Memory Health

Provide a dashboard view of the memory system's health, including entity counts, memory statistics, data quality indicators, and recommendations.

Triggers

• •User says "memory health", "memory stats", "brain check"
• •User says "data quality", "how's my memory system?"
• •User says "how much do you remember?", "what's in your brain?"
• •Periodic self-check (weekly review, morning brief)

Workflow

Step 1: Gather Statistics

Use memory.session_context to get current system state:

memory.session_context(scope="full")

This returns entity counts, memory counts, relationship counts, and predictions.

Step 2: Calculate Health Indicators

From the session context, derive:

Entity Health

• •Total entities by type (people, projects, organizations, topics)
• •Entities with no associated memories (orphans)
• •Entities not mentioned in 90+ days (stale)

Memory Health

• •Total memories by type (fact, preference, observation, learning)
• •Average importance score
• •Invalidated vs. active memories
• •Corrected memories count

Relationship Health

• •Total active relationships
• •Relationships marked as invalid
• •Cooling relationships (no recent activity)

Data Quality

• •Potential duplicate entities (fuzzy name match)
• •Orphan memories (no entity links)
• •Memories below importance threshold (0.3)

Step 3: Present Dashboard

Format:

## Memory System Health Report

### Entities
| Type         | Count | Stale (90d) |
|--------------|-------|-------------|
| People       |    23 |           2 |
| Projects     |    12 |           5 |
| Organizations|     8 |           0 |
| Topics       |    15 |           3 |

### Memories
- **Total:** 847 active memories
- **Average importance:** 0.72
- **By type:** 412 facts, 198 preferences, 156 observations, 81 learnings
- **Corrected:** 12 memories have been corrected
- **Invalidated:** 34 memories marked as no longer true

### Relationships
- **Active:** 67 relationships
- **Cooling:** 8 relationships (no contact in 30+ days)

### Data Quality
- **Potential duplicates:** 3 entity pairs to review
- **Orphan memories:** 5 memories without entity links
- **Low importance:** 23 memories below 0.3 threshold

### Recommendations
1. Review potential duplicates: "John Smith" and "Jon Smith" may be the same person
2. Consider archiving 5 stale projects with no recent activity
3. 8 relationships are cooling - may want to reconnect

Quick Stats Mode

If user just wants numbers:

Your memory at a glance:
- 58 people, 12 projects, 8 orgs
- 847 memories (avg importance: 0.72)
- 67 relationships tracked
- Last consolidation: 2 hours ago

Troubleshooting Mode

When user reports memory issues ("you forgot X", "why don't you remember"):

1. •Search for the specific topic/entity
2. •Check if memories exist but are below recall threshold
3. •Check if memories were invalidated
4. •Report findings:

I searched for memories about "[topic]":
- Found 3 memories, but all below importance 0.4 (not surfacing in context)
- One memory was corrected on [date]
- Recommendation: I can boost the importance of these if they're still relevant

Recommendations Engine

Based on health metrics, suggest:

• •Duplicates found: "Run /fix-duplicates to clean up 3 potential duplicate entities"
• •Stale entities: "Consider archiving [X] project - no activity in 120 days"
• •Cooling relationships: "Haven't heard about [Name] in 45 days - want me to add a follow-up?"
• •Low memory count: "I only have [N] memories about [Entity] - we could add more context"
• •High invalidation rate: "12% of memories about [Entity] were invalidated - the situation may have changed significantly"

Never

• •Expose raw database IDs or technical details to user
• •Make the user feel bad about "memory problems"
• •Automatically delete or modify data based on health checks
• •Claim perfect memory - always acknowledge limitations