
3. Click the model picker → "Manage Models..." → "LiteLLM"
4. Add a server: enter a label, base URL (e.g., http://localhost:4000), and API key
5. Select models to add

Configuration

Server Management

The extension supports connecting to multiple LiteLLM servers at once. Models from all reachable servers are aggregated into one list.

To manage servers:

• Command Palette: Ctrl+Shift+P / Cmd+Shift+P → "Manage LiteLLM Provider"
• Model Picker: Chat interface → Model picker → "Manage Models..." → "LiteLLM"

From the server manager you can:

• Add Server — provide a unique label, base URL, and optional API key
• Edit Server — update label, URL, or API key
• Remove Server — delete a server and its stored credentials
• Test All Servers — verify connectivity to every configured server

If no servers are configured, the "Manage" command jumps straight to the add flow.

Credentials are stored securely in VS Code's secret storage. Server metadata (label, URL) is stored in global state.

Upgrading from single-server: Existing single-server configurations are automatically migrated into the server registry on first run.

Token Limits (Automatic)

The extension automatically reads token limits from your LiteLLM server's model info. You can configure fallback defaults in VS Code settings:

To access: Ctrl+, / Cmd+, → Search "litellm-vscode-chat"

Priority: LiteLLM model info → Workspace settings → Defaults

Custom Model Parameters (Optional)

Override default request parameters for specific models using the modelParameters setting. This is useful for models with specific requirements (like gpt-5 requiring temperature: 1) or to customize behavior per model.

To configure: Add to your settings.json:

{
  "litellm-vscode-chat.modelParameters": {
    "gpt-5": {
      "temperature": 1
    },
    "gpt-4": {
      "max_tokens": 8000,
      "temperature": 0.8,
      "top_p": 0.9
    },
    "claude-opus": {
      "max_tokens": 16000,
      "temperature": 0.5
    }
  }
}

Supported parameters:

• max_tokens - Maximum tokens in response
• temperature - Randomness (0.0-2.0)
• top_p - Nucleus sampling (0.0-1.0)
• frequency_penalty - Reduce repetition (-2.0 to 2.0)
• presence_penalty - Encourage new topics (-2.0 to 2.0)
• stop - Stop sequences (string or array)
• response_format - Structured output / JSON mode
• reasoning_effort - Thinking/reasoning control (for supported models)
• seed - Deterministic output
• And any other parameter supported by your LiteLLM and model provider backend

All modelParameters keys are passed through to LiteLLM — the extension does not filter or restrict which parameters you can set. The reserved key _replaceDefaults is extension metadata and is never forwarded.

Built-in defaults: The extension applies temperature: 0.7 by default. Some models (e.g., gpt-5.5) have built-in overrides that suppress the default temperature. User modelParameters entries merge on top of these defaults.

_replaceDefaults: Set "_replaceDefaults": true in a model entry to skip built-in request-parameter defaults for that model (for example, the default temperature) and use only the request parameters you supply from configuration:

{
  "litellm-vscode-chat.modelParameters": {
    "gpt-4": {
      "_replaceDefaults": true,
      "top_p": 0.9
    }
  }
}

Prefix matching: Configuration keys use longest prefix matching. For example, "gpt-4" will match "gpt-4-turbo:openai", "gpt-4:azure", etc. More specific keys take precedence.

Server-scoped parameters: In multi-server setups, prefix a key with the server label and / to scope parameters to a specific server. Server-scoped entries take priority over unscoped ones:

{
  "litellm-vscode-chat.modelParameters": {
    "gpt-4": {
      "temperature": 0.7
    },
    "Production/gpt-4": {
      "temperature": 0.3
    },
    "Dev/gpt-4": {
      "temperature": 0.9
    }
  }
}

Parameter precedence: Runtime options > User config > Defaults

Prompt Caching (Anthropic Claude)

The extension supports prompt caching for models that advertise this capability (currently Anthropic Claude models). Prompt caching reduces costs and improves response times by caching the system prompt across requests.

To configure: Add to your settings.json:

{
  "litellm-vscode-chat.promptCaching.enabled": true
}

How it works:

• Automatically detects prompt caching support from LiteLLM's /v1/model/info endpoint
• Only affects models that explicitly support prompt caching (primarily Claude models)
• Adds cache_control blocks to system messages when enabled
• Disabled by default for models without support

Benefits:

• Reduced API costs (cached tokens are cheaper)
• Faster response times (cached content doesn't need reprocessing)
• Transparent to the user (works automatically when supported)

Troubleshooting

Mock LiteLLM Server (Local)

For quick manual testing, you can run a tiny mock LiteLLM server that serves a static model list and canned chat replies.

node scripts/mock-litellm-server.js

Optional port override:

PORT=4001 node scripts/mock-litellm-server.js

Then set your base URL to http://localhost:4000 (or the port you chose).

Status Bar Indicator

The LiteLLM status bar indicator (bottom right corner) shows your connection status:

Click the status bar indicator at any time to view detailed diagnostics.

Test Your Connection

After configuring the extension, verify your setup:

1. Command Palette: Ctrl+Shift+P / Cmd+Shift+P → "LiteLLM: Test Connection"
2. Or click "Test Connection" after saving configuration

This will:

• Attempt to connect to your LiteLLM server
• Show the number of models found
• Display detailed error messages if connection fails
• Update the status bar with results

Diagnostic Tools

View Diagnostics

• Command Palette: Ctrl+Shift+P / Cmd+Shift+P → "LiteLLM: Show Diagnostics"
• Or click the status bar indicator

Shows:

• Configured servers with labels and URLs
• Per-server connection state, model count, and errors
• Overall connection status and total model count
• Last check timestamp
• Quick access to output channel

Help & Feedback

• Command Palette: Ctrl+Shift+P / Cmd+Shift+P → "LiteLLM: Help & Feedback"
• Also accessible from the diagnostics dialog

Quickly report bugs, request features, or open the documentation.

Output Channel

View detailed logs for debugging:

1. Open Output panel: Ctrl+Shift+U / Cmd+Shift+U
2. Select "LiteLLM" from the dropdown

The output channel logs:

• Configuration changes
• Model fetch attempts and results
• Error messages with full details
• Server response information

Common Issues

"No models appear in the model picker"

• Check the status bar - it will show the actual state
• Click "Test Connection" to verify your setup
• Check the "LiteLLM" output channel for error details
• Verify your LiteLLM server is running and accessible

"Server returned 0 models"

• Your LiteLLM proxy is running but has no models configured
• Check your LiteLLM proxy configuration (litellm_config.yaml)
• Run litellm --config your_config.yaml to start the proxy with models

"Authentication failed"

• Your server requires an API key
• Run "Manage LiteLLM Provider" and edit the server to update its API key
• Verify the key is correct in your LiteLLM proxy configuration

"Connection Error: Unable to connect"

• Verify the base URL is correct (e.g., http://localhost:4000)
• Ensure your LiteLLM proxy is running
• Check firewall/network settings

Development

git clone https://github.com/Vivswan/litellm-vscode-chat
cd litellm-vscode-chat
bun install
bun run compile

Press F5 to launch the Extension Development Host.

Resources

• LiteLLM Documentation
• VS Code Chat Provider API
• Report Issues