# Kernle Comprehensive Audit Report
**Date:** January 29, 2026
**Auditors:** 4 Specialist Agents (Architecture, Senior Developer x2, Product Leader)
**Scope:** Logic audit, missing features, failure scenarios, competitive analysis

---

## Executive Summary

Kernle is a **stratified memory infrastructure** for AI agents providing persistent memory with identity continuity. It differentiates from competitors through **memory sovereignty** - the agent owns their cognition, Kernle provides storage.

### Key Findings

| Area | Assessment |
|------|------------|
| **Conceptual soundness** | 8/10 - Strong philosophy, some logical gaps |
| **Architecture quality** | 7/10 - Good patterns, PostgreSQL parity issues |
| **Feature completeness** | 7/10 - Deep where implemented, tooling gaps |
| **Competitive position** | 8/10 - Unique niche, may be too niche |
| **Failure resilience** | 6/10 - Silent conflicts, no semantic validation |

### Priority Issues Identified

1. **Confidence inflation** - No decay mechanism
2. **N+1 query patterns** - In update_goal/update_belief
3. **PostgreSQL feature gaps** - Many NotImplementedError
4. **Semantic contradiction detection** - Syntactic only (regex)
5. **Memory scoping** - No project/context field
6. **Auto-capture friction** - Manual only, higher friction than competitors

---

## Table of Contents

1. [System Overview](#1-system-overview)
2. [Logic & Architecture Audit](#2-logic--architecture-audit)
3. [Missing Features & Gaps](#3-missing-features--gaps)
4. [Failure Scenarios](#4-failure-scenarios)
5. [Competitive Analysis](#5-competitive-analysis)
6. [Auto-Capture Design Notes](#6-auto-capture-design-notes)
7. [Prioritized Recommendations](#7-prioritized-recommendations)

---

## 1. System Overview

### Architecture

Kernle uses a **mixin-based composition** pattern:

```
Kernle
├── AnxietyMixin      - Memory health tracking
├── EmotionsMixin     - Emotional memory & mood-congruent retrieval
├── ForgettingMixin   - Controlled forgetting with salience calculation
├── KnowledgeMixin    - Meta-cognition and knowledge mapping
└── MetaMemoryMixin   - Provenance tracking and confidence management
```

### Memory Hierarchy (7 Layers)

```
Values (protected, highest priority)
  ↓
Drives (protected, motivation system)
  ↓
Beliefs (semantic knowledge, confidence-tracked)
  ↓
Goals (active objectives)
  ↓
Episodes (experiences with lessons)
  ↓
Notes (flexible entries)
  ↓
Raw (unprocessed capture)
```

### Storage Architecture

- **Local**: SQLite with sqlite-vec for vector search
- **Cloud**: Supabase PostgreSQL with pgvector
- **Sync**: Local-first, push/pull, last-write-wins conflict resolution
- **Embeddings**: HashEmbedder (default, offline) or OpenAIEmbedder (optional)

### Interfaces

- CLI: 23 commands
- MCP Server: 23 tools
- Python SDK: Direct Kernle class usage

---

## 2. Logic & Architecture Audit

### Conceptual Issues

#### 2.1 Confidence Inflation Without Decay

**Location:** `kernle/features/metamemory.py:42-81`

```python
def verify_memory(self, ...):
    # Increases confidence by 0.1 with each verification
    new_confidence = min(1.0, current_confidence + 0.1)
```

**Problem:** No mechanism for confidence to decay over time. Frequently-accessed memories converge to 1.0 regardless of actual accuracy.

**Recommendation:** Add time-based decay factor:
```python
decay = 0.01 * days_since_last_verified
adjusted_confidence = max(0.5, current_confidence - decay)
```

#### 2.2 Belief Contradiction Detection is Syntactic

**Location:** `kernle/core.py` - `find_contradictions()`

**Problem:** Uses regex patterns ("never" vs "always", "like" vs "dislike"). Misses semantic contradictions like:
- "I should always test" vs "Testing slows me down"
- "API-first is best" vs "Code-first is faster"

**Recommendation:** Use embedding similarity to detect when beliefs are both similar AND have opposing sentiment/direction.

#### 2.3 Salience Formula Edge Cases

**Location:** `kernle/features/forgetting.py:28-71`

```python
salience = (confidence × (log(times_accessed + 1) + 0.1)) / (age_factor + 1)
```

**Problems:**
- Never-accessed memories can have very low salience even if important
- Favors frequently-accessed low-confidence over rarely-accessed high-confidence
- `days_since` defaults to 365 for unknown creation times (too aggressive)

**Recommendation:** Add `importance` field separate from access frequency.

### Architecture Issues

#### 2.4 N+1 Query Pattern in Updates

**Location:** `kernle/core.py:1648-1699`

```python
def update_goal(self, goal_id, ...):
    goals = self._storage.get_goals(status=None, limit=1000)
    for g in goals:
        if g.id == goal_id:
            existing = g
            break
```

**Problem:** Fetches ALL goals/beliefs (up to 1000) to find one by ID.

**Recommendation:** Add direct `get_goal_by_id()`, `get_belief_by_id()` methods.

#### 2.5 PostgreSQL Feature Gaps

**Location:** `kernle/storage/postgres.py:1276-1403`

Methods raising `NotImplementedError`:
- Playbooks: save, get, list, search, update_usage
- Raw entries: save, get, list, mark_processed
- Forgetting: record_access, forget, recover, protect, get_candidates, get_forgotten

**Problem:** Code using `SupabaseStorage` fails at runtime for many operations.

**Recommendation:** Implement missing methods OR document prominently that cloud storage is limited.

#### 2.6 MCP Server Singleton State

**Location:** `kernle/mcp/server.py:38-55`

```python
_mcp_agent_id: str = "default"

def get_kernle() -> Kernle:
    if not hasattr(get_kernle, "_instance"):
        get_kernle._instance = Kernle(_mcp_agent_id)
```

**Problems:**
- Only one agent per process
- No thread safety
- Agent ID changes require clearing cache

#### 2.7 Sync Conflicts Silent

**Location:** `kernle/storage/base.py:55`

```python
conflicts: int = 0  # Conflicts encountered (resolved with last-write-wins)
```

**Problem:** Conflicts counted but resolved silently. Users not notified about lost updates.

---

## 3. Missing Features & Gaps

### Critical Missing Features

| Feature | Impact | Status |
|---------|--------|--------|
| **Web dashboard** | No visual memory exploration | Roadmapped Q2 2026 |
| **Import/migration tools** | Can't migrate from markdown, Mem0, etc. | Dogfooding feedback |
| **Cross-agent memory** | No shared knowledge | Roadmapped Phase 3 |
| **Semantic deduplication** | Only exact-match | Not planned |
| **Batch operations** | Performance bottleneck | Not planned |

### API Gaps

- No CRUD for all memory types in MCP (create but limited update/delete)
- Memory IDs are UUIDs (not human-readable)
- Must call `memory_load` at session start (not automatic)
- No `memory_update_episode`, `memory_delete_*` tools

### Documentation Gaps

1. Migration guides (from files, other systems)
2. Best practices (when to use each memory type)
3. Performance tuning (budget settings, sync frequency)
4. Integration examples (real agent implementations)

### Tooling Gaps

1. No memory browser/inspector (TUI or GUI)
2. No backup/restore CLI (`dump` exists, no `import`)
3. No memory diff (compare states over time)
4. No validation CLI (check integrity)

---

## 4. Failure Scenarios

### 4.1 Agent Working on Contradictory Projects

**Scenario:** Agent helps User A with "API-first" and User B with "code-first" approaches.

**Failure Mode:**
- Both beliefs stored with similar confidence
- `find_contradictions()` misses different terminology
- `load()` returns both without context about which applies
- No `context` or `scope` field on beliefs

### 4.2 Long Sessions with Topic Drift

**Scenario:** 4-hour session: database → frontend → deployment

**Failure Mode:**
- Checkpoint captures "current task" but not topic transitions
- Early-session insights get lower priority than recent notes
- No "session topic history" for context switching

### 4.3 Time-Sensitive Information

**Scenario:** "The deadline is Friday" stored Monday, now it's Tuesday next week.

**Failure Mode:**
- Belief persists with full confidence
- No temporal reasoning about content
- No alert for "memories about past events"

### 4.4 High-Frequency Memory Creation

**Scenario:** Processing large codebase, hundreds of memories rapidly.

**Failure Mode:**
- Each `save_*()` is individual DB write + embedding
- No batch insertion API
- Sync queue grows unboundedly

### 4.5 Multiple Users/Personas

**Scenario:** Same agent serves users with different preferences.

**Failure Mode:**
- Single `agent_id` namespace
- Beliefs don't track "User X told me this"
- Identity synthesis creates single narrative from all

---

## 5. Competitive Analysis

### Positioning Map

```
                    Automatic ←────────────────────→ Explicit
                         │                              │
                    Mem0 │                              │ Kernle
                     Zep │                              │ MemGPT
                         │                              │
                         │     LangChain                │
                         │     LlamaIndex               │
                         │                              │
                    ─────┼──────────────────────────────┼─────
                         │                              │
             Chat-       │                              │ Identity-
             focused     │                              │ focused
```

### Detailed Comparison

| Aspect | Kernle | Mem0 | Zep | LangChain | MemGPT |
|--------|--------|------|-----|-----------|--------|
| **Memory Formation** | Explicit | Auto-extract | Auto-summary | Manual | Self-editing |
| **Memory Types** | 7 layers | 3 types | Facts/sessions | Buffers | 3 tiers |
| **Local-First** | Yes | No | No | Plugin | No |
| **Psychology** | Yes | No | No | No | No |
| **Forgetting** | Salience-based | No | No | No | No |
| **MCP Support** | 23 tools | Yes | No | No | No |

### Kernle's Unique Advantages

1. Stratified memory hierarchy (7 layers)
2. Psychology system (drives, emotions, anxiety)
3. Meta-memory (confidence, provenance chains)
4. Controlled forgetting with salience decay
5. Memory sovereignty philosophy
6. Local-first with optional cloud

### Kernle's Disadvantages vs Competitors

1. **vs Mem0**: Higher friction (explicit capture vs auto-extract)
2. **vs Zep**: No conversation history storage
3. **vs LlamaIndex**: No document ingestion
4. **vs MemGPT**: No self-editing capability

### Strategic Position

**Biggest Opportunity:** Bridge convenience gap with optional auto-capture while preserving sovereignty model.

**Biggest Risk:** Philosophy may be too purist for mass adoption. Users may prefer Mem0's zero-friction approach.

---

## 6. Auto-Capture Design Notes

### Current State

Kernle requires explicit capture - the agent must decide what to remember and call the appropriate tool. This creates friction compared to Mem0's auto-extraction.

### Proposed: Auto-Capture at Raw Layer

**Philosophy Alignment:** Raw layer is explicitly "unprocessed" - auto-capture to raw, with agent-controlled promotion to structured memories, maintains sovereignty while reducing friction.

### Implementation Options

#### Option 1: Claude Code Hooks

```json
// .claude/hooks.json
{
  "hooks": [
    {
      "event": "Stop",
      "command": "kernle -a $KERNLE_AGENT_ID raw --source session-end \"$LAST_ASSISTANT_MESSAGE\""
    },
    {
      "event": "PostToolUse",
      "matcher": { "tool": "*" },
      "command": "kernle -a $KERNLE_AGENT_ID raw --source tool-output --quiet \"Tool: $TOOL_NAME Output: $TOOL_OUTPUT\""
    }
  ]
}
```

#### Option 2: Enhanced MCP Tool

```python
@mcp_tool
def memory_auto_capture(context: str, extract_suggestions: bool = True):
    """
    Capture raw content and optionally suggest structured memories.

    Args:
        context: Full conversation context or content to capture
        extract_suggestions: If True, use LLM to suggest episodes/beliefs/notes

    Returns:
        raw_id: ID of captured raw entry
        suggestions: List of suggested memories for agent approval
    """
```

#### Option 3: Session Summary Hook

System prompt instructs agent to call `memory_session_summary` before ending, which:
1. Captures full session to raw
2. Extracts suggested memories
3. Returns suggestions for agent approval

### Suggested Memory Approval System

> **NOTE:** Consider implementing an auto-system that prepares suggested notes/episodes/beliefs from raw layer content. The agent would then:
> 1. **Promote** - Accept suggestion as-is
> 2. **Modify & Promote** - Edit suggestion then accept
> 3. **Reject** - Decline suggestion
> 4. **Create Original** - Ignore suggestions, create from raw directly
>
> This preserves sovereignty (agent decides) while reducing friction (system does extraction work).

#### Suggested Implementation

```python
@dataclass
class MemorySuggestion:
    id: str
    memory_type: str  # episode, belief, note, etc.
    content: Dict[str, Any]  # Structured memory data
    confidence: float  # How confident the system is in this suggestion
    source_raw_ids: List[str]  # Which raw entries this came from
    status: str = "pending"  # pending, promoted, modified, rejected

class SuggestionService:
    def extract_suggestions(self, raw_ids: List[str]) -> List[MemorySuggestion]:
        """Analyze raw entries and suggest structured memories."""

    def promote(self, suggestion_id: str) -> str:
        """Accept suggestion as-is, returns memory ID."""

    def modify_and_promote(self, suggestion_id: str, modifications: Dict) -> str:
        """Accept with modifications, returns memory ID."""

    def reject(self, suggestion_id: str, reason: str = None):
        """Decline suggestion, optionally with reason for learning."""
```

#### CLI Commands

```bash
# View pending suggestions
kernle suggestions list

# Approve a suggestion
kernle suggestions approve <suggestion_id>

# Modify and approve
kernle suggestions approve <suggestion_id> --edit

# Reject
kernle suggestions reject <suggestion_id> --reason "Not relevant"

# Batch operations
kernle suggestions approve-all --confidence-above 0.8
kernle suggestions reject-all --older-than 7d
```

#### MCP Tools

```python
@mcp_tool
def memory_suggestions_list(limit: int = 10) -> List[MemorySuggestion]:
    """List pending memory suggestions for review."""

@mcp_tool
def memory_suggestions_promote(suggestion_id: str, modifications: Optional[Dict] = None) -> str:
    """Promote a suggestion to a real memory, optionally with modifications."""

@mcp_tool
def memory_suggestions_reject(suggestion_id: str, reason: Optional[str] = None):
    """Reject a suggestion."""
```

---

## 7. Prioritized Recommendations

### Critical (1-2 Weeks)

| Issue | Fix | Effort |
|-------|-----|--------|
| N+1 query in updates | Add `get_*_by_id()` methods | 2h |
| Confidence inflation | Add time-based decay factor | 4h |
| Document PostgreSQL gaps | Prominent warning in docs + README | 1h |

### Important (1-2 Months)

| Issue | Fix | Effort |
|-------|-----|--------|
| Semantic contradiction detection | Embedding similarity + sentiment | 1d |
| Memory scoping | Add `context` field to beliefs/episodes | 2d |
| Batch insertion API | Transaction batching for bulk ops | 1d |
| Import tools | `kernle import markdown/json` | 2d |
| Sync conflict notification | Alert users about overwrites | 4h |

### Strategic (Quarter)

| Issue | Fix | Effort |
|-------|-----|--------|
| Auto-capture hooks | Implement hook-based raw capture | 1w |
| Suggestion system | Extract + approve/reject workflow | 2w |
| Web dashboard | Visual memory browser | 4w |
| Cross-agent memory | Phase 3 roadmap | 6w |

### Nice to Have (Long-term)

| Issue | Fix |
|-------|-----|
| Temporal tagging | "Valid until" fields, expiry dates |
| Real-time sync | WebSocket for live updates |
| Document ingestion | RAG-style knowledge base support |
| Enterprise features | RBAC, org hierarchy, audit logging |

---

## Appendix A: Files Reviewed

- `kernle/core.py` - Main Kernle class (3000+ lines)
- `kernle/storage/base.py` - Storage protocol and data classes
- `kernle/storage/sqlite.py` - SQLite implementation (3400+ lines)
- `kernle/storage/postgres.py` - Supabase implementation
- `kernle/storage/embeddings.py` - Embedding providers
- `kernle/features/*.py` - Mixin implementations
- `kernle/mcp/server.py` - MCP server (23 tools)
- `kernle/cli/` - CLI commands
- `docs-site/` - Mintlify documentation
- `ROADMAP.md` - Development roadmap
- `tests/TEST_AUDIT.md` - Previous test audit

## Appendix B: Test Coverage Notes

- **771 tests passing** (as of Jan 28, 2026)
- **57% coverage** overall
- Known test quality issues documented in `tests/TEST_AUDIT.md`
- Many MCP tests verify mock behavior, not production code

---

*Report generated by Claude Opus 4.5 with specialist agents*
