managarten/apps/context/.agent/memory.md

Memory - Context App

This file tracks important decisions, ongoing work, known issues, and context that should persist across agent sessions.

Current State

App Status

• Stage: Mobile MVP
• Platform: Expo 52 + React Native 0.76
• Database: Supabase (PostgreSQL with RLS)
• AI Providers: Azure OpenAI (GPT-4.1), Google Gemini (Pro, Flash)
• Monetization: RevenueCat (subscriptions + token economy)

Active Work

• Currently in project-agents branch
• Creating agent team documentation

Known Issues

1. API Keys in Mobile App (Critical)

   • Keys are extractable from decompiled app
   • Mitigation: Rate limiting, usage alerts, key rotation
   • Fix: Migrate to backend API (planned)

2. No Rate Limiting

   • Users can spam AI requests
   • Fix: Backend API with Redis-backed rate limiting (planned)

3. Limited Error Handling

   • Some error paths not handled gracefully
   • Fix: Add retry logic and offline support

4. No Automated Tests

   • Only manual testing currently
   • Fix: Add Jest unit tests, Detox E2E tests (planned)

Architecture Decisions

Use Supabase Instead of Custom Backend

Decision: Use Supabase for MVP, migrate to NestJS backend later

Rationale:

• Faster MVP development
• Built-in auth, RLS, realtime
• Easy migration path

Consequences:

• ✅ Rapid development
• ❌ API keys exposed in mobile app

Status: Active - Will migrate to NestJS backend in Phase 2

Token-Based Economy

Decision: Implement token-based economy (1000 tokens = $0.001 USD)

Rationale:

• Transparent costs for users
• Encourages responsible AI usage
• Fair for both light and heavy users

Status: Active - Working well

Multi-Provider AI

Decision: Support Azure OpenAI and Google Gemini

Rationale:

• Avoid vendor lock-in
• Different models for different use cases
• Fallback if one provider has outage

Status: Active - Will add more providers in future

Short IDs for Documents

Decision: Auto-generate IDs like "MD1", "MC2" instead of UUIDs

Rationale:

• Human-friendly references
• Easy to mention in content (@MD1)
• Memorable for users

Status: Active - Working well

Auto-Save with Debounce

Decision: Auto-save 3 seconds after user stops typing

Rationale:

• Modern UX standard
• No friction - users never lose work

Status: Active - Will improve error handling

Migration Path

Phase 1: Mobile MVP (Current)

• ✅ Expo mobile app
• ✅ Supabase for database + auth
• ✅ Direct AI API calls from mobile
• ✅ RevenueCat for monetization

Phase 2: Backend API (Planned)

• NestJS backend with Drizzle ORM
• AI calls proxied through backend
• Migrate auth to mana-core-auth
• Hide API keys server-side

Phase 3: Web App (Planned)

• SvelteKit web app
• Shares backend API with mobile
• Real-time collaboration features

Phase 4: Landing Page (Planned)

• Astro static site
• Marketing content, pricing, docs

Common Patterns

JSONB Metadata for Extensibility

• Always merge metadata, never replace
• Used in documents and token transactions

Service Layer Abstraction

• Each service has one responsibility
• Enables testing and reusability

Debounced Operations

• Auto-save, token counting, search
• Clean up on unmount

Team Notes

For Product Owner

• Users love token transparency
• Feature request: Export as PDF/Markdown (high priority)

For Architect

• Supabase RLS working well
• Consider Redis caching for token balances

For Senior Developer

• Token estimation accuracy is ±10%
• Metadata merge pattern is critical

For Developer

• Always merge metadata with existing
• Use estimateTokens() from tokenCountingService
• Test on both iOS and Android

For Security Engineer

• API key exposure is #1 priority
• RLS policies are solid
• Add rate limiting even in MVP

For QA Lead

• Test token estimation for every AI model
• Auto-save edge cases need regression tests
• Performance benchmarks: Document load <500ms

Lessons Learned

1. Token Estimation is Hard: Improved to ±10% accuracy, will use tiktoken in backend
2. Metadata Updates Need Merging: Always merge, never replace
3. Auto-Save UX is Critical: Better error messages needed
4. Short IDs are Loved: Small UX details make big impact

Future Considerations

Potential Features

• Collaborative editing with real-time sync
• Knowledge graph visualization
• Voice input for mobile
• Offline mode with local storage
• Browser extension for web clipping

Technical Improvements

• Streaming AI responses
• Better token counting (tiktoken)
• Redis caching
• Monitoring and analytics
• Automated tests