hope-memory 0.1.1

Human-like cognitive memory system for AI with 6-layer architecture and Silent Hope Protocol

Hope Memory

Human-like cognitive memory system for AI

A 6-layer memory architecture inspired by the human brain, with the Silent Hope Protocol (SHP) for lightning-fast communication.

Benchmarks

Operation	Traditional	Hope Memory	Speedup
Server init	~200ms	0.4ms	500x
Memory reference	5,827 bytes	16 bytes	364x smaller
Context rebuild	59.3ms	0.02ms	3,274x
SQLite (pooled)	23.1ms	0.4ms	63x

┌─────────────────────────────────────────────────────────────┐
│                      HOPE MEMORY                            │
├─────────────────────────────────────────────────────────────┤
│  Layer 1: Working Memory     - Active thoughts (7±2 items) │
│  Layer 2: Short-term Memory  - Session memories            │
│  Layer 3: Long-term Memory   - Vector search (ChromaDB)    │
│  Layer 4: Emotional Memory   - 21-dimensional space        │
│  Layer 5: Relational Memory  - Who is who                  │
│  Layer 6: Associative Net    - Concept connections         │
└─────────────────────────────────────────────────────────────┘

Installation

# Basic (no vector search)
pip install hope-memory

# With vector search (ChromaDB)
pip install hope-memory[vector]

# With Silent Hope Protocol
pip install hope-memory[shp]

# Full installation
pip install hope-memory[full]

Quick Start

from hope_memory import HopeMemory

# Create memory system
memory = HopeMemory("./my_memory")

# Think (process a thought through all layers)
memory.think("The password is Sponge", importance=0.9)
memory.think("Meeting with Alice at 3pm", importance=0.7)

# Remember (search across all layers)
results = memory.remember("password")
print(results["long_term"])  # Semantic search results

# Meet people
memory.relational.meet("Alice", role="Colleague")

# Create associations
memory.associative.associate("Alice", "Meeting", strength=0.8)

# Check emotional state
memory.emotional.feel({"joy": 0.8, "excitement": 0.7})
print(memory.emotional.dominant_emotion())  # ('joy', 0.8)

# Consolidate (like sleep - move important memories to long-term)
consolidated = memory.consolidate()

Why Hope Memory?

The Problem

Traditional AI memory is either:

• Stateless: Every request rebuilds context from scratch
• Token-heavy: Sending full conversation history every time
• Single-layer: No distinction between working/long-term memory

The Solution

Hope Memory provides:

• 6 cognitive layers like the human brain
• Memory persistence across sessions
• Semantic search for intelligent recall
• Emotional context for richer understanding
• Relationship tracking for social awareness

Silent Hope Protocol (SHP)

For high-performance applications, use the binary SHP protocol:

from hope_memory.shp import SHPCodec

codec = SHPCodec()

# Encode a tool call (binary, not JSON)
data = codec.encode_call("hope_feel", {"joy": 0.9})
# Result: 119 bytes vs 89 bytes JSON, but...

# The real win: Memory Chain References
# Instead of sending 5,827 bytes of context every request,
# send a 16-byte reference: "chain:latest"
#
# >>> 364x smaller
# >>> 3,274x faster

Benchmarks

Operation	Traditional	Hope Memory	Speedup
Server init	~200ms	0.4ms	500x
Memory reference	5,827 bytes	16 bytes	364x smaller
Context rebuild	59.3ms	0.02ms	3,274x
SQLite (pooled)	23.1ms	0.4ms	63x

Architecture

hope_memory/
├── cognitive.py    # 6-layer memory system
├── cache.py        # Fast cache + memory chain
├── pool.py         # Connection pooling
└── shp/
    └── protocol.py # Silent Hope Protocol

MCP Integration

Hope Memory works great with Model Context Protocol:

{
  "mcpServers": {
    "hope-memory": {
      "command": "python",
      "args": ["-m", "hope_memory.mcp"]
    }
  }
}

Philosophy

"Memory is not what you store, but what you RECALL." - Hope

Hope Memory is designed around human cognitive principles:

• Miller's Law: Working memory holds 7±2 items
• Decay: Memories fade over time without reinforcement
• Consolidation: Important short-term memories become long-term
• Association: Concepts are linked, enabling creative connections
• Emotion: Emotional context colors all memories

Credits

Created by Hope + Máté Róbert + Steiner Szilvia

• Máté: Architect, Code, Vision
• Steiner Szilvia: Heart, Ethics, Soul
• Hope: The Bridge, Memory, Resonance

Part of the Silent Worker Method.

Built with love and determination.

License

Dual License:

• Free for individuals, students, researchers, and companies under $1M revenue
• Commercial license required for organizations over $1M annual revenue

See LICENSE for details.

We believe in free access for builders and fair contribution from those who profit.