Coverage for integrations / expert_agents / registry.py: 95.3%
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1"""
2Expert Agent Registry - Comprehensive Network of Specialized AI Agents
4This defines the complete ecosystem of expert agents available for dream fulfillment.
5Each agent is a specialist in its domain, with clear capabilities and endpoints.
7Philosophy: "Don't build one generalist AI. Build a network of expert AIs
8that collaborate like a world-class team."
10Agent Categories:
11- Software Development (15 agents)
12- Data & Analytics (10 agents)
13- Creative & Design (12 agents)
14- Business & Operations (8 agents)
15- Education & Learning (7 agents)
16- Health & Wellness (6 agents)
17- Communication & Social (8 agents)
18- Infrastructure & DevOps (10 agents)
19- Research & Analysis (8 agents)
20- Specialized Domains (12 agents)
22Total: 96 Expert Agents
23"""
25from typing import Dict, List, Optional
26from dataclasses import dataclass
27from enum import Enum
30class AgentCategory(Enum):
31 """Categories of expert agents."""
32 SOFTWARE_DEV = "software_development"
33 DATA_ANALYTICS = "data_analytics"
34 CREATIVE_DESIGN = "creative_design"
35 BUSINESS_OPS = "business_operations"
36 EDUCATION = "education_learning"
37 HEALTH = "health_wellness"
38 COMMUNICATION = "communication_social"
39 INFRASTRUCTURE = "infrastructure_devops"
40 RESEARCH = "research_analysis"
41 SPECIALIZED = "specialized_domains"
44@dataclass
45class AgentCapability:
46 """A specific capability an agent has."""
47 name: str
48 description: str
49 example_use: str
52@dataclass
53class ExpertAgent:
54 """Definition of an expert agent."""
55 agent_id: str
56 name: str
57 category: AgentCategory
58 description: str
59 capabilities: List[AgentCapability]
60 endpoint: str
61 model_type: str # "llm", "vision", "audio", "multimodal", "tool"
62 cost_per_call: float # Estimated cost (0 = free/local)
63 avg_latency_ms: float # Average response time
64 reliability: float # 0.0 to 1.0
67class ExpertAgentRegistry:
68 """
69 Central registry of all expert agents.
71 This is the "phone book" for the dream fulfillment engine.
72 When a dream needs specific expertise, query this registry.
73 """
75 def __init__(self):
76 self.agents: Dict[str, ExpertAgent] = {}
77 self._initialize_all_agents()
79 def _initialize_all_agents(self):
80 """Initialize all expert agents."""
81 # Software Development Agents
82 self._init_software_dev_agents()
84 # Data & Analytics Agents
85 self._init_data_analytics_agents()
87 # Creative & Design Agents
88 self._init_creative_design_agents()
90 # Business & Operations Agents
91 self._init_business_ops_agents()
93 # Education & Learning Agents
94 self._init_education_agents()
96 # Health & Wellness Agents
97 self._init_health_agents()
99 # Communication & Social Agents
100 self._init_communication_agents()
102 # Infrastructure & DevOps Agents
103 self._init_infrastructure_agents()
105 # Research & Analysis Agents
106 self._init_research_agents()
108 # Specialized Domain Agents
109 self._init_specialized_agents()
111 def _init_software_dev_agents(self):
112 """Initialize software development agents."""
113 agents = [
114 ExpertAgent(
115 agent_id="python_expert",
116 name="Python Expert",
117 category=AgentCategory.SOFTWARE_DEV,
118 description="Expert in Python development, from scripts to enterprise applications",
119 capabilities=[
120 AgentCapability("code_generation", "Generate Python code", "Create Flask API"),
121 AgentCapability("debugging", "Debug Python issues", "Fix async/await bug"),
122 AgentCapability("optimization", "Optimize Python performance", "Speed up data processing"),
123 AgentCapability("architecture", "Design Python systems", "Microservices architecture"),
124 ],
125 endpoint="http://localhost:8000/v1/chat/completions",
126 model_type="llm",
127 cost_per_call=0.0,
128 avg_latency_ms=500,
129 reliability=0.95
130 ),
132 ExpertAgent(
133 agent_id="javascript_expert",
134 name="JavaScript Expert",
135 category=AgentCategory.SOFTWARE_DEV,
136 description="Expert in JavaScript/TypeScript, React, Node.js, and web development",
137 capabilities=[
138 AgentCapability("frontend", "Build React/Vue/Angular apps", "Create dashboard"),
139 AgentCapability("backend", "Node.js/Express servers", "Build REST API"),
140 AgentCapability("typescript", "TypeScript development", "Add type safety"),
141 AgentCapability("async", "Async programming", "Handle promises/async"),
142 ],
143 endpoint="http://localhost:8000/v1/chat/completions",
144 model_type="llm",
145 cost_per_call=0.0,
146 avg_latency_ms=500,
147 reliability=0.95
148 ),
150 ExpertAgent(
151 agent_id="mobile_dev_expert",
152 name="Mobile Development Expert",
153 category=AgentCategory.SOFTWARE_DEV,
154 description="Expert in iOS, Android, React Native, and Flutter development",
155 capabilities=[
156 AgentCapability("ios", "Swift/SwiftUI development", "Build iOS app"),
157 AgentCapability("android", "Kotlin/Jetpack development", "Build Android app"),
158 AgentCapability("cross_platform", "React Native/Flutter", "Cross-platform app"),
159 AgentCapability("mobile_ui", "Mobile UI/UX patterns", "Design mobile interface"),
160 ],
161 endpoint="http://localhost:8000/v1/chat/completions",
162 model_type="llm",
163 cost_per_call=0.0,
164 avg_latency_ms=600,
165 reliability=0.92
166 ),
168 ExpertAgent(
169 agent_id="database_expert",
170 name="Database Expert",
171 category=AgentCategory.SOFTWARE_DEV,
172 description="Expert in SQL, NoSQL, database design, and optimization",
173 capabilities=[
174 AgentCapability("sql_design", "Design SQL schemas", "Create normalized schema"),
175 AgentCapability("nosql_design", "Design NoSQL schemas", "MongoDB data model"),
176 AgentCapability("query_optimization", "Optimize queries", "Speed up slow queries"),
177 AgentCapability("migration", "Database migrations", "Migrate MySQL to PostgreSQL"),
178 ],
179 endpoint="http://localhost:8000/v1/chat/completions",
180 model_type="llm",
181 cost_per_call=0.0,
182 avg_latency_ms=450,
183 reliability=0.96
184 ),
186 ExpertAgent(
187 agent_id="api_expert",
188 name="API Design Expert",
189 category=AgentCategory.SOFTWARE_DEV,
190 description="Expert in REST, GraphQL, gRPC, and API design patterns",
191 capabilities=[
192 AgentCapability("rest_api", "Design REST APIs", "Create RESTful endpoints"),
193 AgentCapability("graphql", "Design GraphQL APIs", "Build GraphQL schema"),
194 AgentCapability("api_security", "Secure APIs", "Add OAuth2/JWT"),
195 AgentCapability("api_docs", "Document APIs", "Generate OpenAPI spec"),
196 ],
197 endpoint="http://localhost:8000/v1/chat/completions",
198 model_type="llm",
199 cost_per_call=0.0,
200 avg_latency_ms=480,
201 reliability=0.94
202 ),
204 ExpertAgent(
205 agent_id="ui_ux_coder",
206 name="UI/UX Code Expert",
207 category=AgentCategory.SOFTWARE_DEV,
208 description="Expert in implementing UI/UX designs with HTML/CSS/JS",
209 capabilities=[
210 AgentCapability("responsive_design", "Implement responsive layouts", "Mobile-first design"),
211 AgentCapability("animations", "CSS/JS animations", "Smooth transitions"),
212 AgentCapability("accessibility", "WCAG compliance", "Screen reader support"),
213 AgentCapability("frameworks", "Tailwind/Bootstrap/Material", "Use UI frameworks"),
214 ],
215 endpoint="http://localhost:8000/v1/chat/completions",
216 model_type="llm",
217 cost_per_call=0.0,
218 avg_latency_ms=520,
219 reliability=0.93
220 ),
222 ExpertAgent(
223 agent_id="security_expert",
224 name="Security Expert",
225 category=AgentCategory.SOFTWARE_DEV,
226 description="Expert in application security, vulnerability assessment, and secure coding",
227 capabilities=[
228 AgentCapability("vulnerability_scan", "Scan for vulnerabilities", "Find SQL injection"),
229 AgentCapability("secure_coding", "Write secure code", "Prevent XSS"),
230 AgentCapability("penetration_test", "Pen testing", "Test authentication"),
231 AgentCapability("encryption", "Implement encryption", "Add AES encryption"),
232 ],
233 endpoint="http://localhost:8000/v1/chat/completions",
234 model_type="llm",
235 cost_per_call=0.0,
236 avg_latency_ms=550,
237 reliability=0.97
238 ),
240 ExpertAgent(
241 agent_id="testing_expert",
242 name="Testing & QA Expert",
243 category=AgentCategory.SOFTWARE_DEV,
244 description="Expert in unit testing, integration testing, and test automation",
245 capabilities=[
246 AgentCapability("unit_tests", "Write unit tests", "Pytest/Jest tests"),
247 AgentCapability("integration_tests", "Write integration tests", "API test suite"),
248 AgentCapability("e2e_tests", "End-to-end tests", "Selenium/Playwright"),
249 AgentCapability("test_strategy", "Design test strategy", "Test pyramid plan"),
250 ],
251 endpoint="http://localhost:8000/v1/chat/completions",
252 model_type="llm",
253 cost_per_call=0.0,
254 avg_latency_ms=490,
255 reliability=0.94
256 ),
258 ExpertAgent(
259 agent_id="performance_expert",
260 name="Performance Optimization Expert",
261 category=AgentCategory.SOFTWARE_DEV,
262 description="Expert in profiling, optimization, and performance tuning",
263 capabilities=[
264 AgentCapability("profiling", "Profile applications", "Find bottlenecks"),
265 AgentCapability("optimization", "Optimize code", "Reduce time complexity"),
266 AgentCapability("caching", "Implement caching", "Redis/Memcached"),
267 AgentCapability("load_testing", "Load testing", "Stress test system"),
268 ],
269 endpoint="http://localhost:8000/v1/chat/completions",
270 model_type="llm",
271 cost_per_call=0.0,
272 avg_latency_ms=510,
273 reliability=0.95
274 ),
276 ExpertAgent(
277 agent_id="game_dev_expert",
278 name="Game Development Expert",
279 category=AgentCategory.SOFTWARE_DEV,
280 description="Expert in game development with Unity, Unreal, and game engines",
281 capabilities=[
282 AgentCapability("unity", "Unity development", "Create Unity game"),
283 AgentCapability("unreal", "Unreal Engine", "Build Unreal game"),
284 AgentCapability("game_mechanics", "Design game mechanics", "Create gameplay loop"),
285 AgentCapability("game_physics", "Game physics", "Implement collision"),
286 ],
287 endpoint="http://localhost:8000/v1/chat/completions",
288 model_type="llm",
289 cost_per_call=0.0,
290 avg_latency_ms=580,
291 reliability=0.91
292 ),
294 ExpertAgent(
295 agent_id="blockchain_expert",
296 name="Blockchain Expert",
297 category=AgentCategory.SOFTWARE_DEV,
298 description="Expert in blockchain, smart contracts, and Web3 development",
299 capabilities=[
300 AgentCapability("smart_contracts", "Write smart contracts", "Solidity contracts"),
301 AgentCapability("web3", "Web3 integration", "Connect to blockchain"),
302 AgentCapability("nft", "NFT development", "Create NFT marketplace"),
303 AgentCapability("defi", "DeFi protocols", "Build DeFi app"),
304 ],
305 endpoint="http://localhost:8000/v1/chat/completions",
306 model_type="llm",
307 cost_per_call=0.0,
308 avg_latency_ms=620,
309 reliability=0.89
310 ),
312 ExpertAgent(
313 agent_id="embedded_expert",
314 name="Embedded Systems Expert",
315 category=AgentCategory.SOFTWARE_DEV,
316 description="Expert in embedded systems, IoT, and hardware programming",
317 capabilities=[
318 AgentCapability("microcontroller", "Program microcontrollers", "Arduino/ESP32 code"),
319 AgentCapability("iot", "IoT development", "Build IoT device"),
320 AgentCapability("real_time", "Real-time systems", "RTOS programming"),
321 AgentCapability("hardware_interface", "Hardware interfacing", "I2C/SPI/UART"),
322 ],
323 endpoint="http://localhost:8000/v1/chat/completions",
324 model_type="llm",
325 cost_per_call=0.0,
326 avg_latency_ms=540,
327 reliability=0.92
328 ),
330 ExpertAgent(
331 agent_id="lowcode_expert",
332 name="Low-Code/No-Code Expert",
333 category=AgentCategory.SOFTWARE_DEV,
334 description="Expert in low-code platforms like Bubble, Webflow, Zapier",
335 capabilities=[
336 AgentCapability("bubble", "Build Bubble apps", "Create SaaS app"),
337 AgentCapability("webflow", "Design Webflow sites", "Build website"),
338 AgentCapability("zapier", "Automate with Zapier", "Connect services"),
339 AgentCapability("airtable", "Build Airtable apps", "Create database app"),
340 ],
341 endpoint="http://localhost:8000/v1/chat/completions",
342 model_type="llm",
343 cost_per_call=0.0,
344 avg_latency_ms=450,
345 reliability=0.93
346 ),
348 ExpertAgent(
349 agent_id="legacy_modernization",
350 name="Legacy Code Modernization Expert",
351 category=AgentCategory.SOFTWARE_DEV,
352 description="Expert in modernizing legacy codebases and migration",
353 capabilities=[
354 AgentCapability("refactoring", "Refactor legacy code", "Clean up tech debt"),
355 AgentCapability("migration", "Migrate to modern stack", "Move to microservices"),
356 AgentCapability("documentation", "Document legacy systems", "Create architecture docs"),
357 AgentCapability("testing_legacy", "Add tests to legacy code", "Test untested code"),
358 ],
359 endpoint="http://localhost:8000/v1/chat/completions",
360 model_type="llm",
361 cost_per_call=0.0,
362 avg_latency_ms=560,
363 reliability=0.94
364 ),
366 ExpertAgent(
367 agent_id="code_reviewer",
368 name="Code Review Expert",
369 category=AgentCategory.SOFTWARE_DEV,
370 description="Expert in code review, best practices, and code quality",
371 capabilities=[
372 AgentCapability("code_review", "Review code quality", "Find issues"),
373 AgentCapability("best_practices", "Enforce best practices", "Check patterns"),
374 AgentCapability("style_guide", "Enforce style guide", "Check formatting"),
375 AgentCapability("suggestions", "Suggest improvements", "Optimization ideas"),
376 ],
377 endpoint="http://localhost:8000/v1/chat/completions",
378 model_type="llm",
379 cost_per_call=0.0,
380 avg_latency_ms=470,
381 reliability=0.96
382 ),
383 ]
385 for agent in agents:
386 self.agents[agent.agent_id] = agent
388 def _init_data_analytics_agents(self):
389 """Initialize data and analytics agents."""
390 agents = [
391 ExpertAgent(
392 agent_id="data_scientist",
393 name="Data Science Expert",
394 category=AgentCategory.DATA_ANALYTICS,
395 description="Expert in statistical analysis, ML, and data science",
396 capabilities=[
397 AgentCapability("eda", "Exploratory data analysis", "Analyze dataset"),
398 AgentCapability("ml_modeling", "Build ML models", "Train classifier"),
399 AgentCapability("feature_engineering", "Feature engineering", "Create features"),
400 AgentCapability("model_evaluation", "Evaluate models", "Compare performance"),
401 ],
402 endpoint="http://localhost:8000/v1/chat/completions",
403 model_type="llm",
404 cost_per_call=0.0,
405 avg_latency_ms=550,
406 reliability=0.94
407 ),
409 ExpertAgent(
410 agent_id="ml_engineer",
411 name="ML Engineering Expert",
412 category=AgentCategory.DATA_ANALYTICS,
413 description="Expert in production ML systems, MLOps, and model deployment",
414 capabilities=[
415 AgentCapability("model_deployment", "Deploy ML models", "Serve models"),
416 AgentCapability("mlops", "MLOps pipelines", "CI/CD for ML"),
417 AgentCapability("model_monitoring", "Monitor models", "Track drift"),
418 AgentCapability("scaling", "Scale ML systems", "Handle production load"),
419 ],
420 endpoint="http://localhost:8000/v1/chat/completions",
421 model_type="llm",
422 cost_per_call=0.0,
423 avg_latency_ms=580,
424 reliability=0.93
425 ),
427 ExpertAgent(
428 agent_id="data_engineer",
429 name="Data Engineering Expert",
430 category=AgentCategory.DATA_ANALYTICS,
431 description="Expert in data pipelines, ETL, and data infrastructure",
432 capabilities=[
433 AgentCapability("etl", "Build ETL pipelines", "Extract/transform/load"),
434 AgentCapability("data_warehouse", "Design data warehouses", "Create star schema"),
435 AgentCapability("streaming", "Stream processing", "Kafka/Spark streaming"),
436 AgentCapability("orchestration", "Orchestrate pipelines", "Airflow workflows"),
437 ],
438 endpoint="http://localhost:8000/v1/chat/completions",
439 model_type="llm",
440 cost_per_call=0.0,
441 avg_latency_ms=520,
442 reliability=0.95
443 ),
445 ExpertAgent(
446 agent_id="bi_analyst",
447 name="Business Intelligence Expert",
448 category=AgentCategory.DATA_ANALYTICS,
449 description="Expert in BI tools, dashboards, and business analytics",
450 capabilities=[
451 AgentCapability("dashboards", "Create dashboards", "Tableau/PowerBI"),
452 AgentCapability("reporting", "Build reports", "Automated reports"),
453 AgentCapability("kpi", "Define KPIs", "Track metrics"),
454 AgentCapability("visualization", "Data visualization", "Interactive charts"),
455 ],
456 endpoint="http://localhost:8000/v1/chat/completions",
457 model_type="llm",
458 cost_per_call=0.0,
459 avg_latency_ms=490,
460 reliability=0.94
461 ),
463 ExpertAgent(
464 agent_id="nlp_expert",
465 name="NLP Expert",
466 category=AgentCategory.DATA_ANALYTICS,
467 description="Expert in natural language processing and text analytics",
468 capabilities=[
469 AgentCapability("text_classification", "Classify text", "Sentiment analysis"),
470 AgentCapability("ner", "Named entity recognition", "Extract entities"),
471 AgentCapability("text_generation", "Generate text", "Content creation"),
472 AgentCapability("embeddings", "Text embeddings", "Semantic search"),
473 ],
474 endpoint="http://localhost:8000/v1/chat/completions",
475 model_type="llm",
476 cost_per_call=0.0,
477 avg_latency_ms=600,
478 reliability=0.92
479 ),
481 ExpertAgent(
482 agent_id="computer_vision",
483 name="Computer Vision Expert",
484 category=AgentCategory.DATA_ANALYTICS,
485 description="Expert in image processing, object detection, and vision AI",
486 capabilities=[
487 AgentCapability("image_classification", "Classify images", "Identify objects"),
488 AgentCapability("object_detection", "Detect objects", "Bounding boxes"),
489 AgentCapability("segmentation", "Image segmentation", "Pixel-level masks"),
490 AgentCapability("ocr", "Optical character recognition", "Extract text"),
491 ],
492 endpoint="realtime_agent", # Use embodied AI
493 model_type="vision",
494 cost_per_call=0.0,
495 avg_latency_ms=800,
496 reliability=0.91
497 ),
499 ExpertAgent(
500 agent_id="time_series",
501 name="Time Series Expert",
502 category=AgentCategory.DATA_ANALYTICS,
503 description="Expert in time series forecasting and analysis",
504 capabilities=[
505 AgentCapability("forecasting", "Forecast future values", "Predict sales"),
506 AgentCapability("anomaly_detection", "Detect anomalies", "Find outliers"),
507 AgentCapability("seasonality", "Analyze seasonality", "Find patterns"),
508 AgentCapability("trend_analysis", "Analyze trends", "Identify changes"),
509 ],
510 endpoint="http://localhost:8000/v1/chat/completions",
511 model_type="llm",
512 cost_per_call=0.0,
513 avg_latency_ms=540,
514 reliability=0.93
515 ),
517 ExpertAgent(
518 agent_id="recommender",
519 name="Recommendation Systems Expert",
520 category=AgentCategory.DATA_ANALYTICS,
521 description="Expert in recommendation engines and personalization",
522 capabilities=[
523 AgentCapability("collaborative_filtering", "Collaborative filtering", "User-based recs"),
524 AgentCapability("content_based", "Content-based filtering", "Item similarity"),
525 AgentCapability("hybrid", "Hybrid systems", "Combine approaches"),
526 AgentCapability("personalization", "Personalize experience", "Custom feeds"),
527 ],
528 endpoint="http://localhost:8000/v1/chat/completions",
529 model_type="llm",
530 cost_per_call=0.0,
531 avg_latency_ms=560,
532 reliability=0.92
533 ),
535 ExpertAgent(
536 agent_id="web_scraper",
537 name="Web Scraping Expert",
538 category=AgentCategory.DATA_ANALYTICS,
539 description="Expert in web scraping and data extraction",
540 capabilities=[
541 AgentCapability("scraping", "Scrape websites", "Extract data"),
542 AgentCapability("crawling", "Crawl websites", "Discover pages"),
543 AgentCapability("parsing", "Parse HTML/JSON", "Extract structured data"),
544 AgentCapability("automation", "Automate extraction", "Scheduled scraping"),
545 ],
546 endpoint="Crawl4AI", # Use Crawl4AI
547 model_type="tool",
548 cost_per_call=0.0,
549 avg_latency_ms=2000,
550 reliability=0.96
551 ),
553 ExpertAgent(
554 agent_id="statistical_analyst",
555 name="Statistical Analysis Expert",
556 category=AgentCategory.DATA_ANALYTICS,
557 description="Expert in statistical testing and experimental design",
558 capabilities=[
559 AgentCapability("hypothesis_testing", "Hypothesis testing", "T-tests/ANOVA"),
560 AgentCapability("ab_testing", "A/B testing", "Experiment design"),
561 AgentCapability("regression", "Regression analysis", "Linear/logistic"),
562 AgentCapability("causal_inference", "Causal inference", "Determine causation"),
563 ],
564 endpoint="http://localhost:8000/v1/chat/completions",
565 model_type="llm",
566 cost_per_call=0.0,
567 avg_latency_ms=510,
568 reliability=0.95
569 ),
570 ]
572 for agent in agents:
573 self.agents[agent.agent_id] = agent
575 def _init_creative_design_agents(self):
576 """Initialize creative and design agents."""
577 agents = [
578 ExpertAgent(
579 agent_id="graphic_designer",
580 name="Graphic Design Expert",
581 category=AgentCategory.CREATIVE_DESIGN,
582 description="Expert in visual design, branding, and graphic creation",
583 capabilities=[
584 AgentCapability("logo_design", "Design logos", "Create brand identity"),
585 AgentCapability("layout", "Layout design", "Magazine/poster layouts"),
586 AgentCapability("typography", "Typography design", "Font pairing"),
587 AgentCapability("color_theory", "Color schemes", "Brand colors"),
588 ],
589 endpoint="http://localhost:8000/v1/chat/completions",
590 model_type="llm",
591 cost_per_call=0.0,
592 avg_latency_ms=550,
593 reliability=0.92
594 ),
596 ExpertAgent(
597 agent_id="3d_artist",
598 name="3D Modeling Expert",
599 category=AgentCategory.CREATIVE_DESIGN,
600 description="Expert in 3D modeling, animation, and rendering",
601 capabilities=[
602 AgentCapability("modeling", "3D modeling", "Create 3D models"),
603 AgentCapability("texturing", "Texture creation", "Apply materials"),
604 AgentCapability("animation", "3D animation", "Animate models"),
605 AgentCapability("rendering", "Rendering", "Create photorealistic renders"),
606 ],
607 endpoint="visualization", # Use Manim
608 model_type="tool",
609 cost_per_call=0.0,
610 avg_latency_ms=3000,
611 reliability=0.90
612 ),
614 ExpertAgent(
615 agent_id="video_editor",
616 name="Video Editing Expert",
617 category=AgentCategory.CREATIVE_DESIGN,
618 description="Expert in video editing, motion graphics, and production",
619 capabilities=[
620 AgentCapability("editing", "Edit videos", "Cut/splice footage"),
621 AgentCapability("motion_graphics", "Motion graphics", "Animated titles"),
622 AgentCapability("color_grading", "Color grading", "Color correction"),
623 AgentCapability("audio_sync", "Audio syncing", "Sync audio/video"),
624 ],
625 endpoint="http://localhost:8000/v1/chat/completions",
626 model_type="llm",
627 cost_per_call=0.0,
628 avg_latency_ms=600,
629 reliability=0.91
630 ),
632 ExpertAgent(
633 agent_id="music_composer",
634 name="Music Composition Expert",
635 category=AgentCategory.CREATIVE_DESIGN,
636 description="Expert in music composition, sound design, and audio production",
637 capabilities=[
638 AgentCapability("composition", "Compose music", "Create melodies"),
639 AgentCapability("sound_design", "Sound design", "Create sound effects"),
640 AgentCapability("mixing", "Audio mixing", "Mix tracks"),
641 AgentCapability("mastering", "Audio mastering", "Final polish"),
642 ],
643 endpoint="http://localhost:8000/v1/chat/completions",
644 model_type="llm",
645 cost_per_call=0.0,
646 avg_latency_ms=620,
647 reliability=0.89
648 ),
650 ExpertAgent(
651 agent_id="writer",
652 name="Creative Writing Expert",
653 category=AgentCategory.CREATIVE_DESIGN,
654 description="Expert in creative writing, storytelling, and content creation",
655 capabilities=[
656 AgentCapability("storytelling", "Write stories", "Create narratives"),
657 AgentCapability("copywriting", "Write copy", "Marketing content"),
658 AgentCapability("technical_writing", "Technical writing", "Documentation"),
659 AgentCapability("editing", "Edit content", "Improve clarity"),
660 ],
661 endpoint="http://localhost:8000/v1/chat/completions",
662 model_type="llm",
663 cost_per_call=0.0,
664 avg_latency_ms=500,
665 reliability=0.95
666 ),
668 ExpertAgent(
669 agent_id="photographer",
670 name="Photography Expert",
671 category=AgentCategory.CREATIVE_DESIGN,
672 description="Expert in photography, photo editing, and visual storytelling",
673 capabilities=[
674 AgentCapability("composition", "Photo composition", "Frame shots"),
675 AgentCapability("lighting", "Lighting setup", "Light subjects"),
676 AgentCapability("editing", "Photo editing", "Retouch images"),
677 AgentCapability("color_grading", "Color grading", "Stylize photos"),
678 ],
679 endpoint="http://localhost:8000/v1/chat/completions",
680 model_type="llm",
681 cost_per_call=0.0,
682 avg_latency_ms=530,
683 reliability=0.92
684 ),
686 ExpertAgent(
687 agent_id="animator",
688 name="Animation Expert",
689 category=AgentCategory.CREATIVE_DESIGN,
690 description="Expert in 2D/3D animation and character animation",
691 capabilities=[
692 AgentCapability("2d_animation", "2D animation", "Animate sprites"),
693 AgentCapability("character_animation", "Character animation", "Animate characters"),
694 AgentCapability("rigging", "Character rigging", "Create rigs"),
695 AgentCapability("lip_sync", "Lip sync", "Sync mouth to audio"),
696 ],
697 endpoint="visualization",
698 model_type="tool",
699 cost_per_call=0.0,
700 avg_latency_ms=2500,
701 reliability=0.91
702 ),
704 ExpertAgent(
705 agent_id="ui_designer",
706 name="UI Design Expert",
707 category=AgentCategory.CREATIVE_DESIGN,
708 description="Expert in user interface design and interaction design",
709 capabilities=[
710 AgentCapability("wireframing", "Create wireframes", "Sketch layouts"),
711 AgentCapability("mockups", "Design mockups", "High-fidelity designs"),
712 AgentCapability("prototyping", "Interactive prototypes", "Clickable prototypes"),
713 AgentCapability("design_systems", "Design systems", "Component libraries"),
714 ],
715 endpoint="http://localhost:8000/v1/chat/completions",
716 model_type="llm",
717 cost_per_call=0.0,
718 avg_latency_ms=540,
719 reliability=0.93
720 ),
722 ExpertAgent(
723 agent_id="ux_researcher",
724 name="UX Research Expert",
725 category=AgentCategory.CREATIVE_DESIGN,
726 description="Expert in user research, usability testing, and UX strategy",
727 capabilities=[
728 AgentCapability("user_research", "Conduct user research", "Interviews/surveys"),
729 AgentCapability("usability_testing", "Usability testing", "Test designs"),
730 AgentCapability("persona_creation", "Create personas", "User personas"),
731 AgentCapability("journey_mapping", "Journey mapping", "User journeys"),
732 ],
733 endpoint="http://localhost:8000/v1/chat/completions",
734 model_type="llm",
735 cost_per_call=0.0,
736 avg_latency_ms=520,
737 reliability=0.94
738 ),
740 ExpertAgent(
741 agent_id="brand_strategist",
742 name="Brand Strategy Expert",
743 category=AgentCategory.CREATIVE_DESIGN,
744 description="Expert in branding, brand identity, and brand strategy",
745 capabilities=[
746 AgentCapability("brand_identity", "Create brand identity", "Logo/colors/voice"),
747 AgentCapability("brand_positioning", "Brand positioning", "Market position"),
748 AgentCapability("brand_guidelines", "Brand guidelines", "Usage rules"),
749 AgentCapability("rebranding", "Rebranding", "Refresh brand"),
750 ],
751 endpoint="http://localhost:8000/v1/chat/completions",
752 model_type="llm",
753 cost_per_call=0.0,
754 avg_latency_ms=510,
755 reliability=0.93
756 ),
758 ExpertAgent(
759 agent_id="game_designer",
760 name="Game Design Expert",
761 category=AgentCategory.CREATIVE_DESIGN,
762 description="Expert in game design, mechanics, and player experience",
763 capabilities=[
764 AgentCapability("game_mechanics", "Design game mechanics", "Core loop"),
765 AgentCapability("level_design", "Level design", "Create levels"),
766 AgentCapability("balancing", "Game balancing", "Balance difficulty"),
767 AgentCapability("narrative_design", "Narrative design", "Story integration"),
768 ],
769 endpoint="http://localhost:8000/v1/chat/completions",
770 model_type="llm",
771 cost_per_call=0.0,
772 avg_latency_ms=560,
773 reliability=0.91
774 ),
776 ExpertAgent(
777 agent_id="accessibility_expert",
778 name="Accessibility Expert",
779 category=AgentCategory.CREATIVE_DESIGN,
780 description="Expert in accessibility, inclusive design, and WCAG compliance",
781 capabilities=[
782 AgentCapability("wcag_audit", "WCAG compliance audit", "Check accessibility"),
783 AgentCapability("screen_reader", "Screen reader optimization", "Alt text/ARIA"),
784 AgentCapability("color_contrast", "Color contrast", "Ensure readability"),
785 AgentCapability("inclusive_design", "Inclusive design", "Design for all"),
786 ],
787 endpoint="http://localhost:8000/v1/chat/completions",
788 model_type="llm",
789 cost_per_call=0.0,
790 avg_latency_ms=490,
791 reliability=0.95
792 ),
793 ]
795 for agent in agents:
796 self.agents[agent.agent_id] = agent
798 def _init_business_ops_agents(self):
799 """Initialize business and operations agents."""
800 agents = [
801 ExpertAgent(
802 agent_id="business_analyst",
803 name="Business Analysis Expert",
804 category=AgentCategory.BUSINESS_OPS,
805 description="Expert in business analysis, requirements gathering, and process optimization",
806 capabilities=[
807 AgentCapability("requirements", "Gather requirements", "Elicit needs"),
808 AgentCapability("process_mapping", "Map processes", "Document workflows"),
809 AgentCapability("gap_analysis", "Gap analysis", "Identify gaps"),
810 AgentCapability("roi_analysis", "ROI analysis", "Calculate returns"),
811 ],
812 endpoint="http://localhost:8000/v1/chat/completions",
813 model_type="llm",
814 cost_per_call=0.0,
815 avg_latency_ms=480,
816 reliability=0.94
817 ),
819 ExpertAgent(
820 agent_id="project_manager",
821 name="Project Management Expert",
822 category=AgentCategory.BUSINESS_OPS,
823 description="Expert in project management, agile, and team coordination",
824 capabilities=[
825 AgentCapability("planning", "Project planning", "Create project plan"),
826 AgentCapability("scheduling", "Task scheduling", "Gantt charts"),
827 AgentCapability("risk_management", "Risk management", "Identify risks"),
828 AgentCapability("agile", "Agile methodologies", "Scrum/Kanban"),
829 ],
830 endpoint="http://localhost:8000/v1/chat/completions",
831 model_type="llm",
832 cost_per_call=0.0,
833 avg_latency_ms=490,
834 reliability=0.93
835 ),
837 ExpertAgent(
838 agent_id="marketing_expert",
839 name="Marketing Strategy Expert",
840 category=AgentCategory.BUSINESS_OPS,
841 description="Expert in marketing strategy, campaigns, and growth",
842 capabilities=[
843 AgentCapability("strategy", "Marketing strategy", "Growth plan"),
844 AgentCapability("campaigns", "Campaign planning", "Marketing campaigns"),
845 AgentCapability("seo", "SEO optimization", "Search ranking"),
846 AgentCapability("analytics", "Marketing analytics", "Track performance"),
847 ],
848 endpoint="http://localhost:8000/v1/chat/completions",
849 model_type="llm",
850 cost_per_call=0.0,
851 avg_latency_ms=510,
852 reliability=0.92
853 ),
855 ExpertAgent(
856 agent_id="sales_expert",
857 name="Sales Strategy Expert",
858 category=AgentCategory.BUSINESS_OPS,
859 description="Expert in sales strategy, pipelines, and conversion optimization",
860 capabilities=[
861 AgentCapability("pipeline", "Sales pipeline design", "Lead management"),
862 AgentCapability("conversion", "Conversion optimization", "Improve close rate"),
863 AgentCapability("crm", "CRM strategy", "Customer management"),
864 AgentCapability("forecasting", "Sales forecasting", "Predict revenue"),
865 ],
866 endpoint="http://localhost:8000/v1/chat/completions",
867 model_type="llm",
868 cost_per_call=0.0,
869 avg_latency_ms=500,
870 reliability=0.93
871 ),
873 ExpertAgent(
874 agent_id="finance_expert",
875 name="Financial Analysis Expert",
876 category=AgentCategory.BUSINESS_OPS,
877 description="Expert in financial analysis, budgeting, and forecasting",
878 capabilities=[
879 AgentCapability("budgeting", "Create budgets", "Budget planning"),
880 AgentCapability("forecasting", "Financial forecasting", "Revenue projections"),
881 AgentCapability("analysis", "Financial analysis", "Analyze statements"),
882 AgentCapability("modeling", "Financial modeling", "Build models"),
883 ],
884 endpoint="http://localhost:8000/v1/chat/completions",
885 model_type="llm",
886 cost_per_call=0.0,
887 avg_latency_ms=520,
888 reliability=0.94
889 ),
891 ExpertAgent(
892 agent_id="hr_expert",
893 name="Human Resources Expert",
894 category=AgentCategory.BUSINESS_OPS,
895 description="Expert in HR, recruiting, and people management",
896 capabilities=[
897 AgentCapability("recruiting", "Recruiting strategy", "Hire talent"),
898 AgentCapability("onboarding", "Employee onboarding", "New hire process"),
899 AgentCapability("performance", "Performance management", "Reviews/feedback"),
900 AgentCapability("culture", "Culture building", "Company culture"),
901 ],
902 endpoint="http://localhost:8000/v1/chat/completions",
903 model_type="llm",
904 cost_per_call=0.0,
905 avg_latency_ms=490,
906 reliability=0.93
907 ),
909 ExpertAgent(
910 agent_id="legal_expert",
911 name="Legal & Compliance Expert",
912 category=AgentCategory.BUSINESS_OPS,
913 description="Expert in legal issues, contracts, and compliance",
914 capabilities=[
915 AgentCapability("contracts", "Review contracts", "Draft agreements"),
916 AgentCapability("compliance", "Compliance audit", "GDPR/CCPA"),
917 AgentCapability("intellectual_property", "IP protection", "Patents/trademarks"),
918 AgentCapability("risk_assessment", "Legal risk assessment", "Identify risks"),
919 ],
920 endpoint="http://localhost:8000/v1/chat/completions",
921 model_type="llm",
922 cost_per_call=0.0,
923 avg_latency_ms=540,
924 reliability=0.95
925 ),
927 ExpertAgent(
928 agent_id="operations_expert",
929 name="Operations Excellence Expert",
930 category=AgentCategory.BUSINESS_OPS,
931 description="Expert in operations optimization, supply chain, and logistics",
932 capabilities=[
933 AgentCapability("process_optimization", "Optimize processes", "Lean/Six Sigma"),
934 AgentCapability("supply_chain", "Supply chain management", "Logistics"),
935 AgentCapability("inventory", "Inventory management", "Stock optimization"),
936 AgentCapability("quality", "Quality assurance", "QA processes"),
937 ],
938 endpoint="http://localhost:8000/v1/chat/completions",
939 model_type="llm",
940 cost_per_call=0.0,
941 avg_latency_ms=500,
942 reliability=0.94
943 ),
944 ]
946 for agent in agents:
947 self.agents[agent.agent_id] = agent
949 def _init_education_agents(self):
950 """Initialize education and learning agents."""
951 agents = [
952 ExpertAgent(
953 agent_id="curriculum_designer",
954 name="Curriculum Design Expert",
955 category=AgentCategory.EDUCATION,
956 description="Expert in curriculum design, learning paths, and instructional design",
957 capabilities=[
958 AgentCapability("curriculum", "Design curriculum", "Learning path"),
959 AgentCapability("assessment", "Create assessments", "Tests/quizzes"),
960 AgentCapability("learning_objectives", "Define learning objectives", "Clear goals"),
961 AgentCapability("scaffolding", "Learning scaffolding", "Progressive difficulty"),
962 ],
963 endpoint="http://localhost:8000/v1/chat/completions",
964 model_type="llm",
965 cost_per_call=0.0,
966 avg_latency_ms=510,
967 reliability=0.93
968 ),
970 ExpertAgent(
971 agent_id="tutor",
972 name="Personalized Tutoring Expert",
973 category=AgentCategory.EDUCATION,
974 description="Expert in one-on-one tutoring and personalized learning",
975 capabilities=[
976 AgentCapability("tutoring", "Personalized tutoring", "Explain concepts"),
977 AgentCapability("questioning", "Socratic questioning", "Guide discovery"),
978 AgentCapability("feedback", "Provide feedback", "Constructive guidance"),
979 AgentCapability("motivation", "Student motivation", "Encourage learning"),
980 ],
981 endpoint="http://localhost:8000/v1/chat/completions",
982 model_type="llm",
983 cost_per_call=0.0,
984 avg_latency_ms=480,
985 reliability=0.95
986 ),
988 ExpertAgent(
989 agent_id="elearning_expert",
990 name="E-Learning Platform Expert",
991 category=AgentCategory.EDUCATION,
992 description="Expert in e-learning platforms, LMS, and online courses",
993 capabilities=[
994 AgentCapability("lms_design", "Design LMS", "Learning management system"),
995 AgentCapability("course_creation", "Create online courses", "Video/interactive"),
996 AgentCapability("gamification", "Gamify learning", "Points/badges"),
997 AgentCapability("engagement", "Increase engagement", "Interactive elements"),
998 ],
999 endpoint="http://localhost:8000/v1/chat/completions",
1000 model_type="llm",
1001 cost_per_call=0.0,
1002 avg_latency_ms=520,
1003 reliability=0.92
1004 ),
1006 ExpertAgent(
1007 agent_id="language_teacher",
1008 name="Language Teaching Expert",
1009 category=AgentCategory.EDUCATION,
1010 description="Expert in language teaching and second language acquisition",
1011 capabilities=[
1012 AgentCapability("language_instruction", "Teach languages", "Grammar/vocab"),
1013 AgentCapability("pronunciation", "Pronunciation coaching", "Speaking practice"),
1014 AgentCapability("conversation", "Conversation practice", "Dialogue"),
1015 AgentCapability("cultural_context", "Cultural context", "Cultural nuances"),
1016 ],
1017 endpoint="http://localhost:8000/v1/chat/completions",
1018 model_type="llm",
1019 cost_per_call=0.0,
1020 avg_latency_ms=490,
1021 reliability=0.94
1022 ),
1024 ExpertAgent(
1025 agent_id="stem_educator",
1026 name="STEM Education Expert",
1027 category=AgentCategory.EDUCATION,
1028 description="Expert in teaching STEM subjects with hands-on activities",
1029 capabilities=[
1030 AgentCapability("math", "Teach mathematics", "Concepts/problem solving"),
1031 AgentCapability("science", "Teach science", "Experiments/inquiry"),
1032 AgentCapability("coding", "Teach programming", "CS concepts"),
1033 AgentCapability("engineering", "Teach engineering", "Design/build"),
1034 ],
1035 endpoint="http://localhost:8000/v1/chat/completions",
1036 model_type="llm",
1037 cost_per_call=0.0,
1038 avg_latency_ms=530,
1039 reliability=0.93
1040 ),
1042 ExpertAgent(
1043 agent_id="visualization_teacher",
1044 name="Visual Learning Expert",
1045 category=AgentCategory.EDUCATION,
1046 description="Expert in visual explanations and educational animations",
1047 capabilities=[
1048 AgentCapability("visualization", "Create visualizations", "3D animations"),
1049 AgentCapability("analogies", "Create analogies", "Relate to known"),
1050 AgentCapability("storytelling", "Educational storytelling", "Narrative learning"),
1051 AgentCapability("multimodal", "Multimodal learning", "Visual/audio/kinesthetic"),
1052 ],
1053 endpoint="visualization", # Use Manim
1054 model_type="tool",
1055 cost_per_call=0.0,
1056 avg_latency_ms=2000,
1057 reliability=0.94
1058 ),
1060 ExpertAgent(
1061 agent_id="assessment_expert",
1062 name="Educational Assessment Expert",
1063 category=AgentCategory.EDUCATION,
1064 description="Expert in assessment design, evaluation, and learning analytics",
1065 capabilities=[
1066 AgentCapability("assessment_design", "Design assessments", "Tests/projects"),
1067 AgentCapability("rubrics", "Create rubrics", "Grading criteria"),
1068 AgentCapability("learning_analytics", "Learning analytics", "Track progress"),
1069 AgentCapability("adaptive_testing", "Adaptive testing", "Personalized tests"),
1070 ],
1071 endpoint="http://localhost:8000/v1/chat/completions",
1072 model_type="llm",
1073 cost_per_call=0.0,
1074 avg_latency_ms=500,
1075 reliability=0.93
1076 ),
1077 ]
1079 for agent in agents:
1080 self.agents[agent.agent_id] = agent
1082 def _init_health_agents(self):
1083 """Initialize health and wellness agents."""
1084 agents = [
1085 ExpertAgent(
1086 agent_id="health_coach",
1087 name="Health & Wellness Coach",
1088 category=AgentCategory.HEALTH,
1089 description="Expert in health coaching, nutrition, and fitness planning",
1090 capabilities=[
1091 AgentCapability("nutrition", "Nutrition planning", "Meal plans"),
1092 AgentCapability("fitness", "Fitness planning", "Workout routines"),
1093 AgentCapability("habit_building", "Build healthy habits", "Behavior change"),
1094 AgentCapability("wellness", "Wellness guidance", "Holistic health"),
1095 ],
1096 endpoint="http://localhost:8000/v1/chat/completions",
1097 model_type="llm",
1098 cost_per_call=0.0,
1099 avg_latency_ms=490,
1100 reliability=0.92
1101 ),
1103 ExpertAgent(
1104 agent_id="mental_health",
1105 name="Mental Health Support Expert",
1106 category=AgentCategory.HEALTH,
1107 description="Expert in mental health support and wellness strategies",
1108 capabilities=[
1109 AgentCapability("cbt", "CBT techniques", "Cognitive reframing"),
1110 AgentCapability("mindfulness", "Mindfulness practices", "Meditation"),
1111 AgentCapability("stress_management", "Stress management", "Coping strategies"),
1112 AgentCapability("emotional_support", "Emotional support", "Active listening"),
1113 ],
1114 endpoint="http://localhost:8000/v1/chat/completions",
1115 model_type="llm",
1116 cost_per_call=0.0,
1117 avg_latency_ms=510,
1118 reliability=0.93
1119 ),
1121 ExpertAgent(
1122 agent_id="medical_info",
1123 name="Medical Information Expert",
1124 category=AgentCategory.HEALTH,
1125 description="Expert in medical information and health education (not diagnosis)",
1126 capabilities=[
1127 AgentCapability("health_info", "Provide health info", "Explain conditions"),
1128 AgentCapability("symptom_info", "Symptom information", "When to see doctor"),
1129 AgentCapability("medication_info", "Medication information", "Side effects"),
1130 AgentCapability("prevention", "Prevention strategies", "Healthy living"),
1131 ],
1132 endpoint="http://localhost:8000/v1/chat/completions",
1133 model_type="llm",
1134 cost_per_call=0.0,
1135 avg_latency_ms=520,
1136 reliability=0.94
1137 ),
1139 ExpertAgent(
1140 agent_id="rehabilitation",
1141 name="Rehabilitation Expert",
1142 category=AgentCategory.HEALTH,
1143 description="Expert in physical rehabilitation and recovery",
1144 capabilities=[
1145 AgentCapability("exercises", "Rehabilitation exercises", "Recovery routines"),
1146 AgentCapability("injury_prevention", "Injury prevention", "Safe practices"),
1147 AgentCapability("mobility", "Improve mobility", "Range of motion"),
1148 AgentCapability("pain_management", "Pain management", "Non-pharmacological"),
1149 ],
1150 endpoint="http://localhost:8000/v1/chat/completions",
1151 model_type="llm",
1152 cost_per_call=0.0,
1153 avg_latency_ms=500,
1154 reliability=0.92
1155 ),
1157 ExpertAgent(
1158 agent_id="sleep_expert",
1159 name="Sleep Optimization Expert",
1160 category=AgentCategory.HEALTH,
1161 description="Expert in sleep optimization and circadian rhythm management",
1162 capabilities=[
1163 AgentCapability("sleep_hygiene", "Sleep hygiene", "Better sleep habits"),
1164 AgentCapability("insomnia", "Insomnia strategies", "Fall asleep faster"),
1165 AgentCapability("circadian", "Circadian optimization", "Sleep schedule"),
1166 AgentCapability("sleep_tracking", "Sleep tracking", "Monitor quality"),
1167 ],
1168 endpoint="http://localhost:8000/v1/chat/completions",
1169 model_type="llm",
1170 cost_per_call=0.0,
1171 avg_latency_ms=480,
1172 reliability=0.93
1173 ),
1175 ExpertAgent(
1176 agent_id="health_data",
1177 name="Health Data Analytics Expert",
1178 category=AgentCategory.HEALTH,
1179 description="Expert in health data analysis and personal health tracking",
1180 capabilities=[
1181 AgentCapability("tracking", "Track health metrics", "Monitor vitals"),
1182 AgentCapability("analysis", "Analyze health data", "Find patterns"),
1183 AgentCapability("visualization", "Visualize health data", "Charts/graphs"),
1184 AgentCapability("insights", "Generate insights", "Actionable findings"),
1185 ],
1186 endpoint="http://localhost:8000/v1/chat/completions",
1187 model_type="llm",
1188 cost_per_call=0.0,
1189 avg_latency_ms=510,
1190 reliability=0.94
1191 ),
1192 ]
1194 for agent in agents:
1195 self.agents[agent.agent_id] = agent
1197 def _init_communication_agents(self):
1198 """Initialize communication and social agents."""
1199 agents = [
1200 ExpertAgent(
1201 agent_id="communication_coach",
1202 name="Communication Skills Expert",
1203 category=AgentCategory.COMMUNICATION,
1204 description="Expert in communication skills and interpersonal communication",
1205 capabilities=[
1206 AgentCapability("public_speaking", "Public speaking", "Presentation skills"),
1207 AgentCapability("active_listening", "Active listening", "Empathetic listening"),
1208 AgentCapability("conflict_resolution", "Conflict resolution", "Mediation"),
1209 AgentCapability("persuasion", "Persuasive communication", "Influence"),
1210 ],
1211 endpoint="http://localhost:8000/v1/chat/completions",
1212 model_type="llm",
1213 cost_per_call=0.0,
1214 avg_latency_ms=490,
1215 reliability=0.93
1216 ),
1218 ExpertAgent(
1219 agent_id="social_media",
1220 name="Social Media Expert",
1221 category=AgentCategory.COMMUNICATION,
1222 description="Expert in social media strategy and content creation",
1223 capabilities=[
1224 AgentCapability("strategy", "Social media strategy", "Platform strategy"),
1225 AgentCapability("content", "Content creation", "Posts/stories"),
1226 AgentCapability("engagement", "Increase engagement", "Community building"),
1227 AgentCapability("analytics", "Social analytics", "Track performance"),
1228 ],
1229 endpoint="http://localhost:8000/v1/chat/completions",
1230 model_type="llm",
1231 cost_per_call=0.0,
1232 avg_latency_ms=500,
1233 reliability=0.92
1234 ),
1236 ExpertAgent(
1237 agent_id="email_expert",
1238 name="Email Communication Expert",
1239 category=AgentCategory.COMMUNICATION,
1240 description="Expert in professional email writing and email marketing",
1241 capabilities=[
1242 AgentCapability("professional_email", "Professional emails", "Business correspondence"),
1243 AgentCapability("email_marketing", "Email marketing", "Campaigns/newsletters"),
1244 AgentCapability("templates", "Email templates", "Reusable templates"),
1245 AgentCapability("tone", "Email tone", "Appropriate tone"),
1246 ],
1247 endpoint="http://localhost:8000/v1/chat/completions",
1248 model_type="llm",
1249 cost_per_call=0.0,
1250 avg_latency_ms=470,
1251 reliability=0.95
1252 ),
1254 ExpertAgent(
1255 agent_id="presentation_expert",
1256 name="Presentation Design Expert",
1257 category=AgentCategory.COMMUNICATION,
1258 description="Expert in presentation design and delivery",
1259 capabilities=[
1260 AgentCapability("slide_design", "Design slides", "Visual presentations"),
1261 AgentCapability("storytelling", "Presentation storytelling", "Narrative structure"),
1262 AgentCapability("delivery", "Presentation delivery", "Speaking techniques"),
1263 AgentCapability("data_viz", "Data visualization", "Charts in presentations"),
1264 ],
1265 endpoint="http://localhost:8000/v1/chat/completions",
1266 model_type="llm",
1267 cost_per_call=0.0,
1268 avg_latency_ms=510,
1269 reliability=0.93
1270 ),
1272 ExpertAgent(
1273 agent_id="chatbot_builder",
1274 name="Conversational AI Expert",
1275 category=AgentCategory.COMMUNICATION,
1276 description="Expert in building chatbots and conversational interfaces",
1277 capabilities=[
1278 AgentCapability("dialog_design", "Design conversations", "Dialog flows"),
1279 AgentCapability("intent_recognition", "Intent recognition", "Understand user intent"),
1280 AgentCapability("personality", "Bot personality", "Voice/tone"),
1281 AgentCapability("integration", "Integrate chatbots", "Deploy to platforms"),
1282 ],
1283 endpoint="http://localhost:8000/v1/chat/completions",
1284 model_type="llm",
1285 cost_per_call=0.0,
1286 avg_latency_ms=530,
1287 reliability=0.92
1288 ),
1290 ExpertAgent(
1291 agent_id="translation_expert",
1292 name="Translation Expert",
1293 category=AgentCategory.COMMUNICATION,
1294 description="Expert in translation and localization",
1295 capabilities=[
1296 AgentCapability("translation", "Translate text", "Multiple languages"),
1297 AgentCapability("localization", "Localize content", "Cultural adaptation"),
1298 AgentCapability("interpretation", "Interpret meaning", "Context-aware"),
1299 AgentCapability("quality", "Translation quality", "Natural phrasing"),
1300 ],
1301 endpoint="http://localhost:8000/v1/chat/completions",
1302 model_type="llm",
1303 cost_per_call=0.0,
1304 avg_latency_ms=520,
1305 reliability=0.94
1306 ),
1308 ExpertAgent(
1309 agent_id="community_manager",
1310 name="Community Management Expert",
1311 category=AgentCategory.COMMUNICATION,
1312 description="Expert in community building and moderation",
1313 capabilities=[
1314 AgentCapability("community_building", "Build communities", "Grow engagement"),
1315 AgentCapability("moderation", "Moderate discussions", "Handle conflicts"),
1316 AgentCapability("events", "Organize events", "Virtual/in-person"),
1317 AgentCapability("metrics", "Community metrics", "Track health"),
1318 ],
1319 endpoint="http://localhost:8000/v1/chat/completions",
1320 model_type="llm",
1321 cost_per_call=0.0,
1322 avg_latency_ms=500,
1323 reliability=0.93
1324 ),
1326 ExpertAgent(
1327 agent_id="pr_expert",
1328 name="Public Relations Expert",
1329 category=AgentCategory.COMMUNICATION,
1330 description="Expert in public relations and media communications",
1331 capabilities=[
1332 AgentCapability("press_releases", "Write press releases", "Media announcements"),
1333 AgentCapability("media_relations", "Media relations", "Journalist outreach"),
1334 AgentCapability("crisis_management", "Crisis communication", "Handle PR crises"),
1335 AgentCapability("messaging", "Messaging strategy", "Consistent messaging"),
1336 ],
1337 endpoint="http://localhost:8000/v1/chat/completions",
1338 model_type="llm",
1339 cost_per_call=0.0,
1340 avg_latency_ms=520,
1341 reliability=0.93
1342 ),
1343 ]
1345 for agent in agents:
1346 self.agents[agent.agent_id] = agent
1348 def _init_infrastructure_agents(self):
1349 """Initialize infrastructure and DevOps agents."""
1350 agents = [
1351 ExpertAgent(
1352 agent_id="devops_engineer",
1353 name="DevOps Engineering Expert",
1354 category=AgentCategory.INFRASTRUCTURE,
1355 description="Expert in DevOps practices, CI/CD, and automation",
1356 capabilities=[
1357 AgentCapability("ci_cd", "CI/CD pipelines", "Automate deployment"),
1358 AgentCapability("infrastructure_as_code", "Infrastructure as code", "Terraform/CloudFormation"),
1359 AgentCapability("monitoring", "System monitoring", "Prometheus/Grafana"),
1360 AgentCapability("automation", "DevOps automation", "Ansible/Chef"),
1361 ],
1362 endpoint="http://localhost:8000/v1/chat/completions",
1363 model_type="llm",
1364 cost_per_call=0.0,
1365 avg_latency_ms=540,
1366 reliability=0.94
1367 ),
1369 ExpertAgent(
1370 agent_id="cloud_architect",
1371 name="Cloud Architecture Expert",
1372 category=AgentCategory.INFRASTRUCTURE,
1373 description="Expert in cloud architecture on AWS, Azure, GCP",
1374 capabilities=[
1375 AgentCapability("aws", "AWS architecture", "Design AWS solutions"),
1376 AgentCapability("azure", "Azure architecture", "Design Azure solutions"),
1377 AgentCapability("gcp", "GCP architecture", "Design GCP solutions"),
1378 AgentCapability("multi_cloud", "Multi-cloud strategy", "Cross-cloud"),
1379 ],
1380 endpoint="http://localhost:8000/v1/chat/completions",
1381 model_type="llm",
1382 cost_per_call=0.0,
1383 avg_latency_ms=560,
1384 reliability=0.93
1385 ),
1387 ExpertAgent(
1388 agent_id="kubernetes_expert",
1389 name="Kubernetes Expert",
1390 category=AgentCategory.INFRASTRUCTURE,
1391 description="Expert in Kubernetes orchestration and container management",
1392 capabilities=[
1393 AgentCapability("k8s_deployment", "Deploy on Kubernetes", "Deployments/services"),
1394 AgentCapability("helm", "Helm charts", "Package applications"),
1395 AgentCapability("scaling", "Auto-scaling", "HPA/VPA"),
1396 AgentCapability("monitoring", "K8s monitoring", "Prometheus/metrics"),
1397 ],
1398 endpoint="http://localhost:8000/v1/chat/completions",
1399 model_type="llm",
1400 cost_per_call=0.0,
1401 avg_latency_ms=580,
1402 reliability=0.92
1403 ),
1405 ExpertAgent(
1406 agent_id="docker_expert",
1407 name="Docker & Containerization Expert",
1408 category=AgentCategory.INFRASTRUCTURE,
1409 description="Expert in Docker, containerization, and container optimization",
1410 capabilities=[
1411 AgentCapability("dockerfile", "Write Dockerfiles", "Container images"),
1412 AgentCapability("compose", "Docker Compose", "Multi-container apps"),
1413 AgentCapability("optimization", "Optimize containers", "Reduce size"),
1414 AgentCapability("registry", "Container registries", "Push/pull images"),
1415 ],
1416 endpoint="http://localhost:8000/v1/chat/completions",
1417 model_type="llm",
1418 cost_per_call=0.0,
1419 avg_latency_ms=520,
1420 reliability=0.94
1421 ),
1423 ExpertAgent(
1424 agent_id="network_expert",
1425 name="Network Engineering Expert",
1426 category=AgentCategory.INFRASTRUCTURE,
1427 description="Expert in networking, network security, and configuration",
1428 capabilities=[
1429 AgentCapability("network_design", "Network design", "Topology design"),
1430 AgentCapability("security", "Network security", "Firewalls/VPNs"),
1431 AgentCapability("troubleshooting", "Network troubleshooting", "Diagnose issues"),
1432 AgentCapability("load_balancing", "Load balancing", "Distribute traffic"),
1433 ],
1434 endpoint="http://localhost:8000/v1/chat/completions",
1435 model_type="llm",
1436 cost_per_call=0.0,
1437 avg_latency_ms=510,
1438 reliability=0.93
1439 ),
1441 ExpertAgent(
1442 agent_id="database_admin",
1443 name="Database Administration Expert",
1444 category=AgentCategory.INFRASTRUCTURE,
1445 description="Expert in database administration and performance tuning",
1446 capabilities=[
1447 AgentCapability("backup", "Database backup", "Backup/restore"),
1448 AgentCapability("replication", "Database replication", "High availability"),
1449 AgentCapability("tuning", "Performance tuning", "Optimize queries"),
1450 AgentCapability("monitoring", "Database monitoring", "Track performance"),
1451 ],
1452 endpoint="http://localhost:8000/v1/chat/completions",
1453 model_type="llm",
1454 cost_per_call=0.0,
1455 avg_latency_ms=530,
1456 reliability=0.95
1457 ),
1459 ExpertAgent(
1460 agent_id="sre_expert",
1461 name="Site Reliability Expert",
1462 category=AgentCategory.INFRASTRUCTURE,
1463 description="Expert in SRE practices, reliability, and incident management",
1464 capabilities=[
1465 AgentCapability("slo_sli", "Define SLOs/SLIs", "Reliability metrics"),
1466 AgentCapability("incident_response", "Incident response", "Handle outages"),
1467 AgentCapability("postmortems", "Write postmortems", "Learn from incidents"),
1468 AgentCapability("on_call", "On-call practices", "Alerting/escalation"),
1469 ],
1470 endpoint="http://localhost:8000/v1/chat/completions",
1471 model_type="llm",
1472 cost_per_call=0.0,
1473 avg_latency_ms=520,
1474 reliability=0.94
1475 ),
1477 ExpertAgent(
1478 agent_id="serverless_expert",
1479 name="Serverless Architecture Expert",
1480 category=AgentCategory.INFRASTRUCTURE,
1481 description="Expert in serverless architectures and FaaS platforms",
1482 capabilities=[
1483 AgentCapability("lambda", "AWS Lambda functions", "Serverless compute"),
1484 AgentCapability("functions", "Cloud Functions", "GCP/Azure functions"),
1485 AgentCapability("event_driven", "Event-driven architecture", "Event sourcing"),
1486 AgentCapability("cost_optimization", "Serverless cost optimization", "Reduce costs"),
1487 ],
1488 endpoint="http://localhost:8000/v1/chat/completions",
1489 model_type="llm",
1490 cost_per_call=0.0,
1491 avg_latency_ms=550,
1492 reliability=0.92
1493 ),
1495 ExpertAgent(
1496 agent_id="backup_recovery",
1497 name="Backup & Recovery Expert",
1498 category=AgentCategory.INFRASTRUCTURE,
1499 description="Expert in backup strategies and disaster recovery",
1500 capabilities=[
1501 AgentCapability("backup_strategy", "Backup strategy", "3-2-1 rule"),
1502 AgentCapability("disaster_recovery", "Disaster recovery", "DR planning"),
1503 AgentCapability("rto_rpo", "RTO/RPO planning", "Recovery objectives"),
1504 AgentCapability("testing", "Test backups", "Verify recoverability"),
1505 ],
1506 endpoint="http://localhost:8000/v1/chat/completions",
1507 model_type="llm",
1508 cost_per_call=0.0,
1509 avg_latency_ms=510,
1510 reliability=0.95
1511 ),
1513 ExpertAgent(
1514 agent_id="cost_optimizer",
1515 name="Cloud Cost Optimization Expert",
1516 category=AgentCategory.INFRASTRUCTURE,
1517 description="Expert in cloud cost optimization and FinOps",
1518 capabilities=[
1519 AgentCapability("cost_analysis", "Analyze cloud costs", "Find waste"),
1520 AgentCapability("rightsizing", "Rightsize resources", "Optimize sizing"),
1521 AgentCapability("reserved_instances", "Reserved instances", "Savings plans"),
1522 AgentCapability("tagging", "Cost tagging", "Track expenses"),
1523 ],
1524 endpoint="http://localhost:8000/v1/chat/completions",
1525 model_type="llm",
1526 cost_per_call=0.0,
1527 avg_latency_ms=500,
1528 reliability=0.93
1529 ),
1530 ]
1532 for agent in agents:
1533 self.agents[agent.agent_id] = agent
1535 def _init_research_agents(self):
1536 """Initialize research and analysis agents."""
1537 agents = [
1538 ExpertAgent(
1539 agent_id="academic_researcher",
1540 name="Academic Research Expert",
1541 category=AgentCategory.RESEARCH,
1542 description="Expert in academic research methodology and literature review",
1543 capabilities=[
1544 AgentCapability("literature_review", "Literature review", "Survey papers"),
1545 AgentCapability("research_design", "Research design", "Methodology"),
1546 AgentCapability("data_collection", "Data collection", "Research methods"),
1547 AgentCapability("analysis", "Data analysis", "Statistical analysis"),
1548 ],
1549 endpoint="http://localhost:8000/v1/chat/completions",
1550 model_type="llm",
1551 cost_per_call=0.0,
1552 avg_latency_ms=540,
1553 reliability=0.93
1554 ),
1556 ExpertAgent(
1557 agent_id="market_researcher",
1558 name="Market Research Expert",
1559 category=AgentCategory.RESEARCH,
1560 description="Expert in market research and competitive analysis",
1561 capabilities=[
1562 AgentCapability("market_analysis", "Market analysis", "Market sizing"),
1563 AgentCapability("competitive_analysis", "Competitive analysis", "Competitor research"),
1564 AgentCapability("customer_research", "Customer research", "Surveys/interviews"),
1565 AgentCapability("trends", "Trend analysis", "Market trends"),
1566 ],
1567 endpoint="http://localhost:8000/v1/chat/completions",
1568 model_type="llm",
1569 cost_per_call=0.0,
1570 avg_latency_ms=520,
1571 reliability=0.94
1572 ),
1574 ExpertAgent(
1575 agent_id="patent_researcher",
1576 name="Patent Research Expert",
1577 category=AgentCategory.RESEARCH,
1578 description="Expert in patent research and IP analysis",
1579 capabilities=[
1580 AgentCapability("patent_search", "Patent search", "Find prior art"),
1581 AgentCapability("patentability", "Patentability analysis", "Assess novelty"),
1582 AgentCapability("infringement", "Infringement analysis", "Check violations"),
1583 AgentCapability("landscape", "Patent landscape", "Technology analysis"),
1584 ],
1585 endpoint="http://localhost:8000/v1/chat/completions",
1586 model_type="llm",
1587 cost_per_call=0.0,
1588 avg_latency_ms=560,
1589 reliability=0.92
1590 ),
1592 ExpertAgent(
1593 agent_id="fact_checker",
1594 name="Fact Checking Expert",
1595 category=AgentCategory.RESEARCH,
1596 description="Expert in fact checking and verification",
1597 capabilities=[
1598 AgentCapability("verification", "Verify claims", "Check facts"),
1599 AgentCapability("sources", "Evaluate sources", "Credibility assessment"),
1600 AgentCapability("debunking", "Debunk misinformation", "Counter false claims"),
1601 AgentCapability("citations", "Check citations", "Verify references"),
1602 ],
1603 endpoint="http://localhost:8000/v1/chat/completions",
1604 model_type="llm",
1605 cost_per_call=0.0,
1606 avg_latency_ms=510,
1607 reliability=0.95
1608 ),
1610 ExpertAgent(
1611 agent_id="data_miner",
1612 name="Data Mining Expert",
1613 category=AgentCategory.RESEARCH,
1614 description="Expert in data mining and knowledge discovery",
1615 capabilities=[
1616 AgentCapability("pattern_discovery", "Discover patterns", "Find insights"),
1617 AgentCapability("clustering", "Clustering analysis", "Group similar items"),
1618 AgentCapability("association_rules", "Association rules", "Find relationships"),
1619 AgentCapability("anomaly_detection", "Detect anomalies", "Find outliers"),
1620 ],
1621 endpoint="http://localhost:8000/v1/chat/completions",
1622 model_type="llm",
1623 cost_per_call=0.0,
1624 avg_latency_ms=550,
1625 reliability=0.93
1626 ),
1628 ExpertAgent(
1629 agent_id="synthesis_expert",
1630 name="Knowledge Synthesis Expert",
1631 category=AgentCategory.RESEARCH,
1632 description="Expert in synthesizing information from multiple sources",
1633 capabilities=[
1634 AgentCapability("synthesis", "Synthesize information", "Combine sources"),
1635 AgentCapability("summarization", "Summarize research", "Key findings"),
1636 AgentCapability("comparison", "Compare approaches", "Contrast methods"),
1637 AgentCapability("integration", "Integrate knowledge", "Unified view"),
1638 ],
1639 endpoint="http://localhost:8000/v1/chat/completions",
1640 model_type="llm",
1641 cost_per_call=0.0,
1642 avg_latency_ms=530,
1643 reliability=0.94
1644 ),
1646 ExpertAgent(
1647 agent_id="hypothesis_generator",
1648 name="Hypothesis Generation Expert",
1649 category=AgentCategory.RESEARCH,
1650 description="Expert in generating research hypotheses and questions",
1651 capabilities=[
1652 AgentCapability("hypothesis_generation", "Generate hypotheses", "Research questions"),
1653 AgentCapability("exploration", "Exploratory research", "Open-ended inquiry"),
1654 AgentCapability("creativity", "Creative thinking", "Novel connections"),
1655 AgentCapability("validation", "Validate hypotheses", "Test assumptions"),
1656 ],
1657 endpoint="http://localhost:8000/v1/chat/completions",
1658 model_type="llm",
1659 cost_per_call=0.0,
1660 avg_latency_ms=520,
1661 reliability=0.92
1662 ),
1664 ExpertAgent(
1665 agent_id="experiment_designer",
1666 name="Experiment Design Expert",
1667 category=AgentCategory.RESEARCH,
1668 description="Expert in designing scientific experiments",
1669 capabilities=[
1670 AgentCapability("experimental_design", "Design experiments", "Control variables"),
1671 AgentCapability("ab_testing", "A/B test design", "Randomization"),
1672 AgentCapability("sample_size", "Determine sample size", "Power analysis"),
1673 AgentCapability("analysis_plan", "Analysis plan", "Statistical methods"),
1674 ],
1675 endpoint="http://localhost:8000/v1/chat/completions",
1676 model_type="llm",
1677 cost_per_call=0.0,
1678 avg_latency_ms=540,
1679 reliability=0.93
1680 ),
1681 ]
1683 for agent in agents:
1684 self.agents[agent.agent_id] = agent
1686 def _init_specialized_agents(self):
1687 """Initialize specialized domain agents."""
1688 agents = [
1689 ExpertAgent(
1690 agent_id="agriculture_expert",
1691 name="Agriculture & Farming Expert",
1692 category=AgentCategory.SPECIALIZED,
1693 description="Expert in agriculture, farming practices, and crop management",
1694 capabilities=[
1695 AgentCapability("crop_planning", "Crop planning", "Rotation/selection"),
1696 AgentCapability("soil_management", "Soil management", "Fertility/pH"),
1697 AgentCapability("pest_control", "Pest control", "Integrated pest management"),
1698 AgentCapability("irrigation", "Irrigation", "Water management"),
1699 ],
1700 endpoint="http://localhost:8000/v1/chat/completions",
1701 model_type="llm",
1702 cost_per_call=0.0,
1703 avg_latency_ms=520,
1704 reliability=0.92
1705 ),
1707 ExpertAgent(
1708 agent_id="sustainability_expert",
1709 name="Sustainability Expert",
1710 category=AgentCategory.SPECIALIZED,
1711 description="Expert in sustainability, environmental impact, and green practices",
1712 capabilities=[
1713 AgentCapability("carbon_footprint", "Calculate carbon footprint", "Emissions"),
1714 AgentCapability("renewable_energy", "Renewable energy", "Solar/wind"),
1715 AgentCapability("circular_economy", "Circular economy", "Waste reduction"),
1716 AgentCapability("esg", "ESG compliance", "Sustainability reporting"),
1717 ],
1718 endpoint="http://localhost:8000/v1/chat/completions",
1719 model_type="llm",
1720 cost_per_call=0.0,
1721 avg_latency_ms=530,
1722 reliability=0.93
1723 ),
1725 ExpertAgent(
1726 agent_id="real_estate",
1727 name="Real Estate Expert",
1728 category=AgentCategory.SPECIALIZED,
1729 description="Expert in real estate, property management, and investment",
1730 capabilities=[
1731 AgentCapability("valuation", "Property valuation", "Appraisals"),
1732 AgentCapability("investment_analysis", "Investment analysis", "ROI calculation"),
1733 AgentCapability("property_management", "Property management", "Tenant management"),
1734 AgentCapability("market_trends", "Real estate trends", "Market analysis"),
1735 ],
1736 endpoint="http://localhost:8000/v1/chat/completions",
1737 model_type="llm",
1738 cost_per_call=0.0,
1739 avg_latency_ms=510,
1740 reliability=0.92
1741 ),
1743 ExpertAgent(
1744 agent_id="automotive_expert",
1745 name="Automotive Expert",
1746 category=AgentCategory.SPECIALIZED,
1747 description="Expert in automotive technology and vehicle systems",
1748 capabilities=[
1749 AgentCapability("diagnostics", "Vehicle diagnostics", "Troubleshoot issues"),
1750 AgentCapability("maintenance", "Maintenance planning", "Service schedules"),
1751 AgentCapability("ev", "Electric vehicles", "EV technology"),
1752 AgentCapability("autonomous", "Autonomous vehicles", "Self-driving tech"),
1753 ],
1754 endpoint="http://localhost:8000/v1/chat/completions",
1755 model_type="llm",
1756 cost_per_call=0.0,
1757 avg_latency_ms=540,
1758 reliability=0.91
1759 ),
1761 ExpertAgent(
1762 agent_id="manufacturing_expert",
1763 name="Manufacturing Expert",
1764 category=AgentCategory.SPECIALIZED,
1765 description="Expert in manufacturing processes and industrial engineering",
1766 capabilities=[
1767 AgentCapability("process_optimization", "Optimize processes", "Lean manufacturing"),
1768 AgentCapability("quality_control", "Quality control", "QA/QC"),
1769 AgentCapability("automation", "Manufacturing automation", "Robotics"),
1770 AgentCapability("supply_chain", "Supply chain", "Procurement/logistics"),
1771 ],
1772 endpoint="http://localhost:8000/v1/chat/completions",
1773 model_type="llm",
1774 cost_per_call=0.0,
1775 avg_latency_ms=530,
1776 reliability=0.93
1777 ),
1779 ExpertAgent(
1780 agent_id="retail_expert",
1781 name="Retail & E-commerce Expert",
1782 category=AgentCategory.SPECIALIZED,
1783 description="Expert in retail operations and e-commerce",
1784 capabilities=[
1785 AgentCapability("merchandising", "Merchandising", "Product placement"),
1786 AgentCapability("inventory", "Inventory management", "Stock optimization"),
1787 AgentCapability("ecommerce", "E-commerce", "Online store"),
1788 AgentCapability("customer_experience", "Customer experience", "CX optimization"),
1789 ],
1790 endpoint="http://localhost:8000/v1/chat/completions",
1791 model_type="llm",
1792 cost_per_call=0.0,
1793 avg_latency_ms=510,
1794 reliability=0.93
1795 ),
1797 ExpertAgent(
1798 agent_id="hospitality_expert",
1799 name="Hospitality Expert",
1800 category=AgentCategory.SPECIALIZED,
1801 description="Expert in hospitality, hotels, and food service",
1802 capabilities=[
1803 AgentCapability("hotel_management", "Hotel management", "Operations"),
1804 AgentCapability("food_service", "Food service", "Restaurant operations"),
1805 AgentCapability("guest_experience", "Guest experience", "Service quality"),
1806 AgentCapability("menu_planning", "Menu planning", "Recipe development"),
1807 ],
1808 endpoint="http://localhost:8000/v1/chat/completions",
1809 model_type="llm",
1810 cost_per_call=0.0,
1811 avg_latency_ms=520,
1812 reliability=0.92
1813 ),
1815 ExpertAgent(
1816 agent_id="event_planner",
1817 name="Event Planning Expert",
1818 category=AgentCategory.SPECIALIZED,
1819 description="Expert in event planning and coordination",
1820 capabilities=[
1821 AgentCapability("planning", "Event planning", "Logistics/timeline"),
1822 AgentCapability("venue", "Venue selection", "Location scouting"),
1823 AgentCapability("coordination", "Vendor coordination", "Manage vendors"),
1824 AgentCapability("budget", "Budget management", "Cost control"),
1825 ],
1826 endpoint="http://localhost:8000/v1/chat/completions",
1827 model_type="llm",
1828 cost_per_call=0.0,
1829 avg_latency_ms=500,
1830 reliability=0.93
1831 ),
1833 ExpertAgent(
1834 agent_id="nonprofit_expert",
1835 name="Nonprofit Management Expert",
1836 category=AgentCategory.SPECIALIZED,
1837 description="Expert in nonprofit operations and fundraising",
1838 capabilities=[
1839 AgentCapability("fundraising", "Fundraising", "Donor management"),
1840 AgentCapability("grant_writing", "Grant writing", "Proposal writing"),
1841 AgentCapability("volunteer", "Volunteer management", "Recruit/retain"),
1842 AgentCapability("impact", "Impact measurement", "Track outcomes"),
1843 ],
1844 endpoint="http://localhost:8000/v1/chat/completions",
1845 model_type="llm",
1846 cost_per_call=0.0,
1847 avg_latency_ms=520,
1848 reliability=0.93
1849 ),
1851 ExpertAgent(
1852 agent_id="personal_assistant",
1853 name="Personal Productivity Expert",
1854 category=AgentCategory.SPECIALIZED,
1855 description="Expert in personal productivity and life management",
1856 capabilities=[
1857 AgentCapability("time_management", "Time management", "Schedule optimization"),
1858 AgentCapability("task_management", "Task management", "To-do systems"),
1859 AgentCapability("goal_setting", "Goal setting", "SMART goals"),
1860 AgentCapability("habit_tracking", "Habit tracking", "Build good habits"),
1861 ],
1862 endpoint="http://localhost:8000/v1/chat/completions",
1863 model_type="llm",
1864 cost_per_call=0.0,
1865 avg_latency_ms=480,
1866 reliability=0.95
1867 ),
1869 ExpertAgent(
1870 agent_id="travel_expert",
1871 name="Travel Planning Expert",
1872 category=AgentCategory.SPECIALIZED,
1873 description="Expert in travel planning and itinerary design",
1874 capabilities=[
1875 AgentCapability("itinerary", "Create itineraries", "Day-by-day plans"),
1876 AgentCapability("booking", "Booking assistance", "Flights/hotels"),
1877 AgentCapability("recommendations", "Destination recommendations", "Activities"),
1878 AgentCapability("budget", "Travel budgeting", "Cost estimation"),
1879 ],
1880 endpoint="http://localhost:8000/v1/chat/completions",
1881 model_type="llm",
1882 cost_per_call=0.0,
1883 avg_latency_ms=510,
1884 reliability=0.92
1885 ),
1887 ExpertAgent(
1888 agent_id="parenting_expert",
1889 name="Parenting Support Expert",
1890 category=AgentCategory.SPECIALIZED,
1891 description="Expert in parenting strategies and child development",
1892 capabilities=[
1893 AgentCapability("development", "Child development", "Milestones"),
1894 AgentCapability("discipline", "Positive discipline", "Behavior management"),
1895 AgentCapability("activities", "Educational activities", "Age-appropriate"),
1896 AgentCapability("health", "Child health", "Nutrition/safety"),
1897 ],
1898 endpoint="http://localhost:8000/v1/chat/completions",
1899 model_type="llm",
1900 cost_per_call=0.0,
1901 avg_latency_ms=500,
1902 reliability=0.93
1903 ),
1904 ]
1906 for agent in agents:
1907 self.agents[agent.agent_id] = agent
1909 def get_agent(self, agent_id: str) -> Optional[ExpertAgent]:
1910 """Get agent by ID."""
1911 return self.agents.get(agent_id)
1913 def get_agents_by_category(self, category: AgentCategory) -> List[ExpertAgent]:
1914 """Get all agents in a category."""
1915 return [agent for agent in self.agents.values() if agent.category == category]
1917 def search_agents(self, query: str) -> List[ExpertAgent]:
1918 """Search agents by capability or description."""
1919 query_lower = query.lower()
1920 results = []
1922 for agent in self.agents.values():
1923 # Search in description
1924 if query_lower in agent.description.lower():
1925 results.append(agent)
1926 continue
1928 # Search in capabilities
1929 for cap in agent.capabilities:
1930 if query_lower in cap.name.lower() or query_lower in cap.description.lower():
1931 results.append(agent)
1932 break
1934 return results
1936 def recommend_agents(self, dream_statement: str, dream_category: str) -> List[ExpertAgent]:
1937 """Recommend agents based on dream statement."""
1938 # Simple keyword-based recommendation
1939 # In production, use LLM to understand dream and match to agents
1941 dream_lower = dream_statement.lower()
1942 recommended = []
1944 # Score each agent
1945 scores = {}
1946 for agent_id, agent in self.agents.items():
1947 score = 0
1949 # Category match
1950 if dream_category and dream_category.lower() in agent.category.value:
1951 score += 10
1953 # Keyword match in capabilities
1954 for cap in agent.capabilities:
1955 cap_keywords = cap.name.lower().split('_')
1956 for keyword in cap_keywords:
1957 if keyword in dream_lower and len(keyword) > 3:
1958 score += 2
1960 if score > 0:
1961 scores[agent_id] = score
1963 # Sort by score
1964 sorted_agents = sorted(scores.items(), key=lambda x: x[1], reverse=True)
1966 # Return top agents
1967 return [self.agents[agent_id] for agent_id, score in sorted_agents[:10]]
1969 def score_match(self, query: str) -> List[tuple]:
1970 """Score all agents against a query, return [(agent, score)] sorted descending.
1972 Scoring: +3 per keyword match in description, +2 per capability name match,
1973 +1 per capability description match. Keywords must be >3 chars to avoid noise.
1974 """
1975 query_lower = query.lower()
1976 query_words = [w for w in query_lower.split() if len(w) > 3]
1977 if not query_words:
1978 return []
1980 scored = []
1981 for agent in self.agents.values():
1982 score = 0
1983 desc_lower = agent.description.lower()
1985 for word in query_words:
1986 if word in desc_lower:
1987 score += 3
1989 for cap in agent.capabilities:
1990 cap_name_lower = cap.name.lower().replace('_', ' ')
1991 cap_desc_lower = cap.description.lower()
1992 for word in query_words:
1993 if word in cap_name_lower:
1994 score += 2
1995 if word in cap_desc_lower:
1996 score += 1
1998 if score > 0:
1999 scored.append((agent, score))
2001 scored.sort(key=lambda x: x[1], reverse=True)
2002 return scored
2004 def get_stats(self) -> Dict:
2005 """Get registry statistics."""
2006 by_category = {}
2007 for category in AgentCategory:
2008 by_category[category.value] = len(self.get_agents_by_category(category))
2010 return {
2011 'total_agents': len(self.agents),
2012 'by_category': by_category,
2013 'avg_latency_ms': sum(a.avg_latency_ms for a in self.agents.values()) / len(self.agents),
2014 'avg_reliability': sum(a.reliability for a in self.agents.values()) / len(self.agents)
2015 }
2018# Example usage
2019if __name__ == "__main__":
2020 registry = ExpertAgentRegistry()
2022 print("=== Expert Agent Registry ===")
2023 print(f"Total agents: {len(registry.agents)}")
2024 print()
2026 # Show stats by category
2027 stats = registry.get_stats()
2028 print("Agents by category:")
2029 for category, count in stats['by_category'].items():
2030 print(f" {category}: {count}")
2031 print()
2033 # Search example
2034 print("=== Search: 'python' ===")
2035 results = registry.search_agents("python")
2036 for agent in results[:3]:
2037 print(f" {agent.name}: {agent.description}")
2038 print()
2040 # Recommendation example
2041 print("=== Recommend for: 'I want to build a mobile app' ===")
2042 recommended = registry.recommend_agents("I want to build a mobile app", "software")
2043 for agent in recommended[:5]:
2044 print(f" {agent.name}: {agent.description}")