MindCore — Biomimetic Subconscious Engine

Give your AI agent autonomous thoughts, emotions, and spontaneous impulses.

What It Does

MindCore is a standalone background daemon that simulates a subconscious mind. It rolls dice every second, modeling the random emergence of thoughts like "I want milk tea", "I'm bored", or "I suddenly want to chat".

When a thought's probability accumulates past the firing threshold, the engine outputs a JSON signal telling your AI Agent: "I have something to say."

Architecture

Layer 0: Noise Generators (3000 nodes)
    ├── Pink Noise (1/f, long-range correlation)
    ├── Ornstein-Uhlenbeck (physiological baseline)
    ├── Hawkes Process (emotional chain reaction)
    └── Markov Chain (attention drift)
         ↓
Layer 1: Sensor Layer (150 sensors)
    ├── Body State (hunger/fatigue/bio-rhythms)
    ├── Environment (time/weather/noise)
    └── Social Context (interaction/neglect)
         ↓
Layer 2: Impulse Emergence (150 impulse nodes)
    ├── Synapse Matrix (sensor → impulse mapping)
    ├── Sigmoid Probability + Mood Modulation
    └── Dice Roll → Random Firing
         ↓
Layer 3: Personality Gate (Softmax Sampling)
    ├── Learnable Personality Weights
    └── Short-Term Memory Topic Boost
         ↓
Layer 4: Output Template → JSON signal

Quick Start

# Install dependencies
pip install -r requirements.txt

# Start the engine
python main.py

Requires Python 3.8+. On first run, automatically downloads all-MiniLM-L6-v2 local NLP model (~80MB) for synapse matrix generation.

Key Features

• 150 Daily Impulses across 9 categories (food, social, entertainment, etc.)
• Stochastic, Not Scheduled — Pink Noise + Hawkes Process + Sigmoid probability
• Circadian Rhythms — real clock-driven hunger/thirst/sleep cycles
• Short-Term Memory — 5-slot FIFO buffer with 2-hour exponential decay
• Mood Baseline — continuous valence modulation of impulse probability
• Tunable Frequency — single BURST_BASE_OFFSET parameter controls activity

Integration

MindCore outputs standard JSON and is designed for OpenClaw but compatible with any AI Agent framework that supports external signal injection.

See references/INTEGRATION.md for detailed integration guide.

File Structure

• main.py — Entry point and engine loop
• engine/ — Core 5-layer pipeline implementation
• engine_supervisor.py — Process supervisor for daemon mode
• data/ — Runtime data (sensor state, synapse matrix, memory)
• js_bridge/ — JavaScript bridge for OpenClaw integration

License

AGPL-3.0 (commercial licensing available — contact zmliu0208@gmail.com)