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AI control plane and governed runtime for multi-agent systems, tool execution, and real-world automation.

3 starsPython

Core Rth. A governed AI kernel for engineers who don't trust their LLMs

by christianrth·Mar 3, 2026·1 point·1 comment
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AI Analysis

●●●BangerBig BrainBold Bet

Proposal-first governance + hardware E-stop for AI controlling robots/drones—legitimately novel safety architecture.

Strengths
  • •Governance layer (The Guardian) forces agent proposals through policy DSL before execution—not a wrapper, foundational design
  • •Hardware kill switch for physical agents is correct paranoia; OpenClaw doesn't address actuated systems
  • •Role-based multi-LLM council (Researcher/Coder/Critic) with cost-aware routing beats simple API chaining
Weaknesses
  • •Governance overhead adds latency; unclear if adoption friction outweighs safety gains in practice
  • •Early stage (RC1); no independent security audit or deployment case studies yet
  • •Niche audience—only matters if deploying AI to critical infrastructure; SaaS/web teams don't need this
Category
AI/ML
Target Audience

Enterprise security teams, roboticists, IoT engineers deploying AI agents to critical/physical systems

Similar To

OpenClaw · AutoGPT · LangChain agents

Post Description

Hey HN,

I spent the last 20 years building enterprise software and cybersecurity tools. Last year I started using AI agents for real work — and got terrified. Every tool I tried would happily `rm -rf /` if you phrased it right. One popular agent literally tried to exfiltrate SSH keys (you might have seen that HN post).

So I built Core Rth. It's not a chatbot wrapper. It's a Sovereign Cognitive Kernel — an AI backend where every LLM action is a proposal that must survive a governance layer (The Guardian) before it touches your filesystem, your network, or your hardware.

Here's the thing that makes it different from OpenClaw and friends: Core Rth was designed from day one to control physical systems (IoT via Home Assistant, robots via ROS2, drones via MAVLink). When your AI can actuate a robotic arm or fly a drone, "move fast and break things" is not an option. So we built a Global E-Stop — a hardware kill switch in the Web UI that severs all physical agency instantly.

What's under the hood:

- Guardian: proposal-first governance. No destructive action runs without consent. Ever. - Security Vault: AES-256-GCM encrypted credentials, OS keyring only, zero plaintext keys. - AI Village: parallel agents (Architect, Critic, Synthesizer) debate on a Knowledge Graph before answering. - Model Router: mix OpenAI + Anthropic + local Ollama/vLLM. Route by task type, cost, or privacy. - Mission Control: real-time Web UI with bridge telemetry, policy ledger, and the big red E-Stop.

Try it:

git clone https://github.com/rthgit/CORE-RTH.git cd CORE-RTH pip install -r requirements.txt python scripts/run_core_rth_local_bench_api.py # Open http://127.0.0.1:18030/ui/

Stack: Python 3.10+ / FastAPI / vanilla JS frontend (no React). Source-available license (free for non-commercial/research).

I built this because I needed an AI system I could actually trust with my infrastructure. If you're an engineer or CTO who feels the same way, I'd love your feedback on the architecture.

GitHub: https://github.com/rthgit/CORE-RTH

Created by Christian Quintino De Luca — [email protected]

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