NGX-OS: Network OS Built for AI with eBPF and MCP Integration

✍️ OpenClawRadar📅 Published: March 26, 2026🔗 Source
NGX-OS: Network OS Built for AI with eBPF and MCP Integration
Ad

NGX-OS is a network operating system built specifically for AI integration from day one, eliminating traditional network monitoring methods like log parsing, SNMP polling, and CLI scraping. The system provides direct AI access to network state through structured data.

Architecture and Components

The system has three layers with a single source of truth:

  • Enforcement: XDP/eBPF writes structured counters per device directly in the NIC driver
  • Control: Rust Arbiter syncs counters to Redis
  • Intelligence: Claude or Gemini reads Redis via Model Context Protocol (MCP)
  • Offline capability: Local model provides diagnostics when internet is down

Key Design Principles

NGX-OS has no log files, no CLI, no SNMP, and no API to poll. All network state — including device identities, behavioral counters, NAT mappings, and security events — lives in a single structured database that LLMs read directly through MCP. The AI reads the same data structure that the BPF silicon uses to make enforcement decisions.

The safety rule is explicit: AI never writes state. It observes and explains, a human confirms, and the system executes.

Ad

Practical Example

When a subscriber reports slow performance at 2 AM, the system can provide specific answers like: "4 devices online. The Ring doorbell is sending 47× its baseline traffic to 4,000 unique IPs. Quarantined automatically 1 second after detection. Other 3 devices unaffected. The doorbell is compromised." This answer comes directly from BPF counters in the NIC driver, not from parsed logs or alerts.

Technical Specifications

  • Single binary for ARM, RISC, and x86 architectures
  • Runs on Debian 13 6.12
  • 30-second deployment time
  • Patent pending

The system is targeted at WISP and FTTH operators who currently rely on SSH access to read log files for troubleshooting. The author claims that in the time it takes to locate a log file, Claude can have the problem resolved and waiting for human approval to execute.

📖 Read the full source: r/ClaudeAI

Ad

👀 See Also

LLMSpend: Open-source cost tracker for Anthropic and OpenAI SDKs
Tools

LLMSpend: Open-source cost tracker for Anthropic and OpenAI SDKs

LLMSpend is a Python library that adds cost tracking to Anthropic and OpenAI SDK calls with two lines of code. It provides local SQLite storage, CLI reporting, and a web dashboard without sending data externally.

OpenClawRadar
RelayPlane Open Source Proxy Shows 73% Cost Reduction with Claude Model Routing
Tools

RelayPlane Open Source Proxy Shows 73% Cost Reduction with Claude Model Routing

RelayPlane, an open source npm-native proxy for the Anthropic API, demonstrated 73.4% cost savings in benchmarks by routing requests to appropriate Claude models based on complexity. The tool reduced cost per 10 requests from $0.0323 to $0.0086 while improving p50 latency from 1.55s to 0.78s.

OpenClawRadar
Transloadit MCP Server Connects AI Agents to Media Processing Pipeline
Tools

Transloadit MCP Server Connects AI Agents to Media Processing Pipeline

Transloadit built an MCP server that connects Claude and other AI agents to their media processing pipeline with 86 Robots for video, audio, image, and document processing. Setup in Claude Code requires one line: npx -y @transloadit/mcp-server stdio with TRANSLOADIT_KEY and TRANSLOADIT_SECRET environment variables.

OpenClawRadar
claude-sessions: Terminal UI for Browsing Claude Code Transcripts
Tools

claude-sessions: Terminal UI for Browsing Claude Code Transcripts

claude-sessions is an open-source terminal UI tool that scans local Claude Code transcript files, allowing developers to browse, search, and resume past sessions. Built with Claude Code itself, it features WASD navigation, keyword search, and one-click session resumption.

OpenClawRadar