云梦

An AI-driven visual novel painted by an AI, one scene at a time. You talk and explore within a scene; when the story turns a corner, it paints the next. You click. It paints. The story unfolds.

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How it works

The story unfolds as a sequence of scenes. Each scene is one AI-painted background plus a short tree of beats — moments of narration, dialogue, and the occasional choice. You tap through a scene's beats and the image stays put; only when a choice leads somewhere genuinely new — another place, a new point of view, a jump in time — does the AI paint the next scene.

entering a scene
        │
        ▼
1. Text LLM     directs the whole scene at once — a background prompt
                plus a tree of beats (narration / dialogue / choices)
        │
        ▼
2. Image model  paints the background once, 16:9, no UI baked in
        │
        ▼
[ tap through beats — no model calls, instant ]
        │
        ├─ in-scene choice ──────▶ jump to another beat (instant)
        │
        └─ scene-change choice ──▶ the next scene
                                   (usually pre-generated — see below)

While you're reading one scene, the engine speculatively generates the scenes your choices could lead to — and, for unavoidable next steps, the scene after that. By the time you pick a direction, its image is usually already painted, so the cut feels instant.

Clicking the background itself (not a button) routes through a vision model: it reads where you tapped and decides whether you're exploring the current scene (it inserts a beat — no new image) or moving on (a new scene).

There is no traditional game UI baked into the art. The AI paints the world in whatever style you pick — "stick figure on grid paper" or "cyberpunk noir" — and the dialogue panel and choice buttons are a light HTML layer drawn on top, tuned to sit over the scene.

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One-click deploy

After deploy, set the environment variables (see below) in your Vercel project. Nine are required; TTS is optional (leave blank to run silently); MOCK_IMAGE=true skips image generation for cheap TTS-only testing. The Vercel project's Root Directory must be set to apps/web (the deploy button passes this; if you configure manually, set it in Project Settings).

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Environment variables

Three required providers + optional TTS. Text, Vision, and TTS accept any OpenAI-compatible endpoint (OpenAI, Anthropic via OpenAI-compat proxy, Gemini, OpenRouter, DeepSeek, local Ollama, …). Image goes to Runware (its own task-array protocol, not OpenAI-compatible).

Provider	Variables	Required?	Recommended
Text · story director	TEXT_BASE_URL TEXT_API_KEY TEXT_MODEL	✅	claude-opus-4-7 via Anthropic
Image · UI renderer	IMAGE_BASE_URL IMAGE_API_KEY IMAGE_MODEL	✅	runware:400@6 (FLUX.2 [klein] 9B KV) via Runware
Vision · click reader	VISION_BASE_URL VISION_API_KEY VISION_MODEL	✅	gemini-3-flash via Google
TTS · per-character voice	TTS_BASE_URL TTS_API_KEY TTS_SPEECH_MODEL	optional — leave blank to run silently	mimo-v2.5-tts via Xiaomi MiMo

There's also a flag for cheap testing:

Variable	Effect
MOCK_IMAGE=true	Skip image generation; the renderer returns a static placeholder. Story, voice, and choices still run normally. Great for iterating on TTS without burning Runware credits.

See apps/web/.env.example for the exact shape.

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Local development

Requires Node 20+ and pnpm 9+.

pnpm install
cp apps/web/.env.example apps/web/.env.local
# fill in env vars (9 required + optional TTS/MOCK_IMAGE)
pnpm dev
# open http://localhost:3000

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Project layout

yume/
├── apps/web/              Next.js 16 app — pages + API routes (Vercel root)
└── packages/
    ├── types/             shared TypeScript types
    ├── ai-client/         unified OpenAI-compatible clients + Runware adapter
    ├── tts-client/        Xiaomi MiMo TTS adapter
    └── engine/            multi-agent AI orchestration (open core)

packages/engine is the open core — pure TS, no Next.js or browser dependency. Import it directly to build your own visual-novel front-end (Tauri, Electron, CLI, anywhere).

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Cost & limits

With the recommended trio, each scene is dominated by the text-LLM call. The FLUX.2 [klein] 9B KV image is roughly $0.001 per scene (1792×1024, 4 steps, sub-second); the text call is the rest. Tapping through a scene's beats is free. To keep transitions instant, the engine also pre-generates scenes you might pick but don't — so real spend runs somewhat higher than the scenes you actually see. There is no rate limiting or auth out of the box — if you make your deployment public, your bill will reflect that. Add limits (and consider lowering the prefetch depth) before sharing widely.