fix: parse gpt-oss special token format in LM Studio responses

- Add detection for gpt-oss models in LmStudioHandler
- Implement parseGptOssFormat method to extract actual content from special token format
- Add comprehensive tests for the new parsing logic
- Fixes #6739

src/api/providers/__tests__/lmstudio.spec.ts

@@ -164,4 +164,133 @@ describe("LmStudioHandler", () => {
 			expect(modelInfo.info.contextWindow).toBe(128_000)
 		})
 	})
+
+	describe("gpt-oss special token parsing", () => {
+		it("should parse gpt-oss format with special tokens", async () => {
+			// Mock gpt-oss model response with special tokens
+			mockCreate.mockImplementationOnce(async (options) => {
+				return {
+					[Symbol.asyncIterator]: async function* () {
+						yield {
+							choices: [
+								{
+									delta: {
+										content:
+											'<|start|>assistant<|channel|>commentary to=read_file <|constrain|>json<|message|>{"args":[{"file":{"path":"documentation/program_analysis.md"}}]}',
+									},
+									index: 0,
+								},
+							],
+							usage: null,
+						}
+					},
+				}
+			})
+
+			// Create handler with gpt-oss model
+			const gptOssHandler = new LmStudioHandler({
+				apiModelId: "gpt-oss-20b",
+				lmStudioModelId: "gpt-oss-20b",
+				lmStudioBaseUrl: "http://localhost:1234",
+			})
+
+			const systemPrompt = "You are a helpful assistant."
+			const messages: Anthropic.Messages.MessageParam[] = [
+				{
+					role: "user",
+					content: "Read the file",
+				},
+			]
+
+			const stream = gptOssHandler.createMessage(systemPrompt, messages)
+			const chunks: any[] = []
+			for await (const chunk of stream) {
+				chunks.push(chunk)
+			}
+
+			const textChunks = chunks.filter((chunk) => chunk.type === "text")
+			expect(textChunks).toHaveLength(1)
+			// Should extract just the JSON message content
+			expect(textChunks[0].text).toBe('{"args":[{"file":{"path":"documentation/program_analysis.md"}}]}')
+		})
+
+		it("should handle gpt-oss format without message token", async () => {
+			mockCreate.mockImplementationOnce(async (options) => {
+				return {
+					[Symbol.asyncIterator]: async function* () {
+						yield {
+							choices: [
+								{
+									delta: {
+										content:
+											"<|start|>assistant<|channel|>commentary to=analyze_code <|constrain|>text",
+									},
+									index: 0,
+								},
+							],
+							usage: null,
+						}
+					},
+				}
+			})
+
+			const gptOssHandler = new LmStudioHandler({
+				apiModelId: "gpt-oss-20b",
+				lmStudioModelId: "gpt-oss-20b",
+				lmStudioBaseUrl: "http://localhost:1234",
+			})
+
+			const systemPrompt = "You are a helpful assistant."
+			const messages: Anthropic.Messages.MessageParam[] = [
+				{
+					role: "user",
+					content: "Analyze the code",
+				},
+			]
+
+			const stream = gptOssHandler.createMessage(systemPrompt, messages)
+			const chunks: any[] = []
+			for await (const chunk of stream) {
+				chunks.push(chunk)
+			}
+
+			const textChunks = chunks.filter((chunk) => chunk.type === "text")
+			expect(textChunks).toHaveLength(1)
+			// Should clean up special tokens and function patterns
+			expect(textChunks[0].text).toBe("assistant commentary text")
+		})
+
+		it("should not parse special tokens for non-gpt-oss models", async () => {
+			// Mock response with special-looking content
+			mockCreate.mockImplementationOnce(async (options) => {
+				return {
+					[Symbol.asyncIterator]: async function* () {
+						yield {
+							choices: [
+								{
+									delta: {
+										content:
+											"Here is some content with <|special|> tokens that should not be parsed",
+									},
+									index: 0,
+								},
+							],
+							usage: null,
+						}
+					},
+				}
+			})
+
+			const stream = handler.createMessage("System prompt", [{ role: "user", content: "Test" }])
+			const chunks: any[] = []
+			for await (const chunk of stream) {
+				chunks.push(chunk)
+			}
+
+			const textChunks = chunks.filter((chunk) => chunk.type === "text")
+			expect(textChunks).toHaveLength(1)
+			// Should keep the content as-is for non-gpt-oss models
+			expect(textChunks[0].text).toBe("Here is some content with <|special|> tokens that should not be parsed")
+		})
+	})
 })

src/api/providers/lm-studio.ts

@@ -100,9 +100,24 @@ export class LmStudioHandler extends BaseProvider implements SingleCompletionHan
 				const delta = chunk.choices[0]?.delta
 
 				if (delta?.content) {
-					assistantText += delta.content
-					for (const processedChunk of matcher.update(delta.content)) {
-						yield processedChunk
+					// Check if this is a gpt-oss model with special token format
+					const isGptOss = this.getModel().id?.toLowerCase().includes("gpt-oss")
+
+					if (isGptOss && delta.content.includes("<|") && delta.content.includes("|>")) {
+						// Parse gpt-oss special token format
+						// Format: <|start|>assistant<|channel|>commentary to=read_file <|constrain|>json<|message|>{"args":[...]}
+						const cleanedContent = this.parseGptOssFormat(delta.content)
+						if (cleanedContent) {
+							assistantText += cleanedContent
+							for (const processedChunk of matcher.update(cleanedContent)) {
+								yield processedChunk
+							}
+						}
+					} else {
+						assistantText += delta.content
+						for (const processedChunk of matcher.update(delta.content)) {
+							yield processedChunk
+						}
 					}
 				}
 			}
@@ -169,6 +184,31 @@ export class LmStudioHandler extends BaseProvider implements SingleCompletionHan
 			)
 		}
 	}
+
+	/**
+	 * Parse gpt-oss special token format
+	 * Format example: <|start|>assistant<|channel|>commentary to=read_file <|constrain|>json<|message|>{"args":[...]}
+	 * We want to extract just the actual message content
+	 */
+	private parseGptOssFormat(content: string): string {
+		// Remove all special tokens and extract the actual message
+		// Pattern: <|token|> where token can be any word
+		const specialTokenPattern = /<\|[^|]+\|>/g
+
+		// First, check if this contains the message token
+		const messageMatch = content.match(/<\|message\|>(.+)$/s)
+		if (messageMatch) {
+			// Extract content after <|message|> token
+			return messageMatch[1].trim()
+		}
+
+		// Otherwise, just remove all special tokens
+		const cleaned = content.replace(specialTokenPattern, " ").trim()
+
+		// Also clean up any "to=function_name" patterns that might remain
+		const functionPattern = /\s*to=\w+\s*/g
+		return cleaned.replace(functionPattern, " ").trim()
+	}
 }
 
 export async function getLmStudioModels(baseUrl = "http://localhost:1234") {