Merge main

Author: cte

CHANGELOG.md

@@ -1,5 +1,25 @@
 # Roo Code Changelog
 
+## [3.44.0] - 2026-01-26
+
+![3.44.0 Release - Worktrees](/releases/3.44.0-release.png)
+
+- Add worktree selector and creation UX (PR #10940 by @brunobergher, thanks Cline!)
+- Improve subtask visibility and navigation in history and chat views (PR #10864 by @brunobergher)
+- Add wildcard support for MCP alwaysAllow configuration (PR #10948 by @app/roomote)
+- Fix: Prevent nested condensing from including previously-condensed content (PR #10985 by @hannesrudolph)
+- Fix: VS Code LM token counting returns 0 outside requests, breaking context condensing (#10968 by @srulyt, PR #10983 by @daniel-lxs)
+- Fix: Record truncation event when condensation fails but truncation succeeds (PR #10984 by @hannesrudolph)
+- Replace hyphen encoding with fuzzy matching for MCP tool names (PR #10775 by @daniel-lxs)
+- Remove MCP SERVERS section from system prompt for cleaner prompts (PR #10895 by @daniel-lxs)
+- new_task tool creates checkpoint the same way write_to_file does (PR #10982 by @daniel-lxs)
+- Update Fireworks provider with new models (#10674 by @hannesrudolph, PR #10679 by @ThanhNguyxn)
+- Fix: Truncate AWS Bedrock toolUseId to 64 characters (PR #10902 by @daniel-lxs)
+- Fix: Restore opaque background to settings section headers (PR #10951 by @app/roomote)
+- Fix: Remove unsupported Fireworks model tool fields (PR #10937 by @app/roomote)
+- Update and improve zh-TW Traditional Chinese locale and docs (PR #10953 by @PeterDaveHello)
+- Chore: Remove POWER_STEERING experiment remnants (PR #10980 by @hannesrudolph)
+
 ## [3.43.0] - 2026-01-23
 
 ![3.43.0 Release - Intelligent Context Condensation](/releases/3.43.0-release.png)

packages/types/src/experiment.ts

@@ -7,7 +7,6 @@ import type { Keys, Equals, AssertEqual } from "./type-fu.js"
  */
 
 export const experimentIds = [
-	"powerSteering",
 	"preventFocusDisruption",
 	"imageGeneration",
 	"runSlashCommand",
@@ -24,7 +23,6 @@ export type ExperimentId = z.infer<typeof experimentIdsSchema>
  */
 
 export const experimentsSchema = z.object({
-	powerSteering: z.boolean().optional(),
 	preventFocusDisruption: z.boolean().optional(),
 	imageGeneration: z.boolean().optional(),
 	runSlashCommand: z.boolean().optional(),

releases/3.44.0-release.png

@@ -718,4 +718,206 @@ describe("LiteLLMHandler", () => {
 			})
 		})
 	})
+
+	describe("tool ID normalization", () => {
+		it("should truncate tool IDs longer than 64 characters", async () => {
+			const optionsWithBedrock: ApiHandlerOptions = {
+				...mockOptions,
+				litellmModelId: "bedrock/anthropic.claude-3-sonnet",
+			}
+			handler = new LiteLLMHandler(optionsWithBedrock)
+
+			vi.spyOn(handler as any, "fetchModel").mockResolvedValue({
+				id: "bedrock/anthropic.claude-3-sonnet",
+				info: { ...litellmDefaultModelInfo, maxTokens: 8192 },
+			})
+
+			// Create a tool ID longer than 64 characters
+			const longToolId = "toolu_" + "a".repeat(70) // 76 characters total
+
+			const systemPrompt = "You are a helpful assistant"
+			const messages: Anthropic.Messages.MessageParam[] = [
+				{ role: "user", content: "Hello" },
+				{
+					role: "assistant",
+					content: [
+						{ type: "text", text: "I'll help you with that." },
+						{ type: "tool_use", id: longToolId, name: "read_file", input: { path: "test.txt" } },
+					],
+				},
+				{
+					role: "user",
+					content: [{ type: "tool_result", tool_use_id: longToolId, content: "file contents" }],
+				},
+			]
+
+			const mockStream = {
+				async *[Symbol.asyncIterator]() {
+					yield {
+						choices: [{ delta: { content: "Response" } }],
+						usage: { prompt_tokens: 100, completion_tokens: 20 },
+					}
+				},
+			}
+
+			mockCreate.mockReturnValue({
+				withResponse: vi.fn().mockResolvedValue({ data: mockStream }),
+			})
+
+			const generator = handler.createMessage(systemPrompt, messages)
+			for await (const _chunk of generator) {
+				// Consume
+			}
+
+			// Verify that tool IDs are truncated to 64 characters or less
+			const createCall = mockCreate.mock.calls[0][0]
+			const assistantMessage = createCall.messages.find(
+				(msg: any) => msg.role === "assistant" && msg.tool_calls && msg.tool_calls.length > 0,
+			)
+			const toolMessage = createCall.messages.find((msg: any) => msg.role === "tool")
+
+			expect(assistantMessage).toBeDefined()
+			expect(assistantMessage.tool_calls[0].id.length).toBeLessThanOrEqual(64)
+
+			expect(toolMessage).toBeDefined()
+			expect(toolMessage.tool_call_id.length).toBeLessThanOrEqual(64)
+		})
+
+		it("should not modify tool IDs that are already within 64 characters", async () => {
+			const optionsWithBedrock: ApiHandlerOptions = {
+				...mockOptions,
+				litellmModelId: "bedrock/anthropic.claude-3-sonnet",
+			}
+			handler = new LiteLLMHandler(optionsWithBedrock)
+
+			vi.spyOn(handler as any, "fetchModel").mockResolvedValue({
+				id: "bedrock/anthropic.claude-3-sonnet",
+				info: { ...litellmDefaultModelInfo, maxTokens: 8192 },
+			})
+
+			// Create a tool ID within 64 characters
+			const shortToolId = "toolu_01ABC123" // Well under 64 characters
+
+			const systemPrompt = "You are a helpful assistant"
+			const messages: Anthropic.Messages.MessageParam[] = [
+				{ role: "user", content: "Hello" },
+				{
+					role: "assistant",
+					content: [
+						{ type: "text", text: "I'll help you with that." },
+						{ type: "tool_use", id: shortToolId, name: "read_file", input: { path: "test.txt" } },
+					],
+				},
+				{
+					role: "user",
+					content: [{ type: "tool_result", tool_use_id: shortToolId, content: "file contents" }],
+				},
+			]
+
+			const mockStream = {
+				async *[Symbol.asyncIterator]() {
+					yield {
+						choices: [{ delta: { content: "Response" } }],
+						usage: { prompt_tokens: 100, completion_tokens: 20 },
+					}
+				},
+			}
+
+			mockCreate.mockReturnValue({
+				withResponse: vi.fn().mockResolvedValue({ data: mockStream }),
+			})
+
+			const generator = handler.createMessage(systemPrompt, messages)
+			for await (const _chunk of generator) {
+				// Consume
+			}
+
+			// Verify that tool IDs are unchanged
+			const createCall = mockCreate.mock.calls[0][0]
+			const assistantMessage = createCall.messages.find(
+				(msg: any) => msg.role === "assistant" && msg.tool_calls && msg.tool_calls.length > 0,
+			)
+			const toolMessage = createCall.messages.find((msg: any) => msg.role === "tool")
+
+			expect(assistantMessage).toBeDefined()
+			expect(assistantMessage.tool_calls[0].id).toBe(shortToolId)
+
+			expect(toolMessage).toBeDefined()
+			expect(toolMessage.tool_call_id).toBe(shortToolId)
+		})
+
+		it("should maintain uniqueness with hash suffix when truncating", async () => {
+			const optionsWithBedrock: ApiHandlerOptions = {
+				...mockOptions,
+				litellmModelId: "bedrock/anthropic.claude-3-sonnet",
+			}
+			handler = new LiteLLMHandler(optionsWithBedrock)
+
+			vi.spyOn(handler as any, "fetchModel").mockResolvedValue({
+				id: "bedrock/anthropic.claude-3-sonnet",
+				info: { ...litellmDefaultModelInfo, maxTokens: 8192 },
+			})
+
+			// Create two tool IDs that differ only near the end
+			const longToolId1 = "toolu_" + "a".repeat(60) + "_suffix1"
+			const longToolId2 = "toolu_" + "a".repeat(60) + "_suffix2"
+
+			const systemPrompt = "You are a helpful assistant"
+			const messages: Anthropic.Messages.MessageParam[] = [
+				{ role: "user", content: "Hello" },
+				{
+					role: "assistant",
+					content: [
+						{ type: "text", text: "I'll help." },
+						{ type: "tool_use", id: longToolId1, name: "read_file", input: { path: "test1.txt" } },
+						{ type: "tool_use", id: longToolId2, name: "read_file", input: { path: "test2.txt" } },
+					],
+				},
+				{
+					role: "user",
+					content: [
+						{ type: "tool_result", tool_use_id: longToolId1, content: "file1 contents" },
+						{ type: "tool_result", tool_use_id: longToolId2, content: "file2 contents" },
+					],
+				},
+			]
+
+			const mockStream = {
+				async *[Symbol.asyncIterator]() {
+					yield {
+						choices: [{ delta: { content: "Response" } }],
+						usage: { prompt_tokens: 100, completion_tokens: 20 },
+					}
+				},
+			}
+
+			mockCreate.mockReturnValue({
+				withResponse: vi.fn().mockResolvedValue({ data: mockStream }),
+			})
+
+			const generator = handler.createMessage(systemPrompt, messages)
+			for await (const _chunk of generator) {
+				// Consume
+			}
+
+			// Verify that truncated tool IDs are unique (hash suffix ensures this)
+			const createCall = mockCreate.mock.calls[0][0]
+			const assistantMessage = createCall.messages.find(
+				(msg: any) => msg.role === "assistant" && msg.tool_calls && msg.tool_calls.length > 0,
+			)
+
+			expect(assistantMessage).toBeDefined()
+			expect(assistantMessage.tool_calls).toHaveLength(2)
+
+			const id1 = assistantMessage.tool_calls[0].id
+			const id2 = assistantMessage.tool_calls[1].id
+
+			// Both should be truncated to 64 characters
+			expect(id1.length).toBeLessThanOrEqual(64)
+			expect(id2.length).toBeLessThanOrEqual(64)
+
+			// They should be different (hash suffix ensures uniqueness)
+			expect(id1).not.toBe(id2)
+		})
+	})
 })

src/api/providers/__tests__/lite-llm.spec.ts

@@ -437,6 +437,66 @@ describe("VsCodeLmHandler", () => {
 		})
 	})
 
+	describe("countTokens", () => {
+		beforeEach(() => {
+			handler["client"] = mockLanguageModelChat
+		})
+
+		it("should count tokens when called outside of an active request", async () => {
+			// Ensure no active request cancellation token exists
+			handler["currentRequestCancellation"] = null
+
+			mockLanguageModelChat.countTokens.mockResolvedValueOnce(42)
+
+			const content: Anthropic.Messages.ContentBlockParam[] = [{ type: "text", text: "Hello world" }]
+			const result = await handler.countTokens(content)
+
+			expect(result).toBe(42)
+			expect(mockLanguageModelChat.countTokens).toHaveBeenCalledWith("Hello world", expect.any(Object))
+		})
+
+		it("should count tokens when called during an active request", async () => {
+			// Simulate an active request with a cancellation token
+			const mockCancellation = {
+				token: { isCancellationRequested: false, onCancellationRequested: vi.fn() },
+				cancel: vi.fn(),
+				dispose: vi.fn(),
+			}
+			handler["currentRequestCancellation"] = mockCancellation as any
+
+			mockLanguageModelChat.countTokens.mockResolvedValueOnce(50)
+
+			const content: Anthropic.Messages.ContentBlockParam[] = [{ type: "text", text: "Test content" }]
+			const result = await handler.countTokens(content)
+
+			expect(result).toBe(50)
+			expect(mockLanguageModelChat.countTokens).toHaveBeenCalledWith("Test content", mockCancellation.token)
+		})
+
+		it("should return 0 when no client is available", async () => {
+			handler["client"] = null
+			handler["currentRequestCancellation"] = null
+
+			const content: Anthropic.Messages.ContentBlockParam[] = [{ type: "text", text: "Hello" }]
+			const result = await handler.countTokens(content)
+
+			expect(result).toBe(0)
+		})
+
+		it("should handle image blocks with placeholder", async () => {
+			handler["currentRequestCancellation"] = null
+			mockLanguageModelChat.countTokens.mockResolvedValueOnce(5)
+
+			const content: Anthropic.Messages.ContentBlockParam[] = [
+				{ type: "image", source: { type: "base64", media_type: "image/png", data: "abc" } },
+			]
+			const result = await handler.countTokens(content)
+
+			expect(result).toBe(5)
+			expect(mockLanguageModelChat.countTokens).toHaveBeenCalledWith("[IMAGE]", expect.any(Object))
+		})
+	})
+
 	describe("completePrompt", () => {
 		it("should complete single prompt", async () => {
 			const mockModel = { ...mockLanguageModelChat }

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

@@ -9,6 +9,7 @@ import { ApiHandlerOptions } from "../../shared/api"
 
 import { ApiStream, ApiStreamUsageChunk } from "../transform/stream"
 import { convertToOpenAiMessages } from "../transform/openai-format"
+import { sanitizeOpenAiCallId } from "../../utils/tool-id"
 
 import type { SingleCompletionHandler, ApiHandlerCreateMessageMetadata } from "../index"
 import { RouterProvider } from "./router-provider"
@@ -115,7 +116,9 @@ export class LiteLLMHandler extends RouterProvider implements SingleCompletionHa
 	): ApiStream {
 		const { id: modelId, info } = await this.fetchModel()
 
-		const openAiMessages = convertToOpenAiMessages(messages)
+		const openAiMessages = convertToOpenAiMessages(messages, {
+			normalizeToolCallId: sanitizeOpenAiCallId,
+		})
 
 		// Prepare messages with cache control if enabled and supported
 		let systemMessage: OpenAI.Chat.ChatCompletionMessageParam

src/api/providers/lite-llm.ts

@@ -229,31 +229,37 @@ export class VsCodeLmHandler extends BaseProvider implements SingleCompletionHan
 			return 0
 		}
 
-		if (!this.currentRequestCancellation) {
-			console.warn("Roo Code <Language Model API>: No cancellation token available for token counting")
-			return 0
-		}
-
 		// Validate input
 		if (!text) {
 			console.debug("Roo Code <Language Model API>: Empty text provided for token counting")
 			return 0
 		}
 
+		// Create a temporary cancellation token if we don't have one (e.g., when called outside a request)
+		let cancellationToken: vscode.CancellationToken
+		let tempCancellation: vscode.CancellationTokenSource | null = null
+
+		if (this.currentRequestCancellation) {
+			cancellationToken = this.currentRequestCancellation.token
+		} else {
+			tempCancellation = new vscode.CancellationTokenSource()
+			cancellationToken = tempCancellation.token
+		}
+
 		try {
 			// Handle different input types
 			let tokenCount: number
 
 			if (typeof text === "string") {
-				tokenCount = await this.client.countTokens(text, this.currentRequestCancellation.token)
+				tokenCount = await this.client.countTokens(text, cancellationToken)
 			} 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("Roo Code <Language Model API>: Empty chat message content")
 					return 0
 				}
 				const countMessage = extractTextCountFromMessage(text)
-				tokenCount = await this.client.countTokens(countMessage, this.currentRequestCancellation.token)
+				tokenCount = await this.client.countTokens(countMessage, cancellationToken)
 			} else {
 				console.warn("Roo Code <Language Model API>: Invalid input type for token counting")
 				return 0
@@ -287,6 +293,11 @@ export class VsCodeLmHandler extends BaseProvider implements SingleCompletionHan
 			}
 
 			return 0 // Fallback to prevent stream interruption
+		} finally {
+			// Clean up temporary cancellation token
+			if (tempCancellation) {
+				tempCancellation.dispose()
+			}
 		}
 	}