interactive-story/README.md

Adventure Storyteller

An interactive storytelling app that runs entirely on your own machine, with your own model. Create scenarios and play open-ended adventures where a local LLM narrates the world, keeps track of what is true, and remembers what happened.

This is the Adventure Storyteller fork of AI-DnD. It is deliberately narrower than its upstream: single-user, local-only, and pointed at a model you run yourself. The hosted deployment, the accounts and sessions, the cloud provider support, the Postgres path, and the JavaScript scripting engine have all been removed rather than disabled. What is left is a storyteller you can run offline.

Local-only, by design. The app talks to one place — an Ollama-compatible endpoint on this machine or on a machine you control on your own network — and it refuses to be pointed at a public address. There is no telemetry, no account, no cloud inference, and nothing is fetched at runtime from the Internet.

For the internals, read planning/TECHNICAL-DESIGN.md and planning/CONTEXT-AND-MEMORY.md, which cover the context budgeting, the state model and the memory bank as this fork builds them.

Built with FastAPI and SQLAlchemy on the backend and React (Vite) on the frontend, storing everything in one SQLite file.

On the play screen, the left rail carries 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, and 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, redo, 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/: apply.py clamps, parse.py reads the block back). 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.
• 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, Redo, and retry that roll back state and delete nothing. Undo moves where the story is being read; it removes no accepted turn, so Redo can walk forward into the turns it stepped over. Both restore the world state from a per-node snapshot rather than just the text, and a memory derived from a turn now behind the head stops being retrieved without being deleted or re-embedded. Writing a new turn below a moved-back head is the moment the story forks: the displaced future stays on the line it was written for, and ordinary Redo stops offering it. Nothing a retry replaces is discarded either — 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 scenario format; JSON for everything else. An adventure exports as ai-dnd-adventure-v2, which carries the whole tree: every branch, every take, the fork points, which branches the story has left behind, and the position it is being read at — all of them chosen rather than computed, which is the rule for what a bundle carries. A campaign exported after two Undos imports still undone, with its retained future intact, instead of silently reopening at its newest turn. Files that predate the head position, and files saved in the old single-line format, still import.
• Single user, no accounts. There is no sign-up, no login, no session and no API key anywhere in the product. The storyteller API binds to loopback and is unauthenticated by design, because the only person who can reach it is the person running it. A new install starts with a short pre-played adventure, so the first screen shows real turns and their world-state changes rather than an empty page (backend/app/starter.py).
• A refusal you can rely on. The inference endpoint is checked against an address allowlist when you save it and again before every request, so a public endpoint is refused even if the setting is edited in the database directly. TLS verification is never traded against reachability: a privately issued certificate is verified against your machine's own trust store, and there is no bypass switch.

Screenshots

None yet. The inherited screenshots showed upstream's UI — a Scripts tab, Log in and Sign up, a guest banner, scripting demo scenarios — none of which this fork has since M2, so they were removed rather than left standing as a picture of a product that no longer exists. New ones are taken when the browser smoke test M3 still owes is run.

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 a local Ollama-compatible endpoint:

Where the model runs	Endpoint URL	Notes
Ollama, same machine	http://localhost:11434/v1	the default, and the simplest thing that works
Ollama, a machine on your own network	http://<host>:11434/v1 or https://<host>/v1	see below
LM Studio, same machine	http://localhost:1234/v1

Model name, generation parameters, and (optionally) summary and embedding models for the Memory Bank are configured there too. No config files and no rebuild are needed. There is no API key field, because there is nothing to authenticate to.

What the endpoint policy allows

The address is checked when you save it and again before every request. Only loopback and private-network addresses are accepted; every public address is refused, by address rather than by hostname, so a name that resolves outward is refused too. A well-known cloud inference host is named in the error message only so the refusal says why.

Running the model on a second machine you control is supported and expected — that machine does the inference while the storyteller itself stays bound to loopback on yours. If that machine serves HTTPS with a certificate from a CA you installed, it works: certificates are verified against your operating system's trust store as well as the bundled one. Verification itself is never relaxed, and there is no option to turn it off.

Playing against a local shim

backend/tools/claude_shim.py serves an OpenAI-compatible endpoint on 127.0.0.1:8787 backed by a command-line tool, which is useful for testing the turn engine against a stronger model. Each request spawns one process, which suits the engine: the app assembles the whole prompt every turn and expects a stateless endpoint.

cd backend
.venv/bin/python tools/claude_shim.py      # listens on 127.0.0.1:8787

Set the base URL to http://127.0.0.1:8787/v1 and pick a model the tool offers. Set the reasoning budget to 0 or -1: a positive budget sends a reasoning.max_tokens field that some models reject. Embeddings are not served — leave the embedding model blank, or point the Memory Bank at an endpoint that serves one.

The shim has no authentication and spends whatever quota backs it, so run it on loopback and leave it there.

How a turn works

player input
  → 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]
  → snapshot context (Insights)
  → provider adapter → AI (streamed)
  → extract + referee the world-state delta block, strip it from the prose
  → store & render

Architecture

frontend/   React + Vite SPA  ──HTTP/SSE──►  backend/  FastAPI
                                              ├─ routers/      scenarios, adventures, story cards, chat, settings, debug
                                              ├─ models.py     SQLAlchemy: Scenario, Adventure, Branch, Action, StoryCard, Settings, Memory
                                              ├─ migrations.py hand-rolled, versioned via PRAGMA user_version (79 and counting)
                                              ├─ endpoints.py  the inference-endpoint address policy
                                              ├─ tlstrust.py   one TLS context: the OS trust store unioned with certifi's
                                              ├─ tree.py       forking, promotion, and where a node is placed
                                              ├─ head.py       the active head: where the story is read, and what moving it costs
                                              ├─ 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
                                              ├─ memorybank.py auto-summarization + embedding retrieval
                                              ├─ 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

638 backend tests: unit tests plus full HTTP integration through the real turn engine, with the model provider mocked. They run with no route to the Internet, which is a requirement rather than a convenience — an offline claim proved on a machine that has been online once proves nothing.

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 ones deferred took 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.py hooks 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.py now 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.

Repo notes

• planning/ is this fork's own package: the product specification, the architecture decisions, the milestone plan, the acceptance contract, and a review report for every milestone shipped. Start at planning/README.md.
• planning/archive/ holds the Phase 0 research that chose this base and the completed milestone reports. It is history, not instruction.
• DEVELOPMENT.md is how to set the project up, point it at a model, and run the tests. PROVENANCE.md records what came from upstream and what changed.
• backend/.env.example lists the two environment variables the backend reads. Everything about the model is a runtime setting on the Settings page instead.
• Upstream's own plan/ build log and docs/ project site were removed in the 2026-09-03 documentation pass: they described AI-DnD's hosted, scripted, multi-user product. Both are still in Git history, and in upstream.

License

MIT