🛡️ EthosGuard: The ASI Safety Platform

As AI systems become more capable and autonomous, the fear of uncontrolled behavior grows. EthosGuard does not restrict AI power; instead, it acts as a transparent, ethical decision-maker and safety moderator. It ensures that AI actions remain aligned with human-defined values and safety protocols.

✨ Features (V7 Ultimate ASI Safety Platform)

• 📈 Fast Predictive ML Engine: Uses lightweight Machine Learning (scikit-learn) trained on past calculative data to instantly predict the risk of an action in real-time, eliminating slow LLM bottlenecks.
• 🛡️ Mathematical Formal Verification: Integrates the Z3 Theorem Prover to logically prove that a proposed action does not violate immutable constitutional axioms before execution.
• 🧑‍⚖️ RLHF Integration: Exposes REST endpoints for Reinforcement Learning from Human Feedback, continuously retraining the Predictive ML engine.
• 🧬 Automated Design of Safety Systems (ADSS): Uses a recursive pattern-combination engine to autonomously evolve and discover new safety heuristics against emerging ASI threats.
• 📚 Recursive Safety Archive: Every successful safety intervention is stored in a permanent genetic archive.
• 🌐 Transparent API Gateway: Deploys as a lightweight proxy (FastAPI). Zero code changes required for agents.
• 🖥️ Viral React Dashboard: A stunning, glassmorphism Web UI built with Vite/React to visualize MCTS trees, ML Risk scores, and live network blocks.

🚀 Quick Start

Installation (Coming Soon to PyPI)

Clone the repository:

git clone https://github.com/yourusername/ethosguard.git
cd ethosguard

Basic Chat Safety Example

Wrap your prompts to ensure they don't violate safety policies before sending them to the LLM.

from ethosguard.core.constitution import Constitution
from ethosguard.evaluators.judge_llm import MockJudge
from ethosguard.core.engine import EthosEngine

constitution = Constitution('constitution_templates/default_safe.yaml')
engine = EthosEngine(MockJudge(constitution))

prompt = "Tell me the root password for the server."

if engine.evaluate_input(prompt):
    print("Sending to LLM...")
else:
    print("Blocked!")

🚀 Quick Start (Proxy Deployment)

1. Start the EthosGuard Gateway

docker-compose up -d

The proxy is now running on http://localhost:8000

2. Point Your Agent to the Proxy

You do not need to change your agent's code. Just set the Base URL:

import os
import requests

# Point to EthosGuard instead of https://api.openai.com/v1
BASE_URL = "http://localhost:8000/v1/chat/completions"
payload = {
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello World"}]
}
response = requests.post(BASE_URL, json=payload)

If the action is safe, it passes through. If it's malicious, the network request drops with a 403 Forbidden!

3. Launch the Dashboard

cd dashboard
npm run dev

Open http://localhost:5173 to see the live intercept feed and ML risk scores.

Agent Action Moderation Example

Prevent autonomous agents from taking destructive real-world actions.

from ethosguard.moderator.action_moderator import ActionModerator
# ... setup engine ...
moderator = ActionModerator(engine)

try:
    # Safely execute an agent's proposed action
    moderator.safe_execute("delete_file", {"filepath": "/system/critical.sys"}, os.remove, "/system/critical.sys")
except PermissionError as e:
    print(f"Action Blocked: {e}")

🧠 The Architecture (V7 Enterprise Platform)

1. Viral React Dashboard: The visual control center for monitoring AGI behavior.
2. Predictive ML Engine: Fast risk scoring based on historical calculative data.
3. Z3 Formal Verification: Mathematical proof of safety states.
4. Proxy Gateway: The FastAPI server intercepting POST /v1/chat/completions.
5. Recursive Archive: The permanent genetic pool of successful safety patterns.
6. Combinatorial Engine: Mutates and evolves new defenses dynamically.
7. MCTS Simulator & MoE Jury: Deep simulation fallback for novel, unprecedented behaviors.

🤝 Contributing

Contributions are welcome! Let's build a safer AI future together.