interactive-story/docs/GUIDE.md

AI D&D — design notes

How the engine works and why it is built this way. The README covers what the project does and how to run it; this covers the reasoning behind the parts that had a real choice in them.

Part 1 is the AI layer, which is where most of the design effort went. Parts 2 and 3 are what makes it a service rather than a demo. Part 4 is the web plumbing, kept short.

---

Contents

• Part 0 — Orientation
• Part 1 — The AI layer
  • 1.1 The turn pipeline
  • 1.2 Context assembly is a budget problem
  • 1.3 World state: the AI proposes, Python referees
  • 1.4 Output length, by measurement
  • 1.5 The memory bank
  • 1.6 Streaming
  • 1.7 The scripting sandbox
  • 1.8 Why there is no agent framework
• Part 2 — Data and correctness
• Part 3 — Production concerns
• Part 4 — The web plumbing, briefly
• Part 5 — Measured results and known limitations

---

Part 0 — Orientation

What the thing is

An AI Dungeon clone. You write a scenario, then play an open-ended text adventure where a language model narrates the world. You type "I open the door", the model writes what happens next, and it remembers what came before.

Three things make it more than a chat wrapper:

1. A context engine. The model has a limited input window. The app decides, every single turn, which pieces of the story get to be in the prompt and which get dropped.
2. A world-state engine. The scenario declares stats (hp, trust, day). The model proposes changes to them each turn; a Python engine decides what actually sticks.
3. A scripting sandbox. Real AI Dungeon JavaScript scripts import and run, inside an embedded QuickJS interpreter.

Runs locally against Ollama for free, or hosted against any OpenAI-compatible endpoint.

The stack, and what each part is doing

Piece	What it does here
FastAPI (Python)	The HTTP server. Every URL like /api/adventures/3/actions maps to a Python function. Also does the SSE streaming.
SQLAlchemy	The ORM. Adventure, Action, Memory are Python classes; SQLAlchemy turns them into tables and turns attribute access into SELECTs.
SQLite / Postgres	The database. SQLite is a single file on disk (local). Postgres is a server (hosted, on Neon). Same code talks to both.
React (JavaScript)	The UI. Describes what the screen should look like for a given state; when the state changes it re-renders.
Vite	The frontend build tool and dev server. Bundles React into plain JS the browser can load.
httpx	The HTTP client used to call the model endpoint.
tiktoken	Counts tokens, so the budgeting is arithmetic rather than a guess.
QuickJS	A small embeddable JavaScript engine, used as a sandbox for user scripts.

The whole thing is one process in production: FastAPI serves the API and the built React files from the same port.

The shape of one request

you tap "Do"
  → browser sends POST /api/adventures/3/actions   {type:"do", text:"open the door"}
  → FastAPI route: check ownership, rate limit, turn lock
  → assemble the prompt
  → POST to the model endpoint with stream=true
  → tokens come back one at a time
  → each token is forwarded to the browser as a Server-Sent Event
  → React appends it to the screen as it arrives
  → when the stream ends: parse the state block, referee it, save the action

---

Part 1 — The AI layer

1.1 The turn pipeline

Everything that happens between "player pressed a button" and "text is on screen". Source: backend/app/routers/adventures.py (_generate_turn).

player input
  → onInput script hook          (user JS may rewrite or block it)
  → store the player action
  → retrieve memories            (embed recent story, cosine-rank the bank)
  → snapshot script + world state (so undo/retry can roll back)
  → build_context()              (the budget allocator)
  → onModelContext script hook   (user JS may rewrite the whole prompt)
  → snapshot the exact prompt    (for the Insights panel)
  → provider.generate()          (streamed, token by token)
  → onOutput script hook
  → extract the fenced state block, referee the delta, strip it from the prose
  → save the action
  → fire-and-forget: summarize + embed in the background

Two design choices are visible in that list before any of the details.

The prompt is snapshotted, not reconstructed. Every AI action stores the exact text that was sent to the model. That's what powers the Insights panel — open any turn and see each context component, its token cost, and why it was included. It's also what makes prompt bugs findable. The cost is storage (~74 KB per turn), which turns into a real performance problem later — see 2.5.

Snapshots happen before the model call, not after. state_before and world_state_before are stapled onto the action before the hooks and the delta run. That's the entire mechanism behind undo and retry actually rewinding rather than just deleting text.

---

1.2 Context assembly is a budget problem

Source: backend/app/context/builder.py.

The problem

The model can only read so much. Say the budget is 8,000 tokens. A 200-turn adventure has far more story than that. Something has to be dropped, and what gets dropped decides whether the story stays coherent.

The naive version

Send the last N turns. That breaks in two directions: N turns of short exchanges wastes the window, and N turns of long ones overflows it. It also throws away the things that matter most — the premise, the character sheet, the fact that you promised the innkeeper you'd return.

What this app does

Split the prompt into fixed sections and elastic ones.

Fixed (always included, whatever they cost):

Section	What it is
narrator	The system prompt — how to write.
world_state_guide	The stat legend: what each stat means, its range, its bands.
world_state	Current values of every stat, plus NPCs in scene.
world_state_rule	How to report changes.
ai_instructions	Per-adventure steering.
plot_essentials	AI Dungeon's "Memory" — the premise.
story_summary	The auto-maintained running summary.
used_memories	Top-K retrievals from the memory bank.

Elastic (fit into what's left):

Section	Rule
world_lore	Story cards triggered by keywords in recent text. Capped at 40% of the remaining budget.
history	Story turns, newest first, until the budget runs out.

The algorithm is three lines of arithmetic:

reserved  = sum of every fixed section + author's note + length hint + reminder
available = max(256, context_token_budget - reserved)

Then cards spend up to available * 0.4, and history spends available - cards_used, filling backwards from the newest turn.

The details that are decisions

Cards are capped at 40% of the elastic budget. Story cards are triggered by keyword match, so a scene mentioning six named things could pull in six lore entries and leave no room for the story itself. The cap makes the failure mode "some lore is missing" instead of "the model has no idea what just happened". Cards that don't fit are still reported to Insights with included: false, so the UI can show the lore that got squeezed out.

History fills newest-first and stops. Oldest turns fall out. This is the right direction because the old material is not actually lost — it has been summarized into memories and the running summary, which are in the fixed section.

If even the single newest turn is over budget, it gets hard-truncated rather than dropped. A prompt with no story at all would produce nonsense; a prompt with the tail end of the last turn produces something.

The author's note is injected 3 actions from the end, not at the top. AUTHORS_NOTE_DEPTH = 3. Instructions placed near the end of a prompt have more influence on what comes next than instructions at the top — recency. The author's note is a steering control ("keep it tense"), so it goes where steering works.

The world-state reminder goes dead last. The full emit rule lives up in the system block, hundreds of tokens away from where the model starts writing. A one-line reminder occupies the final slot. Same recency logic, applied to the thing most likely to be forgotten.

Past AI turns get their state block re-attached. The state block is stripped from the text before it's stored, so a replayed history would show the model twenty of its own past turns that contain no state block — which teaches it, by imitation, to stop emitting one. So _history_text() reconstructs the block from the stored delta and re-appends it when building history. The model sees its own pattern and keeps following it.

The performance trap hiding in this

Building the context needs the newest ~6,000 tokens of story. The obvious implementation reads adventure.actions — which loads every row of the adventure — and then throws 90% of it away. At turn 200 that was 839 KB of database reads to use maybe 70 KB, and it grew every single turn.

backend/app/context/history.py fixes it by serving three shapes directly from SQL: a tail, a slice, and a count. window_covering() fetches the newest 32 actions, measures their real token count, and if that's short of the budget it projects how many more it needs from the average length just measured rather than blindly doubling:

average   = tokens / len(actions)
projected = int(budget / average * 1.15) + 8

Each round fetches only what it doesn't already hold, so no row is read twice. Result: the same turn costs 129 KB instead of 839 KB, and stops growing at around turn 50 — the cost is bounded by the context budget instead of by the length of the story.

There's a second rule in that module worth naming: if the actions are already loaded in memory, slice them instead of querying. The scripting pipeline hands the whole history to user scripts (AI Dungeon's API requires it), so on a scripted adventure the rows are already there — issuing a query beside them would mean paying twice.

---

1.3 World state: the AI proposes, Python referees

Source: backend/app/worldstate/engine.py, plan/12-phase-rpg-world-state.md.

The problem

You want an RPG layer — hit points, trust, quest progress. Who owns the numbers?

Three options, and why two lose

Option A: a deterministic dice engine. The player types "attack the goblin", the engine rolls, applies damage, and the model narrates the result. This is what a real RPG does. It loses here because the action space is unbounded — the player can type anything, and mapping arbitrary natural language onto a fixed rules system is a harder problem than the one being solved.

Option B: the model owns the numbers. Let it track hp in the prose and trust it. This fails immediately. Models are bad at arithmetic, worse at remembering a number across twenty turns, and completely unable to obey their own frequency rules — tell one "only change this every 5 turns" and it will change it every turn.

Option C, chosen: the model proposes, the engine disposes. The model narrates and appends a JSON delta of what changed. Python validates and clamps it before anything is stored.

narration: "The blade catches your shoulder. Gwen shouts and drags you back."

```state
{"player.hp": -15, "npc.gwen.trust": 5, "milestones.escaped": true}
```

The engine then applies, in order:

Rule	What it stops
Path must exist in the schema	Hallucinated stats
Value must be the right type	"a lot" instead of -15
Cooldown	Changing a stat more often than the scenario allows
Counters can't decrease	The in-game day going backwards
max_delta_per_turn	Losing 90 hp to a stubbed toe
Clamp to min/max	Negative hp, trust above 100
Milestones are sticky, true only	Un-completing a quest
Flags are two-way booleans	(Deliberately unrestricted — that's what flags are for)

Everything it rejects is reported, not silently swallowed — the Insights panel shows applied, clamped and rejected paths per turn, and the chip under each narration shows what actually changed.

The reliability mechanism: word bands

A stat can carry bands:

"hp": { "min": 0, "max": 100, "initial": 100,
        "bands": [[0,20,"very weak"],[20,40,"hurt"],[40,60,"minor damage"],
                  [60,90,"healthy"],[90,100,"full health"]] }

Two things use them. The live state block shows the current band label — hp 55/100 (minor damage) — so the model reads a word, not just a number. And the stat guide shows the whole ladder once per turn, so the model can see the full scale it's reasoning across.

The point: models reason well over semantics and badly over arithmetic. "He's badly hurt, so a solid hit should take him to very weak" is a judgement a model can make. "55 minus 22 is 33" is one it will get wrong often enough to matter.

The failure philosophy

Nothing in the world-state engine raises. A malformed delta returns {} and the turn continues. The parser is deliberately tolerant — it strips trailing commas and leading + signs on numbers, both of which weaker free models emit and strict JSON rejects. It accepts a fence labelled state, one labelled json, or an unlabelled one, and falls back to a bare JSON object hugging the end of the text — but only if it parses into something that looks like a delta, so prose ending in } is never eaten.

This matters because the hosted demo runs on free-tier models. A stricter parser would mean a good model works and a free one doesn't.

One call, not two

The model narrates and emits the delta in a single request. The alternative — narrate, then a second call to extract structured state — is more reliable per call and costs twice the latency and twice the rate-limit budget. On the free tier (20 requests/minute) that would halve the playable turn rate. The tolerant parser plus the terminal reminder was the cheaper way to buy the same reliability.

---

1.4 Output length, by measurement

The problem

max_output_tokens is a hard wall the endpoint enforces mid-sentence. Hit it and whatever is being written gets cut off. Since the state block is emitted last, the state block is what gets lost. The turn narrates fine and silently records nothing.

First attempt

Tell the model its budget: "keep this turn under about N words".

What the measurement showed

Average turn length went from 174 words to 246 — every run longer than every unhinted run (n=5). Phrased as a budget, the number reads as a target to fill. The hint pushed turns toward the very wall it existed to protect.

The fix

Phrase it as a ceiling, and say explicitly that a typical turn is much shorter:

[Hard limit: this turn must not exceed 412 words. Write only as much as the
moment needs — a typical turn is much shorter. Finish the narration and append
the state block well inside the limit.]

Average came back to 170 words, and the state block survived at tight caps.

And the arithmetic around it

words = int((max_output_tokens - 50) * 0.75 * 0.90)

• - 50 (LENGTH_HEADROOM) — tokens held back for the state block itself.
• * 0.75 (WORDS_PER_TOKEN) — models can't count their own tokens, but they do follow a word budget. English prose is roughly 0.75 words per token.
• * 0.90 (LENGTH_BUFFER) — a word budget is a suggestion the model overshoots; the cap it protects is a hard wall. Aim 10% short so the overshoot lands in slack.
• Below 40 words the hint is dropped entirely — it stops earning its tokens.

---

1.5 The memory bank

Source: backend/app/memorybank.py.

The problem

Story history falls out of the context window as the adventure grows. Turn 4 said you promised the innkeeper you'd return. At turn 90 that's long gone from the prompt — but if you walk back into the inn, it should come back.

The three layers

raw turns   →  memories        →  story summary
(verbatim)     (every 6 turns)    (rewritten every 15 turns)
                    ↓
               embeddings  →  cosine similarity  →  top-K into the prompt

Layer	Cadence	Purpose
Memory	Every 6 actions, starting at 12	One or two past-tense sentences of concrete fact.
Story summary	Every 15 actions	A single ≤250-word overview of the whole plot, rewritten by folding in the new memories.
Retrieval	Every turn	Embed the last 4 actions (≤600 tokens), cosine-rank the bank, inject the top K (default 5).

Retrieval is the part that answers the innkeeper problem: the promise is a memory, the memory has a vector, walking into the inn produces a query vector near it, and it comes back into the prompt.

The decisions inside it

Only settled actions get summarized. The newest action is always held back one turn. Reason: only the last action can be retried. If a memory summarized the newest action and the player then retried it, the memory would describe narration that no longer exists — and because its cursor has already advanced, it would never be regenerated. Holding one action back costs a turn of latency and makes that state unreachable.

Cursors only advance on success. Every AI call in this module is best-effort. If summarization fails, the function returns and the cursor is unchanged, so the same block is retried on a later turn. There is no retry loop, no dead-letter queue, no backoff — the cadence is the retry mechanism. Failures are logged to the debug page.

Summarization is fire-and-forget, in a background task with its own DB session. The player's turn is already on screen; making them wait for a summarization call would add a second or two of latency to every sixth turn for no visible benefit. The task holds strong references to itself (the event loop only keeps weak ones, so a fire-and-forget task can otherwise be garbage-collected mid-run) and a per-adventure guard set stops two from overlapping.

Pinned memories count toward top_k. Pinned ones are always injected; unpinned ones fill up to top_k - len(pinned). Without that, 6 pinned memories plus top_k=5 injects 11 and blows the budget the whole context engine exists to respect.

A dimension mismatch scores 0.0, it doesn't crash. If the user changes their embedding model, old 768-dim vectors get compared against a new 1536-dim query. zip() would happily truncate and score garbage silently. An explicit length check returns 0.0 instead.

Eviction is LRU-ish, and evicted memories are kept. Over capacity (default 200), the least-used, least-recently-used unpinned memories are marked forgotten rather than deleted — so the UI can still show them and you can un-forget one.

Background calls never spend the shared demo key. The summarization and embedding providers are built directly from the user's own settings and never from the demo config, and their call sites are skipped when the turn is running on the demo key. Summarization is unmetered background spend; the demo key is server-funded. Both facts together would be a bill.

---

1.6 Streaming

The model produces tokens one at a time. Waiting for the whole reply before showing anything makes a 20-second generation feel broken.

Server-Sent Events (SSE) is the mechanism: an HTTP response that stays open and pushes data: {...} lines as they become available. It's one-directional (server → browser), which is exactly what's needed here — WebSockets would be a bidirectional connection for a unidirectional problem.

The chain:

model endpoint  --SSE-->  FastAPI  --SSE-->  browser  -->  React state  -->  screen

FastAPI reads the provider's stream, and for each chunk yields data: {"type":"chunk","text":"..."}. The frontend reads the response body with a ReadableStream reader, buffers on \n\n boundaries, and dispatches each parsed event.

Event types: player (the stored player action), reasoning (thinking-model traces, which stream into a separate collapsible panel with their own token budget), chunk (story text), stopped (a script blocked the turn), error, done.

Two production details that only show up when hosted:

• X-Accel-Buffering: no — nginx-style reverse proxies buffer responses by default, which turns a stream into one big delivery at the end. This header tells them to flush each event.
• The security-headers and body-size middlewares are written as pure ASGI rather than Starlette's BaseHTTPMiddleware, because the latter buffers the response body and would break streaming.

The empty-reply case is diagnosed, not reported as "empty". If a reasoning model streams thinking but no story text, it spent its whole budget thinking — the error says so and tells you which three settings to change.

---

1.7 The scripting sandbox

Source: backend/app/scripting/.

Real AI Dungeon scripts are JavaScript files defining modifier(text) and calling it as the last line, with globals like state, history, storyCards. To be compatible, this app runs the same contract in an embedded QuickJS interpreter.

The safety properties are mostly structural:

Property	How
No filesystem, network, or process access	QuickJS has none by default — nothing was removed, nothing was added
Memory cap	16 MB per run
CPU cap	2 seconds per run
No shared state between runs	A fresh Context per hook execution
A broken script can't break a turn	Every failure comes back as .error with text/state/cards unchanged; the pipeline logs it and continues

Data crosses the boundary as JSON — Python serializes {state, text, history, storyCards, info} in, and the script's results out. There is no object bridge to exploit.

One deliberate bug-compatibility: addStoryCard returns the new card's index, so the first card returns 0, which is falsy, so if (!addStoryCard(...)) misfires. That's upstream AI Dungeon's behaviour. It's documented in the code and left alone, because matching real scripts is the whole point of the feature.

---

1.8 Why there is no agent framework

Graph-based agent frameworks (LangGraph and similar) earn their complexity with branching, cyclic, multi-step control flow — a graph of nodes where the path depends on what the model decides, with loops, retries, tool calls, and persisted state between steps.

This turn pipeline is a fixed linear sequence with exactly one model call. There is no routing decision, no tool selection, no loop. The "graph" is:

hook → retrieve → build → hook → call → hook → referee → store

Every turn takes that path. Adding a graph framework would mean carrying its state abstraction, its serialization model, and its debugging surface to express a straight line.

There is also a specific reason a framework's context handling wouldn't fit: the budgeting logic is the product. Buffer-window and summary-memory abstractions are opinionated about how to fit history into a window. Here the Insights panel exposes each context component, its token cost, and the trigger word that pulled it in — which means the assembly has to be explicit and inspectable.

When it would be the right call: if the design went toward the two-call version — narrate, then a separate structured-extraction step, with a retry branch when extraction fails and a tool-calling path for dice — that is a graph, and hand-rolling it would get ugly fast.

---

Part 2 — Data and correctness

2.1 The domain model

User
 ├─ Scenario   (the template)      ── stat_schema, prompt, memory, author's note
 │    └─ StoryCard, Script
 └─ Adventure  (the playthrough)   ── world_state, script_state, cursors
      ├─ Action  (one story entry) ── text, context_snapshot, variants, state_before
      ├─ StoryCard  (its own copy)
      ├─ Memory     (text, embedding, source_start/end, use_count)
      └─ AdventureScript

The decision that shapes everything: template vs instance. A scenario declares what stats exist; an adventure holds what they are right now. Creating an adventure copies the scenario's story cards, scripts and plot fields into it, so editing a scenario later never mutates a game in progress. (There's an explicit opt-in "Update from scenario" flow for when you do want that, which diffs the two and shows you what would change.)

Same reasoning as instantiating a class: shared definition, independent state.

2.2 Two coordinate systems, and the bug class they create

The subtlest thing in the codebase.

There are two ways to identify an action:

• Action.index — a stable number stored on the row. Gaps appear when actions are deleted.
• Position — where an action sits in the filtered, index-ordered list of story actions (non-empty text only). Shifts whenever anything before it is deleted.

The memory cursors (memory_cursor, summary_cursor) are positions. Memory.source_start / source_end are Action.index values.

The two spaces are identical until the first deletion, and diverge forever after. Mixing them means summarization silently skips or duplicates blocks — no crash, no error, just a memory describing the wrong turns.

Three things hold it together:

1. history.position_of_index() is the explicit translation between the spaces, and every crossing goes through it.
2. note_action_removed() is called before a delete: if the removed action sat before a cursor, the cursor decrements, so an unsummarized action can't slide into the "already covered" range and be skipped forever.
3. One definition of "story action", written twice — _STORY_TEXT in SQL and is_story_text() in Python — with a comment on both saying to keep them in step. The SQL version folds newlines and tabs into spaces before trim(), because SQLite's and Postgres' single-argument trim() only strips spaces while Python's .strip() also drops newlines. An action of nothing but a newline would otherwise count as story text in one and not the other, and every cursor after it would be off by one.

2.3 Undo and retry that actually rewind

Most implementations of undo delete the last message. That's wrong here, because a turn mutates three things: the text, the scripting scoreboard (script_state), and the RPG stats (world_state).

The mechanism: every action carries state_before and world_state_before — deep copies taken before the turn's hooks ran. Undo restores from them. Retry rolls back to them, then regenerates.

Retry keeps every attempt. Instead of deleting and replacing, the row survives and each attempt is appended to Action.variants; variant_index names the live one. The UI shows ‹ 2/3 › and you can page back to a discarded take. A variant stores only what differs between attempts — the narration, its reasoning trace, and the state it produced — never the assembled prompt, which is identical across attempts of the same turn and is by far the biggest thing in the snapshot.

Three details that are easy to get wrong:

The row being retried is excluded from its own context. It's still attached to the adventure (it holds the variant history), so without exclude_action_id the model would be shown the attempt it's replacing as established story and would write a continuation of it instead of a replacement.

A retry reuses the turn's index, not the next one. Cooldowns are measured in action indexes, so using next_index() would advance the clock the cooldown rules run on and a retry would quietly unlock stats that should still be on cooldown.

If the regeneration fails, the rollback is reversed. generate_turn wraps the generator in a try/finally: if it ends without saving — a provider error, an empty reply, a script stop, or the browser hanging up — the previous variant is put back in charge. Otherwise the state on the server would drift from the text still on the user's screen.

2.4 The turn lock

One turn at a time per adventure. Double-clicking "Continue" must not run two generations.

The subtlety: the check has to happen in the request phase, not when the SSE generator first runs. A StreamingResponse doesn't start iterating its generator until the response begins, so a check-inside-the-generator lets two rapid requests both pass before either one claims the slot. And because sync FastAPI endpoints run in a threadpool, the test-and-set needs a real threading.Lock.

def acquire_turn_lock(adventure_id):        # in the request handler
    with _active_turns_guard:
        if adventure_id in _active_turns:
            raise HTTPException(409, "A turn is already generating…")
        _active_turns.add(adventure_id)

async def with_turn_lock(adventure_id, gen):  # wraps the SSE generator
    try:
        async for event in gen: yield event
    finally:
        _active_turns.discard(adventure_id)

In-memory, so it's a single-process guarantee. That's honest for the deployment this targets — one Render web service. Two processes would need the lock in the database.

2.5 The 189x egress fix

The setup: Action.context_snapshot holds the entire assembled prompt for a turn — about 74 KB per row, 94% of the database.

The bug: every adventure load pulled that column for every action, to read two small fields out of it (the world-state delta, for the "what changed" chip, and the applied report). SQLAlchemy loads all columns by default.

The fix, in three parts:

1. Move the two small things that are needed for every action into their own column (Action.world_delta).
2. Mark the heavy columns deferred — context_snapshot, variants, reasoning — so they're only fetched when explicitly asked for.
3. Backfill the new column with dialect-specific server-side SQL, so the old data is extracted inside the database and never crosses the wire.

The result: one adventure load went from 38.5 MB to 0.20 MB.

The part that makes it stick: tests/test_egress.py hooks into SQLAlchemy's before_cursor_execute event, captures every statement the ORM sends, and fails if a bulk load ever names those columns again. The regression is caught by asserting on the SQL, not on a timing.

One more detail from that test's design: the count query is written as a real SELECT count(...) rather than query.count(), because SQLAlchemy's .count() wraps the entity select in a subquery, so the emitted SQL names every column — including the deferred ones. No bytes come back either way, but the database still has to read them, and a guard that greps SQL cannot tell the two apart.

There's a companion denormalization for the same reason: variants is deferred, so variant_count exists as its own column to answer "how many attempts?" without fetching them. set_variants() is the only function allowed to write variants, precisely so the two can't drift and the pager can't lie about how many takes a turn has.

2.6 Migrations, hand-rolled

No Alembic. An append-only list of (version, SQL) pairs, with the current version stored in SQLite's PRAGMA user_version or a one-row table on Postgres. 37 versions so far.

• A fresh database is created by Base.metadata.create_all() (always current) and stamped at the latest version — it never replays history.
• An existing database runs every migration above its stored version, in order.

Why this and not Alembic: for a single-file SQLite app that a user might have been running for months, the entire requirement is "add a column, don't lose their data". Alembic's autogenerate, branching, and down-migrations are machinery for a team with a staging environment. This is 250 lines and you can read all of it.

The constraint it creates is written at the top of the file: change models.py (so fresh databases are current) and append a pair here (so existing ones upgrade). Migrations 2–23 predate Postgres support and use SQLite-only syntax — harmless, because every Postgres database starts fresh and never replays them, but anything added since must run on both dialects.

One migration worth reading (#10, repairing duplicate action indexes) uses UPDATE … FROM with a window function rather than a correlated subquery, because SQLite may evaluate a correlated subquery against partially-updated rows and produce duplicates again while "repairing" them.

---

Part 3 — Production concerns

3.1 Two modes, one codebase

AIDND_MULTI_USER switches the whole app between two personalities:

	Local (default)	Hosted
Users	One auto-created "local user"	Guest on first visit, optional account
Auth	None — no cookies, no login UI	Signed session cookie
Rate limits	Off	On
Row caps	Off	On
API docs (/docs)	On	Off
Provider	Whatever Settings points at	User's key, or the shared demo key

The reasoning: a person running this on their own laptop should never be throttled by their own app, never see a login screen, and should get the interactive API docs. A hosted deployment needs all four of those to be the opposite. Rather than two builds, the differences are gated at each site.

Guests upgrade in place. A visitor gets a guest User row on first load. Registering sets email and password_hash on that same row — so every adventure they played as a guest survives with no re-parenting and no migration step. Three kinds of row share the users table: local (email NULL, not guest), guest (email NULL, guest), registered (email set).

3.2 The shared demo key

The demo lets people play with no signup and no API key, on a key the server pays for. That is a spending surface, so it's the most defended code in the project.

resolve_provider_config() is the single place the BYOK-vs-demo decision is made, and on the demo branch it pins two things:

• The model — to a whitelist. A caller-supplied override or a hand-edited settings row can't aim a server-funded key at an expensive model. Anything unrecognised falls back to the first whitelisted model.
• The endpoint — to the configured demo URL. Otherwise the key could be redirected to a URL the user controls and harvested.

Plus a daily per-user turn cap (default 20), checked before the player's input is stored so a capped player doesn't get their message saved with no reply, and counted only after a successful turn.

There's a defensive __post_init__ on the config object that raises if a demo config somehow carries a non-whitelisted model. The comment on it records a real bug: the check tests using_demo, not api_key == DEMO_API_KEY. Keying on the key value looks stricter but is wrong — the demo key is an ordinary OpenRouter key, so a user can legitimately paste that same key into their own settings as BYOK, and then every resolution raised, 500ing even GET /auth/me and taking the whole SPA down. using_demo is what actually means "the server is paying".

Background work (summarization, embeddings) is excluded from the demo key entirely — those are unmetered calls, and unmetered calls on a server-funded key is a bill.

3.3 Secrets

Everything derives from one server-side secret (AIDND_SECRET_KEY).

Thing	Mechanism
Passwords	hashlib.scrypt, N=2^14, r=8, p=1, per-password salt, constant-time compare. Stdlib, so no extra dependency.
Sessions	v1.<user_id>.<HMAC-SHA256>, no expiry — long-lived guest sessions are the point.
Stored LLM API keys	Fernet (AES) encryption at rest, key derived from the secret, enc: prefix so legacy plaintext rows are recognisable and migratable.

The secret auto-generates into a file next to the database for local installs (zero config), but multi-user mode refuses to start without the env var — with an error message that explains why and gives you the command to generate one. Hosted filesystems are ephemeral; a regenerated secret on every deploy would silently log out every user and orphan their stored API keys.

A rotated secret makes stored keys undecryptable. decrypt_secret treats that as "unset" rather than raising, so the user just re-enters their key instead of hitting a 500.

3.4 Abuse guards

Guard	Value
Turn generation	10 / minute
Auth attempts	10 / 5 min, per IP
Guest creation	30 / 5 min, per IP (each guest is a DB row)
Script test runs	30 / minute (each costs up to 2s CPU)
Connection test	10 / minute (outbound HTTP to a user-supplied URL)
Adventures / scenarios / scripts per user	100 / 200 / 200
Actions per adventure	5,000
Request body	2 MB, 20 MB on import endpoints

Rate limits are keyed per user when one is known (accounts survive IP changes) and per IP otherwise, in fixed windows held in memory, with a pruning pass so the per-IP dict can't grow without bound. Import endpoints check bundle list lengths against the same caps live creation enforces — otherwise the cap is trivially bypassed by uploading a file.

Security headers on every response: nosniff, X-Frame-Options: DENY, Referrer-Policy: same-origin, and a CSP allowing exactly what the SPA uses — same-origin everything, inline styles (React needs them), Google Fonts.

3.5 Deployment

One Docker web service on Render, serving the SPA and the API same-origin, with Postgres on Neon.

The Postgres decision was forced: Render's free tier has no persistent disk, so a SQLite file wouldn't survive a deploy. The database lives off-box on Neon's free tier.

Two things worth knowing about the free tier:

• The service sleeps after ~15 minutes idle, and the first request then takes 30–60s.
• /api/health deliberately doesn't touch the database, so a keep-warm pinger wakes the web service without waking the database. Waking a database around the clock costs far more than the cold start is worth.

CI runs the backend tests, the frontend lint and build, and a Docker image build on every push.

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Part 4 — The web plumbing, briefly

For the parts that are just how the web works, not decisions.

Frontend and backend are two programs. In development they're two servers — Vite on 5173 serving React, FastAPI on 8000 serving the API — and Vite proxies /api to FastAPI so the browser thinks it's all one origin (which avoids CORS entirely). In production there's one server: FastAPI serves the built React files as static assets from the same port.

SPA routing. React Router handles URLs like /play/3 in the browser without a round trip. But if you reload that URL, the browser asks the server for /play/3, which isn't a file. So SPAStaticFiles catches the 404 and returns index.html, letting React take over and read the URL itself. API routes are matched before the static mount, so they're unaffected.

Sessions. A cookie is a small value the browser stores and automatically attaches to every request to that site. Here it holds v1.<user_id>.<signature>. The server doesn't store sessions anywhere — it re-verifies the signature on each request, which is why there's no session table.

The 401 retry. If the cookie is missing or stale, any API call returns 401. The frontend catches that once, calls /api/auth/me (which mints a fresh guest session), and retries the original request. So a returning visitor with an expired cookie never sees an error.

React, in one paragraph. A component is a function that returns a description of some UI. useState holds a value; changing it re-renders the component. The streaming turn is the clearest example: each SSE chunk appends to a state string, React re-renders, and the text appears to type itself.

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Part 5 — Measured results and known limitations

Measured results

Database egress per adventure load	38.5 MB → 0.20 MB (~189x)
Prompt snapshot size	~74 KB/turn, 94% of the database
Turn read cost at turn 200	839 KB → 129 KB, flat after ~turn 50
Length-hint phrasing	174 → 246 words phrased as a budget; 170 phrased as a ceiling (n=5)
Backend tests	151, LLM mocked, real QuickJS engine
Schema versions	37
Sandbox limits	16 MB, 2 s CPU, fresh context per run
Context defaults	author's note at depth 3, cards capped at 40% of elastic budget
Memory cadence	memory / 6 turns, summary / 15 turns, top-5 retrieval

Two of the tests encode a performance property rather than a behaviour: test_egress.py asserts on the SQL the ORM emits, and test_history_window.py asserts that the read cost stops growing with story length.

Known limitations

Deliberate trades for a single-user-first app that also happens to be hosted, listed so nobody has to discover them the hard way.

• Single process. The turn lock, the rate limiter and the summarization task all assume one worker. A second worker would need the lock in the database (a row-level advisory lock) and the rate limiter in Redis.
• No vector index. Retrieval does cosine similarity in Python over the whole bank. Fine at the 200-memory cap; at 10,000 it would want pgvector.
• Prompt snapshots are heavy even after the egress fix — they're deferred, not smaller. Compressing them or expiring old ones is the real fix.
• In-memory rate-limit windows reset on restart, so a restart grants a brief extra allowance.
• Background summarization is a fire-and-forget asyncio task, so it does not survive a restart. At real load it belongs in a queue.
• The demo key depends on a free-tier provider's daily cap, which the app can only detect after the fact by string-matching the 429 body.

Cleanup backlog

docs/self-review.md carries an open list of non-bugs — reuse, simplification and efficiency items — kept deliberately separate from the correctness list, which is empty. The largest ones:

• Section.tokens is uncached, so the context gets tokenized two or three times a turn.
• onModelContext flattens system and story into one string before handing it to user scripts; if a script modifies it, the structure is gone and everything ships as user content. Passing structure through the hook would be better but would break AI Dungeon compatibility, which is the point of the feature.
• The import endpoints hand-coerce raw dicts instead of using Pydantic bundle schemas.
• Action has no UniqueConstraint('adventure_id', 'index'); index allocation is ad-hoc per writer, and a database constraint would make the turn-lock race impossible rather than merely fixed.

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Source: github.com/parththakkar106/AI-DnD · Project page