NMPL

No more packet loss

Overview

Networks are unreliable. Packets get lost in transmission. Debugging the where and why is hard and convoluted.

NMPL detects packet loss, localizes it to specific hops/segments, then suggests next steps. When you know exactly where it went wrong, fixing it becomes much easier.

The Problem

Packet loss is sporadic and hard to reproduce:

• Loss only happens under certain conditions (like load)
• Could be one bad router, cable, or ISP segment
• Simple pings miss bursty or path-specific issues

What NMPL Does

• Detect — Continuous probes catch sporadic loss over ICMP and UDP
• Localize — Per-hop visibility shows exactly where packets drop
• Document — JSON/CSV exports for ISP support tickets
• Suggest — Practical next steps based on automated bottleneck patterns

Current Status

• Per-hop loss detection via automated mtr parsing
• Smart bottleneck analysis with forward-loss inheritance checks
• Multi-threaded daemon mode for parallel target monitoring
• Compact SQLite3 database storage for analytics timeline and incidents
• Native FastAPI web dashboard with HTMX live-reloading rows
• JSON/CSV evidence export for ISP support tickets
• Cross-platform support (macOS/Linux)

Quick Start

1. Standalone CLI Probes

python3 -m core 8.8.8.8                # Basic loss test
python3 -m core 8.8.8.8 --watch        # Live single-target monitoring
python3 -m core 8.8.8.8 --mtr          # Traceroute + bottleneck analysis  
python3 -m core 8.8.8.8 --mtr --json=proof.json --csv=hops.csv  # Export ISP evidence

2. Multi-Target Daemon Mode

Monitor multiple endpoints and cloud servers concurrently over parallel execution threads:

python3 cli.py --targets "1.1.1.1,8.8.8.8,://amazonaws.com,github.com" --interval 3.0

Installation

pip install nmpl

# or install from source
git clone https://github.com
cd NMPL
pip install -r requirements.txt

Production Architecture

Cloud & Multi-Target Engine

The core execution engine utilizes a ThreadPoolExecutor to handle scale across arbitrary cloud boundaries:

python3 cli.py --targets "://amazonaws.com,github.com,1.1.1.1" --interval 3.0

Automated Diagnostic Records

When a path degrades, the daemon breaks past its baseline and captures an autonomous trace snapshot, saving a production-ready entry into your local history file for later ISP escalation:

python3 cli.py --report 1