arela/RESEARCH/prompts/hexi-memory-research-prompt.md

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# Deep Research Request: Optimal Memory Architecture for AI Coding Assistants (Hexi-Memory)

## Context

Arela is building an AI Technical Co-Founder with a critical challenge: **perfect memory**.

**Current state:** Tri-Memory System
1. Vector DB (semantic search)
2. Graph DB (structural dependencies)
3. Governance Log (audit trail)

**Problem:** This forgets too much!
- No short-term context (current conversation)
- No project-specific patterns
- No user preferences across projects

**Proposed solution:** Hexi-Memory System (6 layers of redundant memory)

## The Vision

**"Arela should NEVER forget anything important."**

An AI coding assistant that:
- Remembers what you were just working on (Session)
- Learns your project's patterns (Project)
- Knows your preferences across all projects (User)
- Has semantic understanding of your code (Vector)
- Tracks structural dependencies (Graph)
- Maintains audit trail of decisions (Governance)

## Proposed Hexi-Memory Architecture

### Layer 1: Vector Memory (Semantic)
**Purpose:** Semantic search of code/docs  
**Storage:** `.arela/.rag-index.json` (46MB JSON, migrate to proper Vector DB)  
**Retrieval:** Embedding similarity  
**Lifespan:** Project lifetime  
**Example:** "Find all authentication-related code"

### Layer 2: Graph Memory (Structural)
**Purpose:** Dependencies, imports, functions, API calls  
**Storage:** `.arela/memory/graph.db` (SQLite)  
**Retrieval:** Graph traversal, SQL queries  
**Lifespan:** Project lifetime  
**Example:** "What files depend on auth/login.ts?"

### Layer 3: Governance Memory (Historical)
**Purpose:** Decisions, changes, audit trail  
**Storage:** `.arela/memory/audit.db` (SQLite)  
**Retrieval:** Temporal queries  
**Lifespan:** Project lifetime  
**Example:** "Why did we choose Postgres over MongoDB?"

### Layer 4: Session Memory (Short-term) ← NEW
**Purpose:** Current conversation, active task  
**Storage:** In-memory + Redis (optional for persistence)  
**Retrieval:** Recency-based (last N messages)  
**Lifespan:** Current session only (cleared on exit)  
**Example:**
```json
{
  "currentTask": "Implementing login feature",
  "filesOpen": ["src/auth/login.ts", "src/auth/user.ts"],
  "conversationHistory": [
    { "role": "user", "content": "Add login validation" },
    { "role": "assistant", "content": "I'll add email/password validation..." }
  ],
  "activeTicket": "CLAUDE-001",
  "lastEdit": { "file": "src/auth/login.ts", "line": 45 },
  "timestamp": "2025-11-14T18:47:00Z"
}
```

### Layer 5: Project Memory (Medium-term) ← NEW
**Purpose:** Project-specific patterns, decisions, todos  
**Storage:** `.arela/memory/project.db` (SQLite)  
**Retrieval:** Project-scoped queries  
**Lifespan:** Project lifetime  
**Example:**
```json
{
  "projectId": "arela-cli",
  "architecture": "VSA with 8 slices",
  "decisions": [
    {
      "date": "2025-11-14",
      "decision": "Use Infomap for slice detection",
      "rationale": "Better than Louvain for small graphs",
      "alternatives": ["Louvain", "Leiden"]
    }
  ],
  "patterns": [
    "Always use Prisma for DB access",
    "Auth slice handles all authentication",
    "Tests go in test/ directory, not src/"
  ],
  "todos": [
    { "task": "Add tests for login flow", "priority": "high" },
    { "task": "Implement password reset", "priority": "medium" }
  ],
  "techStack": {
    "language": "TypeScript",
    "framework": "Node.js",
    "database": "SQLite",
    "testing": "Vitest"
  },
  "conventions": {
    "naming": "camelCase for functions, PascalCase for classes",
    "imports": "Absolute imports with @ alias",
    "exports": "Named exports, no default exports"
  }
}
```

### Layer 6: User Memory (Long-term) ← NEW
**Purpose:** User preferences, expertise, patterns across ALL projects  
**Storage:** `~/.arela/user.db` (SQLite, global)  
**Retrieval:** User-scoped queries  
**Lifespan:** Forever (persists across all projects)  
**Example:**
```json
{
  "userId": "star",
  "preferences": {
    "language": "TypeScript",
    "framework": "Next.js",
    "database": "Postgres",
    "testing": "Vitest",
    "style": "Functional programming",
    "formatting": "Prettier with 2-space indent"
  },
  "expertise": {
    "frontend": "expert",
    "backend": "intermediate",
    "devops": "beginner",
    "mobile": "intermediate"
  },
  "patterns": [
    "Prefers small PRs (<300 LOC)",
    "Always writes tests first (TDD)",
    "Likes detailed commit messages",
    "Uses VSA architecture",
    "Prefers composition over inheritance"
  ],
  "antiPatterns": [
    "Dislikes microservices for small teams",
    "Avoids premature optimization",
    "No God classes/functions"
  ],
  "learningHistory": [
    {
      "date": "2025-11-14",
      "learned": "User prefers Infomap over Louvain",
      "context": "Slice detection research",
      "confidence": 0.9
    }
  ]
}
```

## Research Questions

### 1. Memory Architecture Best Practices

**Question:** What's the optimal memory architecture for AI coding assistants?

**Sub-questions:**
- How many memory layers are optimal? (3? 6? 10?)
- What should each layer store?
- How to avoid redundancy vs ensure redundancy?
- How to handle memory conflicts (contradictory patterns)?
- What's the right balance between forgetting and remembering?

### 2. Competitive Analysis

**Question:** How do existing tools handle memory?

**Tools to research:**
- **Cursor:** What memory system do they use?
- **GitHub Copilot:** Any memory beyond current file?
- **Windsurf:** What's their memory strategy?
- **Devin:** How do they maintain context?
- **Replit Agent:** Memory architecture?
- **Cody (Sourcegraph):** Memory approach?

**What to find:**
- Memory layers/types
- Storage mechanisms
- Retrieval strategies
- Persistence approach
- Learning capabilities

### 3. Memory Retrieval & Fusion

**Question:** How to efficiently retrieve and combine memories?

**Sub-questions:**
- How to query 6 layers in <200ms?
- How to rank/prioritize memories?
- How to fuse heterogeneous memories (vector + graph + text)?
- How to resolve conflicts (contradictory patterns)?
- How to avoid information overload (too much context)?

**Techniques to research:**
- Parallel querying (query all layers simultaneously)
- Relevance scoring (rank by importance)
- Context windowing (sliding window of recent memories)
- Hierarchical retrieval (summary → details)
- Semantic deduplication (merge similar memories)

### 4. Memory Consolidation

**Question:** How to prevent memory bloat?

**Sub-questions:**
- When to consolidate memories? (merge similar patterns)
- When to forget memories? (remove irrelevant stuff)
- How to detect redundancy?
- How to maintain consistency?
- How to preserve important memories?

**Strategies to research:**
- Periodic consolidation (nightly job)
- Similarity-based merging (cosine similarity)
- Importance scoring (access frequency, recency)
- Decay functions (exponential decay for old memories)
- User-triggered cleanup (manual review)

### 5. Storage Technologies

**Question:** What's the right storage for each layer?

**Options:**
- **SQLite:** Simple, local, SQL queries
- **Redis:** Fast, in-memory, key-value
- **ChromaDB/LanceDB:** Vector databases
- **JSON files:** Simple, human-readable
- **PostgreSQL:** Powerful, relational
- **MongoDB:** Document store, flexible schema

**Criteria:**
- Speed (query latency)
- Size (storage footprint)
- Complexity (setup/maintenance)
- Portability (works on all platforms)
- Cost (free vs paid)

### 6. Learning & Adaptation

**Question:** How should Arela learn from user behavior?

**Sub-questions:**
- How to detect user patterns? (analyze actions)
- How to infer preferences? (implicit vs explicit)
- How to update confidence? (Bayesian updating)
- How to handle changing preferences? (drift detection)
- How to avoid overfitting? (generalization)

**Techniques to research:**
- Implicit feedback (actions, edits, approvals)
- Explicit feedback (user corrections, ratings)
- Pattern mining (frequent itemsets, association rules)
- Confidence scoring (how sure are we?)
- A/B testing (try different approaches)

### 7. Privacy & Security

**Question:** How to handle sensitive data in memory?

**Sub-questions:**
- What data should NEVER be stored? (API keys, passwords)
- How to encrypt sensitive memories?
- How to allow user to delete memories?
- How to comply with GDPR/privacy laws?
- How to prevent memory leaks across projects?

**Best practices to research:**
- Data minimization (store only what's needed)
- Encryption at rest (encrypt sensitive data)
- User control (delete, export, review)
- Anonymization (remove PII)
- Sandboxing (isolate project memories)

### 8. Session Continuity

**Question:** How to maintain perfect session continuity?

**Scenario:**
```
Session 1 (Monday):
User: "Start implementing login feature"
Arela: "I'll create src/auth/login.ts..."
[User closes IDE]

Session 2 (Tuesday):
User: "Continue working on auth"
Arela: "Picking up where we left off on login feature..."
```

**Requirements:**
- Remember active task
- Remember conversation history
- Remember files being edited
- Remember decisions made
- Remember next steps

**Challenges:**
- How to persist session across IDE restarts?
- How to detect session boundaries?
- How to resume interrupted tasks?
- How to handle stale sessions (week-old)?

### 9. Cross-Project Learning

**Question:** How to learn patterns across multiple projects?

**Example:**
```
Project 1: User always uses Prisma for DB
Project 2: User always uses Prisma for DB
Project 3: User starts new project

Arela: "I notice you always use Prisma. Should I set that up?"
```

**Requirements:**
- Detect recurring patterns
- Generalize across projects
- Suggest based on history
- Allow user to override

**Challenges:**
- How to avoid false positives? (coincidence vs pattern)
- How to handle project-specific exceptions?
- How to weight evidence? (3 projects vs 10 projects)

### 10. Memory Query Language

**Question:** How should users/agents query memory?

**Examples:**
```
# Natural language
"What was I working on yesterday?"
"Show me all decisions about authentication"
"What's my preferred testing framework?"

# Structured queries
memory.session.currentTask
memory.project.patterns.filter(p => p.includes("auth"))
memory.user.preferences.language

# SQL-like
SELECT * FROM project_memory WHERE category = 'patterns'
SELECT * FROM user_memory WHERE expertise > 'intermediate'
```

**Requirements:**
- Natural language support (for users)
- Programmatic API (for agents)
- Expressive (can query complex relationships)
- Fast (<100ms for simple queries)

## Our Specific Use Cases

### Use Case 1: Session Resumption

**Scenario:** User closes IDE mid-task, reopens next day

**Expected behavior:**
```
User: "Continue"
Arela: "Picking up where we left off:
  - Task: Implementing login feature
  - Last edit: src/auth/login.ts line 45
  - Next steps: Add password validation, write tests
  - Files open: src/auth/login.ts, src/auth/user.ts"
```

**Memories needed:**
- Session: Active task, files, conversation
- Project: Auth architecture, patterns
- Graph: Dependencies of login.ts
- Vector: Related auth code

### Use Case 2: Pattern Recognition

**Scenario:** User starts 3rd project, Arela suggests patterns

**Expected behavior:**
```
Arela: "I notice you always:
  - Use TypeScript
  - Use Prisma for database
  - Write tests first
  - Prefer VSA architecture
  
Should I set up this project the same way?"
```

**Memories needed:**
- User: Preferences, patterns from past projects
- Project: Tech stack from previous projects

### Use Case 3: Decision Recall

**Scenario:** User asks "Why did we choose X?"

**Expected behavior:**
```
User: "Why did we choose Infomap over Louvain?"
Arela: "On 2025-11-14, we researched slice detection algorithms.
  Decision: Infomap
  Rationale: Better for small, dense graphs
  Alternatives considered: Louvain (too many singletons), Leiden
  Research: RESEARCH/infomap-research.md"
```

**Memories needed:**
- Governance: Decision history
- Project: Architecture decisions
- Vector: Related research documents

### Use Case 4: Context Assembly

**Scenario:** User asks complex question requiring multiple memories

**Expected behavior:**
```
User: "How should I add OAuth to our auth system?"
Arela: [Queries 6 layers]
  - Session: Currently working on auth
  - Project: Auth uses JWT, Prisma, Postgres
  - Graph: Auth slice files: login.ts, user.ts, jwt.ts
  - Vector: Auth-related code snippets
  - User: Prefers simple solutions, intermediate backend expertise
  - Governance: Previous auth decisions
  
Arela: "Based on your current auth setup (JWT + Prisma) and your 
preference for simplicity, I recommend using Passport.js with 
passport-google-oauth20. Here's how to integrate it..."
```

## Success Criteria

Hexi-Memory is successful if:

1. **100% session continuity** - Never forgets active task
2. **Pattern recognition in 3 uses** - Learns preferences quickly
3. **<200ms retrieval** - Query all 6 layers fast
4. **Smart fusion** - Combines relevant memories, ignores noise
5. **Automatic consolidation** - Merges similar patterns weekly
6. **Graceful forgetting** - Removes irrelevant memories monthly
7. **Cross-project learning** - Suggests patterns from past projects
8. **Privacy-preserving** - Never stores secrets/PII
9. **User control** - Can review, edit, delete memories
10. **Conflict resolution** - Handles contradictory patterns

## Expected Output

Please provide:

### 1. Executive Summary (1 page)
- Is Hexi-Memory (6 layers) optimal or overkill?
- What do competitors use?
- Recommended architecture for Arela
- Key insights and recommendations

### 2. Memory Architecture Analysis (5-10 pages)
- Optimal number of layers (3? 6? 10?)
- What each layer should store
- Storage technology for each layer
- Retrieval strategies
- Fusion techniques
- Consolidation approach

### 3. Competitive Analysis (2-3 pages)
- How Cursor handles memory
- How Copilot handles memory
- How Windsurf handles memory
- How Devin handles memory
- Gaps and opportunities

### 4. Implementation Plan (3-5 pages)
- Phase 1: Session memory (v4.1.0)
- Phase 2: Project memory (v4.2.0)
- Phase 3: User memory (v4.3.0)
- Phase 4: Consolidation & learning (v4.4.0)
- Timeline and effort estimates

### 5. Storage Technology Recommendations (2 pages)
- Session: Redis vs in-memory vs SQLite
- Project: SQLite vs Postgres vs MongoDB
- User: SQLite vs cloud storage
- Vector: ChromaDB vs LanceDB vs JSON
- Trade-offs and rationale

### 6. Retrieval & Fusion Strategy (3-5 pages)
- How to query 6 layers in parallel
- How to rank/score memories
- How to fuse heterogeneous data
- How to resolve conflicts
- How to avoid information overload

### 7. Learning & Adaptation (2-3 pages)
- How to detect patterns
- How to infer preferences
- How to update confidence
- How to handle drift
- How to generalize across projects

### 8. Privacy & Security (2 pages)
- What data to never store
- Encryption strategy
- User control mechanisms
- GDPR compliance
- Sandboxing approach

### 9. Code Examples (if available)
- Session memory schema
- Project memory schema
- User memory schema
- Query API examples
- Fusion algorithm pseudocode

### 10. Risk Assessment (1 page)
- What could go wrong?
- Memory bloat
- Performance degradation
- Privacy violations
- Mitigation strategies

### 11. References
- Academic papers on memory systems
- Blog posts from competitors
- Open-source memory architectures
- Best practices and case studies

## Context from Previous Research

We've already validated:
- **3-layer architecture** (programmatic → small model → big model)
- **Token efficiency** (TOON compression, Meta-RAG routing)
- **Tri-memory system** (Vector, Graph, Governance)

**Hexi-Memory is the evolution** - adding Session, Project, and User layers to never forget anything important.

## Integration with Meta-RAG & TOON

**The full stack:**
```
User query
  ↓
Meta-RAG (classify query type)
  ↓
Memory Router (query Hexi-Memory)
  ├─ Session
  ├─ Project
  ├─ User
  ├─ Vector
  ├─ Graph
  └─ Governance
  ↓
Fusion (combine relevant memories)
  ↓
TOON (compress context)
  ↓
Send to LLM
```

**Combined impact:**
- Meta-RAG: Right memories (quality)
- Hexi-Memory: All memories (quantity)
- TOON: Compressed context (efficiency)
- **Result: Perfect context, every time**

## Timeline

**Urgency:** HIGH
- Critical for v4.3.0 (Memory overhaul)
- Needed before v5.0.0 (Extension)
- Research should complete in 2-3 days

## Audience

- **Primary:** Arela development team (technical implementation)
- **Secondary:** AI research community (novel architecture)

---

**Please research this comprehensively. Perfect memory is THE differentiator for Arela.**

**Focus on: What works in production? What's the optimal architecture? How do we never forget anything important?**

**This is the foundation for v5.0.0 (Extension) - we must get this right.** 🧠