JesseMarkowitz
8c65ae99de
94 files, +1,395 -6,578. Three files are new; twenty-four are gone. The milestone is subtraction, and what is left is the single-user local storyteller the specification describes. Removed in full: campaign scripting and its QuickJS sandbox; multi-user accounts, guest sessions, login, registration and the shared demo key; the visitor-analytics tables, dashboard and page beacon; the access log of sign-ins, addresses and devices; per-IP and per-user rate limiting and quotas; Render deployment config; Postgres and psycopg; cloud inference providers, the API-key field and the key encryption that existed to store it; session-cookie signing. None of it was hidden behind a flag — the routes are gone and answer 404. Two things were kept that the brief allowed keeping. The `users` table and its foreign keys stay as an internal ownership detail, because rewriting them out means a migration across most of the schema to delete a column that costs nothing; nothing creates a second user and no request carries an identity. Five inert tables and four inert columns stay for the same reason, so an M1 campaign database opens unchanged. The one addition is app/endpoints.py, which decides where a story may be sent. Loopback, RFC1918, link-local, unique-local and CGNAT — an explicit allowlist of networks, not a guess at what `ipaddress` means by "private", which calls the documentation ranges private and IPv6 loopback reserved. Every address a hostname resolves to must be in it, so a split answer does not squeak through, and the rule runs both when the endpoint is saved and before every outbound request, because a name that resolved to the LAN this morning can resolve elsewhere this afternoon. Known cloud hosts are named in the refusal so the error says why rather than looking like broken DNS. TLS is never traded against it: M1's shared trust context is intact on all four clients and there is no way to skip verification. The hardcoded 120-second model timeout is now a setting. That was not theoretical — on this GPU-less four-core host a cold load of qwen2.5:3b-instruct took 648.9 seconds to produce the first turn, while turns 2 to 5 of the same campaign took 3.6 to 13.1. Connect stays short at 10s so a wrong address still fails fast; the read timeout defaults to 300s and is bounded at 3600, because "wait longer" must stay a number. Two defects found while testing and fixed here. An unknown /api path fell through the SPA catch-all and came back as HTML with status 200, so a client asking for JSON parsed a web page instead of learning the route was gone. And AIDND_CORS_ORIGINS accepted "*", which on an unauthenticated loopback API would hand every page on the Internet a write handle on the campaign database; it now refuses to start. Verified rather than assumed. Offline, on a network with no route out and no DNS: five turns, retry with both takes retained, restart with an identical transcript digest, a failed model call leaving the accepted AI-turn count untouched, and a capture with zero non-loopback unicast packets. Against a real second machine on the LAN over HTTPS with a private CA: four turns, restart, and a capture showing 289 packets to the approved host, 344 loopback, zero anywhere else, zero DNS queries. Cloud and public endpoints refused with their reasons; no API key settable; every removed route 404. 604 backend tests pass, down from 648 by the fifteen retired with the subsystems they tested and up by the twenty-nine added for the endpoint policy and the removed surface. The scripting tests were not deleted: eight files used a JavaScript counter as instrumentation for the state snapshot and rollback machinery, which M2 does not touch, so the counter moved to the world-state engine and those tests still assert what they always did. Frontend lint and build are clean; the image builds, and its wheel-building stage is gone with quickjs. No M3 work. Undo is still destructive and there is still no Redo.
AI D&D
An AI Dungeon-style interactive storytelling app that runs 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: parththakkar106.github.io/AI-DnD
The project page loads instantly and launches the hosted demo in one tap. Play a scenario as a guest: no sign-up and no API key needed. The demo runs on a free tier that sleeps, so the first load after it's been idle takes about 30 to 60 seconds to wake up.
For the internals, read the design notes. They walk through the context budgeting, the world-state referee, and the memory bank, and state the reasoning behind each one (Markdown version).
Built with FastAPI and SQLAlchemy on the backend and React (Vite) on the frontend, running on SQLite locally and Postgres in the cloud. It works with any OpenAI-compatible endpoint: Ollama and LM Studio locally, or OpenRouter, OpenAI, Groq, or vLLM in the cloud. Endpoint, key, and model are all runtime settings, and OpenRouter's free-tier models make the whole experience cost nothing.
The play screen. The left rail shows live world state. The AI proposes changes each turn, and
a Python engine decides what actually sticks. The chip under the narration reports what
changed. The ‹ 2/2 › under a turn steps between the takes it has. Writing below a take that
isn't the live one starts a new branch.
Features
- The full play loop. Do / Say / Story / Continue actions, streamed AI responses (SSE), retry, undo, and edit. Reasoning models are supported: "thinking" streams into a collapsible 💭 panel with its own token budget.
- A branching story tree. The story is a tree, not a list. Any turn can hold more than one
take, and
‹ 2/4 ›steps between them. Stepping is free: the story below simply empties, and the server is told nothing. Writing below a take that isn't the live one is what makes a branch. Branches borrow their ancestors' turns instead of copying them, so a fork costs about 100 bytes, and a 20-fork story loads within 1% of the same story flat. Switching restores that line's world state, script state, and cooldown clocks. A branch panel switches, renames, and deletes; ⌗ See the tree draws every line against the story's own clock (backend/app/tree.py,backend/app/context/lineage.py). - An RPG world-state engine. A scenario can declare stats, flags, milestones, and a named
cast; the adventure carries their live values. The AI proposes deltas, and a Python engine
referees them: it clamps values to range, enforces per-turn caps and cooldowns, keeps counters
monotonic and milestones sticky, then strips the machine-readable block out of the prose
(
backend/app/worldstate/engine.py). Word-labeled bands (40–60: minor damage) make the model reliable at it. No dice and no scripting are required. - AI Dungeon-compatible context engine. Memory, author's note, and story cards (world
info) are triggered by keywords in recent story text, then 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, its token cost, and why it was included.
- JavaScript scripting, AI Dungeon-compatible.
onInput/onModelContext/onOutputmodifiers sharestateand aworldEntriesAPI, and run in an embedded quickjs sandbox (backend/app/scripting/). Real AI Dungeon scripts import and run as is. An in-app CodeMirror editor is included. - Auto-summarization and 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). - Undo and retry that actually roll back state. Undo and retry roll back the world state and script state to a per-node snapshot, not just the text, and prune the memories that covered the removed turns. Nothing a retry replaces is discarded: the old attempt stays as another take of that turn, one keystroke and one click from becoming a branch of its own.
- Import and export. AI Dungeon-compatible formats for scripts and scenarios; JSON for
everything else. An adventure exports as
ai-dnd-adventure-v2, which carries the whole tree: every branch, every take, and the fork points, since those were chosen rather than computed. Files saved in the old single-line format still import. - Optional accounts for hosted deployments. By default the app is single-user with zero
auth friction. Set
AIDND_MULTI_USER=1and visitors play instantly as guests (signed session cookie), can register (email and 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). Each new guest is also given a copy of a short pre-played adventure, so the first screen shows real turns and their world-state changes without spending a demo turn (backend/app/starter.py).
Screenshots
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 |
| Insights: the exact prompt for the next turn, broken into components with token counts and the trigger word that pulled each story card in. | Authoring: stats with ranges, per-turn caps, cooldowns, and word-labeled bands; NPCs the AI addresses by id. |
 |
 |
Scripting: the three AI Dungeon hooks with shared persistent state, run in a quickjs sandbox. |
Home: continue a story in progress or start from a scenario. |
 |
 |
| The tree: one lane per line, from the moment it left its parent to the moment it ends. The horizontal axis is the story's own clock, so a short branch reads as short. | Branches: every line the story has taken, and the three things you can do to one. A line the one you're reading was forked from can't be deleted, and says so. |
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 |
| Claude Code CLI (local) | http://127.0.0.1:8787/v1 |
your Claude subscription instead of an API key; see Playing against Claude locally |
Model name, API key, generation parameters, and (optionally) summary and embedding models for the Memory Bank are all configured there too. No config files and no rebuild are needed.
Playing against Claude locally
backend/tools/claude_shim.py serves an OpenAI-compatible endpoint backed by the
claude command line tool, so you can play the demos against a real model without an
API key. Each request spawns one claude --print process, which suits the turn engine:
the app assembles the whole prompt every turn and expects a stateless endpoint.
cd backend
.venv/Scripts/python.exe tools/claude_shim.py # listens on 127.0.0.1:8787
In Settings, choose the OpenAI-compatible provider, set the base URL to
http://127.0.0.1:8787/v1, put any non-empty string in the API key field, and pick
sonnet. The shim ignores the key and authenticates as you, through the CLI. Set the
reasoning budget to 0 or -1: a positive budget sends a reasoning.max_tokens field
that Claude 5 models reject.
Embeddings are not served. Leave the embedding model blank, or point the Memory Bank at a real endpoint.
Run it against a local backend only. The endpoint has no authentication, and anything
reaching it spends your Claude quota. app/netguard.py blocks localhost endpoints when
AIDND_MULTI_USER is set, so a deployed instance cannot be pointed at it.
How a turn works
player input
→ onInput script modifier
→ assemble context: [narrator prompt] + [world state + stat guide] + [AI instructions]
+ [plot essentials] + [story summary] + [retrieved memories]
+ [triggered story cards] + [history along this branch, token-budgeted]
+ [author's note] + [player action]
→ onModelContext script modifier
→ snapshot context (Insights)
→ provider adapter → AI (streamed)
→ extract + referee the world-state delta block, strip it from the prose
→ onOutput script modifier
→ store & render
Architecture
frontend/ React + Vite SPA ──HTTP/SSE──► backend/ FastAPI
├─ routers/ auth, scenarios, adventures, story cards, scripts, chat, settings, analytics, debug
├─ models.py SQLAlchemy: User, Scenario, Adventure, Branch, Action, StoryCard, Script, Settings, Memory
├─ migrations.py hand-rolled, versioned via PRAGMA user_version (64 and counting)
├─ auth.py guest/registered users, sessions, shared demo key
├─ security.py password hashing, cookie signing, API-key encryption
├─ tree.py forking, promotion, and where a node is placed
├─ attempts.py the takes of one turn, grouped by parent
├─ context/ prompt assembly under a token budget + lineage/history windowing
├─ worldstate/ the stat engine: clamps, cooldowns, bands, milestones
├─ scripting/ quickjs sandbox + AI Dungeon API surface
├─ memorybank.py auto-summarization + embedding retrieval
├─ analytics.py buffered visit counters + the owner's dashboard query
├─ bundle.py the export/import formats, v2 (tree) and a v1 reader
├─ 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.
Tests
549 backend tests: unit tests plus full HTTP integration through the real quickjs scripting engine, with the LLM provider mocked. CI runs them on every push, alongside the frontend lint/build and a Docker image build.
cd backend && pip install -r requirements.txt -r requirements-dev.txt
python -m pytest tests/
Notes on performance
Two of the tests exist because of bugs that were measured rather than guessed at. They are the most interesting engineering in the repo.
- Database egress, cut about 189x. Every adventure load pulled
Action.context_snapshot, the entire assembled prompt at about 74 KB per turn, to read two small fields off it. Moving those fields into their own columns and marking the heavy onesdeferredtook one adventure load from 38.5 MB to 0.20 MB. The backfill runs server-side with dialect-specific SQL, so the old data never crosses the wire.tests/test_egress.pyhooks into SQLAlchemy's cursor events and fails if a bulk load ever names those columns again. - Turn cost, made flat. Assembling a turn walked the whole story, so cost scaled with story
length: 839 KB of reads at turn 200.
backend/app/context/history.pynow serves tails and slices from SQL and measures what it fetched. The same turn now costs 129 KB and stops growing at around turn 50. - Branching that costs nothing to read. A branch stores no turns. It stores where it left its parent, and borrows everything above that. A 40-turn story forked twenty times loads in 31,652 bytes against 31,433 bytes for the same story flat: a 1.007x ratio, or about 103 bytes per branch. Reads stay cheap because the lineage is windowed the same way the history is, so the number of SQL clauses is bounded by the context window rather than by the number of forks.
Visit analytics
The hosted demo keeps its own analytics: an owner-only dashboard at /analytics shows
traffic, which shared scenarios get played, turns and demo-key spend, errors, and a funnel
from visited to played a turn to signed up. It is visible only to the emails listed in
AIDND_ANALYTICS_EMAILS, and the route returns 404 for everyone else.
This is built into the app rather than added with a third-party script, for reasons specific
to this project: the CSP allows only script-src 'self', ad blockers block the popular
trackers, and none of those trackers can see the measurement that matters here, a turn. Counts
are aggregated in memory and flushed as UPSERTs, so a visit is a write and never a read, and
every dashboard query is a GROUP BY that returns tens of rows regardless of traffic volume.
That matters: see the egress note above for what reading rows per request costs on this stack.
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
Render tier has no persistent disk, so the database lives off-box.
- Create a Neon project and copy its pooled connection string.
- In Render, choose New → Blueprint and point it at this repo. Render reads
render.yaml. - Fill in the secrets it prompts for (
sync: falsevars):AIDND_DATABASE_URL(the Neon string);AIDND_DEMO_API_KEYandAIDND_DEMO_MODELSto offer a no-signup demo; andAIDND_ANALYTICS_EMAILS(your own account's email) to see the Visitors dashboard.AIDND_SECRET_KEYis generated automatically and stays stable across deploys. - Deploy. Pushes to
mainauto-deploy after this. The health check is/api/health.
On the free tier the service sleeps after about 15 minutes idle, and the first request after
that takes about 30 to 60 seconds to wake it. Point any keep-warm pinger at /api/health,
which deliberately doesn't touch the database: waking the database around the clock costs far
more than the cold start saves.
If you put another proxy or CDN in front of Render, set AIDND_TRUSTED_PROXY_HOPS to the
number of proxies in the chain. It defaults to 1. The rate limiter reads the client IP that
many entries from the right of X-Forwarded-For, because the trusted edge appends the real
one last. Leave it at 1 behind two proxies and the limiter reads an entry the caller
supplied, so anyone can rotate the header for a fresh rate-limit bucket per request.
Repo notes
plan/holds the phased implementation plan this project was built from, kept as a build log. All fourteen phases are complete. The later files (11, 12, 14) also serve as design notes for the state-revert, world-state, and story-tree work.plan/STATUS.mdis the running thread: what shipped, what was measured, and what is owed next.docs/GUIDE.mdholds design notes: how each subsystem works and why it was built that way, with the measurements behind the decisions. It is also rendered as a reading page.backend/.env.examplelists the few environment variables the backend reads.docs/self-review.mdrecords a full-codebase self-review pass and what came out of it. All correctness findings are resolved.