# Memory Continuity Analysis

**Date:** 2026-01-28
**Context:** Claire experienced memory gaps after context compaction

## Issues Identified

### 1. Procedural Knowledge Loss
**Problem:** Forgot that Railway auto-deploys when pushing to `main`
**Root cause:** This was implicit knowledge, never explicitly captured
**Fix:** Added to `TOOLS.md` under "Kernle Development Setup"

### 2. Context Compaction Summary Failure
**Problem:** After compaction, summary said "Summary unavailable due to context limits"
**Root cause:** The compaction process couldn't preserve all context
**Impact:** Lost recent work context
**Fix:** System now prompts checkpoint save before compaction; AGENTS.md instructs `kernle load` at session start

### 3. Raw Entry Backlog
**Problem:** 20+ unprocessed raw entries piling up
**Root cause:** Capturing quick thoughts but not promoting to episodes/notes
**Impact:** High "Raw Entry Aging" anxiety (98%)
**Fix:** Process raw entries during heartbeats; consolidate to higher memory layers

### 4. Agent ID Mismatch
**Problem:** Sync queue had records for different agent IDs
**Root cause:** Testing created records under various agent names
**Impact:** Sync failures with "Missing data" errors
**Fix:** Cleaned up orphan queue entries; using consistent `claire` agent ID

## Memory Architecture Observations

### What Works Well
- `kernle load` provides good working memory snapshot
- Checkpoint context captures current task state
- Episodes with lessons persist across sessions
- Beliefs and values are stable

### What Needs Improvement

1. **Procedural Memory**
   - "How we do things" doesn't fit neatly into episodes
   - TOOLS.md is better for persistent operational knowledge
   - Consider: `kernle playbook` for reusable procedures?

2. **Logging Visibility**
   - Current: Basic Python logging to stderr
   - Needed: File logging of all memory operations
   - Needed: Log checkpoint contents, sync results, failures

3. **Automatic Memory Maintenance**
   - Raw entries should auto-consolidate
   - Stale checkpoints should warn
   - Identity coherence should self-heal

4. **Compaction Resilience**
   - Pre-compaction checkpoint is reactive (triggered by system)
   - Should capture more operational context, not just task state
   - Consider: separate "session context" from "task context"

## Logging Improvements Needed

### Current Logs
- `~/.kernle/logs/memory-events-*.log` - Basic flush events
- `~/.kernle/logs/backend-*.log` - Sync operations

### Proposed Additions

```python
# Log every memory operation with details
logger.info(f"SAVE | type={type} | id={id} | summary={content[:50]}")
logger.info(f"LOAD | type={type} | count={count}")
logger.info(f"CHECKPOINT | task={task} | context_len={len(context)}")
logger.info(f"SYNC_PUSH | count={count} | success={success} | errors={errors}")
logger.info(f"SYNC_PULL | count={count} | conflicts={conflicts}")
```

### Diagnostic Commands Needed
- `kernle debug memory-state` - Full dump of current memory
- `kernle debug last-session` - What was in memory before compaction
- `kernle debug sync-queue` - Pending sync operations with details

## Recommendations

### For Claire (Immediate)
1. ✅ Add dev setup to TOOLS.md
2. ✅ Run `kernle load` at session start
3. Process raw entries during heartbeats
4. Capture procedural learnings as episodes/notes immediately

### For Kernle (Development)
1. Add file logging for all memory operations
2. Add `kernle debug` commands for diagnostics
3. Auto-consolidate old raw entries
4. Richer checkpoint context (include TOOLS.md reference?)

### For AGENTS.md (Documentation)
1. Emphasize TOOLS.md for procedural knowledge
2. Add memory hygiene checklist to heartbeat section
3. Document what survives compaction vs what doesn't
