AI D&D

An AI Dungeon-style interactive storytelling app you can run entirely on your own machine — with your own AI model. Create scenarios, play open-ended adventures where an LLM narrates the world, and extend the engine with JavaScript scripts compatible with real AI Dungeon scripting.

▶️ Try it live: ai-dnd-1gmp.onrender.com

Play a demo scenario as a guest — no sign-up, no API key needed. (Hosted on Render's free tier, so the first load after it's been idle takes ~30–60s to wake up.)

Built with FastAPI + SQLite on the backend and React (Vite) on the frontend. Works with any OpenAI-compatible endpoint: Ollama and LM Studio locally, or OpenRouter / OpenAI / Groq / vLLM in the cloud — endpoint, key, and model are all runtime settings, and OpenRouter's free-tier models make the whole experience $0.

📸 Screenshots and a demo GIF are coming; for now the fastest tour is running it — one command with Docker.

Features

• The full play loop — Do / Say / Story / Continue actions, streamed AI responses (SSE), retry, undo, and edit. Reasoning models supported: "thinking" streams into a collapsible 💭 panel with its own token budget.
• AI Dungeon-compatible context engine — memory, author's note, and story cards (world info) triggered by keywords in recent story text, assembled under a token budget (backend/app/context/builder.py).
• Insights: total prompt transparency — every turn stores the exact prompt sent to the model; open 🔍 on any AI action to see each context component and why it was included.
• JavaScript scripting, AI Dungeon-compatible — onInput / onModelContext / onOutput modifiers with shared state and a worldEntries API, executed in an embedded quickjs sandbox (backend/app/scripting/). Real AI Dungeon scripts import and run. In-app CodeMirror editor included.
• Auto-summarization + Memory Bank — the modern AI Dungeon memory system: AI-generated memories every few actions, a running story summary, and embedding-based retrieval that pulls old-but-relevant facts back into context, with similarity scores visible in Insights (backend/app/memorybank.py).
• Import/export — AI Dungeon-compatible formats for scripts and scenarios; JSON for everything.
• Optional accounts for hosted deployments — by default the app is single-user with zero auth friction; set AIDND_MULTI_USER=1 and visitors play instantly as guests (signed session cookie), can register (email + password) at any point to keep their data, and each user gets isolated data plus their own encrypted-at-rest API key. A server-funded shared demo key with a daily turn cap lets people try it without bringing a key (backend/app/auth.py).

Quick start

Docker (any OS)

docker compose up --build

Open http://localhost:8000. Your data persists in a named volume across restarts.

Windows

cd backend; python -m venv .venv; .\.venv\Scripts\pip.exe install -r requirements.txt; cd ..
cd frontend; npm install; cd ..
.\start.ps1

Open http://localhost:5173 (dev servers; API docs at http://localhost:8000/docs).

macOS / Linux

./start.sh   # creates the venv and installs dependencies on first run

Open http://localhost:5173.

Connect a model

Open Settings in the app and point it at any OpenAI-compatible endpoint:

Provider	Endpoint URL	Notes
Ollama (local)	http://localhost:11434/v1	free, private; also serves embedding models for the Memory Bank (e.g. nomic-embed-text)
LM Studio (local)	http://localhost:1234/v1	free, private
OpenRouter	https://openrouter.ai/api/v1	:free models cost nothing (no embeddings on the free tier)
OpenAI / Groq / vLLM / …	provider's /v1 URL	anything speaking /v1/chat/completions

Model name, API key, generation parameters, and (optionally) summary/embedding models for the Memory Bank are all configured there too — no config files, no rebuild.

How a turn works

player input
  → onInput script modifier
  → assemble context:  [AI instructions] + [plot essentials] + [story summary]
                       + [retrieved memories] + [triggered story cards]
                       + [story history, token-budgeted] + [author's note] + [player action]
  → onModelContext script modifier
  → snapshot context (Insights)
  → provider adapter → AI (streamed)
  → onOutput script modifier
  → store & render

Architecture

frontend/   React + Vite SPA  ──HTTP/SSE──►  backend/  FastAPI
                                              ├─ routers/      auth, scenarios, adventures, story cards, scripts, settings, debug
                                              ├─ models.py     SQLAlchemy: User, Scenario, Adventure, Action, StoryCard, Script, Settings, Memory
                                              ├─ auth.py       guest/registered users, sessions, shared demo key
                                              ├─ security.py   password hashing, cookie signing, API-key encryption
                                              ├─ context/      prompt assembly under a token budget
                                              ├─ scripting/    quickjs sandbox + AI Dungeon API surface
                                              ├─ memorybank.py auto-summarization + embedding retrieval
                                              ├─ providers/    OpenAI-compatible adapter, streaming
                                              └─ data.db       SQLite (path overridable via AIDND_DB_PATH)

In production the backend serves the built SPA from one port (see Dockerfile); in development Vite proxies /api to FastAPI.

Deploy (Render)

The repo ships a render.yaml blueprint: one Docker web service that serves the SPA and API same-origin, backed by external Neon Postgres (the free tier has no persistent disk, so the database lives off-box).

1. Create a Neon project and copy its pooled connection string.
2. In Render: New → Blueprint, point it at this repo. Render reads render.yaml.
3. Fill the secrets it prompts for (sync: false vars): AIDND_DATABASE_URL (the Neon string) and, to offer a no-signup demo, AIDND_DEMO_API_KEY / AIDND_DEMO_MODELS. AIDND_SECRET_KEY is generated automatically and kept stable across deploys.
4. Deploy. Pushes to main auto-deploy thereafter. Health check: /api/health.

On the free tier the service sleeps after ~15 min idle; the first request then takes ~30–60s to wake.

Repo notes

• plan/ — the phased implementation plan this was built from, kept as a build log (phases 1–6 complete; 7–10 cover the public release).
• backend/.env.example — the few environment variables the backend reads.
• CODE_REVIEW_FINDINGS.md — notes from a self-review pass.

License

MIT