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test(litellm): cover preserveReasoning inference and message conversion branching
Add unit coverage for LITELLM_PRESERVE_REASONING_PATTERN matching per model family, getLiteLLMModels setting preserveReasoning on matched models, and LiteLLMHandler.createMessage branching between convertToR1Format and convertToOpenAiMessages based on info.preserveReasoning. Signed-off-by: daewoongoh <[email protected]>
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‎packages/types/src/__tests__/lite-llm.test.ts‎

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import { LITELLM_PRESERVE_REASONING_PATTERN } from "../providers/lite-llm.js"
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describe("LITELLM_PRESERVE_REASONING_PATTERN", () => {
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it("matches known DeepSeek reasoning aliases", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("deepseek-v4-flash")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("deepseek-v4-pro")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("deepseek/deepseek-reasoner")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("deepseek-v4-mini")).toBe(false)
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})
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it("matches known MiMo reasoning aliases", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("mimo-v2.5")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("mimo-v2.5-pro")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("mimo-v2.6")).toBe(false)
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})
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it("matches known Kimi K2 reasoning aliases across routed providers", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("bedrock/moonshot.kimi-k2-thinking")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("fireworks_ai/accounts/fireworks/models/kimi-k2p7-code")).toBe(
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true,
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)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("kimi-k2.7-code")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("kimi-k2.6")).toBe(false)
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})
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it("matches MiniMax M2/M3 aliases but not other MiniMax generations", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("minimax-m2")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("minimax.m3")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("minimax-m2.5")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("minimax-m2-highspeed")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("minimax-m2-stable")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("minimax-m4")).toBe(false)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("minimax-m1")).toBe(false)
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})
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it("matches GLM-4.7 but excludes the flash variants", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-4.7")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-4.7-flash")).toBe(false)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-4.7-flashx")).toBe(false)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-4.8")).toBe(false)
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})
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it("matches GLM-5 variants but excludes the flash variant", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-5")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-5.1")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-5.2")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-5-turbo")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-5.1-turbo")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("glm-5-flash")).toBe(false)
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})
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it("matches curated Qwen3 plus/max aliases used by opencode-go", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("qwen3.7-plus")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("qwen3.6-max")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("qwen3.5-plus")).toBe(false)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("qwen3.7-mini")).toBe(false)
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})
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it("does not match unrelated model names", () => {
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("gpt-4")).toBe(false)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("claude-3-opus")).toBe(false)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("")).toBe(false)
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})
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it("matches when the fragment appears anywhere in a combined alias/routed-model string", () => {
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// Mirrors how litellm.ts calls it: `${modelName} ${litellmModelName}`
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("my-deepseek-alias deepseek/deepseek-reasoner")).toBe(true)
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expect(LITELLM_PRESERVE_REASONING_PATTERN.test("my-gpt4-alias openai/gpt-4")).toBe(false)
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})
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})

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

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})
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})
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describe("preserveReasoning message conversion", () => {
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const mockStream = {
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async *[Symbol.asyncIterator]() {
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yield {
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choices: [{ delta: { content: "ok" } }],
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usage: { prompt_tokens: 1, completion_tokens: 1 },
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}
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},
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}
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it("uses convertToR1Format (merging tool-result text) when the model info sets preserveReasoning", async () => {
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const optionsWithReasoning: ApiHandlerOptions = {
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...mockOptions,
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litellmModelId: "deepseek-reasoner-alias",
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}
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handler = new LiteLLMHandler(optionsWithReasoning)
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vi.spyOn(handler as any, "fetchModel").mockResolvedValue({
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id: "deepseek-reasoner-alias",
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info: { ...litellmDefaultModelInfo, preserveReasoning: true },
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})
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const systemPrompt = "You are a helpful assistant"
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const messages: Anthropic.Messages.MessageParam[] = [
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{ role: "user", content: "Hello" },
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{
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role: "assistant",
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content: [
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{ type: "text", text: "I'll help." },
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{ type: "tool_use", id: "toolu_123", name: "read_file", input: { path: "test.txt" } },
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],
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// DeepSeek-style interleaved thinking: must be echoed back in the next request.
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reasoning_content: "Let me check the file first.",
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} as Anthropic.Messages.MessageParam & { reasoning_content: string },
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{
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role: "user",
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content: [
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{ type: "tool_result", tool_use_id: "toolu_123", content: "file contents" },
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{ type: "text", text: "Thanks, continue." },
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],
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},
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]
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mockCreate.mockReturnValue({
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withResponse: vi.fn().mockResolvedValue({ data: mockStream }),
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})
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const generator = handler.createMessage(systemPrompt, messages)
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for await (const _chunk of generator) {
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// Consume
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}
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const createCall = mockCreate.mock.calls[0][0]
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// convertToR1Format with mergeToolResultText folds the trailing text into the
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// tool message instead of appending a separate user message.
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const toolMessage = createCall.messages.find((msg: any) => msg.role === "tool")
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expect(toolMessage).toBeDefined()
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expect(toolMessage.content).toBe("file contents\n\nThanks, continue.")
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const trailingUserMessage = createCall.messages.find(
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(msg: any) => msg.role === "user" && msg.content === "Thanks, continue.",
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)
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expect(trailingUserMessage).toBeUndefined()
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// The whole point of routing through convertToR1Format: reasoning_content
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// must survive on the assistant message so the model doesn't reject the
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// follow-up request for missing prior reasoning.
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const assistantMessage = createCall.messages.find(
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(msg: any) => msg.role === "assistant" && msg.tool_calls?.length > 0,
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)
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expect(assistantMessage).toBeDefined()
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expect(assistantMessage.reasoning_content).toBe("Let me check the file first.")
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})
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it("uses convertToOpenAiMessages (no merging) when the model info does not set preserveReasoning", async () => {
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vi.spyOn(handler as any, "fetchModel").mockResolvedValue({
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id: litellmDefaultModelId,
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info: { ...litellmDefaultModelInfo, preserveReasoning: undefined },
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})
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const systemPrompt = "You are a helpful assistant"
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// Task.buildCleanConversationHistory() (src/core/task/Task.ts) already strips the
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// reasoning content block before messages reach this handler when the model's
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// preserveReasoning is not true, so no reasoning_content/reasoning block is present
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// on the assistant message here — this input reflects what the handler actually
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// receives in that case.
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const messages: Anthropic.Messages.MessageParam[] = [
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{ role: "user", content: "Hello" },
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{
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role: "assistant",
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content: [
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{ type: "text", text: "I'll help." },
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{ type: "tool_use", id: "toolu_123", name: "read_file", input: { path: "test.txt" } },
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],
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},
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{
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role: "user",
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content: [
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{ type: "tool_result", tool_use_id: "toolu_123", content: "file contents" },
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{ type: "text", text: "Thanks, continue." },
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],
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},
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]
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mockCreate.mockReturnValue({
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withResponse: vi.fn().mockResolvedValue({ data: mockStream }),
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})
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const generator = handler.createMessage(systemPrompt, messages)
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for await (const _chunk of generator) {
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// Consume
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}
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const createCall = mockCreate.mock.calls[0][0]
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const toolMessage = createCall.messages.find((msg: any) => msg.role === "tool")
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expect(toolMessage).toBeDefined()
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expect(toolMessage.content).toBe("file contents")
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const trailingUserMessage = createCall.messages.find(
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(msg: any) =>
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msg.role === "user" &&
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(msg.content === "Thanks, continue." ||
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(Array.isArray(msg.content) &&
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msg.content.some((part: any) => part.text === "Thanks, continue."))),
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)
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expect(trailingUserMessage).toBeDefined()
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// No reasoning_content is sent to the API in this branch, matching what
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// buildCleanConversationHistory already stripped upstream.
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const assistantMessage = createCall.messages.find(
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(msg: any) => msg.role === "assistant" && msg.tool_calls?.length > 0,
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)
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expect(assistantMessage).toBeDefined()
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expect(assistantMessage.reasoning_content).toBeUndefined()
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})
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})
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describe("session ID header", () => {
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const mockStream = {
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async *[Symbol.asyncIterator]() {

‎src/api/providers/fetchers/__tests__/litellm.spec.ts‎

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description: "model-with-only-max-output-tokens via LiteLLM proxy",
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})
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})
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describe("preserveReasoning inference", () => {
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it("sets preserveReasoning: true when the routed model matches a known reasoning family", async () => {
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const mockResponse = {
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data: {
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data: [
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{
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model_name: "deepseek-reasoner-alias",
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model_info: {
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max_tokens: 8192,
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max_input_tokens: 128000,
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},
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litellm_params: {
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model: "deepseek/deepseek-reasoner",
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},
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},
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{
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model_name: "kimi-k2-thinking",
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model_info: {
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max_tokens: 8192,
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max_input_tokens: 128000,
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},
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litellm_params: {
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model: "bedrock/moonshot.kimi-k2-thinking",
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},
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},
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],
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},
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}
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mockedAxios.get.mockResolvedValue(mockResponse)
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const result = await getLiteLLMModels("test-api-key", "http://localhost:4000")
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expect(result["deepseek-reasoner-alias"]).toMatchObject({ preserveReasoning: true })
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expect(result["kimi-k2-thinking"]).toMatchObject({ preserveReasoning: true })
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})
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it("omits preserveReasoning when the routed model does not match a known reasoning family", async () => {
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const mockResponse = {
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data: {
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data: [
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{
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model_name: "gpt-4-turbo",
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model_info: {
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max_tokens: 8192,
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max_input_tokens: 128000,
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},
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litellm_params: {
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model: "openai/gpt-4-turbo",
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},
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},
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],
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},
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}
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mockedAxios.get.mockResolvedValue(mockResponse)
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const result = await getLiteLLMModels("test-api-key", "http://localhost:4000")
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expect(result["gpt-4-turbo"]).not.toHaveProperty("preserveReasoning")
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})
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it("matches against the model alias even when the routed model name does not match", async () => {
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const mockResponse = {
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data: {
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data: [
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{
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model_name: "glm-5.1-turbo",
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model_info: {
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max_tokens: 8192,
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max_input_tokens: 128000,
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},
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litellm_params: {
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model: "zai/some-custom-deployment",
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},
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},
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],
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},
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}
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mockedAxios.get.mockResolvedValue(mockResponse)
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const result = await getLiteLLMModels("test-api-key", "http://localhost:4000")
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expect(result["glm-5.1-turbo"]).toMatchObject({ preserveReasoning: true })
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})
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})
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})

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