AI Companion App - Architecture Document

Executive Summary

Architecture Decision Record

Decision	Choice	Rationale
Platform	Web-based (PWA)	Faster iteration, cross-platform, lower cost
LLM Provider	Multi-provider (OpenAI primary)	Avoid vendor lock-in, enable A/B testing
Agent Architecture	Multi-agent	Separation of concerns, scalability
System Design	Plugin-based modular	Easy model/tool swapping

1. Platform Selection

CHOSEN Web-based with PWA

Technologies: React 18 + TypeScript, Next.js framework

Progressive Web App for offline capability
Service Workers for background sync
IndexedDB for local data storage
WebRTC for potential voice/video integration

Frontend Stack

React 18 with concurrent features
TypeScript for type safety
Tailwind CSS for styling
Socket.io for real-time

Backend Stack

Node.js with Express
WebSocket for streaming
Redis for session management
PostgreSQL for user data

2. LLM Provider Selection

CHOSEN Modular Multi-Provider

Provider	Use Case	Status
OpenAI GPT-4	Primary, general conversation	Production
Anthropic Claude	Reasoning-heavy tasks	Secondary
Google Gemini	Multi-modal inputs	Future
vLLM (self-hosted)	Enterprise, cost control	Optional

interface LLMProvider {
  async complete(prompt: string, options: CompletionOptions): Promise;
  async stream(prompt: string, options: CompletionOptions): AsyncGenerator;
  getName(): string;
  getCapabilities(): ProviderCapabilities;
}

class OpenAIProvider implements LLMProvider {
  constructor(apiKey: string, model: string = 'gpt-4') { ... }
  async complete(prompt, options) { ... }
}

3. Agent Architecture

CHOSEN Multi-Agent System

Four specialized agents working in coordination via message passing:

💬 Conversation Agent

Main chat interface
Streaming token generation
Typing indicators
Context window management

🧠 Memory Agent

Short-term: conversation history
Long-term: vector DB retrieval
RAG pipeline execution
User preference learning

🔧 Tool Agent

Function calling execution
Tool schema validation
Guardrails enforcement
Calendar, notes, search

📊 Evaluation Agent

LLM-as-judge scoring
Quality assessment
A/B test orchestration
Continuous improvement

4. Plugin System Design

CHOSEN Modular Plugin Architecture

🔌 Model Plugin Interface

loadModel(config) - Initialize model
complete(prompt, opts) - Generate
stream(prompt, opts) - Stream
getEmbedding(text) - Embeddings

🛠️ Tool Plugin Interface

defineSchema() - OpenAPI spec
execute(params) - Run tool
validate(input) - Guardrails
getCapabilities() - Features

💾 Storage Plugin Interface

store(doc) - Save to vector DB
query(embedding, k) - Retrieve
delete(id) - Remove data
getUserHistory(userId) - History

5. Component Diagram

6. Data Flow

User Message → Client → API Gateway → Conversation Agent
                                              ↓
                                    Memory Agent (retrieve context)
                                              ↓
                                    LLM Provider (generate response)
                                              ↓
                                    Tool Agent (execute if needed)
                                              ↓
                                    Evaluation Agent (assess quality)
                                              ↓
                                    Response → Client (stream)
                                              ↓
                                    Memory Agent (store conversation)

7. Next Steps

Task 2: Infrastructure Setup

Configure cloud backend (AWS/GCP)
Set up Kubernetes for orchestration
Configure CI/CD pipelines