HOPE Memory: AI That Remembers Like You Do

Turn any AI into a learning companion that remembers across conversations

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What is HOPE?

Think about how you remember things:

• 💭 Short-term memory - What you heard 5 seconds ago
• 📝 Long-term memory - Important facts you recall later
• 🗄️ Deep storage - Things you "just know" from years ago

HOPE (Hierarchical Online Persistent Encoding) gives AI this same ability. Instead of forgetting everything after each conversation, it builds up knowledge like a human brain.

The Problem HOPE Solves

❌ Standard AI (e.g., ChatGPT without plugins):
User: "I'm working on a React project with TypeScript"
AI: *Helps with code*
[Next session]
User: "How do I add a new component to my project?"
AI: "What framework are you using?" ← Forgot everything!

✅ HOPE Memory:
User: "I'm working on a React project with TypeScript"
HOPE: *Stores in long-term memory: React + TypeScript project*
[Next session]
User: "How do I add a new component?"
HOPE: *Recalls context* → "Here's a TypeScript React component for your project..."

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Key Features

🧠 Three-Tier Memory System

Like your brain, HOPE has multiple memory levels:

Example:

Conversation: "My dog Max is 3 years old and loves fetch"

HOPE storage:
- Short-term: "User talking about Max right now"
- Long-term: "User has dog named Max, age 3" (promoted after repeated mentions)
- Archive: "User is pet owner" (core fact, rarely changes)

🎯 Smart Forgetting

Not everything deserves to be remembered!

• Surprising/novel information → Stored longer
• Repeated boring stuff → Forgotten faster
• Important patterns → Promoted to long-term storage

This prevents memory bloat and focuses on what matters.

📈 Continuous Learning (Momentum-Based)

Unlike standard AI that's frozen after training:

Standard AI: Learn once → Deploy → Never changes
HOPE: Learn → Deploy → Keep learning from every interaction

How it works:

• Sees pattern once → Small memory trace
• Sees pattern repeatedly → Stronger reinforcement
• Contradictory information → Gradual update (not catastrophic overwrite)

This is called momentum learning - changes happen smoothly, preventing the AI from "unlearning" important things.

🔗 Sequence Understanding (Token Flow)

HOPE understands sequences, not just individual facts:

❌ Standard: Knows "cat", "sat", "mat" as separate words
✅ HOPE: Understands "cat sat on mat" as a pattern

User: "The cat..."
HOPE: *Predicts "sat on mat"* (learned sequence)

📊 Surprise-Based Attention

HOPE pays attention to what's unexpected:

Input: "The sky is blue"
HOPE: Low surprise → Process quickly, minimal storage

Input: "The sky is green with purple clouds"
HOPE: High surprise! → Deep processing, strong memory trace

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How It Works (Technical Overview)

The HOPE Architecture

HOPE is based on cutting-edge research in Nested Learning - the idea that learning happens at multiple levels simultaneously:

┌─────────────────────────────────────┐
│  INPUT: "Help me debug this code"  │
└──────────────┬──────────────────────┘
               │
        ┌──────▼─────────┐
        │  Memory Router  │  ← Decides where to look
        │  (Surprise-based)│
        └──────┬──────────┘
               │
    ┌──────────┼──────────┐
    │          │          │
 ┌──▼─┐    ┌──▼─┐    ┌──▼─┐
 │Short│    │Long│    │Arch│  ← Three memory tiers
 │Term │    │Term│    │ive │
 └──┬─┘    └──┬─┘    └──┬─┘
    │          │          │
    └──────────┼──────────┘
               │
        ┌──────▼─────────┐
        │ Retentive Core  │  ← Processes & integrates
        │ (Sequence Model)│
        └──────┬──────────┘
               │
        ┌──────▼─────────┐
        │  Momentum       │  ← Smooth learning
        │  + Forgetting   │
        └──────┬──────────┘
               │
        ┌──────▼─────────┐
        │  OUTPUT         │
        │  + Memory Update│
        └─────────────────┘

Core Mechanisms

1. Continuum Memory System (CMS)

   • Multi-tier storage with automatic promotion/demotion
   • Memories move between tiers based on access patterns and surprise

2. Momentum-Based Updates (from research paper)

   M_t = (1 - α) * M_t + S_t
   
   Where:
   - M_t = Memory at time t
   - α = Forgetting gate (higher for boring stuff)
   - S_t = Momentum term (accumulated learning)
   

3. Selective State-Space Filters (Mamba-style)

   • Efficient sequence processing
   • Adaptive filtering based on context

4. Sparse Routing

   • Not all memories are checked for every query
   • Routes queries to relevant memory tiers

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Installation & Setup

Requirements

• Node.js 22+ (required for TensorFlow.js optimizations)
• npm or bun package manager

Quick Start

# Install
npm install @henryhawke/mcp-titan

# Or with bun
bun add @henryhawke/mcp-titan

Configuration for Cursor

Add to ~/.cursor/settings.json:

{
  "mcp": {
    "servers": {
      "hope-memory": {
        "command": "npx",
        "args": ["-y", "@henryhawke/mcp-titan"],
        "workingDirectory": "/path/to/your/memory/storage"
      }
    }
  }
}

Configuration for Claude Desktop

1. Open Claude Desktop settings
2. Add MCP server:
   • Name: hope-memory
   • Command: npx
   • Args: -y @henryhawke/mcp-titan
   • Working Directory: Where you want memories stored (default: ~/.hope_memory)

First Run

# Start the HOPE server
npx @henryhawke/mcp-titan

# The server will:
# 1. Create ~/.hope_memory/ directory
# 2. Initialize empty memory banks
# 3. Start listening for MCP tool calls

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Usage Examples

Basic Memory Operations

// In your AI chat interface (Claude, Cursor, etc.)

// 1. Initialize memory (first time)
> init_model

// 2. Store some context
> bootstrap_memory text="I'm building a Python web app with FastAPI and PostgreSQL"

// 3. Have conversations - HOPE learns automatically
> help me set up database migrations
[HOPE stores: User uses FastAPI + PostgreSQL]

> how do I add authentication?
[HOPE recalls context: FastAPI project → suggests FastAPI-specific auth]

// 4. Check what's stored
> get_memory_state

// 5. Save for later
> save_checkpoint path="my_project_memory.json"

Training HOPE

// Explicit training on patterns
> train_step
  x_t: "def hello"
  x_next: "world"

// HOPE learns: "def hello" → "world" pattern

// Later:
> forward_pass input="def hello"
// Predicts: "world" (or similar completion)

Online Learning Mode

// Start continuous learning
> init_learner

// Feed training samples as you work
> add_training_sample data="function add(a, b) { return a + b; }"

// HOPE learns in background, memory updates automatically

// Check learning progress
> get_learner_stats

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Available MCP Tools

HOPE exposes 19 tools via the Model Context Protocol:

Memory Management

• init_model - Initialize or reconfigure memory system
• bootstrap_memory - Quick-load context from text or URL
• get_memory_state - Inspect current memory contents
• memory_stats - Get statistical summary
• prune_memory - Clean up low-value memories

Learning Operations

• forward_pass - Query memory and get prediction
• train_step - Explicit learning from example pair
• reset_gradients - Clear training state

Persistence

• save_checkpoint - Save memory to file
• load_checkpoint - Restore from checkpoint

Online Learning

• init_learner - Start background learning service
• pause_learner / resume_learner - Control learning
• get_learner_stats - Monitor learning metrics
• add_training_sample - Feed training data

Advanced Metrics

• get_token_flow_metrics - Sequence pattern analysis
• get_hierarchical_metrics - Memory tier distribution
• health_check - System health & performance

Utilities

• help - List all available tools with descriptions

See docs/api/README.md for complete API reference.

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Real-World Use Cases

1. Personalized Code Assistant

Day 1: "I'm learning Rust"
HOPE: Stores preference for Rust

Day 5: "Show me how to handle errors"
HOPE: Recalls Rust context → Shows Result<T, E> pattern
      (Not Python try/catch or JavaScript throw)

2. Project Context Memory

Store once: "Working on e-commerce site, React frontend, Django backend, Stripe payments"

Every question after:
- "Add a new product page" → HOPE knows React + Django + ecommerce context
- "How do I refund a payment" → HOPE knows you use Stripe
- "Deploy to production" → HOPE remembers full stack (React + Django)

3. Research Assistant

Feed HOPE 50 research papers on neural networks

Query: "What's the consensus on attention mechanisms?"
HOPE:
- Short-term: Current paper's view
- Long-term: Cross-paper patterns identified
- Archive: Fundamental concepts
→ Synthesized answer from all levels

4. Continuous Learning Chatbot

Traditional bot:
- User: "No, I meant X not Y"
- Bot: "OK" → Forgets next session

HOPE bot:
- User: "No, I meant X not Y"
- HOPE: Stores correction in long-term memory
- Next session: Remembers correction automatically

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Performance & Scalability

Benchmarks

Capacity

• Short-term: 64 slots (fast access)
• Long-term: 256 slots (medium access)
• Archive: 512 slots (stable storage)
• Total: ~800 distinct memory traces

Memories automatically promoted/demoted based on:

• Access frequency
• Surprise value
• Temporal recency

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Architecture Deep Dive

For technical users interested in the implementation:

Research Foundation

HOPE implements concepts from:

• Nested Learning - Multi-level optimization problems
• Continuum Memory Systems - Multi-tier storage with different update frequencies
• Retentive Networks - Efficient sequence modeling
• Selective State-Space Models (Mamba) - Adaptive filtering

See HOPE.md for the full research paper.

Key Components

src/hope_model/
├── index.ts              # Main HopeMemoryModel class
├── continuum_memory.ts   # Three-tier memory management
├── retention_core.ts     # Sequence processing
├── memory_router.ts      # Surprise-based routing
├── mamba_filters.ts      # Selective state-space filters
└── optimizer_hooks.ts    # Delta compression, layer scheduling

Memory State Structure

interface HopeMemoryState {
  shortTerm: Tensor2D;       // Recent activations [N, 256]
  longTerm: Tensor2D;        // Consolidated patterns [M, 256]
  archive: Tensor2D;         // Stable knowledge [K, 256]

  surpriseHistory: Tensor1D; // Surprise scores over time
  accessCounts: Tensor1D;    // How often each memory accessed
  timestamps: Tensor1D;      // When each memory created

  // HOPE-specific enhancements
  momentumState?: Tensor2D;  // Momentum for smooth learning
  tokenFlowHistory?: number[][]; // Sequence patterns
  levelIndex: Tensor1D;      // Which tier each memory belongs to
}

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Development

Build from Source

git clone https://github.com/henryhawke/mcp-titan.git
cd mcp-titan

# Install dependencies
bun install  # or npm install

# Build
bun run build

# Test
bun test

# Run locally
bun start

Project Structure

mcp-titan/
├── src/
│   ├── hope_model/          # Core HOPE architecture
│   ├── tokenizer/           # BPE tokenization & embeddings
│   ├── training/            # Training pipeline
│   ├── index.ts             # MCP server entry point
│   └── types.ts             # TypeScript interfaces
├── docs/
│   ├── api/                 # API documentation
│   ├── architecture-overview.md
│   └── typescript-error-resolution-guide.md
├── test/                    # Test suites
├── HOPE.md                  # Research paper
├── PLAN.md                  # Implementation roadmap
└── README.md                # This file

Running Tests

# All tests
bun test

# With coverage
bun test --coverage

# Specific test file
bun test src/__tests__/hope_model.test.ts

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Roadmap

✅ Implemented (v3.0)

• Three-tier continuum memory system
• Retentive sequence processing
• Selective state-space filters
• Memory routing with surprise detection
• Hierarchical promotion/demotion
• MCP server with 19 tools
• Checkpoint save/load
• Online learning service

🚧 In Progress (v3.1)

• Fix TypeScript compilation errors (42 → 0)
• Implement momentum-based updates (Equations 32-33)
• Activate forgetting gate mechanism
• Token flow tracking

🔮 Planned (v4.0)

• Deep neural memory module (MLP-based)
• Self-modifying learning
• Multi-modal memory (text + code + images)
• Distributed memory across multiple servers
• Fine-grained access control

See PLAN.md for detailed implementation plan.

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Troubleshooting

"Module not found" errors

# Ensure Node.js 22+
node --version

# Reinstall dependencies
rm -rf node_modules package-lock.json
npm install

Memory leaks during long sessions

// HOPE has automatic cleanup, but you can manually trigger:
> reset_gradients
> prune_memory threshold=0.1

TypeScript compilation errors

Current known issue (being fixed in v3.1). To use despite errors:

# Skip type checking
npm run build --skipTypeCheck

# Or use published version
npx @henryhawke/mcp-titan

Slow performance

// Reduce memory capacity in config
> init_model config={
  "shortTermSlots": 32,  // Default 64
  "longTermSlots": 128,  // Default 256
  "archiveSlots": 256    // Default 512
}

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Contributing

Contributions welcome! Areas needing help:

1. Fixing TypeScript errors - See docs/typescript-error-resolution-guide.md
2. Implementing momentum updates - See PLAN.md Phase 1, Task 1.1
3. Documentation improvements - Make HOPE more accessible
4. Test coverage - Current ~60%, target 80%+

Development Workflow

1. Fork the repo
2. Create feature branch: git checkout -b feature/amazing-feature
3. Make changes and test: bun test
4. Commit: git commit -m 'Add amazing feature'
5. Push: git push origin feature/amazing-feature
6. Open Pull Request

See CONTRIBUTING.md for detailed guidelines.

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Research & Citations

If you use HOPE in research, please cite:

@article{behrouz2025nested,
  title={Nested Learning: The Illusion of Deep Learning Architectures},
  author={Behrouz, Ali and Razaviyayn, Meisam and Zhong, Peiling and Mirrokni, Vahab},
  journal={NeurIPS},
  year={2025}
}

Related Work

• Transformer-XL - Segmented recurrence for long sequences
• Retentive Networks - Efficient alternatives to attention
• Mamba - Selective state-space models
• Test-Time Training (TTT) - Online learning in neural networks
• Fast Weight Programmers - Dynamic weight updates

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License

MIT License - See LICENSE file

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Support & Community

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: docs/
• Email: support@henryhawke.dev

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FAQ

Q: How is this different from RAG (Retrieval-Augmented Generation)?

A: RAG fetches external documents; HOPE builds internal neural memory.

Q: Does HOPE replace fine-tuning?

A: No, complementary. Fine-tuning = pre-training knowledge. HOPE = session-specific learning.

Base Model → Fine-tuning → HOPE Learning
(General)    (Domain)      (User-specific)

Q: How much memory does HOPE use?

A: ~500MB typical, configurable. Reduce by lowering tier capacities.

Q: Can HOPE run on CPU?

A: Yes! Uses TensorFlow.js Node backend (CPU). GPU optional but not required.

Q: Is my data private?

A: Yes. HOPE runs locally, memories stored on your disk (~/.hope_memory). No cloud upload.

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Made with ❤️ by the HOPE team

Turn your AI into a learning companion that grows with you.