# v0.15.0 Implementation Plan: Unified Memory Model

**Version**: v0.15.0
**Duration**: 8 weeks (2026-11-27 to 2026-01-24)
**Goal**: 統合と深化 - Integrate KB/Task/Code into Unified Memory System
**Strategy**: "削減" ではなく "本質に沿ってブラッシュアップ"

---

## Overview

### Strategic Context

After v0.14.0 (TUI) completion, Clauxton had:
- ✅ 36 MCP tools
- ✅ 1,953+ tests, 90% coverage
- ❌ Unclear positioning ("何でもできる = 何が得意か不明")

**Problem**: Feature richness created fragmentation
**Solution**: Unified Memory Model - single concept for all project memory

### Core Architecture Change

**Before (Fragmented)**:
```
Knowledge Base (KB)  →  kb_add(), kb_search(), kb_list()...
Task Management      →  task_add(), task_list(), task_update()...
Repository Map       →  index_repository(), search_symbols()...
Code Analysis        →  analyze_commits(), extract_patterns()...
```

**After (Unified)**:
```
Memory System        →  memory_add(), memory_search(), memory_get()
                        - type: "knowledge" | "decision" | "code" | "task" | "pattern"
                        - Auto-extraction from commits
                        - Automatic relationships
                        - Question-answering
```

---

## Week 1-2: Core Integration (統合の基盤)

### Goals
1. Define Unified Memory Entry model
2. Implement storage layer
3. Create backward compatibility layer
4. Set up migration utilities

### Day 1-3: Model Definition

**File**: `clauxton/core/memory.py`

```python
from pydantic import BaseModel, Field
from typing import Literal, List, Optional
from datetime import datetime

class MemoryEntry(BaseModel):
    """統一された記憶エントリ"""

    # Core identity
    id: str = Field(..., pattern=r"^MEM-\d{8}-\d{3}$")
    type: Literal["knowledge", "decision", "code", "task", "pattern"]
    title: str = Field(..., min_length=1, max_length=200)
    content: str = Field(..., min_length=1)

    # Metadata
    category: str
    tags: List[str] = []
    created_at: datetime
    updated_at: datetime

    # Relationships (記憶同士の関連)
    related_to: List[str] = []  # Memory IDs
    supersedes: Optional[str] = None  # Replaced memory

    # Context (記憶の文脈)
    source: Literal["manual", "git-commit", "code-analysis", "import"]
    confidence: float = Field(default=1.0, ge=0.0, le=1.0)

    # Source reference
    source_ref: Optional[str] = None  # Commit SHA, file path, etc.

    # Legacy compatibility (deprecated)
    legacy_id: Optional[str] = None  # KB-20251019-001 or TASK-001

class Memory(BaseModel):
    """Memory management system"""
    project_root: Path

    def add(self, entry: MemoryEntry) -> str:
        """Add memory entry"""
        pass

    def get(self, memory_id: str) -> Optional[MemoryEntry]:
        """Get memory by ID"""
        pass

    def search(
        self,
        query: str,
        type_filter: Optional[List[str]] = None,
        limit: int = 10
    ) -> List[MemoryEntry]:
        """Search memories"""
        pass

    def update(self, memory_id: str, **kwargs) -> bool:
        """Update memory"""
        pass

    def delete(self, memory_id: str) -> bool:
        """Delete memory"""
        pass

    def find_related(self, memory_id: str, limit: int = 5) -> List[MemoryEntry]:
        """Find related memories"""
        pass
```

**Tests**: `tests/core/test_memory.py` (25 tests)
- Test memory CRUD operations
- Test type filtering
- Test relationship management
- Test legacy ID compatibility

### Day 4-6: Storage Layer

**File**: `clauxton/core/memory_store.py`

```python
class MemoryStore:
    """
    Storage backend for Memory entries

    Storage format:
    .clauxton/
        memories.yml          # All memory entries
        memories.index        # Fast lookup index
        backups/
            memories_YYYYMMDD_HHMMSS.yml
    """

    def __init__(self, project_root: Path):
        self.memories_file = project_root / ".clauxton" / "memories.yml"
        self.index_file = project_root / ".clauxton" / "memories.index"

    def load_all(self) -> List[MemoryEntry]:
        """Load all memories with caching"""
        pass

    def save(self, entry: MemoryEntry) -> None:
        """Save memory with atomic write"""
        pass

    def rebuild_index(self) -> None:
        """Rebuild search index"""
        pass
```

**Migration**: `clauxton/utils/migrate_to_memory.py`

```python
class MemoryMigrator:
    """Migrate existing KB/Tasks to Memory format"""

    def migrate_knowledge_base(self) -> int:
        """Convert KB entries to Memory entries (type=knowledge)"""
        pass

    def migrate_tasks(self) -> int:
        """Convert Tasks to Memory entries (type=task)"""
        pass

    def create_rollback_backup(self) -> Path:
        """Create backup before migration"""
        pass

    def rollback(self, backup_path: Path) -> None:
        """Rollback migration"""
        pass
```

**CLI**: `clauxton memory migrate`

```bash
clauxton memory migrate --dry-run     # Preview migration
clauxton memory migrate --confirm     # Execute migration
clauxton memory rollback              # Rollback if needed
```

**Tests**: `tests/core/test_memory_store.py` (15 tests)
- Test atomic writes
- Test index rebuilding
- Test backup creation

**Tests**: `tests/utils/test_migrate_to_memory.py` (10 tests)
- Test KB to Memory migration
- Test Task to Memory migration
- Test rollback functionality

### Day 7-10: Backward Compatibility

**File**: `clauxton/core/knowledge_base_compat.py`

```python
class KnowledgeBaseCompat:
    """
    Backward compatibility layer for KB operations
    Maps to Memory system internally
    """

    def __init__(self, memory: Memory):
        self.memory = memory

    def add(self, entry: KnowledgeBaseEntry) -> str:
        """KB entry → Memory entry (type=knowledge)"""
        memory_entry = MemoryEntry(
            id=self._generate_memory_id(),
            type="knowledge",
            title=entry.title,
            content=entry.content,
            category=entry.category,
            tags=entry.tags,
            source="manual",
            legacy_id=entry.id,
            created_at=entry.created_at,
            updated_at=entry.updated_at,
        )
        return self.memory.add(memory_entry)

    def search(self, query: str, limit: int = 10) -> List[KnowledgeBaseEntry]:
        """Search KB → Search Memory (type=knowledge)"""
        memories = self.memory.search(query, type_filter=["knowledge"], limit=limit)
        return [self._to_kb_entry(m) for m in memories]
```

**Similar for**: `task_manager_compat.py`

**Tests**: `tests/core/test_compat.py` (20 tests)
- Test KB API compatibility
- Test Task API compatibility
- Test MCP tool compatibility

**Deliverables**:
- ✅ `MemoryEntry` model with full validation
- ✅ Memory storage backend
- ✅ Migration utilities (KB → Memory, Task → Memory)
- ✅ Backward compatibility layer
- ✅ 50+ tests, 95% coverage

---

## Week 3-4: Smart Memory (賢い記憶)

### Goals
1. Auto-extract memories from Git commits
2. Detect code change patterns
3. Auto-detect memory relationships
4. Consolidate duplicate memories

### Day 11-13: Auto-Extraction from Commits

**File**: `clauxton/semantic/memory_extractor.py`

```python
class MemoryExtractor:
    """Extract memories from Git commits and code changes"""

    def extract_from_commit(self, commit_sha: str) -> List[MemoryEntry]:
        """
        Extract memories from commit message + diff

        Examples:
        - "feat: Add user authentication" → Memory (type=decision)
        - "refactor: Switch to PostgreSQL" → Memory (type=decision)
        - Large file changes → Memory (type=pattern)
        """
        commit = self.repo.commit(commit_sha)

        # Analyze commit message
        decision = self._extract_decision(commit.message)
        if decision:
            yield self._create_decision_memory(decision, commit)

        # Analyze code changes
        patterns = self._detect_patterns(commit.diff())
        for pattern in patterns:
            yield self._create_pattern_memory(pattern, commit)

    def extract_from_recent_commits(
        self,
        since_days: int = 7
    ) -> List[MemoryEntry]:
        """Extract from recent commits"""
        pass

    def _extract_decision(self, commit_message: str) -> Optional[Dict]:
        """
        Identify architectural decisions from commit message

        Patterns:
        - "feat:", "fix:", "refactor:", "perf:"
        - "Switch to X", "Migrate to Y", "Replace X with Y"
        - "Add authentication", "Implement caching"
        """
        pass

    def _detect_patterns(self, diff: str) -> List[Dict]:
        """
        Detect code patterns from diff

        Examples:
        - New API endpoint added
        - Database migration
        - New React component pattern
        """
        pass
```

**CLI**: `clauxton memory extract`

```bash
clauxton memory extract --since 7d        # Extract from last 7 days
clauxton memory extract --commit abc123   # Extract from specific commit
clauxton memory extract --auto-add        # Auto-add to memory
```

**Tests**: `tests/semantic/test_memory_extractor.py` (15 tests)
- Test decision extraction from commits
- Test pattern detection from diffs
- Test auto-add functionality

### Day 14-16: Relationship Detection

**File**: `clauxton/semantic/memory_linker.py`

```python
class MemoryLinker:
    """Auto-detect relationships between memories"""

    def find_relationships(
        self,
        entry: MemoryEntry,
        existing_memories: List[MemoryEntry]
    ) -> List[str]:
        """
        Find related memories using:
        1. Semantic similarity (embeddings)
        2. Shared tags
        3. Same category
        4. Temporal proximity
        5. File/code overlap
        """
        related = []

        # Semantic similarity
        similar = self._find_similar_by_content(entry, existing_memories)
        related.extend(similar)

        # Shared tags (high weight)
        tag_matches = self._find_by_shared_tags(entry, existing_memories)
        related.extend(tag_matches)

        # Deduplicate and rank
        return self._rank_relationships(related)

    def auto_link_all(self) -> int:
        """
        Auto-link all memories
        Returns: Number of relationships created
        """
        pass

    def suggest_merge_candidates(self) -> List[Tuple[str, str, float]]:
        """
        Find duplicate/similar memories that should be merged
        Returns: [(memory_id1, memory_id2, similarity_score)]
        """
        pass
```

**CLI**: `clauxton memory link`

```bash
clauxton memory link --auto              # Auto-link all memories
clauxton memory link MEM-20260127-001    # Link specific memory
clauxton memory suggest-merge            # Find merge candidates
```

**Tests**: `tests/semantic/test_memory_linker.py` (15 tests)
- Test semantic similarity linking
- Test tag-based linking
- Test merge candidate detection

**Deliverables**:
- ✅ Auto-extraction from commits
- ✅ Relationship detection system
- ✅ Merge candidate suggestion
- ✅ 30+ tests, 90% coverage

---

## Week 5-6: Memory Intelligence (記憶の知能化)

### Goals
1. Auto-summarization of memories
2. Question-answering over memories
3. Next task prediction
4. Memory graph visualization

### Day 17-19: Question-Answering

**File**: `clauxton/semantic/memory_qa.py`

```python
class MemoryQA:
    """Question-answering over memory system"""

    def answer_question(self, question: str) -> Dict:
        """
        Answer questions about project using memories

        Examples:
        Q: "Why did we switch to PostgreSQL?"
        A: Found in MEM-20251215-003 (type=decision):
           "Migrated from SQLite to PostgreSQL for better concurrency..."

        Q: "What authentication method do we use?"
        A: Found in MEM-20251201-015 (type=knowledge):
           "Authentication: JWT tokens with refresh tokens..."

        Q: "What should I work on next?"
        A: Based on task memories and recent activity:
           "Suggested: Complete authentication flow (MEM-20260110-042)"
        """

        # 1. Semantic search for relevant memories
        relevant = self.memory.search(
            question,
            limit=10,
            hybrid_search=True  # TF-IDF + semantic
        )

        # 2. Rank by relevance
        ranked = self._rank_by_context(question, relevant)

        # 3. Generate answer
        return {
            "answer": self._generate_answer(question, ranked[:3]),
            "sources": [m.id for m in ranked[:3]],
            "confidence": self._calculate_confidence(ranked),
        }

    def _generate_answer(
        self,
        question: str,
        relevant_memories: List[MemoryEntry]
    ) -> str:
        """
        Generate natural language answer
        Simple approach: Extract most relevant content
        Advanced: Use LLM to synthesize answer (optional)
        """
        pass
```

**MCP Tool**: `answer_question(question: str) -> Dict`

```python
@server.call_tool()
async def answer_question(question: str) -> str:
    """
    Answer questions about the project using memory system

    Args:
        question: Natural language question

    Returns:
        Answer with sources

    Examples:
        "Why did we choose React over Vue?"
        "What is our API design pattern?"
        "What authentication system do we use?"
    """
    qa = MemoryQA(memory)
    result = qa.answer_question(question)

    return format_answer(result)
```

**Tests**: `tests/semantic/test_memory_qa.py` (10 tests)
- Test answering architecture questions
- Test answering pattern questions
- Test confidence scoring

### Day 20-22: Summarization & Prediction

**File**: `clauxton/semantic/memory_summarizer.py`

```python
class MemorySummarizer:
    """Summarize and analyze memory collections"""

    def summarize_project(self) -> Dict:
        """
        Generate project summary from memories

        Returns:
        {
            "architecture_decisions": ["PostgreSQL for DB", "JWT auth", ...],
            "active_patterns": ["REST API design", "React component structure"],
            "recent_changes": ["Migrated to TypeScript", "Added caching"],
            "tech_stack": ["Python", "React", "PostgreSQL", "Redis"],
            "constraints": ["Must support offline mode", ...],
        }
        """
        pass

    def predict_next_tasks(self, context: Dict) -> List[MemoryEntry]:
        """
        Predict likely next tasks based on:
        - Current task memories (status=pending)
        - Recent activity patterns
        - Project phase (from commit activity)
        - Seasonal patterns (e.g., Friday = cleanup tasks)
        """
        pass

    def generate_knowledge_gaps(self) -> List[str]:
        """
        Identify missing knowledge/decisions

        Example:
        - "No authentication decision recorded"
        - "API error handling pattern not documented"
        - "Database backup strategy missing"
        """
        pass
```

**MCP Tools**:
- `get_project_summary() -> Dict`
- `suggest_next_tasks(limit: int = 5) -> List[Dict]`
- `detect_knowledge_gaps() -> List[str]`

**Tests**: `tests/semantic/test_memory_summarizer.py` (10 tests)

### Day 23-24: Memory Graph Visualization

**File**: `clauxton/visualization/memory_graph.py`

```python
class MemoryGraph:
    """Generate memory relationship graph"""

    def generate_graph_data(self) -> Dict:
        """
        Generate graph data for visualization

        Nodes: Memory entries
        Edges: Relationships

        Returns:
        {
            "nodes": [
                {"id": "MEM-001", "type": "knowledge", "title": "...", "size": 10},
                ...
            ],
            "edges": [
                {"source": "MEM-001", "target": "MEM-002", "weight": 0.85},
                ...
            ]
        }
        """
        pass

    def export_to_dot(self) -> str:
        """Export to Graphviz DOT format"""
        pass

    def export_to_mermaid(self) -> str:
        """Export to Mermaid diagram format (for Markdown)"""
        pass
```

**CLI**: `clauxton memory graph`

```bash
clauxton memory graph --output graph.png      # Generate graph image
clauxton memory graph --format mermaid        # Mermaid diagram
clauxton memory graph --type knowledge        # Filter by type
```

**TUI Integration**: Add "Memory Graph" tab to TUI

**Tests**: `tests/visualization/test_memory_graph.py` (5 tests)

**Deliverables**:
- ✅ Question-answering system
- ✅ Project summarization
- ✅ Next task prediction
- ✅ Memory graph visualization
- ✅ 25+ tests, 90% coverage

---

## Week 7-8: UX Polish (体験の洗練)

### Goals
1. Smart default configuration
2. Guided workflows in CLI
3. TUI memory explorer
4. Comprehensive documentation

### Day 25-27: Smart Defaults & Guided Workflows

**File**: `clauxton/cli/memory.py`

```python
@click.group()
def memory():
    """Memory management commands"""
    pass

@memory.command()
@click.option("--interactive", is_flag=True, help="Interactive mode")
def add(interactive):
    """
    Add memory entry (guided workflow)

    Smart defaults:
    - Auto-detect type from content
    - Auto-suggest tags from existing memories
    - Auto-suggest related memories
    - Auto-extract from recent commit if applicable
    """
    if interactive:
        run_guided_memory_add()
    else:
        run_quick_memory_add()

def run_guided_memory_add():
    """
    Guided workflow:

    1. "What do you want to remember?"
       → User enters content

    2. "I detected this might be a 'decision'. Is that correct?"
       → Auto-suggest type

    3. "Related memories found: [MEM-001, MEM-002]. Link them?"
       → Auto-suggest relationships

    4. "Suggested tags: ['authentication', 'api']. Add them?"
       → Auto-suggest tags
    """
    pass
```

**Examples**:

```bash
# Quick add with smart defaults
$ clauxton memory add --quick
Content: We switched to PostgreSQL for better concurrency
→ Auto-detected: type=decision, tags=[database, postgresql]
→ Related: MEM-20251201-008 (Database selection criteria)
→ Memory added: MEM-20260127-042

# Interactive guided workflow
$ clauxton memory add
What do you want to remember?
> We're using JWT for authentication with 1-hour expiry

I detected this might be a 'decision'. Is that correct? [Y/n] y
Suggested tags: ['authentication', 'jwt', 'security']. Use these? [Y/n] y
Related memories found:
  - MEM-20251215-003: API authentication pattern
  - MEM-20260110-018: Session management
Link these memories? [Y/n] y

✓ Memory added: MEM-20260127-043
```

**Tests**: `tests/cli/test_memory_guided.py` (15 tests)

### Day 28-30: TUI Memory Explorer

**File**: `clauxton/tui/memory_explorer.py`

```python
class MemoryExplorer(Screen):
    """
    TUI Screen for exploring memories

    Layout:
    ┌─────────────────────────────────────────────┐
    │ 🧠 Memory Explorer                          │
    ├─────────────────┬───────────────────────────┤
    │ Filters         │ Memory List               │
    │ ☑ Knowledge     │ MEM-001: API Design       │
    │ ☑ Decision      │ MEM-002: Auth Pattern     │
    │ ☑ Task          │ MEM-003: DB Migration     │
    │ ☐ Code          │ ...                       │
    │ ☐ Pattern       │                           │
    │                 │                           │
    │ Search:         │                           │
    │ [____________]  │                           │
    ├─────────────────┴───────────────────────────┤
    │ Memory Details: MEM-001                     │
    │ Type: Knowledge | Category: architecture    │
    │ Tags: api, rest, design                     │
    │                                             │
    │ Content:                                    │
    │ We use RESTful API design with...          │
    │                                             │
    │ Related: MEM-005, MEM-012                   │
    └─────────────────────────────────────────────┘

    Keyboard shortcuts:
    - /: Focus search
    - f: Toggle filters
    - a: Add new memory (guided)
    - e: Edit selected memory
    - d: Delete selected memory
    - g: Show memory graph
    - ?: Ask question (Q&A)
    """
    pass
```

**TUI Integration**: Add to main TUI

```python
# In clauxton/tui/app.py
class ClauxtonTUI(App):
    BINDINGS = [
        ("m", "show_memories", "Memories"),  # New
        ("t", "show_tasks", "Tasks"),
        ("k", "show_kb", "Knowledge Base"),
        ("s", "show_suggestions", "Suggestions"),
    ]
```

**Tests**: `tests/tui/test_memory_explorer.py` (10 tests)

### Day 31-34: Documentation

**Files to Create/Update**:

1. **`docs/MEMORY_SYSTEM.md`** - Comprehensive guide
   - Concept overview
   - Memory types explained
   - CLI usage examples
   - MCP tool reference
   - Migration guide
   - FAQ

2. **`docs/MIGRATION_GUIDE_v0.15.0.md`** - Migration from v0.14.0
   - Breaking changes (if any)
   - Migration steps
   - Rollback procedure
   - Compatibility notes

3. **`README.md`** - Update with Memory System
   - Update feature list
   - Update quick start
   - Update examples

4. **`docs/mcp-tools.md`** - Update MCP tool list
   - New memory tools
   - Deprecated KB/Task tools (but still supported)

5. **Tutorial**: `docs/TUTORIAL_MEMORY_SYSTEM.md`
   - Step-by-step walkthrough
   - Real project examples
   - Best practices

**User Guide Structure**:

```markdown
# Clauxton Memory System

## Quick Start
1. Migrate existing data: `clauxton memory migrate`
2. Add your first memory: `clauxton memory add --interactive`
3. Search memories: `clauxton memory search "authentication"`
4. Ask questions: Use MCP tool `answer_question("How do we handle auth?")`

## Concepts
- What is a Memory Entry?
- Memory Types: knowledge, decision, code, task, pattern
- Relationships: How memories connect
- Auto-extraction: Learning from your commits

## Workflows
- Daily: Extract decisions from commits
- Weekly: Review and consolidate memories
- Monthly: Generate project summary

## Advanced
- Custom memory types
- Memory graph analysis
- Team memory sharing (v0.16.0)
```

### Day 35-36: Testing & Polish

**Integration Tests**: `tests/integration/test_memory_workflow.py`

```python
def test_complete_memory_workflow(tmp_project):
    """
    End-to-end test:
    1. Migrate KB/Tasks to Memory
    2. Add new memory manually
    3. Extract memory from commit
    4. Auto-link memories
    5. Ask question
    6. Generate summary
    7. Export to Markdown
    """
    pass

def test_backward_compatibility(tmp_project):
    """
    Ensure old KB/Task APIs still work after migration
    """
    pass
```

**Performance Tests**: `tests/performance/test_memory_performance.py`

```python
def test_search_performance_1000_memories():
    """Search should be <100ms for 1000 memories"""
    pass

def test_auto_link_performance():
    """Auto-linking should be <5s for 500 memories"""
    pass
```

**User Acceptance Tests**: 15 scenarios covering:
- First-time user migration
- Daily usage workflows
- Q&A accuracy
- TUI usability
- MCP tool integration

**Deliverables**:
- ✅ Smart defaults & guided workflows
- ✅ TUI Memory Explorer
- ✅ Comprehensive documentation (5 guides)
- ✅ 15+ integration tests
- ✅ Performance benchmarks

---

## Release Checklist

### Code Quality
- [ ] All tests pass (target: 2,100+ tests, 90%+ coverage)
- [ ] Mypy type checking passes (strict mode)
- [ ] Ruff linting passes (no warnings)
- [ ] Performance benchmarks meet targets

### Documentation
- [ ] `docs/MEMORY_SYSTEM.md` complete
- [ ] `docs/MIGRATION_GUIDE_v0.15.0.md` complete
- [ ] `docs/TUTORIAL_MEMORY_SYSTEM.md` complete
- [ ] `README.md` updated
- [ ] `CHANGELOG.md` updated
- [ ] MCP tool documentation updated

### Migration Testing
- [ ] Test migration from v0.14.0 with real projects
- [ ] Test rollback procedure
- [ ] Verify backward compatibility

### User Testing
- [ ] 5+ beta testers complete workflows
- [ ] Q&A accuracy >80% validated
- [ ] Auto-extraction accuracy >70% validated
- [ ] TUI usability feedback incorporated

### Release
- [ ] Version bumped to v0.15.0 in `__version__.py` and `pyproject.toml`
- [ ] Git tag created: `v0.15.0`
- [ ] PyPI package published
- [ ] GitHub release created
- [ ] Documentation site updated
- [ ] Announcement post prepared

---

## Success Metrics (Post-Release)

### Week 1-2 (Adoption)
- 📥 PyPI downloads: 500+ (from existing users)
- ⭐ GitHub stars: +50
- 💬 Migration rate: 30% of active users

### Month 1 (Validation)
- 🎯 Memory consolidation: 70%+ of users migrate KB/Tasks
- ⚡ Auto-extraction usage: 50% of users try it
- 💬 Q&A feature: 40% of users try it
- ❤️ User satisfaction: 4.3+/5.0

### Month 3 (Growth)
- 📥 PyPI downloads: 20K/month
- ⭐ GitHub stars: 500+
- 🔄 Retention (30-day): 50%+
- 💰 Pro users: 50+ ($29/year)

---

## Risks & Mitigations

### Risk 1: Migration Complexity
**Risk**: Users struggle with migration, lose data
**Mitigation**:
- Automatic backups before migration
- Dry-run mode to preview changes
- Rollback command
- Comprehensive migration guide

### Risk 2: Backward Compatibility Breaks
**Risk**: Existing integrations/scripts break
**Mitigation**:
- Maintain compatibility layer for 2 versions
- Deprecation warnings in v0.15.0
- Full removal only in v0.17.0

### Risk 3: Performance Degradation
**Risk**: Unified system is slower than specialized systems
**Mitigation**:
- Performance benchmarks as gate
- Caching strategy
- Lazy loading
- Index optimization

### Risk 4: User Confusion
**Risk**: New concept is hard to understand
**Mitigation**:
- Clear documentation with examples
- Guided workflows
- Tutorial video (optional)
- Active community support

---

## Post-Release Roadmap

### v0.15.1 (Patch - 2 weeks after v0.15.0)
- Bug fixes from user feedback
- Performance improvements
- Documentation updates

### v0.15.2 (Minor - 1 month after v0.15.0)
- Memory templates (common patterns)
- Improved auto-extraction
- Enhanced Q&A

### v0.16.0 (Team Features - 3 months after v0.15.0)
- Shared memory workspace
- Team collaboration
- Optional web interface

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## Team & Resources

### Development Team
- **Lead Developer**: 1 person (you)
- **Time Commitment**: 20-30 hours/week
- **Duration**: 8 weeks

### Optional Support
- **Beta Testers**: 5-10 early users
- **Documentation Reviewer**: 1 person (optional)
- **Community Contributors**: Open for PRs

### Budget
- **Development**: Open source (your time)
- **Infrastructure**: $0 (Git-based, no servers)
- **Marketing**: $0 (GitHub, Twitter, Reddit)

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**Last updated**: 2025-11-03
**Status**: 📋 Planning (v0.14.0 complete, v0.15.0 ready to start)
**Next Action**: Begin Week 1 implementation on 2025-11-27
