# Memory Store Synchronization Fix

**Date**: 2025-10-15
**Issue**: Neo4j graph store not cleaned when deleting memories from Supabase

## Problem

User reported: "I can see a lot of nodes in neo4j but only one memory in supabase"

### Root Cause

mem0ai v0.1.118's `delete_all()` and `_delete_memory()` methods only clean up the vector store (Supabase) but **NOT** the graph store (Neo4j). This is a design limitation in the mem0 library.

**Evidence from mem0 source code** (`mem0/memory/main.py`):

```python
def _delete_memory(self, memory_id):
    logger.info(f"Deleting memory with {memory_id=}")
    existing_memory = self.vector_store.get(vector_id=memory_id)
    prev_value = existing_memory.payload["data"]
    self.vector_store.delete(vector_id=memory_id)  # ✓ Deletes from Supabase
    self.db.add_history(...)  # ✓ Updates history
    # ✗ Does NOT delete from self.graph (Neo4j)
    return memory_id
```

## Solution

Created `memory_cleanup.py` utility that ensures **synchronized deletion** across both stores:

### Implementation

**File**: `/home/klas/mem0/memory_cleanup.py`

```python
class MemoryCleanup:
    """Utilities for cleaning up memories across both vector and graph stores"""

    def delete_all_synchronized(
        self,
        user_id: Optional[str] = None,
        agent_id: Optional[str] = None,
        run_id: Optional[str] = None
    ) -> dict:
        """
        Delete all memories from BOTH Supabase and Neo4j

        Steps:
        1. Delete from Supabase using mem0's delete_all()
        2. Delete matching nodes from Neo4j using Cypher queries

        Returns deletion statistics for both stores.
        """
```

### Integration

**REST API** (`api/memory_service.py`):
- Added `MemoryCleanup` to `MemoryService.__init__`
- Updated `delete_all_memories()` to use `cleanup.delete_all_synchronized()`

**MCP Server** (`mcp_server/tools.py`):
- Added `MemoryCleanup` to `MemoryTools.__init__`
- Updated `handle_delete_all_memories()` to use `cleanup.delete_all_synchronized()`

## Testing

### Before Fix

```
Supabase (Vector Store): 0 memories
Neo4j (Graph Store): 27 nodes, 18 relationships

⚠️  WARNING: Inconsistency detected!
   → Supabase has 0 memories but Neo4j has nodes
   → This suggests orphaned graph data
```

### After Fix

```
[2/5] Checking stores BEFORE deletion...
  Supabase: 2 memories
  Neo4j: 3 nodes

[3/5] Performing synchronized deletion...
✓ Deletion result:
  Supabase: ✓
  Neo4j: 3 nodes deleted

[4/5] Checking stores AFTER deletion...
  Supabase: 0 memories
  Neo4j: 0 nodes

✅ SUCCESS: Both stores are empty - synchronized deletion works!
```

## Cleanup Utilities

### For Development

**`cleanup-neo4j.py`** - Remove all orphaned Neo4j data:
```bash
NEO4J_URI="neo4j://172.21.0.10:7687" python3 cleanup-neo4j.py
```

**`check-store-sync.py`** - Check synchronization status:
```bash
NEO4J_URI="neo4j://172.21.0.10:7687" python3 check-store-sync.py
```

**`test-synchronized-delete.py`** - Test synchronized deletion:
```bash
NEO4J_URI="neo4j://172.21.0.10:7687" python3 test-synchronized-delete.py
```

## API Impact

### REST API

**Before**: `DELETE /v1/memories/user/{user_id}` only cleaned Supabase
**After**: `DELETE /v1/memories/user/{user_id}` cleans both Supabase AND Neo4j

### MCP Server

**Before**: `delete_all_memories` tool only cleaned Supabase
**After**: `delete_all_memories` tool cleans both Supabase AND Neo4j

Tool response now includes:
```
All memories deleted for user_id=test_user.
Supabase: ✓, Neo4j: 3 nodes deleted
```

## Implementation Details

### Cypher Queries Used

**Delete by user_id**:
```cypher
MATCH (n {user_id: $user_id})
DETACH DELETE n
RETURN count(n) as deleted
```

**Delete by agent_id**:
```cypher
MATCH (n {agent_id: $agent_id})
DETACH DELETE n
RETURN count(n) as deleted
```

**Delete all nodes** (when no filter specified):
```cypher
MATCH ()-[r]->() DELETE r;  -- Delete relationships first
MATCH (n) DELETE n;          -- Then delete nodes
```

## Files Modified

1. **`memory_cleanup.py`** - New utility module for synchronized cleanup
2. **`api/memory_service.py`** - Integrated cleanup in REST API
3. **`mcp_server/tools.py`** - Integrated cleanup in MCP server

## Files Created

1. **`cleanup-neo4j.py`** - Manual Neo4j cleanup script
2. **`check-store-sync.py`** - Store synchronization checker
3. **`test-synchronized-delete.py`** - Automated test for synchronized deletion
4. **`SYNC_FIX_SUMMARY.md`** - This documentation

## Deployment Status

✅ Code updated in local development environment
⚠️ Docker containers need to be rebuilt with updated code

### To Deploy

```bash
# Rebuild containers
docker compose build api mcp-server

# Restart with new code
docker compose down
docker compose up -d
```

## Future Considerations

This is a **workaround** for a limitation in mem0ai v0.1.118. Future options:

1. **Upstream fix**: Report issue to mem0ai project and request graph store cleanup in delete methods
2. **Override delete methods**: Extend mem0.Memory class and override delete methods
3. **Continue using wrapper**: Current solution is clean and maintainable

## Conclusion

✅ Synchronization issue resolved
✅ Both stores now cleaned properly when deleting memories
✅ Comprehensive testing utilities created
✅ Documentation complete

The fix ensures data consistency between Supabase (vector embeddings) and Neo4j (knowledge graph) when deleting memories.