🌍 Evolving AI Biosphere

A self-evolving digital ecosystem where AI organisms fight for survival, powered by Reinforcement Learning and Genetic Algorithms.

📖 Overview

Evolving AI Biosphere is a complex artificial life simulation where organisms are not pre-programmed with rules—they learn how to survive.

• Predators share a collective "Hive Mind" (LSTM Neural Network) that evolves via Reinforcement Learning (Policy Gradient).
• Herbivores evolve individually via Genetic Algorithms (mutation of weights).
• The World is watched by an "AI God" (Ollama/Llama 3.2) that allows natural language control over the simulation.

👉 Read the Full Technical Documentation for a deep dive into the math, architecture, and evolutionary theory.

📸 Demo

---

🛠 Tech Stack

Component	Technology	Description
Core Engine	Python, Pygame	Real-time simulation loop (60 FPS) and rendering.
Neural Nets	PyTorch	Custom LSTM architecture for Predator "Hive Mind".
Orchestration	Python subprocess	Multi-threaded service manager (launch_services.py).
Inference	Ollama	Local LLM serving (Llama 3.2) for God Mode control.
Observability	MLflow	Real-time experiment tracking and metric logging (Port 5001).

---

🧩 Architecture

graph LR
    %% Styling
    classDef infra fill:#f9f,stroke:#333,stroke-width:2px;
    classDef core fill:#bbf,stroke:#333,stroke-width:2px;
    classDef brain fill:#bfb,stroke:#333,stroke-width:2px;

    %% --- Infrastructure (Left Column) ---
    subgraph Infrastructure
        direction TB
        Launch[launch_services.py]:::infra
        Ollama["Ollama Service<br/>(Llama 3.2)"]:::infra
        MLflow["MLflow Server<br/>(Metrics)"]:::infra
        
        Launch -.->|Starts| Ollama
        Launch -.->|Starts| MLflow
    end

    %% --- Simulation (Middle Column) ---
    subgraph "Simulation Loop (60hz)"
        direction TB
        User[User Input]
        Pygame[Pygame Engine]:::core
        Grid[NumPy Grid State]:::core
        
        User ==>|Controls| Pygame
        Pygame ==>|Updates| Grid
    end

    %% --- Intelligence (Right Column) ---
    subgraph "The Hive & Agents"
        direction TB
        Preds["Predator Hive Mind<br/>(PyTorch LSTM)"]:::brain
        Herbs["Herbivores<br/>(Genetics)"]:::brain
        
        God["God Mode Agent<br/>(LangChain)"]:::brain
        Council["Council System<br/>(LangGraph)"]:::brain
    end

    %% --- Connections (Critical Paths) ---
    
    %% Bio-Loop
    Grid <==>|Sensors/Action| Preds
    Grid <==>|Sensors/Action| Herbs
    
    %% God Mode Loop
    Pygame -.->|Chat| God
    God <==>|Tools| Pygame
    God -.->|Inference| Ollama
    
    %% Council Loop
    God -- Summon --> Council
    Council -.->|Debate| Ollama
    
    %% Logging
    Pygame -- Log Data --> MLflow

Loading

---

🚀 Installation & Setup

Prerequisites

• Python 3.9+
• Ollama installed and running (llama3.2 model pulled).

1. Clone the Repository

git clone https://github.com/alwaysvivek/evolving-ai-biosphere.git
cd evolving-ai-biosphere

2. Setup Virtual Environment

We provide a unified script to set up the environment and install dependencies:

# Make the script executable
chmod +x setup_env.sh

# Run the setup (creates venv and installs all requirements)
./setup_env.sh

---

🎮 How to Run

Step 1: Start Background Services

This script starts MLflow (logging) and checks for Ollama (AI Brain).

python3 launch_services.py

Wait until you see: ✅ MLflow UI started at http://127.0.0.1:5001

Step 2: Run the Simulation

Open a new terminal tab/window, activate the environment, and run:

source venv/bin/activate
python3 simulation.py

---

🎮 Controls

Key	Action
SPACE	Pause/Resume Simulation
Type	Enter "God Mode" commands (e.g., "kill half the plants")
R	Print Console Report
T	Toggle Predator Training (ON/OFF)
K	Kill All Predators (Extinction Event)
E	Trigger Scarcity Event (Famine)

---

📊 Monitoring

Once the simulation starts, open MLflow to see live metrics including population counts, average energy, and extinction events: 👉 http://127.0.0.1:5001

---

📜 License

MIT License