Joseph Doherty
afd1a50958
Phase 2 T43 added a SECOND ACTIVITIES: block to render guest activity separately from you+speaker. Two consecutive ACTIVITIES: headers can read as a duplicate-section bug to the LLM and bloat the prompt. Consolidate to a single ACTIVITIES: block whose body is composed from up to three bullets (you, speaker, guest). The block itself is MUST-tier (always renders); bullet-level trim drops bullets in the order guest -> group node -> you -> other edges, with the speaker bullet as the MUST-tier floor (the speaker's own current activity is the load-bearing slice). Implementation chose Option B from the polish plan: pre-truncate the bullets list at trim time before _build_activity_block runs, rather than introduce a granular tier mode in the trim machinery. Rationale documented in code; the existing block-level trim ladder gains a single new toggle (include_you_activity) and the SHOULD-tier guest_activity_block is gone. Tests: - test_single_activities_block_with_three_bullets_when_3_entities: exactly one ACTIVITIES: header with all three entity bullets. - test_tight_budget_drops_guest_activity_bullet_first: speaker bullet survives, guest bullet absent under tight budget. - Existing test_assemble_with_tight_budget_drops_guest_activity_first still passes (asserts on bullet absence, not block-header absence).
chat
A local-first roleplay chat engine that treats fiction as a simulation, not a chat log.
The LLM is a renderer for structured world state — it does not hold the state. State lives in an event-sourced SQLite database and is projected on demand. Models can be swapped freely behind a stateless generate(prompt, params) -> text interface.
Status: design phase. No code yet. See rp-engine-design.md for the full design and CLAUDE.md for the working summary and conventions.
Why
Conventional RP chatbots have three persistent failure modes:
- Memory loss — old context drops as history grows.
- Quality decay — bots get terse and generic over long conversations.
- Stale state pollution — bots fixate on past props (the "picnic basket" problem: bring a basket to one scene, the bot reaches for it forever).
The fix is to model the world as structured state — locations, time, who's present, what they're doing, what they remember, how they feel about each other — and use the LLM only to render that state into prose.
Scope
Deliberately small, so the design can be made to actually work:
- Single user, single machine.
- Maximum 3 entities per scene:
you+ up to 2 bots. The 3-entity cap is load-bearing — it makes the relationship graph fully enumerable (6 directed edges + 1 group node). - Chat-only. No voice, no real-time.
Multi-session casts and N-entity scenes are explicit non-goals for v1.
How it works (at a glance)
- Entities (
you,botA,botB) have identity, state (mood/goals/status), an activity record (where they are, what they're doing, what they're holding, where their attention is), and per-POV memory. - Containers (car, restaurant booth, room) hold entities in defined slots and provide spatial constraints the model can reason over.
- Relationship graph: 6 directed edges + 1 group node. Asymmetric feelings are first-class — BotA can secretly resent BotB while BotB thinks they're best friends.
- Witnessed-by flags: every memory carries a 3-bit
[you, botA, botB]mask. A speaker can only retrieve memories their bit is set on. This is what stops bots referencing things they couldn't possibly know. - Events have lifecycles (
planned → active → completed) and own their own props. When the picnic ends, the basket goes back into the closed event record. Only narrative gist, acquired objects, learned facts, and relationship changes promote to permanent memory. - Per-POV scene summaries: every witness gets their own version of a closed scene, written from their angle. Different details, different interpretations. This is what gives bots inner lives.
- Event sourcing: state is a projection of an append-only event log. Free rewind, branching ("what if BotA had said yes"), surgical delete with impact preview, and survivable schema changes — all fall out for free.
Architecture
┌──────────────────────────────────────────────┐ ┌────────────────────────┐
│ Mac (always-on) │ │ Inference endpoint │
│ │ │ (stateless) │
│ Web UI │ │ │
│ Orchestrator │ → │ Anthropic API │
│ Event log + projector ← SQLite (one file) │ │ OpenAI / OpenRouter │
│ Persistence + retrieval + prompt builder │ │ Local MLX / llama.cpp │
│ │ │ Rented GPU │
└──────────────────────────────────────────────┘ └────────────────────────┘
The Mac side holds everything that survives — state, history, retrieval, orchestration. Inference is a swappable, stateless service. State outlives any one model.
Stack
- SQLite (single file) for everything structured. WAL mode, foreign keys on, each turn in a transaction.
- sqlite-vss / sqlite-vec for embedding search in the same DB file (Phase 4).
- JSON for snapshots, character templates, scene exports.
- No Postgres. No Redis. No Pinecone. No Docker.
Roadmap
- Core loop — schema, entities + edges, single container, event log + projector, single-bot conversation, one LLM backend, streaming UI, manual rollback.
- Multi-entity — second bot, group node, scene configurations, witness filtering, per-POV memories, activity/containers, scene transitions with compression.
- Events & skips — event queue with triggers, time skips (elision and jump), active threads, significance classifier.
- Polish — vector retrieval, branching, surgical delete + regenerate, snapshots, backup automation, impact-preview UI for rewinds.
Each phase must work end-to-end before the next begins.
Repository
- rp-engine-design.md — full design document.
- CLAUDE.md — working summary and conventions for development with Claude Code.
License
TBD.