Microscope Memory

Microscope Memory is a high-performance, hierarchical cognitive memory engine built for low-latency AI architectures. It operates on a "Zero-JSON" principle, utilizing memory-mapped binary blocks for sub-microsecond retrieval and associative learning.

Core Pillars

• ⚡ Sub-microsecond Latency: Built on memmap2, achieving ~1.2ns raw read speeds and ~1.7µs complex hierarchical queries.
• 🧊 Zero-JSON Architecture: Strict prohibition of text-based parsers in the critical path. Data structures are packed into fixed 256-byte binary blocks.
• 🧠 Hebbian Learning System: Implements associative memory drift, allowing the hierarchy to reorganize based on activation patterns.
• 🏗️ 9-Depth Hierarchy: Multi-scale data organization from Identity (D0) down to Raw Bytes (D8), enabling semantic "zooming".
• 🔐 Merkle Integrity: Integrated Merkle tree verification for deterministic hierarchy state validation.

Performance Benchmarks

🚀 Quickstart (30 Seconds)

The fastest way to experience Microscope Memory is using the init-demo command:

# 1. Initialize demo dataset
./target/release/microscope-mem init-demo

# 2. Build the binary index
./target/release/microscope-mem build

# 3. Think and explore
./target/release/microscope-mem think "What is Hebbian feedback?"

🛠️ Installation

Prerequisites

• Rust 1.75+
• LLVM/Clang (for SIMD optimizations)

From Source

git clone https://github.com/silentnoisehun/microscope-memory.git
cd microscope-memory
cargo build --release

🎯 Use Cases

• 🧠 Autonomous AI Agent Memory: Persistent long-term storage for LLM agents that improves over time via Hebbian drift.
• ⚡ High-Speed RAG Caching: Sub-microsecond semantic retrieval for high-traffic RAG pipelines.
• 🔗 Personal Knowledge Management (PKM): Associative note-taking and knowledge graph discovery.
• 🌐 Federated Knowledge Networks: Synchronized cognitive states across distributed edge nodes using the Resonance Protocol.

🐳 Docker Support

Run Microscope Memory in a container:

docker build -t microscope-mem .
docker run -it microscope-mem init-demo
docker run -it microscope-mem build
docker run -p 6060:6060 microscope-mem spine

📂 Examples

Explore the examples/ directory for integration patterns:

• python_quickstart.py: Connect to the Binary Spine API using Python.

Internal Architecture

The engine organizes data into a 9-depth fractal hierarchy:

• D0: System Identity / Global State
• D1: Layer Aggregates
• D2: Topic Clusters
• D3-D5: Associative Memories & Sentences
• D6-D8: Tokens, Characters, and Raw Bytes

Each block is a C-represented struct ensuring zero-copy alignment with the CPU cache lines.

License

Distributed under the MIT License. See LICENSE for more information.

Developed by Máté Róbert — Part of the autonomous cognitive research series.