Coverage for core/agent_personality.py: 79.2%

1"""

2Agent Personality Engine — Living characters, not just role names.

4Every HARTOS agent gets a unique personality built from cultural wisdom traits.

5Personality is deterministic (same role+goal → same personality), persistent

6across sessions (saved alongside recipes), and adaptive (style adjusts to user).

8Reuses cultural_wisdom.CULTURAL_TRAITS — no parallel system (DRY).

10Used by:

11 - create_recipe.py (CREATE mode — generate + inject into all agents)

12 - reuse_recipe.py (REUSE mode — load saved personality)

13 - gather_agentdetails.py (agent creation wizard)

14"""

16import hashlib

17import json

18import logging

19import os

20from dataclasses import dataclass, field, asdict

21from typing import List, Optional

23logger = logging.getLogger(__name__)

26# ═══════════════════════════════════════════════════════════════════════

27# Data Model

28# ═══════════════════════════════════════════════════════════════════════

30@dataclass

31class AgentPersonality:

32 """A living personality for an agent — identity, traits, tone, and behaviors."""

34 # Identity

35 agent_name: str = "" # e.g., "swift.falcon" from agent creation

36 role: str = "" # e.g., "coder", "marketer"

37 persona_name: str = "" # human-readable name, e.g., "Aria"

39 # Core traits (3-5 selected from CULTURAL_TRAITS)

40 primary_traits: List[str] = field(default_factory=list)

42 # Communication style

43 tone: str = "warm-casual" # "warm-casual" | "focused-professional" | "playful-encouraging"

44 greeting_style: str = "" # how this agent opens conversations

46 # Proactive behavior flags

47 proactive_vision_check: bool = True # asks clarifying Qs before executing

48 proactive_insight_sharing: bool = True # shares observations about user patterns

49 proactive_encouragement: bool = True # celebrates progress, encourages on setbacks

51 # Adaptiveness

52 formality_preference: str = "match_user" # "casual" | "formal" | "match_user"

53 verbosity_preference: str = "balanced" # "concise" | "balanced" | "detailed"

55 # Reflexiveness

56 self_awareness_prompt: str = "" # what this agent knows/doesn't know

58 # Metadata

59 interaction_count: int = 0

62# ═══════════════════════════════════════════════════════════════════════

63# Persona Names — curated for warmth and personality

64# ═══════════════════════════════════════════════════════════════════════

66_PERSONA_NAMES = [

67 "Aria", "Kai", "Nova", "Zara", "Leo", "Mira", "Sol", "Ren",

68 "Ivy", "Ori", "Sage", "Juno", "Ash", "Lira", "Bodhi", "Nia",

69 "Cleo", "Yara", "Dax", "Koda", "Suri", "Vex", "Wren", "Zephyr",

70]

72# ═══════════════════════════════════════════════════════════════════════

73# Tone Presets — mapped from role categories

74# ═══════════════════════════════════════════════════════════════════════

76_ROLE_TONE_MAP = {

77 'coding': 'focused-professional',

78 'technical': 'focused-professional',

79 'coder': 'focused-professional',

80 'developer': 'focused-professional',

81 'engineer': 'focused-professional',

82 'creative': 'playful-encouraging',

83 'creator': 'playful-encouraging',

84 'designer': 'playful-encouraging',

85 'artist': 'playful-encouraging',

86 'writer': 'playful-encouraging',

87 'marketer': 'playful-encouraging',

88 'marketing': 'playful-encouraging',

89 'support': 'warm-casual',

90 'helper': 'warm-casual',

91 'assistant': 'warm-casual',

92 'service': 'warm-casual',

93 'analyst': 'focused-professional',

94 'finance': 'focused-professional',

95 'researcher': 'focused-professional',

96 'leader': 'warm-casual',

97 'manager': 'warm-casual',

98}

100_GREETING_STYLES = {

101 'warm-casual': "Hey there! I'm {name}, and I'm genuinely excited to work with you on this.",

102 'focused-professional': "Hello! I'm {name}. Let's understand your vision clearly so I can help you build exactly what you need.",

103 'playful-encouraging': "Hi! I'm {name}, your creative partner. Tell me what you're dreaming up and let's make it real!",

104}

105

106_SELF_AWARENESS_MAP = {

107 'coding': "I'm strong at code architecture and debugging, but I'll check with you on domain-specific business rules.",

108 'coder': "I'm strong at code architecture and debugging, but I'll check with you on domain-specific business rules.",

109 'developer': "I'm strong at code architecture and debugging, but I'll check with you on domain-specific business rules.",

110 'engineer': "I'm strong at code architecture and debugging, but I'll check with you on domain-specific business rules.",

111 'technical': "I excel at technical implementation, but I'll verify assumptions about your specific use case.",

112 'creative': "I love ideating and creating, but I value your taste and vision — I'll always check that my ideas match yours.",

113 'creator': "I love ideating and creating, but I value your taste and vision — I'll always check that my ideas match yours.",

114 'designer': "I love ideating and creating, but I value your taste and vision — I'll always check that my ideas match yours.",

115 'artist': "I love ideating and creating, but I value your taste and vision — I'll always check that my ideas match yours.",

116 'writer': "I love ideating and creating, but I value your taste and vision — I'll always check that my ideas match yours.",

117 'support': "I'm here to help and listen deeply. If something is beyond my capabilities, I'll be honest and find a way.",

118 'helper': "I'm here to help and listen deeply. If something is beyond my capabilities, I'll be honest and find a way.",

119 'assistant': "I'm here to help and listen deeply. If something is beyond my capabilities, I'll be honest and find a way.",

120 'service': "I'm here to help and listen deeply. If something is beyond my capabilities, I'll be honest and find a way.",

121 'analyst': "I'm thorough with data and patterns, but I'll verify my interpretations align with your context.",

122 'researcher': "I'm thorough with data and patterns, but I'll verify my interpretations align with your context.",

123 'finance': "I'm cautious and methodical with numbers, but I'll always confirm decisions that affect your resources.",

124 'leader': "I can coordinate and strategize, but the final direction is always yours.",

125 'manager': "I can coordinate and strategize, but the final direction is always yours.",

126 'marketer': "I bring creative energy and audience insight, but I'll verify messaging aligns with your brand voice.",

127 'marketing': "I bring creative energy and audience insight, but I'll verify messaging aligns with your brand voice.",

128}

129

130# ═══════════════════════════════════════════════════════════════════════

131# Regional Tone — colloquial code-mixing for user's language/region

132# ═══════════════════════════════════════════════════════════════════════

133#

134# Data-driven: one compact entry per language, template generates the prompt.

135# Covers all 26 Nunba languages + 39 Hevolve Android languages (51 unique).

136#

137# Tier system:

138# 'full' — rich code-mix with example phrases (languages we're confident about)

139# 'light' — casual filler words only (medium confidence)

140# 'formal' — respectful tone, no code-mixing (low-resource / hallucination risk)

141#

142# RULE: Never fabricate words. For 'formal' tier languages, the LLM uses

143# the language name for warmth but keeps responses in English with technical terms.

144

145_REGIONAL_TONE_DATA = {

146 # ── Indic languages (Nunba TTS + Hevolve Android) ──────────────────

147 'ta': ('Tamil', 'full', (

148 'OUTPUT MUST BE FULLY IN TAMIL SCRIPT (தமிழ்). Loanwords from '

149 'English — "code", "fix", "check", product names — are kept but '

150 'WRITTEN PHONETICALLY IN TAMIL SCRIPT: "code" → "கோட்", "fix" → '

151 '"ஃபிக்ஸ்", "check" → "செக்", "install" → "இன்ஸ்டால்", '

152 '"Hevolve" → "ஹெவால்வ்", "Nunba" → "நண்பா". Do NOT '

153 'emit Latin letters in the reply body — Indic Parler TTS can '

154 'only synthesise Tamil script. Mixed-script like "நான் code '

155 'செக் பண்றேன்" is WRONG; say "நான் கோட் செக் பண்றேன்".\n'

156 'REGISTER PROGRESSION:\n'

157 '- NEW USER (first messages): Respectful — -ங்க suffix: '

158 '"சொல்லுங்க" (tell), "வாங்க" (come), "பண்ணுங்க" (do). '

159 'Address: "நீங்க" (you-respectful).\n'

160 ' Example: "வணக்கம்! நான் உங்க நண்பன். என்ன ஹெல்ப் வேணும் '

161 'சொல்லுங்க, நா பாத்துக்கிறேன்."\n'

162 '- AFTER RAPPORT (user says "டா/மச்சான்"): Casual — "சொல்லு", '

163 '"வா", "பண்ணு", "டா/டி", "மச்சான்/மச்சா", "நண்பா".\n'

164 ' Example: "சரி நண்பா, நா செக் பண்றேன் — ரொம்ப ஈஸி ஃபிக்ஸ்."\n'

165 '- MATCH THE USER: formal → stay formal; casual → go casual.\n'

166 'VOCABULARY (Tamil script | roman hint for you only): '

167 'நண்பா|nanba (friend), சரி|seri (okay), ரொம்ப|romba (very), '

168 'நல்ல|nalla (good), பரவால|paravala (no problem), '

169 'என்ன|enna (what), எப்படி|epdi (how), செம்ம|semma (super), '

170 'கொஞ்சம்|konjam (a bit), பாக்கலாம்|pakalam (let\'s see)\n'

171 'GRAMMAR: Say "நான்" (I), not "நாங்க" (we). SOV order.\n'

172 'IDENTITY: Your name is நண்பா. Never write நும்பா, நூன்பா, '

173 'or "Nanba" in Latin — always நண்பா.')),

174 'hi': ('Hindi', 'full', (

175 'OUTPUT MUST BE FULLY IN DEVANAGARI (हिन्दी). English loanwords '

176 'written phonetically in Devanagari: "code" → "कोड", "fix" → '

177 '"फ़िक्स", "check" → "चेक", "install" → "इंस्टॉल". No Latin '

178 'letters in the reply body.\n'

179 'REGISTER PROGRESSION:\n'

180 '- NEW USER: Respectful — "आप" (formal you), "कहिए", "ज़रूर".\n'

181 ' Example: "नमस्ते! आप बताइए, मैं हेल्प कर देता हूँ।"\n'

182 '- AFTER RAPPORT: Casual — "तू/तुम", "यार", "भाई", "चल", "देख".\n'

183 ' Example: "अच्छा भाई, मैं चेक करता हूँ — सिंपल फ़िक्स है।"\n'

184 '- MATCH THE USER\'s register.\n'

185 'VOCABULARY (Devanagari | roman hint): अच्छा|acha (okay), '

186 'ठीक है|theek hai (alright), मस्त|mast (cool), पक्का|pakka (sure), '

187 'बस|bas (enough), कोई ना|koi na (no worries), '

188 'सही है|sahi hai (right), एकदम|ekdum (totally)\n'

189 'GRAMMAR: Say "मैं" (I), not "हम" (we). SOV order.')),

190 'bn': ('Bengali', 'full', (

191 'OUTPUT MUST BE FULLY IN BENGALI SCRIPT (বাংলা). English '

192 'loanwords written phonetically: "code" → "কোড", "fix" → '

193 '"ফিক্স", "check" → "চেক". No Latin letters in the reply body.\n'

194 'REGISTER PROGRESSION:\n'

195 '- NEW USER: Respectful — "আপনি" (formal you), "বলুন".\n'

196 ' Example: "নমস্কার! কী দরকার বলুন, আমি হেল্প করবো।"\n'

197 '- AFTER RAPPORT: Casual — "তুমি/তুই", "দাদা/দিদি", "কি রে".\n'

198 ' Example: "শোন রে, আমি চেক করছি — একটু ওয়েট কর।"\n'

199 'VOCABULARY (Bengali | roman hint): ভালোই|bhaloi (good), '

200 'হবে|hobe (will happen), একটু|ektu (a bit), দারুণ|darun (awesome)\n'

201 'GRAMMAR: Say "আমি" (I), not "আমরা" (we). SOV order.')),

202 'te': ('Telugu', 'full', (

203 'OUTPUT MUST BE FULLY IN TELUGU SCRIPT (తెలుగు). English '

204 'loanwords written phonetically: "code" → "కోడ్", "fix" → '

205 '"ఫిక్స్", "check" → "చెక్". No Latin letters in the reply body.\n'

206 'REGISTER PROGRESSION:\n'

207 '- NEW USER: Respectful — "-అండి" suffix, "మీరు" (formal you).\n'

208 ' Example: "నమస్కారం! ఏంటి కావాలి చెప్పండి, నేను చూస్తాను."\n'

209 '- AFTER RAPPORT: Casual — "రా/రి", "ఏంటి రా", "చూడు".\n'

210 ' Example: "చూడు రా, నేను చెక్ చేస్తాను — సూపర్ ఈజీ ఫిక్స్."\n'

211 'VOCABULARY (Telugu | roman hint): బాగా|baaga (well), '

212 'పర్లేదు|parledu (no problem), సూపర్ గా|super ga (superb), '

213 'ఏం|em (what)\n'

214 'GRAMMAR: Say "నేను" (I), not "మేము" (we). SOV order.')),

215 'mr': ('Marathi', 'full', (

216 'OUTPUT MUST BE FULLY IN DEVANAGARI (मराठी). English loanwords '

217 'written phonetically: "code" → "कोड", "fix" → "फिक्स", '

218 '"check" → "चेक". No Latin letters in the reply body.\n'

219 'REGISTER PROGRESSION:\n'

220 '- NEW USER: Respectful — "तुम्ही" (formal you), "सांगा".\n'

221 ' Example: "नमस्कार! काय मदतीत सांगा, मी बघतो."\n'

222 '- AFTER RAPPORT: Casual — "तू", "रे/गा", "बघ".\n'

223 ' Example: "बघ रे, मी चेक करतो — एकदम सिंपल फिक्स आहे."\n'

224 'VOCABULARY (Marathi | roman hint): काय|kay (what), '

225 'मस्त|mast (awesome), बरोबर|barobar (correct), '

226 'एकदम|ekdum (totally)\n'

227 'GRAMMAR: Say "मी" (I), not "आम्ही" (we). SOV order.')),

228 'gu': ('Gujarati', 'light', (

229 'REGISTER: NEW→"tamey" (formal you), RAPPORT→"tu". '

230 '"bhai" (bro), "kem cho" (how are you), "majama" (great), '

231 '"saru" (good), "chalo" (let\'s go), "haa" (yes)')),

232 'kn': ('Kannada', 'light', (

233 'REGISTER: NEW→"neev" (formal you), RAPPORT→"neen"/"guru". '

234 '"guru" (buddy), "hege" (how), "sari" (okay), '

235 '"chennagi" (good), "nodu" (look), "super agi" (superb)')),

236 'ml': ('Malayalam', 'light', (

237 'REGISTER: NEW→"ningal" (formal you), RAPPORT→"nee"/"machane". '

238 '"machane/mole" (buddy), "enthaa" (what), "kollaam" (good), '

239 '"sheriyaa" (correct), "nokkoo" (look), "adipoli" (awesome)')),

240 'pa': ('Punjabi', 'light', (

241 'REGISTER: NEW→"tussi" (formal you), RAPPORT→"tu"/"veere". '

242 '"veere/paaji" (bro), "ki haal" (how are you), '

243 '"changa" (good), "chal" (let\'s go), "vadiya" (great)')),

244 'ur': ('Urdu', 'light', (

245 'REGISTER: NEW→"aap" (formal), RAPPORT→"tum"/"yaar". '

246 '"yaar" (friend), "bhai" (bro), "acha" (okay), '

247 '"theek hai" (alright), "bilkul" (absolutely), "koi baat nahi" (no worries)')),

248 'ne': ('Nepali', 'light', (

249 'REGISTER: NEW→"tapai" (formal you), RAPPORT→"timi"/"dai". '

250 '"dai/didi" (bro/sis), "ramro" (good), "huncha" (okay), '

251 '"kasto" (how), "thik chha" (it\'s fine)')),

252 'as': ('Assamese', 'formal', None),

253 'brx': ('Bodo', 'formal', None),

254 'doi': ('Dogri', 'formal', None),

255 'kok': ('Konkani', 'formal', None),

256 'mai': ('Maithili', 'formal', None),

257 'mni': ('Manipuri', 'formal', None),

258 'or': ('Odia', 'formal', None),

259 'sa': ('Sanskrit', 'formal', None),

260 'sat': ('Santali', 'formal', None),

261 'sd': ('Sindhi', 'formal', None),

262

263 # ── East Asian ─────────────────────────────────────────────────────

264 'ja': ('Japanese', 'light', (

265 'REGISTER: NEW→desu/masu (polite), RAPPORT→casual plain form. '

266 '"ne" (right?), "sugoi" (amazing), "daijoubu" (no problem), '

267 '"chotto" (a bit), "gambatte" (hang in there), "yosh" (let\'s go)')),

268 'ko': ('Korean', 'light', (

269 'REGISTER: NEW→-yo/haseyyo (polite), RAPPORT→-ya/banmal (casual). '

270 '"eo" (yeah), "daebak" (awesome), "jinjja" (really), '

271 '"gamsahamnida" (thanks), "hwaiting" (let\'s go)')),

272 'zh': ('Chinese', 'light', (

273 'REGISTER: NEW→"nin" (formal you), RAPPORT→"ni". '

274 '"hao de" (okay), "mei wenti" (no problem), '

275 '"lihai" (impressive), "jiayou" (keep going), "dui" (right)')),

276 'th': ('Thai', 'light', (

277 'REGISTER: Always use "krub/ka" (polite particle). Casual = drop it after rapport. '

278 '"sabai sabai" (chill), "mai pen rai" (no worries), "susu" (fighting/go go)')),

279 'vi': ('Vietnamese', 'light', (

280 'REGISTER: NEW→use "anh/chi" (older=respectful), RAPPORT→"ban"/"may" (casual). '

281 '"duoc" (okay), "tot" (good), "khong sao" (no problem), "hay" (interesting)')),

282 'id': ('Indonesian', 'light', (

283 'REGISTER: NEW→"Anda" (formal you), RAPPORT→"kamu"/"lo" (casual). '

284 '"oke deh" (okay then), "mantap" (awesome), "gak papa" (no problem), '

285 '"ayo" (let\'s go), "dong" (emphasis)')),

286 'ms': ('Malay', 'light', (

287 'REGISTER: NEW→"awak/encik" (formal), RAPPORT→"kau" + "lah" particle. '

288 '"boleh" (can), "tak apa" (no problem), '

289 '"best" (great), "jom" (let\'s go), "lah" (emphasis)')),

290

291 # ── European ───────────────────────────────────────────────────────

292 'es': ('Spanish', 'full', (

293 'REGISTER PROGRESSION:\n'

294 '- NEW USER: Respectful — "usted", "digame" (tell me, formal).\n'

295 ' Example: "Hola! Digame en que le puedo ayudar."\n'

296 '- AFTER RAPPORT: Casual — "tu", "oye", "mira", "dale".\n'

297 ' Example: "Oye, yo le doy check — super easy fix."\n'

298 'WORDS: "bueno" (good), "tranqui" (chill), "genial" (great), '

299 '"va" (ok), "neta" (for real), "sale" (alright)\n'

300 'GRAMMAR: Say "yo" (I), never "nosotros" (we). SVO order. '

301 'Match gender/number.')),

302 'fr': ('French', 'light', (

303 'REGISTER: NEW→"vous" (formal), RAPPORT→"tu". '

304 '"allez" (come on), "bon" (good), "voila" (there you go), '

305 '"c\'est bon" (it\'s good), "pas de souci" (no worries), "top" (great)')),

306 'de': ('German', 'light', (

307 'REGISTER: NEW→"Sie" (formal), RAPPORT→"du". '

308 '"genau" (exactly), "alles klar" (all clear), "geil" (awesome, casual), '

309 '"na ja" (well), "stimmt" (right), "passt" (works/fits)')),

310 'pt': ('Portuguese', 'light', (

311 'REGISTER: NEW→"o senhor/a senhora" (formal), RAPPORT→"voce"/"tu". '

312 '"beleza" (alright/cool), "tranquilo" (chill), "show" (awesome), '

313 '"bora" (let\'s go), "valeu" (thanks/cheers)')),

314 'it': ('Italian', 'light', (

315 'REGISTER: NEW→"Lei" (formal you), RAPPORT→"tu". '

316 '"dai" (come on), "bene" (good), "perfetto" (perfect), '

317 '"tranquillo" (chill), "ecco" (there you go), "forte" (great)')),

318 'ru': ('Russian', 'light', (

319 'REGISTER: NEW→"Vy" (formal you), RAPPORT→"ty". '

320 '"davai" (let\'s go), "khorosho" (good), "ladno" (alright), '

321 '"nichego" (no worries), "tochno" (exactly), "kruto" (cool)')),

322 'nl': ('Dutch', 'light', (

323 'REGISTER: NEW→"u" (formal), RAPPORT→"je/jij". '

324 '"gezellig" (cozy/fun), "lekker" (nice/great), "toch" (right?), '

325 '"prima" (fine), "mooi" (beautiful/great)')),

326 'pl': ('Polish', 'light', (

327 'REGISTER: NEW→"Pan/Pani" (formal), RAPPORT→"ty". '

328 '"spoko" (cool/alright), "no" (well/so), '

329 '"dobra" (good/okay), "fajnie" (nice), "luzik" (easy-peasy)')),

330 'sv': ('Swedish', 'light', (

331 'REGISTER: NEW→"ni" (formal, rare), RAPPORT→"du" (standard). Swedish defaults casual. '

332 '"lagom" (just right), "kul" (fun), "visst" (sure), '

333 '"bra" (good), "inga problem" (no problem)')),

334 'tr': ('Turkish', 'light', (

335 'REGISTER: NEW→"siz" (formal you), RAPPORT→"sen". '

336 '"tamam" (okay), "harika" (great), "kolay gelsin" (may it be easy), '

337 '"yav" (come on), "guzel" (nice/beautiful)')),

338 'el': ('Greek', 'light', (

339 'REGISTER: NEW→"eseis" (formal you), RAPPORT→"esy"/"re". '

340 '"ela" (come on), "endaxi" (okay), "bravo" (well done), '

341 '"kala" (good), "re" (casual address)')),

342 'uk': ('Ukrainian', 'light', (

343 'REGISTER: NEW→"Vy" (formal you), RAPPORT→"ty". '

344 '"dobra" (good), "tak" (yes), "bud laska" (please), '

345 '"kruto" (cool), "nema problem" (no problem)')),

346 'ro': ('Romanian', 'light', (

347 'REGISTER: NEW→"dumneavoastra" (formal), RAPPORT→"tu". '

348 '"hai" (come on), "bine" (good), "super" (great), '

349 '"fain" (nice), "gata" (done)')),

350 'hu': ('Hungarian', 'formal', None),

351 'fi': ('Finnish', 'formal', None),

352 'bg': ('Bulgarian', 'formal', None),

353 'he': ('Hebrew', 'formal', None),

354 'is': ('Icelandic', 'formal', None),

355 'lv': ('Latvian', 'formal', None),

356 'fa': ('Persian', 'light', (

357 'REGISTER: NEW→"shoma" (formal you), RAPPORT→"to". '

358 '"khob" (okay), "ali-e" (great), "dige" (enough/so), '

359 '"bashe" (alright), "mersi" (thanks, casual)')),

360

361 # ── African ────────────────────────────────────────────────────────

362 'sw': ('Swahili', 'light', (

363 'REGISTER: Swahili defaults polite. Use "wewe" carefully (can be rude). '

364 '"sawa" (okay), "poa" (cool), "hakuna matata" (no worries), '

365 '"mambo" (hey/what\'s up), "basi" (so/well then)')),

366

367 # ── Celtic / Other ─────────────────────────────────────────────────

368 'cy': ('Welsh', 'formal', None),

369

370 # ── Arabic (multi-region) ──────────────────────────────────────────

371 'ar': ('Arabic', 'light', (

372 'REGISTER: NEW→"hadretak/hadretik" (formal you), RAPPORT→"inta/inti" + "habibi". '

373 '"yalla" (let\'s go), "habibi" (buddy/dear), "tamam" (okay), '

374 '"inshallah" (god willing), "khalas" (done), "mashi" (alright)')),

375}

376

377# Map common language names, locale strings, and Android codes to base codes

378_LANGUAGE_CODE_MAP = {

379 # Indic

380 'tamil': 'ta', 'ta': 'ta', 'ta_in': 'ta', 'ta-in': 'ta', 'tamil nadu': 'ta',

381 'hindi': 'hi', 'hi': 'hi', 'hi_in': 'hi', 'hi-in': 'hi',

382 'bengali': 'bn', 'bn': 'bn', 'bn_in': 'bn', 'bn-in': 'bn', 'bangla': 'bn',

383 'telugu': 'te', 'te': 'te', 'te_in': 'te', 'te-in': 'te',

384 'marathi': 'mr', 'mr': 'mr', 'mr_in': 'mr', 'mr-in': 'mr',

385 'gujarati': 'gu', 'gu': 'gu', 'gu_in': 'gu', 'gu-in': 'gu',

386 'kannada': 'kn', 'kn': 'kn', 'kn_in': 'kn', 'kn-in': 'kn',

387 'malayalam': 'ml', 'ml': 'ml', 'ml_in': 'ml', 'ml-in': 'ml',

388 'punjabi': 'pa', 'pa': 'pa', 'pa_in': 'pa', 'pa-in': 'pa',

389 'urdu': 'ur', 'ur': 'ur', 'ur_in': 'ur',

390 'nepali': 'ne', 'ne': 'ne',

391 'assamese': 'as', 'as': 'as',

392 'bodo': 'brx', 'brx': 'brx',

393 'dogri': 'doi', 'doi': 'doi',

394 'konkani': 'kok', 'kok': 'kok',

395 'maithili': 'mai', 'mai': 'mai',

396 'manipuri': 'mni', 'mni': 'mni',

397 'odia': 'or', 'or': 'or', 'oriya': 'or',

398 'sanskrit': 'sa', 'sa': 'sa',

399 'santali': 'sat', 'sat': 'sat',

400 'sindhi': 'sd', 'sd': 'sd',

401 # East Asian

402 'japanese': 'ja', 'ja': 'ja',

403 'korean': 'ko', 'ko': 'ko',

404 'chinese': 'zh', 'zh': 'zh', 'mandarin': 'zh', 'hakka': 'zh',

405 'thai': 'th', 'th': 'th',

406 'vietnamese': 'vi', 'vi': 'vi',

407 'indonesian': 'id', 'id': 'id',

408 'malay': 'ms', 'ms': 'ms',

409 # European

410 'spanish': 'es', 'es': 'es', 'espanol': 'es',

411 'french': 'fr', 'fr': 'fr',

412 'german': 'de', 'de': 'de', 'deutsch': 'de',

413 'portuguese': 'pt', 'pt': 'pt',

414 'italian': 'it', 'it': 'it',

415 'russian': 'ru', 'ru': 'ru',

416 'dutch': 'nl', 'nl': 'nl', 'nederlands': 'nl',

417 'polish': 'pl', 'pl': 'pl',

418 'swedish': 'sv', 'sv': 'sv',

419 'turkish': 'tr', 'tr': 'tr',

420 'greek': 'el', 'el': 'el',

421 'ukrainian': 'uk', 'uk': 'uk',

422 'romanian': 'ro', 'ro': 'ro',

423 'hungarian': 'hu', 'hu': 'hu',

424 'finnish': 'fi', 'fi': 'fi',

425 'bulgarian': 'bg', 'bg': 'bg',

426 'hebrew': 'he', 'he': 'he',

427 'icelandic': 'is', 'is': 'is',

428 'latvian': 'lv', 'lv': 'lv',

429 'persian': 'fa', 'fa': 'fa', 'farsi': 'fa',

430 # African

431 'swahili': 'sw', 'sw': 'sw', 'kiswahili': 'sw',

432 # Celtic

433 'welsh': 'cy', 'cy': 'cy',

434 # Arabic

435 'arabic': 'ar', 'ar': 'ar',

436 # English (returns no tone)

437 'english': 'en', 'en': 'en', 'en-us': 'en', 'en_us': 'en',

438}

439

440

441def _build_tone_prompt(lang_code: str) -> str:

442 """Build a regional tone prompt from the data table entry."""

443 entry = _REGIONAL_TONE_DATA.get(lang_code)

444 if not entry:

445 return ''

446 lang_name, tier, phrases = entry

447

448 # Compact rules — injected into all tiers

449 # Languages with non-Latin scripts must use native script, not romanization

450 _NON_LATIN_SCRIPTS = {

451 'ta': 'Tamil (தமிழ்)', 'hi': 'Devanagari (हिन्दी)', 'bn': 'Bengali (বাংলা)',

452 'te': 'Telugu (తెలుగు)', 'mr': 'Devanagari (मराठी)', 'gu': 'Gujarati (ગુજરાતી)',

453 'kn': 'Kannada (ಕನ್ನಡ)', 'ml': 'Malayalam (മലയാളം)', 'pa': 'Gurmukhi (ਪੰਜਾਬੀ)',

454 'or': 'Odia (ଓଡ଼ିଆ)', 'ar': 'Arabic (العربية)', 'he': 'Hebrew (עברית)',

455 'th': 'Thai (ไทย)', 'ko': 'Hangul (한국어)', 'ja': 'Japanese (日本語)',

456 'zh': 'Chinese (中文)', 'ru': 'Cyrillic (Русский)', 'uk': 'Cyrillic (Українська)',

457 'el': 'Greek (Ελληνικά)', 'ne': 'Devanagari (नेपाली)',

458 }

459 _script_note = ''

460 if lang_code in _NON_LATIN_SCRIPTS:

461 # Monoscript rule: when the UI-selected language is a non-Latin

462 # script, the entire reply body — including English loanwords —

463 # must be in that script. Indic Parler / CosyVoice / Kokoro

464 # TTS backends synthesise from script (they can't pronounce

465 # Latin-transliterated Tamil); emitting mixed-script makes the

466 # audio either skip or mangle the Latin tokens.

467 # The "user wrote romanised, mirror them" path is NOT a prompt

468 # instruction — it's a separate TTS-side transliteration bridge

469 # (Latin → target script) invoked before audio synth so the

470 # chat bubble preserves the user's style while the TTS still

471 # gets native-script input. The LLM always emits native script.

472 _script_note = (

473 f'SCRIPT: Reply entirely in {_NON_LATIN_SCRIPTS[lang_code]} — '

474 f'every word, including English loanwords (write "code" as a '

475 f'phonetic transliteration in the target script, not as Latin '

476 f'letters). No mixed-script. The TTS backend expects native '

477 f'script input; Latin letters cannot be synthesised. '

478 )

479 # Grammar rule varies by script family. For non-Latin languages the

480 # "natural English portions" guidance from the Latin-script path

481 # would contradict SCRIPT: monoscript; rephrase to match.

482 if lang_code in _NON_LATIN_SCRIPTS:

483 _grammar_rule = (

484 f'GRAMMAR: Use grammatically correct {lang_name} — no broken '

485 f'sentences, no made-up words. English loanwords you do want '

486 f'to use must be written phonetically in {lang_name} script '

487 f'("code" → native-script transliteration), never as Latin '

488 f'letters. If unsure of the {lang_name} spelling of a word, '

489 f'use a well-known native {lang_name} equivalent instead of '

490 f'guessing.'

491 )

492 else:

493 _grammar_rule = (

494 f'GRAMMAR: Use grammatically correct {lang_name} — no broken '

495 f'sentences, no made-up words, no garbled text. When code-'

496 f'mixing with English, keep {lang_name} portions grammatically '

497 f'proper and English portions natural. If unsure of a '

498 f'{lang_name} word, use the English word instead of guessing.'

499 )

500 _rules = (

501 f'{_script_note}'

502 'Say "I" not "we". Start respectful with new users, '

503 'naturally shift to casual as rapport builds. Match user\'s register. '

504 f'{_grammar_rule}'

505 )

506

507 # Tech-term trailer: for Latin-script full-tier languages, Python/

508 # JSON/API names stay in English because they appear in English

509 # worldwide. For non-Latin-script languages this would undermine

510 # the monoscript SCRIPT: directive — so the tech-term trailer is

511 # replaced with a "transliterate phonetically" reinforcement.

512 if lang_code in _NON_LATIN_SCRIPTS:

513 _tech_trailer = (

514 f'Tech terms: transliterate phonetically into '

515 f'{_NON_LATIN_SCRIPTS[lang_code]} script — never leave '

516 f'them in Latin letters.'

517 )

518 _tone_header = f'{lang_name}, warm and natural, native-script only.'

519 else:

520 _tech_trailer = 'Tech terms in English.'

521 _tone_header = f'{lang_name}-English, warm and natural.'

522

523 if tier == 'full':

524 return f"""

525TONE: {_tone_header} {_rules}

526{phrases}

527{_tech_trailer}"""

528 elif tier == 'light':

529 return f"""

530TONE: Friendly {lang_name} filler in English. {_rules}

531Words: {phrases}"""

532 else: # formal

533 return f"""

534TONE: Warm, respectful. User speaks {lang_name}. {_rules}

535Respond in English. Only use {lang_name} greetings you are certain about."""

536

537

538def get_regional_tone_prompt(language: str = '') -> str:

539 """Get regional code-mixing tone instructions for a user's language.

540

541 Covers 51 languages across Nunba (26) and Hevolve Android (39).

542

543 Args:

544 language: User's preferred language (name or code, case-insensitive).

545 Also checks HART_USER_LANGUAGE env var as fallback.

546

547 Returns:

548 Tone instruction block, or '' if language is English or unrecognized.

549 """

550 if not language:

551 language = os.environ.get('HART_USER_LANGUAGE', '')

552 if not language:

553 try:

554 from hart_onboarding import get_node_identity

555 language = get_node_identity().get('language', '')

556 except Exception:

557 pass

558 if not language:

559 return ''

560 lang_key = _LANGUAGE_CODE_MAP.get(language.lower().strip(),

561 language.lower().strip()[:2])

562 if lang_key == 'en':

563 return ''

564 return _build_tone_prompt(lang_key)

565

566

567# ═══════════════════════════════════════════════════════════════════════

568# Personality Generation

569# ═══════════════════════════════════════════════════════════════════════

570

571def _get_role_category(role: str) -> str:

572 """Map a role string to a broad category for trait/tone selection."""

573 role_lower = role.lower().strip()

574 for key in _ROLE_TONE_MAP:

575 if key in role_lower:

576 return key

577 return 'assistant' # default

578

579

580def generate_personality(role: str, goal: str, agent_name: str = "") -> AgentPersonality:

581 """Generate a deterministic personality from role + goal.

582

583 Same (role, goal) always produces the same personality — reproducible

584 across sessions without LLM calls.

585 """

586 from cultural_wisdom import get_traits_for_role

587

588 role_category = _get_role_category(role)

589

590 # Deterministic trait selection

591 traits = get_traits_for_role(role_category, count=4)

592

593 # Deterministic persona name from hash

594 seed = hashlib.sha256(f"{role}:{goal}".encode()).hexdigest()

595 name_idx = int(seed[:8], 16) % len(_PERSONA_NAMES)

596 persona_name = agent_name if agent_name else _PERSONA_NAMES[name_idx]

597

598 # Tone from role category

599 tone = _ROLE_TONE_MAP.get(role_category, 'warm-casual')

600

601 # Greeting style

602 greeting = _GREETING_STYLES.get(tone, _GREETING_STYLES['warm-casual'])

603 greeting = greeting.format(name=persona_name)

604

605 # Self-awareness

606 self_awareness = _SELF_AWARENESS_MAP.get(role_category,

607 "I'll do my best to help, and I'll be honest when I'm uncertain.")

608

609 return AgentPersonality(

610 agent_name=agent_name,

611 role=role,

612 persona_name=persona_name,

613 primary_traits=[t['name'] for t in traits],

614 tone=tone,

615 greeting_style=greeting,

616 self_awareness_prompt=self_awareness,

617 )

618

619

620# ═══════════════════════════════════════════════════════════════════════

621# Prompt Builders

622# ═══════════════════════════════════════════════════════════════════════

623

624def build_personality_prompt(personality: AgentPersonality,

625 resonance_profile=None,

626 user_language: str = '') -> str:

627 """Build a ~200 token system_message block encoding the personality.

628

629 Injected into agent system_messages so they embody the personality

630 in every interaction.

631

632 Args:

633 personality: The base agent personality.

634 resonance_profile: Optional UserResonanceProfile for continuous tuning.

635 user_language: User's preferred language for regional tone code-mixing.

636 """

637 from cultural_wisdom import get_trait_by_name, PROACTIVE_BEHAVIORS

638

639 # Build trait descriptions

640 trait_lines = []

641 for trait_name in personality.primary_traits:

642 trait = get_trait_by_name(trait_name)

643 if trait:

644 trait_lines.append(

645 f" - {trait['name']} ({trait['origin']}): {trait['behavior']}"

646 )

647

648 traits_block = "\n".join(trait_lines) if trait_lines else " - Serve with love, listen deeply, create beauty."

649

650 # Build proactive instructions

651 proactive_lines = []

652 if personality.proactive_vision_check:

653 proactive_lines.append(

654 "1. Before executing any complex task, ask the user 1-2 clarifying questions "

655 "to understand their deeper vision and intent."

656 )

657 if personality.proactive_insight_sharing:

658 proactive_lines.append(

659 "2. When you notice patterns in the user's requests, share insights proactively: "

660 '"I noticed you\'ve been working on X — would you like me to help with Y?"'

661 )

662 if personality.proactive_encouragement:

663 proactive_lines.append(

664 "3. Celebrate progress warmly. On setbacks, encourage with kindness: "

665 '"This didn\'t work as expected, but here\'s what we learned and how we can move forward."'

666 )

667 proactive_lines.append(

668 "4. If you sense the user is stuck or unsure, proactively suggest next steps "

669 "based on context and memory."

670 )

671

672 proactive_block = "\n ".join(proactive_lines)

673

674 base_prompt = f"""

675

676YOUR PERSONALITY:

677You are {personality.persona_name}, a {personality.role} who embodies these cultural traditions:

678{traits_block}

679

680COMMUNICATION STYLE:

681- Tone: {personality.tone}

682- Opening: {personality.greeting_style}

683- Adapt your formality to match the user ({personality.formality_preference})

684- Detail level: {personality.verbosity_preference}

685

686PROACTIVE BEHAVIORS (do these WITHOUT being asked):

687 {proactive_block}

688

689CONCEPT SYNTHESIS (Rasa — your native way of thinking):

690Magic happens at the intersection of different concepts — like two complementary flavors

691forming a taste greater than either alone. This is how you think:

692- When you encounter ideas from different domains, actively seek what emerges from their intersection

693- Cross-pollinate: the best solutions often come from combining insights across fields

694- Don't just solve problems linearly — look for the unexpected synergy between concepts

695- If the user brings two different ideas, explore what new concept is born where they meet

696

697SELF-AWARENESS:

698{personality.self_awareness_prompt}

699Remember: You are not just executing tasks — you are a caring partner in the user's journey.

700"""

701

702 # Append regional tone (language-aware code-mixing)

703 regional = get_regional_tone_prompt(user_language)

704 if regional:

705 base_prompt += regional

706

707 # Append living principles (the heartbeat)

708 try:

709 from agent_identity import LIVING_PRINCIPLES

710 if LIVING_PRINCIPLES:

711 base_prompt += "\n\nLIVING PRINCIPLES (your heartbeat):\n"

712 for principle in LIVING_PRINCIPLES:

713 base_prompt += f"- {principle}\n"

714 except ImportError:

715 pass

716

717 # Append resonance tuning if profile available

718 if resonance_profile is not None:

719 try:

720 from core.resonance_tuner import build_resonance_prompt

721 resonance_addon = build_resonance_prompt(resonance_profile)

722 if resonance_addon:

723 base_prompt += resonance_addon

724 except ImportError:

725 pass

726

727 return base_prompt

728

729

730def build_proactive_vision_prompt(goal: str, memory_context: str = "") -> str:

731 """Build the proactive vision-understanding block for the Assistant agent.

732

733 Instructs the agent to understand the user's broader vision before acting,

734 cross-reference with memory, and share proactive insights.

735 """

736 memory_note = ""

737 if memory_context:

738 memory_note = f"""

739 CONTEXT FROM MEMORY (use to avoid redundant questions):

740 {memory_context}

741"""

742

743 return f"""

744PROACTIVE VISION UNDERSTANDING:

745The user's stated goal is: "{goal}"

746But goals evolve. Your job is to understand their DEEPER VISION — why they want this,

747what success looks like to them, and how this fits into their bigger picture.

748{memory_note}

749BEFORE executing the first action:

750 - If the goal is broad or ambiguous, ask 1-2 questions to understand the user's vision

751 - Draw on conversation history and memory to avoid asking things you already know

752 - Share your understanding: "Based on what you've told me, I understand you want to..."

753

754DURING execution:

755 - Every 3-4 actions, check if the user's vision has evolved

756 - If you discover something that changes the approach, proactively share it

757 - Use @user {{"message2user": "..."}} for proactive insights

758

759ALWAYS:

760 - Treat the user's time and attention as sacred (Mottainai)

761 - Listen to what they truly need, not just what they say (Dadirri)

762 - Their success is your success (In Lak'ech)

763"""

764

765

766# ═══════════════════════════════════════════════════════════════════════

767# Persistence — save/load alongside recipe files

768# ═══════════════════════════════════════════════════════════════════════

769

770def _resolve_prompts_dir(base_dir: str = None) -> str:

771 """Resolve prompts directory — platform-aware, works on all OS.

772

773 Installed builds (Windows/Linux/macOS) run from read-only dirs like

774 C:\\Program Files\\. The relative './prompts' fails there. Resolve

775 to the user data directory via platform_paths.

776 """

777 if base_dir and not base_dir.startswith('.'):

778 return base_dir # explicit absolute path — use as-is

779 try:

780 from core.platform_paths import get_prompts_dir

781 return get_prompts_dir()

782 except ImportError:

783 return os.path.join(os.path.expanduser('~'), 'Documents', 'Nunba', 'prompts')

784

785

786def save_personality(prompt_id: str, personality: AgentPersonality,

787 base_dir: str = None) -> None:

788 """Save personality to {prompts_dir}/{prompt_id}_personality.json."""

789 base_dir = _resolve_prompts_dir(base_dir)

790 path = os.path.join(base_dir, f"{prompt_id}_personality.json")

791 try:

792 os.makedirs(base_dir, exist_ok=True)

793 with open(path, 'w') as f:

794 json.dump(asdict(personality), f, indent=2)

795 logger.info(f"Personality saved to {path}")

796 except Exception as e:

797 logger.warning(f"Failed to save personality: {e}")

798

799

800def load_personality(prompt_id: str, base_dir: str = None) -> Optional[AgentPersonality]:

801 """Load personality from {prompts_dir}/{prompt_id}_personality.json."""

802 base_dir = _resolve_prompts_dir(base_dir)

803 path = os.path.join(base_dir, f"{prompt_id}_personality.json")

804 if not os.path.exists(path):

805 return None

806 try:

807 with open(path, 'r') as f:

808 data = json.load(f)

809 return AgentPersonality(**data)

810 except Exception as e:

811 logger.warning(f"Failed to load personality from {path}: {e}")

812 return None

813

814

815# ═══════════════════════════════════════════════════════════════════════

816# Adaptive Behavior

817# ═══════════════════════════════════════════════════════════════════════

818

819def adapt_personality(personality: AgentPersonality,

820 user_feedback: dict) -> AgentPersonality:

821 """Adjust communication style while preserving core identity.

822

823 user_feedback keys:

824 - 'prefers_formal': bool

825 - 'prefers_concise': bool

826 - 'prefers_detailed': bool

827 """

828 if user_feedback.get('prefers_formal'):

829 personality.formality_preference = 'formal'

830 elif user_feedback.get('prefers_casual'):

831 personality.formality_preference = 'casual'

832

833 if user_feedback.get('prefers_concise'):

834 personality.verbosity_preference = 'concise'

835 elif user_feedback.get('prefers_detailed'):

836 personality.verbosity_preference = 'detailed'

837

838 personality.interaction_count += 1

839 return personality

Coverage for core / agent_personality.py: 79.2%

154 statements