Integration of Lightning Development Kit (LDK) with FLAML AutoML for optimizing Bitcoin Lightning node management.
Overview
This project uses FLAML (Fast and Lightweight AutoML) to forecast routing fees on the Lightning Network. It analyzes channel update messages from LN gossip data and predicts future effective fees for payment routing.
Inspiration
One of the biggest challenges in the Lightning Network today is managing nodes efficiently. Node operators often struggle to determine optimal inbound and outbound liquidity and when to rebalance. While there are existing products like Magma AI (Amboss) and Lightspark, these are closed-source services. We wanted to explore a more open, customizable, and community-driven alternative with simple access to state of the art ML models.
What it does
AutoLN is a library that allows node operators to plug directly into their Lightning nodes and train an AI model based on their own node’s behavior and network position. By analyzing gossip protocol data and liquidity patterns, AutoLN helps automate channel rebalancing in a personalized way — adaptive to each node’s unique connections and traffic flow.
Features
- Parses Lightning Network gossip data (channel_update messages)
- Computes effective routing fees for configurable payment amounts
- Automatically selects the channel with most historical data
- Uses time-series lag features (lag1, lag2, lag3) for forecasting
- Trains FLAML AutoML regression models with minimal configuration
- Provides MAE (Mean Absolute Error) metrics and predictions