JesseMarkowitzandClaude Opus 5
903fa7a74f
Undo deleted. It removed the trailing AI action and the player action in front of it, pruned the memories covering them, and let the tip fall back to whatever survived. That made it the one operation in the application that destroyed accepted story, and it was why there was no Redo: the turns to move forward into no longer existed. Phase 0B demonstrated the head-cursor alternative in a disposable spike; this is that concept as production code. backend/app/head.py is the whole of it. Three questions that used to be one — where the story is being read, how far it is retained, and where it opens — are now three functions, and every caller that moves the head or asks about it goes through this module. The spike put the fork check in the write path and left Retry and Add-take on the old one; sharing the rules is what stops that divergence coming back. lineage.Path now caps every entry at the head, so hiding the retained future costs nothing at the call sites: the transcript, the context builder, attempts.preceding and memory retrieval already funnelled through path_of and narrow together. Path.uncapped() is the deliberate exception, and only Redo and the fork check may use it. The memory bank needs no pruning for the same reason — a memory carries the coordinate of the node its block ends on, so one derived past the head falls outside the capped clause and becomes retrievable again on Redo without having been deleted and re-embedded. Undo alone does not fork. Moving the head is not a decision to abandon anything, since the user may be reading or about to Redo; the first write below the head is where the story states which continuation it means. A head already at the tip forks nothing, so a story that is never undone forks exactly as often as it did before and the branch table does not fill up with one branch per turn. Redo follows the lineage rather than choosing among branches, which is what invalidates it after a divergence with no flag to set or clear. Migrations 78 and 79 give a branch superseded_at and superseded_depth. Nothing reads them to decide behaviour — Redo is decided by the lineage, so a stale or hand-edited value here cannot make the story wrong. They exist so the cleanup and discarded-history features left to a later version have something to select on, and so a divergence is observable in a test. Deleting an action no longer drags a moved-back head forward to the recomputed tip, which would have silently redone the story. can_undo and can_redo ride on AdventureOut and ActionPage because the client can work out neither for itself: the campaign opening may be off the top of the loaded window, and the retained future is never sent to it. This is a checkpoint, not the finished milestone. 601 backend tests pass. Five still assert the destructive contract — they count rows after an undo and expect the story to be shorter — and need rewriting against the new one; the world-state assertions inside them already pass. Export and import do not yet carry the head coordinate, so a bundle still reopens at the deepest node and can silently redo an undone story, which is the Phase 0B finding this milestone exists to close. The browser has no Redo control yet. None of the M3 acceptance coverage (D01-D10, E01-E04, I01-I03, I07, L01-L02) is written. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QF5TcoB86QADgjHz1GZe8u
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.