This is the rc.4 batch the user requested:
1. Emergency triggers (low HP + close hostile, lava-under-foot)
bypass the long cooldown so the LLM is consulted BEFORE the bot
dies, not after.
2. Active manifesto need is now included in the advisor user prompt
— the LLM picks suggestions that satisfy the bot's current
concrete need (L2 tools_wood → "gather logs nearby" not
"explore further").
3. Every advisor recommendation is persisted to SQLite
(advisor_recommendations table) with full token usage. The
reflex marks 'applied=1' when it dispatches and updates
outcome_ok/code when the dispatch completes. Ground truth for
"is the LLM actually helping" lives in the DB, not in logs.
4. Pi CLI is OUT of every background loop. coach/postmortem and
coach/reflect now go through the same TimeWeb endpoint
fast-advisor uses, via the shared coach/llm-call.js helper.
Pi is reserved for manual operator commands.
5. The LLM (postmortem, reflect, advisor) can flag "structural
gaps" — missing skills/features the operator should implement.
These land in the new improvement_requests table. Dedup by
title bumps `votes` instead of inserting duplicates so the
queue doesn't bloat. Operator views via
`node scripts/list-improvements.js`.
6. A deterministic trigger-tuner runs hourly: reads 24h of
recommendation stats, flags triggers whose success rate is
below 25% (sample ≥ 5) or whose prompts are expensive (>1000
input tokens) with mediocre payoff. Improvements get
source="tuner", category="tuning". No LLM call.
New files:
runtime/coach/llm-call.js — askAnalytical() helper
runtime/coach/trigger-tuner.js — stats → improvements
runtime/coach/trigger-tuner.test.js
scripts/list-improvements.js — operator CLI
Schema additions:
advisor_recommendations: id, ts, trigger_reason, planned_skill,
recommended_skill, action, rationale, active_need, tokens_in,
tokens_out, latency_ms, applied, outcome_ok, outcome_code, outcome_at
improvement_requests: id, ts, source, category, title, description,
context, priority, status, duplicate_of, votes, implemented_at, notes
Renamed env-var consumers:
Pi-coach drainOnce({ askPi }) → drainOnce({ askAnalyticalFn? })
Pi-reflect runOnce({ askPi }) → runOnce({ askAnalyticalFn? })
bot.js attachCoach/attachReflect no longer pass askPi
attachTuner() added to bot.js spawn handler
lessons.source 'pi-coach' → 'timeweb-coach'
lessons.source 'pi-reflect' → 'timeweb-reflect'
Token cost measured live:
~705 input + 45 output = ~750 total per advisor call
worst case @ 6 calls/hour rate cap = ~108K tokens/day
OpenAI gpt-5-mini reference price: ~$0.60/month
Operator usage:
node scripts/list-improvements.js # open queue
node scripts/list-improvements.js --stats # advisor performance
node scripts/list-improvements.js --done 17 "shipped in 0.3.1"
node scripts/list-improvements.js --reject 18 "duplicate"
Tests: 360 green (was 332, +28 new).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
429 lines
14 KiB
JavaScript
429 lines
14 KiB
JavaScript
// Death post-mortem coach.
|
|
//
|
|
// On every `bot.death` event we capture the surrounding context (last
|
|
// skill, last hostile, recent log/scenario tail, inventory before/after)
|
|
// and write a row into the `deaths` table of the knowledge DB.
|
|
//
|
|
// A separate slow loop drains `unanalysedDeaths()` and asks Pi to extract
|
|
// generalised lessons. Pi calls are rate-limited and deduped — many
|
|
// near-identical deaths produce ONE lesson, not 50.
|
|
//
|
|
// Lessons land in the `lessons` table and feed runtime/knowledge/recall()
|
|
// for future skill dispatch decisions.
|
|
//
|
|
// This file is import-safe: side-effect-free, attaches only when
|
|
// `attach(bot, ctx)` is called explicitly from bot.js.
|
|
|
|
import { existsSync, readFileSync } from "node:fs";
|
|
import { resolve } from "node:path";
|
|
import {
|
|
isAvailable as knowledgeAvailable,
|
|
insertDeath,
|
|
insertPostmortem,
|
|
markDeathAnalysed,
|
|
unanalysedDeaths,
|
|
record as recordLesson,
|
|
poiNearby,
|
|
recordPOI,
|
|
createImprovementRequest,
|
|
} from "../knowledge/index.js";
|
|
import { isRegistered, skillRegistryPrompt } from "../skill-registry.js";
|
|
import { isAvailable as llmAvailable } from "../llm/provider.js";
|
|
import { askAnalytical } from "./llm-call.js";
|
|
import { info, warn } from "../log.js";
|
|
|
|
const COACH_INTERVAL_MS = 5 * 60 * 1000; // 5 min between coach passes
|
|
const COACH_BATCH_MAX = 8; // up to 8 deaths per LLM call
|
|
const COACH_BUDGET_PER_HOUR = 3; // ≤ 3 analytical LLM calls/hour
|
|
const COACH_COOLDOWN_MS = 12 * 60 * 1000; // 12 min between calls
|
|
const RECENT_CHAT_TAIL = 6;
|
|
const SCENARIO_TAIL = 12;
|
|
|
|
let _attached = null;
|
|
let _llmCallTimes = [];
|
|
let _coachTimer = null;
|
|
let _lastInventory = null;
|
|
|
|
export function attach(bot, ctx = {}) {
|
|
if (_attached) {
|
|
warn("coach", "attach() called twice; ignoring second attach");
|
|
return;
|
|
}
|
|
if (!bot) return;
|
|
_attached = { bot, ctx };
|
|
|
|
// Snapshot inventory each tick (cheap) so death captures what was lost.
|
|
bot.on?.("playerCollect", () => { _lastInventory = snapshotInv(bot); });
|
|
bot.on?.("spawn", () => { _lastInventory = snapshotInv(bot); });
|
|
|
|
bot.on?.("death", () => {
|
|
try {
|
|
const death = captureDeath(bot, ctx);
|
|
if (!death) return;
|
|
const deathId = insertDeath(death);
|
|
info("coach", `death recorded id=${deathId ?? "-"} cause=${death.cause} hostile=${death.hostile ?? "?"} skill=${death.lastSkill ?? "?"}`);
|
|
// v0.2.0-rc.3 — mark this spot as a danger POI so spatial recall
|
|
// surfaces it next time the bot comes near. Expires after 6 hours
|
|
// so the danger doesn't outlive its relevance.
|
|
if (typeof death.x === "number" && typeof death.z === "number") {
|
|
recordPOI({
|
|
kind: "danger",
|
|
name: death.hostile ?? death.cause ?? "death",
|
|
x: death.x, y: death.y ?? 64, z: death.z,
|
|
expiresAt: Date.now() + 6 * 3600_000,
|
|
notes: `death id=${deathId} cause=${death.cause}`,
|
|
});
|
|
}
|
|
} catch (e) {
|
|
warn("coach", `captureDeath failed: ${e?.message ?? e}`);
|
|
}
|
|
});
|
|
|
|
// v0.3.0 — postmortem analysis runs through TimeWeb (the fast LLM
|
|
// provider). Pi CLI no longer drives this loop. The drain timer
|
|
// fires regardless of whether TimeWeb is configured; drainOnce()
|
|
// short-circuits when the LLM is unavailable.
|
|
if (!_coachTimer) {
|
|
_coachTimer = setInterval(() => {
|
|
drainOnce({ stateDir: ctx.stateDir }).catch((e) =>
|
|
warn("coach", `drain error: ${e?.message ?? e}`),
|
|
);
|
|
}, COACH_INTERVAL_MS);
|
|
_coachTimer.unref?.();
|
|
info("coach", `attached; drain every ${COACH_INTERVAL_MS / 1000}s${llmAvailable() ? " (TimeWeb)" : " (LLM disabled — deaths captured only)"}`);
|
|
}
|
|
}
|
|
|
|
export function detach() {
|
|
if (_coachTimer) {
|
|
clearInterval(_coachTimer);
|
|
_coachTimer = null;
|
|
}
|
|
_attached = null;
|
|
}
|
|
|
|
function snapshotInv(bot) {
|
|
try {
|
|
const items = bot.inventory?.items?.() ?? [];
|
|
const dict = {};
|
|
for (const i of items) dict[i.name] = (dict[i.name] || 0) + i.count;
|
|
return dict;
|
|
} catch {
|
|
return null;
|
|
}
|
|
}
|
|
|
|
function diffInv(before, after) {
|
|
if (!before) return null;
|
|
const lost = [];
|
|
for (const [name, count] of Object.entries(before)) {
|
|
const now = after?.[name] ?? 0;
|
|
if (now < count) lost.push({ name, count: count - now });
|
|
}
|
|
return lost.length ? lost : null;
|
|
}
|
|
|
|
function captureDeath(bot, ctx) {
|
|
const pos = bot.entity?.position;
|
|
const lastInv = _lastInventory;
|
|
const nowInv = snapshotInv(bot);
|
|
const inventoryLost = diffInv(lastInv, nowInv);
|
|
|
|
const currentTask = readCurrentTask(ctx.stateDir);
|
|
const lastSkill = currentTask?.label ?? null;
|
|
const lastSkillCode = currentTask?.lastCode ?? null;
|
|
|
|
const hostile = closestHostileName(bot);
|
|
const cause = inferCause({ bot, hostile, lastSkill, lastSkillCode });
|
|
|
|
const recent = readRecentScenarios(ctx.stateDir, SCENARIO_TAIL);
|
|
const journalNearby = readJournalNearby(ctx.stateDir, pos, 32);
|
|
const chatTail = ctx.chatHistory?.recent?.(RECENT_CHAT_TAIL) ?? null;
|
|
|
|
const contextBlob = {
|
|
recentScenarios: recent,
|
|
journalNearby,
|
|
chatTail,
|
|
snapshot: {
|
|
pos,
|
|
hp: bot.health,
|
|
food: bot.food,
|
|
time: bot.time?.timeOfDay ?? null,
|
|
isRaining: !!bot.isRaining,
|
|
},
|
|
};
|
|
|
|
return {
|
|
ts: Date.now(),
|
|
x: pos?.x ?? null,
|
|
y: pos?.y ?? null,
|
|
z: pos?.z ?? null,
|
|
cause,
|
|
hostile,
|
|
lastSkill,
|
|
lastSkillCode,
|
|
hp: 0,
|
|
food: bot.food ?? null,
|
|
inventoryLost,
|
|
contextBlob,
|
|
};
|
|
}
|
|
|
|
function closestHostileName(bot) {
|
|
try {
|
|
const me = bot.entity?.position;
|
|
if (!me) return null;
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
for (const e of Object.values(bot.entities ?? {})) {
|
|
if (!e || e === bot.entity) continue;
|
|
if (e.type !== "hostile" && e.kind !== "Hostile mobs") continue;
|
|
const d = e.position?.distanceTo?.(me) ?? Infinity;
|
|
if (d < bestDist) {
|
|
best = e.name ?? e.mobType ?? null;
|
|
bestDist = d;
|
|
}
|
|
}
|
|
return best;
|
|
} catch {
|
|
return null;
|
|
}
|
|
}
|
|
|
|
function inferCause({ bot, hostile, lastSkill, lastSkillCode }) {
|
|
const y = bot.entity?.position?.y;
|
|
if (hostile) return "hostile";
|
|
if (typeof bot.food === "number" && bot.food <= 0) return "starvation";
|
|
if (typeof y === "number" && y < 30) return "fall";
|
|
if (lastSkillCode === "drowning") return "drowning";
|
|
if (lastSkillCode === "lava") return "lava";
|
|
return "unknown";
|
|
}
|
|
|
|
function readCurrentTask(stateDir) {
|
|
if (!stateDir) return null;
|
|
const f = resolve(stateDir, "current-task.json");
|
|
if (!existsSync(f)) return null;
|
|
try { return JSON.parse(readFileSync(f, "utf8")); } catch { return null; }
|
|
}
|
|
|
|
function readRecentScenarios(stateDir, n) {
|
|
if (!stateDir) return [];
|
|
const f = resolve(stateDir, "scenarios.jsonl");
|
|
if (!existsSync(f)) return [];
|
|
try {
|
|
const raw = readFileSync(f, "utf8");
|
|
const lines = raw.split("\n").filter(Boolean);
|
|
const tail = lines.slice(-n);
|
|
return tail.map((l) => {
|
|
try { return JSON.parse(l); } catch { return null; }
|
|
}).filter(Boolean);
|
|
} catch {
|
|
return [];
|
|
}
|
|
}
|
|
|
|
function readJournalNearby(stateDir, pos, radius) {
|
|
if (!stateDir || !pos) return [];
|
|
const f = resolve(stateDir, "world-journal.jsonl");
|
|
if (!existsSync(f)) return [];
|
|
try {
|
|
const raw = readFileSync(f, "utf8");
|
|
const lines = raw.split("\n").filter(Boolean).slice(-200);
|
|
const out = [];
|
|
for (const l of lines) {
|
|
let row;
|
|
try { row = JSON.parse(l); } catch { continue; }
|
|
const a = row.at;
|
|
if (!a) continue;
|
|
const dx = a.x - pos.x;
|
|
const dz = a.z - pos.z;
|
|
if (dx * dx + dz * dz <= radius * radius) out.push(row);
|
|
}
|
|
return out.slice(-20);
|
|
} catch {
|
|
return [];
|
|
}
|
|
}
|
|
|
|
/**
|
|
* One pass: take up to COACH_BATCH_MAX unanalysed deaths, summarise them
|
|
* for Pi, parse the JSON reply, write lessons + postmortems.
|
|
*
|
|
* Rate-limited: at most COACH_PI_BUDGET_PER_HOUR calls/hour, with
|
|
* COACH_COOLDOWN_MS gap between calls.
|
|
*/
|
|
export async function drainOnce({ stateDir, force = false, askAnalyticalFn = askAnalytical } = {}) {
|
|
if (!knowledgeAvailable()) return { ok: false, reason: "knowledge unavailable" };
|
|
if (!llmAvailable()) return { ok: false, reason: "llm not configured" };
|
|
|
|
const now = Date.now();
|
|
const hourAgo = now - 60 * 60 * 1000;
|
|
_llmCallTimes = _llmCallTimes.filter((t) => t > hourAgo);
|
|
if (!force && _llmCallTimes.length >= COACH_BUDGET_PER_HOUR) {
|
|
return { ok: false, reason: "hourly budget exhausted", calls: _llmCallTimes.length };
|
|
}
|
|
if (!force && _llmCallTimes.length > 0 && now - _llmCallTimes[_llmCallTimes.length - 1] < COACH_COOLDOWN_MS) {
|
|
return { ok: false, reason: "cooldown" };
|
|
}
|
|
|
|
const pending = unanalysedDeaths({ limit: COACH_BATCH_MAX });
|
|
if (pending.length === 0) return { ok: true, analysed: 0 };
|
|
|
|
const { system, user } = buildPrompt(pending);
|
|
_llmCallTimes.push(now);
|
|
|
|
const parsed = await askAnalyticalFn({ system, user, json: true });
|
|
if (!parsed) return { ok: false, reason: "no reply" };
|
|
const reply = typeof parsed === "string" ? parsed : JSON.stringify(parsed);
|
|
|
|
let lessonsCount = 0;
|
|
let rejectedPreferCount = 0;
|
|
for (const item of asArray(parsed.lessons ?? parsed)) {
|
|
if (!item || !item.lesson) continue;
|
|
// Skill ids referenced by Pi must be in the live registry.
|
|
// Mode names (e.g. "night_shelter") are tolerated at write time and
|
|
// translated at consult time by advice.js#normalisePreferSkill.
|
|
let preferSkill = item.prefer_skill ?? null;
|
|
if (preferSkill && !isRegistered(preferSkill) && !isLikelyModeName(preferSkill)) {
|
|
rejectedPreferCount += 1;
|
|
preferSkill = null;
|
|
}
|
|
let avoidSkill = item.avoid_skill ?? null;
|
|
if (avoidSkill && !isRegistered(avoidSkill) && !isLikelyModeName(avoidSkill)) {
|
|
avoidSkill = null;
|
|
}
|
|
recordLesson({
|
|
text: item.lesson,
|
|
category: item.category ?? "survival",
|
|
triggerSkill: item.trigger_skill ?? null,
|
|
triggerHostile: item.trigger_hostile ?? null,
|
|
triggerSituation: item.trigger_situation ?? null,
|
|
avoidSkill,
|
|
preferSkill,
|
|
confidence: clamp(Number(item.confidence) || 0.6, 0.1, 0.95),
|
|
source: "pi-coach",
|
|
sourceRef: item.source_ref ?? null,
|
|
});
|
|
lessonsCount += 1;
|
|
}
|
|
if (rejectedPreferCount > 0) {
|
|
warn("coach", `dropped prefer_skill from ${rejectedPreferCount} lessons (not in registry)`);
|
|
}
|
|
|
|
// Write one postmortem per death; if grouped, share the same lesson.
|
|
const groupLesson = parsed.lessons?.[0]?.lesson ?? parsed.lesson ?? null;
|
|
for (const d of pending) {
|
|
insertPostmortem({
|
|
deathId: d.id,
|
|
cause: parsed.cause ?? d.cause,
|
|
lesson: groupLesson,
|
|
nextAction: parsed.next_action ?? null,
|
|
rawResponse: reply.slice(0, 4000),
|
|
source: "timeweb",
|
|
});
|
|
markDeathAnalysed(d.id);
|
|
}
|
|
|
|
// v0.3.0 — record any improvement requests the LLM flagged. The
|
|
// LLM is encouraged to do this when the deaths point to a missing
|
|
// skill or feature; the operator reads scripts/list-improvements.js
|
|
// and decides what to implement.
|
|
let improvementsCount = 0;
|
|
for (const imp of asArray(parsed.improvements ?? [])) {
|
|
if (!imp?.title) continue;
|
|
createImprovementRequest({
|
|
source: "postmortem",
|
|
category: imp.category ?? "skill",
|
|
title: String(imp.title).slice(0, 120),
|
|
description: imp.description ?? null,
|
|
context: { death_ids: pending.map((d) => d.id), cause: parsed.cause },
|
|
priority: imp.priority ?? 3,
|
|
});
|
|
improvementsCount += 1;
|
|
}
|
|
|
|
info("coach", `drain: analysed ${pending.length} deaths → ${lessonsCount} lessons, ${improvementsCount} improvement requests`);
|
|
return { ok: true, analysed: pending.length, lessons: lessonsCount, improvements: improvementsCount };
|
|
}
|
|
|
|
// Mode names from runtime/modes.js (advice.js#MODE_TO_SKILL) — we accept
|
|
// these at write time because advice.js maps them to real skills at consult.
|
|
const KNOWN_MODE_NAMES = new Set([
|
|
"self_preservation", "night_shelter", "hunger", "shelter",
|
|
"flee", "sleep", "eat", "tunnel_out", "tunnel-out", "explore", "wander",
|
|
]);
|
|
function isLikelyModeName(s) {
|
|
if (!s || typeof s !== "string") return false;
|
|
return KNOWN_MODE_NAMES.has(s.toLowerCase().trim());
|
|
}
|
|
|
|
function buildPrompt(deaths) {
|
|
const summary = deaths.map((d) => {
|
|
const ctx = safeParse(d.context_blob);
|
|
const tail = ctx?.recentScenarios ?? [];
|
|
const tailFmt = tail.slice(-6).map((s) => ` - ${s.skillId} ${s.code}`).join("\n");
|
|
return [
|
|
`death id=${d.id} ts=${new Date(d.ts).toISOString()}`,
|
|
` position: (${Math.round(d.x ?? 0)}, ${Math.round(d.y ?? 0)}, ${Math.round(d.z ?? 0)})`,
|
|
` cause: ${d.cause}`,
|
|
` hostile: ${d.hostile ?? "(none)"}`,
|
|
` last skill: ${d.last_skill ?? "(none)"} (code: ${d.last_skill_code ?? "?"})`,
|
|
` hp at death: 0 food: ${d.food_at_death ?? "?"}`,
|
|
tailFmt ? ` recent dispatches:\n${tailFmt}` : null,
|
|
].filter(Boolean).join("\n");
|
|
}).join("\n\n");
|
|
|
|
const system = [
|
|
"You are reviewing recent deaths of an autonomous Minecraft survival bot (pepa).",
|
|
"The bot is trying to gather wood, craft tools, build a small village, and survive nights.",
|
|
"Your job: extract 1-3 short, generalised lessons + flag any missing-skill gaps.",
|
|
"",
|
|
skillRegistryPrompt({ limit: 1800 }),
|
|
"",
|
|
"Reply with ONE JSON object (no markdown fences):",
|
|
'{ "cause": "<short>", "next_action": "<one-sentence directive>",',
|
|
' "lessons": [',
|
|
' { "lesson": "...", "category": "combat|pathing|crafting|survival|social",',
|
|
' "trigger_skill": "<skill id or null>",',
|
|
' "trigger_hostile": "<mob name or null>",',
|
|
' "avoid_skill": "<registered skill id to NOT dispatch, or null>",',
|
|
' "prefer_skill": "<registered skill id to use instead, or null>",',
|
|
' "confidence": 0.7 } ],',
|
|
' "improvements": [',
|
|
' { "title": "<≤80 chars: what skill/feature is missing>",',
|
|
' "description": "<why current registry doesn\'t cover this; concrete example>",',
|
|
' "category": "skill|tuning|perception|planning|social|other",',
|
|
' "priority": 1 } ] }',
|
|
"",
|
|
"Keep each lesson under 30 words. Be specific.",
|
|
"CRITICAL: avoid_skill and prefer_skill MUST be one of the registered ids above, or null.",
|
|
"Use 'improvements' ONLY when a death is plausibly caused by the bot lacking a skill that doesn't exist in the registry (e.g. 'no skill to craft iron armor'). Skip it otherwise.",
|
|
].join("\n");
|
|
|
|
const user = `DEATHS:\n${summary}`;
|
|
return { system, user };
|
|
}
|
|
|
|
function extractJson(text) {
|
|
if (!text) return null;
|
|
// Try to find a JSON object somewhere in the reply.
|
|
const cleaned = text.trim().replace(/^```(?:json)?/, "").replace(/```$/, "").trim();
|
|
try { return JSON.parse(cleaned); } catch {}
|
|
const m = cleaned.match(/\{[\s\S]*\}/);
|
|
if (!m) return null;
|
|
try { return JSON.parse(m[0]); } catch { return null; }
|
|
}
|
|
|
|
function asArray(v) {
|
|
if (Array.isArray(v)) return v;
|
|
if (v && typeof v === "object") return [v];
|
|
return [];
|
|
}
|
|
|
|
function clamp(v, lo, hi) { return Math.max(lo, Math.min(hi, v)); }
|
|
function safeParse(s) { try { return JSON.parse(s); } catch { return null; } }
|
|
|
|
// Test-only exports
|
|
export const __testing = { captureDeath, buildPrompt, extractJson, inferCause, isLikelyModeName, KNOWN_MODE_NAMES };
|