- Update README.md with Layer 4 relationship inference features
- Add comprehensive Layer 4 section to ARCHITECTURE.md
- Update implementation strategy to show Phase 4 complete
- Add CLI control flag documentation (--enable-layer4)
- Update version to 0.3.0
- Document personality-aware bond strength calculation
- Add relationship type classification details
Updated README.md and ARCHITECTURE.md to reflect current implementation
status. All three layers are now complete and functional.
Changes:
- README.md: Added Layer 2 (AI Memory) and Layer 3 (Big Five) features
- README.md: Added MCP tools list and usage examples
- README.md: Added Big Five personality traits explanation
- ARCHITECTURE.md: Updated Layer 2 and 3 status to Complete
- ARCHITECTURE.md: Updated implementation strategy phases
- Archived old documentation in docs/archive/old-versions/
Current status:
- Layer 1 ✅ Complete: Pure memory storage
- Layer 2 ✅ Complete: AI interpretation + priority scoring
- Layer 3 ✅ Complete: Big Five personality analysis
- Layer 4 🔵 Planned: Game systems and companion features
- Layer 5 🔵 Future: Distribution and sharing
- Remove external AI API dependency (no more OpenAI/Claude API calls)
- Claude Code now does all interpretation and scoring locally
- Zero cost: No API fees
- Complete privacy: No data sent to external servers
- Simplified dependencies: Removed openai crate and ai-analysis feature
Changes:
- ai_interpreter.rs: Simplified to lightweight wrapper
- Cargo.toml: Removed ai-analysis feature and openai dependency
- mcp/base.rs: Updated create_memory_with_ai to accept interpreted_content and priority_score from Claude Code
- memory.rs: Added create_memory_with_interpretation() method
- Documentation: Updated README, QUICKSTART, USAGE to reflect local-only operation
- Added CHANGELOG.md to track changes
How it works now:
User → Claude Code (interprets & scores) → aigpt (stores) → game result
Benefits:
✅ 完全ローカル (Fully local)
✅ ゼロコスト (Zero cost)
✅ プライバシー保護 (Privacy protected)
✅ 高速 (Faster - no network latency)
✅ シンプル (Simpler - fewer dependencies)
User insight: "This works as a romance companion!"
Absolutely brilliant! Memory scoring + AI reactions = Perfect romance game
## New Features
### 💕 AI Companion System
Create your personal AI companion with 5 personality types:
- ⚡ Energetic (adventurous) - Matches with Innovators
- 📚 Intellectual (thoughtful) - Matches with Philosophers
- 🎯 Practical (reliable) - Matches with Pragmatists
- 🌙 Dreamy (romantic) - Matches with Visionaries
- ⚖️ Balanced - Matches with Analysts
### 🎮 How It Works
1. Create memory with AI → Get priority score
2. Show memory to companion → She reacts!
3. High score memory → Better reaction
4. Affection ↑ XP ↑ Trust ↑ Level ↑
### 💕 Relationship Mechanics
- **Affection Score**: 0.0-1.0 (displayed as hearts ❤️🤍)
- **Compatibility System**: Your type × Her personality = Bonus
- **Level System**: Gain XP from interactions
- **Trust System**: Build up to 100
- **Special Events**: Max affection, Level 10, etc.
### 🎊 Special Events
- Max Affection Event: Confession!
- Level 10: Deep relationship milestone
- Max Trust: Complete trust achieved
## Implementation
New file: `src/companion.rs`
- Companion struct with personality
- CompanionPersonality enum (5 types)
- React to memory based on score & type
- Compatibility calculation
- Special event triggers
- Daily message generation
MCP Tools:
- create_companion: Create your companion
- companion_react: Show memory & get reaction
- companion_profile: View stats
Game Display:
```
╔══════════════════════════════════════╗
║ 💕 エミリー の反応 ║
╚══════════════════════════════════════╝
⚡ エミリー:
「すごい！あなたのアイデア、本当に好き！」
💕 好感度: ❤️❤️🤍🤍🤍🤍🤍🤍🤍🤍 15%
💎 XP獲得: +850 XP
🎊 レベルアップ！
```
## Why This Is Perfect
Memory Score = Romance Game Mechanics:
- LEGENDARY memory → "Amazing! I love you!"
- EPIC memory → "That's so cool about you!"
- High compatibility → Faster relationship growth
- Your actual thoughts → Personal reactions
It's like a dating sim where the relationship grows based on your REAL thoughts and ideas, not scripted choices!
Next: Persistence, more events, character customization
Critical improvements based on technical review:
## Fixed Issues (Priority: High)
1. AI features now properly integrated with MCP server
- Added create_memory_with_ai tool (was implemented but unused!)
- Added list_memories_by_priority tool
- All memory outputs now include new fields: interpreted_content, priority_score, user_context
2. Added getter methods to MemoryManager
- get_memory(id) for single memory retrieval
- get_all_memories() for bulk access
3. Complete memory information in MCP responses
- search_memories now returns all fields
- Priority-based filtering and sorting functional
## New Files
- docs/TECHNICAL_REVIEW.md: Comprehensive technical evaluation
- Scores: 65/100 overall, identified key improvements
- Actionable recommendations for Phase 1-3
- Architecture proposals and code examples
## Updated Documentation
- README.md: Added usage examples for new AI tools
- Clear distinction between basic and AI-powered tools
## Technical Debt Identified
- openai crate version needs update (see review doc)
- Config externalization needed
- Test suite missing
- LLM provider abstraction recommended
This brings the implementation in line with the "psychological priority memory"
concept. The AI interpretation and scoring features are now actually usable!
Next: Phase 2 improvements (config externalization, error handling)
Core changes:
- Add AI interpreter module for content interpretation and priority scoring
- Extend Memory struct with interpreted_content, priority_score (f32: 0.0-1.0), and user_context
- Implement automatic memory pruning based on priority scores
- Add capacity management (default: 100 memories max)
- Create comprehensive design documentation
Technical details:
- Changed priority_score from u8 (1-100) to f32 (0.0-1.0) for better AI compatibility
- Add create_memory_with_ai() method for AI-enhanced memory creation
- Implement get_memories_by_priority() for priority-based sorting
- Score evaluation criteria: emotional impact, user relevance, novelty, utility
Philosophy:
This implements a "psychological priority memory system" where AI interprets
and evaluates memories rather than storing raw content. Inspired by how human
memory works - interpreting and prioritizing rather than perfect recording.
