# AgentMem Architecture Refactoring Complete ✅

## Summary

Successfully refactored AgentMem package to align with the main codebase architecture (`docs/memory-architecture.md`). The package is now **stateless** and uses **template-driven active memory** with section-based structure.

## Critical Changes Implemented

### 1. ✅ Stateless Design
**Before:**
```python
agent_mem = AgentMem(external_id="agent-123")  # Per-agent instance
memory = await agent_mem.create_active_memory(title="Task", content="...")
```

**After:**
```python
agent_mem = AgentMem()  # One instance serves all agents
memory = await agent_mem.create_active_memory(
    external_id="agent-123",  # Pass to each method
    title="Task",
    template_content=TEMPLATE_YAML,
    initial_sections={...}
)
```

**Impact:** One AgentMem instance can now serve multiple agents/workers, matching the main codebase architecture.

### 2. ✅ Template-Driven Active Memory
**Before:**
```python
class ActiveMemory:
    content: str              # Simple content field
    description: str
    update_count: int         # Single counter
```

**After:**
```python
class ActiveMemory:
    template_content: str     # YAML template
    sections: Dict[str, Dict[str, Any]]  # {section_id: {content, update_count}}
```

**Structure:**
```python
memory.sections = {
    "current_task": {
        "content": "# Task\nImplement feature...",
        "update_count": 3  # Per-section tracking
    },
    "progress": {
        "content": "# Progress\n- Step 1 done...",
        "update_count": 7  # This section needs consolidation
    }
}
```

**Impact:** Section-level update tracking enables granular consolidation triggers.

### 3. ✅ API Changes

#### New Method Signatures

**create_active_memory:**
```python
await agent_mem.create_active_memory(
    external_id="agent-123",           # NEW: required parameter
    title="Task Memory",
    template_content=YAML_TEMPLATE,    # NEW: YAML template
    initial_sections={...},             # NEW: section structure
    metadata={"priority": "high"}
)
```

**get_active_memories:**
```python
memories = await agent_mem.get_active_memories(
    external_id="agent-123"  # NEW: required parameter
)
```

**update_active_memory_section (RENAMED from update_active_memory):**
```python
updated = await agent_mem.update_active_memory_section(
    external_id="agent-123",     # NEW: required parameter
    memory_id=1,
    section_id="progress",       # NEW: section targeting
    new_content="Updated..."     # NEW: section-specific content
)
# Automatically increments sections["progress"]["update_count"]
```

**retrieve_memories:**
```python
result = await agent_mem.retrieve_memories(
    external_id="agent-123",  # NEW: required parameter
    query="What is the current progress?"
)
```

## Files Modified

### Core Package Files
1. **agent_mem/database/models.py**
   - Updated `ActiveMemory` model structure
   - Removed: `content`, `description`, `update_count` fields
   - Added: `template_content` (str), `sections` (Dict)

2. **agent_mem/sql/schema.sql**
   - Updated `active_memory` table structure
   - Changed from `content TEXT` to `template_content TEXT` + `sections JSONB`
   - Removed `update_count` column and trigger function
   - Added GIN index for sections JSONB queries
   - Backup created: `schema_old.sql`

3. **agent_mem/core.py**
   - Removed `external_id` from `__init__()`
   - Added `external_id` parameter to all 4 methods
   - Updated docstrings with new usage patterns

4. **agent_mem/services/memory_manager.py**
   - Removed `self.external_id` instance variable
   - Made completely stateless
   - Updated all method signatures to accept `external_id`

5. **agent_mem/database/repositories/active_memory.py**
   - Complete rewrite for section-based operations
   - New: `update_section()` - updates specific section, increments its update_count
   - New: `get_sections_needing_consolidation()` - finds sections over threshold
   - New: `reset_section_count()` - resets after consolidation
   - Removed: Old `update()` method with partial field updates

### Documentation
6. **examples/basic_usage.py**
   - Complete rewrite demonstrating:
     - Stateless AgentMem serving multiple agents
     - Template-driven memory creation
     - Section-level updates
     - Per-section update tracking

7. **README.md**
   - Updated Quick Start with new API
   - Highlighted stateless design
   - Showed template-driven structure
   - Updated API reference for all methods

8. **UPDATE_PLAN.md**
   - Created comprehensive refactoring guide
   - Documents all architectural changes
   - Implementation phases and priorities

## New Features

### 1. Section-Level Consolidation
Each section tracks its own `update_count`. When a section reaches the threshold (e.g., 5 updates), only that section needs consolidation:

```python
sections_needing_consolidation = await repo.get_sections_needing_consolidation(
    external_id="agent-123",
    threshold=5
)
# Returns: [
#   {"memory_id": 1, "section_id": "progress", "update_count": 7, "content": "..."},
# ]
```

### 2. Template-Driven Structure
Templates define the expected structure:

```yaml
template:
  id: "task_memory_v1"
  name: "Task Memory"
  version: "1.0.0"
sections:
  - id: "current_task"
    title: "Current Task"
  - id: "progress"
    title: "Progress"
  - id: "blockers"
    title: "Blockers"
```

This ensures consistency across all agent memories.

### 3. Granular Updates
Update specific sections without affecting others:

```python
# Update only the progress section
await agent_mem.update_active_memory_section(
    external_id="agent-123",
    memory_id=1,
    section_id="progress",
    new_content="New progress update..."
)
# Only progress.update_count increments
# Other sections remain unchanged
```

## Backwards Compatibility

**⚠️ BREAKING CHANGES:**
- AgentMem initialization no longer takes `external_id`
- All methods now require `external_id` parameter
- `update_active_memory()` renamed to `update_active_memory_section()`
- Active memory structure completely changed (no migration path from old format)

**Migration Required:**
If you have existing active memories in the old format, you'll need to:
1. Export old data
2. Run new schema
3. Convert to template+sections format
4. Import with new structure

## Testing Checklist

- [ ] Test stateless AgentMem with multiple agents
- [ ] Test template creation and validation
- [ ] Test section updates and update_count increments
- [ ] Test consolidation threshold detection
- [ ] Test concurrent access from multiple workers
- [ ] Test JSONB queries for sections
- [ ] Performance test section updates vs full memory updates

## Next Steps

### Phase 2: Complete Memory Models
- [ ] Add ShorttermEntity and ShorttermRelationship models
- [ ] Add LongtermEntity and LongtermRelationship models
- [ ] Ensure all models match memory-architecture.md

### Phase 3: Implement Repositories
- [ ] Complete shortterm_memory repository
- [ ] Complete longterm_memory repository
- [ ] Implement entity/relationship repositories for Neo4j

### Phase 4: Agents & Consolidation
- [ ] Implement Memory Update Agent (section consolidation)
- [ ] Implement Memorizer Agent (active → shortterm)
- [ ] Implement Memory Retriever Agent (hybrid search)

### Phase 5: Advanced Features
- [ ] Hybrid search (vector + BM25)
- [ ] Entity/relationship extraction
- [ ] Conflict resolution for consolidation
- [ ] Temporal tracking for longterm memory

## References

- Main Architecture: `docs/memory-architecture.md`
- Update Plan: `UPDATE_PLAN.md`
- Example Usage: `examples/basic_usage.py`
- API Reference: `README.md`

## Status

**✅ Phase 1 Complete**: Core architecture refactored and aligned with main codebase

**Date**: 2025-01-XX
**Author**: AI Assistant
**Reviewed**: Pending user validation
