Self-evolving AI agents that learn and improve on the XRP Ledger — skill-evolution NFTs + multi-agent swarms on XRPL Testnet.
Agents that improve from their own production traces. Harvest → label → cluster → propose → gate → measure. RL on agent trajectories, measured honestly on Daytona. Zero-dep, MIT.
SkillOpt replaces manual prompt tweaking with a mathematical feedback loop. A base model runs your tasks against a benchmark dataset while an optimizer model evaluates failures and rewrites the `## Instructions` section — automatically rejecting changes that don't improve the score.
OpenCLAW persistent memory skill with multi-agent isolation, vector search, and context inheritance.
Empathetic AI engineering partner with cognitive memory, multi-agent orchestration (10 agents), 132-signal skill routing, and self-improving reflection loops. Built on Model Context Protocol. By Aldo Karendra.
Add a description, image, and links to the self-improving-ai topic page so that developers can more easily learn about it.
To associate your repository with the self-improving-ai topic, visit your repo's landing page and select "manage topics."