Add capability-probed structured outputs and a prototype/instance creation graph.
Probe API profiles for json_schema/tools, flatten worker artifacts onto a strict schema, and let users spawn or revise graph nodes instead of a one-shot DAG. Co-authored-by: Cursor <cursoragent@cursor.com>
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Cursor
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import { describe, expect, it } from "vitest";
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import {
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applyOpenAiGeneration,
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applyRejectedGenerationField,
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classifyChatModel,
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fetchWithGenerationCompat,
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parseRejectedGenerationField,
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sanitizeReasoningEffort,
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sanitizeVerbosity,
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} from "../src/llm/generation-compat.js";
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import { buildRequestBody } from "../src/llm/client.js";
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import type { LlmConfig } from "../src/config/env.js";
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const stGeneration = {
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temperature: 1,
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topP: 0.95,
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topK: 40,
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minP: 0.05,
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frequencyPenalty: 0,
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presencePenalty: 0,
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repetitionPenalty: 1.05,
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maxOutputTokens: 60000,
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seed: -1,
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reasoningEffort: "auto",
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verbosity: "auto",
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};
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describe("sanitizeReasoningEffort", () => {
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it("omits SillyTavern auto and empty values", () => {
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expect(sanitizeReasoningEffort("auto")).toBeUndefined();
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expect(sanitizeReasoningEffort("AUTO")).toBeUndefined();
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expect(sanitizeReasoningEffort("")).toBeUndefined();
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expect(sanitizeReasoningEffort(" ")).toBeUndefined();
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expect(sanitizeReasoningEffort(undefined)).toBeUndefined();
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});
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it("keeps values the provider enum accepts", () => {
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expect(sanitizeReasoningEffort("none")).toBe("none");
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expect(sanitizeReasoningEffort("minimal")).toBe("minimal");
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expect(sanitizeReasoningEffort("low")).toBe("low");
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expect(sanitizeReasoningEffort("medium")).toBe("medium");
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expect(sanitizeReasoningEffort("high")).toBe("high");
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expect(sanitizeReasoningEffort("xhigh")).toBe("xhigh");
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expect(sanitizeReasoningEffort("max")).toBe("max");
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});
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it("maps SillyTavern min to minimal and ignores unknown values", () => {
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expect(sanitizeReasoningEffort("min")).toBe("minimal");
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expect(sanitizeReasoningEffort("MIN")).toBe("minimal");
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expect(sanitizeReasoningEffort("unknown")).toBeUndefined();
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});
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});
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describe("sanitizeVerbosity", () => {
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it("omits auto and unknown values", () => {
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expect(sanitizeVerbosity("auto")).toBeUndefined();
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expect(sanitizeVerbosity("loud")).toBeUndefined();
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expect(sanitizeVerbosity("medium")).toBe("medium");
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});
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});
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describe("classifyChatModel", () => {
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it("treats o-series, gpt-5, kimi-k2.5 and deepseek-reasoner as no-sampling", () => {
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expect(classifyChatModel("o3-mini").omitSampling).toBe(true);
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expect(classifyChatModel("openai/o4-mini").omitSampling).toBe(true);
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expect(classifyChatModel("gpt-5").omitSampling).toBe(true);
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expect(classifyChatModel("deepseek-reasoner").omitSampling).toBe(true);
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expect(classifyChatModel("kimi-k2.5").omitSampling).toBe(true);
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});
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it("keeps sampling for ordinary chat models", () => {
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expect(classifyChatModel("gpt-4o").omitSampling).toBe(false);
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expect(classifyChatModel("deepseek-v4-pro").omitSampling).toBe(false);
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expect(classifyChatModel("gpt-5-chat-latest").omitSampling).toBe(false);
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});
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});
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describe("applyOpenAiGeneration", () => {
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it("does not send ST extras or auto sentinels on a chat model", () => {
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const body: Record<string, unknown> = { model: "deepseek-v4-pro" };
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applyOpenAiGeneration(body, stGeneration, "deepseek-v4-pro");
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expect(body.temperature).toBe(1);
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expect(body.top_p).toBe(0.95);
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expect(body.max_tokens).toBe(60000);
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expect(body.top_k).toBeUndefined();
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expect(body.min_p).toBeUndefined();
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expect(body.repetition_penalty).toBeUndefined();
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expect(body.reasoning_effort).toBeUndefined();
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expect(body.verbosity).toBeUndefined();
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expect(body.seed).toBeUndefined();
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});
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it("drops sampling params for reasoning models and uses max_completion_tokens", () => {
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const body: Record<string, unknown> = { model: "o3-mini" };
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applyOpenAiGeneration(body, stGeneration, "o3-mini");
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expect(body.temperature).toBeUndefined();
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expect(body.top_p).toBeUndefined();
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expect(body.frequency_penalty).toBeUndefined();
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expect(body.presence_penalty).toBeUndefined();
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expect(body.max_tokens).toBeUndefined();
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expect(body.max_completion_tokens).toBe(60000);
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});
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});
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describe("parseRejectedGenerationField", () => {
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it("reads OpenAI unsupported parameter errors", () => {
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expect(
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parseRejectedGenerationField(
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JSON.stringify({
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error: {
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message: "Unsupported parameter: 'temperature' is not supported with this model.",
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param: "temperature",
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},
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}),
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),
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).toBe("temperature");
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});
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it("reads strict JSON deserializer unknown variant errors", () => {
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expect(
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parseRejectedGenerationField(
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JSON.stringify({
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error: {
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message:
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"Failed to deserialize the JSON body into the target type: reasoning_effort: unknown variant `auto`, expected one of `none`, `minimal`",
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},
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}),
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),
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).toBe("reasoning_effort");
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});
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it("drops top_p when temperature and top_p are mutually exclusive", () => {
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expect(
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parseRejectedGenerationField(
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"temperature and top_p cannot both be specified for this model",
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),
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).toBe("top_p");
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});
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});
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describe("applyRejectedGenerationField", () => {
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it("renames max_completion_tokens to max_tokens once", () => {
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const body: Record<string, unknown> = { max_completion_tokens: 100 };
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const tried = new Set<string>();
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expect(applyRejectedGenerationField(body, "max_completion_tokens", tried)).toBe(
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true,
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);
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expect(body.max_tokens).toBe(100);
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expect(body.max_completion_tokens).toBeUndefined();
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expect(applyRejectedGenerationField(body, "max_tokens", tried)).toBe(true);
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expect(body.max_completion_tokens).toBeUndefined();
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expect(body.max_tokens).toBeUndefined();
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});
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});
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describe("fetchWithGenerationCompat", () => {
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it("retries a 400 after stripping the rejected field", async () => {
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const sent: Array<Record<string, unknown>> = [];
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const response = await fetchWithGenerationCompat(async (body) => {
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sent.push({ ...body });
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if ("temperature" in body) {
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return new Response(
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JSON.stringify({
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error: {
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message: "Unsupported parameter: 'temperature'",
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param: "temperature",
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},
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}),
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{ status: 400 },
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);
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}
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return new Response(JSON.stringify({ ok: true }), { status: 200 });
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}, { model: "o3", temperature: 1, messages: [] });
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expect(response.ok).toBe(true);
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expect(sent).toHaveLength(2);
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expect(sent[0].temperature).toBe(1);
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expect(sent[1].temperature).toBeUndefined();
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});
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});
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describe("buildRequestBody", () => {
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const messages = [{ role: "user" as const, content: "hi" }];
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it("does not send reasoning_effort when the preset says auto", () => {
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const config: LlmConfig = {
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baseUrl: "https://example.test/v1",
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apiKey: "k",
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model: "deepseek-v4-pro",
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};
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const body = buildRequestBody(config, messages, {
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generation: { reasoningEffort: "auto", temperature: 0.5 },
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});
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expect(body.reasoning_effort).toBeUndefined();
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expect(body.temperature).toBe(0.5);
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});
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it("sends a supported effort value", () => {
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const config: LlmConfig = {
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baseUrl: "https://example.test/v1",
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apiKey: "k",
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model: "gpt-5",
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};
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const body = buildRequestBody(config, messages, {
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generation: { reasoningEffort: "High" },
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});
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expect(body.reasoning_effort).toBe("high");
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expect(body.temperature).toBeUndefined();
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});
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it("uses profile default reasoningEffort when generation omits it", () => {
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const config: LlmConfig = {
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baseUrl: "https://example.test/v1",
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apiKey: "k",
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model: "glm-5.3",
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reasoningEffort: "high",
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};
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const body = buildRequestBody(config, messages, {
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generation: { temperature: 1 },
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});
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expect(body.reasoning_effort).toBe("high");
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});
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it("lets generation reasoningEffort override the profile default", () => {
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const config: LlmConfig = {
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baseUrl: "https://example.test/v1",
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apiKey: "k",
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model: "glm-5.3",
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reasoningEffort: "max",
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};
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const body = buildRequestBody(config, messages, {
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generation: { reasoningEffort: "low" },
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});
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expect(body.reasoning_effort).toBe("low");
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});
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it("does not forward top_k from an imported ST preset", () => {
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const config: LlmConfig = {
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baseUrl: "https://example.test/v1",
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apiKey: "k",
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model: "deepseek-v4-pro",
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};
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const body = buildRequestBody(config, messages, {
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generation: stGeneration,
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});
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expect(body.top_k).toBeUndefined();
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expect(body.min_p).toBeUndefined();
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});
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});
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