Every attempt at a turn is now its own row at the same (branch, depth),
with `live` naming the one the story tells. The JSON repeating group on
`actions.variants` is read one last time, by a migration that writes it
out as the sibling rows it always described, and then goes unread.
The snapshots turn around with it: an action carries the state it left
behind rather than the state it started from, because attempts at one
turn share a starting position and differ exactly in their outcome.
Rolling back is "what the node in front left behind", one lookup on the
path, and it is what undo and retry now both read.
And the memory holdback goes. It existed because retry rewrote a row
under a mark that had already moved past it; a retry writes a sibling
now, and replacing what a coordinate says withdraws what was derived
from it — the same repair undo and delete already made.
The assembled prompt is still stored once per turn: it moves with the
live flag, so a superseded attempt keeps only the few hundred bytes that
were its own. Measured on the 600-action fixture: 700 rows for the same
600-turn story, prompt archive byte-identical at 0.50 MB, index 1.8 kB
and page load 62.7 kB unmoved.
347 tests green. `tests/test_story_tree_baseline.py` and
`tests/test_retry_variants.py` pass unmodified — SP4 was allowed to move
the baseline for the variant-count semantics and did not need to.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017Dvvqn9ZDR4ixeFPHNbww7
The memory bank and the story summary each kept a cursor: how many story
actions they had already covered. A count is a position in a list, and this
list moves — delete an action in front of the mark and every later one slides
down a slot, so the mark now covers one it has never read. All the cursor
bookkeeping existed to patch that up.
Both marks are now (branch_id, depth): the node up to and including which the
work is done. A depth is a coordinate along a path, not an offset into a list,
so nothing in front of it can move it. That deletes rather than rewrites
`position_of_index`, `note_action_removed`, `_rewind_cursors_to_index`,
`prune_dangling_memories` and the every-pass clamp in `run_post_turn`.
A memory hangs off the node its block ends on, so a fork inherits its
ancestors' memories without copying any, and retrieval selects through the
branch clause over the *whole* lineage — recall is long-range by definition and
cannot be windowed. Measured: 1,807 B on a story forked twenty times against
1,823 B on a flat one of the same length.
Migrations 53-56 translate the old counts into nodes. They rewrite `adventures`
and not `actions`, so this one needs no VACUUM FULL.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017Dvvqn9ZDR4ixeFPHNbww7
Phase 14 SP1. The tree goes into the schema and nothing reads it yet: a
`branches` table, `branch_id`/`depth` on actions and memories, a head pointer
on adventures, migrations 46-52, and a server-side backfill that re-reads every
existing adventure as a tree with one branch. `depth` holds the number `index`
already held, gaps included, so no story changes — a linear story *is* a tree
with one branch, which is what makes the SP0 baseline passing unmodified the
pass condition rather than a hope.
The writer had to come with it. No migration will ever visit a row written
after it ran, so columns backfilled today and populated next subphase would
leave a hole exactly the width of one deploy, and from SP2 on a row without a
branch is a row no read can see. `app/tree.py` owns that: one module, because a
node written without a branch fails by disappearing rather than by raising.
Three things the schema itself insisted on:
- `adventures.head_branch_id` is a plain integer, not a foreign key. Pointing
both ways makes the two tables a cycle create_all cannot order, and its
escape hatch needs an ALTER SQLite does not have. It is a cache, and a head
naming a branch that is gone recovers onto the root.
- `lineage` is NOT NULL, so the backfill inserts `'[]'` and fills it in a
second pass guarded on `json_array_length(lineage) = 0` — not `= '[]'`,
because Postgres `json` has no equality operator.
- SQLite will not drop a column a foreign key names, which is how two existing
tests broke: they simulated an old database by rewinding the stamp while
leaving the new columns in place. Every ADD COLUMN migration is now
idempotent, and `tests/test_tree_migration.py` builds a genuine schema 45 by
rebuilding three tables from frozen DDL so the real ALTERs run.
297 tests green, 14 of them new. `branches` costs 0.1 kB of a 733.5 kB turn;
page load and index are byte-identical to the recorded figures.
The deploy that ships this needs one `VACUUM FULL actions;` on the direct
endpoint afterwards — it rewrites every row.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017Dvvqn9ZDR4ixeFPHNbww7
Migration 38 left memories.embedding in place so a rollback could still find
the vectors. Production has since been verified reading from embedding_blob,
so migration 42 drops it: 4 MB of a 99.6 MB database holding nothing anyone
reads.
Removing it surfaced a live bug. Changing your embedding model is supposed to
throw the bank's vectors away and let the post-turn pass rebuild them, because
two models' vectors are not comparable. The settings route did that by nulling
memories.embedding -- correct until 38 moved the vectors, after which it
cleared the dead column and left the blob intact with `embedded` still true.
_embed_pending filters on `embedded IS FALSE`, so it never saw those rows and
the bank went on ranking against the old model's vectors permanently.
Nothing would have reported it. cosine returns 0.0 on a width mismatch, so a
different-width model scores every memory zero and retrieval returns whichever
rows happen to sort first; a same-width model scores plausible garbage.
The bulk clear now sets both columns. It stays a bulk UPDATE rather than going
through set_vector -- loading the rows is the cost that whole path exists to
avoid -- so set_vector's docstring now names it as the one caller that
legitimately writes those columns by hand. No cache invalidation is added:
clearing `embedded` drops the rows out of the catalogue query, and set_vector
evicts each entry as the re-embed puts it back.
test_embedding_blob.py now rebuilds the pre-38 schema by hand where it tests
the backfill, since create_all no longer produces the column it converts from,
and asserts 42 removes it at the end of a full bootstrap -- 38 reads that
column and 42 drops it, so an upgrade that reordered them would arrive with an
empty bank.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017Dvvqn9ZDR4ixeFPHNbww7
Retrieval walked adventure.memories, so every turn loaded every row of the
bank with its vector attached -- 3.1 MB, 96% of everything a turn read. It
now asks SQL which memories are in play (an id and a flag per row), ranks
against vectors held in process, and fetches text only for the five it picks.
Two more callers were doing the same thing and the production SQL could not
see them: _evict_over_capacity walked the bank to count it, and _embed_pending
walked it to find the rows with no vector. Both are counts and filters the
database can do without sending anything back.
one turn 3,258.7 kB -> 723.4 kB cold, 122.3 kB warm
run_post_turn 3,139.1 kB -> 0.7 kB
Insights 3,223.7 kB -> 117.9 kB
Memories drawer ~3.1 MB -> 23.6 kB
A played turn is turn plus post-turn work: 6.4 MB down to 123 kB.
The cache needs no invalidation callbacks, which is what makes it safe. A
vector can only change through set_vector, which drops that one entry;
anything that removes a memory from play leaves the catalogue query, and
entries missing from the catalogue are dropped on the next read. So eviction,
deletion and pruning have nothing to remember to call.
memories.embedded joins the blob, for the same reason actions.variant_count
sits beside actions.variants: with the vector deferred, every "is this
embedded?" check would otherwise be a 6 KB lazy load, once per row.
Capacity drops 200 -> 80, on retrieval quality as much as cost -- ranking two
hundred memories to pick five buries the five. Eviction was measured at scale
first: trimming 100 to 80 costs 0.8 kB and reads no vectors.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CYEJKobJ2Re4Dv7qUoSA7
A 1536-dimension vector spelled out as JSON decimals is ~31 KB. The same
numbers packed as float32 are 6,144 bytes, and the whole bank is read on
every turn, so those bytes are paid over and over.
It is a format change, not a precision trade: the endpoints compute in
float32 and render that into JSON, so converting back recovers the original
bits exactly. Nothing is re-embedded and no API call is made -- migration 38
is a pure repack of what is already stored.
Unlike migrations 36 and 37 this backfill cannot be expressed in portable
SQL, so it comes through Python, batched, and pays a one-time read of every
vector to stop paying three megabytes a turn.
The JSON column stays, still written through set_vector, so a rollback finds
the vectors intact. Reading from the blob comes next; a follow-up migration
drops the old column once that is verified.
Migration SQL can now be a {dialect: sql} map -- BLOB and BYTEA have no
common spelling, and every Postgres deploy replays this one.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CYEJKobJ2Re4Dv7qUoSA7
Two reads still grew without bound after the snapshot fix.
`Action.variants` holds every discarded retry attempt, but a list response
only needs how many there are — so each retry permanently added ~5 KB to
every later load of that adventure. Defer the column and keep the count
beside it (migration 37, backfilled server-side), with set_variants() as the
one write path that keeps the two in step.
`story_actions()` walked adventure.actions, then every caller threw almost
all of it away: the builder concatenates the story and immediately cuts it
back to the token budget, the NPC check looks at the last 6, retrieval at the
last 4, the cursor clamp only wants a count. A turn on a 200-action adventure
read 839 KB to use ~70 KB, and grew with every turn played. app/context/
history.py serves those shapes from SQL; window_covering() measures the
actions it fetched and projects how many more it needs, fetching only the
part it does not already hold. Memorybank cursors move to position_of_index()
and settled_count()/settled_slice() — same arithmetic, no full list.
The scripting pipeline still receives the whole history per AI Dungeon's API,
and every helper reuses adventure.actions when it is already loaded, so a
scripted adventure pays what it always did and never twice.
Measured at production shape: retry tax 5.1 KB -> 0; turn 200 839 KB -> 129 KB
and flat from ~turn 50; a 200-turn playthrough 84.5 MB -> 23.0 MB; a delete
115 KB -> 5 KB.
Verified the window builds a byte-identical prompt to the full story across
budgets from 1K to 100K tokens, with and without the retry exclusion - this
is a cost change and nothing else. Cursor helpers checked against the old list
arithmetic, including after deleting a middle action. Counts are real
SELECT count(...): Query.count() wraps the entity select in a subquery, so the
SQL named every deferred column and the egress guard could not tell it apart
from a bulk fetch. 139 tests pass; the four new guards verified by sabotage.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UeQVy5bEjLhfgWNc27Efet
Three fallout bugs from keeping the retried action row alive (906ba42),
plus two long-standing cursor bugs the same investigation turned up.
Retry context leak: the row being regenerated is still attached to the
adventure, so it was replayed as established story and the model wrote a
continuation of the attempt it was meant to replace — the story visibly
blended both takes. It leaked into four places, not one: history replay,
story-card trigger matching, in-scene NPC detection, and the memory-bank
similarity query. Adds a shared context.story_actions(exclude_action_id),
threaded through build_context and retrieve_memories.
Memory holdback: a memory could summarize the just-generated turn; retry
rewrites Action.text but memory_cursor has already advanced, so the memory
was never regenerated and went on describing narration no longer in the
story. settled_story_actions() holds the newest action back one turn —
only the last action is retryable, so that makes it unreachable. The
settled list is always a prefix, so cursors stay valid and nothing is
skipped. The run_post_turn clamp deliberately still uses the full count:
clamping to settled rewinds legacy adventures a step and double-covers an
action.
Cursor bookkeeping: memory_cursor is a position into story_actions() while
Memory.source_* are Action.index values, and the two diverge as soon as
anything is deleted. Deleting a middle action slid a never-summarized
action into the covered range, skipping it forever; and pruning a memory
left the actions it covered stranded behind the cursor. Adds
note_action_removed() (called before the delete in delete_action and
undo_turn) and a rewind in prune_dangling_memories. delete_action also
now prunes at all, which it never did.
Not addressed: editing an already-summarized action still leaves its
memory stale, and the cumulative story summary can't have one fact
un-mixed from it.
117 backend tests pass, including new test_memory_settling.py (12) and
two retry-context regression tests.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UeQVy5bEjLhfgWNc27Efet
AI Chat is a plain scratchpad for talking to a model directly — no story
context, scripts or world state — for poking at models, prompts and endpoints
without starting an adventure. Power users only: the router 404s (rather than
403s) for everyone else and the nav link is hidden. The conversation lives in
localStorage, so there's no new table or migration.
is_power_user() now also returns True in local mode: it's the operator's own
machine and their own key, the same reasoning that makes the provider debug log
local-only.
Alongside that, the rule keeping the shared demo key off paid models now lives
in exactly one place. It had been duplicated into the chat router, which is how
one copy eventually drifts:
- resolve_provider_config() takes an optional model_override and is the only
place the whitelist is applied, so turns, AI Chat and the connection test all
inherit it. An override is a per-request preference, never a grant.
- ProviderConfig.__post_init__ refuses to exist when api_key is the demo key
and the model isn't whitelisted. It keys on the key itself rather than the
using_demo flag, so a mislabelled config can't slip past, and it raises so a
future path that bypasses the resolver fails loudly instead of billing.
- The demo branch still pins endpoint_url too — a user-controlled endpoint
would leak the key itself, which is worse than spending it.
Provider gained chat(messages, ...) beside generate(), both delegating to a
shared _stream(url, body); completion-mode endpoints get the messages flattened
into a labelled transcript. Settings' /models fetch moved to
list_endpoint_models() and is shared with /api/chat/config.
Tests: 10 new in tests/test_chat.py (70 total). These deliberately do not stub
resolve_provider_config — the point is to exercise the real BYOK-vs-demo
decision and assert on what the provider actually received: off-whitelist
override pinned, off-whitelist Settings.model pinned, redirected endpoint
pinned, BYOK passed through untouched.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FGY1yvzSeKgTtRfeVtDmx
The shared per-adventure script_state ("scoreboard" scripts write to) was
never reverted by undo, and retry re-ran the output hook on top of the already-
mutated state, double-applying its changes (e.g. "+10 gold" became +20).
Each action now snapshots script_state as it was immediately before its own
hooks ran (new Action.state_before column, migration 25):
- undo restores the turn's first-action snapshot, prunes memories that
summarized the removed actions, and takes the turn lock against races.
- retry restores the AI action's snapshot before regenerating.
Story-card mutations are not reverted (documented limit). Adds the project's
first test suite: unit + full HTTP integration through the real scripting
engine (14 tests). See plan/11-state-revert-and-retry-fix.md.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Guest-first multi-user mode behind AIDND_MULTI_USER (local installs
unchanged): signed-cookie guest sessions bootstrapped by /api/auth/me,
register upgrades the guest in place, login/logout, per-IP rate limits.
Every router scoped by user_id; Settings become per-user with the API
key Fernet-encrypted at rest and write-only through the API. Users
without a key get a server-funded demo key (OpenRouter free models,
20 turns/day, memory bank disabled on demo turns). Public read-only
demo scenarios (seed_demo.py); debug log restricted to local mode.
Frontend: auth modal + guest nudge, 401 re-establish/retry, demo
banner and key management in Settings.
Migrations 13-23 adopt existing data under a local user and encrypt
stored keys. Verified: migration on a copy of real data.db, two-session
isolation + register/login via curl and Chrome, demo cap 429, live
OpenRouter turn through the encrypted-key path, vite build + oxlint.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KFsGHju9szibJJa2YJcdbg
Backend:
- provider: fall back to parsing a plain JSON body when a server ignores
stream=true (was: silent empty turn); error if response has no text (#5)
- memorybank: clamp cursors after undo/retry shrinks the action list, and
translate summary_cursor (list position) to an Action.index boundary
before comparing with Memory.source_end (#7)
- memorybank: pinned memories now count toward the top_k budget (#8)
- memorybank: cosine() returns 0.0 on dimension mismatch; changing the
embedding model clears stored vectors so they re-embed (#9)
- scripting: MAX_STORY_CARDS cap now counts cards inserted during the
hook, so a script can't add unbounded cards in one turn (#10)
- settings: /test tolerates non-dict JSON from /models (#13)
- scenarios: import accepts worldInformation as a story-card source (#14)
Frontend:
- per-key debounce timers in PlotPanel and ScenarioEditor — editing two
things within 600ms no longer drops the first save (#15, #16)
- Continue button no longer discards typed input (#17)
- failed retry resyncs actions from the server instead of leaving the
removed action missing (#18)
- Settings save/test surface errors instead of hanging on Testing… (#19)
- InsightsPanel ignores stale responses from superseded requests (#20)
- placeholder scan includes story-card trigger keys (#21)
addStoryCard returning the 0-based index (falsy for the first card) matches
real AI Dungeon per the scripting guidebook — kept, documented (#11).
Statuses updated in CODE_REVIEW_FINDINGS.md; stale entries for previously
fixed items (#1-4, #6, #12) corrected.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KFsGHju9szibJJa2YJcdbg
