Learn & remember anything. An agent-native, topic-agnostic spaced-repetition recall engine.

The engine knows nothing about any subject. A topic is a plug-in: config + cards + (later) practice prompts, all data, no code. Spanish-conversation practice is the first instance, not a special case.

• Scheduling: FSRS-6 via ts-fsrs
• Storage: files are the source of truth (item.md w/ frontmatter); a derived bun:sqlite index makes due-queries O(due)
• Grading: deterministic rubric (evaluateAnswer) — code decides the rating, not the agent, so FSRS stays consistent
• Stack: Bun + TypeScript

One command — seeds a starter pack (Conversational Mexican Spanish), boots the app, opens your browser:

bunx @waits/recallit start

Keyless by default: real cards, real grading, no API spend. Paste an ANTHROPIC_API_KEY at the prompt (or set it in your environment) to enable the live AI tutor. Data persists to ~/.recallit.

src/
  types.ts      generic domain types (no topic specifics)
  paths.ts      data-dir layout
  card.ts       item.md <-> RecallCard (+ FSRS) serialization
  scheduler.ts  ts-fsrs wrapper: gradeCard, previewSchedule (pure)
  evaluate.ts   deterministic answer grader
  db.ts         derived sqlite index: due-queries + rebuild
  topic.ts      topic CRUD + active-topic selection
  store.ts      card CRUD + reviewCard (file + index + log, consistent)
  pack.ts       topic-pack spec: manifest/card schemas + loadPack
  install.ts    installPack: validate + materialize a pack into ~/.recallit
  resolve.ts    resolve `topic add` source (dir/tarball/git/npm) to a pack dir
  server.ts     Bun HTTP+WS voice host
  index.ts      public API barrel (also the engine's importable package surface)
  cli.ts        headless CLI harness

Data lives under ~/.recallit (override with RECALLIT_DATA_DIR). Subjects ship as packs under packs/<id>/; install with topic add (see ARCHITECTURE.md → Topic packs).

bun install
bun test            # unit + integration
bun run typecheck   # tsc --noEmit
bun run lint        # biome

export RECALLIT_DATA_DIR=./data
bun run cli topic create capitals --name "World Capitals" --goal cards_recalled
bun run cli card add --front "France" --back "Paris"
bun run cli due
bun run cli answer <cardId> "Paris"    # evaluate spoken/typed answer -> auto-grade -> reschedule
bun run cli preview <cardId>           # next interval per rating
bun run cli stats

The engine primitives are exposed to a Claude agent as in-process MCP tools. The agent drives the review turn-by-turn; the turn machine (turn.ts) enforces "respond before reveal" and the rating is always engine-computed (evaluate.ts), so the agent never fudges scheduling.

export ANTHROPIC_API_KEY=sk-ant-...
export RECALLIT_DATA_DIR=./data
bun run cli agent                      # interactive: agent presents, you answer, it grades + reschedules

Live integration test (opt-in; needs a key, makes real calls):

RECALLIT_LIVE_TEST=1 ANTHROPIC_API_KEY=sk-ant-... bun test test/agent.live.test.ts

A Bun HTTP+WS server hosts a voice review session: the browser records push-to-talk audio, the server transcribes it (STT), stores the attempt next to the card, and feeds the transcript into the same turn machine; the agent's prompts are spoken back (TTS).

export ANTHROPIC_API_KEY=sk-ant-...      # agent loop
export ELEVENLABS_API_KEY=...            # TTS + STT (Scribe) — covers voice on its own
export ELEVENLABS_VOICE_ID=...           # optional; per-topic voice
bun run serve                            # http://localhost:3000

By default both TTS and STT use ElevenLabs (Scribe). To use OpenAI gpt-4o-transcribe for STT instead, set RECALLIT_STT=openai and provide OPENAI_API_KEY (needs credits). STT/TTS sit behind SttProvider/TtsProvider interfaces (src/voice/), so providers are swappable and the WS/turn wiring is tested offline with mocks. Optional live round-trip: RECALLIT_LIVE_TEST=1 bun test test/voice.live.test.ts.

During practice the agent captures new items as cards via mine_card (src/mining.ts), which enforces the one-new-thing (i+1) rule in code: a mined card must introduce exactly one new element vs the learner's known set — more-than-one-new and duplicates are rejected. checkCardQuality (src/quality.ts) flags low-confidence cards ("needs-review") so confidently-wrong content doesn't silently enter the schedule. buildPracticePrompt adds tiered correction guidance (recast → explicit → metalinguistic).

The Spanish topic ships as a portable pack (data, not engine code) under packs/spanish-mx-rgv/ — a manifest.json + cards.json (41 cards biased toward i+1 sentences with RGV vocab) + 8 wife-conversation scenarios/ + bundled assets/*.mp3 native audio. Install it into your data dir with topic add:

bun run src/cli.ts topic add packs/spanish-mx-rgv      # install (materializes cards + audio)
bun run seed:spanish                                   # same thing, with --force (re-seed)
bun run src/cli.ts topic add packs/spanish-mx-rgv --no-audio   # skip bundled audio

A pack is the durable, versioned unit of a subject (see ARCHITECTURE.md → Topic packs); installPack (src/install.ts) validates the manifest, checks the engine range, and projects it into data/topics/<id>/ through the engine primitives.

bun run cli daily runs the full multi-phase regimen as one autonomous agent loop — the orchestration is just a prompt (buildDailySessionPrompt) over the existing primitives, parameterized by the topic's modality (voice topics add shadowing + roleplay; text topics run review + reflect). Each phase is checkpointed (src/checkpoint.ts) under a stable per-day session id, so a killed session resumes from the last completed phase. Habit state (streak, longest, danger-zone) lives in progress.json; the browser client shows due / done-today / streak / goal-metric and warns when a streak is at risk (GET /api/progress).