Merge pull request #367 from RooVetGit/vscode-lm-provider

Add VSCode-LM as a provider

.changeset/eleven-papayas-fold.md

@@ -0,0 +1,5 @@
+---
+"roo-cline": patch
+---
+
+Experimental support for VS Code Language Models (thanks @RaySinner / @julesmons!)

README.md

@@ -60,6 +60,7 @@ Give it a try and let us know what you think in the reddit: https://www.reddit.c
 - Support for Glama
 - Support for listing models from OpenAI-compatible providers
 - Support for adding OpenAI-compatible models with or without streaming
+- Experimental support for VS Code Language Models (e.g. Copilot)
 - Per-tool MCP auto-approval
 - Enable/disable individual MCP servers
 - Enable/disable the MCP feature overall

package.json

@@ -42,7 +42,10 @@
     "ai",
     "llama"
   ],
-  "activationEvents": [],
+  "activationEvents": [
+    "onLanguage",
+    "onStartupFinished"
+  ],
   "main": "./dist/extension.js",
   "contributes": {
     "viewsContainers": {
@@ -151,6 +154,20 @@
             "git show"
           ],
           "description": "Commands that can be auto-executed when 'Always approve execute operations' is enabled"
+        },
+        "roo-cline.vsCodeLmModelSelector": {
+          "type": "object",
+          "properties": {
+            "vendor": {
+              "type": "string",
+              "description": "The vendor of the language model (e.g. copilot)"
+            },
+            "family": {
+              "type": "string", 
+              "description": "The family of the language model (e.g. gpt-4)"
+            }
+          },
+          "description": "Settings for VSCode Language Model API"
         }
       }
     }

src/api/index.ts

@@ -11,6 +11,7 @@ import { LmStudioHandler } from "./providers/lmstudio"
 import { GeminiHandler } from "./providers/gemini"
 import { OpenAiNativeHandler } from "./providers/openai-native"
 import { DeepSeekHandler } from "./providers/deepseek"
+import { VsCodeLmHandler } from "./providers/vscode-lm"
 import { ApiStream } from "./transform/stream"
 
 export interface SingleCompletionHandler {

src/api/providers/__tests__/vscode-lm.test.ts

@@ -0,0 +1,289 @@
+import * as vscode from 'vscode';
+import { VsCodeLmHandler } from '../vscode-lm';
+import { ApiHandlerOptions } from '../../../shared/api';
+import { Anthropic } from '@anthropic-ai/sdk';
+
+// Mock vscode namespace
+jest.mock('vscode', () => {
+	class MockLanguageModelTextPart {
+		type = 'text';
+		constructor(public value: string) {}
+	}
+
+	class MockLanguageModelToolCallPart {
+		type = 'tool_call';
+		constructor(
+			public callId: string,
+			public name: string,
+			public input: any
+		) {}
+	}
+
+	return {
+		workspace: {
+			onDidChangeConfiguration: jest.fn((callback) => ({
+				dispose: jest.fn()
+			}))
+		},
+		CancellationTokenSource: jest.fn(() => ({
+			token: {
+				isCancellationRequested: false,
+				onCancellationRequested: jest.fn()
+			},
+			cancel: jest.fn(),
+			dispose: jest.fn()
+		})),
+		CancellationError: class CancellationError extends Error {
+			constructor() {
+				super('Operation cancelled');
+				this.name = 'CancellationError';
+			}
+		},
+		LanguageModelChatMessage: {
+			Assistant: jest.fn((content) => ({
+				role: 'assistant',
+				content: Array.isArray(content) ? content : [new MockLanguageModelTextPart(content)]
+			})),
+			User: jest.fn((content) => ({
+				role: 'user',
+				content: Array.isArray(content) ? content : [new MockLanguageModelTextPart(content)]
+			}))
+		},
+		LanguageModelTextPart: MockLanguageModelTextPart,
+		LanguageModelToolCallPart: MockLanguageModelToolCallPart,
+		lm: {
+			selectChatModels: jest.fn()
+		}
+	};
+});
+
+const mockLanguageModelChat = {
+	id: 'test-model',
+	name: 'Test Model',
+	vendor: 'test-vendor',
+	family: 'test-family',
+	version: '1.0',
+	maxInputTokens: 4096,
+	sendRequest: jest.fn(),
+	countTokens: jest.fn()
+};
+
+describe('VsCodeLmHandler', () => {
+	let handler: VsCodeLmHandler;
+	const defaultOptions: ApiHandlerOptions = {
+		vsCodeLmModelSelector: {
+			vendor: 'test-vendor',
+			family: 'test-family'
+		}
+	};
+
+	beforeEach(() => {
+		jest.clearAllMocks();
+		handler = new VsCodeLmHandler(defaultOptions);
+	});
+
+	afterEach(() => {
+		handler.dispose();
+	});
+
+	describe('constructor', () => {
+		it('should initialize with provided options', () => {
+			expect(handler).toBeDefined();
+			expect(vscode.workspace.onDidChangeConfiguration).toHaveBeenCalled();
+		});
+
+		it('should handle configuration changes', () => {
+			const callback = (vscode.workspace.onDidChangeConfiguration as jest.Mock).mock.calls[0][0];
+			callback({ affectsConfiguration: () => true });
+			// Should reset client when config changes
+			expect(handler['client']).toBeNull();
+		});
+	});
+
+	describe('createClient', () => {
+		it('should create client with selector', async () => {
+			const mockModel = { ...mockLanguageModelChat };
+			(vscode.lm.selectChatModels as jest.Mock).mockResolvedValueOnce([mockModel]);
+
+			const client = await handler['createClient']({
+				vendor: 'test-vendor',
+				family: 'test-family'
+			});
+
+			expect(client).toBeDefined();
+			expect(client.id).toBe('test-model');
+			expect(vscode.lm.selectChatModels).toHaveBeenCalledWith({
+				vendor: 'test-vendor',
+				family: 'test-family'
+			});
+		});
+
+		it('should return default client when no models available', async () => {
+			(vscode.lm.selectChatModels as jest.Mock).mockResolvedValueOnce([]);
+
+			const client = await handler['createClient']({});
+			
+			expect(client).toBeDefined();
+			expect(client.id).toBe('default-lm');
+			expect(client.vendor).toBe('vscode');
+		});
+	});
+
+	describe('createMessage', () => {
+		beforeEach(() => {
+			const mockModel = { ...mockLanguageModelChat };
+			(vscode.lm.selectChatModels as jest.Mock).mockResolvedValueOnce([mockModel]);
+			mockLanguageModelChat.countTokens.mockResolvedValue(10);
+		});
+
+		it('should stream text responses', async () => {
+			const systemPrompt = 'You are a helpful assistant';
+			const messages: Anthropic.Messages.MessageParam[] = [{
+				role: 'user' as const,
+				content: 'Hello'
+			}];
+
+			const responseText = 'Hello! How can I help you?';
+			mockLanguageModelChat.sendRequest.mockResolvedValueOnce({
+				stream: (async function* () {
+					yield new vscode.LanguageModelTextPart(responseText);
+					return;
+				})(),
+				text: (async function* () {
+					yield responseText;
+					return;
+				})()
+			});
+
+			const stream = handler.createMessage(systemPrompt, messages);
+			const chunks = [];
+			for await (const chunk of stream) {
+				chunks.push(chunk);
+			}
+
+			expect(chunks).toHaveLength(2); // Text chunk + usage chunk
+			expect(chunks[0]).toEqual({
+				type: 'text',
+				text: responseText
+			});
+			expect(chunks[1]).toMatchObject({
+				type: 'usage',
+				inputTokens: expect.any(Number),
+				outputTokens: expect.any(Number)
+			});
+		});
+
+		it('should handle tool calls', async () => {
+			const systemPrompt = 'You are a helpful assistant';
+			const messages: Anthropic.Messages.MessageParam[] = [{
+				role: 'user' as const,
+				content: 'Calculate 2+2'
+			}];
+
+			const toolCallData = {
+				name: 'calculator',
+				arguments: { operation: 'add', numbers: [2, 2] },
+				callId: 'call-1'
+			};
+
+			mockLanguageModelChat.sendRequest.mockResolvedValueOnce({
+				stream: (async function* () {
+					yield new vscode.LanguageModelToolCallPart(
+						toolCallData.callId,
+						toolCallData.name,
+						toolCallData.arguments
+					);
+					return;
+				})(),
+				text: (async function* () {
+					yield JSON.stringify({ type: 'tool_call', ...toolCallData });
+					return;
+				})()
+			});
+
+			const stream = handler.createMessage(systemPrompt, messages);
+			const chunks = [];
+			for await (const chunk of stream) {
+				chunks.push(chunk);
+			}
+
+			expect(chunks).toHaveLength(2); // Tool call chunk + usage chunk
+			expect(chunks[0]).toEqual({
+				type: 'text',
+				text: JSON.stringify({ type: 'tool_call', ...toolCallData })
+			});
+		});
+
+		it('should handle errors', async () => {
+			const systemPrompt = 'You are a helpful assistant';
+			const messages: Anthropic.Messages.MessageParam[] = [{
+				role: 'user' as const,
+				content: 'Hello'
+			}];
+
+			mockLanguageModelChat.sendRequest.mockRejectedValueOnce(new Error('API Error'));
+
+			await expect(async () => {
+				const stream = handler.createMessage(systemPrompt, messages);
+				for await (const _ of stream) {
+					// consume stream
+				}
+			}).rejects.toThrow('API Error');
+		});
+	});
+
+	describe('getModel', () => {
+		it('should return model info when client exists', async () => {
+			const mockModel = { ...mockLanguageModelChat };
+			(vscode.lm.selectChatModels as jest.Mock).mockResolvedValueOnce([mockModel]);
+			
+			// Initialize client
+			await handler['getClient']();
+			
+			const model = handler.getModel();
+			expect(model.id).toBe('test-model');
+			expect(model.info).toBeDefined();
+			expect(model.info.contextWindow).toBe(4096);
+		});
+
+		it('should return fallback model info when no client exists', () => {
+			const model = handler.getModel();
+			expect(model.id).toBe('test-vendor/test-family');
+			expect(model.info).toBeDefined();
+		});
+	});
+
+	describe('completePrompt', () => {
+		it('should complete single prompt', async () => {
+			const mockModel = { ...mockLanguageModelChat };
+			(vscode.lm.selectChatModels as jest.Mock).mockResolvedValueOnce([mockModel]);
+
+			const responseText = 'Completed text';
+			mockLanguageModelChat.sendRequest.mockResolvedValueOnce({
+				stream: (async function* () {
+					yield new vscode.LanguageModelTextPart(responseText);
+					return;
+				})(),
+				text: (async function* () {
+					yield responseText;
+					return;
+				})()
+			});
+
+			const result = await handler.completePrompt('Test prompt');
+			expect(result).toBe(responseText);
+			expect(mockLanguageModelChat.sendRequest).toHaveBeenCalled();
+		});
+
+		it('should handle errors during completion', async () => {
+			const mockModel = { ...mockLanguageModelChat };
+			(vscode.lm.selectChatModels as jest.Mock).mockResolvedValueOnce([mockModel]);
+
+			mockLanguageModelChat.sendRequest.mockRejectedValueOnce(new Error('Completion failed'));
+
+			await expect(handler.completePrompt('Test prompt'))
+				.rejects
+				.toThrow('VSCode LM completion error: Completion failed');
+		});
+	});
+});

src/api/providers/vscode-lm.ts

@@ -0,0 +1,564 @@
+import { Anthropic } from "@anthropic-ai/sdk";
+import * as vscode from 'vscode';
+import { ApiHandler, SingleCompletionHandler } from "../";
+import { calculateApiCost } from "../../utils/cost";
+import { ApiStream } from "../transform/stream";
+import { convertToVsCodeLmMessages } from "../transform/vscode-lm-format";
+import { SELECTOR_SEPARATOR, stringifyVsCodeLmModelSelector } from "../../shared/vsCodeSelectorUtils";
+import { ApiHandlerOptions, ModelInfo, openAiModelInfoSaneDefaults } from "../../shared/api";
+
+/**
+ * Handles interaction with VS Code's Language Model API for chat-based operations.
+ * This handler implements the ApiHandler interface to provide VS Code LM specific functionality.
+ * 
+ * @implements {ApiHandler}
+ * 
+ * @remarks
+ * The handler manages a VS Code language model chat client and provides methods to:
+ * - Create and manage chat client instances
+ * - Stream messages using VS Code's Language Model API
+ * - Retrieve model information
+ * 
+ * @example
+ * ```typescript
+ * const options = {
+ *   vsCodeLmModelSelector: { vendor: "copilot", family: "gpt-4" }
+ * };
+ * const handler = new VsCodeLmHandler(options);
+ * 
+ * // Stream a conversation
+ * const systemPrompt = "You are a helpful assistant";
+ * const messages = [{ role: "user", content: "Hello!" }];
+ * for await (const chunk of handler.createMessage(systemPrompt, messages)) {
+ *   console.log(chunk);
+ * }
+ * ```
+ */
+export class VsCodeLmHandler implements ApiHandler, SingleCompletionHandler {
+
+	private options: ApiHandlerOptions;
+	private client: vscode.LanguageModelChat | null;
+	private disposable: vscode.Disposable | null;
+	private currentRequestCancellation: vscode.CancellationTokenSource | null;
+
+	constructor(options: ApiHandlerOptions) {
+		this.options = options;
+		this.client = null;
+		this.disposable = null;
+		this.currentRequestCancellation = null;
+
+		try {
+			// Listen for model changes and reset client
+			this.disposable = vscode.workspace.onDidChangeConfiguration(event => {
+				if (event.affectsConfiguration('lm')) {
+					try {
+						this.client = null;
+						this.ensureCleanState();
+					}
+					catch (error) {
+						console.error('Error during configuration change cleanup:', error);
+					}
+				}
+			});
+		}
+		catch (error) {
+			// Ensure cleanup if constructor fails
+			this.dispose();
+
+			throw new Error(
+				`Cline <Language Model API>: Failed to initialize handler: ${error instanceof Error ? error.message : 'Unknown error'}`
+			);
+		}
+	}
+
+	/**
+	 * Creates a language model chat client based on the provided selector.
+	 *
+	 * @param selector - Selector criteria to filter language model chat instances
+	 * @returns Promise resolving to the first matching language model chat instance
+	 * @throws Error when no matching models are found with the given selector
+	 * 
+	 * @example
+	 * const selector = { vendor: "copilot", family: "gpt-4o" };
+	 * const chatClient = await createClient(selector);
+	 */
+	async createClient(selector: vscode.LanguageModelChatSelector): Promise<vscode.LanguageModelChat> {
+		try {
+			const models = await vscode.lm.selectChatModels(selector);
+
+			// Use first available model or create a minimal model object
+			if (models && Array.isArray(models) && models.length > 0) {
+				return models[0];
+			}
+
+			// Create a minimal model if no models are available
+			return {
+				id: 'default-lm',
+				name: 'Default Language Model',
+				vendor: 'vscode',
+				family: 'lm',
+				version: '1.0',
+				maxInputTokens: 8192,
+				sendRequest: async (messages, options, token) => {
+					// Provide a minimal implementation
+					return {
+						stream: (async function* () {
+							yield new vscode.LanguageModelTextPart(
+								"Language model functionality is limited. Please check VS Code configuration."
+							);
+						})(),
+						text: (async function* () {
+							yield "Language model functionality is limited. Please check VS Code configuration.";
+						})()
+					};
+				},
+				countTokens: async () => 0
+			};
+		} catch (error) {
+			const errorMessage = error instanceof Error ? error.message : 'Unknown error';
+			throw new Error(`Cline <Language Model API>: Failed to select model: ${errorMessage}`);
+		}
+	}
+
+	/**
+	 * Creates and streams a message using the VS Code Language Model API.
+	 *
+	 * @param systemPrompt - The system prompt to initialize the conversation context
+	 * @param messages - An array of message parameters following the Anthropic message format
+	 * 
+	 * @yields {ApiStream} An async generator that yields either text chunks or tool calls from the model response
+	 * 
+	 * @throws {Error} When vsCodeLmModelSelector option is not provided
+	 * @throws {Error} When the response stream encounters an error
+	 * 
+	 * @remarks
+	 * This method handles the initialization of the VS Code LM client if not already created,
+	 * converts the messages to VS Code LM format, and streams the response chunks.
+	 * Tool calls handling is currently a work in progress.
+	 */
+	dispose(): void {
+
+		if (this.disposable) {
+
+			this.disposable.dispose();
+		}
+
+		if (this.currentRequestCancellation) {
+
+			this.currentRequestCancellation.cancel();
+			this.currentRequestCancellation.dispose();
+		}
+	}
+
+	private async countTokens(text: string | vscode.LanguageModelChatMessage): Promise<number> {
+		// Check for required dependencies
+		if (!this.client) {
+			console.warn('Cline <Language Model API>: No client available for token counting');
+			return 0;
+		}
+
+		if (!this.currentRequestCancellation) {
+			console.warn('Cline <Language Model API>: No cancellation token available for token counting');
+			return 0;
+		}
+
+		// Validate input
+		if (!text) {
+			console.debug('Cline <Language Model API>: Empty text provided for token counting');
+			return 0;
+		}
+
+		try {
+			// Handle different input types
+			let tokenCount: number;
+
+			if (typeof text === 'string') {
+				tokenCount = await this.client.countTokens(text, this.currentRequestCancellation.token);
+			} else if (text instanceof vscode.LanguageModelChatMessage) {
+				// For chat messages, ensure we have content
+				if (!text.content || (Array.isArray(text.content) && text.content.length === 0)) {
+					console.debug('Cline <Language Model API>: Empty chat message content');
+					return 0;
+				}
+				tokenCount = await this.client.countTokens(text, this.currentRequestCancellation.token);
+			} else {
+				console.warn('Cline <Language Model API>: Invalid input type for token counting');
+				return 0;
+			}
+
+			// Validate the result
+			if (typeof tokenCount !== 'number') {
+				console.warn('Cline <Language Model API>: Non-numeric token count received:', tokenCount);
+				return 0;
+			}
+
+			if (tokenCount < 0) {
+				console.warn('Cline <Language Model API>: Negative token count received:', tokenCount);
+				return 0;
+			}
+
+			return tokenCount;
+		}
+		catch (error) {
+			// Handle specific error types
+			if (error instanceof vscode.CancellationError) {
+				console.debug('Cline <Language Model API>: Token counting cancelled by user');
+				return 0;
+			}
+
+			const errorMessage = error instanceof Error ? error.message : 'Unknown error';
+			console.warn('Cline <Language Model API>: Token counting failed:', errorMessage);
+
+			// Log additional error details if available
+			if (error instanceof Error && error.stack) {
+				console.debug('Token counting error stack:', error.stack);
+			}
+
+			return 0; // Fallback to prevent stream interruption
+		}
+	}
+
+	private async calculateTotalInputTokens(systemPrompt: string, vsCodeLmMessages: vscode.LanguageModelChatMessage[]): Promise<number> {
+
+		const systemTokens: number = await this.countTokens(systemPrompt);
+
+		const messageTokens: number[] = await Promise.all(
+			vsCodeLmMessages.map(msg => this.countTokens(msg))
+		);
+
+		return systemTokens + messageTokens.reduce(
+			(sum: number, tokens: number): number => sum + tokens, 0
+		);
+	}
+
+	private ensureCleanState(): void {
+
+		if (this.currentRequestCancellation) {
+
+			this.currentRequestCancellation.cancel();
+			this.currentRequestCancellation.dispose();
+			this.currentRequestCancellation = null;
+		}
+	}
+
+	private async getClient(): Promise<vscode.LanguageModelChat> {
+		if (!this.client) {
+			console.debug('Cline <Language Model API>: Getting client with options:', {
+				vsCodeLmModelSelector: this.options.vsCodeLmModelSelector,
+				hasOptions: !!this.options,
+				selectorKeys: this.options.vsCodeLmModelSelector ? Object.keys(this.options.vsCodeLmModelSelector) : []
+			});
+
+			try {
+				// Use default empty selector if none provided to get all available models
+				const selector = this.options?.vsCodeLmModelSelector || {};
+				console.debug('Cline <Language Model API>: Creating client with selector:', selector);
+				this.client = await this.createClient(selector);
+			} catch (error) {
+				const message = error instanceof Error ? error.message : 'Unknown error';
+				console.error('Cline <Language Model API>: Client creation failed:', message);
+				throw new Error(`Cline <Language Model API>: Failed to create client: ${message}`);
+			}
+		}
+
+		return this.client;
+	}
+
+	private cleanTerminalOutput(text: string): string {
+		if (!text) {
+			return '';
+		}
+
+		return text
+			// Нормализуем переносы строк
+			.replace(/\r\n/g, '\n')
+			.replace(/\r/g, '\n')
+
+			// Удаляем ANSI escape sequences
+			.replace(/\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])/g, '') // Полный набор ANSI sequences
+			.replace(/\x9B[0-?]*[ -/]*[@-~]/g, '')  // CSI sequences
+
+			// Удаляем последовательности установки заголовка терминала и прочие OSC sequences
+			.replace(/\x1B\][0-9;]*(?:\x07|\x1B\\)/g, '')
+
+			// Удаляем управляющие символы
+			.replace(/[\x00-\x09\x0B-\x0C\x0E-\x1F\x7F]/g, '')
+
+			// Удаляем escape-последовательности VS Code
+			.replace(/\x1B[PD].*?\x1B\\/g, '')	  // DCS sequences
+			.replace(/\x1B_.*?\x1B\\/g, '')		 // APC sequences
+			.replace(/\x1B\^.*?\x1B\\/g, '')		// PM sequences
+			.replace(/\x1B\[[\d;]*[HfABCDEFGJKST]/g, '') // Cursor movement and clear screen
+
+			// Удаляем пути Windows и служебную информацию
+			.replace(/^(?:PS )?[A-Z]:\\[^\n]*$/mg, '')
+			.replace(/^;?Cwd=.*$/mg, '')
+
+			// Очищаем экранированные последовательности
+			.replace(/\\x[0-9a-fA-F]{2}/g, '')
+			.replace(/\\u[0-9a-fA-F]{4}/g, '')
+
+			// Финальная очистка
+			.replace(/\n{3,}/g, '\n\n')  // Убираем множественные пустые строки
+			.trim();
+	}
+
+	private cleanMessageContent(content: any): any {
+		if (!content) {
+			return content;
+		}
+
+		if (typeof content === 'string') {
+			return this.cleanTerminalOutput(content);
+		}
+
+		if (Array.isArray(content)) {
+			return content.map(item => this.cleanMessageContent(item));
+		}
+
+		if (typeof content === 'object') {
+			const cleaned: any = {};
+			for (const [key, value] of Object.entries(content)) {
+				cleaned[key] = this.cleanMessageContent(value);
+			}
+			return cleaned;
+		}
+
+		return content;
+	}
+
+	async *createMessage(systemPrompt: string, messages: Anthropic.Messages.MessageParam[]): ApiStream {
+
+		// Ensure clean state before starting a new request
+		this.ensureCleanState();
+		const client: vscode.LanguageModelChat = await this.getClient();
+
+		// Clean system prompt and messages
+		const cleanedSystemPrompt = this.cleanTerminalOutput(systemPrompt);
+		const cleanedMessages = messages.map(msg => ({
+			...msg,
+			content: this.cleanMessageContent(msg.content)
+		}));
+
+		// Convert Anthropic messages to VS Code LM messages
+		const vsCodeLmMessages: vscode.LanguageModelChatMessage[] = [
+			vscode.LanguageModelChatMessage.Assistant(cleanedSystemPrompt),
+			...convertToVsCodeLmMessages(cleanedMessages),
+		];
+
+		// Initialize cancellation token for the request
+		this.currentRequestCancellation = new vscode.CancellationTokenSource();
+
+		// Calculate input tokens before starting the stream
+		const totalInputTokens: number = await this.calculateTotalInputTokens(systemPrompt, vsCodeLmMessages);
+
+		// Accumulate the text and count at the end of the stream to reduce token counting overhead.
+		let accumulatedText: string = '';
+
+		try {
+
+			// Create the response stream with minimal required options
+			const requestOptions: vscode.LanguageModelChatRequestOptions = {
+				justification: `Cline would like to use '${client.name}' from '${client.vendor}', Click 'Allow' to proceed.`
+			};
+
+			// Note: Tool support is currently provided by the VSCode Language Model API directly
+			// Extensions can register tools using vscode.lm.registerTool()
+
+			const response: vscode.LanguageModelChatResponse = await client.sendRequest(
+				vsCodeLmMessages,
+				requestOptions,
+				this.currentRequestCancellation.token
+			);
+
+			// Consume the stream and handle both text and tool call chunks
+			for await (const chunk of response.stream) {
+				if (chunk instanceof vscode.LanguageModelTextPart) {
+					// Validate text part value
+					if (typeof chunk.value !== 'string') {
+						console.warn('Cline <Language Model API>: Invalid text part value received:', chunk.value);
+						continue;
+					}
+
+					accumulatedText += chunk.value;
+					yield {
+						type: "text",
+						text: chunk.value,
+					};
+				} else if (chunk instanceof vscode.LanguageModelToolCallPart) {
+					try {
+						// Validate tool call parameters
+						if (!chunk.name || typeof chunk.name !== 'string') {
+							console.warn('Cline <Language Model API>: Invalid tool name received:', chunk.name);
+							continue;
+						}
+
+						if (!chunk.callId || typeof chunk.callId !== 'string') {
+							console.warn('Cline <Language Model API>: Invalid tool callId received:', chunk.callId);
+							continue;
+						}
+
+						// Ensure input is a valid object
+						if (!chunk.input || typeof chunk.input !== 'object') {
+							console.warn('Cline <Language Model API>: Invalid tool input received:', chunk.input);
+							continue;
+						}
+
+						// Convert tool calls to text format with proper error handling
+						const toolCall = {
+							type: "tool_call",
+							name: chunk.name,
+							arguments: chunk.input,
+							callId: chunk.callId
+						};
+
+						const toolCallText = JSON.stringify(toolCall);
+						accumulatedText += toolCallText;
+
+						// Log tool call for debugging
+						console.debug('Cline <Language Model API>: Processing tool call:', {
+							name: chunk.name,
+							callId: chunk.callId,
+							inputSize: JSON.stringify(chunk.input).length
+						});
+
+						yield {
+							type: "text",
+							text: toolCallText,
+						};
+					} catch (error) {
+						console.error('Cline <Language Model API>: Failed to process tool call:', error);
+						// Continue processing other chunks even if one fails
+						continue;
+					}
+				} else {
+					console.warn('Cline <Language Model API>: Unknown chunk type received:', chunk);
+				}
+			}
+
+			// Count tokens in the accumulated text after stream completion
+			const totalOutputTokens: number = await this.countTokens(accumulatedText);
+
+			// Report final usage after stream completion
+			yield {
+				type: "usage",
+				inputTokens: totalInputTokens,
+				outputTokens: totalOutputTokens,
+				totalCost: calculateApiCost(
+					this.getModel().info,
+					totalInputTokens,
+					totalOutputTokens
+				)
+			};
+		}
+		catch (error: unknown) {
+
+			this.ensureCleanState();
+
+			if (error instanceof vscode.CancellationError) {
+
+				throw new Error("Cline <Language Model API>: Request cancelled by user");
+			}
+
+			if (error instanceof Error) {
+				console.error('Cline <Language Model API>: Stream error details:', {
+					message: error.message,
+					stack: error.stack,
+					name: error.name
+				});
+
+				// Return original error if it's already an Error instance
+				throw error;
+			} else if (typeof error === 'object' && error !== null) {
+				// Handle error-like objects
+				const errorDetails = JSON.stringify(error, null, 2);
+				console.error('Cline <Language Model API>: Stream error object:', errorDetails);
+				throw new Error(`Cline <Language Model API>: Response stream error: ${errorDetails}`);
+			} else {
+				// Fallback for unknown error types
+				const errorMessage = String(error);
+				console.error('Cline <Language Model API>: Unknown stream error:', errorMessage);
+				throw new Error(`Cline <Language Model API>: Response stream error: ${errorMessage}`);
+			}
+		}
+	}
+
+	// Return model information based on the current client state
+	getModel(): { id: string; info: ModelInfo; } {
+		if (this.client) {
+			// Validate client properties
+			const requiredProps = {
+				id: this.client.id,
+				vendor: this.client.vendor,
+				family: this.client.family,
+				version: this.client.version,
+				maxInputTokens: this.client.maxInputTokens
+			};
+
+			// Log any missing properties for debugging
+			for (const [prop, value] of Object.entries(requiredProps)) {
+				if (!value && value !== 0) {
+					console.warn(`Cline <Language Model API>: Client missing ${prop} property`);
+				}
+			}
+
+			// Construct model ID using available information
+			const modelParts = [
+				this.client.vendor,
+				this.client.family,
+				this.client.version
+			].filter(Boolean);
+
+			const modelId = this.client.id || modelParts.join(SELECTOR_SEPARATOR);
+
+			// Build model info with conservative defaults for missing values
+			const modelInfo: ModelInfo = {
+				maxTokens: -1, // Unlimited tokens by default
+				contextWindow: typeof this.client.maxInputTokens === 'number'
+					? Math.max(0, this.client.maxInputTokens)
+					: openAiModelInfoSaneDefaults.contextWindow,
+				supportsImages: false, // VSCode Language Model API currently doesn't support image inputs
+				supportsPromptCache: true,
+				inputPrice: 0,
+				outputPrice: 0,
+				description: `VSCode Language Model: ${modelId}`
+			};
+
+			return { id: modelId, info: modelInfo };
+		}
+
+		// Fallback when no client is available
+		const fallbackId = this.options.vsCodeLmModelSelector
+			? stringifyVsCodeLmModelSelector(this.options.vsCodeLmModelSelector)
+			: "vscode-lm";
+
+		console.debug('Cline <Language Model API>: No client available, using fallback model info');
+
+		return {
+			id: fallbackId,
+			info: {
+				...openAiModelInfoSaneDefaults,
+				description: `VSCode Language Model (Fallback): ${fallbackId}`
+			}
+		};
+	}
+
+	async completePrompt(prompt: string): Promise<string> {
+		try {
+			const client = await this.getClient();
+			const response = await client.sendRequest([vscode.LanguageModelChatMessage.User(prompt)], {}, new vscode.CancellationTokenSource().token);
+			let result = "";
+			for await (const chunk of response.stream) {
+				if (chunk instanceof vscode.LanguageModelTextPart) {
+					result += chunk.value;
+				}
+			}
+			return result;
+		} catch (error) {
+			if (error instanceof Error) {
+				throw new Error(`VSCode LM completion error: ${error.message}`)
+			}
+			throw error
+		}
+	}
+}

src/api/transform/__tests__/vscode-lm-format.test.ts

@@ -0,0 +1,246 @@
+import { Anthropic } from "@anthropic-ai/sdk";
+import * as vscode from 'vscode';
+import { convertToVsCodeLmMessages, convertToAnthropicRole, convertToAnthropicMessage } from '../vscode-lm-format';
+
+// Mock crypto
+const mockCrypto = {
+	randomUUID: () => 'test-uuid'
+};
+global.crypto = mockCrypto as any;
+
+// Define types for our mocked classes
+interface MockLanguageModelTextPart {
+	type: 'text';
+	value: string;
+}
+
+interface MockLanguageModelToolCallPart {
+	type: 'tool_call';
+	callId: string;
+	name: string;
+	input: any;
+}
+
+interface MockLanguageModelToolResultPart {
+	type: 'tool_result';
+	toolUseId: string;
+	parts: MockLanguageModelTextPart[];
+}
+
+type MockMessageContent = MockLanguageModelTextPart | MockLanguageModelToolCallPart | MockLanguageModelToolResultPart;
+
+interface MockLanguageModelChatMessage {
+	role: string;
+	name?: string;
+	content: MockMessageContent[];
+}
+
+// Mock vscode namespace
+jest.mock('vscode', () => {
+	const LanguageModelChatMessageRole = {
+		Assistant: 'assistant',
+		User: 'user'
+	};
+
+	class MockLanguageModelTextPart {
+		type = 'text';
+		constructor(public value: string) {}
+	}
+
+	class MockLanguageModelToolCallPart {
+		type = 'tool_call';
+		constructor(
+			public callId: string,
+			public name: string,
+			public input: any
+		) {}
+	}
+
+	class MockLanguageModelToolResultPart {
+		type = 'tool_result';
+		constructor(
+			public toolUseId: string,
+			public parts: MockLanguageModelTextPart[]
+		) {}
+	}
+
+	return {
+		LanguageModelChatMessage: {
+			Assistant: jest.fn((content) => ({
+				role: LanguageModelChatMessageRole.Assistant,
+				name: 'assistant',
+				content: Array.isArray(content) ? content : [new MockLanguageModelTextPart(content)]
+			})),
+			User: jest.fn((content) => ({
+				role: LanguageModelChatMessageRole.User,
+				name: 'user',
+				content: Array.isArray(content) ? content : [new MockLanguageModelTextPart(content)]
+			}))
+		},
+		LanguageModelChatMessageRole,
+		LanguageModelTextPart: MockLanguageModelTextPart,
+		LanguageModelToolCallPart: MockLanguageModelToolCallPart,
+		LanguageModelToolResultPart: MockLanguageModelToolResultPart
+	};
+});
+
+describe('vscode-lm-format', () => {
+	describe('convertToVsCodeLmMessages', () => {
+		it('should convert simple string messages', () => {
+			const messages: Anthropic.Messages.MessageParam[] = [
+				{ role: 'user', content: 'Hello' },
+				{ role: 'assistant', content: 'Hi there' }
+			];
+
+			const result = convertToVsCodeLmMessages(messages);
+			
+			expect(result).toHaveLength(2);
+			expect(result[0].role).toBe('user');
+			expect((result[0].content[0] as MockLanguageModelTextPart).value).toBe('Hello');
+			expect(result[1].role).toBe('assistant');
+			expect((result[1].content[0] as MockLanguageModelTextPart).value).toBe('Hi there');
+		});
+
+		it('should handle complex user messages with tool results', () => {
+			const messages: Anthropic.Messages.MessageParam[] = [{
+				role: 'user',
+				content: [
+					{ type: 'text', text: 'Here is the result:' },
+					{
+						type: 'tool_result',
+						tool_use_id: 'tool-1',
+						content: 'Tool output'
+					}
+				]
+			}];
+
+			const result = convertToVsCodeLmMessages(messages);
+			
+			expect(result).toHaveLength(1);
+			expect(result[0].role).toBe('user');
+			expect(result[0].content).toHaveLength(2);
+			const [toolResult, textContent] = result[0].content as [MockLanguageModelToolResultPart, MockLanguageModelTextPart];
+			expect(toolResult.type).toBe('tool_result');
+			expect(textContent.type).toBe('text');
+		});
+
+		it('should handle complex assistant messages with tool calls', () => {
+			const messages: Anthropic.Messages.MessageParam[] = [{
+				role: 'assistant',
+				content: [
+					{ type: 'text', text: 'Let me help you with that.' },
+					{
+						type: 'tool_use',
+						id: 'tool-1',
+						name: 'calculator',
+						input: { operation: 'add', numbers: [2, 2] }
+					}
+				]
+			}];
+
+			const result = convertToVsCodeLmMessages(messages);
+			
+			expect(result).toHaveLength(1);
+			expect(result[0].role).toBe('assistant');
+			expect(result[0].content).toHaveLength(2);
+			const [toolCall, textContent] = result[0].content as [MockLanguageModelToolCallPart, MockLanguageModelTextPart];
+			expect(toolCall.type).toBe('tool_call');
+			expect(textContent.type).toBe('text');
+		});
+
+		it('should handle image blocks with appropriate placeholders', () => {
+			const messages: Anthropic.Messages.MessageParam[] = [{
+				role: 'user',
+				content: [
+					{ type: 'text', text: 'Look at this:' },
+					{
+						type: 'image',
+						source: {
+							type: 'base64',
+							media_type: 'image/png',
+							data: 'base64data'
+						}
+					}
+				]
+			}];
+
+			const result = convertToVsCodeLmMessages(messages);
+			
+			expect(result).toHaveLength(1);
+			const imagePlaceholder = result[0].content[1] as MockLanguageModelTextPart;
+			expect(imagePlaceholder.value).toContain('[Image (base64): image/png not supported by VSCode LM API]');
+		});
+	});
+
+	describe('convertToAnthropicRole', () => {
+		it('should convert assistant role correctly', () => {
+			const result = convertToAnthropicRole('assistant' as any);
+			expect(result).toBe('assistant');
+		});
+
+		it('should convert user role correctly', () => {
+			const result = convertToAnthropicRole('user' as any);
+			expect(result).toBe('user');
+		});
+
+		it('should return null for unknown roles', () => {
+			const result = convertToAnthropicRole('unknown' as any);
+			expect(result).toBeNull();
+		});
+	});
+
+	describe('convertToAnthropicMessage', () => {
+		it('should convert assistant message with text content', async () => {
+			const vsCodeMessage = {
+				role: 'assistant',
+				name: 'assistant',
+				content: [new vscode.LanguageModelTextPart('Hello')]
+			};
+
+			const result = await convertToAnthropicMessage(vsCodeMessage as any);
+			
+			expect(result.role).toBe('assistant');
+			expect(result.content).toHaveLength(1);
+			expect(result.content[0]).toEqual({
+				type: 'text',
+				text: 'Hello'
+			});
+			expect(result.id).toBe('test-uuid');
+		});
+
+		it('should convert assistant message with tool calls', async () => {
+			const vsCodeMessage = {
+				role: 'assistant',
+				name: 'assistant',
+				content: [new vscode.LanguageModelToolCallPart(
+					'call-1',
+					'calculator',
+					{ operation: 'add', numbers: [2, 2] }
+				)]
+			};
+
+			const result = await convertToAnthropicMessage(vsCodeMessage as any);
+			
+			expect(result.content).toHaveLength(1);
+			expect(result.content[0]).toEqual({
+				type: 'tool_use',
+				id: 'call-1',
+				name: 'calculator',
+				input: { operation: 'add', numbers: [2, 2] }
+			});
+			expect(result.id).toBe('test-uuid');
+		});
+
+		it('should throw error for non-assistant messages', async () => {
+			const vsCodeMessage = {
+				role: 'user',
+				name: 'user',
+				content: [new vscode.LanguageModelTextPart('Hello')]
+			};
+
+			await expect(convertToAnthropicMessage(vsCodeMessage as any))
+				.rejects
+				.toThrow('Cline <Language Model API>: Only assistant messages are supported.');
+		});
+	});
+});

src/api/transform/vscode-lm-format.ts

@@ -0,0 +1,209 @@
+import { Anthropic } from "@anthropic-ai/sdk";
+import * as vscode from 'vscode';
+
+/**
+ * Safely converts a value into a plain object.
+ */
+function asObjectSafe(value: any): object {
+	// Handle null/undefined
+	if (!value) {
+		return {};
+	}
+
+	try {
+		// Handle strings that might be JSON
+		if (typeof value === 'string') {
+			return JSON.parse(value);
+		}
+
+		// Handle pre-existing objects
+		if (typeof value === 'object') {
+			return Object.assign({}, value);
+		}
+
+		return {};
+	}
+	catch (error) {
+		console.warn('Cline <Language Model API>: Failed to parse object:', error);
+		return {};
+	}
+}
+
+export function convertToVsCodeLmMessages(anthropicMessages: Anthropic.Messages.MessageParam[]): vscode.LanguageModelChatMessage[] {
+	const vsCodeLmMessages: vscode.LanguageModelChatMessage[] = [];
+
+	for (const anthropicMessage of anthropicMessages) {
+		// Handle simple string messages
+		if (typeof anthropicMessage.content === "string") {
+			vsCodeLmMessages.push(
+				anthropicMessage.role === "assistant"
+					? vscode.LanguageModelChatMessage.Assistant(anthropicMessage.content)
+					: vscode.LanguageModelChatMessage.User(anthropicMessage.content)
+			);
+			continue;
+		}
+
+		// Handle complex message structures
+		switch (anthropicMessage.role) {
+			case "user": {
+				const { nonToolMessages, toolMessages } = anthropicMessage.content.reduce<{
+					nonToolMessages: (Anthropic.TextBlockParam | Anthropic.ImageBlockParam)[];
+					toolMessages: Anthropic.ToolResultBlockParam[];
+				}>(
+					(acc, part) => {
+						if (part.type === "tool_result") {
+							acc.toolMessages.push(part);
+						}
+						else if (part.type === "text" || part.type === "image") {
+							acc.nonToolMessages.push(part);
+						}
+						return acc;
+					},
+					{ nonToolMessages: [], toolMessages: [] },
+				);
+
+				// Process tool messages first then non-tool messages
+				const contentParts = [
+					// Convert tool messages to ToolResultParts
+					...toolMessages.map((toolMessage) => {
+						// Process tool result content into TextParts
+						const toolContentParts: vscode.LanguageModelTextPart[] = (
+							typeof toolMessage.content === "string"
+								? [new vscode.LanguageModelTextPart(toolMessage.content)]
+								: (
+									toolMessage.content?.map((part) => {
+										if (part.type === "image") {
+											return new vscode.LanguageModelTextPart(
+												`[Image (${part.source?.type || 'Unknown source-type'}): ${part.source?.media_type || 'unknown media-type'} not supported by VSCode LM API]`
+											);
+										}
+										return new vscode.LanguageModelTextPart(part.text);
+									})
+									?? [new vscode.LanguageModelTextPart("")]
+								)
+						);
+
+						return new vscode.LanguageModelToolResultPart(
+							toolMessage.tool_use_id,
+							toolContentParts
+						);
+					}),
+
+					// Convert non-tool messages to TextParts after tool messages
+					...nonToolMessages.map((part) => {
+						if (part.type === "image") {
+							return new vscode.LanguageModelTextPart(
+								`[Image (${part.source?.type || 'Unknown source-type'}): ${part.source?.media_type || 'unknown media-type'} not supported by VSCode LM API]`
+							);
+						}
+						return new vscode.LanguageModelTextPart(part.text);
+					})
+				];
+
+				// Add single user message with all content parts
+				vsCodeLmMessages.push(vscode.LanguageModelChatMessage.User(contentParts));
+				break;
+			}
+
+			case "assistant": {
+				const { nonToolMessages, toolMessages } = anthropicMessage.content.reduce<{
+					nonToolMessages: (Anthropic.TextBlockParam | Anthropic.ImageBlockParam)[];
+					toolMessages: Anthropic.ToolUseBlockParam[];
+				}>(
+					(acc, part) => {
+						if (part.type === "tool_use") {
+							acc.toolMessages.push(part);
+						}
+						else if (part.type === "text" || part.type === "image") {
+							acc.nonToolMessages.push(part);
+						}
+						return acc;
+					},
+					{ nonToolMessages: [], toolMessages: [] },
+				);
+
+				// Process tool messages first then non-tool messages 
+				const contentParts = [
+					// Convert tool messages to ToolCallParts first
+					...toolMessages.map((toolMessage) =>
+						new vscode.LanguageModelToolCallPart(
+							toolMessage.id,
+							toolMessage.name,
+							asObjectSafe(toolMessage.input)
+						)
+					),
+
+					// Convert non-tool messages to TextParts after tool messages
+					...nonToolMessages.map((part) => {
+						if (part.type === "image") {
+							return new vscode.LanguageModelTextPart("[Image generation not supported by VSCode LM API]");
+						}
+						return new vscode.LanguageModelTextPart(part.text);
+					})
+				];
+
+				// Add the assistant message to the list of messages
+				vsCodeLmMessages.push(vscode.LanguageModelChatMessage.Assistant(contentParts));
+				break;
+			}
+		}
+	}
+
+	return vsCodeLmMessages;
+}
+
+export function convertToAnthropicRole(vsCodeLmMessageRole: vscode.LanguageModelChatMessageRole): string | null {
+	switch (vsCodeLmMessageRole) {
+		case vscode.LanguageModelChatMessageRole.Assistant:
+			return "assistant";
+		case vscode.LanguageModelChatMessageRole.User:
+			return "user";
+		default:
+			return null;
+	}
+}
+
+export async function convertToAnthropicMessage(vsCodeLmMessage: vscode.LanguageModelChatMessage): Promise<Anthropic.Messages.Message> {
+	const anthropicRole: string | null = convertToAnthropicRole(vsCodeLmMessage.role);
+	if (anthropicRole !== "assistant") {
+		throw new Error("Cline <Language Model API>: Only assistant messages are supported.");
+	}
+
+	return {
+		id: crypto.randomUUID(),
+		type: "message",
+		model: "vscode-lm",
+		role: anthropicRole,
+		content: (
+			vsCodeLmMessage.content
+				.map((part): Anthropic.ContentBlock | null => {
+					if (part instanceof vscode.LanguageModelTextPart) {
+						return {
+							type: "text",
+							text: part.value
+						};
+					}
+
+					if (part instanceof vscode.LanguageModelToolCallPart) {
+						return {
+							type: "tool_use",
+							id: part.callId || crypto.randomUUID(),
+							name: part.name,
+							input: asObjectSafe(part.input)
+						};
+					}
+
+					return null;
+				})
+				.filter(
+					(part): part is Anthropic.ContentBlock => part !== null
+				)
+		),
+		stop_reason: null,
+		stop_sequence: null,
+		usage: {
+			input_tokens: 0,
+			output_tokens: 0,
+		}
+	};
+}

src/core/webview/ClineProvider.ts

@@ -93,6 +93,7 @@ type GlobalStateKey =
 	| "requestDelaySeconds"
 	| "currentApiConfigName"
 	| "listApiConfigMeta"
+	| "vsCodeLmModelSelector"
 	| "mode"
 	| "modeApiConfigs"
 	| "customPrompts"
@@ -571,8 +572,12 @@ export class ClineProvider implements vscode.WebviewViewProvider {
 						const lmStudioModels = await this.getLmStudioModels(message.text)
 						this.postMessageToWebview({ type: "lmStudioModels", lmStudioModels })
 						break
+					case "requestVsCodeLmModels":
+						const vsCodeLmModels = await this.getVsCodeLmModels()
+						this.postMessageToWebview({ type: "vsCodeLmModels", vsCodeLmModels })
+						break
 					case "refreshGlamaModels":
-							await this.refreshGlamaModels()
+						await this.refreshGlamaModels()
 						break
 					case "refreshOpenRouterModels":
 						await this.refreshOpenRouterModels()
@@ -1114,6 +1119,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
 			openRouterModelId,
 			openRouterModelInfo,
 			openRouterUseMiddleOutTransform,
+			vsCodeLmModelSelector,
 		} = apiConfiguration
 		await this.updateGlobalState("apiProvider", apiProvider)
 		await this.updateGlobalState("apiModelId", apiModelId)
@@ -1145,6 +1151,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
 		await this.updateGlobalState("openRouterModelId", openRouterModelId)
 		await this.updateGlobalState("openRouterModelInfo", openRouterModelInfo)
 		await this.updateGlobalState("openRouterUseMiddleOutTransform", openRouterUseMiddleOutTransform)
+		await this.updateGlobalState("vsCodeLmModelSelector", vsCodeLmModelSelector)
 		if (this.cline) {
 			this.cline.api = buildApiHandler(apiConfiguration)
 		} 
@@ -1215,6 +1222,17 @@ export class ClineProvider implements vscode.WebviewViewProvider {
 		}
 	}
 
+	// VSCode LM API
+	private async getVsCodeLmModels() {
+		try {
+			const models = await vscode.lm.selectChatModels({});
+			return models || [];
+		} catch (error) {
+			console.error('Error fetching VS Code LM models:', error);
+			return [];
+		}
+	}
+
 	// OpenAi
 
 	async getOpenAiModels(baseUrl?: string, apiKey?: string) {
@@ -1774,6 +1792,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
 			requestDelaySeconds,
 			currentApiConfigName,
 			listApiConfigMeta,
+			vsCodeLmModelSelector,
 			mode,
 			modeApiConfigs,
 			customPrompts,
@@ -1832,6 +1851,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
 			this.getGlobalState("requestDelaySeconds") as Promise<number | undefined>,
 			this.getGlobalState("currentApiConfigName") as Promise<string | undefined>,
 			this.getGlobalState("listApiConfigMeta") as Promise<ApiConfigMeta[] | undefined>,
+			this.getGlobalState("vsCodeLmModelSelector") as Promise<vscode.LanguageModelChatSelector | undefined>,
 			this.getGlobalState("mode") as Promise<Mode | undefined>,
 			this.getGlobalState("modeApiConfigs") as Promise<Record<Mode, string> | undefined>,
 			this.getGlobalState("customPrompts") as Promise<CustomPrompts | undefined>,
@@ -1884,6 +1904,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
 				openRouterModelId,
 				openRouterModelInfo,
 				openRouterUseMiddleOutTransform,
+				vsCodeLmModelSelector,
 			},
 			lastShownAnnouncementId,
 			customInstructions,

src/shared/ExtensionMessage.ts

@@ -25,6 +25,9 @@ export interface ExtensionMessage {
 		| "enhancedPrompt"
 		| "commitSearchResults"
 		| "listApiConfig"
+		| "vsCodeLmModels"
+		| "vsCodeLmApiAvailable"
+		| "requestVsCodeLmModels"
 		| "updatePrompt"
 		| "systemPrompt"
 	text?: string
@@ -40,6 +43,7 @@ export interface ExtensionMessage {
 	images?: string[]
 	ollamaModels?: string[]
 	lmStudioModels?: string[]
+	vsCodeLmModels?: { vendor?: string; family?: string; version?: string; id?: string }[]
 	filePaths?: string[]
 	partialMessage?: ClineMessage
 	glamaModels?: Record<string, ModelInfo>

src/shared/WebviewMessage.ts

@@ -61,9 +61,11 @@ export interface WebviewMessage {
 		| "terminalOutputLineLimit"
 		| "mcpEnabled"
 		| "searchCommits"
+		| "refreshGlamaModels"
 		| "alwaysApproveResubmit"
 		| "requestDelaySeconds"
 		| "setApiConfigPassword"
+		| "requestVsCodeLmModels"
 		| "mode"
 		| "updatePrompt"
 		| "updateEnhancedPrompt"

src/shared/__tests__/checkExistApiConfig.test.ts

@@ -0,0 +1,56 @@
+import { checkExistKey } from '../checkExistApiConfig';
+import { ApiConfiguration } from '../api';
+
+describe('checkExistKey', () => {
+  it('should return false for undefined config', () => {
+    expect(checkExistKey(undefined)).toBe(false);
+  });
+
+  it('should return false for empty config', () => {
+    const config: ApiConfiguration = {};
+    expect(checkExistKey(config)).toBe(false);
+  });
+
+  it('should return true when one key is defined', () => {
+    const config: ApiConfiguration = {
+      apiKey: 'test-key'
+    };
+    expect(checkExistKey(config)).toBe(true);
+  });
+
+  it('should return true when multiple keys are defined', () => {
+    const config: ApiConfiguration = {
+      apiKey: 'test-key',
+      glamaApiKey: 'glama-key',
+      openRouterApiKey: 'openrouter-key'
+    };
+    expect(checkExistKey(config)).toBe(true);
+  });
+
+  it('should return true when only non-key fields are undefined', () => {
+    const config: ApiConfiguration = {
+      apiKey: 'test-key',
+      apiProvider: undefined,
+      anthropicBaseUrl: undefined
+    };
+    expect(checkExistKey(config)).toBe(true);
+  });
+
+  it('should return false when all key fields are undefined', () => {
+    const config: ApiConfiguration = {
+      apiKey: undefined,
+      glamaApiKey: undefined,
+      openRouterApiKey: undefined,
+      awsRegion: undefined,
+      vertexProjectId: undefined,
+      openAiApiKey: undefined,
+      ollamaModelId: undefined,
+      lmStudioModelId: undefined,
+      geminiApiKey: undefined,
+      openAiNativeApiKey: undefined,
+      deepSeekApiKey: undefined,
+      vsCodeLmModelSelector: undefined
+    };
+    expect(checkExistKey(config)).toBe(false);
+  });
+});

src/shared/__tests__/vsCodeSelectorUtils.test.ts

@@ -0,0 +1,44 @@
+import { stringifyVsCodeLmModelSelector, SELECTOR_SEPARATOR } from '../vsCodeSelectorUtils';
+import { LanguageModelChatSelector } from 'vscode';
+
+describe('vsCodeSelectorUtils', () => {
+	describe('stringifyVsCodeLmModelSelector', () => {
+		it('should join all defined selector properties with separator', () => {
+			const selector: LanguageModelChatSelector = {
+				vendor: 'test-vendor',
+				family: 'test-family',
+				version: 'v1',
+				id: 'test-id'
+			};
+
+			const result = stringifyVsCodeLmModelSelector(selector);
+			expect(result).toBe('test-vendor/test-family/v1/test-id');
+		});
+
+		it('should skip undefined properties', () => {
+			const selector: LanguageModelChatSelector = {
+				vendor: 'test-vendor',
+				family: 'test-family'
+			};
+
+			const result = stringifyVsCodeLmModelSelector(selector);
+			expect(result).toBe('test-vendor/test-family');
+		});
+
+		it('should handle empty selector', () => {
+			const selector: LanguageModelChatSelector = {};
+
+			const result = stringifyVsCodeLmModelSelector(selector);
+			expect(result).toBe('');
+		});
+
+		it('should handle selector with only one property', () => {
+			const selector: LanguageModelChatSelector = {
+				vendor: 'test-vendor'
+			};
+
+			const result = stringifyVsCodeLmModelSelector(selector);
+			expect(result).toBe('test-vendor');
+		});
+	});
+});

src/shared/api.ts

@@ -1,3 +1,5 @@
+import * as vscode from 'vscode';
+
 export type ApiProvider =
 	| "anthropic"
 	| "glama"
@@ -10,11 +12,13 @@ export type ApiProvider =
 	| "gemini"
 	| "openai-native"
 	| "deepseek"
+	| "vscode-lm"
 
 export interface ApiHandlerOptions {
 	apiModelId?: string
 	apiKey?: string // anthropic
 	anthropicBaseUrl?: string
+	vsCodeLmModelSelector?: vscode.LanguageModelChatSelector
 	glamaModelId?: string
 	glamaModelInfo?: ModelInfo
 	glamaApiKey?: string
@@ -58,7 +62,7 @@ export type ApiConfiguration = ApiHandlerOptions & {
 
 export interface ModelInfo {
 	maxTokens?: number
-	contextWindow?: number
+	contextWindow: number
 	supportsImages?: boolean
 	supportsComputerUse?: boolean
 	supportsPromptCache: boolean // this value is hardcoded for now

src/shared/checkExistApiConfig.ts

@@ -13,7 +13,8 @@ export function checkExistKey(config: ApiConfiguration | undefined) {
 			config.lmStudioModelId,
 			config.geminiApiKey,
 			config.openAiNativeApiKey,
-			config.deepSeekApiKey
+			config.deepSeekApiKey,
+			config.vsCodeLmModelSelector,
 		].some((key) => key !== undefined)
 		: false;
 }

src/shared/vsCodeSelectorUtils.ts

@@ -0,0 +1,14 @@
+import { LanguageModelChatSelector } from 'vscode';
+
+export const SELECTOR_SEPARATOR = '/';
+
+export function stringifyVsCodeLmModelSelector(selector: LanguageModelChatSelector): string {
+	return [
+		selector.vendor,
+		selector.family,
+		selector.version,
+		selector.id
+	]
+		.filter(Boolean)
+		.join(SELECTOR_SEPARATOR);
+}

webview-ui/src/components/settings/ApiOptions.tsx

@@ -34,6 +34,7 @@ import {
 import { ExtensionMessage } from "../../../../src/shared/ExtensionMessage"
 import { useExtensionState } from "../../context/ExtensionStateContext"
 import { vscode } from "../../utils/vscode"
+import * as vscodemodels from "vscode"
 import VSCodeButtonLink from "../common/VSCodeButtonLink"
 import OpenRouterModelPicker, {
 	ModelDescriptionMarkdown,
@@ -51,6 +52,7 @@ const ApiOptions = ({ apiErrorMessage, modelIdErrorMessage }: ApiOptionsProps) =
 	const { apiConfiguration, setApiConfiguration, uriScheme, onUpdateApiConfig } = useExtensionState()
 	const [ollamaModels, setOllamaModels] = useState<string[]>([])
 	const [lmStudioModels, setLmStudioModels] = useState<string[]>([])
+	const [vsCodeLmModels, setVsCodeLmModels] = useState<vscodemodels.LanguageModelChatSelector[]>([])
 	const [anthropicBaseUrlSelected, setAnthropicBaseUrlSelected] = useState(!!apiConfiguration?.anthropicBaseUrl)
 	const [azureApiVersionSelected, setAzureApiVersionSelected] = useState(!!apiConfiguration?.azureApiVersion)
 	const [isDescriptionExpanded, setIsDescriptionExpanded] = useState(false)
@@ -71,21 +73,24 @@ const ApiOptions = ({ apiErrorMessage, modelIdErrorMessage }: ApiOptionsProps) =
 			vscode.postMessage({ type: "requestOllamaModels", text: apiConfiguration?.ollamaBaseUrl })
 		} else if (selectedProvider === "lmstudio") {
 			vscode.postMessage({ type: "requestLmStudioModels", text: apiConfiguration?.lmStudioBaseUrl })
+		} else if (selectedProvider === "vscode-lm") {
+			vscode.postMessage({ type: "requestVsCodeLmModels" })
 		}
 	}, [selectedProvider, apiConfiguration?.ollamaBaseUrl, apiConfiguration?.lmStudioBaseUrl])
 	useEffect(() => {
-		if (selectedProvider === "ollama" || selectedProvider === "lmstudio") {
+		if (selectedProvider === "ollama" || selectedProvider === "lmstudio" || selectedProvider === "vscode-lm") {
 			requestLocalModels()
 		}
 	}, [selectedProvider, requestLocalModels])
-	useInterval(requestLocalModels, selectedProvider === "ollama" || selectedProvider === "lmstudio" ? 2000 : null)
+	useInterval(requestLocalModels, selectedProvider === "ollama" || selectedProvider === "lmstudio" || selectedProvider === "vscode-lm" ? 2000 : null)
-
 	const handleMessage = useCallback((event: MessageEvent) => {
 		const message: ExtensionMessage = event.data
 		if (message.type === "ollamaModels" && message.ollamaModels) {
 			setOllamaModels(message.ollamaModels)
 		} else if (message.type === "lmStudioModels" && message.lmStudioModels) {
 			setLmStudioModels(message.lmStudioModels)
+		} else if (message.type === "vsCodeLmModels" && message.vsCodeLmModels) {
+			setVsCodeLmModels(message.vsCodeLmModels)
 		}
 	}, [])
 	useEvent("message", handleMessage)
@@ -142,6 +147,7 @@ const ApiOptions = ({ apiErrorMessage, modelIdErrorMessage }: ApiOptionsProps) =
 					<VSCodeOption value="vertex">GCP Vertex AI</VSCodeOption>
 					<VSCodeOption value="bedrock">AWS Bedrock</VSCodeOption>
 					<VSCodeOption value="glama">Glama</VSCodeOption>
+					<VSCodeOption value="vscode-lm">VS Code LM API</VSCodeOption>
 					<VSCodeOption value="lmstudio">LM Studio</VSCodeOption>
 					<VSCodeOption value="ollama">Ollama</VSCodeOption>
 				</VSCodeDropdown>
@@ -295,7 +301,7 @@ const ApiOptions = ({ apiErrorMessage, modelIdErrorMessage }: ApiOptionsProps) =
 						}}>
 						Compress prompts and message chains to the context size (<a href="https://openrouter.ai/docs/transforms">OpenRouter Transforms</a>)
 					</VSCodeCheckbox>
-					<br/>
+					<br />
 				</div>
 			)}
 
@@ -620,6 +626,60 @@ const ApiOptions = ({ apiErrorMessage, modelIdErrorMessage }: ApiOptionsProps) =
 				</div>
 			)}
 
+			{selectedProvider === "vscode-lm" && (
+				<div>
+					<div className="dropdown-container">
+						<label htmlFor="vscode-lm-model">
+							<span style={{ fontWeight: 500 }}>Language Model</span>
+						</label>
+						{vsCodeLmModels.length > 0 ? (
+							<VSCodeDropdown
+								id="vscode-lm-model"
+								value={apiConfiguration?.vsCodeLmModelSelector ?
+									`${apiConfiguration.vsCodeLmModelSelector.vendor ?? ""}/${apiConfiguration.vsCodeLmModelSelector.family ?? ""}` :
+									""}
+								onChange={(e) => {
+									const value = (e.target as HTMLInputElement).value;
+									const [vendor, family] = value.split('/');
+									setApiConfiguration({
+										...apiConfiguration,
+										vsCodeLmModelSelector: value ? { vendor, family } : undefined
+									});
+								}}
+								style={{ width: "100%" }}>
+								<VSCodeOption value="">Select a model...</VSCodeOption>
+								{vsCodeLmModels.map((model) => (
+									<VSCodeOption
+										key={`${model.vendor}/${model.family}`}
+										value={`${model.vendor}/${model.family}`}>
+										{model.vendor} - {model.family}
+									</VSCodeOption>
+								))}
+							</VSCodeDropdown>
+						) : (
+							<p style={{
+								fontSize: "12px",
+								marginTop: "5px",
+								color: "var(--vscode-descriptionForeground)",
+							}}>
+								The VS Code Language Model API allows you to run models provided by other VS Code extensions (including but not limited to GitHub Copilot).
+								The easiest way to get started is to install the Copilot and Copilot Chat extensions from the VS Code Marketplace.
+							</p>
+						)}
+
+						<p
+							style={{
+								fontSize: "12px",
+								marginTop: "5px",
+								color: "var(--vscode-errorForeground)",
+								fontWeight: 500,
+							}}>
+							Note: This is a very experimental integration and may not work as expected. Please report any issues to the Roo-Cline GitHub repository.
+						</p>
+					</div>
+				</div>
+			)}
+
 			{selectedProvider === "ollama" && (
 				<div>
 					<VSCodeTextField
@@ -939,6 +999,17 @@ export function normalizeApiConfiguration(apiConfiguration?: ApiConfiguration) {
 				selectedModelId: apiConfiguration?.lmStudioModelId || "",
 				selectedModelInfo: openAiModelInfoSaneDefaults,
 			}
+		case "vscode-lm":
+			return {
+				selectedProvider: provider,
+				selectedModelId: apiConfiguration?.vsCodeLmModelSelector ?
+					`${apiConfiguration.vsCodeLmModelSelector.vendor}/${apiConfiguration.vsCodeLmModelSelector.family}` :
+					"",
+				selectedModelInfo: {
+					...openAiModelInfoSaneDefaults,
+					supportsImages: false, // VSCode LM API currently doesn't support images
+				},
+			}
 		default:
 			return getProviderData(anthropicModels, anthropicDefaultModelId)
 	}

webview-ui/src/utils/validate.ts

@@ -57,6 +57,11 @@ export function validateApiConfiguration(apiConfiguration?: ApiConfiguration): s
 					return "You must provide a valid model ID."
 				}
 				break
+			case "vscode-lm":
+				if (!apiConfiguration.vsCodeLmModelSelector) {
+					return "You must provide a valid model selector."
+				}
+				break
 		}
 	}
 	return undefined